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This compact and efficient device allows developers, educators, and students to build AI projects with ease. Despite its small size, it offers exceptional performance for machine learning, computer vision, and robotics. The developer kit provides access to the same NVIDIA CUDA-X software and AI libraries that power sophisticated AI models, making it an ideal entry point for those seeking hands-on experience in AI development.\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eAI-Powered Performance in a Compact Form Factor\u003c\/strong\u003e\u003cbr\u003eThe \u003cstrong\u003eNVIDIA Jetson Nano 4GB Developer Kit\u003c\/strong\u003e is equipped with a quad-core ARM Cortex-A57 processor and a 128-core Maxwell GPU, delivering robust performance for AI applications. This powerful hardware supports multiple neural networks running in parallel, enabling real-time AI inference on edge devices. With 4GB of LPDDR4 memory, the Jetson Nano ensures smooth handling of AI models and data streams. This performance-packed development kit enables developers to create advanced AI applications for robotics, drones, smart devices, and edge AI solutions with minimal effort.\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eUser-Friendly, Scalable, and Ready-to-Use AI Development Kit\u003c\/strong\u003e\u003cbr\u003eDesigned to lower barriers to AI development, the \u003cstrong\u003eNVIDIA Jetson Nano 4GB Developer Kit\u003c\/strong\u003e provides a plug-and-play experience. Its user-friendly interface allows developers, educators, and hobbyists to get started with AI in minutes. The system features USB ports, HDMI, and Ethernet connectivity, offering extensive compatibility with peripherals, sensors, and cameras. With support for the NVIDIA JetPack SDK, developers can access a suite of pre-trained AI models, tools, and libraries, enabling faster development cycles. The kit is scalable, allowing users to start with simple projects and transition to more advanced AI applications as their skills progress.\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eAI at the Edge – Real-Time Inference and Low Latency\u003c\/strong\u003e\u003cbr\u003eFor edge AI applications, the \u003cstrong\u003eNVIDIA Jetson Nano 4GB Developer Kit\u003c\/strong\u003e is the perfect solution. It processes AI tasks at the edge, reducing latency and enabling real-time decision-making for smart cameras, security systems, industrial IoT, and more. With its energy-efficient design, it consumes minimal power, making it ideal for use in portable or battery-powered devices. Developers can also integrate the Jetson Nano into smart home systems and edge computing devices to achieve real-time automation and AI-driven control.\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eTechnical Specifications of NVIDIA Jetson Nano 4GB Developer Kit\u003c\/strong\u003e\u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cstrong\u003eProcessor\u003c\/strong\u003e: Quad-core ARM Cortex-A57 CPU\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eGPU\u003c\/strong\u003e: 128-core Maxwell GPU\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eMemory\u003c\/strong\u003e: 4GB LPDDR4\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eI\/O Ports\u003c\/strong\u003e: USB 3.0, USB 2.0, HDMI 2.0, Ethernet, GPIO, CSI Camera Connector\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eConnectivity\u003c\/strong\u003e: Ethernet, Wi-Fi (via adapter)\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eSoftware Support\u003c\/strong\u003e: NVIDIA JetPack SDK, CUDA, cuDNN, TensorRT, AI libraries\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eGTIN\u003c\/strong\u003e: 812674024019\u003c\/li\u003e\n\u003c\/ul\u003e","brand":"Nvidia","offers":[{"title":"Default Title","offer_id":44853901099172,"sku":"945-13450-0000-100","price":265.0,"currency_code":"SGD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0605\/0136\/0804\/products\/nvidia-jetson-nano-4gb-developer-kit-945-13450-0000-100-935815.jpg?v=1702465292"},{"product_id":"asus-ascent-gx10-compact-desktop-ai-supercomputer-1tb-gx10-gg0007bn","title":"ASUS Ascent GX10 Compact AI Supercomputer - 1TB","description":"\u003cdiv\u003e\n\u003cstyle\u003e\n.pd-acc { border: 1px solid #e2e2e2; border-radius: 8px; margin: 10px 0; overflow: hidden; }\n.pd-acc summary { cursor: pointer; padding: 14px 16px; font-weight: 700; font-size: 1.05em; list-style: none; display: flex; justify-content: space-between; align-items: center; background: #f7f7f7; }\n.pd-acc[open] \u003e summary { border-bottom: 1px solid #e2e2e2; }\n.pd-acc summary::-webkit-details-marker { display: none; }\n.pd-acc summary::after { content: \"+\"; font-size: 1.4em; font-weight: 400; line-height: 1; margin-left: 12px; }\n.pd-acc[open] \u003e summary::after { content: \"\\2212\"; }\n.pd-acc .pd-acc-body { padding: 8px 16px 14px; }\n.pd-spec, .pd-spec tr, .pd-spec td { border: none !important; background: transparent !important; }\n.pd-spec { width: 100%; border-collapse: collapse !important; margin: 6px 0 18px; }\n.pd-spec td { padding: 7px 10px 7px 0 !important; border-bottom: 1px solid #ececec !important; vertical-align: top; font-size: 0.95em; text-align: left; }\n.pd-spec td:first-child { width: 38%; font-weight: 600; color: #333; }\n.pd-spec tr:last-child td { border-bottom: none !important; }\n\u003c\/style\u003e\n\u003ch2\u003e\n\u003cstrong\u003eASUS Ascent GX10 Compact Desktop AI Supercomputer - 1TB (GX10-GG0007BN) \/ (90MS0371-M00070) - 1 Year Onsite Warranty \u003c\/strong\u003e\u003cspan style=\"color: rgb(255, 42, 0);\"\u003e\u003cstrong\u003e+ Limited-Time Offer: Free Upgrade to 3 Years Warranty.\u003c\/strong\u003e\u003c\/span\u003e\n\u003c\/h2\u003e\n\u003ch3\u003e\u003cstrong\u003eASUS Ascent GX10 — Petaflop AI Power in a 1.48 kg Desktop\u003c\/strong\u003e\u003c\/h3\u003e\n\u003cp\u003eBuilt on the NVIDIA GB10 Grace Blackwell Superchip, the ASUS Ascent GX10 delivers up to 1 petaFLOP of FP4 AI performance with 128 GB of unified CPU–GPU memory — enough to run large language models up to 200 billion parameters locally. It is the NVIDIA DGX Spark platform in ASUS's premium Stellar Grey chassis, at a sharper price point. Need more storage? See the \u003ca href=\"\/products\/asus-ascent-gx10-compact-desktop-ai-supercomputer-2tb-gx10-gg0030bn\"\u003e2TB\u003c\/a\u003e and \u003ca href=\"\/products\/asus-ascent-gx10-compact-desktop-ai-supercomputer-4tb-gx10-gg0007bn\"\u003e4TB\u003c\/a\u003e models.\u003c\/p\u003e\n\u003ch3\u003e\u003cstrong\u003eDevelop, Fine-Tune and Deploy AI Locally\u003c\/strong\u003e\u003c\/h3\u003e\n\u003cp\u003eRun CUDA, TensorRT, NVIDIA NIM, PyTorch, TensorFlow and Jupyter on Ubuntu Linux \/ NVIDIA DGX OS. Fine-tune models, build agentic AI, run RAG workloads and vector databases — all on-premise, keeping data private and cloud bills at zero.\u003c\/p\u003e\n\u003ch3\u003e\u003cstrong\u003eQuietFlow Cooling, Cluster-Ready Networking\u003c\/strong\u003e\u003c\/h3\u003e\n\u003cp\u003eA triple-fan, dual vapor chamber QuietFlow cooling system sustains continuous AI processing at whisper-quiet levels, while the built-in NVIDIA ConnectX-7 SmartNIC lets you link two GX10 units for models beyond 200B parameters — use the \u003ca href=\"\/products\/nvidia-qsfp112-400g-dac-cable-40cm-x0101g00400a\"\u003eNVIDIA QSFP112 DAC cable\u003c\/a\u003e.\u003c\/p\u003e\n\u003cp\u003e\u003ciframe width=\"560\" height=\"315\" src=\"https:\/\/www.youtube.com\/embed\/ASm_Cg5txOI?si=NwZBr8flFQ39APye\" title=\"YouTube video player\"\u003e\u003c\/iframe\u003e\u003c\/p\u003e\n\u003cdetails class=\"pd-acc\"\u003e\n\u003csummary style=\"cursor: pointer; font-weight: 700;\"\u003eASUS Ascent GX10 Datasheet — Technical Specifications\u003c\/summary\u003e\n\u003cdiv class=\"pd-acc-body\"\u003e\n\u003cp\u003e\u003cstrong\u003eGeneral\u003c\/strong\u003e\u003c\/p\u003e\n\u003ctable class=\"pd-spec\"\u003e\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eModel\u003c\/td\u003e\n\u003ctd\u003eASUS Ascent GX10 — 1TB\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eSKU \/ Part Number\u003c\/td\u003e\n\u003ctd\u003eGX10-GG0007BN \/ 90MS0371-M00070\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eProduct Type\u003c\/td\u003e\n\u003ctd\u003eUltra-Compact Desktop AI Supercomputer (NVIDIA DGX Spark Platform)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eOperating System\u003c\/td\u003e\n\u003ctd\u003eUbuntu Linux \/ NVIDIA DGX OS\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eColor\u003c\/td\u003e\n\u003ctd\u003eStellar Grey, Premium Metal Chassis\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eWarranty\u003c\/td\u003e\n\u003ctd\u003e1 Year Onsite Warranty (Limited-Time Free Upgrade to 3 Years)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/tbody\u003e\u003c\/table\u003e\n\u003cp\u003e\u003cstrong\u003eProcessor — NVIDIA GB10 Grace Blackwell Superchip\u003c\/strong\u003e\u003c\/p\u003e\n\u003ctable class=\"pd-spec\"\u003e\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eCPU\u003c\/td\u003e\n\u003ctd\u003e20-Core Arm v9.2-A — 10 × Cortex-X925 + 10 × Cortex-A725\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eGPU\u003c\/td\u003e\n\u003ctd\u003eNVIDIA Blackwell (Integrated), 5th-Gen Tensor Cores\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eAI Performance\u003c\/td\u003e\n\u003ctd\u003eUp to 1 PetaFLOP FP4\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eLLM Support\u003c\/td\u003e\n\u003ctd\u003eUp to 200 Billion Parameter Models (Single Unit)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/tbody\u003e\u003c\/table\u003e\n\u003cp\u003e\u003cstrong\u003eMemory \u0026amp; Storage\u003c\/strong\u003e\u003c\/p\u003e\n\u003ctable class=\"pd-spec\"\u003e\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eMemory\u003c\/td\u003e\n\u003ctd\u003e128 GB LPDDR5X Coherent Unified Memory (Shared CPU–GPU)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eStorage\u003c\/td\u003e\n\u003ctd\u003e1 TB M.2 NVMe PCIe SSD\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/tbody\u003e\u003c\/table\u003e\n\u003cp\u003e\u003cstrong\u003eNetworking\u003c\/strong\u003e\u003c\/p\u003e\n\u003ctable class=\"pd-spec\"\u003e\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eSmartNIC\u003c\/td\u003e\n\u003ctd\u003eNVIDIA ConnectX-7 — Dual-System Scaling \u0026amp; Cluster Networking\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eEthernet\u003c\/td\u003e\n\u003ctd\u003e1 × 10 GbE RJ-45\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eWireless\u003c\/td\u003e\n\u003ctd\u003eWi-Fi 7, Bluetooth 5.4\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/tbody\u003e\u003c\/table\u003e\n\u003cp\u003e\u003cstrong\u003ePorts \u0026amp; Connectivity\u003c\/strong\u003e\u003c\/p\u003e\n\u003ctable class=\"pd-spec\"\u003e\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eUSB\u003c\/td\u003e\n\u003ctd\u003e4 × USB 3.2 Gen 2x2 Type-C (20 Gbps)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eDisplay\u003c\/td\u003e\n\u003ctd\u003e1 × HDMI 2.1b; DisplayPort 2.1 via USB-C Alt Mode\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eSecurity\u003c\/td\u003e\n\u003ctd\u003eKensington Lock Slot\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ePower Input\u003c\/td\u003e\n\u003ctd\u003eUSB-C PD 3.1 (180W EPR), 240W External Adapter\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/tbody\u003e\u003c\/table\u003e\n\u003cp\u003e\u003cstrong\u003eCooling \u0026amp; Physical\u003c\/strong\u003e\u003c\/p\u003e\n\u003ctable class=\"pd-spec\"\u003e\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eCooling\u003c\/td\u003e\n\u003ctd\u003eQuietFlow — Triple-Fan, Dual Vapor Chamber\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eDimensions\u003c\/td\u003e\n\u003ctd\u003e150 × 150 × 51 mm\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eWeight\u003c\/td\u003e\n\u003ctd\u003eApproximately 1.48 kg\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/tbody\u003e\u003c\/table\u003e\n\u003cp\u003e\u003cstrong\u003eSoftware Support\u003c\/strong\u003e\u003c\/p\u003e\n\u003ctable class=\"pd-spec\"\u003e\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eAI Stack\u003c\/td\u003e\n\u003ctd\u003eCUDA, TensorRT, NVIDIA NIM, PyTorch, TensorFlow, Jupyter, NVIDIA AI Software Stack\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eWorkloads\u003c\/td\u003e\n\u003ctd\u003eLLM Fine-Tuning, Local Inference, Agentic AI, RAG, Deep Learning Training\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/tbody\u003e\u003c\/table\u003e\n\u003c\/div\u003e\n\u003c\/details\u003e\n\u003cdetails class=\"pd-acc\"\u003e\n\u003csummary style=\"cursor: pointer; font-weight: 700;\"\u003ePackage Contents \u0026amp; Ordering Information\u003c\/summary\u003e\n\u003cdiv class=\"pd-acc-body\"\u003e\n\u003cp\u003e\u003cstrong\u003ePackage Contents\u003c\/strong\u003e\u003c\/p\u003e\n\u003cp\u003eASUS Ascent GX10 AI Supercomputer (1TB)\u003cbr\u003e\n240W Power Adapter\u003cbr\u003e\nPower Cord\u003cbr\u003e\nUser Manual\u003cbr\u003e\nWarranty Card\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eOrdering Information — AI Supercomputers at SourceIT\u003c\/strong\u003e\u003c\/p\u003e\n\u003ctable class=\"pd-spec\"\u003e\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eASUS Ascent GX10 1TB\u003c\/td\u003e\n\u003ctd\u003eGX10-GG0007BN (This Product)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eASUS Ascent GX10 2TB\u003c\/td\u003e\n\u003ctd\u003e\u003ca href=\"\/products\/asus-ascent-gx10-compact-desktop-ai-supercomputer-2tb-gx10-gg0030bn\"\u003eGX10-GG0030BN — In Stock\u003c\/a\u003e\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eASUS Ascent GX10 4TB\u003c\/td\u003e\n\u003ctd\u003e\u003ca href=\"\/products\/asus-ascent-gx10-compact-desktop-ai-supercomputer-4tb-gx10-gg0007bn\"\u003eGX10-GG0031BN — In Stock\u003c\/a\u003e\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eNVIDIA DGX Spark 4TB\u003c\/td\u003e\n\u003ctd\u003e\u003ca href=\"\/products\/nvidia-dgx-spark-ai-supercomputer-4tb-940-54242-0007-000\"\u003e940-54242-0007-000 — In Stock\u003c\/a\u003e\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eNVIDIA QSFP112 400G DAC Cable (Links 2 Units)\u003c\/td\u003e\n\u003ctd\u003e\u003ca href=\"\/products\/nvidia-qsfp112-400g-dac-cable-40cm-x0101g00400a\"\u003eX0101G00400A — In Stock\u003c\/a\u003e\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/tbody\u003e\u003c\/table\u003e\n\u003c\/div\u003e\n\u003c\/details\u003e\n\u003cdetails class=\"pd-acc\"\u003e\n\u003csummary style=\"cursor: pointer; font-weight: 700;\"\u003eDatasheet \u0026amp; Downloads\u003c\/summary\u003e\n\u003cdiv class=\"pd-acc-body\"\u003e\n\u003cp\u003e• \u003ca href=\"https:\/\/www.asus.com\/networking-iot-servers\/aiot-industrial-solutions\/embedded-computers-edge-ai-systems\/asus-ascent-gx10\/\" rel=\"noopener\" target=\"_blank\"\u003eASUS Ascent GX10 — Official Product Page \u0026amp; Specifications\u003c\/a\u003e\u003cbr\u003e\n• \u003ca href=\"https:\/\/www.nvidia.com\/en-us\/products\/workstations\/dgx-spark\/\" rel=\"noopener\" target=\"_blank\"\u003eNVIDIA DGX Spark Platform — Official Specifications\u003c\/a\u003e\u003cbr\u003e\n• \u003ca href=\"https:\/\/sourceit.com.sg\/blogs\/news\/nvidia-dgx-spark-setup-and-enterprise-deployment-guide\"\u003eDGX Spark Platform Setup \u0026amp; Enterprise Deployment Guide — SourceIT\u003c\/a\u003e\u003c\/p\u003e\n\u003c\/div\u003e\n\u003c\/details\u003e\n\u003cdetails class=\"pd-acc\"\u003e\n\u003csummary style=\"cursor: pointer; font-weight: 700;\"\u003eFrequently Asked Questions — ASUS Ascent GX10\u003c\/summary\u003e\n\u003cdiv class=\"pd-acc-body\"\u003e\n\u003cp\u003e\u003cstrong\u003eWhat is the difference between the ASUS Ascent GX10 and the NVIDIA DGX Spark?\u003c\/strong\u003e\u003cbr\u003e\nBoth run the same NVIDIA GB10 Grace Blackwell platform — 128 GB unified memory, up to 1 PFLOP FP4, ConnectX-7 networking. The GX10 comes in 1TB\/2TB\/4TB storage tiers at sharper pricing with onsite warranty; the \u003ca href=\"\/products\/nvidia-dgx-spark-ai-supercomputer-4tb-940-54242-0007-000\"\u003eDGX Spark\u003c\/a\u003e is NVIDIA's first-party 4TB unit.\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eWhat AI models can the GX10 run?\u003c\/strong\u003e\u003cbr\u003e\nUp to 200-billion-parameter models on a single unit — local inference and fine-tuning of Llama, Qwen, DeepSeek, Mistral-class models. Link two GX10s via ConnectX-7 for even larger workloads.\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eWhich storage size should I choose?\u003c\/strong\u003e\u003cbr\u003e\n1TB (this model) suits development and single-model work; choose \u003ca href=\"\/products\/asus-ascent-gx10-compact-desktop-ai-supercomputer-2tb-gx10-gg0030bn\"\u003e2TB\u003c\/a\u003e or \u003ca href=\"\/products\/asus-ascent-gx10-compact-desktop-ai-supercomputer-4tb-gx10-gg0007bn\"\u003e4TB\u003c\/a\u003e if you keep multiple large models, datasets or vector databases on-device.\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eDo I need special power or cooling?\u003c\/strong\u003e\u003cbr\u003e\nNo — it draws from a standard socket via a 240W adapter (USB-C PD 3.1) and cools itself with the whisper-quiet QuietFlow triple-fan design.\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eWhat warranty do I get in Singapore?\u003c\/strong\u003e\u003cbr\u003e\n1 year onsite warranty with a limited-time free upgrade to 3 years, plus a GST invoice from SourceIT. Volume and enterprise procurement enquiries welcome.\u003c\/p\u003e\n\u003c\/div\u003e\n\u003c\/details\u003e\n\u003c\/div\u003e","brand":"Asus","offers":[{"title":"Default Title","offer_id":51166918541476,"sku":"GX10-GG0007BN \/ 90MS0371-M00070","price":7255.0,"currency_code":"SGD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0605\/0136\/0804\/files\/asus-ascent-gx10-compact-desktop-ai-supercomputer-1tb-gx10-gg0007bn-7973426.png?v=1761560946"},{"product_id":"nvidia-rtx-pro™-6000-blackwell-workstation-edition-900-5g144-2500-000","title":"NVIDIA RTX PRO™ 6000 Blackwell Workstation Edition","description":"\u003cdiv\u003e\n\u003cstyle\u003e\n.pd-acc { border: 1px solid #e2e2e2; border-radius: 8px; margin: 10px 0; overflow: hidden; }\n.pd-acc summary { cursor: pointer; padding: 14px 16px; font-weight: 700; font-size: 1.05em; list-style: none; display: flex; justify-content: space-between; align-items: center; background: #f7f7f7; }\n.pd-acc[open] \u003e summary { border-bottom: 1px solid #e2e2e2; }\n.pd-acc summary::-webkit-details-marker { display: none; }\n.pd-acc summary::after { content: \"+\"; font-size: 1.4em; font-weight: 400; line-height: 1; margin-left: 12px; }\n.pd-acc[open] \u003e summary::after { content: \"\\2212\"; }\n.pd-acc .pd-acc-body { padding: 8px 16px 14px; }\n.pd-spec, .pd-spec tr, .pd-spec td { border: none !important; background: transparent !important; }\n.pd-spec { width: 100%; border-collapse: collapse !important; margin: 6px 0 18px; }\n.pd-spec td { padding: 7px 10px 7px 0 !important; border-bottom: 1px solid #ececec !important; vertical-align: top; font-size: 0.95em; text-align: left; }\n.pd-spec td:first-child { width: 38%; font-weight: 600; color: #333; }\n.pd-spec tr:last-child td { border-bottom: none !important; }\n\u003c\/style\u003e\n\u003ch2\u003e\u003cstrong\u003eNVIDIA RTX PRO™ 6000 Blackwell Workstation Edition 96 GB GDDR7 with ECC (900-5G144-2500-000) - 3 Years Local Warranty\u003c\/strong\u003e\u003c\/h2\u003e\n\u003ch3\u003e\u003cstrong\u003eRTX PRO 6000 Blackwell Workstation Edition — 96 GB of GDDR7 for Local AI and Neural Rendering\u003c\/strong\u003e\u003c\/h3\u003e\n\u003cp\u003eThe \u003cstrong\u003eNVIDIA RTX PRO™ 6000 Blackwell Workstation Edition\u003c\/strong\u003e is the top of NVIDIA's Blackwell professional desktop range: 24,064 CUDA cores, 96 GB of GDDR7 with ECC on a 512-bit bus, and 1,792 GB\/s of memory bandwidth. That memory capacity is the headline for Singapore teams doing local AI — it holds large language models, diffusion pipelines and multi-billion-parameter fine-tuning jobs entirely in VRAM, without the round trips to system memory or the cloud that turn an experiment into an overnight run.\u003c\/p\u003e\n\u003ch3\u003e\u003cstrong\u003e4,000 AI TOPS from Fifth-Generation Tensor Cores\u003c\/strong\u003e\u003c\/h3\u003e\n\u003cp\u003eFifth-generation Tensor Cores deliver up to \u003cstrong\u003e4,000 AI TOPS\u003c\/strong\u003e (effective FP4 with sparsity) alongside 126 TFLOPS of FP32 for classic compute. Fourth-generation RT Cores add 382 TFLOPS of ray tracing throughput for neural rendering, real-time visualisation and physically accurate lighting. Ninth-generation NVENC and sixth-generation NVDEC engines — four of each, with 4:2:2 support — make this equally credible as a broadcast and post-production card.\u003c\/p\u003e\n\u003ch3\u003e\u003cstrong\u003eMulti-Instance GPU: One Card, Up to Four Isolated Workspaces\u003c\/strong\u003e\u003c\/h3\u003e\n\u003cp\u003eMIG partitions the card into up to \u003cstrong\u003efour fully isolated 24 GB instances\u003c\/strong\u003e (or two at 48 GB, or one at 96 GB), each with its own memory and compute. For a design studio or research group that means several engineers or several containerised workloads sharing one card with hard resource boundaries rather than fighting over it.\u003c\/p\u003e\n\u003ch3\u003e\u003cstrong\u003e600 W, Double Flow-Through, PCIe 5.0 x16\u003c\/strong\u003e\u003c\/h3\u003e\n\u003cp\u003eAt 600 W total board power with a double flow-through active cooler in a 5.4in x 12in dual-slot extended-height form factor, this card needs a workstation chassis with real airflow and a PSU with headroom, fed by a single PCIe CEM5 16-pin connector. Confirm chassis clearance and power budget before ordering — our team can check your specific workstation model.\u003c\/p\u003e\n\u003cdetails class=\"pd-acc\"\u003e\u003csummary style=\"cursor: pointer; font-weight: 700;\"\u003eNVIDIA RTX PRO™ 6000 Blackwell Workstation Edition Datasheet — Technical Specifications\u003c\/summary\u003e\u003cdiv class=\"pd-acc-body\"\u003e\n\u003ch4\u003eGeneral\u003c\/h4\u003e\n\u003ctable class=\"pd-spec\"\u003e\n\u003ctr\u003e\n\u003ctd\u003ePart Number\u003c\/td\u003e\n\u003ctd\u003e900-5G144-2500-000\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eModel\u003c\/td\u003e\n\u003ctd\u003eNVIDIA RTX PRO™ 6000 Blackwell Workstation Edition\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eGPU Architecture\u003c\/td\u003e\n\u003ctd\u003eNVIDIA Blackwell\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eProduct Family\u003c\/td\u003e\n\u003ctd\u003eNVIDIA RTX PRO Blackwell Workstation \/ Professional Desktop GPUs\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eWarranty\u003c\/td\u003e\n\u003ctd\u003e3 Years Local Warranty (Singapore)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/table\u003e\n\u003ch4\u003eMemory\u003c\/h4\u003e\n\u003ctable class=\"pd-spec\"\u003e\n\u003ctr\u003e\n\u003ctd\u003eGPU Memory\u003c\/td\u003e\n\u003ctd\u003e96 GB GDDR7 with ECC\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eMemory Interface\u003c\/td\u003e\n\u003ctd\u003e512-bit\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eMemory Bandwidth\u003c\/td\u003e\n\u003ctd\u003e1,792 GB\/s\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eECC\u003c\/td\u003e\n\u003ctd\u003eYes — error-correcting code memory\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/table\u003e\n\u003ch4\u003eCompute \u0026amp; Performance\u003c\/h4\u003e\n\u003ctable class=\"pd-spec\"\u003e\n\u003ctr\u003e\n\u003ctd\u003eCUDA Cores\u003c\/td\u003e\n\u003ctd\u003e24,064\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eTensor Cores\u003c\/td\u003e\n\u003ctd\u003eFifth-generation Tensor Cores — 752 (core count per NVIDIA RTX PRO Blackwell architecture whitepaper)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eRT Cores\u003c\/td\u003e\n\u003ctd\u003eFourth-generation RT Cores — 188 (core count per NVIDIA RTX PRO Blackwell architecture whitepaper)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eSingle-Precision (FP32) Performance\u003c\/td\u003e\n\u003ctd\u003e126 TFLOPS\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eRT Core Performance\u003c\/td\u003e\n\u003ctd\u003e382 TFLOPS\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eAI Performance\u003c\/td\u003e\n\u003ctd\u003e4,000 AI TOPS (effective FP4 TOPS with sparsity, peak rates based on GPU Boost Clock)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eMulti-Instance GPU (MIG)\u003c\/td\u003e\n\u003ctd\u003eYes — up to 4x 24 GB, up to 2x 48 GB, or 1x 96 GB (maximum 4 instances)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/table\u003e\n\u003ch4\u003eMedia Engines\u003c\/h4\u003e\n\u003ctable class=\"pd-spec\"\u003e\n\u003ctr\u003e\n\u003ctd\u003eEncoders (NVENC)\u003c\/td\u003e\n\u003ctd\u003e4x NVENC (ninth generation)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eDecoders (NVDEC)\u003c\/td\u003e\n\u003ctd\u003e4x NVDEC (sixth generation), 4:2:2 support\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/table\u003e\n\u003ch4\u003eDisplay\u003c\/h4\u003e\n\u003ctable class=\"pd-spec\"\u003e\n\u003ctr\u003e\n\u003ctd\u003eDisplay Connectors\u003c\/td\u003e\n\u003ctd\u003e4x DisplayPort 2.1b\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eMaximum Simultaneous Displays\u003c\/td\u003e\n\u003ctd\u003e4x 4096 x 2160 @ 120 Hz; 4x 5120 x 2880 @ 60 Hz; 2x 7680 x 4320 @ 60 Hz\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/table\u003e\n\u003ch4\u003ePower, Form Factor \u0026amp; Interface\u003c\/h4\u003e\n\u003ctable class=\"pd-spec\"\u003e\n\u003ctr\u003e\n\u003ctd\u003eMaximum Power Consumption\u003c\/td\u003e\n\u003ctd\u003e600 W total board power\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eGraphics Bus\u003c\/td\u003e\n\u003ctd\u003ePCIe 5.0 x16\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eForm Factor\u003c\/td\u003e\n\u003ctd\u003e5.4in (H) x 12in (L), dual slot, extended height\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eThermal Solution\u003c\/td\u003e\n\u003ctd\u003eActive — double flow-through\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ePower Connector\u003c\/td\u003e\n\u003ctd\u003e1x PCIe CEM5 16-pin\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/table\u003e\n\u003cp\u003e\u003cem\u003eSpecifications are taken from NVIDIA's published datasheet for this model. Fields NVIDIA does not publish for a given card are shown as such rather than estimated.\u003c\/em\u003e\u003c\/p\u003e\n\u003c\/div\u003e\u003c\/details\u003e\u003cdetails class=\"pd-acc\"\u003e\u003csummary style=\"cursor: pointer; font-weight: 700;\"\u003eCompatibility \u0026amp; Software Support\u003c\/summary\u003e\u003cdiv class=\"pd-acc-body\"\u003e\n\u003ctable class=\"pd-spec\"\u003e\n\u003ctr\u003e\n\u003ctd\u003eGraphics APIs\u003c\/td\u003e\n\u003ctd\u003eDirectX 12 (Shader Model 6.6), OpenGL 4.6, Vulkan 1.4\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eCompute APIs\u003c\/td\u003e\n\u003ctd\u003eCUDA 12.8, OpenCL 3.0, DirectCompute\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eOperating Systems\u003c\/td\u003e\n\u003ctd\u003eWindows 10\/11 64-bit; Linux — RHEL 8.10, SUSE Linux Enterprise Desktop 15.6, OpenSUSE 15.6, Fedora 41, Ubuntu 22.04; FreeBSD 14.2; Solaris 11 U4 (per NVIDIA RTX PRO Blackwell Quick Start Guide)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eDrivers\u003c\/td\u003e\n\u003ctd\u003eNVIDIA RTX Enterprise Driver — Production Branch and New Feature Branch\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eProfessional Applications\u003c\/td\u003e\n\u003ctd\u003eCertified across leading CAD, DCC, BIM, medical imaging and simulation ISV applications\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eAI Frameworks\u003c\/td\u003e\n\u003ctd\u003eCUDA, cuDNN, TensorRT, PyTorch, TensorFlow, NVIDIA NIM and NGC containers\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eOmniverse \/ Digital Twin\u003c\/td\u003e\n\u003ctd\u003eSupported via NVIDIA Omniverse and OpenUSD workflows\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eChassis Check\u003c\/td\u003e\n\u003ctd\u003e5.4in (H) x 12in (L), dual slot, extended height — confirm slot clearance, power headroom and airflow before ordering\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/table\u003e\n\u003cp\u003eSourceIT can verify fit against your specific workstation or server model before you order. For ISV certification status on a named application version, check the NVIDIA \u003ca href=\"https:\/\/www.nvidia.com\/en-us\/products\/workstations\/product-literature\/\" target=\"_blank\" rel=\"noopener\"\u003eprofessional product literature library\u003c\/a\u003e.\u003c\/p\u003e\n\u003c\/div\u003e\u003c\/details\u003e\u003cdetails class=\"pd-acc\"\u003e\u003csummary style=\"cursor: pointer; font-weight: 700;\"\u003ePackage Contents \u0026amp; NVIDIA RTX PRO Blackwell Ordering Information\u003c\/summary\u003e\u003cdiv class=\"pd-acc-body\"\u003e\n\u003cp\u003e\u003cstrong\u003eWhat is in the box:\u003c\/strong\u003e NVIDIA's RTX PRO Blackwell Quick Start Guide lists a Quick Start Card and an auxiliary power cable (2x PCIe 8-pin PSU to 1x PCIe Gen5 16-pin GPU adapter). NVIDIA does not publish a per-SKU box-contents list on the datasheet.\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eNVIDIA RTX PRO Blackwell range available from SourceIT:\u003c\/strong\u003e\u003c\/p\u003e\n\u003ctable class=\"pd-spec\"\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003ePart Number\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e\u003cstrong\u003eModel \u0026amp; Memory\u003c\/strong\u003e\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e900-5G144-2500-000\u003c\/td\u003e\n\u003ctd\u003e\u003cstrong\u003eRTX PRO 6000 Blackwell Workstation Edition — 96 GB GDDR7 with ECC (this item)\u003c\/strong\u003e\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e900-5G153-2500-000\u003c\/td\u003e\n\u003ctd\u003e\n\u003ca href=\"\/products\/nvidia-rtx-pro%E2%84%A2-6000-blackwell-max-q-workstation-edition-900-5g153-2500-000\"\u003eRTX PRO 6000 Blackwell Max-Q Workstation Edition\u003c\/a\u003e — 96 GB GDDR7 with ECC\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e900-2G153-0000-000\u003c\/td\u003e\n\u003ctd\u003e\n\u003ca href=\"\/products\/nvidia-rtx-pro%E2%84%A2-6000-blackwell-server-edition\"\u003eRTX PRO 6000 Blackwell Server Edition\u003c\/a\u003e — 96 GB GDDR7 with ECC\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e900-5G153-2570-000\u003c\/td\u003e\n\u003ctd\u003e\n\u003ca href=\"\/products\/nvidia-rtx-pro%E2%84%A2-5000-blackwell-generation\"\u003eRTX PRO 5000 Blackwell 72 GB\u003c\/a\u003e — 72 GB GDDR7 with ECC\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e900-5G153-2550-000\u003c\/td\u003e\n\u003ctd\u003e\n\u003ca href=\"\/products\/nvidia-rtx-pro%E2%84%A2-5000-blackwell-generation-900-5g153-2550-000\"\u003eRTX PRO 5000 Blackwell 48 GB\u003c\/a\u003e — 48 GB GDDR7 with ECC\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e900-5G147-2550-000\u003c\/td\u003e\n\u003ctd\u003e\n\u003ca href=\"\/products\/nvidia-rtx-pro%E2%84%A2-4500-blackwell-generation-900-5g147-2550-000\"\u003eRTX PRO 4500 Blackwell\u003c\/a\u003e — 32 GB GDDR7 with ECC\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e900-5G147-2570-000\u003c\/td\u003e\n\u003ctd\u003e\n\u003ca href=\"\/products\/nvidia-rtx-pro%E2%84%A2-4000-blackwell-generation-900-5g147-2570-000\"\u003eRTX PRO 4000 Blackwell\u003c\/a\u003e — 24 GB GDDR7 with ECC\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e900-5G195-2501-000\u003c\/td\u003e\n\u003ctd\u003e\n\u003ca href=\"\/products\/nvidia-rtx-pro%E2%84%A2-4000-blackwell-generation-sff-edition-900-5g195-2501-000\"\u003eRTX PRO 4000 Blackwell SFF Edition\u003c\/a\u003e — 24 GB GDDR7 with ECC\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e900-5G195-2551-000\u003c\/td\u003e\n\u003ctd\u003e\n\u003ca href=\"\/products\/nvidia-rtx-pro%E2%84%A2-2000-blackwell-generation-900-5g195-2551-000\"\u003eRTX PRO 2000 Blackwell\u003c\/a\u003e — 16 GB GDDR7 with ECC\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/table\u003e\n\u003cp\u003eChoosing between them: memory capacity is usually the deciding factor for AI work and scene size, while form factor and power decide what will physically fit. The \u003cstrong\u003eMax-Q\u003c\/strong\u003e variant exists to give full 96 GB capacity at 300 W for chassis that cannot take a 600 W card or that need more than one GPU. The \u003cstrong\u003eServer Edition\u003c\/strong\u003e is passively cooled and belongs in a server, not a workstation. The \u003cstrong\u003eSFF Edition\u003c\/strong\u003e trades bandwidth and PCIe lanes for a 70 W low-profile board.\u003c\/p\u003e\n\u003c\/div\u003e\u003c\/details\u003e\u003cdetails class=\"pd-acc\"\u003e\u003csummary style=\"cursor: pointer; font-weight: 700;\"\u003eDatasheet \u0026amp; Downloads\u003c\/summary\u003e\u003cdiv class=\"pd-acc-body\"\u003e\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https:\/\/www.nvidia.com\/content\/dam\/en-zz\/Solutions\/data-center\/rtx-pro-6000-blackwell-workstation-edition\/workstation-blackwell-rtx-pro-6000-workstation-edition-nvidia-us-3519208-web.pdf\" target=\"_blank\" rel=\"noopener\"\u003eNVIDIA RTX PRO™ 6000 Blackwell Workstation Edition official NVIDIA datasheet\u003c\/a\u003e\u003c\/li\u003e\n\u003cli\u003e\u003ca href=\"https:\/\/www.nvidia.com\/en-us\/products\/workstations\/professional-desktop-gpus\/rtx-pro-6000\/\" target=\"_blank\" rel=\"noopener\"\u003eNVIDIA RTX PRO™ 6000 Blackwell Workstation Edition product page on NVIDIA.com\u003c\/a\u003e\u003c\/li\u003e\n\u003cli\u003e\u003ca href=\"https:\/\/www.nvidia.com\/content\/dam\/en-zz\/Solutions\/design-visualization\/quadro-product-literature\/NVIDIA-RTX-Blackwell-PRO-GPU-Architecture-v1.0.pdf\" target=\"_blank\" rel=\"noopener\"\u003eNVIDIA RTX PRO Blackwell GPU Architecture Whitepaper (PDF)\u003c\/a\u003e\u003c\/li\u003e\n\u003cli\u003e\u003ca href=\"https:\/\/images.nvidia.com\/aem-dam\/Solutions\/design-visualization\/quadro-product-literature\/rtx-pro-blackwell-online-qsg-210x148mm-20260317-r18.pdf\" target=\"_blank\" rel=\"noopener\"\u003eNVIDIA RTX PRO Blackwell Quick Start Guide (PDF)\u003c\/a\u003e\u003c\/li\u003e\n\u003cli\u003e\u003ca href=\"https:\/\/www.nvidia.com\/en-us\/products\/workstations\/product-literature\/\" target=\"_blank\" rel=\"noopener\"\u003eNVIDIA professional workstation product literature library\u003c\/a\u003e\u003c\/li\u003e\n\u003c\/ul\u003e\u003c\/div\u003e\u003c\/details\u003e\u003cdetails class=\"pd-acc\"\u003e\u003csummary style=\"cursor: pointer; font-weight: 700;\"\u003eFrequently Asked Questions — RTX PRO 6000 Blackwell Workstation Edition\u003c\/summary\u003e\u003cdiv class=\"pd-acc-body\"\u003e\n\u003cp\u003e\u003cstrong\u003eHow much GPU memory does it have, and why does that matter?\u003c\/strong\u003e\u003cbr\u003e96 GB GDDR7 with ECC. For AI work, VRAM capacity decides which models fit on the card at all — a model that does not fit runs an order of magnitude slower or not at all. For visualisation it decides scene and texture budget. ECC means single-bit memory errors are corrected rather than silently corrupting a long job.\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eWhat is the difference between the Workstation Edition and the Max-Q version?\u003c\/strong\u003e\u003cbr\u003eSame 96 GB of GDDR7 with ECC and the same 24,064 CUDA cores. The Workstation Edition runs at 600 W for 4,000 AI TOPS and 126 TFLOPS FP32; the \u003ca href=\"\/products\/nvidia-rtx-pro%E2%84%A2-6000-blackwell-max-q-workstation-edition-900-5g153-2500-000\"\u003eMax-Q\u003c\/a\u003e runs at 300 W for 3,511 AI TOPS and 110 TFLOPS. Choose the Workstation Edition for maximum single-card performance, Max-Q when chassis power or multi-GPU density is the constraint.\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eWhat power supply and chassis clearance do I need?\u003c\/strong\u003e\u003cbr\u003eThis card is 600 w total board power in a 5.4in (H) x 12in (L), dual slot, extended height form factor with active cooling, powered via 1x PCIe CEM5 16-pin. Check both physical clearance and PSU headroom against your workstation before ordering — send us your machine model and we will confirm fit.\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eDoes it support Multi-Instance GPU?\u003c\/strong\u003e\u003cbr\u003eYes — up to 4x 24 GB, up to 2x 48 GB, or 1x 96 GB (maximum 4 instances). Each instance gets isolated memory and compute, so several users, containers or services can share one card with hard boundaries instead of contending for it.\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eIs this a professional card or a gaming card?\u003c\/strong\u003e\u003cbr\u003eProfessional. RTX PRO Blackwell cards carry ECC memory, NVIDIA RTX Enterprise drivers with ISV application certification, longer driver support branches and a professional warranty. Consumer GeForce cards have none of these, which is why they are not accepted in most enterprise and regulated deployments regardless of raw frame rate.\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eDoes it come with local warranty in Singapore?\u003c\/strong\u003e\u003cbr\u003eYes. Every RTX PRO 6000 Blackwell Workstation Edition sold by SourceIT comes with 3 years local Singapore warranty and a GST invoice.\u003c\/p\u003e\n\u003c\/div\u003e\u003c\/details\u003e\n\u003c\/div\u003e","brand":"Nvidia","offers":[{"title":"Default Title","offer_id":51167246123172,"sku":"900-5G144-2500-000","price":26685.0,"currency_code":"SGD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0605\/0136\/0804\/files\/nvidia-rtx-pro-6000-blackwell-workstation-edition-900-5g144-2500-000-7881029.webp?v=1761645031"},{"product_id":"nvidia-rtx-pro™-6000-blackwell-max-q-workstation-edition-900-5g153-2500-000","title":"NVIDIA RTX PRO™ 6000 Blackwell Max-Q Workstation Edition","description":"\u003cdiv\u003e\n\u003cstyle\u003e\n.pd-acc { border: 1px solid #e2e2e2; border-radius: 8px; margin: 10px 0; overflow: hidden; }\n.pd-acc summary { cursor: pointer; padding: 14px 16px; font-weight: 700; font-size: 1.05em; list-style: none; display: flex; justify-content: space-between; align-items: center; background: #f7f7f7; }\n.pd-acc[open] \u003e summary { border-bottom: 1px solid #e2e2e2; }\n.pd-acc summary::-webkit-details-marker { display: none; }\n.pd-acc summary::after { content: \"+\"; font-size: 1.4em; font-weight: 400; line-height: 1; margin-left: 12px; }\n.pd-acc[open] \u003e summary::after { content: \"\\2212\"; }\n.pd-acc .pd-acc-body { padding: 8px 16px 14px; }\n.pd-spec, .pd-spec tr, .pd-spec td { border: none !important; background: transparent !important; }\n.pd-spec { width: 100%; border-collapse: collapse !important; margin: 6px 0 18px; }\n.pd-spec td { padding: 7px 10px 7px 0 !important; border-bottom: 1px solid #ececec !important; vertical-align: top; font-size: 0.95em; text-align: left; }\n.pd-spec td:first-child { width: 38%; font-weight: 600; color: #333; }\n.pd-spec tr:last-child td { border-bottom: none !important; }\n\u003c\/style\u003e\n\u003ch2\u003e\u003cstrong\u003eNVIDIA RTX PRO™ 6000 Blackwell Max-Q Workstation Edition 96 GB GDDR7 with ECC (900-5G153-2500-000) - 3 Years Local Warranty\u003c\/strong\u003e\u003c\/h2\u003e\n\u003ch3\u003e\u003cstrong\u003eRTX PRO 6000 Blackwell Max-Q Workstation Edition — 96 GB in a 300 W Envelope\u003c\/strong\u003e\u003c\/h3\u003e\n\u003cp\u003eThe \u003cstrong\u003eNVIDIA RTX PRO™ 6000 Blackwell Max-Q Workstation Edition\u003c\/strong\u003e answers a specific problem: you need the full 96 GB of GDDR7 with ECC and all 24,064 CUDA cores, but your chassis or your power budget cannot absorb a 600 W card. Max-Q keeps the same memory capacity and the same 1,792 GB\/s of bandwidth at \u003cstrong\u003e300 W total board power\u003c\/strong\u003e in a standard 4.4in x 10.5in dual-slot full-height card.\u003c\/p\u003e\n\u003ch3\u003e\u003cstrong\u003e3,511 AI TOPS for Multi-GPU Workstations\u003c\/strong\u003e\u003c\/h3\u003e\n\u003cp\u003eFifth-generation Tensor Cores deliver up to 3,511 AI TOPS (effective FP4 with sparsity) with 110 TFLOPS FP32 and 333 TFLOPS of RT Core performance. Because each card draws half the power of the Workstation Edition, Max-Q is the variant to specify when you want \u003cstrong\u003etwo or more 96 GB cards in one chassis\u003c\/strong\u003e — 192 GB or more of aggregate ECC VRAM in a workstation that stays inside its thermal and power design.\u003c\/p\u003e\n\u003ch3\u003e\u003cstrong\u003eMulti-Instance GPU for Shared Teams\u003c\/strong\u003e\u003c\/h3\u003e\n\u003cp\u003eMIG splits the card into up to \u003cstrong\u003efour isolated 24 GB instances\u003c\/strong\u003e, two at 48 GB, or one at 96 GB, giving hard boundaries between users or containers on shared hardware.\u003c\/p\u003e\n\u003ch3\u003e\u003cstrong\u003eStandard Form Factor, Standard Power\u003c\/strong\u003e\u003c\/h3\u003e\n\u003cp\u003e4.4in x 10.5in, dual slot, full height, active cooling, PCIe 5.0 x16 and a single PCIe CEM5 16-pin connector. It fits the workstation chassis you already buy, which is the whole point of the Max-Q variant.\u003c\/p\u003e\n\u003cdetails class=\"pd-acc\"\u003e\u003csummary style=\"cursor: pointer; font-weight: 700;\"\u003eNVIDIA RTX PRO™ 6000 Blackwell Max-Q Workstation Edition Datasheet — Technical Specifications\u003c\/summary\u003e\u003cdiv class=\"pd-acc-body\"\u003e\n\u003ch4\u003eGeneral\u003c\/h4\u003e\n\u003ctable class=\"pd-spec\"\u003e\n\u003ctr\u003e\n\u003ctd\u003ePart Number\u003c\/td\u003e\n\u003ctd\u003e900-5G153-2500-000\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eModel\u003c\/td\u003e\n\u003ctd\u003eNVIDIA RTX PRO™ 6000 Blackwell Max-Q Workstation Edition\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eGPU Architecture\u003c\/td\u003e\n\u003ctd\u003eNVIDIA Blackwell\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eProduct Family\u003c\/td\u003e\n\u003ctd\u003eNVIDIA RTX PRO Blackwell Workstation \/ Professional Desktop GPUs\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eWarranty\u003c\/td\u003e\n\u003ctd\u003e3 Years Local Warranty (Singapore)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/table\u003e\n\u003ch4\u003eMemory\u003c\/h4\u003e\n\u003ctable class=\"pd-spec\"\u003e\n\u003ctr\u003e\n\u003ctd\u003eGPU Memory\u003c\/td\u003e\n\u003ctd\u003e96 GB GDDR7 with ECC\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eMemory Interface\u003c\/td\u003e\n\u003ctd\u003e512-bit\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eMemory Bandwidth\u003c\/td\u003e\n\u003ctd\u003e1,792 GB\/s\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eECC\u003c\/td\u003e\n\u003ctd\u003eYes — error-correcting code memory\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/table\u003e\n\u003ch4\u003eCompute \u0026amp; Performance\u003c\/h4\u003e\n\u003ctable class=\"pd-spec\"\u003e\n\u003ctr\u003e\n\u003ctd\u003eCUDA Cores\u003c\/td\u003e\n\u003ctd\u003e24,064\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eTensor Cores\u003c\/td\u003e\n\u003ctd\u003eFifth-generation Tensor Cores — 752 (core count per NVIDIA RTX PRO Blackwell architecture whitepaper)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eRT Cores\u003c\/td\u003e\n\u003ctd\u003eFourth-generation RT Cores — 188 (core count per NVIDIA RTX PRO Blackwell architecture whitepaper)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eSingle-Precision (FP32) Performance\u003c\/td\u003e\n\u003ctd\u003e110 TFLOPS\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eRT Core Performance\u003c\/td\u003e\n\u003ctd\u003e333 TFLOPS\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eAI Performance\u003c\/td\u003e\n\u003ctd\u003e3,511 AI TOPS (effective FP4 TOPS with sparsity, peak rates based on GPU Boost Clock)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eMulti-Instance GPU (MIG)\u003c\/td\u003e\n\u003ctd\u003eYes — up to 4x 24 GB, up to 2x 48 GB, or 1x 96 GB (maximum 4 instances)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/table\u003e\n\u003ch4\u003eMedia Engines\u003c\/h4\u003e\n\u003ctable class=\"pd-spec\"\u003e\n\u003ctr\u003e\n\u003ctd\u003eEncoders (NVENC)\u003c\/td\u003e\n\u003ctd\u003e4x NVENC (ninth generation)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eDecoders (NVDEC)\u003c\/td\u003e\n\u003ctd\u003e4x NVDEC (sixth generation), 4:2:2 support\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/table\u003e\n\u003ch4\u003eDisplay\u003c\/h4\u003e\n\u003ctable class=\"pd-spec\"\u003e\n\u003ctr\u003e\n\u003ctd\u003eDisplay Connectors\u003c\/td\u003e\n\u003ctd\u003e4x DisplayPort 2.1b\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eMaximum Simultaneous Displays\u003c\/td\u003e\n\u003ctd\u003e4x 4096 x 2160 @ 120 Hz; 4x 5120 x 2880 @ 60 Hz; 2x 7680 x 4320 @ 60 Hz\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/table\u003e\n\u003ch4\u003ePower, Form Factor \u0026amp; Interface\u003c\/h4\u003e\n\u003ctable class=\"pd-spec\"\u003e\n\u003ctr\u003e\n\u003ctd\u003eMaximum Power Consumption\u003c\/td\u003e\n\u003ctd\u003e300 W total board power\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eGraphics Bus\u003c\/td\u003e\n\u003ctd\u003ePCIe 5.0 x16\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eForm Factor\u003c\/td\u003e\n\u003ctd\u003e4.4in (H) x 10.5in (L), dual slot, full height\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eThermal Solution\u003c\/td\u003e\n\u003ctd\u003eActive\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ePower Connector\u003c\/td\u003e\n\u003ctd\u003e1x PCIe CEM5 16-pin\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/table\u003e\n\u003cp\u003e\u003cem\u003eSpecifications are taken from NVIDIA's published datasheet for this model. Fields NVIDIA does not publish for a given card are shown as such rather than estimated.\u003c\/em\u003e\u003c\/p\u003e\n\u003c\/div\u003e\u003c\/details\u003e\u003cdetails class=\"pd-acc\"\u003e\u003csummary style=\"cursor: pointer; font-weight: 700;\"\u003eCompatibility \u0026amp; Software Support\u003c\/summary\u003e\u003cdiv class=\"pd-acc-body\"\u003e\n\u003ctable class=\"pd-spec\"\u003e\n\u003ctr\u003e\n\u003ctd\u003eGraphics APIs\u003c\/td\u003e\n\u003ctd\u003eDirectX 12 (Shader Model 6.6), OpenGL 4.6, Vulkan 1.4\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eCompute APIs\u003c\/td\u003e\n\u003ctd\u003eCUDA 12.8, OpenCL 3.0, DirectCompute\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eOperating Systems\u003c\/td\u003e\n\u003ctd\u003eWindows 10\/11 64-bit; Linux — RHEL 8.10, SUSE Linux Enterprise Desktop 15.6, OpenSUSE 15.6, Fedora 41, Ubuntu 22.04; FreeBSD 14.2; Solaris 11 U4 (per NVIDIA RTX PRO Blackwell Quick Start Guide)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eDrivers\u003c\/td\u003e\n\u003ctd\u003eNVIDIA RTX Enterprise Driver — Production Branch and New Feature Branch\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eProfessional Applications\u003c\/td\u003e\n\u003ctd\u003eCertified across leading CAD, DCC, BIM, medical imaging and simulation ISV applications\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eAI Frameworks\u003c\/td\u003e\n\u003ctd\u003eCUDA, cuDNN, TensorRT, PyTorch, TensorFlow, NVIDIA NIM and NGC containers\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eOmniverse \/ Digital Twin\u003c\/td\u003e\n\u003ctd\u003eSupported via NVIDIA Omniverse and OpenUSD workflows\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eChassis Check\u003c\/td\u003e\n\u003ctd\u003e4.4in (H) x 10.5in (L), dual slot, full height — confirm slot clearance, power headroom and airflow before ordering\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/table\u003e\n\u003cp\u003eSourceIT can verify fit against your specific workstation or server model before you order. For ISV certification status on a named application version, check the NVIDIA \u003ca href=\"https:\/\/www.nvidia.com\/en-us\/products\/workstations\/product-literature\/\" target=\"_blank\" rel=\"noopener\"\u003eprofessional product literature library\u003c\/a\u003e.\u003c\/p\u003e\n\u003c\/div\u003e\u003c\/details\u003e\u003cdetails class=\"pd-acc\"\u003e\u003csummary style=\"cursor: pointer; font-weight: 700;\"\u003ePackage Contents \u0026amp; NVIDIA RTX PRO Blackwell Ordering Information\u003c\/summary\u003e\u003cdiv class=\"pd-acc-body\"\u003e\n\u003cp\u003e\u003cstrong\u003eWhat is in the box:\u003c\/strong\u003e NVIDIA's RTX PRO Blackwell Quick Start Guide lists a Quick Start Card and an auxiliary power cable (2x PCIe 8-pin PSU to 1x PCIe Gen5 16-pin GPU adapter). NVIDIA does not publish a per-SKU box-contents list on the datasheet.\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eNVIDIA RTX PRO Blackwell range available from SourceIT:\u003c\/strong\u003e\u003c\/p\u003e\n\u003ctable class=\"pd-spec\"\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003ePart Number\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e\u003cstrong\u003eModel \u0026amp; Memory\u003c\/strong\u003e\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e900-5G144-2500-000\u003c\/td\u003e\n\u003ctd\u003e\n\u003ca href=\"\/products\/nvidia-rtx-pro%E2%84%A2-6000-blackwell-workstation-edition-900-5g144-2500-000\"\u003eRTX PRO 6000 Blackwell Workstation Edition\u003c\/a\u003e — 96 GB GDDR7 with ECC\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e900-5G153-2500-000\u003c\/td\u003e\n\u003ctd\u003e\u003cstrong\u003eRTX PRO 6000 Blackwell Max-Q Workstation Edition — 96 GB GDDR7 with ECC (this item)\u003c\/strong\u003e\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e900-2G153-0000-000\u003c\/td\u003e\n\u003ctd\u003e\n\u003ca href=\"\/products\/nvidia-rtx-pro%E2%84%A2-6000-blackwell-server-edition\"\u003eRTX PRO 6000 Blackwell Server Edition\u003c\/a\u003e — 96 GB GDDR7 with ECC\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e900-5G153-2570-000\u003c\/td\u003e\n\u003ctd\u003e\n\u003ca href=\"\/products\/nvidia-rtx-pro%E2%84%A2-5000-blackwell-generation\"\u003eRTX PRO 5000 Blackwell 72 GB\u003c\/a\u003e — 72 GB GDDR7 with ECC\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e900-5G153-2550-000\u003c\/td\u003e\n\u003ctd\u003e\n\u003ca href=\"\/products\/nvidia-rtx-pro%E2%84%A2-5000-blackwell-generation-900-5g153-2550-000\"\u003eRTX PRO 5000 Blackwell 48 GB\u003c\/a\u003e — 48 GB GDDR7 with ECC\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e900-5G147-2550-000\u003c\/td\u003e\n\u003ctd\u003e\n\u003ca href=\"\/products\/nvidia-rtx-pro%E2%84%A2-4500-blackwell-generation-900-5g147-2550-000\"\u003eRTX PRO 4500 Blackwell\u003c\/a\u003e — 32 GB GDDR7 with ECC\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e900-5G147-2570-000\u003c\/td\u003e\n\u003ctd\u003e\n\u003ca href=\"\/products\/nvidia-rtx-pro%E2%84%A2-4000-blackwell-generation-900-5g147-2570-000\"\u003eRTX PRO 4000 Blackwell\u003c\/a\u003e — 24 GB GDDR7 with ECC\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e900-5G195-2501-000\u003c\/td\u003e\n\u003ctd\u003e\n\u003ca href=\"\/products\/nvidia-rtx-pro%E2%84%A2-4000-blackwell-generation-sff-edition-900-5g195-2501-000\"\u003eRTX PRO 4000 Blackwell SFF Edition\u003c\/a\u003e — 24 GB GDDR7 with ECC\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e900-5G195-2551-000\u003c\/td\u003e\n\u003ctd\u003e\n\u003ca href=\"\/products\/nvidia-rtx-pro%E2%84%A2-2000-blackwell-generation-900-5g195-2551-000\"\u003eRTX PRO 2000 Blackwell\u003c\/a\u003e — 16 GB GDDR7 with ECC\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/table\u003e\n\u003cp\u003eChoosing between them: memory capacity is usually the deciding factor for AI work and scene size, while form factor and power decide what will physically fit. The \u003cstrong\u003eMax-Q\u003c\/strong\u003e variant exists to give full 96 GB capacity at 300 W for chassis that cannot take a 600 W card or that need more than one GPU. The \u003cstrong\u003eServer Edition\u003c\/strong\u003e is passively cooled and belongs in a server, not a workstation. The \u003cstrong\u003eSFF Edition\u003c\/strong\u003e trades bandwidth and PCIe lanes for a 70 W low-profile board.\u003c\/p\u003e\n\u003c\/div\u003e\u003c\/details\u003e\u003cdetails class=\"pd-acc\"\u003e\u003csummary style=\"cursor: pointer; font-weight: 700;\"\u003eDatasheet \u0026amp; Downloads\u003c\/summary\u003e\u003cdiv class=\"pd-acc-body\"\u003e\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https:\/\/www.nvidia.com\/content\/dam\/en-zz\/Solutions\/products\/workstations\/professional-desktop-gpus\/rtx-pro-6000-max-q\/workstation-datasheet-blackwell-rtx-pro-6000-max-q-nvidia-3519233.pdf\" target=\"_blank\" rel=\"noopener\"\u003eNVIDIA RTX PRO™ 6000 Blackwell Max-Q Workstation Edition official NVIDIA datasheet\u003c\/a\u003e\u003c\/li\u003e\n\u003cli\u003e\u003ca href=\"https:\/\/www.nvidia.com\/en-us\/products\/workstations\/professional-desktop-gpus\/rtx-pro-6000-max-q\/\" target=\"_blank\" rel=\"noopener\"\u003eNVIDIA RTX PRO™ 6000 Blackwell Max-Q Workstation Edition product page on NVIDIA.com\u003c\/a\u003e\u003c\/li\u003e\n\u003cli\u003e\u003ca href=\"https:\/\/www.nvidia.com\/content\/dam\/en-zz\/Solutions\/design-visualization\/quadro-product-literature\/NVIDIA-RTX-Blackwell-PRO-GPU-Architecture-v1.0.pdf\" target=\"_blank\" rel=\"noopener\"\u003eNVIDIA RTX PRO Blackwell GPU Architecture Whitepaper (PDF)\u003c\/a\u003e\u003c\/li\u003e\n\u003cli\u003e\u003ca href=\"https:\/\/images.nvidia.com\/aem-dam\/Solutions\/design-visualization\/quadro-product-literature\/rtx-pro-blackwell-online-qsg-210x148mm-20260317-r18.pdf\" target=\"_blank\" rel=\"noopener\"\u003eNVIDIA RTX PRO Blackwell Quick Start Guide (PDF)\u003c\/a\u003e\u003c\/li\u003e\n\u003cli\u003e\u003ca href=\"https:\/\/www.nvidia.com\/en-us\/products\/workstations\/product-literature\/\" target=\"_blank\" rel=\"noopener\"\u003eNVIDIA professional workstation product literature library\u003c\/a\u003e\u003c\/li\u003e\n\u003c\/ul\u003e\u003c\/div\u003e\u003c\/details\u003e\u003cdetails class=\"pd-acc\"\u003e\u003csummary style=\"cursor: pointer; font-weight: 700;\"\u003eFrequently Asked Questions — RTX PRO 6000 Blackwell Max-Q Workstation Edition\u003c\/summary\u003e\u003cdiv class=\"pd-acc-body\"\u003e\n\u003cp\u003e\u003cstrong\u003eHow much GPU memory does it have, and why does that matter?\u003c\/strong\u003e\u003cbr\u003e96 GB GDDR7 with ECC. For AI work, VRAM capacity decides which models fit on the card at all — a model that does not fit runs an order of magnitude slower or not at all. For visualisation it decides scene and texture budget. ECC means single-bit memory errors are corrected rather than silently corrupting a long job.\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eWhat do I give up versus the full 600 W Workstation Edition?\u003c\/strong\u003e\u003cbr\u003eRoughly 12 percent of peak throughput for half the power. The \u003ca href=\"\/products\/nvidia-rtx-pro%E2%84%A2-6000-blackwell-workstation-edition-900-5g144-2500-000\"\u003eWorkstation Edition\u003c\/a\u003e delivers 4,000 AI TOPS and 126 TFLOPS FP32 at 600 W; Max-Q delivers 3,511 AI TOPS and 110 TFLOPS at 300 W. Memory, bandwidth and CUDA core count are identical. In multi-GPU or power-constrained builds Max-Q usually wins on total system throughput.\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eWhat power supply and chassis clearance do I need?\u003c\/strong\u003e\u003cbr\u003eThis card is 300 w total board power in a 4.4in (H) x 10.5in (L), dual slot, full height form factor with active cooling, powered via 1x PCIe CEM5 16-pin. Check both physical clearance and PSU headroom against your workstation before ordering — send us your machine model and we will confirm fit.\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eDoes it support Multi-Instance GPU?\u003c\/strong\u003e\u003cbr\u003eYes — up to 4x 24 GB, up to 2x 48 GB, or 1x 96 GB (maximum 4 instances). Each instance gets isolated memory and compute, so several users, containers or services can share one card with hard boundaries instead of contending for it.\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eIs this a professional card or a gaming card?\u003c\/strong\u003e\u003cbr\u003eProfessional. RTX PRO Blackwell cards carry ECC memory, NVIDIA RTX Enterprise drivers with ISV application certification, longer driver support branches and a professional warranty. Consumer GeForce cards have none of these, which is why they are not accepted in most enterprise and regulated deployments regardless of raw frame rate.\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eDoes it come with local warranty in Singapore?\u003c\/strong\u003e\u003cbr\u003eYes. Every RTX PRO 6000 Blackwell Max-Q Workstation Edition sold by SourceIT comes with 3 years local Singapore warranty and a GST invoice.\u003c\/p\u003e\n\u003c\/div\u003e\u003c\/details\u003e\n\u003c\/div\u003e","brand":"Nvidia","offers":[{"title":"Default Title","offer_id":51167248613540,"sku":"900-5G153-2500-000","price":26685.0,"currency_code":"SGD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0605\/0136\/0804\/files\/nvidia-rtx-pro-6000-blackwell-max-q-workstation-edition-900-5g153-2500-000-9593279.webp?v=1761645031"},{"product_id":"nvidia-rtx-pro™-5000-blackwell-generation-900-5g153-2550-000","title":"NVIDIA RTX PRO™ 5000 Blackwell Generation","description":"\u003cdiv\u003e\n\u003cstyle\u003e\n.pd-acc { border: 1px solid #e2e2e2; border-radius: 8px; margin: 10px 0; overflow: hidden; }\n.pd-acc summary { cursor: pointer; padding: 14px 16px; font-weight: 700; font-size: 1.05em; list-style: none; display: flex; justify-content: space-between; align-items: center; background: #f7f7f7; }\n.pd-acc[open] \u003e summary { border-bottom: 1px solid #e2e2e2; }\n.pd-acc summary::-webkit-details-marker { display: none; }\n.pd-acc summary::after { content: \"+\"; font-size: 1.4em; font-weight: 400; line-height: 1; margin-left: 12px; }\n.pd-acc[open] \u003e summary::after { content: \"\\2212\"; }\n.pd-acc .pd-acc-body { padding: 8px 16px 14px; }\n.pd-spec, .pd-spec tr, .pd-spec td { border: none !important; background: transparent !important; }\n.pd-spec { width: 100%; border-collapse: collapse !important; margin: 6px 0 18px; }\n.pd-spec td { padding: 7px 10px 7px 0 !important; border-bottom: 1px solid #ececec !important; vertical-align: top; font-size: 0.95em; text-align: left; }\n.pd-spec td:first-child { width: 38%; font-weight: 600; color: #333; }\n.pd-spec tr:last-child td { border-bottom: none !important; }\n\u003c\/style\u003e\n\u003ch2\u003e\u003cstrong\u003eNVIDIA RTX PRO™ 5000 Blackwell Generation 48 GB GDDR7 with ECC (900-5G153-2550-000) - 3 Years Local Warranty\u003c\/strong\u003e\u003c\/h2\u003e\n\u003ch3\u003e\u003cstrong\u003eRTX PRO 5000 Blackwell 48 GB — 48 GB GDDR7 with ECC for Professional AI and Visualisation\u003c\/strong\u003e\u003c\/h3\u003e\n\u003cp\u003eThe \u003cstrong\u003eNVIDIA RTX PRO™ 5000 Blackwell\u003c\/strong\u003e pairs 48 GB GDDR7 with ECC with 1,344 GB\/s of memory bandwidth across a 384-bit interface and 14,080 CUDA cores. It is the balance point of the RTX PRO Blackwell desktop range: enough VRAM for serious model work and large scenes, in a standard 300 W card that fits a normal workstation.\u003c\/p\u003e\n\u003ch3\u003e\u003cstrong\u003e2,064 AI TOPS from Fifth-Generation Tensor Cores\u003c\/strong\u003e\u003c\/h3\u003e\n\u003cp\u003eFifth-generation Tensor Cores deliver 2,064 AI TOPS (effective FP4 TOPS with sparsity, peak rates based on GPU Boost Clock), with 65 TFLOPS of FP32 compute and 196 TFLOPS of fourth-generation RT Core performance for ray tracing and neural rendering. 3x NVENC (ninth generation) and 3x NVDEC (sixth generation) handle encode and decode for video and streaming pipelines.\u003c\/p\u003e\n\u003ch3\u003e\u003cstrong\u003eMulti-Instance GPU for Shared Workstations\u003c\/strong\u003e\u003c\/h3\u003e\n\u003cp\u003eMIG support lets this card be partitioned — up to 2x 24 GB or 1x 48 GB (maximum 2 instances) — so two users or two containerised workloads can share one GPU with isolated memory and compute rather than contending for it.\u003c\/p\u003e\n\u003ch3\u003e\u003cstrong\u003eStandard Workstation Fit, Backed Locally\u003c\/strong\u003e\u003c\/h3\u003e\n\u003cp\u003e4.4in (H) x 10.5in (L), dual slot, full height with active cooling on a PCIe 5.0 x16 interface, powered through a 1x PCIe CEM5 16-pin connector. Supplied by SourceIT Pte Ltd in Singapore with a GST invoice and \u003cstrong\u003e3 years local warranty\u003c\/strong\u003e — supported here rather than through an overseas RMA queue.\u003c\/p\u003e\n\u003cdetails class=\"pd-acc\"\u003e\u003csummary style=\"cursor: pointer; font-weight: 700;\"\u003eNVIDIA RTX PRO™ 5000 Blackwell Datasheet — Technical Specifications\u003c\/summary\u003e\u003cdiv class=\"pd-acc-body\"\u003e\n\u003ch4\u003eGeneral\u003c\/h4\u003e\n\u003ctable class=\"pd-spec\"\u003e\n\u003ctr\u003e\n\u003ctd\u003ePart Number\u003c\/td\u003e\n\u003ctd\u003e900-5G153-2550-000\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eModel\u003c\/td\u003e\n\u003ctd\u003eNVIDIA RTX PRO™ 5000 Blackwell\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eGPU Architecture\u003c\/td\u003e\n\u003ctd\u003eNVIDIA Blackwell\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eProduct Family\u003c\/td\u003e\n\u003ctd\u003eNVIDIA RTX PRO Blackwell Workstation \/ Professional Desktop GPUs\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eWarranty\u003c\/td\u003e\n\u003ctd\u003e3 Years Local Warranty (Singapore)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/table\u003e\n\u003ch4\u003eMemory\u003c\/h4\u003e\n\u003ctable class=\"pd-spec\"\u003e\n\u003ctr\u003e\n\u003ctd\u003eGPU Memory\u003c\/td\u003e\n\u003ctd\u003e48 GB GDDR7 with ECC\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eMemory Interface\u003c\/td\u003e\n\u003ctd\u003e384-bit\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eMemory Bandwidth\u003c\/td\u003e\n\u003ctd\u003e1,344 GB\/s\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eECC\u003c\/td\u003e\n\u003ctd\u003eYes — error-correcting code memory\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/table\u003e\n\u003ch4\u003eCompute \u0026amp; Performance\u003c\/h4\u003e\n\u003ctable class=\"pd-spec\"\u003e\n\u003ctr\u003e\n\u003ctd\u003eCUDA Cores\u003c\/td\u003e\n\u003ctd\u003e14,080\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eTensor Cores\u003c\/td\u003e\n\u003ctd\u003eFifth-generation Tensor Cores\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eRT Cores\u003c\/td\u003e\n\u003ctd\u003eFourth-generation RT Cores\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eSingle-Precision (FP32) Performance\u003c\/td\u003e\n\u003ctd\u003e65 TFLOPS\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eRT Core Performance\u003c\/td\u003e\n\u003ctd\u003e196 TFLOPS\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eAI Performance\u003c\/td\u003e\n\u003ctd\u003e2,064 AI TOPS (effective FP4 TOPS with sparsity, peak rates based on GPU Boost Clock)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eMulti-Instance GPU (MIG)\u003c\/td\u003e\n\u003ctd\u003eYes — up to 2x 24 GB or 1x 48 GB (maximum 2 instances)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/table\u003e\n\u003ch4\u003eMedia Engines\u003c\/h4\u003e\n\u003ctable class=\"pd-spec\"\u003e\n\u003ctr\u003e\n\u003ctd\u003eEncoders (NVENC)\u003c\/td\u003e\n\u003ctd\u003e3x NVENC (ninth generation)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eDecoders (NVDEC)\u003c\/td\u003e\n\u003ctd\u003e3x NVDEC (sixth generation)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/table\u003e\n\u003ch4\u003eDisplay\u003c\/h4\u003e\n\u003ctable class=\"pd-spec\"\u003e\n\u003ctr\u003e\n\u003ctd\u003eDisplay Connectors\u003c\/td\u003e\n\u003ctd\u003e4x DisplayPort 2.1b\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eMaximum Simultaneous Displays\u003c\/td\u003e\n\u003ctd\u003e4x 4096 x 2160 @ 120 Hz; 4x 5120 x 2880 @ 60 Hz; 2x 7680 x 4320 @ 60 Hz\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/table\u003e\n\u003ch4\u003ePower, Form Factor \u0026amp; Interface\u003c\/h4\u003e\n\u003ctable class=\"pd-spec\"\u003e\n\u003ctr\u003e\n\u003ctd\u003eMaximum Power Consumption\u003c\/td\u003e\n\u003ctd\u003e300 W total board power\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eGraphics Bus\u003c\/td\u003e\n\u003ctd\u003ePCIe 5.0 x16\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eForm Factor\u003c\/td\u003e\n\u003ctd\u003e4.4in (H) x 10.5in (L), dual slot, full height\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eThermal Solution\u003c\/td\u003e\n\u003ctd\u003eActive\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ePower Connector\u003c\/td\u003e\n\u003ctd\u003e1x PCIe CEM5 16-pin\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/table\u003e\n\u003cp\u003e\u003cem\u003eSpecifications are taken from NVIDIA's published datasheet for this model. Fields NVIDIA does not publish for a given card are shown as such rather than estimated.\u003c\/em\u003e\u003c\/p\u003e\n\u003c\/div\u003e\u003c\/details\u003e\u003cdetails class=\"pd-acc\"\u003e\u003csummary style=\"cursor: pointer; font-weight: 700;\"\u003eCompatibility \u0026amp; Software Support\u003c\/summary\u003e\u003cdiv class=\"pd-acc-body\"\u003e\n\u003ctable class=\"pd-spec\"\u003e\n\u003ctr\u003e\n\u003ctd\u003eGraphics APIs\u003c\/td\u003e\n\u003ctd\u003eDirectX 12 (Shader Model 6.6), OpenGL 4.6, Vulkan 1.4\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eCompute APIs\u003c\/td\u003e\n\u003ctd\u003eCUDA 12.8, OpenCL 3.0, DirectCompute\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eOperating Systems\u003c\/td\u003e\n\u003ctd\u003eWindows 10\/11 64-bit; Linux — RHEL 8.10, SUSE Linux Enterprise Desktop 15.6, OpenSUSE 15.6, Fedora 41, Ubuntu 22.04; FreeBSD 14.2; Solaris 11 U4 (per NVIDIA RTX PRO Blackwell Quick Start Guide)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eDrivers\u003c\/td\u003e\n\u003ctd\u003eNVIDIA RTX Enterprise Driver — Production Branch and New Feature Branch\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eProfessional Applications\u003c\/td\u003e\n\u003ctd\u003eCertified across leading CAD, DCC, BIM, medical imaging and simulation ISV applications\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eAI Frameworks\u003c\/td\u003e\n\u003ctd\u003eCUDA, cuDNN, TensorRT, PyTorch, TensorFlow, NVIDIA NIM and NGC containers\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eOmniverse \/ Digital Twin\u003c\/td\u003e\n\u003ctd\u003eSupported via NVIDIA Omniverse and OpenUSD workflows\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eChassis Check\u003c\/td\u003e\n\u003ctd\u003e4.4in (H) x 10.5in (L), dual slot, full height — confirm slot clearance, power headroom and airflow before ordering\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/table\u003e\n\u003cp\u003eSourceIT can verify fit against your specific workstation or server model before you order. For ISV certification status on a named application version, check the NVIDIA \u003ca href=\"https:\/\/www.nvidia.com\/en-us\/products\/workstations\/product-literature\/\" target=\"_blank\" rel=\"noopener\"\u003eprofessional product literature library\u003c\/a\u003e.\u003c\/p\u003e\n\u003c\/div\u003e\u003c\/details\u003e\u003cdetails class=\"pd-acc\"\u003e\u003csummary style=\"cursor: pointer; font-weight: 700;\"\u003ePackage Contents \u0026amp; NVIDIA RTX PRO Blackwell Ordering Information\u003c\/summary\u003e\u003cdiv class=\"pd-acc-body\"\u003e\n\u003cp\u003e\u003cstrong\u003eWhat is in the box:\u003c\/strong\u003e NVIDIA's RTX PRO Blackwell Quick Start Guide lists a Quick Start Card and an auxiliary power cable (2x PCIe 8-pin PSU to 1x PCIe Gen5 16-pin GPU adapter). NVIDIA does not publish a per-SKU box-contents list on the datasheet.\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eNVIDIA RTX PRO Blackwell range available from SourceIT:\u003c\/strong\u003e\u003c\/p\u003e\n\u003ctable class=\"pd-spec\"\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003ePart Number\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e\u003cstrong\u003eModel \u0026amp; Memory\u003c\/strong\u003e\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e900-5G144-2500-000\u003c\/td\u003e\n\u003ctd\u003e\n\u003ca href=\"\/products\/nvidia-rtx-pro%E2%84%A2-6000-blackwell-workstation-edition-900-5g144-2500-000\"\u003eRTX PRO 6000 Blackwell Workstation Edition\u003c\/a\u003e — 96 GB GDDR7 with ECC\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e900-5G153-2500-000\u003c\/td\u003e\n\u003ctd\u003e\n\u003ca href=\"\/products\/nvidia-rtx-pro%E2%84%A2-6000-blackwell-max-q-workstation-edition-900-5g153-2500-000\"\u003eRTX PRO 6000 Blackwell Max-Q Workstation Edition\u003c\/a\u003e — 96 GB GDDR7 with ECC\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e900-2G153-0000-000\u003c\/td\u003e\n\u003ctd\u003e\n\u003ca href=\"\/products\/nvidia-rtx-pro%E2%84%A2-6000-blackwell-server-edition\"\u003eRTX PRO 6000 Blackwell Server Edition\u003c\/a\u003e — 96 GB GDDR7 with ECC\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e900-5G153-2570-000\u003c\/td\u003e\n\u003ctd\u003e\n\u003ca href=\"\/products\/nvidia-rtx-pro%E2%84%A2-5000-blackwell-generation\"\u003eRTX PRO 5000 Blackwell 72 GB\u003c\/a\u003e — 72 GB GDDR7 with ECC\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e900-5G153-2550-000\u003c\/td\u003e\n\u003ctd\u003e\u003cstrong\u003eRTX PRO 5000 Blackwell 48 GB — 48 GB GDDR7 with ECC (this item)\u003c\/strong\u003e\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e900-5G147-2550-000\u003c\/td\u003e\n\u003ctd\u003e\n\u003ca href=\"\/products\/nvidia-rtx-pro%E2%84%A2-4500-blackwell-generation-900-5g147-2550-000\"\u003eRTX PRO 4500 Blackwell\u003c\/a\u003e — 32 GB GDDR7 with ECC\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e900-5G147-2570-000\u003c\/td\u003e\n\u003ctd\u003e\n\u003ca href=\"\/products\/nvidia-rtx-pro%E2%84%A2-4000-blackwell-generation-900-5g147-2570-000\"\u003eRTX PRO 4000 Blackwell\u003c\/a\u003e — 24 GB GDDR7 with ECC\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e900-5G195-2501-000\u003c\/td\u003e\n\u003ctd\u003e\n\u003ca href=\"\/products\/nvidia-rtx-pro%E2%84%A2-4000-blackwell-generation-sff-edition-900-5g195-2501-000\"\u003eRTX PRO 4000 Blackwell SFF Edition\u003c\/a\u003e — 24 GB GDDR7 with ECC\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e900-5G195-2551-000\u003c\/td\u003e\n\u003ctd\u003e\n\u003ca href=\"\/products\/nvidia-rtx-pro%E2%84%A2-2000-blackwell-generation-900-5g195-2551-000\"\u003eRTX PRO 2000 Blackwell\u003c\/a\u003e — 16 GB GDDR7 with ECC\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/table\u003e\n\u003cp\u003eChoosing between them: memory capacity is usually the deciding factor for AI work and scene size, while form factor and power decide what will physically fit. The \u003cstrong\u003eMax-Q\u003c\/strong\u003e variant exists to give full 96 GB capacity at 300 W for chassis that cannot take a 600 W card or that need more than one GPU. The \u003cstrong\u003eServer Edition\u003c\/strong\u003e is passively cooled and belongs in a server, not a workstation. The \u003cstrong\u003eSFF Edition\u003c\/strong\u003e trades bandwidth and PCIe lanes for a 70 W low-profile board.\u003c\/p\u003e\n\u003c\/div\u003e\u003c\/details\u003e\u003cdetails class=\"pd-acc\"\u003e\u003csummary style=\"cursor: pointer; font-weight: 700;\"\u003eDatasheet \u0026amp; Downloads\u003c\/summary\u003e\u003cdiv class=\"pd-acc-body\"\u003e\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https:\/\/www.nvidia.com\/content\/dam\/en-zz\/Solutions\/products\/workstations\/professional-desktop-gpus\/rtx-pro-5000-blackwell\/workstation-datasheet-blackwell-rtx-pro-5000-5488550-nvidia.pdf\" target=\"_blank\" rel=\"noopener\"\u003eNVIDIA RTX PRO™ 5000 Blackwell official NVIDIA datasheet\u003c\/a\u003e\u003c\/li\u003e\n\u003cli\u003e\u003ca href=\"https:\/\/www.nvidia.com\/en-us\/products\/workstations\/professional-desktop-gpus\/rtx-pro-5000\/\" target=\"_blank\" rel=\"noopener\"\u003eNVIDIA RTX PRO™ 5000 Blackwell product page on NVIDIA.com\u003c\/a\u003e\u003c\/li\u003e\n\u003cli\u003e\u003ca href=\"https:\/\/www.nvidia.com\/content\/dam\/en-zz\/Solutions\/design-visualization\/quadro-product-literature\/NVIDIA-RTX-Blackwell-PRO-GPU-Architecture-v1.0.pdf\" target=\"_blank\" rel=\"noopener\"\u003eNVIDIA RTX PRO Blackwell GPU Architecture Whitepaper (PDF)\u003c\/a\u003e\u003c\/li\u003e\n\u003cli\u003e\u003ca href=\"https:\/\/images.nvidia.com\/aem-dam\/Solutions\/design-visualization\/quadro-product-literature\/rtx-pro-blackwell-online-qsg-210x148mm-20260317-r18.pdf\" target=\"_blank\" rel=\"noopener\"\u003eNVIDIA RTX PRO Blackwell Quick Start Guide (PDF)\u003c\/a\u003e\u003c\/li\u003e\n\u003cli\u003e\u003ca href=\"https:\/\/www.nvidia.com\/en-us\/products\/workstations\/product-literature\/\" target=\"_blank\" rel=\"noopener\"\u003eNVIDIA professional workstation product literature library\u003c\/a\u003e\u003c\/li\u003e\n\u003c\/ul\u003e\u003c\/div\u003e\u003c\/details\u003e\u003cdetails class=\"pd-acc\"\u003e\u003csummary style=\"cursor: pointer; font-weight: 700;\"\u003eFrequently Asked Questions — RTX PRO 5000 Blackwell 48 GB\u003c\/summary\u003e\u003cdiv class=\"pd-acc-body\"\u003e\n\u003cp\u003e\u003cstrong\u003eHow much GPU memory does it have, and why does that matter?\u003c\/strong\u003e\u003cbr\u003e48 GB GDDR7 with ECC. For AI work, VRAM capacity decides which models fit on the card at all — a model that does not fit runs an order of magnitude slower or not at all. For visualisation it decides scene and texture budget. ECC means single-bit memory errors are corrected rather than silently corrupting a long job.\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eHow does it compare to the RTX PRO 4000 Blackwell?\u003c\/strong\u003e\u003cbr\u003eThe \u003ca href=\"\/products\/nvidia-rtx-pro%E2%84%A2-4000-blackwell-generation-900-5g147-2570-000\"\u003eRTX PRO 4000\u003c\/a\u003e steps up to 24 GB of GDDR7, 8,960 CUDA cores, 672 GB\/s and 1,178 AI TOPS at 145 W. The RTX PRO 2000 delivers 16 GB, 4,352 cores, 288 GB\/s and 545 AI TOPS at 70 W. If your models and scenes fit in 16 GB, the 2000 is the efficient choice; if they do not, no amount of compute compensates.\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eWhat power supply and chassis clearance do I need?\u003c\/strong\u003e\u003cbr\u003eThis card is 300 w total board power in a 4.4in (H) x 10.5in (L), dual slot, full height form factor with active cooling, powered via 1x PCIe CEM5 16-pin. Check both physical clearance and PSU headroom against your workstation before ordering — send us your machine model and we will confirm fit.\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eDoes it support Multi-Instance GPU?\u003c\/strong\u003e\u003cbr\u003eYes — up to 2x 24 GB or 1x 48 GB (maximum 2 instances). Each instance gets isolated memory and compute, so several users, containers or services can share one card with hard boundaries instead of contending for it.\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eIs this a professional card or a gaming card?\u003c\/strong\u003e\u003cbr\u003eProfessional. RTX PRO Blackwell cards carry ECC memory, NVIDIA RTX Enterprise drivers with ISV application certification, longer driver support branches and a professional warranty. Consumer GeForce cards have none of these, which is why they are not accepted in most enterprise and regulated deployments regardless of raw frame rate.\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eDoes it come with local warranty in Singapore?\u003c\/strong\u003e\u003cbr\u003eYes. Every RTX PRO 5000 Blackwell 48 GB sold by SourceIT comes with 3 years local Singapore warranty and a GST invoice.\u003c\/p\u003e\n\u003c\/div\u003e\u003c\/details\u003e\n\u003c\/div\u003e","brand":"Nvidia","offers":[{"title":"Default Title","offer_id":51167250415780,"sku":"900-5G153-2550-000","price":14725.0,"currency_code":"SGD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0605\/0136\/0804\/files\/nvidia-rtx-pro-5000-blackwell-generation-900-5g153-2550-000-7312271.webp?v=1761645030"},{"product_id":"nvidia-rtx-pro™-4500-blackwell-generation-900-5g147-2550-000","title":"NVIDIA RTX PRO™ 4500 Blackwell Generation","description":"\u003cdiv\u003e\n\u003cstyle\u003e\n.pd-acc { border: 1px solid #e2e2e2; border-radius: 8px; margin: 10px 0; overflow: hidden; }\n.pd-acc summary { cursor: pointer; padding: 14px 16px; font-weight: 700; font-size: 1.05em; list-style: none; display: flex; justify-content: space-between; align-items: center; background: #f7f7f7; }\n.pd-acc[open] \u003e summary { border-bottom: 1px solid #e2e2e2; }\n.pd-acc summary::-webkit-details-marker { display: none; }\n.pd-acc summary::after { content: \"+\"; font-size: 1.4em; font-weight: 400; line-height: 1; margin-left: 12px; }\n.pd-acc[open] \u003e summary::after { content: \"\\2212\"; }\n.pd-acc .pd-acc-body { padding: 8px 16px 14px; }\n.pd-spec, .pd-spec tr, .pd-spec td { border: none !important; background: transparent !important; }\n.pd-spec { width: 100%; border-collapse: collapse !important; margin: 6px 0 18px; }\n.pd-spec td { padding: 7px 10px 7px 0 !important; border-bottom: 1px solid #ececec !important; vertical-align: top; font-size: 0.95em; text-align: left; }\n.pd-spec td:first-child { width: 38%; font-weight: 600; color: #333; }\n.pd-spec tr:last-child td { border-bottom: none !important; }\n\u003c\/style\u003e\n\u003ch2\u003e\u003cstrong\u003eNVIDIA RTX PRO™ 4500 Blackwell Generation 32 GB GDDR7 with ECC (900-5G147-2550-000) - 3 Years Local Warranty\u003c\/strong\u003e\u003c\/h2\u003e\n\u003ch3\u003e\u003cstrong\u003eRTX PRO 4500 Blackwell — 32 GB GDDR7 with ECC for Professional AI and Visualisation\u003c\/strong\u003e\u003c\/h3\u003e\n\u003cp\u003eThe \u003cstrong\u003eNVIDIA RTX PRO™ 4500 Blackwell\u003c\/strong\u003e pairs 32 GB GDDR7 with ECC with 896 GB\/s of memory bandwidth across a 256-bit interface and 10,496 CUDA cores. It is the balance point of the RTX PRO Blackwell desktop range: enough VRAM for serious model work and large scenes, in a standard 200 W card that fits a normal workstation.\u003c\/p\u003e\n\u003ch3\u003e\u003cstrong\u003e1,617 AI TOPS from Fifth-Generation Tensor Cores\u003c\/strong\u003e\u003c\/h3\u003e\n\u003cp\u003eFifth-generation Tensor Cores deliver 1,617 AI TOPS (effective FP4 TOPS with sparsity, peak rates based on GPU Boost Clock), with 51 TFLOPS of FP32 compute and 153 TFLOPS of fourth-generation RT Core performance for ray tracing and neural rendering. 2x NVENC (ninth generation) and 2x NVDEC (sixth generation) handle encode and decode for video and streaming pipelines.\u003c\/p\u003e\n\u003ch3\u003e\u003cstrong\u003eECC Memory for Work That Has to Be Right\u003c\/strong\u003e\u003c\/h3\u003e\n\u003cp\u003eAll 32 GB GDDR7 carries error-correcting code. For simulation, medical imaging, financial modelling and long training runs, ECC is the difference between a result you can sign off and one you have to re-run because a single bit flipped somewhere in a twelve-hour job.\u003c\/p\u003e\n\u003ch3\u003e\u003cstrong\u003eStandard Workstation Fit, Backed Locally\u003c\/strong\u003e\u003c\/h3\u003e\n\u003cp\u003e4.4in (H) x 10.5in (L), dual slot, full height with active cooling on a PCIe 5.0 x16 interface, powered through a 1x PCIe CEM5 16-pin connector. Supplied by SourceIT Pte Ltd in Singapore with a GST invoice and \u003cstrong\u003e3 years local warranty\u003c\/strong\u003e — supported here rather than through an overseas RMA queue.\u003c\/p\u003e\n\u003cdetails class=\"pd-acc\"\u003e\u003csummary style=\"cursor: pointer; font-weight: 700;\"\u003eNVIDIA RTX PRO™ 4500 Blackwell Datasheet — Technical Specifications\u003c\/summary\u003e\u003cdiv class=\"pd-acc-body\"\u003e\n\u003ch4\u003eGeneral\u003c\/h4\u003e\n\u003ctable class=\"pd-spec\"\u003e\n\u003ctr\u003e\n\u003ctd\u003ePart Number\u003c\/td\u003e\n\u003ctd\u003e900-5G147-2550-000\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eModel\u003c\/td\u003e\n\u003ctd\u003eNVIDIA RTX PRO™ 4500 Blackwell\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eGPU Architecture\u003c\/td\u003e\n\u003ctd\u003eNVIDIA Blackwell\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eProduct Family\u003c\/td\u003e\n\u003ctd\u003eNVIDIA RTX PRO Blackwell Workstation \/ Professional Desktop GPUs\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eWarranty\u003c\/td\u003e\n\u003ctd\u003e3 Years Local Warranty (Singapore)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/table\u003e\n\u003ch4\u003eMemory\u003c\/h4\u003e\n\u003ctable class=\"pd-spec\"\u003e\n\u003ctr\u003e\n\u003ctd\u003eGPU Memory\u003c\/td\u003e\n\u003ctd\u003e32 GB GDDR7 with ECC\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eMemory Interface\u003c\/td\u003e\n\u003ctd\u003e256-bit\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eMemory Bandwidth\u003c\/td\u003e\n\u003ctd\u003e896 GB\/s\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eECC\u003c\/td\u003e\n\u003ctd\u003eYes — error-correcting code memory\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/table\u003e\n\u003ch4\u003eCompute \u0026amp; Performance\u003c\/h4\u003e\n\u003ctable class=\"pd-spec\"\u003e\n\u003ctr\u003e\n\u003ctd\u003eCUDA Cores\u003c\/td\u003e\n\u003ctd\u003e10,496\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eTensor Cores\u003c\/td\u003e\n\u003ctd\u003eFifth-generation Tensor Cores\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eRT Cores\u003c\/td\u003e\n\u003ctd\u003eFourth-generation RT Cores\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eSingle-Precision (FP32) Performance\u003c\/td\u003e\n\u003ctd\u003e51 TFLOPS\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eRT Core Performance\u003c\/td\u003e\n\u003ctd\u003e153 TFLOPS\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eAI Performance\u003c\/td\u003e\n\u003ctd\u003e1,617 AI TOPS (effective FP4 TOPS with sparsity, peak rates based on GPU Boost Clock)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eMulti-Instance GPU (MIG)\u003c\/td\u003e\n\u003ctd\u003eNot published by NVIDIA for this model\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/table\u003e\n\u003ch4\u003eMedia Engines\u003c\/h4\u003e\n\u003ctable class=\"pd-spec\"\u003e\n\u003ctr\u003e\n\u003ctd\u003eEncoders (NVENC)\u003c\/td\u003e\n\u003ctd\u003e2x NVENC (ninth generation)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eDecoders (NVDEC)\u003c\/td\u003e\n\u003ctd\u003e2x NVDEC (sixth generation)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/table\u003e\n\u003ch4\u003eDisplay\u003c\/h4\u003e\n\u003ctable class=\"pd-spec\"\u003e\n\u003ctr\u003e\n\u003ctd\u003eDisplay Connectors\u003c\/td\u003e\n\u003ctd\u003e4x DisplayPort 2.1b\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eMaximum Simultaneous Displays\u003c\/td\u003e\n\u003ctd\u003e4x 3840 x 2160 @ 165 Hz; 2x 7680 x 4320 @ 100 Hz\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/table\u003e\n\u003ch4\u003ePower, Form Factor \u0026amp; Interface\u003c\/h4\u003e\n\u003ctable class=\"pd-spec\"\u003e\n\u003ctr\u003e\n\u003ctd\u003eMaximum Power Consumption\u003c\/td\u003e\n\u003ctd\u003e200 W total board power\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eGraphics Bus\u003c\/td\u003e\n\u003ctd\u003ePCIe 5.0 x16\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eForm Factor\u003c\/td\u003e\n\u003ctd\u003e4.4in (H) x 10.5in (L), dual slot, full height\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eThermal Solution\u003c\/td\u003e\n\u003ctd\u003eActive\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ePower Connector\u003c\/td\u003e\n\u003ctd\u003e1x PCIe CEM5 16-pin\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/table\u003e\n\u003cp\u003e\u003cem\u003eSpecifications are taken from NVIDIA's published datasheet for this model. Fields NVIDIA does not publish for a given card are shown as such rather than estimated.\u003c\/em\u003e\u003c\/p\u003e\n\u003c\/div\u003e\u003c\/details\u003e\u003cdetails class=\"pd-acc\"\u003e\u003csummary style=\"cursor: pointer; font-weight: 700;\"\u003eCompatibility \u0026amp; Software Support\u003c\/summary\u003e\u003cdiv class=\"pd-acc-body\"\u003e\n\u003ctable class=\"pd-spec\"\u003e\n\u003ctr\u003e\n\u003ctd\u003eGraphics APIs\u003c\/td\u003e\n\u003ctd\u003eDirectX 12 (Shader Model 6.6), OpenGL 4.6, Vulkan 1.4\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eCompute APIs\u003c\/td\u003e\n\u003ctd\u003eCUDA 12.8, OpenCL 3.0, DirectCompute\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eOperating Systems\u003c\/td\u003e\n\u003ctd\u003eWindows 10\/11 64-bit; Linux — RHEL 8.10, SUSE Linux Enterprise Desktop 15.6, OpenSUSE 15.6, Fedora 41, Ubuntu 22.04; FreeBSD 14.2; Solaris 11 U4 (per NVIDIA RTX PRO Blackwell Quick Start Guide)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eDrivers\u003c\/td\u003e\n\u003ctd\u003eNVIDIA RTX Enterprise Driver — Production Branch and New Feature Branch\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eProfessional Applications\u003c\/td\u003e\n\u003ctd\u003eCertified across leading CAD, DCC, BIM, medical imaging and simulation ISV applications\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eAI Frameworks\u003c\/td\u003e\n\u003ctd\u003eCUDA, cuDNN, TensorRT, PyTorch, TensorFlow, NVIDIA NIM and NGC containers\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eOmniverse \/ Digital Twin\u003c\/td\u003e\n\u003ctd\u003eSupported via NVIDIA Omniverse and OpenUSD workflows\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eChassis Check\u003c\/td\u003e\n\u003ctd\u003e4.4in (H) x 10.5in (L), dual slot, full height — confirm slot clearance, power headroom and airflow before ordering\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/table\u003e\n\u003cp\u003eSourceIT can verify fit against your specific workstation or server model before you order. For ISV certification status on a named application version, check the NVIDIA \u003ca href=\"https:\/\/www.nvidia.com\/en-us\/products\/workstations\/product-literature\/\" target=\"_blank\" rel=\"noopener\"\u003eprofessional product literature library\u003c\/a\u003e.\u003c\/p\u003e\n\u003c\/div\u003e\u003c\/details\u003e\u003cdetails class=\"pd-acc\"\u003e\u003csummary style=\"cursor: pointer; font-weight: 700;\"\u003ePackage Contents \u0026amp; NVIDIA RTX PRO Blackwell Ordering Information\u003c\/summary\u003e\u003cdiv class=\"pd-acc-body\"\u003e\n\u003cp\u003e\u003cstrong\u003eWhat is in the box:\u003c\/strong\u003e NVIDIA's RTX PRO Blackwell Quick Start Guide lists a Quick Start Card and an auxiliary power cable (2x PCIe 8-pin PSU to 1x PCIe Gen5 16-pin GPU adapter). NVIDIA does not publish a per-SKU box-contents list on the datasheet.\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eNVIDIA RTX PRO Blackwell range available from SourceIT:\u003c\/strong\u003e\u003c\/p\u003e\n\u003ctable class=\"pd-spec\"\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003ePart Number\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e\u003cstrong\u003eModel \u0026amp; Memory\u003c\/strong\u003e\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e900-5G144-2500-000\u003c\/td\u003e\n\u003ctd\u003e\n\u003ca href=\"\/products\/nvidia-rtx-pro%E2%84%A2-6000-blackwell-workstation-edition-900-5g144-2500-000\"\u003eRTX PRO 6000 Blackwell Workstation Edition\u003c\/a\u003e — 96 GB GDDR7 with ECC\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e900-5G153-2500-000\u003c\/td\u003e\n\u003ctd\u003e\n\u003ca href=\"\/products\/nvidia-rtx-pro%E2%84%A2-6000-blackwell-max-q-workstation-edition-900-5g153-2500-000\"\u003eRTX PRO 6000 Blackwell Max-Q Workstation Edition\u003c\/a\u003e — 96 GB GDDR7 with ECC\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e900-2G153-0000-000\u003c\/td\u003e\n\u003ctd\u003e\n\u003ca href=\"\/products\/nvidia-rtx-pro%E2%84%A2-6000-blackwell-server-edition\"\u003eRTX PRO 6000 Blackwell Server Edition\u003c\/a\u003e — 96 GB GDDR7 with ECC\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e900-5G153-2570-000\u003c\/td\u003e\n\u003ctd\u003e\n\u003ca href=\"\/products\/nvidia-rtx-pro%E2%84%A2-5000-blackwell-generation\"\u003eRTX PRO 5000 Blackwell 72 GB\u003c\/a\u003e — 72 GB GDDR7 with ECC\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e900-5G153-2550-000\u003c\/td\u003e\n\u003ctd\u003e\n\u003ca href=\"\/products\/nvidia-rtx-pro%E2%84%A2-5000-blackwell-generation-900-5g153-2550-000\"\u003eRTX PRO 5000 Blackwell 48 GB\u003c\/a\u003e — 48 GB GDDR7 with ECC\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e900-5G147-2550-000\u003c\/td\u003e\n\u003ctd\u003e\u003cstrong\u003eRTX PRO 4500 Blackwell — 32 GB GDDR7 with ECC (this item)\u003c\/strong\u003e\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e900-5G147-2570-000\u003c\/td\u003e\n\u003ctd\u003e\n\u003ca href=\"\/products\/nvidia-rtx-pro%E2%84%A2-4000-blackwell-generation-900-5g147-2570-000\"\u003eRTX PRO 4000 Blackwell\u003c\/a\u003e — 24 GB GDDR7 with ECC\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e900-5G195-2501-000\u003c\/td\u003e\n\u003ctd\u003e\n\u003ca href=\"\/products\/nvidia-rtx-pro%E2%84%A2-4000-blackwell-generation-sff-edition-900-5g195-2501-000\"\u003eRTX PRO 4000 Blackwell SFF Edition\u003c\/a\u003e — 24 GB GDDR7 with ECC\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e900-5G195-2551-000\u003c\/td\u003e\n\u003ctd\u003e\n\u003ca href=\"\/products\/nvidia-rtx-pro%E2%84%A2-2000-blackwell-generation-900-5g195-2551-000\"\u003eRTX PRO 2000 Blackwell\u003c\/a\u003e — 16 GB GDDR7 with ECC\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/table\u003e\n\u003cp\u003eChoosing between them: memory capacity is usually the deciding factor for AI work and scene size, while form factor and power decide what will physically fit. The \u003cstrong\u003eMax-Q\u003c\/strong\u003e variant exists to give full 96 GB capacity at 300 W for chassis that cannot take a 600 W card or that need more than one GPU. The \u003cstrong\u003eServer Edition\u003c\/strong\u003e is passively cooled and belongs in a server, not a workstation. The \u003cstrong\u003eSFF Edition\u003c\/strong\u003e trades bandwidth and PCIe lanes for a 70 W low-profile board.\u003c\/p\u003e\n\u003c\/div\u003e\u003c\/details\u003e\u003cdetails class=\"pd-acc\"\u003e\u003csummary style=\"cursor: pointer; font-weight: 700;\"\u003eDatasheet \u0026amp; Downloads\u003c\/summary\u003e\u003cdiv class=\"pd-acc-body\"\u003e\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https:\/\/www.nvidia.com\/content\/dam\/en-zz\/Solutions\/data-center\/rtx-pro-4500-blackwell\/workstation-datasheet-blackwell-rtx-pro-4500-we-nvidia-us-5108623-web.pdf\" target=\"_blank\" rel=\"noopener\"\u003eNVIDIA RTX PRO™ 4500 Blackwell official NVIDIA datasheet\u003c\/a\u003e\u003c\/li\u003e\n\u003cli\u003e\u003ca href=\"https:\/\/www.nvidia.com\/en-us\/products\/workstations\/professional-desktop-gpus\/rtx-pro-4500\/\" target=\"_blank\" rel=\"noopener\"\u003eNVIDIA RTX PRO™ 4500 Blackwell product page on NVIDIA.com\u003c\/a\u003e\u003c\/li\u003e\n\u003cli\u003e\u003ca href=\"https:\/\/www.nvidia.com\/content\/dam\/en-zz\/Solutions\/design-visualization\/quadro-product-literature\/NVIDIA-RTX-Blackwell-PRO-GPU-Architecture-v1.0.pdf\" target=\"_blank\" rel=\"noopener\"\u003eNVIDIA RTX PRO Blackwell GPU Architecture Whitepaper (PDF)\u003c\/a\u003e\u003c\/li\u003e\n\u003cli\u003e\u003ca href=\"https:\/\/images.nvidia.com\/aem-dam\/Solutions\/design-visualization\/quadro-product-literature\/rtx-pro-blackwell-online-qsg-210x148mm-20260317-r18.pdf\" target=\"_blank\" rel=\"noopener\"\u003eNVIDIA RTX PRO Blackwell Quick Start Guide (PDF)\u003c\/a\u003e\u003c\/li\u003e\n\u003cli\u003e\u003ca href=\"https:\/\/www.nvidia.com\/en-us\/products\/workstations\/product-literature\/\" target=\"_blank\" rel=\"noopener\"\u003eNVIDIA professional workstation product literature library\u003c\/a\u003e\u003c\/li\u003e\n\u003c\/ul\u003e\u003c\/div\u003e\u003c\/details\u003e\u003cdetails class=\"pd-acc\"\u003e\u003csummary style=\"cursor: pointer; font-weight: 700;\"\u003eFrequently Asked Questions — RTX PRO 4500 Blackwell\u003c\/summary\u003e\u003cdiv class=\"pd-acc-body\"\u003e\n\u003cp\u003e\u003cstrong\u003eHow much GPU memory does it have, and why does that matter?\u003c\/strong\u003e\u003cbr\u003e32 GB GDDR7 with ECC. For AI work, VRAM capacity decides which models fit on the card at all — a model that does not fit runs an order of magnitude slower or not at all. For visualisation it decides scene and texture budget. ECC means single-bit memory errors are corrected rather than silently corrupting a long job.\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eHow does it compare to the RTX PRO 4000 Blackwell?\u003c\/strong\u003e\u003cbr\u003eThe \u003ca href=\"\/products\/nvidia-rtx-pro%E2%84%A2-4000-blackwell-generation-900-5g147-2570-000\"\u003eRTX PRO 4000\u003c\/a\u003e steps up to 24 GB of GDDR7, 8,960 CUDA cores, 672 GB\/s and 1,178 AI TOPS at 145 W. The RTX PRO 2000 delivers 16 GB, 4,352 cores, 288 GB\/s and 545 AI TOPS at 70 W. If your models and scenes fit in 16 GB, the 2000 is the efficient choice; if they do not, no amount of compute compensates.\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eWhat power supply and chassis clearance do I need?\u003c\/strong\u003e\u003cbr\u003eThis card is 200 w total board power in a 4.4in (H) x 10.5in (L), dual slot, full height form factor with active cooling, powered via 1x PCIe CEM5 16-pin. Check both physical clearance and PSU headroom against your workstation before ordering — send us your machine model and we will confirm fit.\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eDoes it support Multi-Instance GPU or vGPU?\u003c\/strong\u003e\u003cbr\u003eNVIDIA does not publish MIG or vGPU support for this model on its datasheet, so we do not claim it. If GPU partitioning or virtualisation is a requirement, the \u003ca href=\"\/products\/nvidia-rtx-pro%E2%84%A2-6000-blackwell-server-edition\"\u003eRTX PRO 6000 Blackwell Server Edition\u003c\/a\u003e and the RTX PRO 6000 and 5000 workstation cards are the models with published MIG support.\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eIs this a professional card or a gaming card?\u003c\/strong\u003e\u003cbr\u003eProfessional. RTX PRO Blackwell cards carry ECC memory, NVIDIA RTX Enterprise drivers with ISV application certification, longer driver support branches and a professional warranty. Consumer GeForce cards have none of these, which is why they are not accepted in most enterprise and regulated deployments regardless of raw frame rate.\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eDoes it come with local warranty in Singapore?\u003c\/strong\u003e\u003cbr\u003eYes. Every RTX PRO 4500 Blackwell sold by SourceIT comes with 3 years local Singapore warranty and a GST invoice.\u003c\/p\u003e\n\u003c\/div\u003e\u003c\/details\u003e\n\u003c\/div\u003e","brand":"Nvidia","offers":[{"title":"Default Title","offer_id":51167250972836,"sku":"900-5G147-2550-000","price":8735.0,"currency_code":"SGD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0605\/0136\/0804\/files\/nvidia-rtx-pro-4500-blackwell-generation-900-5g147-2550-000-1118980.webp?v=1761645030"},{"product_id":"nvidia-rtx-pro™-4000-blackwell-generation-900-5g147-2570-000","title":"NVIDIA RTX PRO™ 4000 Blackwell Generation","description":"\u003cdiv\u003e\n\u003cstyle\u003e\n.pd-acc { border: 1px solid #e2e2e2; border-radius: 8px; margin: 10px 0; overflow: hidden; }\n.pd-acc summary { cursor: pointer; padding: 14px 16px; font-weight: 700; font-size: 1.05em; list-style: none; display: flex; justify-content: space-between; align-items: center; background: #f7f7f7; }\n.pd-acc[open] \u003e summary { border-bottom: 1px solid #e2e2e2; }\n.pd-acc summary::-webkit-details-marker { display: none; }\n.pd-acc summary::after { content: \"+\"; font-size: 1.4em; font-weight: 400; line-height: 1; margin-left: 12px; }\n.pd-acc[open] \u003e summary::after { content: \"\\2212\"; }\n.pd-acc .pd-acc-body { padding: 8px 16px 14px; }\n.pd-spec, .pd-spec tr, .pd-spec td { border: none !important; background: transparent !important; }\n.pd-spec { width: 100%; border-collapse: collapse !important; margin: 6px 0 18px; }\n.pd-spec td { padding: 7px 10px 7px 0 !important; border-bottom: 1px solid #ececec !important; vertical-align: top; font-size: 0.95em; text-align: left; }\n.pd-spec td:first-child { width: 38%; font-weight: 600; color: #333; }\n.pd-spec tr:last-child td { border-bottom: none !important; }\n\u003c\/style\u003e\n\u003ch2\u003e\u003cstrong\u003eNVIDIA RTX PRO™ 4000 Blackwell Generation 24 GB GDDR7 with ECC (900-5G147-2570-000) - 3 Years Local Warranty\u003c\/strong\u003e\u003c\/h2\u003e\n\u003ch3\u003e\u003cstrong\u003eRTX PRO 4000 Blackwell — 24 GB ECC in a Single-Slot Card\u003c\/strong\u003e\u003c\/h3\u003e\n\u003cp\u003eThe \u003cstrong\u003eNVIDIA RTX PRO™ 4000 Blackwell\u003c\/strong\u003e is the most useful card in the range for the largest number of desks: 24 GB of GDDR7 with ECC, 8,960 CUDA cores and 672 GB\/s of bandwidth, all in a \u003cstrong\u003esingle-slot, full-height, 145 W\u003c\/strong\u003e card. Single-slot matters — it fits workstations where a dual-slot card simply will not go, and it leaves the adjacent slot free.\u003c\/p\u003e\n\u003ch3\u003e\u003cstrong\u003eBlackwell AI Performance at 145 W\u003c\/strong\u003e\u003c\/h3\u003e\n\u003cp\u003eFifth-generation Tensor Cores deliver 1,178 AI TOPS on the NVIDIA datasheet (theoretical FP4 using sparsity), with 37 TFLOPS FP32 and 112 TFLOPS of RT Core throughput. Note that NVIDIA's product web page quotes slightly higher figures (1,290 TOPS, 40 TFLOPS, 122 TFLOPS); we quote the datasheet numbers here and flag the difference rather than picking the flattering one.\u003c\/p\u003e\n\u003ch3\u003e\u003cstrong\u003eFour DisplayPort 2.1b Outputs for Multi-Monitor Desks\u003c\/strong\u003e\u003c\/h3\u003e\n\u003cp\u003eFour full-size DisplayPort 2.1b connectors drive up to four 3840 x 2160 displays at 165 Hz, or two 7680 x 4320 displays at 100 Hz. For CAD, GIS, trading floors and control rooms that is a complete multi-monitor solution from one single-slot card.\u003c\/p\u003e\n\u003ch3\u003e\u003cstrong\u003eDeploys Into Existing Workstations\u003c\/strong\u003e\u003c\/h3\u003e\n\u003cp\u003ePCIe 5.0 x16, 4.4in x 9.5in, single slot, active cooling, one PCIe CEM5 16-pin power connector. Supplied by SourceIT Pte Ltd with a GST invoice and \u003cstrong\u003e3 years local warranty\u003c\/strong\u003e.\u003c\/p\u003e\n\u003cdetails class=\"pd-acc\"\u003e\u003csummary style=\"cursor: pointer; font-weight: 700;\"\u003eNVIDIA RTX PRO™ 4000 Blackwell Datasheet — Technical Specifications\u003c\/summary\u003e\u003cdiv class=\"pd-acc-body\"\u003e\n\u003ch4\u003eGeneral\u003c\/h4\u003e\n\u003ctable class=\"pd-spec\"\u003e\n\u003ctr\u003e\n\u003ctd\u003ePart Number\u003c\/td\u003e\n\u003ctd\u003e900-5G147-2570-000\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eModel\u003c\/td\u003e\n\u003ctd\u003eNVIDIA RTX PRO™ 4000 Blackwell\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eGPU Architecture\u003c\/td\u003e\n\u003ctd\u003eNVIDIA Blackwell\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eProduct Family\u003c\/td\u003e\n\u003ctd\u003eNVIDIA RTX PRO Blackwell Workstation \/ Professional Desktop GPUs\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eWarranty\u003c\/td\u003e\n\u003ctd\u003e3 Years Local Warranty (Singapore)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/table\u003e\n\u003ch4\u003eMemory\u003c\/h4\u003e\n\u003ctable class=\"pd-spec\"\u003e\n\u003ctr\u003e\n\u003ctd\u003eGPU Memory\u003c\/td\u003e\n\u003ctd\u003e24 GB GDDR7 with ECC\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eMemory Interface\u003c\/td\u003e\n\u003ctd\u003e192-bit\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eMemory Bandwidth\u003c\/td\u003e\n\u003ctd\u003e672 GB\/s\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eECC\u003c\/td\u003e\n\u003ctd\u003eYes — error-correcting code memory\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/table\u003e\n\u003ch4\u003eCompute \u0026amp; Performance\u003c\/h4\u003e\n\u003ctable class=\"pd-spec\"\u003e\n\u003ctr\u003e\n\u003ctd\u003eCUDA Cores\u003c\/td\u003e\n\u003ctd\u003e8,960\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eTensor Cores\u003c\/td\u003e\n\u003ctd\u003eFifth-generation Tensor Cores\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eRT Cores\u003c\/td\u003e\n\u003ctd\u003eFourth-generation RT Cores\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eSingle-Precision (FP32) Performance\u003c\/td\u003e\n\u003ctd\u003e37 TFLOPS (datasheet; NVIDIA product page lists 40 TFLOPS)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eRT Core Performance\u003c\/td\u003e\n\u003ctd\u003e112 TFLOPS (datasheet; NVIDIA product page lists 122 TFLOPS)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eAI Performance\u003c\/td\u003e\n\u003ctd\u003e1,178 AI TOPS (datasheet, theoretical FP4 TOPS using sparsity; NVIDIA product page lists 1,290 TOPS)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eMulti-Instance GPU (MIG)\u003c\/td\u003e\n\u003ctd\u003eNot published by NVIDIA for this model\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/table\u003e\n\u003ch4\u003eMedia Engines\u003c\/h4\u003e\n\u003ctable class=\"pd-spec\"\u003e\n\u003ctr\u003e\n\u003ctd\u003eEncoders (NVENC)\u003c\/td\u003e\n\u003ctd\u003e2x NVENC (ninth generation)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eDecoders (NVDEC)\u003c\/td\u003e\n\u003ctd\u003e2x NVDEC (sixth generation)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/table\u003e\n\u003ch4\u003eDisplay\u003c\/h4\u003e\n\u003ctable class=\"pd-spec\"\u003e\n\u003ctr\u003e\n\u003ctd\u003eDisplay Connectors\u003c\/td\u003e\n\u003ctd\u003e4x DisplayPort 2.1b (full-size)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eMaximum Simultaneous Displays\u003c\/td\u003e\n\u003ctd\u003e4x 3840 x 2160 @ 165 Hz; 2x 7680 x 4320 @ 100 Hz\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/table\u003e\n\u003ch4\u003ePower, Form Factor \u0026amp; Interface\u003c\/h4\u003e\n\u003ctable class=\"pd-spec\"\u003e\n\u003ctr\u003e\n\u003ctd\u003eMaximum Power Consumption\u003c\/td\u003e\n\u003ctd\u003e145 W total board power\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eGraphics Bus\u003c\/td\u003e\n\u003ctd\u003ePCIe 5.0 x16\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eForm Factor\u003c\/td\u003e\n\u003ctd\u003e4.4in (H) x 9.5in (L), single slot, full height\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eThermal Solution\u003c\/td\u003e\n\u003ctd\u003eActive\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ePower Connector\u003c\/td\u003e\n\u003ctd\u003e1x PCIe CEM5 16-pin\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/table\u003e\n\u003cp\u003e\u003cem\u003eSpecifications are taken from NVIDIA's published datasheet for this model. Fields NVIDIA does not publish for a given card are shown as such rather than estimated.\u003c\/em\u003e\u003c\/p\u003e\n\u003c\/div\u003e\u003c\/details\u003e\u003cdetails class=\"pd-acc\"\u003e\u003csummary style=\"cursor: pointer; font-weight: 700;\"\u003eCompatibility \u0026amp; Software Support\u003c\/summary\u003e\u003cdiv class=\"pd-acc-body\"\u003e\n\u003ctable class=\"pd-spec\"\u003e\n\u003ctr\u003e\n\u003ctd\u003eGraphics APIs\u003c\/td\u003e\n\u003ctd\u003eDirectX 12 (Shader Model 6.6), OpenGL 4.6, Vulkan 1.4\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eCompute APIs\u003c\/td\u003e\n\u003ctd\u003eCUDA 12.8, OpenCL 3.0, DirectCompute\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eOperating Systems\u003c\/td\u003e\n\u003ctd\u003eWindows 10\/11 64-bit; Linux — RHEL 8.10, SUSE Linux Enterprise Desktop 15.6, OpenSUSE 15.6, Fedora 41, Ubuntu 22.04; FreeBSD 14.2; Solaris 11 U4 (per NVIDIA RTX PRO Blackwell Quick Start Guide)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eDrivers\u003c\/td\u003e\n\u003ctd\u003eNVIDIA RTX Enterprise Driver — Production Branch and New Feature Branch\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eProfessional Applications\u003c\/td\u003e\n\u003ctd\u003eCertified across leading CAD, DCC, BIM, medical imaging and simulation ISV applications\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eAI Frameworks\u003c\/td\u003e\n\u003ctd\u003eCUDA, cuDNN, TensorRT, PyTorch, TensorFlow, NVIDIA NIM and NGC containers\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eOmniverse \/ Digital Twin\u003c\/td\u003e\n\u003ctd\u003eSupported via NVIDIA Omniverse and OpenUSD workflows\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eChassis Check\u003c\/td\u003e\n\u003ctd\u003e4.4in (H) x 9.5in (L), single slot, full height — confirm slot clearance, power headroom and airflow before ordering\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/table\u003e\n\u003cp\u003eSourceIT can verify fit against your specific workstation or server model before you order. For ISV certification status on a named application version, check the NVIDIA \u003ca href=\"https:\/\/www.nvidia.com\/en-us\/products\/workstations\/product-literature\/\" target=\"_blank\" rel=\"noopener\"\u003eprofessional product literature library\u003c\/a\u003e.\u003c\/p\u003e\n\u003c\/div\u003e\u003c\/details\u003e\u003cdetails class=\"pd-acc\"\u003e\u003csummary style=\"cursor: pointer; font-weight: 700;\"\u003ePackage Contents \u0026amp; NVIDIA RTX PRO Blackwell Ordering Information\u003c\/summary\u003e\u003cdiv class=\"pd-acc-body\"\u003e\n\u003cp\u003e\u003cstrong\u003eWhat is in the box:\u003c\/strong\u003e NVIDIA's RTX PRO Blackwell Quick Start Guide lists a Quick Start Card and an auxiliary power cable (2x PCIe 8-pin PSU to 1x PCIe Gen5 16-pin GPU adapter). NVIDIA does not publish a per-SKU box-contents list on the datasheet.\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eNVIDIA RTX PRO Blackwell range available from SourceIT:\u003c\/strong\u003e\u003c\/p\u003e\n\u003ctable class=\"pd-spec\"\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003ePart Number\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e\u003cstrong\u003eModel \u0026amp; Memory\u003c\/strong\u003e\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e900-5G144-2500-000\u003c\/td\u003e\n\u003ctd\u003e\n\u003ca href=\"\/products\/nvidia-rtx-pro%E2%84%A2-6000-blackwell-workstation-edition-900-5g144-2500-000\"\u003eRTX PRO 6000 Blackwell Workstation Edition\u003c\/a\u003e — 96 GB GDDR7 with ECC\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e900-5G153-2500-000\u003c\/td\u003e\n\u003ctd\u003e\n\u003ca href=\"\/products\/nvidia-rtx-pro%E2%84%A2-6000-blackwell-max-q-workstation-edition-900-5g153-2500-000\"\u003eRTX PRO 6000 Blackwell Max-Q Workstation Edition\u003c\/a\u003e — 96 GB GDDR7 with ECC\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e900-2G153-0000-000\u003c\/td\u003e\n\u003ctd\u003e\n\u003ca href=\"\/products\/nvidia-rtx-pro%E2%84%A2-6000-blackwell-server-edition\"\u003eRTX PRO 6000 Blackwell Server Edition\u003c\/a\u003e — 96 GB GDDR7 with ECC\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e900-5G153-2570-000\u003c\/td\u003e\n\u003ctd\u003e\n\u003ca href=\"\/products\/nvidia-rtx-pro%E2%84%A2-5000-blackwell-generation\"\u003eRTX PRO 5000 Blackwell 72 GB\u003c\/a\u003e — 72 GB GDDR7 with ECC\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e900-5G153-2550-000\u003c\/td\u003e\n\u003ctd\u003e\n\u003ca href=\"\/products\/nvidia-rtx-pro%E2%84%A2-5000-blackwell-generation-900-5g153-2550-000\"\u003eRTX PRO 5000 Blackwell 48 GB\u003c\/a\u003e — 48 GB GDDR7 with ECC\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e900-5G147-2550-000\u003c\/td\u003e\n\u003ctd\u003e\n\u003ca href=\"\/products\/nvidia-rtx-pro%E2%84%A2-4500-blackwell-generation-900-5g147-2550-000\"\u003eRTX PRO 4500 Blackwell\u003c\/a\u003e — 32 GB GDDR7 with ECC\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e900-5G147-2570-000\u003c\/td\u003e\n\u003ctd\u003e\u003cstrong\u003eRTX PRO 4000 Blackwell — 24 GB GDDR7 with ECC (this item)\u003c\/strong\u003e\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e900-5G195-2501-000\u003c\/td\u003e\n\u003ctd\u003e\n\u003ca href=\"\/products\/nvidia-rtx-pro%E2%84%A2-4000-blackwell-generation-sff-edition-900-5g195-2501-000\"\u003eRTX PRO 4000 Blackwell SFF Edition\u003c\/a\u003e — 24 GB GDDR7 with ECC\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e900-5G195-2551-000\u003c\/td\u003e\n\u003ctd\u003e\n\u003ca href=\"\/products\/nvidia-rtx-pro%E2%84%A2-2000-blackwell-generation-900-5g195-2551-000\"\u003eRTX PRO 2000 Blackwell\u003c\/a\u003e — 16 GB GDDR7 with ECC\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/table\u003e\n\u003cp\u003eChoosing between them: memory capacity is usually the deciding factor for AI work and scene size, while form factor and power decide what will physically fit. The \u003cstrong\u003eMax-Q\u003c\/strong\u003e variant exists to give full 96 GB capacity at 300 W for chassis that cannot take a 600 W card or that need more than one GPU. The \u003cstrong\u003eServer Edition\u003c\/strong\u003e is passively cooled and belongs in a server, not a workstation. The \u003cstrong\u003eSFF Edition\u003c\/strong\u003e trades bandwidth and PCIe lanes for a 70 W low-profile board.\u003c\/p\u003e\n\u003c\/div\u003e\u003c\/details\u003e\u003cdetails class=\"pd-acc\"\u003e\u003csummary style=\"cursor: pointer; font-weight: 700;\"\u003eDatasheet \u0026amp; Downloads\u003c\/summary\u003e\u003cdiv class=\"pd-acc-body\"\u003e\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https:\/\/www.nvidia.com\/content\/dam\/en-zz\/Solutions\/products\/workstations\/professional-desktop-gpus\/rtx-pro-4000\/workstation-datasheet-rtx-pro-4000-nvidia-us-web.pdf\" target=\"_blank\" rel=\"noopener\"\u003eNVIDIA RTX PRO™ 4000 Blackwell official NVIDIA datasheet\u003c\/a\u003e\u003c\/li\u003e\n\u003cli\u003e\u003ca href=\"https:\/\/www.nvidia.com\/en-us\/products\/workstations\/professional-desktop-gpus\/rtx-pro-4000\/\" target=\"_blank\" rel=\"noopener\"\u003eNVIDIA RTX PRO™ 4000 Blackwell product page on NVIDIA.com\u003c\/a\u003e\u003c\/li\u003e\n\u003cli\u003e\u003ca href=\"https:\/\/www.nvidia.com\/content\/dam\/en-zz\/Solutions\/design-visualization\/quadro-product-literature\/NVIDIA-RTX-Blackwell-PRO-GPU-Architecture-v1.0.pdf\" target=\"_blank\" rel=\"noopener\"\u003eNVIDIA RTX PRO Blackwell GPU Architecture Whitepaper (PDF)\u003c\/a\u003e\u003c\/li\u003e\n\u003cli\u003e\u003ca href=\"https:\/\/images.nvidia.com\/aem-dam\/Solutions\/design-visualization\/quadro-product-literature\/rtx-pro-blackwell-online-qsg-210x148mm-20260317-r18.pdf\" target=\"_blank\" rel=\"noopener\"\u003eNVIDIA RTX PRO Blackwell Quick Start Guide (PDF)\u003c\/a\u003e\u003c\/li\u003e\n\u003cli\u003e\u003ca href=\"https:\/\/www.nvidia.com\/en-us\/products\/workstations\/product-literature\/\" target=\"_blank\" rel=\"noopener\"\u003eNVIDIA professional workstation product literature library\u003c\/a\u003e\u003c\/li\u003e\n\u003c\/ul\u003e\u003c\/div\u003e\u003c\/details\u003e\u003cdetails class=\"pd-acc\"\u003e\u003csummary style=\"cursor: pointer; font-weight: 700;\"\u003eFrequently Asked Questions — RTX PRO 4000 Blackwell\u003c\/summary\u003e\u003cdiv class=\"pd-acc-body\"\u003e\n\u003cp\u003e\u003cstrong\u003eHow much GPU memory does it have, and why does that matter?\u003c\/strong\u003e\u003cbr\u003e24 GB GDDR7 with ECC. For AI work, VRAM capacity decides which models fit on the card at all — a model that does not fit runs an order of magnitude slower or not at all. For visualisation it decides scene and texture budget. ECC means single-bit memory errors are corrected rather than silently corrupting a long job.\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eIs there a low-profile version?\u003c\/strong\u003e\u003cbr\u003eYes — the \u003ca href=\"\/products\/nvidia-rtx-pro%E2%84%A2-4000-blackwell-generation-sff-edition-900-5g195-2501-000\"\u003eRTX PRO 4000 Blackwell SFF Edition\u003c\/a\u003e has the same 24 GB of GDDR7 with ECC and the same 8,960 CUDA cores in a 70 W half-height board, at the cost of bandwidth (432 GB\/s vs 672 GB\/s), PCIe lanes (x8 vs x16) and Mini DisplayPort instead of full-size connectors.\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eWhat power supply and chassis clearance do I need?\u003c\/strong\u003e\u003cbr\u003eThis card is 145 w total board power in a 4.4in (H) x 9.5in (L), single slot, full height form factor with active cooling, powered via 1x PCIe CEM5 16-pin. Check both physical clearance and PSU headroom against your workstation before ordering — send us your machine model and we will confirm fit.\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eDoes it support Multi-Instance GPU or vGPU?\u003c\/strong\u003e\u003cbr\u003eNVIDIA does not publish MIG or vGPU support for this model on its datasheet, so we do not claim it. If GPU partitioning or virtualisation is a requirement, the \u003ca href=\"\/products\/nvidia-rtx-pro%E2%84%A2-6000-blackwell-server-edition\"\u003eRTX PRO 6000 Blackwell Server Edition\u003c\/a\u003e and the RTX PRO 6000 and 5000 workstation cards are the models with published MIG support.\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eIs this a professional card or a gaming card?\u003c\/strong\u003e\u003cbr\u003eProfessional. RTX PRO Blackwell cards carry ECC memory, NVIDIA RTX Enterprise drivers with ISV application certification, longer driver support branches and a professional warranty. Consumer GeForce cards have none of these, which is why they are not accepted in most enterprise and regulated deployments regardless of raw frame rate.\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eDoes it come with local warranty in Singapore?\u003c\/strong\u003e\u003cbr\u003eYes. Every RTX PRO 4000 Blackwell sold by SourceIT comes with 3 years local Singapore warranty and a GST invoice.\u003c\/p\u003e\n\u003c\/div\u003e\u003c\/details\u003e\n\u003c\/div\u003e","brand":"Nvidia","offers":[{"title":"Default Title","offer_id":51167251333284,"sku":"900-5G147-2570-000","price":5550.0,"currency_code":"SGD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0605\/0136\/0804\/files\/nvidia-rtx-pro-4000-blackwell-generation-900-5g147-2570-000-2504044.webp?v=1761645029"},{"product_id":"nvidia-rtx-pro™-4000-blackwell-generation-sff-edition-900-5g195-2501-000","title":"NVIDIA RTX PRO™ 4000 Blackwell Generation SFF Edition","description":"\u003cdiv\u003e\n\u003cstyle\u003e\n.pd-acc { border: 1px solid #e2e2e2; border-radius: 8px; margin: 10px 0; overflow: hidden; }\n.pd-acc summary { cursor: pointer; padding: 14px 16px; font-weight: 700; font-size: 1.05em; list-style: none; display: flex; justify-content: space-between; align-items: center; background: #f7f7f7; }\n.pd-acc[open] \u003e summary { border-bottom: 1px solid #e2e2e2; }\n.pd-acc summary::-webkit-details-marker { display: none; }\n.pd-acc summary::after { content: \"+\"; font-size: 1.4em; font-weight: 400; line-height: 1; margin-left: 12px; }\n.pd-acc[open] \u003e summary::after { content: \"\\2212\"; }\n.pd-acc .pd-acc-body { padding: 8px 16px 14px; }\n.pd-spec, .pd-spec tr, .pd-spec td { border: none !important; background: transparent !important; }\n.pd-spec { width: 100%; border-collapse: collapse !important; margin: 6px 0 18px; }\n.pd-spec td { padding: 7px 10px 7px 0 !important; border-bottom: 1px solid #ececec !important; vertical-align: top; font-size: 0.95em; text-align: left; }\n.pd-spec td:first-child { width: 38%; font-weight: 600; color: #333; }\n.pd-spec tr:last-child td { border-bottom: none !important; }\n\u003c\/style\u003e\n\u003ch2\u003e\u003cstrong\u003eNVIDIA RTX PRO™ 4000 Blackwell SFF Edition 24 GB GDDR7 with ECC (900-5G195-2501-000) - 3 Years Local Warranty\u003c\/strong\u003e\u003c\/h2\u003e\n\u003ch3\u003e\u003cstrong\u003eRTX PRO 4000 Blackwell SFF Edition — 24 GB ECC in a 70 W Low-Profile Card\u003c\/strong\u003e\u003c\/h3\u003e\n\u003cp\u003eThe \u003cstrong\u003eNVIDIA RTX PRO™ 4000 Blackwell SFF Edition\u003c\/strong\u003e is the card for small-form-factor workstations, edge nodes and rack-dense deployments where a full-height GPU is not an option. It carries the same \u003cstrong\u003e24 GB of GDDR7 with ECC and 8,960 CUDA cores\u003c\/strong\u003e as the full-size RTX PRO 4000, inside a 2.7in x 6.6in half-height, low-profile board drawing just \u003cstrong\u003e70 W\u003c\/strong\u003e — with no auxiliary power connector required.\u003c\/p\u003e\n\u003ch3\u003e\u003cstrong\u003e770 AI TOPS Without a Power Cable\u003c\/strong\u003e\u003c\/h3\u003e\n\u003cp\u003eNVIDIA's product page rates the SFF Edition at 770 AI TOPS (theoretical FP4 using sparsity), 24 TFLOPS FP32 and 73 TFLOPS RT Core performance. These figures come from NVIDIA's product page rather than the datasheet, which omits them — we flag the source rather than presenting them as datasheet-verified. Two ninth-generation NVENC and two sixth-generation NVDEC engines handle video workloads.\u003c\/p\u003e\n\u003ch3\u003e\u003cstrong\u003eKnow the Trade-Offs Against the Full-Size RTX PRO 4000\u003c\/strong\u003e\u003c\/h3\u003e\n\u003cp\u003eSame memory, different envelope. The SFF runs at 432 GB\/s rather than 672 GB\/s, connects over \u003cstrong\u003ePCIe 5.0 x8\u003c\/strong\u003e rather than x16, uses \u003cstrong\u003efour Mini DisplayPort 2.1b\u003c\/strong\u003e outputs rather than full-size DP, and occupies two low-profile slots. If your chassis takes the full-height card, \u003ca href=\"\/products\/nvidia-rtx-pro%E2%84%A2-4000-blackwell-generation-900-5g147-2570-000\"\u003ethe standard RTX PRO 4000 Blackwell\u003c\/a\u003e is faster for the same VRAM.\u003c\/p\u003e\n\u003ch3\u003e\u003cstrong\u003eIdeal for SFF Workstations and Edge AI in Singapore\u003c\/strong\u003e\u003c\/h3\u003e\n\u003cp\u003eA 70 W, slot-powered, low-profile card with 24 GB of ECC VRAM is a rare combination, and it is what makes local AI inference practical in compact chassis and edge cabinets. Supplied by SourceIT Pte Ltd with a GST invoice and \u003cstrong\u003e3 years local warranty\u003c\/strong\u003e.\u003c\/p\u003e\n\u003cdetails class=\"pd-acc\"\u003e\u003csummary style=\"cursor: pointer; font-weight: 700;\"\u003eNVIDIA RTX PRO™ 4000 Blackwell SFF Edition Datasheet — Technical Specifications\u003c\/summary\u003e\u003cdiv class=\"pd-acc-body\"\u003e\n\u003ch4\u003eGeneral\u003c\/h4\u003e\n\u003ctable class=\"pd-spec\"\u003e\n\u003ctr\u003e\n\u003ctd\u003ePart Number\u003c\/td\u003e\n\u003ctd\u003e900-5G195-2501-000\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eModel\u003c\/td\u003e\n\u003ctd\u003eNVIDIA RTX PRO™ 4000 Blackwell SFF Edition\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eGPU Architecture\u003c\/td\u003e\n\u003ctd\u003eNVIDIA Blackwell\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eProduct Family\u003c\/td\u003e\n\u003ctd\u003eNVIDIA RTX PRO Blackwell Workstation \/ Professional Desktop GPUs\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eWarranty\u003c\/td\u003e\n\u003ctd\u003e3 Years Local Warranty (Singapore)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/table\u003e\n\u003ch4\u003eMemory\u003c\/h4\u003e\n\u003ctable class=\"pd-spec\"\u003e\n\u003ctr\u003e\n\u003ctd\u003eGPU Memory\u003c\/td\u003e\n\u003ctd\u003e24 GB GDDR7 with ECC\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eMemory Interface\u003c\/td\u003e\n\u003ctd\u003e192-bit\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eMemory Bandwidth\u003c\/td\u003e\n\u003ctd\u003e432 GB\/s\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eECC\u003c\/td\u003e\n\u003ctd\u003eYes — error-correcting code memory\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/table\u003e\n\u003ch4\u003eCompute \u0026amp; Performance\u003c\/h4\u003e\n\u003ctable class=\"pd-spec\"\u003e\n\u003ctr\u003e\n\u003ctd\u003eCUDA Cores\u003c\/td\u003e\n\u003ctd\u003e8,960\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eTensor Cores\u003c\/td\u003e\n\u003ctd\u003eFifth-generation Tensor Cores\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eRT Cores\u003c\/td\u003e\n\u003ctd\u003eFourth-generation RT Cores\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eSingle-Precision (FP32) Performance\u003c\/td\u003e\n\u003ctd\u003e24 TFLOPS (NVIDIA product page — not stated on the datasheet)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eRT Core Performance\u003c\/td\u003e\n\u003ctd\u003e73 TFLOPS (NVIDIA product page — not stated on the datasheet)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eAI Performance\u003c\/td\u003e\n\u003ctd\u003e770 AI TOPS (NVIDIA product page, theoretical FP4 TOPS using sparsity — not stated on the datasheet)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eMulti-Instance GPU (MIG)\u003c\/td\u003e\n\u003ctd\u003eNot published by NVIDIA for this model\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/table\u003e\n\u003ch4\u003eMedia Engines\u003c\/h4\u003e\n\u003ctable class=\"pd-spec\"\u003e\n\u003ctr\u003e\n\u003ctd\u003eEncoders (NVENC)\u003c\/td\u003e\n\u003ctd\u003e2x NVENC (ninth generation)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eDecoders (NVDEC)\u003c\/td\u003e\n\u003ctd\u003e2x NVDEC (sixth generation)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/table\u003e\n\u003ch4\u003eDisplay\u003c\/h4\u003e\n\u003ctable class=\"pd-spec\"\u003e\n\u003ctr\u003e\n\u003ctd\u003eDisplay Connectors\u003c\/td\u003e\n\u003ctd\u003e4x Mini DisplayPort 2.1b\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eMaximum Simultaneous Displays\u003c\/td\u003e\n\u003ctd\u003e4x 3840 x 2160 @ 165 Hz; 2x 7680 x 4320 @ 100 Hz\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/table\u003e\n\u003ch4\u003ePower, Form Factor \u0026amp; Interface\u003c\/h4\u003e\n\u003ctable class=\"pd-spec\"\u003e\n\u003ctr\u003e\n\u003ctd\u003eMaximum Power Consumption\u003c\/td\u003e\n\u003ctd\u003e70 W total board power\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eGraphics Bus\u003c\/td\u003e\n\u003ctd\u003ePCIe 5.0 x8 (uses a full-length PCIe interface)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eForm Factor\u003c\/td\u003e\n\u003ctd\u003e2.7in (H) x 6.6in (L), dual slot, half height \/ low profile\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eThermal Solution\u003c\/td\u003e\n\u003ctd\u003eActive\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ePower Connector\u003c\/td\u003e\n\u003ctd\u003eNone — 70 W drawn from the PCIe slot, no auxiliary power connector published\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/table\u003e\n\u003cp\u003e\u003cem\u003eSpecifications are taken from NVIDIA's published datasheet for this model. Fields NVIDIA does not publish for a given card are shown as such rather than estimated.\u003c\/em\u003e\u003c\/p\u003e\n\u003c\/div\u003e\u003c\/details\u003e\u003cdetails class=\"pd-acc\"\u003e\u003csummary style=\"cursor: pointer; font-weight: 700;\"\u003eCompatibility \u0026amp; Software Support\u003c\/summary\u003e\u003cdiv class=\"pd-acc-body\"\u003e\n\u003ctable class=\"pd-spec\"\u003e\n\u003ctr\u003e\n\u003ctd\u003eGraphics APIs\u003c\/td\u003e\n\u003ctd\u003eDirectX 12 (Shader Model 6.6), OpenGL 4.6, Vulkan 1.4\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eCompute APIs\u003c\/td\u003e\n\u003ctd\u003eCUDA 12.8, OpenCL 3.0, DirectCompute\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eOperating Systems\u003c\/td\u003e\n\u003ctd\u003eWindows 10\/11 64-bit; Linux — RHEL 8.10, SUSE Linux Enterprise Desktop 15.6, OpenSUSE 15.6, Fedora 41, Ubuntu 22.04; FreeBSD 14.2; Solaris 11 U4 (per NVIDIA RTX PRO Blackwell Quick Start Guide)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eDrivers\u003c\/td\u003e\n\u003ctd\u003eNVIDIA RTX Enterprise Driver — Production Branch and New Feature Branch\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eProfessional Applications\u003c\/td\u003e\n\u003ctd\u003eCertified across leading CAD, DCC, BIM, medical imaging and simulation ISV applications\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eAI Frameworks\u003c\/td\u003e\n\u003ctd\u003eCUDA, cuDNN, TensorRT, PyTorch, TensorFlow, NVIDIA NIM and NGC containers\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eOmniverse \/ Digital Twin\u003c\/td\u003e\n\u003ctd\u003eSupported via NVIDIA Omniverse and OpenUSD workflows\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eChassis Check\u003c\/td\u003e\n\u003ctd\u003e2.7in (H) x 6.6in (L), dual slot, half height \/ low profile — confirm slot clearance, power headroom and airflow before ordering\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/table\u003e\n\u003cp\u003eSourceIT can verify fit against your specific workstation or server model before you order. For ISV certification status on a named application version, check the NVIDIA \u003ca href=\"https:\/\/www.nvidia.com\/en-us\/products\/workstations\/product-literature\/\" target=\"_blank\" rel=\"noopener\"\u003eprofessional product literature library\u003c\/a\u003e.\u003c\/p\u003e\n\u003c\/div\u003e\u003c\/details\u003e\u003cdetails class=\"pd-acc\"\u003e\u003csummary style=\"cursor: pointer; font-weight: 700;\"\u003ePackage Contents \u0026amp; NVIDIA RTX PRO Blackwell Ordering Information\u003c\/summary\u003e\u003cdiv class=\"pd-acc-body\"\u003e\n\u003cp\u003e\u003cstrong\u003eWhat is in the box:\u003c\/strong\u003e NVIDIA does not publish a formal box-contents list for this model. As a 70 W slot-powered card it requires no auxiliary power cable. If you need a specific bracket height confirmed for your chassis, contact SourceIT before ordering.\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eNVIDIA RTX PRO Blackwell range available from SourceIT:\u003c\/strong\u003e\u003c\/p\u003e\n\u003ctable class=\"pd-spec\"\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003ePart Number\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e\u003cstrong\u003eModel \u0026amp; Memory\u003c\/strong\u003e\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e900-5G144-2500-000\u003c\/td\u003e\n\u003ctd\u003e\n\u003ca href=\"\/products\/nvidia-rtx-pro%E2%84%A2-6000-blackwell-workstation-edition-900-5g144-2500-000\"\u003eRTX PRO 6000 Blackwell Workstation Edition\u003c\/a\u003e — 96 GB GDDR7 with ECC\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e900-5G153-2500-000\u003c\/td\u003e\n\u003ctd\u003e\n\u003ca href=\"\/products\/nvidia-rtx-pro%E2%84%A2-6000-blackwell-max-q-workstation-edition-900-5g153-2500-000\"\u003eRTX PRO 6000 Blackwell Max-Q Workstation Edition\u003c\/a\u003e — 96 GB GDDR7 with ECC\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e900-2G153-0000-000\u003c\/td\u003e\n\u003ctd\u003e\n\u003ca href=\"\/products\/nvidia-rtx-pro%E2%84%A2-6000-blackwell-server-edition\"\u003eRTX PRO 6000 Blackwell Server Edition\u003c\/a\u003e — 96 GB GDDR7 with ECC\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e900-5G153-2570-000\u003c\/td\u003e\n\u003ctd\u003e\n\u003ca href=\"\/products\/nvidia-rtx-pro%E2%84%A2-5000-blackwell-generation\"\u003eRTX PRO 5000 Blackwell 72 GB\u003c\/a\u003e — 72 GB GDDR7 with ECC\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e900-5G153-2550-000\u003c\/td\u003e\n\u003ctd\u003e\n\u003ca href=\"\/products\/nvidia-rtx-pro%E2%84%A2-5000-blackwell-generation-900-5g153-2550-000\"\u003eRTX PRO 5000 Blackwell 48 GB\u003c\/a\u003e — 48 GB GDDR7 with ECC\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e900-5G147-2550-000\u003c\/td\u003e\n\u003ctd\u003e\n\u003ca href=\"\/products\/nvidia-rtx-pro%E2%84%A2-4500-blackwell-generation-900-5g147-2550-000\"\u003eRTX PRO 4500 Blackwell\u003c\/a\u003e — 32 GB GDDR7 with ECC\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e900-5G147-2570-000\u003c\/td\u003e\n\u003ctd\u003e\n\u003ca href=\"\/products\/nvidia-rtx-pro%E2%84%A2-4000-blackwell-generation-900-5g147-2570-000\"\u003eRTX PRO 4000 Blackwell\u003c\/a\u003e — 24 GB GDDR7 with ECC\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e900-5G195-2501-000\u003c\/td\u003e\n\u003ctd\u003e\u003cstrong\u003eRTX PRO 4000 Blackwell SFF Edition — 24 GB GDDR7 with ECC (this item)\u003c\/strong\u003e\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e900-5G195-2551-000\u003c\/td\u003e\n\u003ctd\u003e\n\u003ca href=\"\/products\/nvidia-rtx-pro%E2%84%A2-2000-blackwell-generation-900-5g195-2551-000\"\u003eRTX PRO 2000 Blackwell\u003c\/a\u003e — 16 GB GDDR7 with ECC\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/table\u003e\n\u003cp\u003eChoosing between them: memory capacity is usually the deciding factor for AI work and scene size, while form factor and power decide what will physically fit. The \u003cstrong\u003eMax-Q\u003c\/strong\u003e variant exists to give full 96 GB capacity at 300 W for chassis that cannot take a 600 W card or that need more than one GPU. The \u003cstrong\u003eServer Edition\u003c\/strong\u003e is passively cooled and belongs in a server, not a workstation. The \u003cstrong\u003eSFF Edition\u003c\/strong\u003e trades bandwidth and PCIe lanes for a 70 W low-profile board.\u003c\/p\u003e\n\u003c\/div\u003e\u003c\/details\u003e\u003cdetails class=\"pd-acc\"\u003e\u003csummary style=\"cursor: pointer; font-weight: 700;\"\u003eDatasheet \u0026amp; Downloads\u003c\/summary\u003e\u003cdiv class=\"pd-acc-body\"\u003e\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https:\/\/images.nvidia.com\/aem-dam\/Solutions\/design-visualization\/quadro-product-literature\/workstation-datasheet-blackwell-rtx-pro-4000-sff-nvidia-us-4016700.pdf\" target=\"_blank\" rel=\"noopener\"\u003eNVIDIA RTX PRO™ 4000 Blackwell SFF Edition official NVIDIA datasheet\u003c\/a\u003e\u003c\/li\u003e\n\u003cli\u003e\u003ca href=\"https:\/\/www.nvidia.com\/en-us\/products\/workstations\/professional-desktop-gpus\/rtx-pro-4000-sff\/\" target=\"_blank\" rel=\"noopener\"\u003eNVIDIA RTX PRO™ 4000 Blackwell SFF Edition product page on NVIDIA.com\u003c\/a\u003e\u003c\/li\u003e\n\u003cli\u003e\u003ca href=\"https:\/\/www.nvidia.com\/content\/dam\/en-zz\/Solutions\/design-visualization\/quadro-product-literature\/NVIDIA-RTX-Blackwell-PRO-GPU-Architecture-v1.0.pdf\" target=\"_blank\" rel=\"noopener\"\u003eNVIDIA RTX PRO Blackwell GPU Architecture Whitepaper (PDF)\u003c\/a\u003e\u003c\/li\u003e\n\u003cli\u003e\u003ca href=\"https:\/\/images.nvidia.com\/aem-dam\/Solutions\/design-visualization\/quadro-product-literature\/rtx-pro-blackwell-online-qsg-210x148mm-20260317-r18.pdf\" target=\"_blank\" rel=\"noopener\"\u003eNVIDIA RTX PRO Blackwell Quick Start Guide (PDF)\u003c\/a\u003e\u003c\/li\u003e\n\u003cli\u003e\u003ca href=\"https:\/\/www.nvidia.com\/en-us\/products\/workstations\/product-literature\/\" target=\"_blank\" rel=\"noopener\"\u003eNVIDIA professional workstation product literature library\u003c\/a\u003e\u003c\/li\u003e\n\u003c\/ul\u003e\u003c\/div\u003e\u003c\/details\u003e\u003cdetails class=\"pd-acc\"\u003e\u003csummary style=\"cursor: pointer; font-weight: 700;\"\u003eFrequently Asked Questions — RTX PRO 4000 Blackwell SFF Edition\u003c\/summary\u003e\u003cdiv class=\"pd-acc-body\"\u003e\n\u003cp\u003e\u003cstrong\u003eHow much GPU memory does it have, and why does that matter?\u003c\/strong\u003e\u003cbr\u003e24 GB GDDR7 with ECC. For AI work, VRAM capacity decides which models fit on the card at all — a model that does not fit runs an order of magnitude slower or not at all. For visualisation it decides scene and texture budget. ECC means single-bit memory errors are corrected rather than silently corrupting a long job.\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eHow does the SFF Edition differ from the standard RTX PRO 4000 Blackwell?\u003c\/strong\u003e\u003cbr\u003eSame 24 GB GDDR7 with ECC and 8,960 CUDA cores, but the SFF runs at 70 W with 432 GB\/s bandwidth over PCIe 5.0 x8 on a half-height low-profile board with Mini DisplayPort outputs. The \u003ca href=\"\/products\/nvidia-rtx-pro%E2%84%A2-4000-blackwell-generation-900-5g147-2570-000\"\u003estandard RTX PRO 4000\u003c\/a\u003e runs at 145 W with 672 GB\/s over PCIe 5.0 x16, single slot full height, with full-size DisplayPort. Take the SFF only if your chassis requires low profile.\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eWhat power supply and chassis clearance do I need?\u003c\/strong\u003e\u003cbr\u003eThis card is 70 w total board power in a 2.7in (H) x 6.6in (L), dual slot, half height \/ low profile form factor with active cooling, powered via None. Check both physical clearance and PSU headroom against your workstation before ordering — send us your machine model and we will confirm fit.\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eDoes it support Multi-Instance GPU or vGPU?\u003c\/strong\u003e\u003cbr\u003eNVIDIA does not publish MIG or vGPU support for this model on its datasheet, so we do not claim it. If GPU partitioning or virtualisation is a requirement, the \u003ca href=\"\/products\/nvidia-rtx-pro%E2%84%A2-6000-blackwell-server-edition\"\u003eRTX PRO 6000 Blackwell Server Edition\u003c\/a\u003e and the RTX PRO 6000 and 5000 workstation cards are the models with published MIG support.\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eIs this a professional card or a gaming card?\u003c\/strong\u003e\u003cbr\u003eProfessional. RTX PRO Blackwell cards carry ECC memory, NVIDIA RTX Enterprise drivers with ISV application certification, longer driver support branches and a professional warranty. Consumer GeForce cards have none of these, which is why they are not accepted in most enterprise and regulated deployments regardless of raw frame rate.\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eDoes it come with local warranty in Singapore?\u003c\/strong\u003e\u003cbr\u003eYes. Every RTX PRO 4000 Blackwell SFF Edition sold by SourceIT comes with 3 years local Singapore warranty and a GST invoice.\u003c\/p\u003e\n\u003c\/div\u003e\u003c\/details\u003e\n\u003c\/div\u003e","brand":"Nvidia","offers":[{"title":"Default Title","offer_id":51167259623588,"sku":"900-5G195-2501-000","price":5550.0,"currency_code":"SGD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0605\/0136\/0804\/files\/nvidia-rtx-pro-4000-blackwell-generation-sff-edition-900-5g195-2501-000-8572969.webp?v=1761645032"},{"product_id":"nvidia-rtx-pro™-2000-blackwell-generation-900-5g195-2551-000","title":"NVIDIA RTX PRO™ 2000 Blackwell Generation","description":"\u003cdiv\u003e\n\u003cstyle\u003e\n.pd-acc { border: 1px solid #e2e2e2; border-radius: 8px; margin: 10px 0; overflow: hidden; }\n.pd-acc summary { cursor: pointer; padding: 14px 16px; font-weight: 700; font-size: 1.05em; list-style: none; display: flex; justify-content: space-between; align-items: center; background: #f7f7f7; }\n.pd-acc[open] \u003e summary { border-bottom: 1px solid #e2e2e2; }\n.pd-acc summary::-webkit-details-marker { display: none; }\n.pd-acc summary::after { content: \"+\"; font-size: 1.4em; font-weight: 400; line-height: 1; margin-left: 12px; }\n.pd-acc[open] \u003e summary::after { content: \"\\2212\"; }\n.pd-acc .pd-acc-body { padding: 8px 16px 14px; }\n.pd-spec, .pd-spec tr, .pd-spec td { border: none !important; background: transparent !important; }\n.pd-spec { width: 100%; border-collapse: collapse !important; margin: 6px 0 18px; }\n.pd-spec td { padding: 7px 10px 7px 0 !important; border-bottom: 1px solid #ececec !important; vertical-align: top; font-size: 0.95em; text-align: left; }\n.pd-spec td:first-child { width: 38%; font-weight: 600; color: #333; }\n.pd-spec tr:last-child td { border-bottom: none !important; }\n\u003c\/style\u003e\n\u003ch2\u003e\u003cstrong\u003eNVIDIA RTX PRO™ 2000 Blackwell Generation 16 GB GDDR7 with ECC (900-5G195-2551-000) - 3 Years Local Warranty\u003c\/strong\u003e\u003c\/h2\u003e\n\u003ch3\u003e\u003cstrong\u003eRTX PRO 2000 Blackwell — 16 GB ECC Professional GPU at 70 W\u003c\/strong\u003e\u003c\/h3\u003e\n\u003cp\u003eThe \u003cstrong\u003eNVIDIA RTX PRO™ 2000 Blackwell\u003c\/strong\u003e is the entry point into the Blackwell professional range and the natural replacement for ageing T-series and RTX A2000 cards. It delivers 16 GB of GDDR7 with ECC and 4,352 CUDA cores inside a 2.7in x 6.6in low-profile, dual-slot board drawing just \u003cstrong\u003e70 W\u003c\/strong\u003e with no auxiliary power connector.\u003c\/p\u003e\n\u003ch3\u003e\u003cstrong\u003e545 AI TOPS for Everyday Professional AI\u003c\/strong\u003e\u003c\/h3\u003e\n\u003cp\u003eFifth-generation Tensor Cores deliver 545 AI TOPS (effective FP4 with sparsity), with 17 TFLOPS FP32 and 52 TFLOPS of fourth-generation RT Core performance. That is enough for AI-assisted design tools, local inference on small and mid-size models, real-time viewport ray tracing and GPU-accelerated video work — on a card that fits almost any chassis.\u003c\/p\u003e\n\u003ch3\u003e\u003cstrong\u003eFour Mini DisplayPort 2.1b Outputs\u003c\/strong\u003e\u003c\/h3\u003e\n\u003cp\u003eFour Mini DisplayPort 2.1b connectors drive up to four simultaneous displays at 4K 165 Hz, or two at 4K 360 Hz or 8K 100 Hz with DSC. Combined with the low-profile form factor, this makes the RTX PRO 2000 a strong fit for trading desks, control rooms and digital signage as well as CAD workstations.\u003c\/p\u003e\n\u003ch3\u003e\u003cstrong\u003e16 GB of ECC Memory at This Tier\u003c\/strong\u003e\u003c\/h3\u003e\n\u003cp\u003eECC on an entry professional card is the point of difference against a consumer GPU: silent memory errors do not quietly corrupt a simulation or a long render. Supplied by SourceIT Pte Ltd with a GST invoice and \u003cstrong\u003e3 years local warranty\u003c\/strong\u003e.\u003c\/p\u003e\n\u003cdetails class=\"pd-acc\"\u003e\u003csummary style=\"cursor: pointer; font-weight: 700;\"\u003eNVIDIA RTX PRO™ 2000 Blackwell Datasheet — Technical Specifications\u003c\/summary\u003e\u003cdiv class=\"pd-acc-body\"\u003e\n\u003ch4\u003eGeneral\u003c\/h4\u003e\n\u003ctable class=\"pd-spec\"\u003e\n\u003ctr\u003e\n\u003ctd\u003ePart Number\u003c\/td\u003e\n\u003ctd\u003e900-5G195-2551-000\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eModel\u003c\/td\u003e\n\u003ctd\u003eNVIDIA RTX PRO™ 2000 Blackwell\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eGPU Architecture\u003c\/td\u003e\n\u003ctd\u003eNVIDIA Blackwell\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eProduct Family\u003c\/td\u003e\n\u003ctd\u003eNVIDIA RTX PRO Blackwell Workstation \/ Professional Desktop GPUs\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eWarranty\u003c\/td\u003e\n\u003ctd\u003e3 Years Local Warranty (Singapore)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/table\u003e\n\u003ch4\u003eMemory\u003c\/h4\u003e\n\u003ctable class=\"pd-spec\"\u003e\n\u003ctr\u003e\n\u003ctd\u003eGPU Memory\u003c\/td\u003e\n\u003ctd\u003e16 GB GDDR7 with ECC\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eMemory Interface\u003c\/td\u003e\n\u003ctd\u003e128-bit\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eMemory Bandwidth\u003c\/td\u003e\n\u003ctd\u003e288 GB\/s\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eECC\u003c\/td\u003e\n\u003ctd\u003eYes — error-correcting code memory\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/table\u003e\n\u003ch4\u003eCompute \u0026amp; Performance\u003c\/h4\u003e\n\u003ctable class=\"pd-spec\"\u003e\n\u003ctr\u003e\n\u003ctd\u003eCUDA Cores\u003c\/td\u003e\n\u003ctd\u003e4,352\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eTensor Cores\u003c\/td\u003e\n\u003ctd\u003eFifth-generation Tensor Cores\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eRT Cores\u003c\/td\u003e\n\u003ctd\u003eFourth-generation RT Cores\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eSingle-Precision (FP32) Performance\u003c\/td\u003e\n\u003ctd\u003e17 TFLOPS\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eRT Core Performance\u003c\/td\u003e\n\u003ctd\u003e52 TFLOPS\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eAI Performance\u003c\/td\u003e\n\u003ctd\u003e545 AI TOPS (effective FP4 TOPS with sparsity, peak rates based on GPU Boost Clock)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eMulti-Instance GPU (MIG)\u003c\/td\u003e\n\u003ctd\u003eNot published by NVIDIA for this model\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/table\u003e\n\u003ch4\u003eMedia Engines\u003c\/h4\u003e\n\u003ctable class=\"pd-spec\"\u003e\n\u003ctr\u003e\n\u003ctd\u003eEncoders (NVENC)\u003c\/td\u003e\n\u003ctd\u003e1x NVENC (ninth generation)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eDecoders (NVDEC)\u003c\/td\u003e\n\u003ctd\u003e1x NVDEC (sixth generation)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/table\u003e\n\u003ch4\u003eDisplay\u003c\/h4\u003e\n\u003ctable class=\"pd-spec\"\u003e\n\u003ctr\u003e\n\u003ctd\u003eDisplay Connectors\u003c\/td\u003e\n\u003ctd\u003e4x Mini DisplayPort 2.1b\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eMaximum Simultaneous Displays\u003c\/td\u003e\n\u003ctd\u003eUp to four simultaneous displays; 4x 4K @ 165 Hz, or 2x 4K @ 360 Hz \/ 8K @ 100 Hz with DSC\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/table\u003e\n\u003ch4\u003ePower, Form Factor \u0026amp; Interface\u003c\/h4\u003e\n\u003ctable class=\"pd-spec\"\u003e\n\u003ctr\u003e\n\u003ctd\u003eMaximum Power Consumption\u003c\/td\u003e\n\u003ctd\u003e70 W maximum power consumption\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eGraphics Bus\u003c\/td\u003e\n\u003ctd\u003ePCIe 5.0 x8 (uses a full-length PCIe interface)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eForm Factor\u003c\/td\u003e\n\u003ctd\u003e2.7in (H) x 6.6in (L), dual slot, low-profile height\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eThermal Solution\u003c\/td\u003e\n\u003ctd\u003eActive\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ePower Connector\u003c\/td\u003e\n\u003ctd\u003eNone — 70 W drawn from the PCIe slot, no auxiliary power connector published\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/table\u003e\n\u003cp\u003e\u003cem\u003eSpecifications are taken from NVIDIA's published datasheet for this model. Fields NVIDIA does not publish for a given card are shown as such rather than estimated.\u003c\/em\u003e\u003c\/p\u003e\n\u003c\/div\u003e\u003c\/details\u003e\u003cdetails class=\"pd-acc\"\u003e\u003csummary style=\"cursor: pointer; font-weight: 700;\"\u003eCompatibility \u0026amp; Software Support\u003c\/summary\u003e\u003cdiv class=\"pd-acc-body\"\u003e\n\u003ctable class=\"pd-spec\"\u003e\n\u003ctr\u003e\n\u003ctd\u003eGraphics APIs\u003c\/td\u003e\n\u003ctd\u003eDirectX 12 (Shader Model 6.6), OpenGL 4.6, Vulkan 1.4\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eCompute APIs\u003c\/td\u003e\n\u003ctd\u003eCUDA 12.8, OpenCL 3.0, DirectCompute\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eOperating Systems\u003c\/td\u003e\n\u003ctd\u003eWindows 10\/11 64-bit; Linux — RHEL 8.10, SUSE Linux Enterprise Desktop 15.6, OpenSUSE 15.6, Fedora 41, Ubuntu 22.04; FreeBSD 14.2; Solaris 11 U4 (per NVIDIA RTX PRO Blackwell Quick Start Guide)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eDrivers\u003c\/td\u003e\n\u003ctd\u003eNVIDIA RTX Enterprise Driver — Production Branch and New Feature Branch\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eProfessional Applications\u003c\/td\u003e\n\u003ctd\u003eCertified across leading CAD, DCC, BIM, medical imaging and simulation ISV applications\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eAI Frameworks\u003c\/td\u003e\n\u003ctd\u003eCUDA, cuDNN, TensorRT, PyTorch, TensorFlow, NVIDIA NIM and NGC containers\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eOmniverse \/ Digital Twin\u003c\/td\u003e\n\u003ctd\u003eSupported via NVIDIA Omniverse and OpenUSD workflows\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eChassis Check\u003c\/td\u003e\n\u003ctd\u003e2.7in (H) x 6.6in (L), dual slot, low-profile height — confirm slot clearance, power headroom and airflow before ordering\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/table\u003e\n\u003cp\u003eSourceIT can verify fit against your specific workstation or server model before you order. For ISV certification status on a named application version, check the NVIDIA \u003ca href=\"https:\/\/www.nvidia.com\/en-us\/products\/workstations\/product-literature\/\" target=\"_blank\" rel=\"noopener\"\u003eprofessional product literature library\u003c\/a\u003e.\u003c\/p\u003e\n\u003c\/div\u003e\u003c\/details\u003e\u003cdetails class=\"pd-acc\"\u003e\u003csummary style=\"cursor: pointer; font-weight: 700;\"\u003ePackage Contents \u0026amp; NVIDIA RTX PRO Blackwell Ordering Information\u003c\/summary\u003e\u003cdiv class=\"pd-acc-body\"\u003e\n\u003cp\u003e\u003cstrong\u003eWhat is in the box:\u003c\/strong\u003e NVIDIA does not publish a formal box-contents list for this model. As a 70 W slot-powered card it requires no auxiliary power cable. If you need a specific bracket height confirmed for your chassis, contact SourceIT before ordering.\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eNVIDIA RTX PRO Blackwell range available from SourceIT:\u003c\/strong\u003e\u003c\/p\u003e\n\u003ctable class=\"pd-spec\"\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003ePart Number\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e\u003cstrong\u003eModel \u0026amp; Memory\u003c\/strong\u003e\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e900-5G144-2500-000\u003c\/td\u003e\n\u003ctd\u003e\n\u003ca href=\"\/products\/nvidia-rtx-pro%E2%84%A2-6000-blackwell-workstation-edition-900-5g144-2500-000\"\u003eRTX PRO 6000 Blackwell Workstation Edition\u003c\/a\u003e — 96 GB GDDR7 with ECC\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e900-5G153-2500-000\u003c\/td\u003e\n\u003ctd\u003e\n\u003ca href=\"\/products\/nvidia-rtx-pro%E2%84%A2-6000-blackwell-max-q-workstation-edition-900-5g153-2500-000\"\u003eRTX PRO 6000 Blackwell Max-Q Workstation Edition\u003c\/a\u003e — 96 GB GDDR7 with ECC\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e900-2G153-0000-000\u003c\/td\u003e\n\u003ctd\u003e\n\u003ca href=\"\/products\/nvidia-rtx-pro%E2%84%A2-6000-blackwell-server-edition\"\u003eRTX PRO 6000 Blackwell Server Edition\u003c\/a\u003e — 96 GB GDDR7 with ECC\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e900-5G153-2570-000\u003c\/td\u003e\n\u003ctd\u003e\n\u003ca href=\"\/products\/nvidia-rtx-pro%E2%84%A2-5000-blackwell-generation\"\u003eRTX PRO 5000 Blackwell 72 GB\u003c\/a\u003e — 72 GB GDDR7 with ECC\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e900-5G153-2550-000\u003c\/td\u003e\n\u003ctd\u003e\n\u003ca href=\"\/products\/nvidia-rtx-pro%E2%84%A2-5000-blackwell-generation-900-5g153-2550-000\"\u003eRTX PRO 5000 Blackwell 48 GB\u003c\/a\u003e — 48 GB GDDR7 with ECC\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e900-5G147-2550-000\u003c\/td\u003e\n\u003ctd\u003e\n\u003ca href=\"\/products\/nvidia-rtx-pro%E2%84%A2-4500-blackwell-generation-900-5g147-2550-000\"\u003eRTX PRO 4500 Blackwell\u003c\/a\u003e — 32 GB GDDR7 with ECC\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e900-5G147-2570-000\u003c\/td\u003e\n\u003ctd\u003e\n\u003ca href=\"\/products\/nvidia-rtx-pro%E2%84%A2-4000-blackwell-generation-900-5g147-2570-000\"\u003eRTX PRO 4000 Blackwell\u003c\/a\u003e — 24 GB GDDR7 with ECC\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e900-5G195-2501-000\u003c\/td\u003e\n\u003ctd\u003e\n\u003ca href=\"\/products\/nvidia-rtx-pro%E2%84%A2-4000-blackwell-generation-sff-edition-900-5g195-2501-000\"\u003eRTX PRO 4000 Blackwell SFF Edition\u003c\/a\u003e — 24 GB GDDR7 with ECC\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e900-5G195-2551-000\u003c\/td\u003e\n\u003ctd\u003e\u003cstrong\u003eRTX PRO 2000 Blackwell — 16 GB GDDR7 with ECC (this item)\u003c\/strong\u003e\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/table\u003e\n\u003cp\u003eChoosing between them: memory capacity is usually the deciding factor for AI work and scene size, while form factor and power decide what will physically fit. The \u003cstrong\u003eMax-Q\u003c\/strong\u003e variant exists to give full 96 GB capacity at 300 W for chassis that cannot take a 600 W card or that need more than one GPU. The \u003cstrong\u003eServer Edition\u003c\/strong\u003e is passively cooled and belongs in a server, not a workstation. The \u003cstrong\u003eSFF Edition\u003c\/strong\u003e trades bandwidth and PCIe lanes for a 70 W low-profile board.\u003c\/p\u003e\n\u003c\/div\u003e\u003c\/details\u003e\u003cdetails class=\"pd-acc\"\u003e\u003csummary style=\"cursor: pointer; font-weight: 700;\"\u003eDatasheet \u0026amp; Downloads\u003c\/summary\u003e\u003cdiv class=\"pd-acc-body\"\u003e\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https:\/\/images.nvidia.com\/aem-dam\/Solutions\/design-visualization\/quadro-product-literature\/workstation-datasheet-blackwell-rtx-pro-2000-nvidia-us-4016661.pdf\" target=\"_blank\" rel=\"noopener\"\u003eNVIDIA RTX PRO™ 2000 Blackwell official NVIDIA datasheet\u003c\/a\u003e\u003c\/li\u003e\n\u003cli\u003e\u003ca href=\"https:\/\/www.nvidia.com\/en-us\/products\/workstations\/professional-desktop-gpus\/rtx-pro-2000\/\" target=\"_blank\" rel=\"noopener\"\u003eNVIDIA RTX PRO™ 2000 Blackwell product page on NVIDIA.com\u003c\/a\u003e\u003c\/li\u003e\n\u003cli\u003e\u003ca href=\"https:\/\/www.nvidia.com\/content\/dam\/en-zz\/Solutions\/design-visualization\/quadro-product-literature\/NVIDIA-RTX-Blackwell-PRO-GPU-Architecture-v1.0.pdf\" target=\"_blank\" rel=\"noopener\"\u003eNVIDIA RTX PRO Blackwell GPU Architecture Whitepaper (PDF)\u003c\/a\u003e\u003c\/li\u003e\n\u003cli\u003e\u003ca href=\"https:\/\/images.nvidia.com\/aem-dam\/Solutions\/design-visualization\/quadro-product-literature\/rtx-pro-blackwell-online-qsg-210x148mm-20260317-r18.pdf\" target=\"_blank\" rel=\"noopener\"\u003eNVIDIA RTX PRO Blackwell Quick Start Guide (PDF)\u003c\/a\u003e\u003c\/li\u003e\n\u003cli\u003e\u003ca href=\"https:\/\/www.nvidia.com\/en-us\/products\/workstations\/product-literature\/\" target=\"_blank\" rel=\"noopener\"\u003eNVIDIA professional workstation product literature library\u003c\/a\u003e\u003c\/li\u003e\n\u003c\/ul\u003e\u003c\/div\u003e\u003c\/details\u003e\u003cdetails class=\"pd-acc\"\u003e\u003csummary style=\"cursor: pointer; font-weight: 700;\"\u003eFrequently Asked Questions — RTX PRO 2000 Blackwell\u003c\/summary\u003e\u003cdiv class=\"pd-acc-body\"\u003e\n\u003cp\u003e\u003cstrong\u003eHow much GPU memory does it have, and why does that matter?\u003c\/strong\u003e\u003cbr\u003e16 GB GDDR7 with ECC. For AI work, VRAM capacity decides which models fit on the card at all — a model that does not fit runs an order of magnitude slower or not at all. For visualisation it decides scene and texture budget. ECC means single-bit memory errors are corrected rather than silently corrupting a long job.\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eHow does it compare to the RTX PRO 4000 Blackwell?\u003c\/strong\u003e\u003cbr\u003eThe \u003ca href=\"\/products\/nvidia-rtx-pro%E2%84%A2-4000-blackwell-generation-900-5g147-2570-000\"\u003eRTX PRO 4000\u003c\/a\u003e steps up to 24 GB of GDDR7, 8,960 CUDA cores, 672 GB\/s and 1,178 AI TOPS at 145 W. The RTX PRO 2000 delivers 16 GB, 4,352 cores, 288 GB\/s and 545 AI TOPS at 70 W. If your models and scenes fit in 16 GB, the 2000 is the efficient choice; if they do not, no amount of compute compensates.\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eWhat power supply and chassis clearance do I need?\u003c\/strong\u003e\u003cbr\u003eThis card is 70 w maximum power consumption in a 2.7in (H) x 6.6in (L), dual slot, low-profile height form factor with active cooling, powered via None. Check both physical clearance and PSU headroom against your workstation before ordering — send us your machine model and we will confirm fit.\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eDoes it support Multi-Instance GPU or vGPU?\u003c\/strong\u003e\u003cbr\u003eNVIDIA does not publish MIG or vGPU support for this model on its datasheet, so we do not claim it. If GPU partitioning or virtualisation is a requirement, the \u003ca href=\"\/products\/nvidia-rtx-pro%E2%84%A2-6000-blackwell-server-edition\"\u003eRTX PRO 6000 Blackwell Server Edition\u003c\/a\u003e and the RTX PRO 6000 and 5000 workstation cards are the models with published MIG support.\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eIs this a professional card or a gaming card?\u003c\/strong\u003e\u003cbr\u003eProfessional. RTX PRO Blackwell cards carry ECC memory, NVIDIA RTX Enterprise drivers with ISV application certification, longer driver support branches and a professional warranty. Consumer GeForce cards have none of these, which is why they are not accepted in most enterprise and regulated deployments regardless of raw frame rate.\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eDoes it come with local warranty in Singapore?\u003c\/strong\u003e\u003cbr\u003eYes. Every RTX PRO 2000 Blackwell sold by SourceIT comes with 3 years local Singapore warranty and a GST invoice.\u003c\/p\u003e\n\u003c\/div\u003e\u003c\/details\u003e\n\u003c\/div\u003e","brand":"Nvidia","offers":[{"title":"Default Title","offer_id":51167295373476,"sku":"900-5G195-2551-000","price":2990.0,"currency_code":"SGD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0605\/0136\/0804\/files\/nvidia-rtx-pro-2000-blackwell-generation-900-5g195-2551-000-8093555.webp?v=1761645031"},{"product_id":"nvidia-dgx-spark-ai-supercomputer-4tb-940-54242-0007-000","title":"NVIDIA DGX Spark AI Supercomputer - 4TB","description":"\u003cdiv\u003e\n\u003cstyle\u003e\n.pd-acc { border: 1px solid #e2e2e2; border-radius: 8px; margin: 10px 0; overflow: hidden; }\n.pd-acc summary { cursor: pointer; padding: 14px 16px; font-weight: 700; font-size: 1.05em; list-style: none; display: flex; justify-content: space-between; align-items: center; background: #f7f7f7; }\n.pd-acc[open] \u003e summary { border-bottom: 1px solid #e2e2e2; }\n.pd-acc summary::-webkit-details-marker { display: none; }\n.pd-acc summary::after { content: \"+\"; font-size: 1.4em; font-weight: 400; line-height: 1; margin-left: 12px; }\n.pd-acc[open] \u003e summary::after { content: \"\\2212\"; }\n.pd-acc .pd-acc-body { padding: 8px 16px 14px; }\n.pd-spec, .pd-spec tr, .pd-spec td { border: none !important; background: transparent !important; }\n.pd-spec { width: 100%; border-collapse: collapse !important; margin: 6px 0 18px; }\n.pd-spec td { padding: 7px 10px 7px 0 !important; border-bottom: 1px solid #ececec !important; vertical-align: top; font-size: 0.95em; text-align: left; }\n.pd-spec td:first-child { width: 38%; font-weight: 600; color: #333; }\n.pd-spec tr:last-child td { border-bottom: none !important; }\n\u003c\/style\u003e\n\u003ch2\u003e\u003cstrong\u003eNVIDIA DGX Spark AI Supercomputer 4TB (940-54242-0007-000) - 1 Year Local Warranty\u003c\/strong\u003e\u003c\/h2\u003e\n\u003ch3\u003e\u003cstrong\u003eNVIDIA DGX Spark — A Petaflop of AI Performance on Your Desk\u003c\/strong\u003e\u003c\/h3\u003e\n\u003cp\u003eThe NVIDIA DGX Spark puts up to 1 petaFLOP of FP4 AI compute in a 1.2 kg desktop box. Powered by the NVIDIA GB10 Grace Blackwell Superchip with 128 GB of coherent unified memory, it runs inference on models up to 200 billion parameters and fine-tunes models up to 70 billion parameters — locally, on your desk, with no cloud dependency.\u003c\/p\u003e\n\u003ch3\u003e\u003cstrong\u003eEnterprise AI Without the Cloud — Privacy, Compliance, Control\u003c\/strong\u003e\u003c\/h3\u003e\n\u003cp\u003eDGX Spark brings the NVIDIA DGX platform to enterprises, research labs and advanced developers who need on-premise AI for data privacy, compliance and IP protection — finance, healthcare, research, defense and government. The full NVIDIA AI software stack comes preloaded on DGX OS: CUDA, TensorRT, PyTorch and TensorFlow containers, NVIDIA NIM and NGC-certified containers.\u003c\/p\u003e\n\u003ch3\u003e\u003cstrong\u003eScale to 405B Parameters with Two Linked Sparks\u003c\/strong\u003e\u003c\/h3\u003e\n\u003cp\u003eThe built-in NVIDIA ConnectX-7 SmartNIC (200 Gbps, RDMA-capable) lets you link two DGX Spark units into a single system that handles models up to 405 billion parameters — pair yours with a second unit and the \u003ca href=\"\/products\/nvidia-qsfp112-400g-dac-cable-40cm-x0101g00400a\"\u003eNVIDIA QSFP112 DAC cable (X0101G00400A)\u003c\/a\u003e.\u003c\/p\u003e\n\u003ch3\u003e\u003cstrong\u003eDeployment-Ready in Singapore\u003c\/strong\u003e\u003c\/h3\u003e\n\u003cp\u003eIn stock at SourceIT with GST invoice, 1 year local warranty, and our \u003ca href=\"https:\/\/sourceit.com.sg\/blogs\/news\/nvidia-dgx-spark-setup-and-enterprise-deployment-guide\"\u003eNVIDIA DGX Spark Setup and Enterprise Deployment Guide\u003c\/a\u003e to get you running from day one.\u003c\/p\u003e\n\u003cdetails class=\"pd-acc\"\u003e\n\u003csummary style=\"cursor: pointer; font-weight: 700;\"\u003eNVIDIA DGX Spark Datasheet — Technical Specifications\u003c\/summary\u003e\n\u003cdiv class=\"pd-acc-body\"\u003e\n\u003cp\u003e\u003cstrong\u003eGeneral\u003c\/strong\u003e\u003c\/p\u003e\n\u003ctable class=\"pd-spec\"\u003e\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eModel\u003c\/td\u003e\n\u003ctd\u003eNVIDIA DGX Spark AI Supercomputer — 4TB\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ePart Number\u003c\/td\u003e\n\u003ctd\u003e940-54242-0007-000\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eGTIN\u003c\/td\u003e\n\u003ctd\u003e812674029197\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eCategory\u003c\/td\u003e\n\u003ctd\u003eCompact Desktop AI Supercomputer\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eWarranty\u003c\/td\u003e\n\u003ctd\u003e1 Year Local Warranty (Singapore)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/tbody\u003e\u003c\/table\u003e\n\u003cp\u003e\u003cstrong\u003eProcessor — NVIDIA GB10 Grace Blackwell Superchip\u003c\/strong\u003e\u003c\/p\u003e\n\u003ctable class=\"pd-spec\"\u003e\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eArchitecture\u003c\/td\u003e\n\u003ctd\u003eCPU + GPU Unified (Grace Blackwell), NVLink-C2C Interconnect\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eCPU\u003c\/td\u003e\n\u003ctd\u003e20-Core Arm — 10 × Cortex-X925 Performance + 10 × Cortex-A725 Efficiency\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eGPU\u003c\/td\u003e\n\u003ctd\u003eNVIDIA Blackwell Architecture\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eTensor Cores\u003c\/td\u003e\n\u003ctd\u003e5th Generation (FP4 \/ FP8 Optimised)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eRT Cores\u003c\/td\u003e\n\u003ctd\u003e4th Generation\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eGB10 TDP\u003c\/td\u003e\n\u003ctd\u003e140 W\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/tbody\u003e\u003c\/table\u003e\n\u003cp\u003e\u003cstrong\u003eAI Performance\u003c\/strong\u003e\u003c\/p\u003e\n\u003ctable class=\"pd-spec\"\u003e\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003ePeak AI Compute\u003c\/td\u003e\n\u003ctd\u003eUp to 1 PFLOP (1,000 TOPS) FP4 with Sparsity\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eInference\u003c\/td\u003e\n\u003ctd\u003eModels Up to 200 Billion Parameters (Single Unit)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eFine-Tuning\u003c\/td\u003e\n\u003ctd\u003eModels Up to 70 Billion Parameters\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eClustered (2 Units)\u003c\/td\u003e\n\u003ctd\u003eModels Up to 405 Billion Parameters via ConnectX-7\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/tbody\u003e\u003c\/table\u003e\n\u003cp\u003e\u003cstrong\u003eMemory \u0026amp; Storage\u003c\/strong\u003e\u003c\/p\u003e\n\u003ctable class=\"pd-spec\"\u003e\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eSystem Memory\u003c\/td\u003e\n\u003ctd\u003e128 GB LPDDR5x Coherent Unified Memory (Shared CPU–GPU)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eMemory Interface\u003c\/td\u003e\n\u003ctd\u003e256-bit, 273 GB\/s Bandwidth\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eStorage\u003c\/td\u003e\n\u003ctd\u003e4 TB NVMe M.2 Self-Encrypting SSD\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/tbody\u003e\u003c\/table\u003e\n\u003cp\u003e\u003cstrong\u003eNetworking \u0026amp; I\/O\u003c\/strong\u003e\u003c\/p\u003e\n\u003ctable class=\"pd-spec\"\u003e\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eSmartNIC\u003c\/td\u003e\n\u003ctd\u003eNVIDIA ConnectX-7 — Up to 200 Gbps, RDMA Capable, Multi-Node Clustering\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eEthernet\u003c\/td\u003e\n\u003ctd\u003e1 × 10 GbE RJ-45\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eWireless\u003c\/td\u003e\n\u003ctd\u003eWi-Fi 7, Bluetooth 5.4\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eUSB\u003c\/td\u003e\n\u003ctd\u003e4 × USB Type-C\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eDisplay\u003c\/td\u003e\n\u003ctd\u003e1 × HDMI 2.1a; Up to 3 × DisplayPort via USB-C Alt Mode\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eVideo Engines\u003c\/td\u003e\n\u003ctd\u003e1 × NVENC, 1 × NVDEC\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/tbody\u003e\u003c\/table\u003e\n\u003cp\u003e\u003cstrong\u003eSoftware\u003c\/strong\u003e\u003c\/p\u003e\n\u003ctable class=\"pd-spec\"\u003e\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eOperating System\u003c\/td\u003e\n\u003ctd\u003eNVIDIA DGX OS (Ubuntu LTS-Based) with Preloaded AI Stack\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eAI Stack\u003c\/td\u003e\n\u003ctd\u003eNVIDIA AI Enterprise, CUDA Toolkit, TensorRT, NVIDIA NIM, PyTorch \u0026amp; TensorFlow Containers, NGC-Certified Containers\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eSecurity\u003c\/td\u003e\n\u003ctd\u003eSecure Boot (UEFI), TPM-Based Security, Self-Encrypting SSD\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/tbody\u003e\u003c\/table\u003e\n\u003cp\u003e\u003cstrong\u003ePower \u0026amp; Physical\u003c\/strong\u003e\u003c\/p\u003e\n\u003ctable class=\"pd-spec\"\u003e\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003ePower Supply\u003c\/td\u003e\n\u003ctd\u003e240 W External Adapter\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eDimensions\u003c\/td\u003e\n\u003ctd\u003e150 mm × 150 mm × 50.5 mm\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eWeight\u003c\/td\u003e\n\u003ctd\u003eApproximately 1.2 kg\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ePower Cord\u003c\/td\u003e\n\u003ctd\u003eUS Type-B Cord Included (Factory Sealed); UK Power Cord Provided Separately by SourceIT\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/tbody\u003e\u003c\/table\u003e\n\u003c\/div\u003e\n\u003c\/details\u003e\n\u003cdetails class=\"pd-acc\"\u003e\n\u003csummary style=\"cursor: pointer; font-weight: 700;\"\u003eWorkloads \u0026amp; Use Cases\u003c\/summary\u003e\n\u003cdiv class=\"pd-acc-body\"\u003e\n\u003cp\u003e\u003cstrong\u003ePrimary:\u003c\/strong\u003e LLM training, fine-tuning and large-scale inference — faster experimentation, shorter training cycles and real-time deployment for business-critical workloads.\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eAlso ideal for:\u003c\/strong\u003e generative AI (image, video, multimodal), RAG systems and vector databases, agentic AI development, data science and embeddings, computer vision, scientific computing\/HPC research, and edge AI with low-latency local inference.\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eRegulated industries:\u003c\/strong\u003e keeps sensitive data on-premise for finance, healthcare, research, defense and government deployments where public cloud is not an option.\u003c\/p\u003e\n\u003c\/div\u003e\n\u003c\/details\u003e\n\u003cdetails class=\"pd-acc\"\u003e\n\u003csummary style=\"cursor: pointer; font-weight: 700;\"\u003eOrdering Information — AI Supercomputers at SourceIT\u003c\/summary\u003e\n\u003cdiv class=\"pd-acc-body\"\u003e\n\u003ctable class=\"pd-spec\"\u003e\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eNVIDIA DGX Spark 4TB\u003c\/td\u003e\n\u003ctd\u003e940-54242-0007-000 (This Product)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eASUS Ascent GX10 1TB (GB10 Platform)\u003c\/td\u003e\n\u003ctd\u003e\u003ca href=\"\/products\/asus-ascent-gx10-compact-desktop-ai-supercomputer-1tb-gx10-gg0007bn\"\u003eGX10-GG0007BN — In Stock\u003c\/a\u003e\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eASUS Ascent GX10 2TB (GB10 Platform)\u003c\/td\u003e\n\u003ctd\u003e\u003ca href=\"\/products\/asus-ascent-gx10-compact-desktop-ai-supercomputer-2tb-gx10-gg0030bn\"\u003eGX10-GG0030BN — In Stock\u003c\/a\u003e\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eASUS Ascent GX10 4TB (GB10 Platform)\u003c\/td\u003e\n\u003ctd\u003e\u003ca href=\"\/products\/asus-ascent-gx10-compact-desktop-ai-supercomputer-4tb-gx10-gg0007bn\"\u003eGX10-GG0031BN — In Stock\u003c\/a\u003e\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eNVIDIA QSFP112 400G DAC Cable (Links 2 Units)\u003c\/td\u003e\n\u003ctd\u003e\u003ca href=\"\/products\/nvidia-qsfp112-400g-dac-cable-40cm-x0101g00400a\"\u003eX0101G00400A — In Stock\u003c\/a\u003e\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eMSI XpertStation WS300 (DGX Station GB300, 748GB)\u003c\/td\u003e\n\u003ctd\u003e\u003ca href=\"\/products\/msi-xpertstation-ws300-nvidia-dgx-station-ai-supercomputer\"\u003eIn Stock — Enquire\u003c\/a\u003e\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eHP ZGX Fury G1n GB300 AI Workstation\u003c\/td\u003e\n\u003ctd\u003e\u003ca href=\"\/products\/hp-zgx-fury-g1n-gb300-ai-workstation-with-nvidia-blackwell-ultra\"\u003eEnquire\u003c\/a\u003e\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/tbody\u003e\u003c\/table\u003e\n\u003c\/div\u003e\n\u003c\/details\u003e\n\u003cdetails class=\"pd-acc\"\u003e\n\u003csummary style=\"cursor: pointer; font-weight: 700;\"\u003eDatasheet, Downloads \u0026amp; Setup Guide\u003c\/summary\u003e\n\u003cdiv class=\"pd-acc-body\"\u003e\n\u003cp\u003e• \u003ca href=\"https:\/\/www.nvidia.com\/en-us\/products\/workstations\/dgx-spark\/\" rel=\"noopener\" target=\"_blank\"\u003eNVIDIA DGX Spark — Official Product Page \u0026amp; Specifications\u003c\/a\u003e\u003cbr\u003e\n• \u003ca href=\"https:\/\/sourceit.com.sg\/blogs\/news\/nvidia-dgx-spark-setup-and-enterprise-deployment-guide\"\u003eNVIDIA DGX Spark Setup and Enterprise Deployment Guide — SourceIT\u003c\/a\u003e\u003c\/p\u003e\n\u003c\/div\u003e\n\u003c\/details\u003e\n\u003cdetails class=\"pd-acc\"\u003e\n\u003csummary style=\"cursor: pointer; font-weight: 700;\"\u003eFrequently Asked Questions — NVIDIA DGX Spark\u003c\/summary\u003e\n\u003cdiv class=\"pd-acc-body\"\u003e\n\u003cp\u003e\u003cstrong\u003eWhat AI models can the DGX Spark run?\u003c\/strong\u003e\u003cbr\u003e\nA single DGX Spark handles inference on models up to 200 billion parameters and fine-tuning up to 70 billion parameters — think Llama, Qwen, DeepSeek, Mistral and Flux-class models locally. Two linked units scale to 405 billion parameters.\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eHow do I connect two DGX Spark units?\u003c\/strong\u003e\u003cbr\u003e\nVia the built-in ConnectX-7 SmartNIC using the \u003ca href=\"\/products\/nvidia-qsfp112-400g-dac-cable-40cm-x0101g00400a\"\u003eNVIDIA QSFP112 400G DAC cable\u003c\/a\u003e — a direct connection that turns two Sparks into one system for larger models and distributed training.\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eWhat is the difference between the NVIDIA DGX Spark and the ASUS Ascent GX10?\u003c\/strong\u003e\u003cbr\u003e\nBoth are built on the same NVIDIA GB10 Grace Blackwell platform with 128 GB unified memory and up to 1 PFLOP FP4. The DGX Spark is NVIDIA's first-party unit with 4 TB storage; the \u003ca href=\"\/products\/asus-ascent-gx10-compact-desktop-ai-supercomputer-1tb-gx10-gg0007bn\"\u003eASUS GX10\u003c\/a\u003e offers 1 TB, 2 TB and 4 TB tiers at different price points with onsite warranty.\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eDo I need special power or cooling?\u003c\/strong\u003e\u003cbr\u003e\nNo. The DGX Spark draws just 240 W from a standard wall socket with an external adapter and enterprise-grade active cooling — it runs on a desk, not a data centre. Note: the factory-sealed box includes a US power cord; SourceIT provides a UK\/Singapore cord separately.\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eWhat software comes preinstalled?\u003c\/strong\u003e\u003cbr\u003e\nNVIDIA DGX OS with the full NVIDIA AI stack: CUDA, TensorRT, NVIDIA NIM, PyTorch\/TensorFlow containers and NGC-certified containers — ready for development on day one. See our \u003ca href=\"https:\/\/sourceit.com.sg\/blogs\/news\/nvidia-dgx-spark-setup-and-enterprise-deployment-guide\"\u003esetup and deployment guide\u003c\/a\u003e.\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eIs the DGX Spark covered by local warranty in Singapore?\u003c\/strong\u003e\u003cbr\u003e\nYes. Every DGX Spark sold by SourceIT comes with 1 year local warranty and a GST invoice. Volume and enterprise procurement enquiries are welcome.\u003c\/p\u003e\n\u003c\/div\u003e\n\u003c\/details\u003e\n\u003cp\u003e\u003ciframe title=\"YouTube video player\" src=\"https:\/\/www.youtube.com\/embed\/Sd2Ie68OD_o?si=s0psw3fnKrfzFmAM\" height=\"315\" width=\"560\"\u003e\u003c\/iframe\u003e\u003c\/p\u003e\n\u003c\/div\u003e","brand":"Nvidia","offers":[{"title":"Default Title","offer_id":51167295733924,"sku":"940-54242-0007-000","price":8800.0,"currency_code":"SGD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0605\/0136\/0804\/files\/nvidia-dgx-spark-ai-supercomputer-4tb-4080342.jpg?v=1782950834"},{"product_id":"asus-ascent-gx10-compact-desktop-ai-supercomputer-2tb-gx10-gg0030bn","title":"ASUS Ascent GX10 Compact AI Supercomputer - 2TB","description":"\u003cdiv\u003e\n\u003cstyle\u003e\n.pd-acc { border: 1px solid #e2e2e2; border-radius: 8px; margin: 10px 0; overflow: hidden; }\n.pd-acc summary { cursor: pointer; padding: 14px 16px; font-weight: 700; font-size: 1.05em; list-style: none; display: flex; justify-content: space-between; align-items: center; background: #f7f7f7; }\n.pd-acc[open] \u003e summary { border-bottom: 1px solid #e2e2e2; }\n.pd-acc summary::-webkit-details-marker { display: none; }\n.pd-acc summary::after { content: \"+\"; font-size: 1.4em; font-weight: 400; line-height: 1; margin-left: 12px; }\n.pd-acc[open] \u003e summary::after { content: \"\\2212\"; }\n.pd-acc .pd-acc-body { padding: 8px 16px 14px; }\n.pd-spec, .pd-spec tr, .pd-spec td { border: none !important; background: transparent !important; }\n.pd-spec { width: 100%; border-collapse: collapse !important; margin: 6px 0 18px; }\n.pd-spec td { padding: 7px 10px 7px 0 !important; border-bottom: 1px solid #ececec !important; vertical-align: top; font-size: 0.95em; text-align: left; }\n.pd-spec td:first-child { width: 38%; font-weight: 600; color: #333; }\n.pd-spec tr:last-child td { border-bottom: none !important; }\n\u003c\/style\u003e\n\u003ch2\u003e\n\u003cstrong\u003eASUS Ascent GX10 Compact Desktop AI Supercomputer - 2TB (GX10-GG0030BN) \/ (90MS0371-M000Y0) - 1 Year Onsite Warranty \u003c\/strong\u003e\u003cspan style=\"color: rgb(255, 42, 0);\"\u003e\u003cstrong\u003e+ Limited-Time Offer: Free Upgrade to 3 Years Warranty.\u003c\/strong\u003e\u003c\/span\u003e\n\u003c\/h2\u003e\n\u003ch3\u003e\u003cstrong\u003eASUS Ascent GX10 — Petaflop AI Power in a 1.48 kg Desktop\u003c\/strong\u003e\u003c\/h3\u003e\n\u003cp\u003eBuilt on the NVIDIA GB10 Grace Blackwell Superchip, the ASUS Ascent GX10 delivers up to 1 petaFLOP of FP4 AI performance with 128 GB of unified CPU–GPU memory — enough to run large language models up to 200 billion parameters locally. This 2TB model doubles the storage of the entry unit for multiple models and larger datasets. Also available in \u003ca href=\"\/products\/asus-ascent-gx10-compact-desktop-ai-supercomputer-1tb-gx10-gg0007bn\"\u003e1TB\u003c\/a\u003e and \u003ca href=\"\/products\/asus-ascent-gx10-compact-desktop-ai-supercomputer-4tb-gx10-gg0007bn\"\u003e4TB\u003c\/a\u003e.\u003c\/p\u003e\n\u003ch3\u003e\u003cstrong\u003eDevelop, Fine-Tune and Deploy AI Locally\u003c\/strong\u003e\u003c\/h3\u003e\n\u003cp\u003eRun CUDA, TensorRT, NVIDIA NIM, PyTorch, TensorFlow and Jupyter on Ubuntu Linux \/ NVIDIA DGX OS. Fine-tune models, build agentic AI, run RAG workloads and vector databases — all on-premise, keeping data private and cloud bills at zero.\u003c\/p\u003e\n\u003ch3\u003e\u003cstrong\u003eQuietFlow Cooling, Cluster-Ready Networking\u003c\/strong\u003e\u003c\/h3\u003e\n\u003cp\u003eA triple-fan, dual vapor chamber QuietFlow cooling system sustains continuous AI processing at whisper-quiet levels, while the built-in NVIDIA ConnectX-7 SmartNIC lets you link two GX10 units for models beyond 200B parameters — use the \u003ca href=\"\/products\/nvidia-qsfp112-400g-dac-cable-40cm-x0101g00400a\"\u003eNVIDIA QSFP112 DAC cable\u003c\/a\u003e.\u003c\/p\u003e\n\u003cp\u003e\u003ciframe width=\"560\" height=\"315\" src=\"https:\/\/www.youtube.com\/embed\/ASm_Cg5txOI?si=NwZBr8flFQ39APye\" title=\"YouTube video player\"\u003e\u003c\/iframe\u003e\u003c\/p\u003e\n\u003cdetails class=\"pd-acc\"\u003e\n\u003csummary style=\"cursor: pointer; font-weight: 700;\"\u003eASUS Ascent GX10 Datasheet — Technical Specifications\u003c\/summary\u003e\n\u003cdiv class=\"pd-acc-body\"\u003e\n\u003cp\u003e\u003cstrong\u003eGeneral\u003c\/strong\u003e\u003c\/p\u003e\n\u003ctable class=\"pd-spec\"\u003e\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eModel\u003c\/td\u003e\n\u003ctd\u003eASUS Ascent GX10 — 2TB\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eSKU \/ Part Number\u003c\/td\u003e\n\u003ctd\u003eGX10-GG0030BN \/ 90MS0371-M000Y0\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eProduct Type\u003c\/td\u003e\n\u003ctd\u003eUltra-Compact Desktop AI Supercomputer (NVIDIA DGX Spark Platform)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eOperating System\u003c\/td\u003e\n\u003ctd\u003eUbuntu Linux \/ NVIDIA DGX OS\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eColor\u003c\/td\u003e\n\u003ctd\u003eStellar Grey, Premium Metal Chassis\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eWarranty\u003c\/td\u003e\n\u003ctd\u003e1 Year Onsite Warranty (Limited-Time Free Upgrade to 3 Years)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/tbody\u003e\u003c\/table\u003e\n\u003cp\u003e\u003cstrong\u003eProcessor — NVIDIA GB10 Grace Blackwell Superchip\u003c\/strong\u003e\u003c\/p\u003e\n\u003ctable class=\"pd-spec\"\u003e\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eCPU\u003c\/td\u003e\n\u003ctd\u003e20-Core Arm v9.2-A — 10 × Cortex-X925 + 10 × Cortex-A725\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eGPU\u003c\/td\u003e\n\u003ctd\u003eNVIDIA Blackwell (Integrated), 5th-Gen Tensor Cores\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eAI Performance\u003c\/td\u003e\n\u003ctd\u003eUp to 1 PetaFLOP FP4\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eLLM Support\u003c\/td\u003e\n\u003ctd\u003eUp to 200 Billion Parameter Models (Single Unit)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/tbody\u003e\u003c\/table\u003e\n\u003cp\u003e\u003cstrong\u003eMemory \u0026amp; Storage\u003c\/strong\u003e\u003c\/p\u003e\n\u003ctable class=\"pd-spec\"\u003e\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eMemory\u003c\/td\u003e\n\u003ctd\u003e128 GB LPDDR5X Coherent Unified Memory (Shared CPU–GPU)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eStorage\u003c\/td\u003e\n\u003ctd\u003e2 TB M.2 NVMe PCIe SSD\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/tbody\u003e\u003c\/table\u003e\n\u003cp\u003e\u003cstrong\u003eNetworking\u003c\/strong\u003e\u003c\/p\u003e\n\u003ctable class=\"pd-spec\"\u003e\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eSmartNIC\u003c\/td\u003e\n\u003ctd\u003eNVIDIA ConnectX-7 — Dual-System Scaling \u0026amp; Cluster Networking\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eEthernet\u003c\/td\u003e\n\u003ctd\u003e1 × 10 GbE RJ-45\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eWireless\u003c\/td\u003e\n\u003ctd\u003eWi-Fi 7, Bluetooth 5.4\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/tbody\u003e\u003c\/table\u003e\n\u003cp\u003e\u003cstrong\u003ePorts \u0026amp; Connectivity\u003c\/strong\u003e\u003c\/p\u003e\n\u003ctable class=\"pd-spec\"\u003e\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eUSB\u003c\/td\u003e\n\u003ctd\u003e4 × USB 3.2 Gen 2x2 Type-C (20 Gbps)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eDisplay\u003c\/td\u003e\n\u003ctd\u003e1 × HDMI 2.1b; DisplayPort 2.1 via USB-C Alt Mode\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eSecurity\u003c\/td\u003e\n\u003ctd\u003eKensington Lock Slot\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ePower Input\u003c\/td\u003e\n\u003ctd\u003eUSB-C PD 3.1 (180W EPR), 240W External Adapter\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/tbody\u003e\u003c\/table\u003e\n\u003cp\u003e\u003cstrong\u003eCooling \u0026amp; Physical\u003c\/strong\u003e\u003c\/p\u003e\n\u003ctable class=\"pd-spec\"\u003e\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eCooling\u003c\/td\u003e\n\u003ctd\u003eQuietFlow — Triple-Fan, Dual Vapor Chamber\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eDimensions\u003c\/td\u003e\n\u003ctd\u003e150 × 150 × 51 mm\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eWeight\u003c\/td\u003e\n\u003ctd\u003eApproximately 1.48 kg\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/tbody\u003e\u003c\/table\u003e\n\u003cp\u003e\u003cstrong\u003eSoftware Support\u003c\/strong\u003e\u003c\/p\u003e\n\u003ctable class=\"pd-spec\"\u003e\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eAI Stack\u003c\/td\u003e\n\u003ctd\u003eCUDA, TensorRT, NVIDIA NIM, PyTorch, TensorFlow, Jupyter, NVIDIA AI Software Stack\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eWorkloads\u003c\/td\u003e\n\u003ctd\u003eLLM Fine-Tuning, Local Inference, Agentic AI, RAG, Deep Learning Training\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/tbody\u003e\u003c\/table\u003e\n\u003c\/div\u003e\n\u003c\/details\u003e\n\u003cdetails class=\"pd-acc\"\u003e\n\u003csummary style=\"cursor: pointer; font-weight: 700;\"\u003ePackage Contents \u0026amp; Ordering Information\u003c\/summary\u003e\n\u003cdiv class=\"pd-acc-body\"\u003e\n\u003cp\u003e\u003cstrong\u003ePackage Contents\u003c\/strong\u003e\u003c\/p\u003e\n\u003cp\u003eASUS Ascent GX10 AI Supercomputer (2TB)\u003cbr\u003e\n240W Power Adapter\u003cbr\u003e\nPower Cord\u003cbr\u003e\nUser Manual\u003cbr\u003e\nWarranty Card\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eOrdering Information — AI Supercomputers at SourceIT\u003c\/strong\u003e\u003c\/p\u003e\n\u003ctable class=\"pd-spec\"\u003e\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eASUS Ascent GX10 2TB\u003c\/td\u003e\n\u003ctd\u003eGX10-GG0030BN (This Product)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eASUS Ascent GX10 1TB\u003c\/td\u003e\n\u003ctd\u003e\u003ca href=\"\/products\/asus-ascent-gx10-compact-desktop-ai-supercomputer-1tb-gx10-gg0007bn\"\u003eGX10-GG0007BN — In Stock\u003c\/a\u003e\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eASUS Ascent GX10 4TB\u003c\/td\u003e\n\u003ctd\u003e\u003ca href=\"\/products\/asus-ascent-gx10-compact-desktop-ai-supercomputer-4tb-gx10-gg0007bn\"\u003eGX10-GG0031BN — In Stock\u003c\/a\u003e\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eNVIDIA DGX Spark 4TB\u003c\/td\u003e\n\u003ctd\u003e\u003ca href=\"\/products\/nvidia-dgx-spark-ai-supercomputer-4tb-940-54242-0007-000\"\u003e940-54242-0007-000 — In Stock\u003c\/a\u003e\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eNVIDIA QSFP112 400G DAC Cable (Links 2 Units)\u003c\/td\u003e\n\u003ctd\u003e\u003ca href=\"\/products\/nvidia-qsfp112-400g-dac-cable-40cm-x0101g00400a\"\u003eX0101G00400A — In Stock\u003c\/a\u003e\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/tbody\u003e\u003c\/table\u003e\n\u003c\/div\u003e\n\u003c\/details\u003e\n\u003cdetails class=\"pd-acc\"\u003e\n\u003csummary style=\"cursor: pointer; font-weight: 700;\"\u003eDatasheet \u0026amp; Downloads\u003c\/summary\u003e\n\u003cdiv class=\"pd-acc-body\"\u003e\n\u003cp\u003e• \u003ca href=\"https:\/\/www.asus.com\/networking-iot-servers\/aiot-industrial-solutions\/embedded-computers-edge-ai-systems\/asus-ascent-gx10\/\" rel=\"noopener\" target=\"_blank\"\u003eASUS Ascent GX10 — Official Product Page \u0026amp; Specifications\u003c\/a\u003e\u003cbr\u003e\n• \u003ca href=\"https:\/\/www.nvidia.com\/en-us\/products\/workstations\/dgx-spark\/\" rel=\"noopener\" target=\"_blank\"\u003eNVIDIA DGX Spark Platform — Official Specifications\u003c\/a\u003e\u003cbr\u003e\n• \u003ca href=\"https:\/\/sourceit.com.sg\/blogs\/news\/nvidia-dgx-spark-setup-and-enterprise-deployment-guide\"\u003eDGX Spark Platform Setup \u0026amp; Enterprise Deployment Guide — SourceIT\u003c\/a\u003e\u003c\/p\u003e\n\u003c\/div\u003e\n\u003c\/details\u003e\n\u003cdetails class=\"pd-acc\"\u003e\n\u003csummary style=\"cursor: pointer; font-weight: 700;\"\u003eFrequently Asked Questions — ASUS Ascent GX10\u003c\/summary\u003e\n\u003cdiv class=\"pd-acc-body\"\u003e\n\u003cp\u003e\u003cstrong\u003eWhat is the difference between the ASUS Ascent GX10 and the NVIDIA DGX Spark?\u003c\/strong\u003e\u003cbr\u003e\nBoth run the same NVIDIA GB10 Grace Blackwell platform — 128 GB unified memory, up to 1 PFLOP FP4, ConnectX-7 networking. The GX10 comes in 1TB\/2TB\/4TB storage tiers at sharper pricing with onsite warranty; the \u003ca href=\"\/products\/nvidia-dgx-spark-ai-supercomputer-4tb-940-54242-0007-000\"\u003eDGX Spark\u003c\/a\u003e is NVIDIA's first-party 4TB unit.\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eWhat AI models can the GX10 run?\u003c\/strong\u003e\u003cbr\u003e\nUp to 200-billion-parameter models on a single unit — local inference and fine-tuning of Llama, Qwen, DeepSeek, Mistral-class models. Link two GX10s via ConnectX-7 for even larger workloads.\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eWhich storage size should I choose?\u003c\/strong\u003e\u003cbr\u003e\n2TB (this model) is the sweet spot for teams working with several large models or datasets; choose \u003ca href=\"\/products\/asus-ascent-gx10-compact-desktop-ai-supercomputer-4tb-gx10-gg0007bn\"\u003e4TB\u003c\/a\u003e for heavy multi-model and RAG\/vector-database workloads.\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eDo I need special power or cooling?\u003c\/strong\u003e\u003cbr\u003e\nNo — it draws from a standard socket via a 240W adapter (USB-C PD 3.1) and cools itself with the whisper-quiet QuietFlow triple-fan design.\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eWhat warranty do I get in Singapore?\u003c\/strong\u003e\u003cbr\u003e\n1 year onsite warranty with a limited-time free upgrade to 3 years, plus a GST invoice from SourceIT. Volume and enterprise procurement enquiries welcome.\u003c\/p\u003e\n\u003c\/div\u003e\n\u003c\/details\u003e\n\u003c\/div\u003e","brand":"Asus","offers":[{"title":"Default Title","offer_id":51167328895140,"sku":"GX10-GG0030BN \/ 90MS0371-M000Y0","price":8450.0,"currency_code":"SGD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0605\/0136\/0804\/files\/asus-ascent-gx10-compact-desktop-ai-supercomputer-1tb-gx10-gg0007bn-7973426.png?v=1761560946"},{"product_id":"asus-ascent-gx10-compact-desktop-ai-supercomputer-4tb-gx10-gg0007bn","title":"ASUS Ascent GX10 Compact AI Supercomputer - 4TB","description":"\u003cdiv\u003e\n\u003cstyle\u003e\n.pd-acc { border: 1px solid #e2e2e2; border-radius: 8px; margin: 10px 0; overflow: hidden; }\n.pd-acc summary { cursor: pointer; padding: 14px 16px; font-weight: 700; font-size: 1.05em; list-style: none; display: flex; justify-content: space-between; align-items: center; background: #f7f7f7; }\n.pd-acc[open] \u003e summary { border-bottom: 1px solid #e2e2e2; }\n.pd-acc summary::-webkit-details-marker { display: none; }\n.pd-acc summary::after { content: \"+\"; font-size: 1.4em; font-weight: 400; line-height: 1; margin-left: 12px; }\n.pd-acc[open] \u003e summary::after { content: \"\\2212\"; }\n.pd-acc .pd-acc-body { padding: 8px 16px 14px; }\n.pd-spec, .pd-spec tr, .pd-spec td { border: none !important; background: transparent !important; }\n.pd-spec { width: 100%; border-collapse: collapse !important; margin: 6px 0 18px; }\n.pd-spec td { padding: 7px 10px 7px 0 !important; border-bottom: 1px solid #ececec !important; vertical-align: top; font-size: 0.95em; text-align: left; }\n.pd-spec td:first-child { width: 38%; font-weight: 600; color: #333; }\n.pd-spec tr:last-child td { border-bottom: none !important; }\n\u003c\/style\u003e\n\u003ch2\u003e\n\u003cstrong\u003eASUS Ascent GX10 Compact Desktop AI Supercomputer - 4TB (GX10-GG0031BN) \/ (90MS0371-M000Z0) - 1 Year Onsite Warranty \u003c\/strong\u003e\u003cspan style=\"color: rgb(255, 42, 0);\"\u003e\u003cstrong\u003e+ Limited-Time Offer: Free Upgrade to 3 Years Warranty.\u003c\/strong\u003e\u003c\/span\u003e\n\u003c\/h2\u003e\n\u003ch3\u003e\u003cstrong\u003eASUS Ascent GX10 — Petaflop AI Power in a 1.48 kg Desktop\u003c\/strong\u003e\u003c\/h3\u003e\n\u003cp\u003eBuilt on the NVIDIA GB10 Grace Blackwell Superchip, the ASUS Ascent GX10 delivers up to 1 petaFLOP of FP4 AI performance with 128 GB of unified CPU–GPU memory — enough to run large language models up to 200 billion parameters locally. This flagship 4TB model carries the most storage in the range for multi-model, dataset-heavy workflows. Also available in \u003ca href=\"\/products\/asus-ascent-gx10-compact-desktop-ai-supercomputer-1tb-gx10-gg0007bn\"\u003e1TB\u003c\/a\u003e and \u003ca href=\"\/products\/asus-ascent-gx10-compact-desktop-ai-supercomputer-2tb-gx10-gg0030bn\"\u003e2TB\u003c\/a\u003e.\u003c\/p\u003e\n\u003ch3\u003e\u003cstrong\u003eDevelop, Fine-Tune and Deploy AI Locally\u003c\/strong\u003e\u003c\/h3\u003e\n\u003cp\u003eRun CUDA, TensorRT, NVIDIA NIM, PyTorch, TensorFlow and Jupyter on Ubuntu Linux \/ NVIDIA DGX OS. Fine-tune models, build agentic AI, run RAG workloads and vector databases — all on-premise, keeping data private and cloud bills at zero.\u003c\/p\u003e\n\u003ch3\u003e\u003cstrong\u003eQuietFlow Cooling, Cluster-Ready Networking\u003c\/strong\u003e\u003c\/h3\u003e\n\u003cp\u003eA triple-fan, dual vapor chamber QuietFlow cooling system sustains continuous AI processing at whisper-quiet levels, while the built-in NVIDIA ConnectX-7 SmartNIC lets you link two GX10 units for models beyond 200B parameters — use the \u003ca href=\"\/products\/nvidia-qsfp112-400g-dac-cable-40cm-x0101g00400a\"\u003eNVIDIA QSFP112 DAC cable\u003c\/a\u003e.\u003c\/p\u003e\n\u003cp\u003e\u003ciframe width=\"560\" height=\"315\" src=\"https:\/\/www.youtube.com\/embed\/ASm_Cg5txOI?si=NwZBr8flFQ39APye\" title=\"YouTube video player\"\u003e\u003c\/iframe\u003e\u003c\/p\u003e\n\u003cdetails class=\"pd-acc\"\u003e\n\u003csummary style=\"cursor: pointer; font-weight: 700;\"\u003eASUS Ascent GX10 Datasheet — Technical Specifications\u003c\/summary\u003e\n\u003cdiv class=\"pd-acc-body\"\u003e\n\u003cp\u003e\u003cstrong\u003eGeneral\u003c\/strong\u003e\u003c\/p\u003e\n\u003ctable class=\"pd-spec\"\u003e\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eModel\u003c\/td\u003e\n\u003ctd\u003eASUS Ascent GX10 — 4TB\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eSKU \/ Part Number\u003c\/td\u003e\n\u003ctd\u003eGX10-GG0031BN \/ 90MS0371-M000Z0\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eProduct Type\u003c\/td\u003e\n\u003ctd\u003eUltra-Compact Desktop AI Supercomputer (NVIDIA DGX Spark Platform)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eOperating System\u003c\/td\u003e\n\u003ctd\u003eUbuntu Linux \/ NVIDIA DGX OS\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eColor\u003c\/td\u003e\n\u003ctd\u003eStellar Grey, Premium Metal Chassis\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eWarranty\u003c\/td\u003e\n\u003ctd\u003e1 Year Onsite Warranty (Limited-Time Free Upgrade to 3 Years)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/tbody\u003e\u003c\/table\u003e\n\u003cp\u003e\u003cstrong\u003eProcessor — NVIDIA GB10 Grace Blackwell Superchip\u003c\/strong\u003e\u003c\/p\u003e\n\u003ctable class=\"pd-spec\"\u003e\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eCPU\u003c\/td\u003e\n\u003ctd\u003e20-Core Arm v9.2-A — 10 × Cortex-X925 + 10 × Cortex-A725\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eGPU\u003c\/td\u003e\n\u003ctd\u003eNVIDIA Blackwell (Integrated), 5th-Gen Tensor Cores\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eAI Performance\u003c\/td\u003e\n\u003ctd\u003eUp to 1 PetaFLOP FP4\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eLLM Support\u003c\/td\u003e\n\u003ctd\u003eUp to 200 Billion Parameter Models (Single Unit)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/tbody\u003e\u003c\/table\u003e\n\u003cp\u003e\u003cstrong\u003eMemory \u0026amp; Storage\u003c\/strong\u003e\u003c\/p\u003e\n\u003ctable class=\"pd-spec\"\u003e\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eMemory\u003c\/td\u003e\n\u003ctd\u003e128 GB LPDDR5X Coherent Unified Memory (Shared CPU–GPU)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eStorage\u003c\/td\u003e\n\u003ctd\u003e4 TB M.2 NVMe PCIe SSD\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/tbody\u003e\u003c\/table\u003e\n\u003cp\u003e\u003cstrong\u003eNetworking\u003c\/strong\u003e\u003c\/p\u003e\n\u003ctable class=\"pd-spec\"\u003e\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eSmartNIC\u003c\/td\u003e\n\u003ctd\u003eNVIDIA ConnectX-7 — Dual-System Scaling \u0026amp; Cluster Networking\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eEthernet\u003c\/td\u003e\n\u003ctd\u003e1 × 10 GbE RJ-45\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eWireless\u003c\/td\u003e\n\u003ctd\u003eWi-Fi 7, Bluetooth 5.4\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/tbody\u003e\u003c\/table\u003e\n\u003cp\u003e\u003cstrong\u003ePorts \u0026amp; Connectivity\u003c\/strong\u003e\u003c\/p\u003e\n\u003ctable class=\"pd-spec\"\u003e\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eUSB\u003c\/td\u003e\n\u003ctd\u003e4 × USB 3.2 Gen 2x2 Type-C (20 Gbps)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eDisplay\u003c\/td\u003e\n\u003ctd\u003e1 × HDMI 2.1b; DisplayPort 2.1 via USB-C Alt Mode\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eSecurity\u003c\/td\u003e\n\u003ctd\u003eKensington Lock Slot\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ePower Input\u003c\/td\u003e\n\u003ctd\u003eUSB-C PD 3.1 (180W EPR), 240W External Adapter\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/tbody\u003e\u003c\/table\u003e\n\u003cp\u003e\u003cstrong\u003eCooling \u0026amp; Physical\u003c\/strong\u003e\u003c\/p\u003e\n\u003ctable class=\"pd-spec\"\u003e\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eCooling\u003c\/td\u003e\n\u003ctd\u003eQuietFlow — Triple-Fan, Dual Vapor Chamber\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eDimensions\u003c\/td\u003e\n\u003ctd\u003e150 × 150 × 51 mm\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eWeight\u003c\/td\u003e\n\u003ctd\u003eApproximately 1.48 kg\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/tbody\u003e\u003c\/table\u003e\n\u003cp\u003e\u003cstrong\u003eSoftware Support\u003c\/strong\u003e\u003c\/p\u003e\n\u003ctable class=\"pd-spec\"\u003e\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eAI Stack\u003c\/td\u003e\n\u003ctd\u003eCUDA, TensorRT, NVIDIA NIM, PyTorch, TensorFlow, Jupyter, NVIDIA AI Software Stack\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eWorkloads\u003c\/td\u003e\n\u003ctd\u003eLLM Fine-Tuning, Local Inference, Agentic AI, RAG, Deep Learning Training\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/tbody\u003e\u003c\/table\u003e\n\u003c\/div\u003e\n\u003c\/details\u003e\n\u003cdetails class=\"pd-acc\"\u003e\n\u003csummary style=\"cursor: pointer; font-weight: 700;\"\u003ePackage Contents \u0026amp; Ordering Information\u003c\/summary\u003e\n\u003cdiv class=\"pd-acc-body\"\u003e\n\u003cp\u003e\u003cstrong\u003ePackage Contents\u003c\/strong\u003e\u003c\/p\u003e\n\u003cp\u003eASUS Ascent GX10 AI Supercomputer (4TB)\u003cbr\u003e\n240W Power Adapter\u003cbr\u003e\nPower Cord\u003cbr\u003e\nUser Manual\u003cbr\u003e\nWarranty Card\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eOrdering Information — AI Supercomputers at SourceIT\u003c\/strong\u003e\u003c\/p\u003e\n\u003ctable class=\"pd-spec\"\u003e\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eASUS Ascent GX10 4TB\u003c\/td\u003e\n\u003ctd\u003eGX10-GG0031BN (This Product)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eASUS Ascent GX10 1TB\u003c\/td\u003e\n\u003ctd\u003e\u003ca href=\"\/products\/asus-ascent-gx10-compact-desktop-ai-supercomputer-1tb-gx10-gg0007bn\"\u003eGX10-GG0007BN — In Stock\u003c\/a\u003e\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eASUS Ascent GX10 2TB\u003c\/td\u003e\n\u003ctd\u003e\u003ca href=\"\/products\/asus-ascent-gx10-compact-desktop-ai-supercomputer-2tb-gx10-gg0030bn\"\u003eGX10-GG0030BN — In Stock\u003c\/a\u003e\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eNVIDIA DGX Spark 4TB\u003c\/td\u003e\n\u003ctd\u003e\u003ca href=\"\/products\/nvidia-dgx-spark-ai-supercomputer-4tb-940-54242-0007-000\"\u003e940-54242-0007-000 — In Stock\u003c\/a\u003e\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eNVIDIA QSFP112 400G DAC Cable (Links 2 Units)\u003c\/td\u003e\n\u003ctd\u003e\u003ca href=\"\/products\/nvidia-qsfp112-400g-dac-cable-40cm-x0101g00400a\"\u003eX0101G00400A — In Stock\u003c\/a\u003e\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/tbody\u003e\u003c\/table\u003e\n\u003c\/div\u003e\n\u003c\/details\u003e\n\u003cdetails class=\"pd-acc\"\u003e\n\u003csummary style=\"cursor: pointer; font-weight: 700;\"\u003eDatasheet \u0026amp; Downloads\u003c\/summary\u003e\n\u003cdiv class=\"pd-acc-body\"\u003e\n\u003cp\u003e• \u003ca href=\"https:\/\/www.asus.com\/networking-iot-servers\/aiot-industrial-solutions\/embedded-computers-edge-ai-systems\/asus-ascent-gx10\/\" rel=\"noopener\" target=\"_blank\"\u003eASUS Ascent GX10 — Official Product Page \u0026amp; Specifications\u003c\/a\u003e\u003cbr\u003e\n• \u003ca href=\"https:\/\/www.nvidia.com\/en-us\/products\/workstations\/dgx-spark\/\" rel=\"noopener\" target=\"_blank\"\u003eNVIDIA DGX Spark Platform — Official Specifications\u003c\/a\u003e\u003cbr\u003e\n• \u003ca href=\"https:\/\/sourceit.com.sg\/blogs\/news\/nvidia-dgx-spark-setup-and-enterprise-deployment-guide\"\u003eDGX Spark Platform Setup \u0026amp; Enterprise Deployment Guide — SourceIT\u003c\/a\u003e\u003c\/p\u003e\n\u003c\/div\u003e\n\u003c\/details\u003e\n\u003cdetails class=\"pd-acc\"\u003e\n\u003csummary style=\"cursor: pointer; font-weight: 700;\"\u003eFrequently Asked Questions — ASUS Ascent GX10\u003c\/summary\u003e\n\u003cdiv class=\"pd-acc-body\"\u003e\n\u003cp\u003e\u003cstrong\u003eWhat is the difference between the ASUS Ascent GX10 and the NVIDIA DGX Spark?\u003c\/strong\u003e\u003cbr\u003e\nBoth run the same NVIDIA GB10 Grace Blackwell platform — 128 GB unified memory, up to 1 PFLOP FP4, ConnectX-7 networking. The GX10 4TB matches the \u003ca href=\"\/products\/nvidia-dgx-spark-ai-supercomputer-4tb-940-54242-0007-000\"\u003eDGX Spark's\u003c\/a\u003e storage at a sharper price, with onsite warranty.\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eWhat AI models can the GX10 run?\u003c\/strong\u003e\u003cbr\u003e\nUp to 200-billion-parameter models on a single unit — local inference and fine-tuning of Llama, Qwen, DeepSeek, Mistral-class models. Link two GX10s via ConnectX-7 for even larger workloads.\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eWho is the 4TB model for?\u003c\/strong\u003e\u003cbr\u003e\nTeams keeping multiple large models, training datasets, checkpoints and vector databases on-device — maximum headroom without external storage.\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eDo I need special power or cooling?\u003c\/strong\u003e\u003cbr\u003e\nNo — it draws from a standard socket via a 240W adapter (USB-C PD 3.1) and cools itself with the whisper-quiet QuietFlow triple-fan design.\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eWhat warranty do I get in Singapore?\u003c\/strong\u003e\u003cbr\u003e\n1 year onsite warranty with a limited-time free upgrade to 3 years, plus a GST invoice from SourceIT. Volume and enterprise procurement enquiries welcome.\u003c\/p\u003e\n\u003c\/div\u003e\n\u003c\/details\u003e\n\u003c\/div\u003e","brand":"Asus","offers":[{"title":"Default Title","offer_id":51167329550500,"sku":"GX10-GG0031BN \/ 90MS0371-M000Z0","price":10600.0,"currency_code":"SGD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0605\/0136\/0804\/files\/asus-ascent-gx10-compact-desktop-ai-supercomputer-1tb-gx10-gg0007bn-7973426.png?v=1761560946"},{"product_id":"asus-qsfp-cable-for-asus-ascent-gx10-40cm-asu-90ma0000-p01000","title":"ASUS QSFP Cable for ASUS Ascent GX10 40cm","description":"\u003ch2\u003e\n\u003cstrong\u003eASUS QSFP Cable for ASUS Ascent GX10 40cm (ASU-90MA0000-P01000) - Local Warranty\u003c\/strong\u003e\n\u003c\/h2\u003e\n\u003ch3 data-start=\"120\" data-end=\"452\"\u003e\u003cstrong data-start=\"142\" data-end=\"202\"\u003eHigh-Performance QSFP Cable for ASUS Ascent GX10 Systems\u003c\/strong\u003e\u003c\/h3\u003e\n\u003cp data-start=\"120\" data-end=\"452\"\u003eThe \u003cstrong data-start=\"209\" data-end=\"276\"\u003eASUS QSFP Cable for ASUS Ascent GX10 40cm (ASU-90MA0000-P01000)\u003c\/strong\u003e is engineered to deliver reliable high-bandwidth connectivity for enterprise and AI computing environments. Designed specifically for compatibility with ASUS Ascent GX10 platforms, this short-range QSFP interconnect cable ensures stable, low-latency data transmission between components in high-performance computing infrastructures. Its precision design supports demanding workloads where consistent throughput and minimal signal loss are critical for optimal system performance.\u003c\/p\u003e\n\u003ch3 data-start=\"120\" data-end=\"452\"\u003e\u003cstrong data-start=\"760\" data-end=\"824\"\u003eOptimised Connectivity for AI and High-Performance Computing\u003c\/strong\u003e\u003c\/h3\u003e\n\u003cp data-start=\"120\" data-end=\"452\"\u003eBuilt to support modern AI training workloads, GPU compute clusters, and data-intensive applications, the ASUS QSFP cable enables seamless communication between networking interfaces and computing nodes. The 40cm length is ideal for rack-mounted systems, helping maintain clean cable management while reducing signal interference. This ensures efficient data exchange in environments such as AI research labs, enterprise data centres, and advanced computing deployments.\u003c\/p\u003e\n\u003ch3 data-start=\"120\" data-end=\"452\"\u003e\u003cstrong data-start=\"1300\" data-end=\"1356\"\u003eReliable Signal Integrity and Enterprise-Grade Build\u003c\/strong\u003e\u003c\/h3\u003e\n\u003cp data-start=\"120\" data-end=\"452\"\u003eThe cable is manufactured using high-quality materials to maintain signal integrity across high-speed connections. Its robust QSFP connectors provide secure locking and stable connections even in dense server environments. The durable construction is designed to withstand continuous operation in professional IT environments, ensuring dependable performance for mission-critical infrastructure.\u003c\/p\u003e\n\u003ch3 data-start=\"120\" data-end=\"452\"\u003e\u003cstrong data-start=\"1757\" data-end=\"1811\"\u003eCompact 40cm Design for Efficient Rack Integration\u003c\/strong\u003e\u003c\/h3\u003e\n\u003cp data-start=\"120\" data-end=\"452\"\u003eWith a carefully engineered \u003cstrong data-start=\"1842\" data-end=\"1863\"\u003e40cm cable length\u003c\/strong\u003e, this ASUS QSFP cable helps maintain tidy rack layouts and optimal airflow in server cabinets. Short-reach interconnect cables like this are commonly used within AI servers and networking equipment where minimal cable slack improves cooling efficiency and simplifies maintenance. This makes it an ideal accessory for high-density AI clusters and enterprise compute systems built around ASUS hardware platforms.\u003c\/p\u003e\n\u003ch3 data-start=\"120\" data-end=\"452\"\u003e\u003cstrong data-start=\"2277\" data-end=\"2337\"\u003eSeamless Integration with ASUS Enterprise Infrastructure\u003c\/strong\u003e\u003c\/h3\u003e\n\u003cp data-start=\"120\" data-end=\"452\"\u003eWhen deploying advanced computing platforms such as the ASUS Ascent GX10, reliable interconnects are essential for maintaining consistent system performance. This dedicated QSFP cable ensures full compatibility with ASUS hardware ecosystems, enabling IT teams to build scalable and efficient compute environments. Whether deployed in research institutions, enterprise AI labs, or HPC clusters, it supports the connectivity backbone required for next-generation computing workloads.\u003ciframe width=\"560\" height=\"315\" src=\"https:\/\/www.youtube.com\/embed\/ASm_Cg5txOI?si=NwZBr8flFQ39APye\" title=\"YouTube video player\" style=\"font-size: 0.875rem;\"\u003e\u003c\/iframe\u003e\u003c\/p\u003e\n\u003ch3 data-start=\"1481\" data-end=\"1789\"\u003e\u003cstrong data-start=\"1481\" data-end=\"1508\"\u003eTechnical Specification\u003c\/strong\u003e\u003c\/h3\u003e\n\u003cp data-start=\"95\" data-end=\"342\"\u003e\u003cstrong data-start=\"95\" data-end=\"106\"\u003eGeneral\u003c\/strong\u003e\u003cbr data-start=\"106\" data-end=\"109\"\u003e• Product Name: ASUS QSFP Cable for ASUS Ascent GX10\u003cbr data-start=\"161\" data-end=\"164\"\u003e• Model Number: ASU-90MA0000-P01000\u003cbr data-start=\"199\" data-end=\"202\"\u003e• Manufacturer: ASUS\u003cbr data-start=\"222\" data-end=\"225\"\u003e• Product Type: High-speed DAC interconnect cable\u003cbr data-start=\"274\" data-end=\"277\"\u003e• Category: AI system \/ HPC \/ GPU server connectivity accessory\u003c\/p\u003e\n\u003cp data-start=\"344\" data-end=\"642\"\u003e\u003cstrong data-start=\"344\" data-end=\"367\"\u003eCable Specification\u003c\/strong\u003e\u003cbr data-start=\"367\" data-end=\"370\"\u003e• Cable Type: QSFP112 Direct Attach Copper (DAC) Cable\u003cbr data-start=\"424\" data-end=\"427\"\u003e• Connector Type: QSFP112 to QSFP112\u003cbr data-start=\"463\" data-end=\"466\"\u003e• Cable Length: 40 cm\u003cbr data-start=\"487\" data-end=\"490\"\u003e• Bandwidth: Up to 400 Gbps\u003cbr data-start=\"517\" data-end=\"520\"\u003e• Data Rate per Lane: 112 Gbps PAM-4 signaling\u003cbr data-start=\"566\" data-end=\"569\"\u003e• Medium: Copper DAC cable\u003cbr data-start=\"595\" data-end=\"598\"\u003e• Form Factor: Pluggable QSFP module cable\u003c\/p\u003e\n\u003cp data-start=\"644\" data-end=\"869\"\u003e\u003cstrong data-start=\"644\" data-end=\"661\"\u003eCompatibility\u003c\/strong\u003e\u003cbr data-start=\"661\" data-end=\"664\"\u003e• Designed For: ASUS Ascent GX10 AI Supercomputer\u003cbr data-start=\"713\" data-end=\"716\"\u003e• Compatible Systems: AI compute clusters, GPU servers, HPC nodes\u003cbr data-start=\"781\" data-end=\"784\"\u003e• Usage: GPU interconnect, node-to-node connectivity, high-speed data communication\u003c\/p\u003e\n\u003cp data-start=\"871\" data-end=\"1128\"\u003e\u003cstrong data-start=\"871\" data-end=\"886\"\u003ePerformance\u003c\/strong\u003e\u003cbr data-start=\"886\" data-end=\"889\"\u003e• Ultra-high bandwidth connectivity up to 400Gbps\u003cbr data-start=\"938\" data-end=\"941\"\u003e• Low latency communication for AI workloads\u003cbr data-start=\"985\" data-end=\"988\"\u003e• Optimized for distributed computing and GPU cluster environments\u003cbr data-start=\"1054\" data-end=\"1057\"\u003e• High-speed networking support for modern data center infrastructure\u003c\/p\u003e\n\u003cp data-start=\"1130\" data-end=\"1332\"\u003e\u003cstrong data-start=\"1130\" data-end=\"1155\"\u003eApplication Scenarios\u003c\/strong\u003e\u003cbr data-start=\"1155\" data-end=\"1158\"\u003e• AI model training infrastructure\u003cbr data-start=\"1192\" data-end=\"1195\"\u003e• High-performance computing clusters\u003cbr data-start=\"1232\" data-end=\"1235\"\u003e• GPU server interconnects\u003cbr data-start=\"1261\" data-end=\"1264\"\u003e• Data center networking\u003cbr data-start=\"1288\" data-end=\"1291\"\u003e• Short-distance rack-level connections\u003c\/p\u003e\n\u003cp data-start=\"1334\" data-end=\"1545\"\u003e\u003cstrong data-start=\"1334\" data-end=\"1352\"\u003eDesign \u0026amp; Build\u003c\/strong\u003e\u003cbr data-start=\"1352\" data-end=\"1355\"\u003e• Enterprise-grade cable construction\u003cbr data-start=\"1392\" data-end=\"1395\"\u003e• Shielded copper cable for signal integrity\u003cbr data-start=\"1439\" data-end=\"1442\"\u003e• Secure QSFP connectors for stable connection\u003cbr data-start=\"1488\" data-end=\"1491\"\u003e• Compact 40cm cable optimized for rack environments\u003c\/p\u003e\n\u003cp data-start=\"1547\" data-end=\"1824\" data-is-last-node=\"\" data-is-only-node=\"\"\u003e\u003cstrong data-start=\"1547\" data-end=\"1563\"\u003eKey Features\u003c\/strong\u003e\u003cbr data-start=\"1563\" data-end=\"1566\"\u003e• High-bandwidth 400G connectivity\u003cbr data-start=\"1600\" data-end=\"1603\"\u003e• Low latency communication for AI and HPC workloads\u003cbr data-start=\"1655\" data-end=\"1658\"\u003e• Compact cable length for rack deployments\u003cbr data-start=\"1701\" data-end=\"1704\"\u003e• Reliable high-speed interconnect for GPU compute systems\u003cbr data-start=\"1762\" data-end=\"1765\"\u003e• Designed specifically for ASUS Ascent GX10 infrastructure\u003c\/p\u003e","brand":"Asus","offers":[{"title":"Default Title","offer_id":51174052004004,"sku":"ASU-90MA0000-P01000","price":120.0,"currency_code":"SGD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0605\/0136\/0804\/files\/NVIDIAQSFP112400GDACCABLE40CM.webp?v=1762399304"},{"product_id":"dell-pro-max-ai-desktop-pcs-with-nvidia-gb10-blackwell-gpu-2tb","title":"Dell Pro Max AI Desktop PCs with NVIDIA GB10 Blackwell GPU - 2TB","description":"\u003ch2\u003e\u003cstrong\u003eDell Pro Max GB10 FCM1253 NVIDIA GB10 Grace CPU \/NVIDIA GB10 Blackwell\/ 128GB LPDDR5\/ 2TB SSD\/ WIFI (DPMFCM1253GB10128G2TB) - 3 Years Onsite Warranty\u003c\/strong\u003e\u003c\/h2\u003e\n\u003cdiv style=\"text-align: left;\"\u003e\n\u003ch3 data-end=\"592\" data-start=\"122\"\u003e\u003cstrong data-end=\"161\" data-start=\"122\"\u003eHigh-Performance AI Computing Power\u003c\/strong\u003e\u003c\/h3\u003e\n\u003cp data-end=\"592\" data-start=\"122\"\u003eThe Dell Pro Max AI Desktop PC with NVIDIA GB10 Blackwell GPU is built for teams pushing intensive AI workloads, from generative AI pipelines to high-volume data processing. Its architecture delivers strong parallel throughput for model training, inference, simulations, and analytics that require consistent speed and low latency. This setup suits environments where fast iteration cycles and stable compute performance matter.\u003c\/p\u003e\n\u003ch3 data-end=\"1031\" data-start=\"594\"\u003e\u003cstrong data-end=\"640\" data-start=\"594\"\u003eNext-Gen NVIDIA Blackwell GPU Acceleration\u003c\/strong\u003e\u003c\/h3\u003e\n\u003cp data-end=\"1031\" data-start=\"594\"\u003eRunning on the NVIDIA GB10 Blackwell platform, the system handles multimodal AI, LLM fine-tuning, dense vector search, and rendering tasks with ease. The Blackwell architecture boosts performance per watt, improves memory bandwidth efficiency, and cuts processing latency. It’s tuned for production-grade AI workloads where quicker training turnaround and predictable performance are key.\u003c\/p\u003e\n\u003ch3 data-end=\"1464\" data-start=\"1033\"\u003e\u003cstrong data-end=\"1072\" data-start=\"1033\"\u003eOptimized Workflow and Multitasking\u003c\/strong\u003e\u003c\/h3\u003e\n\u003cp data-end=\"1464\" data-start=\"1033\"\u003eEngineered for heavy, concurrent workloads, the desktop maintains responsive performance while managing large datasets, model tests, and deployment builds. Developers and data teams can run multiple training jobs and background processes without workstation slowdowns. Its thermal and power engineering supports long continuous runtimes, ideal for overnight training or batch compute jobs.\u003c\/p\u003e\n\u003ch3 data-end=\"1861\" data-start=\"1466\"\u003e\u003cstrong data-end=\"1514\" data-start=\"1466\"\u003e2TB High-Speed NVMe Storage for AI Pipelines\u003c\/strong\u003e\u003c\/h3\u003e\n\u003cp data-end=\"1861\" data-start=\"1466\"\u003eEquipped with a 2TB NVMe SSD, the system offers fast read\/write speeds essential for dataset ingestion, caching, and model checkpointing. Large file transfers and multi-GB workloads load quickly, reducing bottlenecks and improving productivity for AI development cycles. This helps teams experiment faster and scale up data-intensive workflows.\u003c\/p\u003e\n\u003ch3 data-end=\"2243\" data-start=\"1863\"\u003e\u003cstrong data-end=\"1911\" data-start=\"1863\"\u003eEnterprise-Ready Reliability and Scalability\u003c\/strong\u003e\u003c\/h3\u003e\n\u003cp data-end=\"2243\" data-start=\"1863\"\u003eDesigned for long-term deployment, the Dell Pro Max AI Desktop PC fits into enterprise IT environments with secure connectivity, stable thermals, and future-ready upgrade paths. The chassis supports component expansion, ensuring organizations can scale compute resources as AI workloads grow while maintaining system reliability.\u003c\/p\u003e\n\u003ch3 data-end=\"2879\" data-start=\"2245\"\u003e\u003cstrong data-end=\"2294\" data-start=\"2245\"\u003eTechnical Specification \u003c\/strong\u003e\u003c\/h3\u003e\n\u003cp data-start=\"72\" data-end=\"774\"\u003e\u003cstrong data-start=\"72\" data-end=\"83\"\u003eGeneral\u003c\/strong\u003e\u003cbr data-start=\"83\" data-end=\"86\"\u003eModel: Dell Pro Max with GB10\u003cbr data-start=\"115\" data-end=\"118\"\u003ePart Number: DPMFCM1253GB10128G2TB\u003cbr data-start=\"152\" data-end=\"155\"\u003eProduct Name: Dell Pro Max GB10 FCM1253 NVIDIA Grace Blackwell AI Workstation\u003cbr data-start=\"232\" data-end=\"235\"\u003eManufacturer: \u003cspan class=\"hover:entity-accent entity-underline inline cursor-pointer align-baseline\"\u003e\u003cspan class=\"whitespace-normal\"\u003eDell Technologies\u003c\/span\u003e\u003c\/span\u003e\u003cbr data-start=\"286\" data-end=\"289\"\u003eProduct Type: Personal AI Supercomputer \/ AI Workstation\u003cbr data-start=\"345\" data-end=\"348\"\u003eForm Factor: Compact Desktop AI System\u003cbr data-start=\"386\" data-end=\"389\"\u003eDeployment: Generative AI, AI Development, Large Language Models (LLM), Edge AI, AI Inferencing, Data Science\u003cbr data-start=\"498\" data-end=\"501\"\u003eArchitecture: NVIDIA Grace Blackwell Platform\u003cbr data-start=\"546\" data-end=\"549\"\u003eTarget Workloads: AI Model Development, LLM Fine-Tuning, AI Agents, Machine Learning, Deep Learning, Generative AI\u003cbr data-start=\"663\" data-end=\"666\"\u003eOperating System: NVIDIA DGX™ OS 7\u003cbr data-start=\"700\" data-end=\"703\"\u003eWarranty: 3 Years Onsite Warranty \u003cspan class=\"\" data-state=\"closed\"\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp data-start=\"776\" data-end=\"932\"\u003e\u003cstrong data-start=\"776\" data-end=\"804\"\u003eProcessor Specifications\u003c\/strong\u003e\u003cbr data-start=\"804\" data-end=\"807\"\u003eProcessor Architecture: NVIDIA GB10 Grace Blackwell Superchip\u003cbr data-start=\"868\" data-end=\"871\"\u003eCPU Type: Arm-Based AI Processor\u003cbr data-start=\"903\" data-end=\"906\"\u003eCPU Core Count: 20 Cores\u003c\/p\u003e\n\u003cp data-start=\"934\" data-end=\"1031\"\u003eCPU Configuration:\u003cbr data-start=\"952\" data-end=\"955\"\u003e• 10 x Cortex-X925 Performance Cores\u003cbr data-start=\"991\" data-end=\"994\"\u003e• 10 x Cortex-A725 Efficiency Cores\u003c\/p\u003e\n\u003cp data-start=\"1033\" data-end=\"1191\"\u003eCPU-GPU Interconnect: NVIDIA NVLink™-C2C\u003cbr data-start=\"1073\" data-end=\"1076\"\u003eAI Accelerated Computing: Supported\u003cbr data-start=\"1111\" data-end=\"1114\"\u003eUnified Compute Architecture: Supported \u003cspan class=\"\" data-state=\"closed\"\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp data-start=\"1193\" data-end=\"1651\"\u003e\u003cstrong data-start=\"1193\" data-end=\"1215\"\u003eGPU Specifications\u003c\/strong\u003e\u003cbr data-start=\"1215\" data-end=\"1218\"\u003eGPU Architecture: NVIDIA GB10 Blackwell GPU\u003cbr data-start=\"1261\" data-end=\"1264\"\u003eCUDA Cores: 6144 CUDA Cores\u003cbr data-start=\"1291\" data-end=\"1294\"\u003eTensor Core Generation: 5th Generation Tensor Cores\u003cbr data-start=\"1345\" data-end=\"1348\"\u003eRT Core Generation: 4th Generation RT Cores\u003cbr data-start=\"1391\" data-end=\"1394\"\u003eAI Performance: Up to 1 PetaFLOP FP4 AI Compute Performance\u003cbr data-start=\"1453\" data-end=\"1456\"\u003eAI Optimization: Supported\u003cbr data-start=\"1482\" data-end=\"1485\"\u003eGenerative AI Support: Supported\u003cbr data-start=\"1517\" data-end=\"1520\"\u003eLarge Language Model Processing: Supported\u003cbr data-start=\"1562\" data-end=\"1565\"\u003eSupports AI Models: Up to 200 Billion Parameters \u003cspan class=\"\" data-state=\"closed\"\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp data-start=\"1653\" data-end=\"1927\"\u003e\u003cstrong data-start=\"1653\" data-end=\"1678\"\u003eMemory Specifications\u003c\/strong\u003e\u003cbr data-start=\"1678\" data-end=\"1681\"\u003eMemory Type: LPDDR5X Unified Memory\u003cbr data-start=\"1716\" data-end=\"1719\"\u003eMemory Capacity: 128GB\u003cbr data-start=\"1741\" data-end=\"1744\"\u003eMemory Architecture: Unified CPU-GPU Shared Memory\u003cbr data-start=\"1794\" data-end=\"1797\"\u003eMemory Interface: 256-bit\u003cbr data-start=\"1822\" data-end=\"1825\"\u003eMemory Bandwidth: Up to 273 GB\/s\u003cbr data-start=\"1857\" data-end=\"1860\"\u003eMemory Speed: Up to 8533 MT\/s \u003cspan class=\"\" data-state=\"closed\"\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp data-start=\"1929\" data-end=\"2222\"\u003e\u003cstrong data-start=\"1929\" data-end=\"1955\"\u003eStorage Specifications\u003c\/strong\u003e\u003cbr data-start=\"1955\" data-end=\"1958\"\u003eStorage Capacity: 2TB\u003cbr data-start=\"1979\" data-end=\"1982\"\u003eStorage Type: M.2 PCIe Gen4 NVMe SSD\u003cbr data-start=\"2018\" data-end=\"2021\"\u003eStorage Architecture: High-Speed AI Optimized Storage\u003cbr data-start=\"2074\" data-end=\"2077\"\u003eSSD Configuration: TLC \/ QLC NVMe SSD depending on configuration\u003cbr data-start=\"2141\" data-end=\"2144\"\u003eSelf Encrypting Drive Support: Supported \u003cspan class=\"\" data-state=\"closed\"\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp data-start=\"2224\" data-end=\"2410\"\u003e\u003cstrong data-start=\"2224\" data-end=\"2253\"\u003eNetworking Specifications\u003c\/strong\u003e\u003cbr data-start=\"2253\" data-end=\"2256\"\u003eNetwork Technology: NVIDIA ConnectX®-7 SmartNIC\u003cbr data-start=\"2303\" data-end=\"2306\"\u003eHigh-Speed Networking: Supported\u003cbr data-start=\"2338\" data-end=\"2341\"\u003eLow-Latency Networking: Supported\u003cbr data-start=\"2374\" data-end=\"2377\"\u003eAI Cluster Expansion: Supported\u003c\/p\u003e\n\u003cp data-start=\"2412\" data-end=\"2585\"\u003eSupported Features:\u003cbr data-start=\"2431\" data-end=\"2434\"\u003e• ConnectX®-7 AI Networking\u003cbr data-start=\"2461\" data-end=\"2464\"\u003e• Distributed AI Processing\u003cbr data-start=\"2491\" data-end=\"2494\"\u003e• Multi-System AI Scaling\u003cbr data-start=\"2519\" data-end=\"2522\"\u003e• High Bandwidth AI Communication\u003cbr data-start=\"2555\" data-end=\"2558\"\u003e• AI Cluster Connectivity\u003c\/p\u003e\n\u003cp data-start=\"2587\" data-end=\"2692\"\u003eDual System Expansion: Supported via NVIDIA high-speed interconnect \u003cspan class=\"\" data-state=\"closed\"\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp data-start=\"2694\" data-end=\"2859\"\u003e\u003cstrong data-start=\"2694\" data-end=\"2719\"\u003eWireless Connectivity\u003c\/strong\u003e\u003cbr data-start=\"2719\" data-end=\"2722\"\u003eWireless LAN: Wi-Fi 7\u003cbr data-start=\"2743\" data-end=\"2746\"\u003eBluetooth: Bluetooth 5 Wireless Technology\u003cbr data-start=\"2788\" data-end=\"2791\"\u003eWireless Networking: Supported \u003cspan class=\"\" data-state=\"closed\"\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp data-start=\"2861\" data-end=\"2964\"\u003e\u003cstrong data-start=\"2861\" data-end=\"2877\"\u003eConnectivity\u003c\/strong\u003e\u003cbr data-start=\"2877\" data-end=\"2880\"\u003ePorts:\u003cbr data-start=\"2886\" data-end=\"2889\"\u003e• USB-C Ports\u003cbr data-start=\"2902\" data-end=\"2905\"\u003e• USB-A Ports\u003cbr data-start=\"2918\" data-end=\"2921\"\u003e• HDMI\u003cbr data-start=\"2927\" data-end=\"2930\"\u003e• DisplayPort\u003cbr data-start=\"2943\" data-end=\"2946\"\u003e• RJ-45 Ethernet\u003c\/p\u003e\n\u003cp data-start=\"2966\" data-end=\"3135\"\u003eDisplay Support: Multi-Monitor Support\u003cbr data-start=\"3004\" data-end=\"3007\"\u003eDisplay Output: Up to 4 Displays Supported\u003cbr data-start=\"3049\" data-end=\"3052\"\u003eHigh-Speed Peripheral Connectivity: Supported \u003cspan class=\"\" data-state=\"closed\"\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp data-start=\"3137\" data-end=\"3202\"\u003e\u003cstrong data-start=\"3137\" data-end=\"3163\"\u003eSoftware \u0026amp; AI Platform\u003c\/strong\u003e\u003cbr data-start=\"3163\" data-end=\"3166\"\u003eOperating System: NVIDIA DGX™ OS 7\u003c\/p\u003e\n\u003cp data-start=\"3204\" data-end=\"3443\"\u003eSupported AI Functions:\u003cbr data-start=\"3227\" data-end=\"3230\"\u003e• AI Model Development\u003cbr data-start=\"3252\" data-end=\"3255\"\u003e• AI Fine-Tuning\u003cbr data-start=\"3271\" data-end=\"3274\"\u003e• AI Inferencing\u003cbr data-start=\"3290\" data-end=\"3293\"\u003e• AI Agent Development\u003cbr data-start=\"3315\" data-end=\"3318\"\u003e• Generative AI Workflows\u003cbr data-start=\"3343\" data-end=\"3346\"\u003e• Large Language Model Deployment\u003cbr data-start=\"3379\" data-end=\"3382\"\u003e• Machine Learning Development\u003cbr data-start=\"3412\" data-end=\"3415\"\u003e• Multimodal AI Processing\u003c\/p\u003e\n\u003cp data-start=\"3445\" data-end=\"3652\"\u003eAI Resource Management: Supported\u003cbr data-start=\"3478\" data-end=\"3481\"\u003eSystem Monitoring: Supported\u003cbr data-start=\"3509\" data-end=\"3512\"\u003eCUDA Development Environment: Supported\u003cbr data-start=\"3551\" data-end=\"3554\"\u003eTensorFlow Support: Supported\u003cbr data-start=\"3583\" data-end=\"3586\"\u003ePyTorch Support: Supported \u003cspan class=\"\" data-state=\"closed\"\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp data-start=\"3654\" data-end=\"4166\"\u003e\u003cstrong data-start=\"3654\" data-end=\"3680\"\u003ePerformance \u0026amp; Features\u003c\/strong\u003e\u003cbr data-start=\"3680\" data-end=\"3683\"\u003eUp to 1 PetaFLOP AI compute performance\u003cbr data-start=\"3722\" data-end=\"3725\"\u003eSupports up to 200B parameter AI models\u003cbr data-start=\"3764\" data-end=\"3767\"\u003eUnified memory architecture for accelerated AI workloads\u003cbr data-start=\"3823\" data-end=\"3826\"\u003eOptimized for local LLM inferencing and fine-tuning\u003cbr data-start=\"3877\" data-end=\"3880\"\u003eDesigned for enterprise AI development environments\u003cbr data-start=\"3931\" data-end=\"3934\"\u003eHigh-speed CPU-GPU communication through NVLink™-C2C\u003cbr data-start=\"3986\" data-end=\"3989\"\u003eSupports AI cluster scaling and distributed workloads\u003cbr data-start=\"4042\" data-end=\"4045\"\u003eLow latency AI processing architecture\u003cbr data-start=\"4083\" data-end=\"4086\"\u003eEnterprise-grade AI workstation platform \u003cspan class=\"\" data-state=\"closed\"\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp data-start=\"4168\" data-end=\"4423\"\u003e\u003cstrong data-start=\"4168\" data-end=\"4196\"\u003eCooling \u0026amp; Thermal Design\u003c\/strong\u003e\u003cbr data-start=\"4196\" data-end=\"4199\"\u003eCooling Type: Advanced Active Cooling System\u003cbr data-start=\"4243\" data-end=\"4246\"\u003eThermal Optimization: AI Workload Optimized\u003cbr data-start=\"4289\" data-end=\"4292\"\u003eHigh Efficiency Cooling Architecture: Supported\u003cbr data-start=\"4339\" data-end=\"4342\"\u003eContinuous AI Workload Support: Supported \u003cspan class=\"\" data-state=\"closed\"\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp data-start=\"4425\" data-end=\"4664\"\u003e\u003cstrong data-start=\"4425\" data-end=\"4446\"\u003eSecurity Features\u003c\/strong\u003e\u003cbr data-start=\"4446\" data-end=\"4449\"\u003eSelf Encrypting Storage Support\u003cbr data-start=\"4480\" data-end=\"4483\"\u003eEnterprise Data Protection Support\u003cbr data-start=\"4517\" data-end=\"4520\"\u003eLocal AI Processing Capability\u003cbr data-start=\"4550\" data-end=\"4553\"\u003eSecure AI Development Environment\u003cbr data-start=\"4586\" data-end=\"4589\"\u003eData Privacy Optimized Architecture \u003cspan class=\"\" data-state=\"closed\"\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp data-start=\"4666\" data-end=\"4964\"\u003e\u003cstrong data-start=\"4666\" data-end=\"4691\"\u003eDesign \u0026amp; Construction\u003c\/strong\u003e\u003cbr data-start=\"4691\" data-end=\"4694\"\u003eForm Factor: Compact Desktop AI Workstation\u003cbr data-start=\"4737\" data-end=\"4740\"\u003eChassis Type: Dell Pro Max GB10 Chassis\u003cbr data-start=\"4779\" data-end=\"4782\"\u003eDeployment Environment: Enterprise AI Development, Research Labs, Edge AI, Data Science\u003cbr data-start=\"4869\" data-end=\"4872\"\u003eConstruction: Enterprise Grade AI Computing Platform \u003cspan class=\"\" data-state=\"closed\"\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp data-start=\"4966\" data-end=\"5135\"\u003e\u003cstrong data-start=\"4966\" data-end=\"4986\"\u003ePackage Contents\u003c\/strong\u003e\u003cbr data-start=\"4986\" data-end=\"4989\"\u003eDell Pro Max GB10 AI Workstation\u003cbr data-start=\"5021\" data-end=\"5024\"\u003ePower Adapter\u003cbr data-start=\"5037\" data-end=\"5040\"\u003ePower Cord\u003cbr data-start=\"5050\" data-end=\"5053\"\u003eDocumentation\u003cbr data-start=\"5066\" data-end=\"5069\"\u003ePreloaded NVIDIA DGX™ OS 7 \u003cspan class=\"\" data-state=\"closed\"\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp data-start=\"5137\" data-end=\"5521\"\u003e\u003cstrong data-start=\"5137\" data-end=\"5153\"\u003eKey Features\u003c\/strong\u003e\u003cbr data-start=\"5153\" data-end=\"5156\"\u003eNVIDIA GB10 Grace Blackwell Superchip\u003cbr data-start=\"5193\" data-end=\"5196\"\u003e128GB LPDDR5X unified memory\u003cbr data-start=\"5224\" data-end=\"5227\"\u003e2TB PCIe Gen4 NVMe SSD\u003cbr data-start=\"5249\" data-end=\"5252\"\u003e6144 CUDA cores\u003cbr data-start=\"5267\" data-end=\"5270\"\u003eUp to 1 PetaFLOP AI performance\u003cbr data-start=\"5301\" data-end=\"5304\"\u003eSupports up to 200B parameter AI models\u003cbr data-start=\"5343\" data-end=\"5346\"\u003eNVIDIA ConnectX®-7 SmartNIC\u003cbr data-start=\"5373\" data-end=\"5376\"\u003eNVIDIA DGX™ OS 7 preloaded\u003cbr data-start=\"5402\" data-end=\"5405\"\u003eWi-Fi 7 and Bluetooth connectivity\u003cbr data-start=\"5439\" data-end=\"5442\"\u003eCompact desktop AI supercomputer design \u003cspan class=\"\" data-state=\"closed\"\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp data-start=\"5523\" data-end=\"5870\"\u003e\u003cstrong data-start=\"5523\" data-end=\"5544\"\u003eTypical Use Cases\u003c\/strong\u003e\u003cbr data-start=\"5544\" data-end=\"5547\"\u003eLarge Language Model (LLM) deployment\u003cbr data-start=\"5584\" data-end=\"5587\"\u003eGenerative AI development\u003cbr data-start=\"5612\" data-end=\"5615\"\u003eAI inferencing workloads\u003cbr data-start=\"5639\" data-end=\"5642\"\u003eAI model fine-tuning\u003cbr data-start=\"5662\" data-end=\"5665\"\u003eMachine learning development\u003cbr data-start=\"5693\" data-end=\"5696\"\u003eData science environments\u003cbr data-start=\"5721\" data-end=\"5724\"\u003eEnterprise AI prototyping\u003cbr data-start=\"5749\" data-end=\"5752\"\u003eEdge AI deployment\u003cbr data-start=\"5770\" data-end=\"5773\"\u003ePrivate on-premise AI computing\u003cbr data-start=\"5804\" data-end=\"5807\"\u003eDistributed AI clusters\u003c\/p\u003e\n\u003c\/div\u003e","brand":"Dell","offers":[{"title":"Default Title","offer_id":51198140514468,"sku":"DPMFCM1253GB10128G2TB","price":9735.0,"currency_code":"SGD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0605\/0136\/0804\/files\/dell-pro-max-ai-desktop-pcs-with-nvidia-gb10-blackwell-gpu-4tb-8810604.png?v=1765505830"},{"product_id":"dell-pro-max-ai-desktop-pcs-with-nvidia-gb10-blackwell-gpu-4tb","title":"Dell Pro Max AI Desktop PCs with NVIDIA GB10 Blackwell GPU - 4TB","description":"\u003ch2\u003e\u003cstrong\u003eDell Pro Max GB10 FCM1253 NVIDIA GB10 Grace CPU\/ NVIDIA GB10 Blackwell\/ 128GB LPDDR5\/ 4TB SSD\/ WIFI (DPMFCM1253GB10128G4TB) - 3 Years Onsite Warranty\u003c\/strong\u003e\u003c\/h2\u003e\n\u003cdiv style=\"text-align: left;\"\u003e\n\u003ch3 data-start=\"140\" data-end=\"592\"\u003e\u003cstrong data-start=\"140\" data-end=\"198\"\u003eHigh-Performance AI Acceleration for Serious Workloads\u003c\/strong\u003e\u003c\/h3\u003e\n\u003cp data-start=\"140\" data-end=\"592\"\u003eThe Dell Pro Max AI Desktop PC with NVIDIA GB10 Blackwell GPU is built for organisations handling demanding AI development, large language model training, and high-volume inference pipelines. Its workstation-grade architecture prioritises stability and sustained compute performance, delivering reliable throughput for parallel processing, simulation, and long continuous runtime operations.\u003c\/p\u003e\n\u003ch3 data-start=\"594\" data-end=\"1051\"\u003e\u003cstrong data-start=\"594\" data-end=\"641\"\u003eNVIDIA GB10 Blackwell GPU for Next-Level AI\u003c\/strong\u003e\u003c\/h3\u003e\n\u003cp data-start=\"594\" data-end=\"1051\"\u003ePowered by the next-generation NVIDIA GB10 Blackwell GPU, this system accelerates generative AI models, multimodal vision-language tasks, scientific simulations, and large-scale data analytics. Blackwell architecture enhances compute density, memory bandwidth efficiency, and latency reduction—ideal for teams that require rapid iteration, reduced training time, and consistent production-level performance.\u003c\/p\u003e\n\u003ch3 data-start=\"1053\" data-end=\"1554\"\u003e\u003cstrong data-start=\"1053\" data-end=\"1107\"\u003eOptimised for Multitasking and Developer Workflows\u003c\/strong\u003e\u003c\/h3\u003e\n\u003cp data-start=\"1053\" data-end=\"1554\"\u003eDesigned for heavy concurrent workloads, the system runs multiple frameworks and pipelines smoothly—whether you're training models, preparing datasets, running inference servers, or testing deployment builds locally. Its thermal system and power management maintain sustained performance during peak loads, making it suitable for research labs, engineering teams, and enterprise AI developers who rely on predictable workstation responsiveness.\u003c\/p\u003e\n\u003ch3 data-start=\"1556\" data-end=\"2006\"\u003e\u003cstrong data-start=\"1556\" data-end=\"1604\"\u003e4TB High-Speed NVMe Storage for AI Pipelines\u003c\/strong\u003e\u003c\/h3\u003e\n\u003cp data-start=\"1556\" data-end=\"2006\"\u003eThe 4TB NVMe SSD delivers fast access speeds for large datasets, embeddings, multimodal assets, and model checkpoint files. High throughput reduces delays in preprocessing, caching, and loading, allowing teams to iterate faster across complex AI workflow cycles. The expanded capacity supports extensive storage requirements for LLM fine-tuning, diffusion models, and high-resolution data pipelines.\u003c\/p\u003e\n\u003ch3 data-start=\"2008\" data-end=\"2430\"\u003e\u003cstrong data-start=\"2008\" data-end=\"2052\"\u003eReliable, Scalable, and Enterprise Ready\u003c\/strong\u003e\u003c\/h3\u003e\n\u003cp data-start=\"2008\" data-end=\"2430\"\u003eBuilt for long-term operational environments, the Dell Pro Max AI Desktop PC integrates securely into enterprise infrastructure. Its upgrade-friendly chassis supports future expansion of storage, memory, and accelerators. Stable thermals, long-lifecycle components, and enterprise-grade power delivery ensure dependable performance for organisations scaling up AI operations.\u003c\/p\u003e\n\u003ch3 data-start=\"2432\" data-end=\"3126\"\u003e\u003cstrong data-start=\"2432\" data-end=\"2481\"\u003eTechnical Specification \u003c\/strong\u003e\u003c\/h3\u003e\n\u003cp data-start=\"72\" data-end=\"702\"\u003e\u003cstrong data-start=\"72\" data-end=\"83\"\u003eGeneral\u003c\/strong\u003e\u003cbr data-start=\"83\" data-end=\"86\"\u003eModel: Dell Pro Max with GB10\u003cbr data-start=\"115\" data-end=\"118\"\u003ePart Number: DPMFCM1253GB10128G4TB\u003cbr data-start=\"152\" data-end=\"155\"\u003eProduct Name: Dell Pro Max GB10 FCM1253 NVIDIA Grace Blackwell AI Workstation\u003cbr data-start=\"232\" data-end=\"235\"\u003eManufacturer: \u003cspan class=\"hover:entity-accent entity-underline inline cursor-pointer align-baseline\"\u003e\u003cspan class=\"whitespace-normal\"\u003eDell Technologies\u003c\/span\u003e\u003c\/span\u003e\u003cbr data-start=\"286\" data-end=\"289\"\u003eProduct Type: Personal AI Supercomputer \/ AI Workstation\u003cbr data-start=\"345\" data-end=\"348\"\u003eForm Factor: Compact Desktop AI System\u003cbr data-start=\"386\" data-end=\"389\"\u003eDeployment: Generative AI, Large Language Models (LLM), AI Development, Edge AI, AI Inferencing, Data Science\u003cbr data-start=\"498\" data-end=\"501\"\u003eArchitecture: NVIDIA Grace Blackwell Platform\u003cbr data-start=\"546\" data-end=\"549\"\u003eTarget Workloads: AI Model Development, LLM Fine-Tuning, AI Agents, Machine Learning, Deep Learning, Generative AI\u003cbr data-start=\"663\" data-end=\"666\"\u003eOperating System: NVIDIA DGX™ OS 7\u003c\/p\u003e\n\u003cp data-start=\"704\" data-end=\"860\"\u003e\u003cstrong data-start=\"704\" data-end=\"732\"\u003eProcessor Specifications\u003c\/strong\u003e\u003cbr data-start=\"732\" data-end=\"735\"\u003eProcessor Architecture: NVIDIA GB10 Grace Blackwell Superchip\u003cbr data-start=\"796\" data-end=\"799\"\u003eCPU Type: Arm-Based AI Processor\u003cbr data-start=\"831\" data-end=\"834\"\u003eCPU Core Count: 20 Cores\u003c\/p\u003e\n\u003cp data-start=\"862\" data-end=\"959\"\u003eCPU Configuration:\u003cbr data-start=\"880\" data-end=\"883\"\u003e• 10 x Cortex-X925 Performance Cores\u003cbr data-start=\"919\" data-end=\"922\"\u003e• 10 x Cortex-A725 Efficiency Cores\u003c\/p\u003e\n\u003cp data-start=\"961\" data-end=\"1153\"\u003eProcessor Cache: 16MB L2 Cache\u003cbr data-start=\"991\" data-end=\"994\"\u003eCPU-GPU Interconnect: NVIDIA NVLink™-C2C\u003cbr data-start=\"1034\" data-end=\"1037\"\u003eAI Accelerated Computing: Supported\u003cbr data-start=\"1072\" data-end=\"1075\"\u003eUnified Compute Architecture: Supported \u003cspan class=\"\" data-state=\"closed\"\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp data-start=\"1155\" data-end=\"1597\"\u003e\u003cstrong data-start=\"1155\" data-end=\"1177\"\u003eGPU Specifications\u003c\/strong\u003e\u003cbr data-start=\"1177\" data-end=\"1180\"\u003eGPU Architecture: NVIDIA GB10 Blackwell GPU\u003cbr data-start=\"1223\" data-end=\"1226\"\u003eCUDA Cores: 6144 CUDA Cores\u003cbr data-start=\"1253\" data-end=\"1256\"\u003eTensor Core Generation: 5th Generation Tensor Cores\u003cbr data-start=\"1307\" data-end=\"1310\"\u003eRT Core Generation: 4th Generation RT Cores\u003cbr data-start=\"1353\" data-end=\"1356\"\u003eAI Compute Performance: Up to 1 PetaFLOP FP4 AI Performance\u003cbr data-start=\"1415\" data-end=\"1418\"\u003eAI Optimization: Supported\u003cbr data-start=\"1444\" data-end=\"1447\"\u003eGenerative AI Support: Supported\u003cbr data-start=\"1479\" data-end=\"1482\"\u003eLLM Processing: Supported\u003cbr data-start=\"1507\" data-end=\"1510\"\u003eSupports AI Models: Up to 200 Billion Parameters \u003cspan class=\"\" data-state=\"closed\"\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp data-start=\"1599\" data-end=\"1851\"\u003e\u003cstrong data-start=\"1599\" data-end=\"1624\"\u003eMemory Specifications\u003c\/strong\u003e\u003cbr data-start=\"1624\" data-end=\"1627\"\u003eMemory Type: LPDDR5X Unified Memory\u003cbr data-start=\"1662\" data-end=\"1665\"\u003eMemory Capacity: 128GB\u003cbr data-start=\"1687\" data-end=\"1690\"\u003eMemory Architecture: Coherent Unified CPU-GPU Shared Memory\u003cbr data-start=\"1749\" data-end=\"1752\"\u003eMemory Bandwidth: Up to 273 GB\/s\u003cbr data-start=\"1784\" data-end=\"1787\"\u003eMemory Interface: 256-bit \u003cspan class=\"\" data-state=\"closed\"\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp data-start=\"1853\" data-end=\"2103\"\u003e\u003cstrong data-start=\"1853\" data-end=\"1879\"\u003eStorage Specifications\u003c\/strong\u003e\u003cbr data-start=\"1879\" data-end=\"1882\"\u003eStorage Capacity: 4TB\u003cbr data-start=\"1903\" data-end=\"1906\"\u003eStorage Type: M.2 2242 PCIe Gen4 NVMe SSD\u003cbr data-start=\"1947\" data-end=\"1950\"\u003eSSD Type: TLC SSD\u003cbr data-start=\"1967\" data-end=\"1970\"\u003eSelf Encrypting Drive (SED): Supported\u003cbr data-start=\"2008\" data-end=\"2011\"\u003eStorage Architecture: High-Speed AI Optimized Storage \u003cspan class=\"\" data-state=\"closed\"\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp data-start=\"2105\" data-end=\"2294\"\u003e\u003cstrong data-start=\"2105\" data-end=\"2134\"\u003eNetworking Specifications\u003c\/strong\u003e\u003cbr data-start=\"2134\" data-end=\"2137\"\u003eNetwork Technology: NVIDIA ConnectX®-7 SmartNIC\u003cbr data-start=\"2184\" data-end=\"2187\"\u003eHigh-Speed Networking: Supported\u003cbr data-start=\"2219\" data-end=\"2222\"\u003eLow-Latency AI Networking: Supported\u003cbr data-start=\"2258\" data-end=\"2261\"\u003eAI Cluster Expansion: Supported\u003c\/p\u003e\n\u003cp data-start=\"2296\" data-end=\"2507\"\u003eSupported Features:\u003cbr data-start=\"2315\" data-end=\"2318\"\u003e• Multi-System Scaling\u003cbr data-start=\"2340\" data-end=\"2343\"\u003e• AI Cluster Connectivity\u003cbr data-start=\"2368\" data-end=\"2371\"\u003e• High Bandwidth AI Networking\u003cbr data-start=\"2401\" data-end=\"2404\"\u003e• Distributed AI Processing\u003cbr data-start=\"2431\" data-end=\"2434\"\u003e• Dual-System Interconnect Support \u003cspan class=\"\" data-state=\"closed\"\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp data-start=\"2509\" data-end=\"2677\"\u003e\u003cstrong data-start=\"2509\" data-end=\"2534\"\u003eWireless Connectivity\u003c\/strong\u003e\u003cbr data-start=\"2534\" data-end=\"2537\"\u003eWireless LAN: Wi-Fi 7\u003cbr data-start=\"2558\" data-end=\"2561\"\u003eBluetooth: Bluetooth 5.x Wireless Technology\u003cbr data-start=\"2605\" data-end=\"2608\"\u003eWireless Networking: Supported \u003cspan class=\"\" data-state=\"closed\"\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp data-start=\"2679\" data-end=\"2740\"\u003e\u003cstrong data-start=\"2679\" data-end=\"2695\"\u003eConnectivity\u003c\/strong\u003e\u003cbr data-start=\"2695\" data-end=\"2698\"\u003eUSB Ports:\u003cbr data-start=\"2708\" data-end=\"2711\"\u003e• 3 x USB 3.2 Gen2x2 Type-C\u003c\/p\u003e\n\u003cp data-start=\"2742\" data-end=\"2888\"\u003eUSB Speed: Up to 20Gbps\u003cbr data-start=\"2765\" data-end=\"2768\"\u003eDisplay Output Support: Supported\u003cbr data-start=\"2801\" data-end=\"2804\"\u003eHigh-Speed Peripheral Connectivity: Supported \u003cspan class=\"\" data-state=\"closed\"\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp data-start=\"2890\" data-end=\"2955\"\u003e\u003cstrong data-start=\"2890\" data-end=\"2916\"\u003eSoftware \u0026amp; AI Platform\u003c\/strong\u003e\u003cbr data-start=\"2916\" data-end=\"2919\"\u003eOperating System: NVIDIA DGX™ OS 7\u003c\/p\u003e\n\u003cp data-start=\"2957\" data-end=\"3167\"\u003eSupported AI Functions:\u003cbr data-start=\"2980\" data-end=\"2983\"\u003e• AI Model Development\u003cbr data-start=\"3005\" data-end=\"3008\"\u003e• AI Fine-Tuning\u003cbr data-start=\"3024\" data-end=\"3027\"\u003e• AI Inferencing\u003cbr data-start=\"3043\" data-end=\"3046\"\u003e• Generative AI Workflows\u003cbr data-start=\"3071\" data-end=\"3074\"\u003e• AI Agent Development\u003cbr data-start=\"3096\" data-end=\"3099\"\u003e• Large Language Model Deployment\u003cbr data-start=\"3132\" data-end=\"3135\"\u003e• Machine Learning Development\u003c\/p\u003e\n\u003cp data-start=\"3169\" data-end=\"3312\"\u003eAI Development Environment: Supported\u003cbr data-start=\"3206\" data-end=\"3209\"\u003eAI Resource Management: Supported\u003cbr data-start=\"3242\" data-end=\"3245\"\u003eSystem Monitoring: Supported \u003cspan class=\"\" data-state=\"closed\"\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp data-start=\"3314\" data-end=\"3779\"\u003e\u003cstrong data-start=\"3314\" data-end=\"3340\"\u003ePerformance \u0026amp; Features\u003c\/strong\u003e\u003cbr data-start=\"3340\" data-end=\"3343\"\u003eUp to 1 PetaFLOP AI compute performance\u003cbr data-start=\"3382\" data-end=\"3385\"\u003eSupports up to 200B parameter AI models\u003cbr data-start=\"3424\" data-end=\"3427\"\u003eUnified memory architecture for accelerated AI workloads\u003cbr data-start=\"3483\" data-end=\"3486\"\u003eOptimized for local LLM inferencing and fine-tuning\u003cbr data-start=\"3537\" data-end=\"3540\"\u003eDesigned for enterprise AI development environments\u003cbr data-start=\"3591\" data-end=\"3594\"\u003eHigh-speed CPU-GPU communication via NVLink™-C2C\u003cbr data-start=\"3642\" data-end=\"3645\"\u003eCompact AI supercomputer architecture\u003cbr data-start=\"3682\" data-end=\"3685\"\u003eSupports AI cluster scaling and distributed workloads \u003cspan class=\"\" data-state=\"closed\"\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp data-start=\"3781\" data-end=\"4031\"\u003e\u003cstrong data-start=\"3781\" data-end=\"3809\"\u003eCooling \u0026amp; Thermal Design\u003c\/strong\u003e\u003cbr data-start=\"3809\" data-end=\"3812\"\u003eCooling Type: Advanced Active Cooling System\u003cbr data-start=\"3856\" data-end=\"3859\"\u003eThermal Optimization: AI Workload Optimized\u003cbr data-start=\"3902\" data-end=\"3905\"\u003eCompact Airflow Design: Supported\u003cbr data-start=\"3938\" data-end=\"3941\"\u003eEnterprise Continuous Workload Support: Supported \u003cspan class=\"\" data-state=\"closed\"\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp data-start=\"4033\" data-end=\"4269\"\u003e\u003cstrong data-start=\"4033\" data-end=\"4054\"\u003eSecurity Features\u003c\/strong\u003e\u003cbr data-start=\"4054\" data-end=\"4057\"\u003eSelf Encrypting SSD Support\u003cbr data-start=\"4084\" data-end=\"4087\"\u003eEnterprise Data Protection Support\u003cbr data-start=\"4121\" data-end=\"4124\"\u003eLocal AI Processing Capability\u003cbr data-start=\"4154\" data-end=\"4157\"\u003eSecure AI Development Environment\u003cbr data-start=\"4190\" data-end=\"4193\"\u003eData Privacy Optimized Architecture \u003cspan class=\"\" data-state=\"closed\"\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp data-start=\"4271\" data-end=\"4600\"\u003e\u003cstrong data-start=\"4271\" data-end=\"4296\"\u003eDesign \u0026amp; Construction\u003c\/strong\u003e\u003cbr data-start=\"4296\" data-end=\"4299\"\u003eForm Factor: Compact Desktop AI Workstation\u003cbr data-start=\"4342\" data-end=\"4345\"\u003eChassis Type: Dell Pro Max GB10 L6 Chassis\u003cbr data-start=\"4387\" data-end=\"4390\"\u003eColor: Black \/ Magnetite\u003cbr data-start=\"4414\" data-end=\"4417\"\u003eDeployment Environment: Enterprise AI Development, Data Science, Research Labs, Edge AI\u003cbr data-start=\"4504\" data-end=\"4507\"\u003eConstruction: Enterprise Grade AI Computing Platform \u003cspan class=\"\" data-state=\"closed\"\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp data-start=\"4602\" data-end=\"4701\"\u003e\u003cstrong data-start=\"4602\" data-end=\"4629\"\u003ePhysical Specifications\u003c\/strong\u003e\u003cbr data-start=\"4629\" data-end=\"4632\"\u003eDimensions:\u003cbr data-start=\"4643\" data-end=\"4646\"\u003e• Width: 150 mm\u003cbr data-start=\"4661\" data-end=\"4664\"\u003e• Height: 50.5 mm\u003cbr data-start=\"4681\" data-end=\"4684\"\u003e• Depth: 150 mm\u003c\/p\u003e\n\u003cp data-start=\"4703\" data-end=\"4766\"\u003eWeight: Approx. 1.2 kg \u003cspan class=\"\" data-state=\"closed\"\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp data-start=\"4768\" data-end=\"4938\"\u003e\u003cstrong data-start=\"4768\" data-end=\"4788\"\u003ePackage Contents\u003c\/strong\u003e\u003cbr data-start=\"4788\" data-end=\"4791\"\u003eDell Pro Max GB10 AI Workstation\u003cbr data-start=\"4823\" data-end=\"4826\"\u003ePower Adapter\u003cbr data-start=\"4839\" data-end=\"4842\"\u003ePower Cord\u003cbr data-start=\"4852\" data-end=\"4855\"\u003eDocumentation\u003cbr data-start=\"4868\" data-end=\"4871\"\u003ePreloaded NVIDIA DGX™ OS 7 \u003cspan class=\"\" data-state=\"closed\"\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp data-start=\"4940\" data-end=\"5322\"\u003e\u003cstrong data-start=\"4940\" data-end=\"4956\"\u003eKey Features\u003c\/strong\u003e\u003cbr data-start=\"4956\" data-end=\"4959\"\u003eNVIDIA GB10 Grace Blackwell Superchip\u003cbr data-start=\"4996\" data-end=\"4999\"\u003e128GB LPDDR5X unified memory\u003cbr data-start=\"5027\" data-end=\"5030\"\u003e4TB PCIe Gen4 NVMe SSD\u003cbr data-start=\"5052\" data-end=\"5055\"\u003e6144 CUDA cores\u003cbr data-start=\"5070\" data-end=\"5073\"\u003eUp to 1 PetaFLOP AI performance\u003cbr data-start=\"5104\" data-end=\"5107\"\u003eSupports up to 200B parameter AI models\u003cbr data-start=\"5146\" data-end=\"5149\"\u003eNVIDIA ConnectX®-7 networking\u003cbr data-start=\"5178\" data-end=\"5181\"\u003eNVIDIA DGX™ OS 7 preloaded\u003cbr data-start=\"5207\" data-end=\"5210\"\u003eWi-Fi 7 and Bluetooth support\u003cbr data-start=\"5239\" data-end=\"5242\"\u003eCompact desktop AI supercomputer design \u003cspan class=\"\" data-state=\"closed\"\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp data-start=\"5324\" data-end=\"5672\"\u003e\u003cstrong data-start=\"5324\" data-end=\"5345\"\u003eTypical Use Cases\u003c\/strong\u003e\u003cbr data-start=\"5345\" data-end=\"5348\"\u003eLarge Language Model (LLM) deployment\u003cbr data-start=\"5385\" data-end=\"5388\"\u003eGenerative AI development\u003cbr data-start=\"5413\" data-end=\"5416\"\u003eAI inferencing workloads\u003cbr data-start=\"5440\" data-end=\"5443\"\u003eAI model fine-tuning\u003cbr data-start=\"5463\" data-end=\"5466\"\u003eMachine learning development\u003cbr data-start=\"5494\" data-end=\"5497\"\u003eData science environments\u003cbr data-start=\"5522\" data-end=\"5525\"\u003eEnterprise AI prototyping\u003cbr data-start=\"5550\" data-end=\"5553\"\u003eEdge AI deployment\u003cbr data-start=\"5571\" data-end=\"5574\"\u003ePrivate on-premise AI computing\u003cbr data-start=\"5605\" data-end=\"5608\"\u003eDistributed AI clusters\u003c\/p\u003e\n\u003c\/div\u003e","brand":"Dell","offers":[{"title":"Default Title","offer_id":51198141071524,"sku":"DPMFCM1253GB10128G4TB","price":10385.0,"currency_code":"SGD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0605\/0136\/0804\/files\/dell-pro-max-ai-desktop-pcs-with-nvidia-gb10-blackwell-gpu-4tb-8810604.png?v=1765505830"},{"product_id":"gigabyte-ai-top-atom-personal-ai-computer-4tb","title":"Gigabyte AI TOP ATOM Personal AI Computer 4TB (PCIe5.0)","description":"\u003ch2\u003e\n\u003cstrong\u003eGigabyte AI TOP ATOM Personal AI Computer 4TB (GB10 \/ 128GB LPDDR5x \/ \u003cspan style=\"color: rgb(255, 42, 0);\"\u003ePCIe5.0\u003c\/span\u003e 1400 4TB NVMe M.2 SSD \/ NVIDIA DGX™ OS, Ubuntu Linux (ATAGB10-9000) - 3 Year Local Warranty \u003c\/strong\u003e\n\u003c\/h2\u003e\n\u003cp data-start=\"1731\" data-end=\"2325\" data-is-last-node=\"\" data-is-only-node=\"\"\u003e\u003ciframe title=\"YouTube video player\" src=\"https:\/\/www.youtube.com\/embed\/Sd2Ie68OD_o?si=s0psw3fnKrfzFmAM\" height=\"315\" width=\"560\"\u003e\u003c\/iframe\u003e\u003c\/p\u003e\n\u003ch3 data-end=\"267\" data-start=\"205\"\u003e\u003cstrong data-end=\"265\" data-start=\"205\"\u003ePersonal AI Supercomputer for Developers and Researchers\u003c\/strong\u003e\u003c\/h3\u003e\n\u003cp data-end=\"735\" data-start=\"269\"\u003eThe Gigabyte AI TOP ATOM Personal AI Computer 4TB (ATAGB10-9000) is a cutting-edge AI computing platform designed for developers, researchers, data scientists, and organizations looking to run advanced artificial intelligence workloads locally. Powered by the NVIDIA GB10 architecture and equipped with 128GB LPDDR5x unified memory, this compact AI system enables users to develop, test, and deploy AI applications without relying entirely on cloud infrastructure.\u003c\/p\u003e\n\u003ch3 data-end=\"796\" data-start=\"737\"\u003e\u003cstrong data-end=\"794\" data-start=\"737\"\u003eOptimized for Generative AI and Large Language Models\u003c\/strong\u003e\u003c\/h3\u003e\n\u003cp data-end=\"1205\" data-start=\"798\"\u003eBuilt for modern AI workloads, the Gigabyte AI TOP ATOM supports large language models (LLMs), generative AI, machine learning, deep learning, computer vision, and AI-assisted software development. The powerful architecture provides the performance required for local inference, model fine-tuning, AI experimentation, and advanced research projects while maintaining low latency and enhanced data privacy.\u003c\/p\u003e\n\u003ch3 data-end=\"1259\" data-start=\"1207\"\u003e\u003cstrong data-end=\"1257\" data-start=\"1207\"\u003eHigh-Speed Memory and Storage for AI Workloads\u003c\/strong\u003e\u003c\/h3\u003e\n\u003cp data-end=\"1647\" data-start=\"1261\"\u003eFeaturing 128GB LPDDR5x memory and a PCIe 5.0 4TB NVMe M.2 SSD, the system delivers exceptional responsiveness for handling large datasets, AI models, and complex computational tasks. The high-capacity storage allows users to maintain extensive AI libraries, training datasets, development environments, and enterprise applications while benefiting from ultra-fast data access speeds.\u003c\/p\u003e\n\u003ch3 data-end=\"1702\" data-start=\"1649\"\u003e\u003cstrong data-end=\"1700\" data-start=\"1649\"\u003eEnterprise-Ready AI Platform with NVIDIA DGX OS\u003c\/strong\u003e\u003c\/h3\u003e\n\u003cp data-end=\"2073\" data-start=\"1704\"\u003ePreloaded with NVIDIA DGX™ OS based on Ubuntu Linux, the Gigabyte AI TOP ATOM provides an optimized software environment for AI development and deployment. The platform is designed to support modern AI frameworks, data science tools, and machine learning workflows, helping users accelerate innovation while maintaining a stable and efficient development environment.\u003c\/p\u003e\n\u003ch3 data-end=\"2117\" data-start=\"2075\"\u003e\u003cstrong data-end=\"2115\" data-start=\"2075\"\u003eTechnical Specifications \u003c\/strong\u003e\u003c\/h3\u003e\n\u003cdiv data-is-intersecting=\"true\" data-turn-id-container=\"request-WEB:1eef009e-1c68-42b2-857a-adf7e87e4f85-212\" class=\"\"\u003e\n\u003csection data-turn=\"assistant\" data-scroll-anchor=\"false\" data-testid=\"conversation-turn-40\" data-turn-id-container=\"request-WEB:1eef009e-1c68-42b2-857a-adf7e87e4f85-212\" data-turn-id=\"request-WEB:1eef009e-1c68-42b2-857a-adf7e87e4f85-212\" dir=\"auto\" class=\"text-token-text-primary w-full focus:outline-none has-data-writing-block:pointer-events-none [\u0026amp;:has([data-writing-block])\u0026gt;*]:pointer-events-auto R6Vx5W_threadScrollVars scroll-mb-[calc(var(--scroll-root-safe-area-inset-bottom,0px)+var(--thread-response-height))] scroll-mt-[calc(var(--header-height)+min(200px,max(70px,20svh)))]\"\u003e\n\u003cdiv class=\"text-base my-auto mx-auto pb-10 [--thread-content-margin:var(--thread-content-margin-xs,calc(var(--spacing)*4))] @w-sm\/main:[--thread-content-margin:var(--thread-content-margin-sm,calc(var(--spacing)*6))] @w-lg\/main:[--thread-content-margin:var(--thread-content-margin-lg,calc(var(--spacing)*16))] px-(--thread-content-margin)\"\u003e\n\u003cdiv class=\"[--thread-content-max-width:40rem] @w-lg\/main:[--thread-content-max-width:48rem] mx-auto max-w-(--thread-content-max-width) flex-1 group\/turn-messages focus-visible:outline-hidden relative flex w-full min-w-0 flex-col agent-turn\"\u003e\n\u003cdiv class=\"flex max-w-full flex-col gap-4 grow\"\u003e\n\u003cdiv data-turn-start-message=\"true\" class=\"min-h-8 text-message relative flex w-full flex-col items-end gap-2 text-start break-words whitespace-normal outline-none keyboard-focused:focus-ring [.text-message+\u0026amp;]:mt-1\" data-message-model-slug=\"gpt-5-5\" dir=\"auto\" data-message-id=\"523928d6-527f-4f23-9c73-521a539e8457\" data-message-author-role=\"assistant\" tabindex=\"0\"\u003e\n\u003cdiv class=\"flex w-full flex-col gap-1 empty:hidden\"\u003e\n\u003cdiv class=\"markdown prose dark:prose-invert wrap-break-word w-full light markdown-new-styling\"\u003e\n\u003cp data-end=\"632\" data-start=\"0\"\u003e\u003cstrong data-end=\"11\" data-start=\"0\"\u003eGeneral\u003c\/strong\u003e\u003cbr data-end=\"14\" data-start=\"11\"\u003eModel: AI TOP ATOM Personal AI Computer\u003cbr data-end=\"56\" data-start=\"53\"\u003ePart Number: ATAGB10-9000\u003cbr data-end=\"84\" data-start=\"81\"\u003eProduct Name: Gigabyte AI TOP ATOM Personal AI Computer 4TB\u003cbr data-end=\"146\" data-start=\"143\"\u003eManufacturer: \u003cspan class=\"hover:entity-accent entity-underline inline cursor-pointer align-baseline\"\u003e\u003cspan class=\"whitespace-normal\"\u003eGigabyte Technology\u003c\/span\u003e\u003c\/span\u003e\u003cbr data-end=\"200\" data-start=\"197\"\u003eProduct Type: Personal AI Supercomputer\u003cbr data-end=\"242\" data-start=\"239\"\u003ePlatform: NVIDIA GB10 Grace Blackwell Superchip\u003cbr data-end=\"292\" data-start=\"289\"\u003eForm Factor: Compact Desktop AI Workstation\u003cbr data-end=\"338\" data-start=\"335\"\u003eOperating System: NVIDIA DGX™ OS, Ubuntu Linux\u003cbr data-end=\"387\" data-start=\"384\"\u003eTarget Users: AI Developers, Researchers, Data Scientists, Universities, Enterprise AI Teams\u003cbr data-end=\"482\" data-start=\"479\"\u003eDeployment Type: AI Development, LLM Inference, Generative AI, Edge AI, Machine Learning\u003cbr data-end=\"573\" data-start=\"570\"\u003eWarranty: Manufacturer Warranty (Configuration Dependent)\u003c\/p\u003e\n\u003cp data-end=\"991\" data-start=\"634\"\u003e\u003cstrong data-end=\"662\" data-start=\"634\"\u003eProcessor Specifications\u003c\/strong\u003e\u003cbr data-end=\"665\" data-start=\"662\"\u003eProcessor Platform: NVIDIA GB10 Grace Blackwell Superchip\u003cbr data-end=\"725\" data-start=\"722\"\u003eCPU Architecture: NVIDIA Grace ARM Architecture\u003cbr data-end=\"775\" data-start=\"772\"\u003eCPU Configuration: 20-Core CPU\u003cbr data-end=\"808\" data-start=\"805\"\u003eCPU Design: 10× Cortex-X925 + 10× Cortex-A725\u003cbr data-end=\"856\" data-start=\"853\"\u003eCPU Performance: Optimized for AI and High-Performance Computing Workloads\u003cbr data-end=\"933\" data-start=\"930\"\u003eMemory Bandwidth: High-Speed Unified Memory Architecture\u003c\/p\u003e\n\u003cp data-end=\"1376\" data-start=\"993\"\u003e\u003cstrong data-end=\"1015\" data-start=\"993\"\u003eGPU Specifications\u003c\/strong\u003e\u003cbr data-end=\"1018\" data-start=\"1015\"\u003eGPU Architecture: NVIDIA Blackwell GPU\u003cbr data-end=\"1059\" data-start=\"1056\"\u003eTensor Core Generation: Latest NVIDIA Tensor Cores\u003cbr data-end=\"1112\" data-start=\"1109\"\u003eRay Tracing Cores: Supported\u003cbr data-end=\"1143\" data-start=\"1140\"\u003eAI Performance: Up to 1 PetaFLOP AI Compute (FP4)\u003cbr data-end=\"1195\" data-start=\"1192\"\u003eCUDA Support: Supported\u003cbr data-end=\"1221\" data-start=\"1218\"\u003eTensorRT Support: Supported\u003cbr data-end=\"1251\" data-start=\"1248\"\u003eNVIDIA AI Software Stack: Supported\u003cbr data-end=\"1289\" data-start=\"1286\"\u003eGenerative AI Workloads: Supported\u003cbr data-end=\"1326\" data-start=\"1323\"\u003eLarge Language Model (LLM) Processing: Supported\u003c\/p\u003e\n\u003cp data-end=\"1636\" data-start=\"1378\"\u003e\u003cstrong data-end=\"1403\" data-start=\"1378\"\u003eMemory Specifications\u003c\/strong\u003e\u003cbr data-end=\"1406\" data-start=\"1403\"\u003eMemory Capacity: 128 GB\u003cbr data-end=\"1432\" data-start=\"1429\"\u003eMemory Type: LPDDR5x Unified Memory\u003cbr data-end=\"1470\" data-start=\"1467\"\u003eMemory Architecture: Shared CPU-GPU Unified Memory\u003cbr data-end=\"1523\" data-start=\"1520\"\u003eMemory Bandwidth: Up to 273 GB\/s\u003cbr data-end=\"1558\" data-start=\"1555\"\u003eECC Support: Supported\u003cbr data-end=\"1583\" data-start=\"1580\"\u003eAI Model Capacity: Supports Large AI Models Locally\u003c\/p\u003e\n\u003cp data-end=\"1870\" data-start=\"1638\"\u003e\u003cstrong data-end=\"1664\" data-start=\"1638\"\u003eStorage Specifications\u003c\/strong\u003e\u003cbr data-end=\"1667\" data-start=\"1664\"\u003eInstalled Storage: 4 TB\u003cbr data-end=\"1693\" data-start=\"1690\"\u003eStorage Type: PCIe Gen5 NVMe M.2 SSD\u003cbr data-end=\"1732\" data-start=\"1729\"\u003eSSD Interface: PCIe 5.0\u003cbr data-end=\"1758\" data-start=\"1755\"\u003eStorage Expansion: Configuration Dependent\u003cbr data-end=\"1803\" data-start=\"1800\"\u003eHigh-Speed Data Access: Supported\u003cbr data-end=\"1839\" data-start=\"1836\"\u003eAI Dataset Storage: Supported\u003c\/p\u003e\n\u003cp data-end=\"2258\" data-start=\"1872\"\u003e\u003cstrong data-end=\"1906\" data-start=\"1872\"\u003eAI \u0026amp; Machine Learning Features\u003c\/strong\u003e\u003cbr data-end=\"1909\" data-start=\"1906\"\u003eLarge Language Model (LLM) Support: Supported\u003cbr data-end=\"1957\" data-start=\"1954\"\u003eGenerative AI Development: Supported\u003cbr data-end=\"1996\" data-start=\"1993\"\u003eRetrieval-Augmented Generation (RAG): Supported\u003cbr data-end=\"2046\" data-start=\"2043\"\u003eFine-Tuning Workloads: Supported\u003cbr data-end=\"2081\" data-start=\"2078\"\u003eInference Workloads: Supported\u003cbr data-end=\"2114\" data-start=\"2111\"\u003eAI Agent Development: Supported\u003cbr data-end=\"2148\" data-start=\"2145\"\u003eDeep Learning Framework Support: Supported\u003cbr data-end=\"2193\" data-start=\"2190\"\u003eLocal AI Computing: Supported\u003cbr data-end=\"2225\" data-start=\"2222\"\u003eEdge AI Applications: Supported\u003c\/p\u003e\n\u003cp data-end=\"2635\" data-start=\"2260\"\u003e\u003cstrong data-end=\"2294\" data-start=\"2260\"\u003eSoftware \u0026amp; Development Support\u003c\/strong\u003e\u003cbr data-end=\"2297\" data-start=\"2294\"\u003eOperating System: NVIDIA DGX™ OS\u003cbr data-end=\"2332\" data-start=\"2329\"\u003eSecondary OS Support: Ubuntu Linux\u003cbr data-end=\"2369\" data-start=\"2366\"\u003eCUDA Toolkit: Supported\u003cbr data-end=\"2395\" data-start=\"2392\"\u003eNVIDIA TensorRT: Supported\u003cbr data-end=\"2424\" data-start=\"2421\"\u003eNVIDIA NIM Microservices: Supported\u003cbr data-end=\"2462\" data-start=\"2459\"\u003eDocker Containers: Supported\u003cbr data-end=\"2493\" data-start=\"2490\"\u003eKubernetes: Supported\u003cbr data-end=\"2517\" data-start=\"2514\"\u003ePython Development: Supported\u003cbr data-end=\"2549\" data-start=\"2546\"\u003eAI Framework Support:\u003cbr data-end=\"2573\" data-start=\"2570\"\u003e• PyTorch\u003cbr data-end=\"2585\" data-start=\"2582\"\u003e• TensorFlow\u003cbr data-end=\"2600\" data-start=\"2597\"\u003e• JAX\u003cbr data-end=\"2608\" data-start=\"2605\"\u003e• ONNX Runtime\u003cbr data-is-only-node=\"\" data-end=\"2625\" data-start=\"2622\"\u003e• RAPIDS\u003c\/p\u003e\n\u003cp data-end=\"2838\" data-start=\"2637\"\u003e\u003cstrong data-end=\"2666\" data-start=\"2637\"\u003eNetworking Specifications\u003c\/strong\u003e\u003cbr data-end=\"2669\" data-start=\"2666\"\u003eEthernet: 10 Gigabit Ethernet (10GbE)\u003cbr data-end=\"2709\" data-start=\"2706\"\u003eWireless Connectivity: Wi-Fi 7\u003cbr data-end=\"2742\" data-start=\"2739\"\u003eBluetooth: Bluetooth 5.x\u003cbr data-end=\"2769\" data-start=\"2766\"\u003eHigh-Speed Networking: Supported\u003cbr data-end=\"2804\" data-start=\"2801\"\u003eRemote AI Development: Supported\u003c\/p\u003e\n\u003cp data-end=\"3052\" data-start=\"2840\"\u003e\u003cstrong data-end=\"2864\" data-start=\"2840\"\u003eConnectivity \u0026amp; Ports\u003c\/strong\u003e\u003cbr data-end=\"2867\" data-start=\"2864\"\u003eUSB Type-C Ports: Supported\u003cbr data-end=\"2897\" data-start=\"2894\"\u003eUSB Type-A Ports: Supported\u003cbr data-end=\"2927\" data-start=\"2924\"\u003eHDMI Output: Supported\u003cbr data-end=\"2952\" data-start=\"2949\"\u003eDisplay Connectivity: Supported\u003cbr data-end=\"2986\" data-start=\"2983\"\u003eRJ-45 Ethernet Port: Supported\u003cbr data-end=\"3019\" data-start=\"3016\"\u003ePeripheral Expansion: Supported\u003c\/p\u003e\n\u003cp data-end=\"3230\" data-start=\"3054\"\u003e\u003cstrong data-end=\"3075\" data-start=\"3054\"\u003eSecurity Features\u003c\/strong\u003e\u003cbr data-end=\"3078\" data-start=\"3075\"\u003eSecure Boot: Supported\u003cbr data-end=\"3103\" data-start=\"3100\"\u003eEncrypted Storage Support: Supported\u003cbr data-end=\"3142\" data-start=\"3139\"\u003eEnterprise Security Features: Supported\u003cbr data-end=\"3184\" data-start=\"3181\"\u003eOperating System Security Updates: Supported\u003c\/p\u003e\n\u003cp data-end=\"3384\" data-start=\"3232\"\u003e\u003cstrong data-end=\"3256\" data-start=\"3232\"\u003ePower Specifications\u003c\/strong\u003e\u003cbr data-end=\"3259\" data-start=\"3256\"\u003ePower Efficiency: Optimized for Desktop AI Computing\u003cbr data-end=\"3314\" data-start=\"3311\"\u003ePower Supply: Included\u003cbr data-end=\"3339\" data-start=\"3336\"\u003eEnergy Efficient AI Architecture: Supported\u003c\/p\u003e\n\u003cp data-end=\"3572\" data-start=\"3386\"\u003e\u003cstrong data-end=\"3412\" data-start=\"3386\"\u003eCooling Specifications\u003c\/strong\u003e\u003cbr data-end=\"3415\" data-start=\"3412\"\u003eCooling System: Advanced Active Cooling\u003cbr data-end=\"3457\" data-start=\"3454\"\u003eThermal Optimization: Supported\u003cbr data-end=\"3491\" data-start=\"3488\"\u003eAI Workload Cooling Design: Supported\u003cbr data-end=\"3531\" data-start=\"3528\"\u003eContinuous Operation Support: Supported\u003c\/p\u003e\n\u003cp data-end=\"3921\" data-start=\"3574\"\u003e\u003cstrong data-end=\"3597\" data-start=\"3574\"\u003eSupported Workloads\u003c\/strong\u003e\u003cbr data-end=\"3600\" data-start=\"3597\"\u003eAI Model Inference\u003cbr data-end=\"3621\" data-start=\"3618\"\u003eGenerative AI Applications\u003cbr data-end=\"3650\" data-start=\"3647\"\u003eLarge Language Models (LLMs)\u003cbr data-end=\"3681\" data-start=\"3678\"\u003eRetrieval-Augmented Generation (RAG)\u003cbr data-end=\"3720\" data-start=\"3717\"\u003eMachine Learning Development\u003cbr data-end=\"3751\" data-start=\"3748\"\u003eData Science Workloads\u003cbr data-end=\"3776\" data-start=\"3773\"\u003eComputer Vision Applications\u003cbr data-end=\"3807\" data-start=\"3804\"\u003eNatural Language Processing (NLP)\u003cbr data-end=\"3843\" data-start=\"3840\"\u003eAI Agent Development\u003cbr data-end=\"3866\" data-start=\"3863\"\u003eEdge AI Deployments\u003cbr data-end=\"3888\" data-start=\"3885\"\u003eResearch and Academic Computing\u003c\/p\u003e\n\u003cp data-end=\"4151\" data-start=\"3923\"\u003e\u003cstrong data-end=\"3948\" data-start=\"3923\"\u003eDesign \u0026amp; Construction\u003c\/strong\u003e\u003cbr data-end=\"3951\" data-start=\"3948\"\u003eForm Factor: Compact Desktop Workstation\u003cbr data-end=\"3994\" data-start=\"3991\"\u003eChassis Design: AI Computing Optimized\u003cbr data-end=\"4035\" data-start=\"4032\"\u003eDeployment Flexibility: Desktop, Lab, Office, Research Environment\u003cbr data-end=\"4104\" data-start=\"4101\"\u003eConstruction: Enterprise-Grade Compact Design\u003c\/p\u003e\n\u003cp data-end=\"4529\" data-start=\"4153\"\u003e\u003cstrong data-end=\"4165\" data-start=\"4153\"\u003eFeatures\u003c\/strong\u003e\u003cbr data-end=\"4168\" data-start=\"4165\"\u003eNVIDIA GB10 Grace Blackwell Superchip\u003cbr data-end=\"4208\" data-start=\"4205\"\u003e128 GB LPDDR5x Unified Memory\u003cbr data-end=\"4240\" data-start=\"4237\"\u003e4 TB PCIe Gen5 NVMe SSD\u003cbr data-end=\"4266\" data-start=\"4263\"\u003eNVIDIA Blackwell GPU Architecture\u003cbr data-end=\"4302\" data-start=\"4299\"\u003eUp to 1 PetaFLOP AI Performance (FP4)\u003cbr data-end=\"4342\" data-start=\"4339\"\u003eNVIDIA DGX OS Preloaded\u003cbr data-end=\"4368\" data-start=\"4365\"\u003eUbuntu Linux Support\u003cbr data-end=\"4391\" data-start=\"4388\"\u003e10GbE Networking\u003cbr data-end=\"4410\" data-start=\"4407\"\u003eWi-Fi 7 Connectivity\u003cbr data-end=\"4433\" data-start=\"4430\"\u003eCompact Desktop Form Factor\u003cbr data-end=\"4463\" data-start=\"4460\"\u003eEnterprise AI Development Ready\u003cbr data-end=\"4497\" data-start=\"4494\"\u003eLocal AI Processing Capability\u003c\/p\u003e\n\u003cp data-end=\"5094\" data-start=\"4531\"\u003e\u003cstrong data-end=\"4557\" data-start=\"4531\"\u003ePerformance \u0026amp; Features\u003c\/strong\u003e\u003cbr data-end=\"4560\" data-start=\"4557\"\u003eDesigned for local AI development and inference workloads\u003cbr data-end=\"4620\" data-start=\"4617\"\u003eEnables running advanced generative AI models without cloud dependency\u003cbr data-end=\"4693\" data-start=\"4690\"\u003eSupports large-scale language model experimentation and deployment\u003cbr data-end=\"4762\" data-start=\"4759\"\u003eProvides high-bandwidth unified memory architecture for AI workloads\u003cbr data-end=\"4833\" data-start=\"4830\"\u003eOptimized for researchers, developers, and enterprise AI teams\u003cbr data-end=\"4898\" data-start=\"4895\"\u003eSupports modern AI frameworks and NVIDIA software ecosystem\u003cbr data-end=\"4960\" data-start=\"4957\"\u003eOffers workstation-class performance in a compact desktop footprint\u003cbr data-end=\"5030\" data-start=\"5027\"\u003eBuilt for continuous AI computing and development environments\u003c\/p\u003e\n\u003cp data-end=\"5386\" data-start=\"5096\"\u003e\u003cstrong data-end=\"5123\" data-start=\"5096\"\u003ePhysical Specifications\u003c\/strong\u003e\u003cbr data-end=\"5126\" data-start=\"5123\"\u003eForm Factor: Compact Desktop AI Computer\u003cbr data-end=\"5169\" data-start=\"5166\"\u003eProcessor: NVIDIA GB10 Grace Blackwell Superchip\u003cbr data-end=\"5220\" data-start=\"5217\"\u003eMemory: 128 GB LPDDR5x Unified Memory\u003cbr data-end=\"5260\" data-start=\"5257\"\u003eStorage: 4 TB PCIe Gen5 NVMe SSD\u003cbr data-end=\"5295\" data-start=\"5292\"\u003eNetworking: 10GbE, Wi-Fi 7, Bluetooth 5.x\u003cbr data-end=\"5339\" data-start=\"5336\"\u003eOperating System: NVIDIA DGX OS, Ubuntu Linux\u003c\/p\u003e\n\u003cp data-end=\"5575\" data-start=\"5388\"\u003e\u003cstrong data-end=\"5408\" data-start=\"5388\"\u003ePackage Contents\u003c\/strong\u003e\u003cbr data-end=\"5411\" data-start=\"5408\"\u003eGigabyte AI TOP ATOM Personal AI Computer\u003cbr data-end=\"5455\" data-start=\"5452\"\u003ePower Adapter \/ Power Cable\u003cbr data-end=\"5485\" data-start=\"5482\"\u003eQuick Start Guide\u003cbr data-end=\"5505\" data-start=\"5502\"\u003eDocumentation\u003cbr data-end=\"5521\" data-start=\"5518\"\u003ePreloaded NVIDIA DGX OS and Ubuntu Linux Environment\u003c\/p\u003e\n\u003cp data-end=\"6020\" data-start=\"5577\"\u003e\u003cstrong data-end=\"5593\" data-start=\"5577\"\u003eKey Features\u003c\/strong\u003e\u003cbr data-end=\"5596\" data-start=\"5593\"\u003eNVIDIA GB10 Grace Blackwell Superchip\u003cbr data-end=\"5636\" data-start=\"5633\"\u003e128 GB LPDDR5x Unified Memory\u003cbr data-end=\"5668\" data-start=\"5665\"\u003e4 TB PCIe Gen5 NVMe SSD\u003cbr data-end=\"5694\" data-start=\"5691\"\u003eUp to 1 PetaFLOP AI Compute Performance\u003cbr data-end=\"5736\" data-start=\"5733\"\u003eNVIDIA Blackwell GPU Architecture\u003cbr data-end=\"5772\" data-start=\"5769\"\u003eNVIDIA DGX OS Preinstalled\u003cbr data-end=\"5801\" data-start=\"5798\"\u003e10GbE Networking Connectivity\u003cbr data-end=\"5833\" data-start=\"5830\"\u003eWi-Fi 7 and Bluetooth Support\u003cbr data-end=\"5865\" data-start=\"5862\"\u003eGenerative AI and LLM Ready\u003cbr data-end=\"5895\" data-start=\"5892\"\u003eCompact Desktop AI Workstation Design\u003cbr data-end=\"5935\" data-start=\"5932\"\u003eEnterprise and Research AI Development Platform\u003cbr data-end=\"5985\" data-start=\"5982\"\u003eOptimized for Local AI Processing\u003c\/p\u003e\n\u003cp data-end=\"6424\" data-start=\"6022\"\u003e\u003cstrong data-end=\"6043\" data-start=\"6022\"\u003eTypical Use Cases\u003c\/strong\u003e\u003cbr data-end=\"6046\" data-start=\"6043\"\u003eAI Model Development\u003cbr data-end=\"6069\" data-start=\"6066\"\u003eLarge Language Model (LLM) Inference\u003cbr data-end=\"6108\" data-start=\"6105\"\u003eGenerative AI Applications\u003cbr data-end=\"6137\" data-start=\"6134\"\u003eMachine Learning Training and Testing\u003cbr data-end=\"6177\" data-start=\"6174\"\u003eData Science Research\u003cbr data-end=\"6201\" data-start=\"6198\"\u003eUniversity and Academic Research\u003cbr data-end=\"6236\" data-start=\"6233\"\u003eAI Agent Development\u003cbr data-end=\"6259\" data-start=\"6256\"\u003eComputer Vision Projects\u003cbr data-end=\"6286\" data-start=\"6283\"\u003eNatural Language Processing Applications\u003cbr data-end=\"6329\" data-start=\"6326\"\u003eEnterprise AI Prototyping\u003cbr data-end=\"6357\" data-start=\"6354\"\u003eEdge AI Deployments\u003cbr data-end=\"6379\" data-start=\"6376\"\u003eLocal AI Computing Without Cloud Dependency\u003c\/p\u003e\n\u003cp data-is-only-node=\"\" data-is-last-node=\"\" data-end=\"6707\" data-start=\"6426\"\u003e\u003cstrong data-end=\"6450\" data-start=\"6426\"\u003eOrdering Information\u003c\/strong\u003e\u003cbr data-end=\"6453\" data-start=\"6450\"\u003eModel: Gigabyte AI TOP ATOM Personal AI Computer 4TB\u003cbr data-end=\"6508\" data-start=\"6505\"\u003ePart Number: ATAGB10-9000\u003cbr data-end=\"6536\" data-start=\"6533\"\u003eConfiguration: NVIDIA GB10 \/ 128GB LPDDR5x \/ PCIe Gen5 4TB NVMe SSD\u003cbr\u003e\u003c\/p\u003e\n\u003c\/div\u003e\n\u003c\/div\u003e\n\u003c\/div\u003e\n\u003c\/div\u003e\n\u003c\/div\u003e\n\u003c\/div\u003e\n\u003c\/section\u003e\n\u003c\/div\u003e","brand":"Nvidia","offers":[{"title":"Default Title","offer_id":51211369414820,"sku":"ATAGB10-9000","price":9265.0,"currency_code":"SGD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0605\/0136\/0804\/files\/gigabyte-ai-top-atom-personal-ai-computer-4tb-3819840.jpg?v=1767711611"},{"product_id":"nvidia-jetson-agx-thor-128gb-developer-kit","title":"NVIDIA Jetson AGX THOR 128GB Developer Kit","description":"\u003ch2\u003e\n\u003cstrong\u003eNVIDIA Jetson AGX THOR 128GB Developer Kit (945-14070-0080-000) - 6 Months Local Warranty \u003c\/strong\u003e\n\u003c\/h2\u003e\n\u003ch3 data-start=\"281\" data-end=\"699\"\u003e\u003cstrong data-start=\"281\" data-end=\"331\"\u003eAI-Optimised Edge Computing Developer Platform\u003c\/strong\u003e\u003c\/h3\u003e\n\u003cp data-start=\"281\" data-end=\"699\"\u003eThe \u003cstrong data-start=\"338\" data-end=\"384\"\u003eNVIDIA Jetson AGX THOR 128GB Developer Kit\u003c\/strong\u003e is a powerful AI edge computing platform designed for robotics, autonomous machines, intelligent devices, and embedded AI applications. It delivers advanced compute performance for perception, navigation, path planning, sensor fusion, deep learning inference, and more — all within a compact developer form factor.\u003c\/p\u003e\n\u003ch3 data-start=\"701\" data-end=\"1043\"\u003e\u003cstrong data-start=\"701\" data-end=\"740\"\u003e128GB High-Capacity Onboard Storage\u003c\/strong\u003e\u003c\/h3\u003e\n\u003cp data-start=\"701\" data-end=\"1043\"\u003eWith \u003cstrong data-start=\"748\" data-end=\"776\"\u003e128GB of onboard storage\u003c\/strong\u003e, this kit offers room for large datasets, multi-model storage, and local data caching without needing external drives. The integrated storage supports faster I\/O and reduces latency in data-intensive workflows such as real-time analytics and multi-sensor processing.\u003c\/p\u003e\n\u003ch3 data-start=\"1045\" data-end=\"1378\"\u003e\u003cstrong data-start=\"1045\" data-end=\"1079\"\u003eNVIDIA-Powered AI Acceleration\u003c\/strong\u003e\u003c\/h3\u003e\n\u003cp data-start=\"1045\" data-end=\"1378\"\u003eBased on NVIDIA’s cutting-edge embedded architecture, Jetson AGX THOR accelerates deep learning, computer vision, and parallel workloads. Its GPU, AI processors, and CUDA cores work together to deliver high throughput and efficient inference performance for demanding AI applications at the edge.\u003c\/p\u003e\n\u003ch3 data-start=\"1380\" data-end=\"1787\"\u003e\u003cstrong data-start=\"1380\" data-end=\"1419\"\u003eFlexible Connectivity and Expansion\u003c\/strong\u003e\u003c\/h3\u003e\n\u003cp data-start=\"1380\" data-end=\"1787\"\u003eThe developer kit includes a broad set of high-speed I\/O interfaces suitable for robotics and machine designs: PCIe for external accelerators, USB for peripherals, Ethernet for network connectivity, and MIPI\/CSI camera interfaces for sensor integration. This lets developers build complete AI systems that integrate LiDAR, depth sensors, IMUs, and high-res cameras.\u003c\/p\u003e\n\u003ch3 data-start=\"1789\" data-end=\"2138\"\u003e\u003cstrong data-start=\"1789\" data-end=\"1834\"\u003eDeveloper-Friendly Software and Ecosystem\u003c\/strong\u003e\u003c\/h3\u003e\n\u003cp data-start=\"1789\" data-end=\"2138\"\u003eJetson AGX THOR supports NVIDIA JetPack SDK, CUDA, cuDNN, TensorRT, and popular AI frameworks, making it easy to prototype, optimise, and deploy AI models in real-world environments. The ecosystem accelerates development cycles and brings production-ready workflows from prototype to field deployment.\u003c\/p\u003e\n\u003ch3 data-start=\"2140\" data-end=\"2650\"\u003e\u003cstrong data-start=\"2140\" data-end=\"2188\"\u003eTechnical Specification (GTIN: 095185269580)\u003c\/strong\u003e\u003c\/h3\u003e\n\u003cp data-start=\"2140\" data-end=\"2650\"\u003e• \u003cstrong data-start=\"2193\" data-end=\"2239\"\u003eNVIDIA Jetson AGX THOR 128GB Developer Kit\u003c\/strong\u003e (945-14070-0080-000)\u003cbr data-start=\"2260\" data-end=\"2263\"\u003e• 128GB onboard storage for models and data\u003cbr data-start=\"2306\" data-end=\"2309\"\u003e• Integrated NVIDIA GPU with AI acceleration\u003cbr data-start=\"2353\" data-end=\"2356\"\u003e• High-bandwidth memory for parallel compute\u003cbr data-start=\"2400\" data-end=\"2403\"\u003e• High-speed I\/O: PCIe, USB, Ethernet, camera\/sensor interfaces\u003cbr data-start=\"2466\" data-end=\"2469\" data-is-only-node=\"\"\u003e• Supports NVIDIA JetPack, TensorRT, CUDA, and AI frameworks\u003cbr data-start=\"2529\" data-end=\"2532\"\u003e• Designed for robotics, autonomous systems, embedded AI\u003cbr data-start=\"2588\" data-end=\"2591\"\u003e• Production-grade performance with developer flexibility\u003c\/p\u003e\n","brand":"Nvidia","offers":[{"title":"Default Title","offer_id":51231647760548,"sku":"945-14070-0080-000","price":8925.0,"currency_code":"SGD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0605\/0136\/0804\/files\/nvidia-jetson-agx-thor-128gb-developer-kit-4322314.webp?v=1770255066"},{"product_id":"nvidia-rtx-pro™-5000-blackwell-generation","title":"NVIDIA RTX PRO™ 5000 Blackwell Generation","description":"\u003cdiv\u003e\n\u003cstyle\u003e\n.pd-acc { border: 1px solid #e2e2e2; border-radius: 8px; margin: 10px 0; overflow: hidden; }\n.pd-acc summary { cursor: pointer; padding: 14px 16px; font-weight: 700; font-size: 1.05em; list-style: none; display: flex; justify-content: space-between; align-items: center; background: #f7f7f7; }\n.pd-acc[open] \u003e summary { border-bottom: 1px solid #e2e2e2; }\n.pd-acc summary::-webkit-details-marker { display: none; }\n.pd-acc summary::after { content: \"+\"; font-size: 1.4em; font-weight: 400; line-height: 1; margin-left: 12px; }\n.pd-acc[open] \u003e summary::after { content: \"\\2212\"; }\n.pd-acc .pd-acc-body { padding: 8px 16px 14px; }\n.pd-spec, .pd-spec tr, .pd-spec td { border: none !important; background: transparent !important; }\n.pd-spec { width: 100%; border-collapse: collapse !important; margin: 6px 0 18px; }\n.pd-spec td { padding: 7px 10px 7px 0 !important; border-bottom: 1px solid #ececec !important; vertical-align: top; font-size: 0.95em; text-align: left; }\n.pd-spec td:first-child { width: 38%; font-weight: 600; color: #333; }\n.pd-spec tr:last-child td { border-bottom: none !important; }\n\u003c\/style\u003e\n\u003ch2\u003e\u003cstrong\u003eNVIDIA RTX PRO™ 5000 Blackwell Generation 72 GB GDDR7 with ECC (900-5G153-2570-000) - 3 Years Local Warranty\u003c\/strong\u003e\u003c\/h2\u003e\n\u003ch3\u003e\u003cstrong\u003eRTX PRO 5000 Blackwell 72 GB — 72 GB GDDR7 with ECC for Professional AI and Visualisation\u003c\/strong\u003e\u003c\/h3\u003e\n\u003cp\u003eThe \u003cstrong\u003eNVIDIA RTX PRO™ 5000 Blackwell\u003c\/strong\u003e pairs 72 GB GDDR7 with ECC with 1,344 GB\/s of memory bandwidth across a 384-bit interface and 14,080 CUDA cores. It is the balance point of the RTX PRO Blackwell desktop range: enough VRAM for serious model work and large scenes, in a standard 300 W card that fits a normal workstation.\u003c\/p\u003e\n\u003ch3\u003e\u003cstrong\u003e2,064 AI TOPS from Fifth-Generation Tensor Cores\u003c\/strong\u003e\u003c\/h3\u003e\n\u003cp\u003eFifth-generation Tensor Cores deliver 2,064 AI TOPS (effective FP4 TOPS with sparsity, peak rates based on GPU Boost Clock), with 65 TFLOPS of FP32 compute and 196 TFLOPS of fourth-generation RT Core performance for ray tracing and neural rendering. 3x NVENC (ninth generation) and 3x NVDEC (sixth generation) handle encode and decode for video and streaming pipelines.\u003c\/p\u003e\n\u003ch3\u003e\u003cstrong\u003eMulti-Instance GPU for Shared Workstations\u003c\/strong\u003e\u003c\/h3\u003e\n\u003cp\u003eMIG support lets this card be partitioned — up to 2x 36 GB or 1x 72 GB (maximum 2 instances) — so two users or two containerised workloads can share one GPU with isolated memory and compute rather than contending for it.\u003c\/p\u003e\n\u003ch3\u003e\u003cstrong\u003eStandard Workstation Fit, Backed Locally\u003c\/strong\u003e\u003c\/h3\u003e\n\u003cp\u003e4.4in (H) x 10.5in (L), dual slot, full height with active cooling on a PCIe 5.0 x16 interface, powered through a 1x PCIe CEM5 16-pin connector. Supplied by SourceIT Pte Ltd in Singapore with a GST invoice and \u003cstrong\u003e3 years local warranty\u003c\/strong\u003e — supported here rather than through an overseas RMA queue.\u003c\/p\u003e\n\u003cdetails class=\"pd-acc\"\u003e\u003csummary style=\"cursor: pointer; font-weight: 700;\"\u003eNVIDIA RTX PRO™ 5000 Blackwell Datasheet — Technical Specifications\u003c\/summary\u003e\u003cdiv class=\"pd-acc-body\"\u003e\n\u003ch4\u003eGeneral\u003c\/h4\u003e\n\u003ctable class=\"pd-spec\"\u003e\n\u003ctr\u003e\n\u003ctd\u003ePart Number\u003c\/td\u003e\n\u003ctd\u003e900-5G153-2570-000\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eModel\u003c\/td\u003e\n\u003ctd\u003eNVIDIA RTX PRO™ 5000 Blackwell\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eGPU Architecture\u003c\/td\u003e\n\u003ctd\u003eNVIDIA Blackwell\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eProduct Family\u003c\/td\u003e\n\u003ctd\u003eNVIDIA RTX PRO Blackwell Workstation \/ Professional Desktop GPUs\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eWarranty\u003c\/td\u003e\n\u003ctd\u003e3 Years Local Warranty (Singapore)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/table\u003e\n\u003ch4\u003eMemory\u003c\/h4\u003e\n\u003ctable class=\"pd-spec\"\u003e\n\u003ctr\u003e\n\u003ctd\u003eGPU Memory\u003c\/td\u003e\n\u003ctd\u003e72 GB GDDR7 with ECC\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eMemory Interface\u003c\/td\u003e\n\u003ctd\u003e384-bit\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eMemory Bandwidth\u003c\/td\u003e\n\u003ctd\u003e1,344 GB\/s\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eECC\u003c\/td\u003e\n\u003ctd\u003eYes — error-correcting code memory\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/table\u003e\n\u003ch4\u003eCompute \u0026amp; Performance\u003c\/h4\u003e\n\u003ctable class=\"pd-spec\"\u003e\n\u003ctr\u003e\n\u003ctd\u003eCUDA Cores\u003c\/td\u003e\n\u003ctd\u003e14,080\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eTensor Cores\u003c\/td\u003e\n\u003ctd\u003eFifth-generation Tensor Cores\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eRT Cores\u003c\/td\u003e\n\u003ctd\u003eFourth-generation RT Cores\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eSingle-Precision (FP32) Performance\u003c\/td\u003e\n\u003ctd\u003e65 TFLOPS\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eRT Core Performance\u003c\/td\u003e\n\u003ctd\u003e196 TFLOPS\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eAI Performance\u003c\/td\u003e\n\u003ctd\u003e2,064 AI TOPS (effective FP4 TOPS with sparsity, peak rates based on GPU Boost Clock)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eMulti-Instance GPU (MIG)\u003c\/td\u003e\n\u003ctd\u003eYes — up to 2x 36 GB or 1x 72 GB (maximum 2 instances)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/table\u003e\n\u003ch4\u003eMedia Engines\u003c\/h4\u003e\n\u003ctable class=\"pd-spec\"\u003e\n\u003ctr\u003e\n\u003ctd\u003eEncoders (NVENC)\u003c\/td\u003e\n\u003ctd\u003e3x NVENC (ninth generation)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eDecoders (NVDEC)\u003c\/td\u003e\n\u003ctd\u003e3x NVDEC (sixth generation)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/table\u003e\n\u003ch4\u003eDisplay\u003c\/h4\u003e\n\u003ctable class=\"pd-spec\"\u003e\n\u003ctr\u003e\n\u003ctd\u003eDisplay Connectors\u003c\/td\u003e\n\u003ctd\u003e4x DisplayPort 2.1b\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eMaximum Simultaneous Displays\u003c\/td\u003e\n\u003ctd\u003e4x 4096 x 2160 @ 120 Hz; 4x 5120 x 2880 @ 60 Hz; 2x 7680 x 4320 @ 60 Hz\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/table\u003e\n\u003ch4\u003ePower, Form Factor \u0026amp; Interface\u003c\/h4\u003e\n\u003ctable class=\"pd-spec\"\u003e\n\u003ctr\u003e\n\u003ctd\u003eMaximum Power Consumption\u003c\/td\u003e\n\u003ctd\u003e300 W total board power\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eGraphics Bus\u003c\/td\u003e\n\u003ctd\u003ePCIe 5.0 x16\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eForm Factor\u003c\/td\u003e\n\u003ctd\u003e4.4in (H) x 10.5in (L), dual slot, full height\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eThermal Solution\u003c\/td\u003e\n\u003ctd\u003eActive\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ePower Connector\u003c\/td\u003e\n\u003ctd\u003e1x PCIe CEM5 16-pin\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/table\u003e\n\u003cp\u003e\u003cem\u003eSpecifications are taken from NVIDIA's published datasheet for this model. Fields NVIDIA does not publish for a given card are shown as such rather than estimated.\u003c\/em\u003e\u003c\/p\u003e\n\u003c\/div\u003e\u003c\/details\u003e\u003cdetails class=\"pd-acc\"\u003e\u003csummary style=\"cursor: pointer; font-weight: 700;\"\u003eCompatibility \u0026amp; Software Support\u003c\/summary\u003e\u003cdiv class=\"pd-acc-body\"\u003e\n\u003ctable class=\"pd-spec\"\u003e\n\u003ctr\u003e\n\u003ctd\u003eGraphics APIs\u003c\/td\u003e\n\u003ctd\u003eDirectX 12 (Shader Model 6.6), OpenGL 4.6, Vulkan 1.4\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eCompute APIs\u003c\/td\u003e\n\u003ctd\u003eCUDA 12.8, OpenCL 3.0, DirectCompute\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eOperating Systems\u003c\/td\u003e\n\u003ctd\u003eWindows 10\/11 64-bit; Linux — RHEL 8.10, SUSE Linux Enterprise Desktop 15.6, OpenSUSE 15.6, Fedora 41, Ubuntu 22.04; FreeBSD 14.2; Solaris 11 U4 (per NVIDIA RTX PRO Blackwell Quick Start Guide)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eDrivers\u003c\/td\u003e\n\u003ctd\u003eNVIDIA RTX Enterprise Driver — Production Branch and New Feature Branch\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eProfessional Applications\u003c\/td\u003e\n\u003ctd\u003eCertified across leading CAD, DCC, BIM, medical imaging and simulation ISV applications\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eAI Frameworks\u003c\/td\u003e\n\u003ctd\u003eCUDA, cuDNN, TensorRT, PyTorch, TensorFlow, NVIDIA NIM and NGC containers\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eOmniverse \/ Digital Twin\u003c\/td\u003e\n\u003ctd\u003eSupported via NVIDIA Omniverse and OpenUSD workflows\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eChassis Check\u003c\/td\u003e\n\u003ctd\u003e4.4in (H) x 10.5in (L), dual slot, full height — confirm slot clearance, power headroom and airflow before ordering\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/table\u003e\n\u003cp\u003eSourceIT can verify fit against your specific workstation or server model before you order. For ISV certification status on a named application version, check the NVIDIA \u003ca href=\"https:\/\/www.nvidia.com\/en-us\/products\/workstations\/product-literature\/\" target=\"_blank\" rel=\"noopener\"\u003eprofessional product literature library\u003c\/a\u003e.\u003c\/p\u003e\n\u003c\/div\u003e\u003c\/details\u003e\u003cdetails class=\"pd-acc\"\u003e\u003csummary style=\"cursor: pointer; font-weight: 700;\"\u003ePackage Contents \u0026amp; NVIDIA RTX PRO Blackwell Ordering Information\u003c\/summary\u003e\u003cdiv class=\"pd-acc-body\"\u003e\n\u003cp\u003e\u003cstrong\u003eWhat is in the box:\u003c\/strong\u003e NVIDIA's RTX PRO Blackwell Quick Start Guide lists a Quick Start Card and an auxiliary power cable (2x PCIe 8-pin PSU to 1x PCIe Gen5 16-pin GPU adapter). NVIDIA does not publish a per-SKU box-contents list on the datasheet.\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eNVIDIA RTX PRO Blackwell range available from SourceIT:\u003c\/strong\u003e\u003c\/p\u003e\n\u003ctable class=\"pd-spec\"\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003ePart Number\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e\u003cstrong\u003eModel \u0026amp; Memory\u003c\/strong\u003e\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e900-5G144-2500-000\u003c\/td\u003e\n\u003ctd\u003e\n\u003ca href=\"\/products\/nvidia-rtx-pro%E2%84%A2-6000-blackwell-workstation-edition-900-5g144-2500-000\"\u003eRTX PRO 6000 Blackwell Workstation Edition\u003c\/a\u003e — 96 GB GDDR7 with ECC\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e900-5G153-2500-000\u003c\/td\u003e\n\u003ctd\u003e\n\u003ca href=\"\/products\/nvidia-rtx-pro%E2%84%A2-6000-blackwell-max-q-workstation-edition-900-5g153-2500-000\"\u003eRTX PRO 6000 Blackwell Max-Q Workstation Edition\u003c\/a\u003e — 96 GB GDDR7 with ECC\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e900-2G153-0000-000\u003c\/td\u003e\n\u003ctd\u003e\n\u003ca href=\"\/products\/nvidia-rtx-pro%E2%84%A2-6000-blackwell-server-edition\"\u003eRTX PRO 6000 Blackwell Server Edition\u003c\/a\u003e — 96 GB GDDR7 with ECC\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e900-5G153-2570-000\u003c\/td\u003e\n\u003ctd\u003e\u003cstrong\u003eRTX PRO 5000 Blackwell 72 GB — 72 GB GDDR7 with ECC (this item)\u003c\/strong\u003e\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e900-5G153-2550-000\u003c\/td\u003e\n\u003ctd\u003e\n\u003ca href=\"\/products\/nvidia-rtx-pro%E2%84%A2-5000-blackwell-generation-900-5g153-2550-000\"\u003eRTX PRO 5000 Blackwell 48 GB\u003c\/a\u003e — 48 GB GDDR7 with ECC\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e900-5G147-2550-000\u003c\/td\u003e\n\u003ctd\u003e\n\u003ca href=\"\/products\/nvidia-rtx-pro%E2%84%A2-4500-blackwell-generation-900-5g147-2550-000\"\u003eRTX PRO 4500 Blackwell\u003c\/a\u003e — 32 GB GDDR7 with ECC\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e900-5G147-2570-000\u003c\/td\u003e\n\u003ctd\u003e\n\u003ca href=\"\/products\/nvidia-rtx-pro%E2%84%A2-4000-blackwell-generation-900-5g147-2570-000\"\u003eRTX PRO 4000 Blackwell\u003c\/a\u003e — 24 GB GDDR7 with ECC\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e900-5G195-2501-000\u003c\/td\u003e\n\u003ctd\u003e\n\u003ca href=\"\/products\/nvidia-rtx-pro%E2%84%A2-4000-blackwell-generation-sff-edition-900-5g195-2501-000\"\u003eRTX PRO 4000 Blackwell SFF Edition\u003c\/a\u003e — 24 GB GDDR7 with ECC\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e900-5G195-2551-000\u003c\/td\u003e\n\u003ctd\u003e\n\u003ca href=\"\/products\/nvidia-rtx-pro%E2%84%A2-2000-blackwell-generation-900-5g195-2551-000\"\u003eRTX PRO 2000 Blackwell\u003c\/a\u003e — 16 GB GDDR7 with ECC\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/table\u003e\n\u003cp\u003eChoosing between them: memory capacity is usually the deciding factor for AI work and scene size, while form factor and power decide what will physically fit. The \u003cstrong\u003eMax-Q\u003c\/strong\u003e variant exists to give full 96 GB capacity at 300 W for chassis that cannot take a 600 W card or that need more than one GPU. The \u003cstrong\u003eServer Edition\u003c\/strong\u003e is passively cooled and belongs in a server, not a workstation. The \u003cstrong\u003eSFF Edition\u003c\/strong\u003e trades bandwidth and PCIe lanes for a 70 W low-profile board.\u003c\/p\u003e\n\u003c\/div\u003e\u003c\/details\u003e\u003cdetails class=\"pd-acc\"\u003e\u003csummary style=\"cursor: pointer; font-weight: 700;\"\u003eDatasheet \u0026amp; Downloads\u003c\/summary\u003e\u003cdiv class=\"pd-acc-body\"\u003e\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https:\/\/www.nvidia.com\/content\/dam\/en-zz\/Solutions\/products\/workstations\/professional-desktop-gpus\/rtx-pro-5000-blackwell\/workstation-datasheet-blackwell-rtx-pro-5000-5488550-nvidia.pdf\" target=\"_blank\" rel=\"noopener\"\u003eNVIDIA RTX PRO™ 5000 Blackwell official NVIDIA datasheet\u003c\/a\u003e\u003c\/li\u003e\n\u003cli\u003e\u003ca href=\"https:\/\/www.nvidia.com\/en-us\/products\/workstations\/professional-desktop-gpus\/rtx-pro-5000\/\" target=\"_blank\" rel=\"noopener\"\u003eNVIDIA RTX PRO™ 5000 Blackwell product page on NVIDIA.com\u003c\/a\u003e\u003c\/li\u003e\n\u003cli\u003e\u003ca href=\"https:\/\/www.nvidia.com\/content\/dam\/en-zz\/Solutions\/design-visualization\/quadro-product-literature\/NVIDIA-RTX-Blackwell-PRO-GPU-Architecture-v1.0.pdf\" target=\"_blank\" rel=\"noopener\"\u003eNVIDIA RTX PRO Blackwell GPU Architecture Whitepaper (PDF)\u003c\/a\u003e\u003c\/li\u003e\n\u003cli\u003e\u003ca href=\"https:\/\/images.nvidia.com\/aem-dam\/Solutions\/design-visualization\/quadro-product-literature\/rtx-pro-blackwell-online-qsg-210x148mm-20260317-r18.pdf\" target=\"_blank\" rel=\"noopener\"\u003eNVIDIA RTX PRO Blackwell Quick Start Guide (PDF)\u003c\/a\u003e\u003c\/li\u003e\n\u003cli\u003e\u003ca href=\"https:\/\/www.nvidia.com\/en-us\/products\/workstations\/product-literature\/\" target=\"_blank\" rel=\"noopener\"\u003eNVIDIA professional workstation product literature library\u003c\/a\u003e\u003c\/li\u003e\n\u003c\/ul\u003e\u003c\/div\u003e\u003c\/details\u003e\u003cdetails class=\"pd-acc\"\u003e\u003csummary style=\"cursor: pointer; font-weight: 700;\"\u003eFrequently Asked Questions — RTX PRO 5000 Blackwell 72 GB\u003c\/summary\u003e\u003cdiv class=\"pd-acc-body\"\u003e\n\u003cp\u003e\u003cstrong\u003eHow much GPU memory does it have, and why does that matter?\u003c\/strong\u003e\u003cbr\u003e72 GB GDDR7 with ECC. For AI work, VRAM capacity decides which models fit on the card at all — a model that does not fit runs an order of magnitude slower or not at all. For visualisation it decides scene and texture budget. ECC means single-bit memory errors are corrected rather than silently corrupting a long job.\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eHow does it compare to the RTX PRO 4000 Blackwell?\u003c\/strong\u003e\u003cbr\u003eThe \u003ca href=\"\/products\/nvidia-rtx-pro%E2%84%A2-4000-blackwell-generation-900-5g147-2570-000\"\u003eRTX PRO 4000\u003c\/a\u003e steps up to 24 GB of GDDR7, 8,960 CUDA cores, 672 GB\/s and 1,178 AI TOPS at 145 W. The RTX PRO 2000 delivers 16 GB, 4,352 cores, 288 GB\/s and 545 AI TOPS at 70 W. If your models and scenes fit in 16 GB, the 2000 is the efficient choice; if they do not, no amount of compute compensates.\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eWhat power supply and chassis clearance do I need?\u003c\/strong\u003e\u003cbr\u003eThis card is 300 w total board power in a 4.4in (H) x 10.5in (L), dual slot, full height form factor with active cooling, powered via 1x PCIe CEM5 16-pin. Check both physical clearance and PSU headroom against your workstation before ordering — send us your machine model and we will confirm fit.\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eDoes it support Multi-Instance GPU?\u003c\/strong\u003e\u003cbr\u003eYes — up to 2x 36 GB or 1x 72 GB (maximum 2 instances). Each instance gets isolated memory and compute, so several users, containers or services can share one card with hard boundaries instead of contending for it.\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eIs this a professional card or a gaming card?\u003c\/strong\u003e\u003cbr\u003eProfessional. RTX PRO Blackwell cards carry ECC memory, NVIDIA RTX Enterprise drivers with ISV application certification, longer driver support branches and a professional warranty. Consumer GeForce cards have none of these, which is why they are not accepted in most enterprise and regulated deployments regardless of raw frame rate.\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eDoes it come with local warranty in Singapore?\u003c\/strong\u003e\u003cbr\u003eYes. Every RTX PRO 5000 Blackwell 72 GB sold by SourceIT comes with 3 years local Singapore warranty and a GST invoice.\u003c\/p\u003e\n\u003c\/div\u003e\u003c\/details\u003e\n\u003c\/div\u003e","brand":"Nvidia","offers":[{"title":"Default Title","offer_id":51231648120996,"sku":"900-5G153-2570-000","price":21165.0,"currency_code":"SGD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0605\/0136\/0804\/files\/nvidia-rtx-pro-5000-blackwell-generation-3270248.webp?v=1770349927"},{"product_id":"dell-pro-max-16-mc16250-u7-265h-vpro-rtx-pro-500-16g-512gb-ssd","title":"Dell Pro Max 16 MC16250 U7-265H vPro\/ RTX PRO 500\/ 16G\/ 512GB SSD","description":"\u003ch2\u003e\u003cstrong\u003e\u003cspan\u003eDell Pro Max 16 MC16250 U7-265H vPro\/RTX PRO 500\/16G\/512GB SSD (DPMMC16250C716G512DIS) \u003c\/span\u003e- 3 Year Local Onsite Warranty\u003c\/strong\u003e\u003c\/h2\u003e\n\u003cp\u003e\u003cstrong\u003e\u003c\/strong\u003e\u003c\/p\u003e\n\u003ch3 data-start=\"282\" data-end=\"331\"\u003e\u003cstrong data-start=\"282\" data-end=\"331\"\u003eHigh-Performance Intel Core Ultra 7 with vPro\u003c\/strong\u003e\u003c\/h3\u003e\n\u003cp data-start=\"333\" data-end=\"735\"\u003eThe \u003cstrong data-start=\"337\" data-end=\"388\"\u003eDell Pro Max 16 MC16250 (DPMMC16250C716G512DIS)\u003c\/strong\u003e is powered by the \u003cstrong data-start=\"407\" data-end=\"449\"\u003eIntel Core Ultra 7 265H vPro processor\u003c\/strong\u003e, delivering up to 16 cores with turbo speeds around 5.3GHz. This platform includes AI acceleration (NPU) and enterprise-grade vPro manageability, making it ideal for corporate deployments, remote management, and high-performance computing tasks. \u003cspan class=\"\" data-state=\"closed\"\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003ch3 data-start=\"737\" data-end=\"791\"\u003e\u003cstrong data-start=\"737\" data-end=\"791\"\u003eProfessional RTX PRO 500 GPU for Workstation Tasks\u003c\/strong\u003e\u003c\/h3\u003e\n\u003cp data-start=\"793\" data-end=\"1195\"\u003eEquipped with the \u003cstrong data-start=\"811\" data-end=\"856\"\u003eNVIDIA RTX PRO 500 (Blackwell generation)\u003c\/strong\u003e GPU with dedicated GDDR7 memory, this system is built for professional workloads such as 3D rendering, CAD, AI model inference, and content creation. It offers significantly better stability and optimisation compared to consumer GPUs, making it suitable for engineers, designers, and AI developers. \u003cspan class=\"\" data-state=\"closed\"\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003ch3 data-start=\"1197\" data-end=\"1246\"\u003e\u003cstrong data-start=\"1197\" data-end=\"1246\"\u003eEfficient Multitasking with DDR5 and Fast SSD\u003c\/strong\u003e\u003c\/h3\u003e\n\u003cp data-start=\"1248\" data-end=\"1560\"\u003eThis configuration includes \u003cstrong data-start=\"1276\" data-end=\"1293\"\u003e16GB DDR5 RAM\u003c\/strong\u003e and a \u003cstrong data-start=\"1300\" data-end=\"1323\"\u003e512GB PCIe NVMe SSD\u003c\/strong\u003e, providing fast system responsiveness, quick boot times, and efficient multitasking. It is well-suited for running multiple enterprise applications, virtual environments, and data-heavy workflows. \u003cspan class=\"\" data-state=\"closed\"\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003ch3 data-start=\"1562\" data-end=\"1611\"\u003e\u003cstrong data-start=\"1562\" data-end=\"1611\"\u003e16-Inch Professional Display for Productivity\u003c\/strong\u003e\u003c\/h3\u003e\n\u003cp data-start=\"1613\" data-end=\"1889\"\u003eThe laptop features a \u003cstrong data-start=\"1635\" data-end=\"1673\"\u003e16-inch FHD+ (1920 x 1200) display\u003c\/strong\u003e, offering a larger workspace with better vertical viewing compared to standard FHD panels. This improves productivity for multitasking, coding, design work, and data analysis. \u003cspan class=\"\" data-state=\"closed\"\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003ch3 data-start=\"1891\" data-end=\"1945\"\u003e\u003cstrong data-start=\"1891\" data-end=\"1945\"\u003eEnterprise-Grade Design with Advanced Connectivity\u003c\/strong\u003e\u003c\/h3\u003e\n\u003cp data-start=\"1947\" data-end=\"2273\"\u003eBuilt as a mobile workstation, the Dell Pro Max 16 supports advanced connectivity including USB-C, Thunderbolt, HDMI, Ethernet, WiFi, and optional 5G. Combined with enterprise security and durability, it is designed for professionals who need performance both in-office and on the move. \u003cspan class=\"\" data-state=\"closed\"\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003ch3 data-end=\"2270\" data-start=\"2242\"\u003e\u003cstrong data-end=\"2270\" data-start=\"2242\"\u003eTechnical Specifications\u003c\/strong\u003e\u003c\/h3\u003e\n\u003cp data-start=\"131\" data-end=\"368\"\u003e\u003cstrong data-start=\"131\" data-end=\"144\"\u003eProcessor\u003c\/strong\u003e\u003cbr data-start=\"144\" data-end=\"147\"\u003e• Intel Core Ultra 7 265H (vPro Enterprise)\u003cbr data-start=\"190\" data-end=\"193\"\u003e• Up to ~5.3GHz Turbo Boost\u003cbr data-start=\"220\" data-end=\"223\"\u003e• 16 cores, 16 threads\u003cbr data-start=\"245\" data-end=\"248\"\u003e• 24MB Intel Smart Cache\u003cbr data-start=\"272\" data-end=\"275\"\u003e• Integrated AI Boost NPU (~13 TOPS) for AI workloads \u003cspan class=\"\" data-state=\"closed\"\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp data-start=\"370\" data-end=\"607\"\u003e\u003cstrong data-start=\"370\" data-end=\"398\"\u003eGraphics (Dedicated GPU)\u003c\/strong\u003e\u003cbr data-start=\"398\" data-end=\"401\"\u003e• NVIDIA RTX PRO 500 (Blackwell Generation)\u003cbr data-start=\"444\" data-end=\"447\"\u003e• 6GB GDDR7 VRAM\u003cbr data-start=\"463\" data-end=\"466\"\u003e• AI acceleration up to ~75 TOPS\u003cbr data-start=\"498\" data-end=\"501\"\u003e• Designed for CAD, AI workloads, rendering, and professional apps \u003cspan class=\"\" data-state=\"closed\"\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp data-start=\"609\" data-end=\"777\"\u003e\u003cstrong data-start=\"609\" data-end=\"619\"\u003eMemory\u003c\/strong\u003e\u003cbr data-start=\"619\" data-end=\"622\"\u003e• 16GB DDR5 RAM (2 x 8GB)\u003cbr data-start=\"647\" data-end=\"650\"\u003e• Memory speed up to 5600 MT\/s\u003cbr data-start=\"680\" data-end=\"683\"\u003e• Dual-channel configuration\u003cbr data-start=\"711\" data-end=\"714\"\u003e• Expandable up to 64GB \u003cspan class=\"\" data-state=\"closed\"\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp data-start=\"779\" data-end=\"953\"\u003e\u003cstrong data-start=\"779\" data-end=\"790\"\u003eStorage\u003c\/strong\u003e\u003cbr data-start=\"790\" data-end=\"793\"\u003e• 512GB M.2 PCIe Gen4 NVMe SSD\u003cbr data-start=\"823\" data-end=\"826\"\u003e• Fast boot and high-speed data access\u003cbr data-start=\"864\" data-end=\"867\"\u003e• Supports additional SSD expansion (dual M.2) \u003cspan class=\"\" data-state=\"closed\"\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp data-start=\"955\" data-end=\"1137\"\u003e\u003cstrong data-start=\"955\" data-end=\"966\"\u003eDisplay\u003c\/strong\u003e\u003cbr data-start=\"966\" data-end=\"969\"\u003e• 16\" WUXGA (1920 x 1200) IPS display\u003cbr data-start=\"1006\" data-end=\"1009\"\u003e• Anti-glare, non-touch\u003cbr data-start=\"1032\" data-end=\"1035\"\u003e• Brightness ~300 nits\u003cbr data-start=\"1057\" data-end=\"1060\"\u003e• 16:10 aspect ratio for productivity \u003cspan class=\"\" data-state=\"closed\"\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp data-start=\"1139\" data-end=\"1238\"\u003e\u003cstrong data-start=\"1139\" data-end=\"1159\"\u003eOperating System\u003c\/strong\u003e\u003cbr data-start=\"1159\" data-end=\"1162\"\u003e• Windows 11 Pro (business-ready OS) \u003cspan class=\"\" data-state=\"closed\"\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp data-start=\"1240\" data-end=\"1419\"\u003e\u003cstrong data-start=\"1240\" data-end=\"1264\"\u003eForm Factor \u0026amp; Design\u003c\/strong\u003e\u003cbr data-start=\"1264\" data-end=\"1267\"\u003e• 16-inch mobile workstation\u003cbr data-start=\"1295\" data-end=\"1298\"\u003e• Premium enterprise chassis\u003cbr data-start=\"1326\" data-end=\"1329\"\u003e• Designed for durability and heavy workloads\u003cbr data-start=\"1374\" data-end=\"1377\"\u003e• Portable workstation-class performance\u003c\/p\u003e\n\u003cp data-start=\"1421\" data-end=\"1571\"\u003e\u003cstrong data-start=\"1421\" data-end=\"1443\"\u003eChipset \/ Platform\u003c\/strong\u003e\u003cbr data-start=\"1443\" data-end=\"1446\"\u003e• Intel vPro Enterprise platform\u003cbr data-start=\"1478\" data-end=\"1481\"\u003e• Advanced remote management and security features \u003cspan class=\"\" data-state=\"closed\"\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp data-start=\"1573\" data-end=\"1848\"\u003e\u003cstrong data-start=\"1573\" data-end=\"1597\"\u003ePorts \u0026amp; Connectivity\u003c\/strong\u003e\u003cbr data-start=\"1597\" data-end=\"1600\"\u003e• USB-C \/ Thunderbolt ports (high-speed data + display + power)\u003cbr data-start=\"1663\" data-end=\"1666\"\u003e• Multiple USB-A ports\u003cbr data-start=\"1688\" data-end=\"1691\"\u003e• HDMI output\u003cbr data-start=\"1704\" data-end=\"1707\"\u003e• RJ-45 Ethernet\u003cbr data-start=\"1723\" data-end=\"1726\"\u003e• Audio combo jack\u003cbr data-start=\"1744\" data-end=\"1747\"\u003e• LAN: Gigabit Ethernet\u003cbr data-start=\"1770\" data-end=\"1773\"\u003e• Wireless: WiFi 6E + Bluetooth 5.x \u003cspan class=\"\" data-state=\"closed\"\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp data-start=\"1850\" data-end=\"1971\"\u003e\u003cstrong data-start=\"1850\" data-end=\"1871\"\u003eExpansion \u0026amp; Slots\u003c\/strong\u003e\u003cbr data-start=\"1871\" data-end=\"1874\"\u003e• Dual M.2 SSD slots\u003cbr data-start=\"1894\" data-end=\"1897\"\u003e• Upgradeable RAM (SODIMM)\u003cbr data-start=\"1923\" data-end=\"1926\"\u003e• Modular workstation configuration support\u003c\/p\u003e\n\u003cp data-start=\"1973\" data-end=\"2119\"\u003e\u003cstrong data-start=\"1973\" data-end=\"1991\"\u003eAudio \u0026amp; Camera\u003c\/strong\u003e\u003cbr data-start=\"1991\" data-end=\"1994\"\u003e• Integrated HD audio\u003cbr data-start=\"2015\" data-end=\"2018\"\u003e• Dual microphones\u003cbr data-start=\"2036\" data-end=\"2039\"\u003e• FHD IR webcam (supports Windows Hello) \u003cspan class=\"\" data-state=\"closed\"\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp data-start=\"2121\" data-end=\"2295\"\u003e\u003cstrong data-start=\"2121\" data-end=\"2152\"\u003eSecurity (Enterprise Grade)\u003c\/strong\u003e\u003cbr data-start=\"2152\" data-end=\"2155\"\u003e• TPM 2.0 hardware security\u003cbr data-start=\"2182\" data-end=\"2185\"\u003e• Intel vPro security features\u003cbr data-start=\"2215\" data-end=\"2218\"\u003e• IR camera for facial recognition\u003cbr data-start=\"2252\" data-end=\"2255\"\u003e• Enterprise-grade endpoint protection\u003c\/p\u003e\n\u003cp data-start=\"2297\" data-end=\"2471\"\u003e\u003cstrong data-start=\"2297\" data-end=\"2316\"\u003eBattery \u0026amp; Power\u003c\/strong\u003e\u003cbr data-start=\"2316\" data-end=\"2319\"\u003e• 6-cell battery (mobile workstation class)\u003cbr data-start=\"2362\" data-end=\"2365\"\u003e• Fast charging support\u003cbr data-start=\"2388\" data-end=\"2391\"\u003e• High-performance power adapter (~130W) \u003cspan class=\"\" data-state=\"closed\"\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp data-start=\"2473\" data-end=\"2638\"\u003e\u003cstrong data-start=\"2473\" data-end=\"2505\"\u003ePhysical Dimensions \u0026amp; Weight\u003c\/strong\u003e\u003cbr data-start=\"2505\" data-end=\"2508\"\u003e• Approx. 16-inch chassis\u003cbr data-start=\"2533\" data-end=\"2536\"\u003e• Weight ~2.2 kg\u003cbr data-start=\"2552\" data-end=\"2555\"\u003e• Portable for a workstation-class device \u003cspan class=\"\" data-state=\"closed\"\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp data-start=\"2640\" data-end=\"2864\"\u003e\u003cstrong data-start=\"2640\" data-end=\"2652\"\u003eUse Case\u003c\/strong\u003e\u003cbr data-start=\"2652\" data-end=\"2655\"\u003e• Engineering (CAD, BIM, SolidWorks)\u003cbr data-start=\"2691\" data-end=\"2694\"\u003e• AI \/ machine learning workloads (entry–mid level)\u003cbr data-start=\"2745\" data-end=\"2748\"\u003e• Video editing and content creation\u003cbr data-start=\"2784\" data-end=\"2787\"\u003e• Data analysis and simulation\u003cbr data-start=\"2817\" data-end=\"2820\"\u003e• Enterprise mobile workstation deployment\u003cstrong data-end=\"2283\" data-start=\"2274\"\u003e\u003c\/strong\u003e\u003c\/p\u003e\n\u003ch3\u003e\u003cstrong\u003eDetailed Specification:\u003c\/strong\u003e\u003c\/h3\u003e\n\u003cp\u003eDell Pro Max 16 (MC16250) XCTO Base\u003cbr\u003eIntel Core Ultra 7 265H Processor with vPro\u003cbr\u003eFHD+IR Camera, HDR\u003cbr\u003eDell Pro 14-16 Plus EcoLoop Slim Backpack? - CP5724S\u003cbr\u003eIntel Core Ultra 7 265H, vPro Enterprise (24MB, 16 cores, 16 threads, up to 5.30 GHz Turbo, 45W)\u003cbr\u003e16 FHD+ LCD with 300 nits, Non-touch, FHD HDR IR Camera, Microphone, WLAN\u003cbr\u003eDell Pro Max 16 Bottom Door, Discrete\u003cbr\u003eNVIDIA RTX PRO 500 Blackwell 6GB GDDR7\u003cbr\u003e16GB: 2x8GB, DDR5, 5600 MT\/s, SoDIMM, Dual Channel, non-ECC\u003cbr\u003ePalmrest, FPR+CV3\u003cbr\u003e6 cell, 96Whr, ExpressCharge(TM) Capable, standard battery\u003cbr\u003e512GB, M.2 2230, Gen4 PCIe NVMe, SSD, Class 35\u003cbr\u003eEnglish US backlit Copilot key keyboard with numeric keypad\u003cbr\u003eWindows 11 Pro\u003cbr\u003eIntel Wi-Fi 6E AX211, 2x2, 802.11ax, Bluetooth 5.3 wireless card\u003cbr\u003eIntel vPro Enterprise Technology Enabled\u003cbr\u003e3 Years ProSupport Warranty\u003c\/p\u003e","brand":"Dell","offers":[{"title":"Default Title","offer_id":51300048830628,"sku":"DPMMC16250C716G512DIS","price":3330.0,"currency_code":"SGD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0605\/0136\/0804\/files\/dell-pro-max-16-mc16250-u7-265h-vpro-rtx-pro-500-32g-1tb-ssd-6509459.jpg?v=1775832612"},{"product_id":"dell-pro-max-16-mc16250-u7-265h-vpro-rtx-pro-500-32g-1tb-ssd","title":"Dell Pro Max 16 MC16250 U7-265H vPro \/RTX PRO 500 \/32G \/1TB SSD","description":"\u003ch2\u003e\u003cstrong\u003e\u003cspan\u003eDell Pro Max 16 MC16250 U7-265H vPro \/RTX PRO 500 \/32G \/1TB SSD (DPMMC16250C732G1TBDIS) \u003c\/span\u003e- 3 Year Local Onsite Warranty\u003c\/strong\u003e\u003c\/h2\u003e\n\u003cp\u003e\u003cstrong\u003e\u003c\/strong\u003e\u003c\/p\u003e\n\u003ch3 data-start=\"283\" data-end=\"335\"\u003e\u003cstrong data-start=\"283\" data-end=\"335\"\u003eIntel Core Ultra 7 with vPro and AI Acceleration\u003c\/strong\u003e\u003c\/h3\u003e\n\u003cp data-start=\"337\" data-end=\"708\"\u003eThe \u003cstrong data-start=\"341\" data-end=\"392\"\u003eDell Pro Max 16 MC16250 (DPMMC16250C732G1TBDIS)\u003c\/strong\u003e is powered by the \u003cstrong data-start=\"411\" data-end=\"453\"\u003eIntel Core Ultra 7 265H vPro processor\u003c\/strong\u003e, delivering up to 16 cores and boost speeds around 5.3GHz. It includes an integrated NPU (~13 TOPS) for AI acceleration, making it suitable for modern enterprise workloads, automation, and AI-assisted applications. \u003cspan class=\"\" data-state=\"closed\"\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003ch3 data-start=\"710\" data-end=\"764\"\u003e\u003cstrong data-start=\"710\" data-end=\"764\"\u003eProfessional RTX PRO 500 GPU for Workstation Tasks\u003c\/strong\u003e\u003c\/h3\u003e\n\u003cp data-start=\"766\" data-end=\"1090\"\u003eEquipped with the \u003cstrong data-start=\"784\" data-end=\"840\"\u003eNVIDIA RTX PRO 500 (Blackwell generation, 6GB GDDR7)\u003c\/strong\u003e, this system is built for professional workloads such as 3D rendering, CAD, AI inference, and content creation. It offers certified drivers and stability required for enterprise and ISV-supported applications. \u003cspan class=\"\" data-state=\"closed\"\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003ch3 data-start=\"1092\" data-end=\"1144\"\u003e\u003cstrong data-start=\"1092\" data-end=\"1144\"\u003eEnhanced Multitasking with 32GB DDR5 and 1TB SSD\u003c\/strong\u003e\u003c\/h3\u003e\n\u003cp data-start=\"1146\" data-end=\"1474\"\u003eThis configuration features \u003cstrong data-start=\"1174\" data-end=\"1191\"\u003e32GB DDR5 RAM\u003c\/strong\u003e, enabling heavy multitasking and smooth handling of large datasets, virtual machines, and professional software. The \u003cstrong data-start=\"1309\" data-end=\"1330\"\u003e1TB PCIe NVMe SSD\u003c\/strong\u003e provides fast boot times, high-speed data access, and ample storage for project files and applications. \u003cspan class=\"\" data-state=\"closed\"\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003ch3 data-start=\"1476\" data-end=\"1525\"\u003e\u003cstrong data-start=\"1476\" data-end=\"1525\"\u003e16-Inch Display for Professional Productivity\u003c\/strong\u003e\u003c\/h3\u003e\n\u003cp data-start=\"1527\" data-end=\"1832\"\u003eThe laptop comes with a \u003cstrong data-start=\"1551\" data-end=\"1589\"\u003e16-inch FHD+ (1920 x 1200) display\u003c\/strong\u003e, offering more vertical screen space for productivity tasks such as coding, design, and multitasking. It supports optional higher-resolution panels, making it adaptable for different professional needs. \u003cspan class=\"\" data-state=\"closed\"\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003ch3 data-start=\"1834\" data-end=\"1891\"\u003e\u003cstrong data-start=\"1834\" data-end=\"1891\"\u003eEnterprise-Grade Build, Connectivity, and Reliability\u003c\/strong\u003e\u003c\/h3\u003e\n\u003cp data-start=\"1893\" data-end=\"2268\"\u003eBuilt as a professional workstation, it includes \u003cstrong data-start=\"1942\" data-end=\"1998\"\u003eThunderbolt 4, HDMI, Ethernet, and WiFi 6E\/7 options\u003c\/strong\u003e, ensuring seamless connectivity across enterprise environments. With ISV certifications, strong thermals, and durable build quality, it is designed for engineers, developers, and business professionals working on demanding tasks. \u003cspan class=\"\" data-state=\"closed\"\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003ch3 data-start=\"2270\" data-end=\"2297\"\u003e\u003cstrong data-start=\"2270\" data-end=\"2297\"\u003eTechnical Specification\u003c\/strong\u003e\u003c\/h3\u003e\n\u003cp data-start=\"129\" data-end=\"364\"\u003e\u003cstrong data-start=\"129\" data-end=\"142\"\u003eProcessor\u003c\/strong\u003e\u003cbr data-start=\"142\" data-end=\"145\"\u003e• Intel Core Ultra 7 265H (vPro Enterprise)\u003cbr data-start=\"188\" data-end=\"191\"\u003e• Up to ~5.3GHz Turbo Boost\u003cbr data-start=\"218\" data-end=\"221\"\u003e• 16 cores, 16 threads\u003cbr data-start=\"243\" data-end=\"246\"\u003e• 24MB Intel Smart Cache\u003cbr data-start=\"270\" data-end=\"273\"\u003e• Built-in AI Boost NPU (~13 TOPS) for AI workloads \u003cspan class=\"\" data-state=\"closed\"\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp data-start=\"366\" data-end=\"615\"\u003e\u003cstrong data-start=\"366\" data-end=\"394\"\u003eGraphics (Dedicated GPU)\u003c\/strong\u003e\u003cbr data-start=\"394\" data-end=\"397\"\u003e• NVIDIA RTX PRO 500 (Blackwell Generation)\u003cbr data-start=\"440\" data-end=\"443\"\u003e• 6GB GDDR7 VRAM\u003cbr data-start=\"459\" data-end=\"462\"\u003e• AI acceleration and professional GPU performance\u003cbr data-start=\"512\" data-end=\"515\"\u003e• Suitable for CAD, rendering, AI, and engineering workloads \u003cspan class=\"\" data-state=\"closed\"\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp data-start=\"617\" data-end=\"788\"\u003e\u003cstrong data-start=\"617\" data-end=\"627\"\u003eMemory\u003c\/strong\u003e\u003cbr data-start=\"627\" data-end=\"630\"\u003e• 32GB DDR5 RAM\u003cbr data-start=\"645\" data-end=\"648\"\u003e• High-speed memory (up to ~5600–6400 MT\/s)\u003cbr data-start=\"691\" data-end=\"694\"\u003e• Dual-channel configuration\u003cbr data-start=\"722\" data-end=\"725\"\u003e• Expandable up to 64GB \u003cspan class=\"\" data-state=\"closed\"\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp data-start=\"790\" data-end=\"965\"\u003e\u003cstrong data-start=\"790\" data-end=\"801\"\u003eStorage\u003c\/strong\u003e\u003cbr data-start=\"801\" data-end=\"804\"\u003e• 1TB M.2 PCIe Gen4 NVMe SSD\u003cbr data-start=\"832\" data-end=\"835\"\u003e• High-speed read\/write performance\u003cbr data-start=\"870\" data-end=\"873\"\u003e• Supports additional SSD expansion (dual M.2 slots) \u003cspan class=\"\" data-state=\"closed\"\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp data-start=\"967\" data-end=\"1143\"\u003e\u003cstrong data-start=\"967\" data-end=\"978\"\u003eDisplay\u003c\/strong\u003e\u003cbr data-start=\"978\" data-end=\"981\"\u003e• 16\" FHD+ (1920 x 1200) anti-glare display\u003cbr data-start=\"1024\" data-end=\"1027\"\u003e• 16:10 aspect ratio for productivity\u003cbr data-start=\"1064\" data-end=\"1067\"\u003e• Non-touch panel (config dependent) \u003cspan class=\"\" data-state=\"closed\"\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp data-start=\"1145\" data-end=\"1244\"\u003e\u003cstrong data-start=\"1145\" data-end=\"1165\"\u003eOperating System\u003c\/strong\u003e\u003cbr data-start=\"1165\" data-end=\"1168\"\u003e• Windows 11 Pro (business-ready OS) \u003cspan class=\"\" data-state=\"closed\"\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp data-start=\"1246\" data-end=\"1423\"\u003e\u003cstrong data-start=\"1246\" data-end=\"1270\"\u003eForm Factor \u0026amp; Design\u003c\/strong\u003e\u003cbr data-start=\"1270\" data-end=\"1273\"\u003e• 16-inch mobile workstation\u003cbr data-start=\"1301\" data-end=\"1304\"\u003e• Premium enterprise chassis\u003cbr data-start=\"1332\" data-end=\"1335\"\u003e• Built for durability and sustained performance\u003cbr data-start=\"1383\" data-end=\"1386\"\u003e• Portable workstation-class design\u003c\/p\u003e\n\u003cp data-start=\"1425\" data-end=\"1539\"\u003e\u003cstrong data-start=\"1425\" data-end=\"1447\"\u003ePlatform \/ Chipset\u003c\/strong\u003e\u003cbr data-start=\"1447\" data-end=\"1450\"\u003e• Intel vPro Enterprise platform\u003cbr data-start=\"1482\" data-end=\"1485\"\u003e• Advanced remote management and enterprise security\u003c\/p\u003e\n\u003cp data-start=\"1541\" data-end=\"1762\"\u003e\u003cstrong data-start=\"1541\" data-end=\"1565\"\u003ePorts \u0026amp; Connectivity\u003c\/strong\u003e\u003cbr data-start=\"1565\" data-end=\"1568\"\u003e• USB-C \/ Thunderbolt (data, display, charging)\u003cbr data-start=\"1615\" data-end=\"1618\"\u003e• Multiple USB-A ports\u003cbr data-start=\"1640\" data-end=\"1643\"\u003e• HDMI output\u003cbr data-start=\"1656\" data-end=\"1659\"\u003e• RJ-45 Ethernet\u003cbr data-start=\"1675\" data-end=\"1678\"\u003e• Audio combo jack\u003cbr data-start=\"1696\" data-end=\"1699\"\u003e• LAN: Gigabit Ethernet\u003cbr data-start=\"1722\" data-end=\"1725\"\u003e• Wireless: WiFi 6E + Bluetooth 5.x\u003c\/p\u003e\n\u003cp data-start=\"1764\" data-end=\"1877\"\u003e\u003cstrong data-start=\"1764\" data-end=\"1785\"\u003eExpansion \u0026amp; Slots\u003c\/strong\u003e\u003cbr data-start=\"1785\" data-end=\"1788\"\u003e• Dual M.2 SSD slots\u003cbr data-start=\"1808\" data-end=\"1811\"\u003e• Upgradeable RAM (SODIMM)\u003cbr data-start=\"1837\" data-end=\"1840\"\u003e• Modular workstation configuration\u003c\/p\u003e\n\u003cp data-start=\"1879\" data-end=\"1995\"\u003e\u003cstrong data-start=\"1879\" data-end=\"1897\"\u003eAudio \u0026amp; Camera\u003c\/strong\u003e\u003cbr data-start=\"1897\" data-end=\"1900\"\u003e• Integrated HD audio system\u003cbr data-start=\"1928\" data-end=\"1931\"\u003e• Dual microphones\u003cbr data-start=\"1949\" data-end=\"1952\"\u003e• FHD IR webcam (Windows Hello supported)\u003c\/p\u003e\n\u003cp data-start=\"1997\" data-end=\"2172\"\u003e\u003cstrong data-start=\"1997\" data-end=\"2028\"\u003eSecurity (Enterprise Grade)\u003c\/strong\u003e\u003cbr data-start=\"2028\" data-end=\"2031\"\u003e• TPM 2.0 hardware security\u003cbr data-start=\"2058\" data-end=\"2061\"\u003e• Intel vPro security features\u003cbr data-start=\"2091\" data-end=\"2094\"\u003e• IR camera with facial recognition\u003cbr data-start=\"2129\" data-end=\"2132\"\u003e• Enterprise-grade endpoint protection\u003c\/p\u003e\n\u003cp data-start=\"2174\" data-end=\"2343\"\u003e\u003cstrong data-start=\"2174\" data-end=\"2193\"\u003eBattery \u0026amp; Power\u003c\/strong\u003e\u003cbr data-start=\"2193\" data-end=\"2196\"\u003e• 6-cell battery (~96Wh typical)\u003cbr data-start=\"2228\" data-end=\"2231\"\u003e• Fast charging support\u003cbr data-start=\"2254\" data-end=\"2257\"\u003e• High-performance power adapter (~130W class) \u003cspan class=\"\" data-state=\"closed\"\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp data-start=\"2345\" data-end=\"2490\"\u003e\u003cstrong data-start=\"2345\" data-end=\"2377\"\u003ePhysical Dimensions \u0026amp; Weight\u003c\/strong\u003e\u003cbr data-start=\"2377\" data-end=\"2380\"\u003e• Approx. 16-inch chassis\u003cbr data-start=\"2405\" data-end=\"2408\"\u003e• Weight ~2.2 kg (varies by configuration) \u003cspan class=\"\" data-state=\"closed\"\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp data-start=\"2492\" data-end=\"2740\"\u003e\u003cstrong data-start=\"2492\" data-end=\"2504\"\u003eUse Case\u003c\/strong\u003e\u003cbr data-start=\"2504\" data-end=\"2507\"\u003e• Engineering (AutoCAD, SolidWorks, BIM)\u003cbr data-start=\"2547\" data-end=\"2550\"\u003e• AI \/ machine learning workloads (entry to mid-level)\u003cbr data-start=\"2604\" data-end=\"2607\"\u003e• Video editing and content creation\u003cbr data-start=\"2643\" data-end=\"2646\"\u003e• Data analysis, simulation, and virtualization\u003cbr data-start=\"2693\" data-end=\"2696\"\u003e• Enterprise mobile workstation deployment\u003c\/p\u003e\n\u003ch3\u003e\u003cstrong\u003eDetailed Specification:\u003c\/strong\u003e\u003c\/h3\u003e\n\u003cp\u003eDell Pro Max 16 (MC16250) XCTO Base\u003cbr\u003eIntel Core Ultra 7 265H Processor with vPro\u003cbr\u003eFHD+IR Camera, HDR\u003cbr\u003eDell Pro 14-16 Plus EcoLoop Slim Backpack? - CP5724S\u003cbr\u003eIntel Core Ultra 7 265H, vPro Enterprise (24MB, 16 cores, 16 threads, up to 5.30 GHz Turbo, 45W)\u003cbr\u003e16 FHD+ LCD with 300 nits, Non-touch, FHD HDR IR Camera, Microphone, WLAN\u003cbr\u003eDell Pro Max 16 Bottom Door, Discrete\u003cbr\u003eNVIDIA RTX PRO 500 Blackwell 6GB GDDR7\u003cbr\u003e32GB: 2x16GB, DDR5, 5600 MT\/s, SoDIMM, Dual Channel, non-ECC\u003cbr\u003ePalmrest, FPR+CV3\u003cbr\u003e6 cell, 96Whr, ExpressCharge(TM) Capable, standard battery\u003cbr\u003e1TB, M.2 2280, Gen4 PCIe NVMe, Class 40, SED\u003cbr\u003eEnglish US backlit Copilot key keyboard with numeric keypad\u003cbr\u003eWindows 11 Pro\u003cbr\u003eIntel Wi-Fi 6E AX211, 2x2, 802.11ax, Bluetooth 5.3 wireless card\u003cbr\u003eIntel vPro Enterprise Technology Enabled\u003cbr\u003e3 Years ProSupport Warranty\u003c\/p\u003e","brand":"Dell","offers":[{"title":"Default Title","offer_id":51300050010276,"sku":"DPMMC16250C732G1TBDIS","price":3345.0,"currency_code":"SGD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0605\/0136\/0804\/files\/dell-pro-max-16-mc16250-u7-265h-vpro-rtx-pro-500-32g-1tb-ssd-6509459.jpg?v=1775832612"},{"product_id":"msi-edgexpert-personal-ai-supercomputer-4tb","title":"MSI EdgeXpert Personal AI Supercomputer - 4TB","description":"\u003ch2\u003e\u003cstrong\u003eMSI EdgeXpert Personal AI Supercomputer 4TB (ARM 20-core CPU\/AI Blackwell GPU\/128GB LPDDR5x\/4TB\/WiFi 7\/BT 5.3\/NVIDIA DGX OS) (9S6-C9311-61S) - 3 Year Local Warranty \u003c\/strong\u003e\u003c\/h2\u003e\n\u003ch3 data-start=\"193\" data-end=\"249\"\u003e\u003cstrong data-start=\"193\" data-end=\"247\"\u003eExtreme AI Performance with NVIDIA Grace Blackwell\u003c\/strong\u003e\u003c\/h3\u003e\n\u003cp data-start=\"251\" data-end=\"681\"\u003eThe MSI EdgeXpert Personal AI Supercomputer is built on the NVIDIA GB10 Grace Blackwell architecture, combining a powerful Blackwell GPU with a 20-core Arm CPU to deliver up to 1000 AI TOPS (FP4) of performance, enabling advanced workloads such as large language model (LLM) training, inference, generative AI pipelines, and real-time data processing directly on a compact desktop system without relying on cloud infrastructure.\u003c\/p\u003e\n\u003ch3 data-start=\"683\" data-end=\"736\"\u003e\u003cstrong data-start=\"683\" data-end=\"734\"\u003eUnified Memory Architecture for Large AI Models\u003c\/strong\u003e\u003c\/h3\u003e\n\u003cp data-start=\"738\" data-end=\"1051\"\u003eEquipped with 128GB LPDDR5x unified memory, the system allows seamless data sharing between CPU and GPU through high-speed interconnect, significantly reducing latency and bottlenecks, making it ideal for handling large datasets, complex AI models, and memory-intensive workloads efficiently in a single system.\u003c\/p\u003e\n\u003ch3 data-start=\"1053\" data-end=\"1104\"\u003e\u003cstrong data-start=\"1053\" data-end=\"1102\"\u003eLocal AI Development with Massive Scalability\u003c\/strong\u003e\u003c\/h3\u003e\n\u003cp data-start=\"1106\" data-end=\"1410\"\u003eDesigned for enterprise and research environments, the EdgeXpert supports large AI models up to hundreds of billions of parameters and can scale further by linking multiple units together, enabling users to build, fine-tune, and deploy advanced AI systems locally with high performance and flexibility.\u003c\/p\u003e\n\u003ch3 data-start=\"1412\" data-end=\"1471\"\u003e\u003cstrong data-start=\"1412\" data-end=\"1469\"\u003eCompact Form Factor with Data Center-Class Capability\u003c\/strong\u003e\u003c\/h3\u003e\n\u003cp data-start=\"1473\" data-end=\"1751\"\u003eDespite delivering near data center-level performance, the system is housed in a compact desktop chassis, making it suitable for office desks, labs, and edge deployments where space is limited, while still providing powerful compute capabilities for demanding AI applications.\u003c\/p\u003e\n\u003ch3 data-start=\"1753\" data-end=\"1800\"\u003e\u003cstrong data-start=\"1753\" data-end=\"1798\"\u003eFull AI Software Stack with NVIDIA DGX OS\u003c\/strong\u003e\u003c\/h3\u003e\n\u003cp data-start=\"1802\" data-end=\"2078\"\u003ePreloaded with NVIDIA DGX OS, the system provides a complete AI development environment with support for CUDA, TensorRT, PyTorch, and TensorFlow, allowing developers and enterprises to rapidly develop, test, and deploy AI models with optimized performance and minimal setup.\u003c\/p\u003e\n\u003ch3 data-start=\"2080\" data-end=\"2110\"\u003e\u003cstrong data-start=\"2080\" data-end=\"2108\"\u003eTechnical Specifications\u003c\/strong\u003e\u003c\/h3\u003e\n\u003cp data-start=\"144\" data-end=\"430\"\u003e\u003cstrong data-start=\"144\" data-end=\"176\"\u003eProcessor (SoC Architecture)\u003c\/strong\u003e\u003cbr data-start=\"176\" data-end=\"179\"\u003e• NVIDIA GB10 Grace Blackwell Superchip\u003cbr data-start=\"218\" data-end=\"221\"\u003e• 20-core ARM CPU (10 × Cortex-X925 + 10 × Cortex-A725)\u003cbr data-start=\"276\" data-end=\"279\"\u003e• Unified CPU + GPU architecture via NVLink-C2C\u003cbr data-start=\"326\" data-end=\"329\"\u003e• Designed for AI orchestration, preprocessing, and inference \u003cspan class=\"\" data-state=\"closed\"\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp data-start=\"432\" data-end=\"631\"\u003e\u003cstrong data-start=\"432\" data-end=\"452\"\u003eGPU (AI Compute)\u003c\/strong\u003e\u003cbr data-start=\"452\" data-end=\"455\"\u003e• NVIDIA Blackwell GPU architecture\u003cbr data-start=\"490\" data-end=\"493\"\u003e• 5th Gen Tensor Cores + advanced AI acceleration\u003cbr data-start=\"542\" data-end=\"545\"\u003e• Up to ~1000 AI TOPS (FP4 sparse performance) \u003cspan class=\"\" data-state=\"closed\"\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp data-start=\"633\" data-end=\"835\"\u003e\u003cstrong data-start=\"633\" data-end=\"666\"\u003eMemory (Unified Architecture)\u003c\/strong\u003e\u003cbr data-start=\"666\" data-end=\"669\"\u003e• 128GB LPDDR5X unified memory\u003cbr data-start=\"699\" data-end=\"702\"\u003e• Shared between CPU + GPU (coherent memory model)\u003cbr data-start=\"752\" data-end=\"755\"\u003e• 256-bit interface, ~273 GB\/s bandwidth \u003cspan class=\"\" data-state=\"closed\"\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp data-start=\"837\" data-end=\"991\"\u003e\u003cstrong data-start=\"837\" data-end=\"848\"\u003eStorage\u003c\/strong\u003e\u003cbr data-start=\"848\" data-end=\"851\"\u003e• 4TB NVMe M.2 PCIe SSD (self-encrypting)\u003cbr data-start=\"892\" data-end=\"895\"\u003e• High-speed storage for datasets, models, and pipelines \u003cspan class=\"\" data-state=\"closed\"\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp data-start=\"993\" data-end=\"1335\"\u003e\u003cstrong data-start=\"993\" data-end=\"1024\"\u003eAI Performance \u0026amp; Capability\u003c\/strong\u003e\u003cbr data-start=\"1024\" data-end=\"1027\"\u003e• ~1 petaFLOP AI compute (FP4 class) \u003cspan class=\"\" data-state=\"closed\"\u003e\u003c\/span\u003e\u003cbr data-start=\"1101\" data-end=\"1104\"\u003e• Supports LLMs up to ~200B parameters locally \u003cspan class=\"\" data-state=\"closed\"\u003e\u003c\/span\u003e\u003cbr data-start=\"1188\" data-end=\"1191\"\u003e• Multi-node scaling (2 units) up to ~405B models \u003cspan class=\"\" data-state=\"closed\"\u003e\u003c\/span\u003e\u003cbr data-start=\"1278\" data-end=\"1281\"\u003e• Optimised for training, fine-tuning, and inference\u003c\/p\u003e\n\u003cp data-start=\"1337\" data-end=\"1554\"\u003e\u003cstrong data-start=\"1337\" data-end=\"1368\"\u003eOperating System \u0026amp; AI Stack\u003c\/strong\u003e\u003cbr data-start=\"1368\" data-end=\"1371\"\u003e• NVIDIA DGX OS (pre-installed)\u003cbr data-start=\"1402\" data-end=\"1405\"\u003e• Supports CUDA, TensorRT, PyTorch, TensorFlow, RAPIDS\u003cbr data-start=\"1459\" data-end=\"1462\"\u003e• Seamless scaling to NVIDIA DGX Cloud \/ data centre \u003cspan class=\"\" data-state=\"closed\"\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp data-start=\"1556\" data-end=\"1736\"\u003e\u003cstrong data-start=\"1556\" data-end=\"1585\"\u003eNetworking \u0026amp; Connectivity\u003c\/strong\u003e\u003cbr data-start=\"1585\" data-end=\"1588\"\u003e• 1 × 10GbE RJ45 LAN\u003cbr data-start=\"1608\" data-end=\"1611\"\u003e• NVIDIA ConnectX-7 Smart NIC (high-speed AI networking)\u003cbr data-start=\"1667\" data-end=\"1670\"\u003e• WiFi 7\u003cbr data-start=\"1678\" data-end=\"1681\"\u003e• Bluetooth 5.3 \u003cspan class=\"\" data-state=\"closed\"\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp data-start=\"1738\" data-end=\"1876\"\u003e\u003cstrong data-start=\"1738\" data-end=\"1753\"\u003ePorts \u0026amp; I\/O\u003c\/strong\u003e\u003cbr data-start=\"1753\" data-end=\"1756\"\u003e• 4 × USB 3.2 Type-C\u003cbr data-start=\"1776\" data-end=\"1779\"\u003e• 1 × HDMI 2.1a\u003cbr data-start=\"1794\" data-end=\"1797\"\u003e• Multi-display via USB-C DisplayPort \u003cspan class=\"\" data-state=\"closed\"\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp data-start=\"1878\" data-end=\"2049\"\u003e\u003cstrong data-start=\"1878\" data-end=\"1902\"\u003eForm Factor \u0026amp; Design\u003c\/strong\u003e\u003cbr data-start=\"1902\" data-end=\"1905\"\u003e• Ultra-compact desktop AI system (~1.2L chassis)\u003cbr data-start=\"1954\" data-end=\"1957\"\u003e• Dimensions ~151 × 151 × 52 mm\u003cbr data-start=\"1988\" data-end=\"1991\"\u003e• Weight ~1.2 kg \u003cspan class=\"\" data-state=\"closed\"\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp data-start=\"2051\" data-end=\"2143\"\u003e\u003cstrong data-start=\"2051\" data-end=\"2060\"\u003ePower\u003c\/strong\u003e\u003cbr data-start=\"2060\" data-end=\"2063\"\u003e• External power adapter (~280W class) \u003cspan class=\"\" data-state=\"closed\"\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp data-start=\"2145\" data-end=\"2288\"\u003e\u003cstrong data-start=\"2145\" data-end=\"2169\"\u003eCooling \u0026amp; Efficiency\u003c\/strong\u003e\u003cbr data-start=\"2169\" data-end=\"2172\"\u003e• Optimised thermal design for sustained AI workloads\u003cbr data-start=\"2225\" data-end=\"2228\"\u003e• High performance per watt (ARM + Blackwell architecture)\u003c\/p\u003e\n\u003cp data-start=\"2290\" data-end=\"2448\"\u003e\u003cstrong data-start=\"2290\" data-end=\"2317\"\u003eSecurity \u0026amp; Data Control\u003c\/strong\u003e\u003cbr data-start=\"2317\" data-end=\"2320\"\u003e• Local AI processing (no cloud dependency)\u003cbr data-start=\"2363\" data-end=\"2366\"\u003e• Full data sovereignty for enterprise workloads\u003cbr data-start=\"2414\" data-end=\"2417\"\u003e• Self-encrypting SSD support\u003c\/p\u003e","brand":"Nvidia","offers":[{"title":"Default Title","offer_id":51315547701412,"sku":"9S6-C9311-61S","price":6895.0,"currency_code":"SGD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0605\/0136\/0804\/files\/msi-edgexpert-personal-ai-supercomputer-4tb-8770171.jpg?v=1777335073"},{"product_id":"hp-zgx-nano-g1n-ai-station-4tb","title":"HP ZGX Nano G1n AI Station - 4TB","description":"\u003c!-- ZGX-FLASH-SALE-START --\u003e\u003cdiv id=\"zgx-promo\" data-zgx-end=\"2026-09-14T23:59:00+08:00\" style=\"display:block; margin:0 0 18px; font-family:inherit; border-radius:14px; padding:1.5px; background:linear-gradient(120deg,#14532d,#22c55e,#14532d);\"\u003e\u003cdiv style=\"background:#fff; border-radius:12.5px; padding:14px 16px;\"\u003e\n\u003cdiv style=\"display:flex; align-items:center; gap:8px; flex-wrap:wrap;\"\u003e\n\u003cspan style=\"display:inline-flex; align-items:center; gap:6px; background:#14532d; color:#fff; border-radius:999px; 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deliver powerful local computing performance for artificial intelligence, machine learning, data science, and advanced analytics workloads. Combining enterprise-class capabilities with a compact form factor, this AI station enables organizations to run demanding AI applications without relying entirely on cloud infrastructure.\u003c\/p\u003e\n\u003ch3 data-start=\"683\" data-end=\"739\"\u003e\u003cstrong data-start=\"683\" data-end=\"737\"\u003eBuilt for AI Development and Large Language Models\u003c\/strong\u003e\u003c\/h3\u003e\n\u003cp data-start=\"741\" data-end=\"1167\"\u003eDesigned to support modern AI workflows, the HP ZGX Nano G1n provides the performance required for AI model development, local inference, generative AI applications, large language models (LLMs), and data-intensive processing tasks. The integrated 4TB high-speed storage offers ample capacity for AI datasets, machine learning models, research projects, and enterprise workloads while ensuring rapid access to critical data.\u003c\/p\u003e\n\u003ch3 data-start=\"1169\" data-end=\"1222\"\u003e\u003cstrong data-start=\"1169\" data-end=\"1220\"\u003eSpace-Saving Design with Enterprise Reliability\u003c\/strong\u003e\u003c\/h3\u003e\n\u003cp data-start=\"1224\" data-end=\"1601\"\u003eThe HP ZGX Nano G1n features a compact desktop footprint that allows deployment in offices, research labs, innovation centers, and edge computing environments. Despite its small size, it is engineered to deliver professional-grade reliability, making it suitable for organizations looking to deploy AI infrastructure without the complexity of traditional server environments.\u003c\/p\u003e\n\u003ch3 data-start=\"1603\" data-end=\"1655\"\u003e\u003cstrong data-start=\"1603\" data-end=\"1653\"\u003eAccelerate Innovation with Local AI Processing\u003c\/strong\u003e\u003c\/h3\u003e\n\u003cp data-start=\"1657\" data-end=\"2019\"\u003eOrganizations can leverage the HP ZGX Nano G1n AI Station to accelerate software development, AI experimentation, model testing, and business analytics while maintaining greater control over sensitive data. Local processing helps reduce latency, improve responsiveness, and support privacy requirements for enterprise AI deployments across multiple industries.\u003c\/p\u003e\n\u003ch3 data-start=\"2021\" data-end=\"2063\"\u003e\u003cstrong data-start=\"2021\" data-end=\"2061\"\u003eTechnical Specifications\u003c\/strong\u003e\u003c\/h3\u003e\n\u003cp\u003e\u003cstrong\u003eGeneral\u003c\/strong\u003e\u003cbr\u003eModel: ZGX Nano G1n AI Station – 4TB\u003cbr\u003ePart Number: DD8J0PA\u003cbr\u003eProduct Name: HP ZGX Nano G1n AI Station – 4TB\u003cbr\u003eManufacturer: HP Inc.\u003cbr\u003eProduct Type: AI Workstation \/ Personal AI Supercomputer\u003cbr\u003eSeries: HP ZGX Nano AI Station\u003cbr\u003eForm Factor: Mini AI Workstation\u003cbr\u003eOperating System: NVIDIA DGX OS 7 (Ubuntu 24.04 Based)\u003cbr\u003eDeployment: AI Development, LLM Inference, Fine-Tuning, Data Science, Edge AI, Research Computing\u003cbr\u003eWarranty: 1 Year Limited Warranty\u003cbr\u003ePlatform: NVIDIA Grace Blackwell Architecture\u003cbr\u003eAI Framework Support: NVIDIA AI Software Stack Compatible\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eProcessor Specifications\u003c\/strong\u003e\u003cbr\u003eProcessor: NVIDIA GB10 Grace Blackwell Superchip\u003cbr\u003eCPU Architecture: ARM-Based\u003cbr\u003eCPU Cores: 20 Cores\u003cbr\u003eCPU Configuration:\u003cbr\u003e• 10 × Cortex-X925 Performance Cores\u003cbr\u003e• 10 × Cortex-A725 Efficiency Cores\u003c\/p\u003e\n\u003cp\u003eGPU Architecture: NVIDIA Blackwell\u003cbr\u003eTensor Cores: 5th Generation Tensor Cores\u003cbr\u003eRay Tracing Cores: 4th Generation RT Cores\u003cbr\u003eHardware Video Encoder: 1 × NVENC\u003cbr\u003eHardware Video Decoder: 1 × NVDEC\u003cbr\u003eAI Optimization: Built for AI Training, Fine-Tuning and Inference Workloads\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eMemory Specifications\u003c\/strong\u003e\u003cbr\u003eInstalled Memory: 128 GB LPDDR5x\u003cbr\u003eMemory Speed: 8533 MT\/s\u003cbr\u003eMemory Type: Unified System Memory\u003cbr\u003eMemory Interface: 256-Bit\u003cbr\u003eMemory Bandwidth: 273 GB\/s\u003cbr\u003eECC Support: Integrated Reliability Features\u003cbr\u003eMemory Architecture: Coherent Unified Memory Design\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eStorage Specifications\u003c\/strong\u003e\u003cbr\u003eInstalled Storage: 4 TB PCIe Gen4 NVMe SSD\u003cbr\u003eStorage Type: M.2 NVMe SSD\u003cbr\u003eSSD Technology: TLC NAND\u003cbr\u003eDrive Security: OPAL Self-Encrypting Drive (SED)\u003cbr\u003eExpansion Support: Configuration Dependent\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eGraphics Specifications\u003c\/strong\u003e\u003cbr\u003eGraphics Controller: NVIDIA Blackwell Architecture GPU\u003cbr\u003eGraphics Type: Integrated AI GPU\u003cbr\u003eCUDA Support: Supported\u003cbr\u003eTensor Core Acceleration: Supported\u003cbr\u003eRay Tracing Support: Supported\u003cbr\u003eAI Workload Acceleration: Supported\u003cbr\u003eLarge Language Model Processing: Supported\u003cbr\u003eGenerative AI Workloads: Supported\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eAI Capabilities\u003c\/strong\u003e\u003cbr\u003eLocal LLM Deployment: Supported\u003cbr\u003eModel Prototyping: Supported\u003cbr\u003eModel Fine-Tuning: Supported\u003cbr\u003eInference Workloads: Supported\u003cbr\u003eComputer Vision Applications: Supported\u003cbr\u003eRobotics Development: Supported\u003cbr\u003eEdge AI Deployment: Supported\u003cbr\u003eData Science Workloads: Supported\u003cbr\u003eSupports Models Up To: 200 Billion Parameters Locally\u003cbr\u003eScalable AI Clustering: Supported via ConnectX Networking\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eNetworking \u0026amp; Connectivity\u003c\/strong\u003e\u003cbr\u003eWireless LAN: Wi-Fi 7\u003cbr\u003eWireless Chipset: MediaTek MT7925\u003cbr\u003eBluetooth Version: Bluetooth 5.4\u003cbr\u003eWired Ethernet: 10GbE RJ45\u003cbr\u003eHigh-Speed Networking: NVIDIA ConnectX-7 200GbE\u003cbr\u003eCluster Interconnect Support: Supported\u003cbr\u003eMulti-System Scaling: Supported\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003ePorts \u0026amp; Interfaces\u003c\/strong\u003e\u003cbr\u003eUSB Type-C Power Connector × 1\u003cbr\u003eUSB Type-C 20Gbps Ports × 3\u003cbr\u003eHDMI 2.1a × 1\u003cbr\u003e10GbE RJ45 Port × 1\u003cbr\u003eQSFP 200GbE Ports × 2\u003cbr\u003eAudio Over HDMI: Supported\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eSoftware \u0026amp; Management\u003c\/strong\u003e\u003cbr\u003eOperating System: NVIDIA DGX OS\u003cbr\u003eHP ZGX Toolkit: Supported\u003cbr\u003eMLflow Integration: Supported\u003cbr\u003eOllama Integration: Supported\u003cbr\u003eVisual Studio Code Integration: Supported\u003cbr\u003eModel Discovery Tools: Supported\u003cbr\u003eModel Export Tools: Supported\u003cbr\u003eLocal AI Serving: Supported\u003cbr\u003eDeveloper Workflow Optimization: Supported\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003ePower Specifications\u003c\/strong\u003e\u003cbr\u003ePower Adapter: External USB-C Power Adapter\u003cbr\u003ePower Rating: 240W\u003cbr\u003ePower Efficiency: Up to 89%\u003cbr\u003eActive PFC: Supported\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eDesign \u0026amp; Construction\u003c\/strong\u003e\u003cbr\u003eForm Factor: Mini AI Workstation\u003cbr\u003eColor: Black\u003cbr\u003eConstruction: Enterprise-Grade Compact Chassis\u003cbr\u003eCooling Design: Active Intelligent Cooling\u003cbr\u003eDesktop Deployment: Supported\u003cbr\u003eCluster Deployment: Supported\u003cbr\u003eEnterprise AI Ready: Supported\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003ePhysical Specifications\u003c\/strong\u003e\u003cbr\u003eDimensions (W × D × H): 150 × 150 × 51 mm\u003cbr\u003eWeight: Approximately 1.25 kg\u003cbr\u003eForm Factor: Mini Desktop AI Station\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003ePackage Contents\u003c\/strong\u003e\u003cbr\u003eHP ZGX Nano G1n AI Station\u003cbr\u003e240W USB-C Power Adapter\u003cbr\u003ePower Cord\u003cbr\u003eQuick Start Guide\u003cbr\u003eDocumentation\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eKey Features\u003c\/strong\u003e\u003cbr\u003eNVIDIA GB10 Grace Blackwell Superchip\u003cbr\u003e20-Core ARM CPU Architecture\u003cbr\u003e128GB Unified LPDDR5x Memory\u003cbr\u003e273GB\/s Memory Bandwidth\u003cbr\u003e4TB PCIe Gen4 NVMe SSD\u003cbr\u003eNVIDIA Blackwell GPU Architecture\u003cbr\u003eWi-Fi 7 and Bluetooth 5.4\u003cbr\u003e10GbE Ethernet Connectivity\u003cbr\u003eNVIDIA ConnectX-7 200GbE Networking\u003cbr\u003eNVIDIA DGX OS Included\u003cbr\u003eSupports AI Model Fine-Tuning and Inference\u003cbr\u003eUltra-Compact 150mm Chassis Design\u003cbr\u003eScalable Multi-Node AI Deployment\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003ePerformance \u0026amp; 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color: #333; }\n.pd-spec tr:last-child td { border-bottom: none !important; }\n\u003c\/style\u003e\n\u003ch2\u003e\u003cstrong\u003eNVIDIA RTX PRO™ 6000 Blackwell Server Edition 96 GB GDDR7 with ECC (900-2G153-0000-000) - 3 Years Local Warranty\u003c\/strong\u003e\u003c\/h2\u003e\n\u003ch3\u003e\u003cstrong\u003eRTX PRO 6000 Blackwell Server Edition — Passive 96 GB Accelerator for NVIDIA-Certified Servers\u003c\/strong\u003e\u003c\/h3\u003e\n\u003cp\u003eThe \u003cstrong\u003eNVIDIA RTX PRO™ 6000 Blackwell Server Edition\u003c\/strong\u003e brings 96 GB of GDDR7 with ECC and 24,064 CUDA cores into a \u003cstrong\u003epassively cooled, dual-slot FHFL server card\u003c\/strong\u003e designed to be driven by chassis airflow rather than its own fans. It is the data-centre member of the RTX PRO Blackwell family, intended for NVIDIA-Certified servers running AI inference, virtual workstations, rendering and digital twin workloads.\u003c\/p\u003e\n\u003ch3\u003e\u003cstrong\u003e4 PFLOPS of Peak FP4 AI Performance\u003c\/strong\u003e\u003c\/h3\u003e\n\u003cp\u003eNVIDIA rates the Server Edition at \u003cstrong\u003e4 PFLOPS peak FP4 AI\u003c\/strong\u003e, with 120 TFLOPS FP32 and 355 TFLOPS of RT Core throughput across 752 fifth-generation Tensor Cores and 188 fourth-generation RT Cores. Memory bandwidth is 1,597 GB\/s across a 512-bit interface, and four NVENC, four NVDEC and four JPEG engines handle high-density video and imaging pipelines.\u003c\/p\u003e\n\u003ch3\u003e\u003cstrong\u003eConfidential Computing, Secure Boot and MIG\u003c\/strong\u003e\u003c\/h3\u003e\n\u003cp\u003eUnlike the workstation cards, the Server Edition publishes the enterprise security features that regulated Singapore workloads need: \u003cstrong\u003eConfidential Computing support\u003c\/strong\u003e and \u003cstrong\u003esecure boot with hardware root of trust\u003c\/strong\u003e. Multi-Instance GPU partitions the card into up to four isolated 24 GB instances so a single accelerator can serve multiple tenants or services with hard separation.\u003c\/p\u003e\n\u003ch3\u003e\u003cstrong\u003eConfigurable to 600 W, PCIe 5.0 x16\u003c\/strong\u003e\u003c\/h3\u003e\n\u003cp\u003ePower is configurable up to 600 W through a single PCIe CEM5 16-pin connector on a PCIe 5.0 x16 interface. Because the cooler is passive, this card must be installed in a server designed for it — talk to us about NVIDIA-Certified platforms before ordering. NVIDIA AI Enterprise support is available for this product.\u003c\/p\u003e\n\u003cdetails class=\"pd-acc\"\u003e\u003csummary style=\"cursor: pointer; font-weight: 700;\"\u003eNVIDIA RTX PRO™ 6000 Blackwell Server Edition Datasheet — Technical Specifications\u003c\/summary\u003e\u003cdiv class=\"pd-acc-body\"\u003e\n\u003ch4\u003eGeneral\u003c\/h4\u003e\n\u003ctable class=\"pd-spec\"\u003e\n\u003ctr\u003e\n\u003ctd\u003ePart Number\u003c\/td\u003e\n\u003ctd\u003e900-2G153-0000-000\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eModel\u003c\/td\u003e\n\u003ctd\u003eNVIDIA RTX PRO™ 6000 Blackwell Server Edition\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eGPU Architecture\u003c\/td\u003e\n\u003ctd\u003eNVIDIA Blackwell\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eProduct Family\u003c\/td\u003e\n\u003ctd\u003eNVIDIA RTX PRO Blackwell Server Edition\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eWarranty\u003c\/td\u003e\n\u003ctd\u003e3 Years Local Warranty (Singapore)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/table\u003e\n\u003ch4\u003eMemory\u003c\/h4\u003e\n\u003ctable class=\"pd-spec\"\u003e\n\u003ctr\u003e\n\u003ctd\u003eGPU Memory\u003c\/td\u003e\n\u003ctd\u003e96 GB GDDR7 with ECC\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eMemory Interface\u003c\/td\u003e\n\u003ctd\u003e512-bit\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eMemory Bandwidth\u003c\/td\u003e\n\u003ctd\u003e1,597 GB\/s\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eECC\u003c\/td\u003e\n\u003ctd\u003eYes — error-correcting code memory\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/table\u003e\n\u003ch4\u003eCompute \u0026amp; Performance\u003c\/h4\u003e\n\u003ctable class=\"pd-spec\"\u003e\n\u003ctr\u003e\n\u003ctd\u003eCUDA Cores\u003c\/td\u003e\n\u003ctd\u003e24,064\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eTensor Cores\u003c\/td\u003e\n\u003ctd\u003eFifth-generation Tensor Cores — 752\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eRT Cores\u003c\/td\u003e\n\u003ctd\u003eFourth-generation RT Cores — 188\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eSingle-Precision (FP32) Performance\u003c\/td\u003e\n\u003ctd\u003e120 TFLOPS\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eRT Core Performance\u003c\/td\u003e\n\u003ctd\u003e355 TFLOPS\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eAI Performance\u003c\/td\u003e\n\u003ctd\u003e4 PFLOPS peak FP4 AI\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eMulti-Instance GPU (MIG)\u003c\/td\u003e\n\u003ctd\u003eYes — up to 4 MIG instances @ 24 GB\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/table\u003e\n\u003ch4\u003eMedia Engines\u003c\/h4\u003e\n\u003ctable class=\"pd-spec\"\u003e\n\u003ctr\u003e\n\u003ctd\u003eEncoders (NVENC)\u003c\/td\u003e\n\u003ctd\u003e4x NVENC (ninth generation)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eDecoders (NVDEC)\u003c\/td\u003e\n\u003ctd\u003e4x NVDEC (sixth generation); 4x JPEG\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/table\u003e\n\u003ch4\u003eDisplay\u003c\/h4\u003e\n\u003ctable class=\"pd-spec\"\u003e\n\u003ctr\u003e\n\u003ctd\u003eDisplay Connectors\u003c\/td\u003e\n\u003ctd\u003e4x DisplayPort 2.1\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eMaximum Simultaneous Displays\u003c\/td\u003e\n\u003ctd\u003eNot published by NVIDIA for this model\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/table\u003e\n\u003ch4\u003ePower, Form Factor \u0026amp; Interface\u003c\/h4\u003e\n\u003ctable class=\"pd-spec\"\u003e\n\u003ctr\u003e\n\u003ctd\u003eMaximum Power Consumption\u003c\/td\u003e\n\u003ctd\u003eUp to 600 W (configurable)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eGraphics Bus\u003c\/td\u003e\n\u003ctd\u003ePCI Express 5.0 x16\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eForm Factor\u003c\/td\u003e\n\u003ctd\u003e4.4in (H) x 10.5in (L), dual slot (air-cooled, FHFL)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eThermal Solution\u003c\/td\u003e\n\u003ctd\u003ePassive — server chassis airflow required\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ePower Connector\u003c\/td\u003e\n\u003ctd\u003e1x PCIe CEM5 16-pin\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/table\u003e\n\u003ch4\u003eEnterprise Security\u003c\/h4\u003e\n\u003ctable class=\"pd-spec\"\u003e\n\u003ctr\u003e\n\u003ctd\u003eConfidential Computing\u003c\/td\u003e\n\u003ctd\u003eSupported\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eSecure Boot\u003c\/td\u003e\n\u003ctd\u003eYes — secure boot with hardware root of trust\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eNVIDIA AI Enterprise\u003c\/td\u003e\n\u003ctd\u003eSupported\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/table\u003e\n\u003cp\u003e\u003cem\u003eSpecifications are taken from NVIDIA's published datasheet for this model. Fields NVIDIA does not publish for a given card are shown as such rather than estimated.\u003c\/em\u003e\u003c\/p\u003e\n\u003c\/div\u003e\u003c\/details\u003e\u003cdetails class=\"pd-acc\"\u003e\u003csummary style=\"cursor: pointer; font-weight: 700;\"\u003eCompatibility \u0026amp; Software Support\u003c\/summary\u003e\u003cdiv class=\"pd-acc-body\"\u003e\n\u003ctable class=\"pd-spec\"\u003e\n\u003ctr\u003e\n\u003ctd\u003eGraphics APIs\u003c\/td\u003e\n\u003ctd\u003eDirectX 12 (Shader Model 6.6), OpenGL 4.6, Vulkan 1.4\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eCompute APIs\u003c\/td\u003e\n\u003ctd\u003eCUDA 12.8, OpenCL 3.0, DirectCompute\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ePlatforms\u003c\/td\u003e\n\u003ctd\u003eNVIDIA-Certified servers and leading cloud \/ data-centre partners\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eSoftware Stack\u003c\/td\u003e\n\u003ctd\u003eNVIDIA AI Enterprise, CUDA-X libraries, NVIDIA NIM microservices, Omniverse\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eVirtualisation\u003c\/td\u003e\n\u003ctd\u003eMulti-Instance GPU (up to 4 instances @ 24 GB); Confidential Computing supported\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eCooling Requirement\u003c\/td\u003e\n\u003ctd\u003ePassive card — requires a server chassis rated for passive GPU airflow\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eChassis Check\u003c\/td\u003e\n\u003ctd\u003e4.4in (H) x 10.5in (L), dual slot (air-cooled, FHFL) — confirm slot clearance, power headroom and airflow before ordering\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/table\u003e\n\u003cp\u003eSourceIT can verify fit against your specific workstation or server model before you order. For ISV certification status on a named application version, check the NVIDIA \u003ca href=\"https:\/\/www.nvidia.com\/en-us\/products\/workstations\/product-literature\/\" target=\"_blank\" rel=\"noopener\"\u003eprofessional product literature library\u003c\/a\u003e.\u003c\/p\u003e\n\u003c\/div\u003e\u003c\/details\u003e\u003cdetails class=\"pd-acc\"\u003e\u003csummary style=\"cursor: pointer; font-weight: 700;\"\u003ePackage Contents \u0026amp; NVIDIA RTX PRO Blackwell Ordering Information\u003c\/summary\u003e\u003cdiv class=\"pd-acc-body\"\u003e\n\u003cp\u003e\u003cstrong\u003eWhat is in the box:\u003c\/strong\u003e NVIDIA does not publish a box-contents list for the Server Edition. This is a passively cooled OEM server card and is normally supplied as the board with documentation; contact SourceIT if you need a specific bracket or accessory confirmed for your chassis.\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eNVIDIA RTX PRO Blackwell range available from SourceIT:\u003c\/strong\u003e\u003c\/p\u003e\n\u003ctable class=\"pd-spec\"\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003ePart Number\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e\u003cstrong\u003eModel \u0026amp; Memory\u003c\/strong\u003e\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e900-5G144-2500-000\u003c\/td\u003e\n\u003ctd\u003e\n\u003ca href=\"\/products\/nvidia-rtx-pro%E2%84%A2-6000-blackwell-workstation-edition-900-5g144-2500-000\"\u003eRTX PRO 6000 Blackwell Workstation Edition\u003c\/a\u003e — 96 GB GDDR7 with ECC\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e900-5G153-2500-000\u003c\/td\u003e\n\u003ctd\u003e\n\u003ca href=\"\/products\/nvidia-rtx-pro%E2%84%A2-6000-blackwell-max-q-workstation-edition-900-5g153-2500-000\"\u003eRTX PRO 6000 Blackwell Max-Q Workstation Edition\u003c\/a\u003e — 96 GB GDDR7 with ECC\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e900-2G153-0000-000\u003c\/td\u003e\n\u003ctd\u003e\u003cstrong\u003eRTX PRO 6000 Blackwell Server Edition — 96 GB GDDR7 with ECC (this item)\u003c\/strong\u003e\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e900-5G153-2570-000\u003c\/td\u003e\n\u003ctd\u003e\n\u003ca href=\"\/products\/nvidia-rtx-pro%E2%84%A2-5000-blackwell-generation\"\u003eRTX PRO 5000 Blackwell 72 GB\u003c\/a\u003e — 72 GB GDDR7 with ECC\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e900-5G153-2550-000\u003c\/td\u003e\n\u003ctd\u003e\n\u003ca href=\"\/products\/nvidia-rtx-pro%E2%84%A2-5000-blackwell-generation-900-5g153-2550-000\"\u003eRTX PRO 5000 Blackwell 48 GB\u003c\/a\u003e — 48 GB GDDR7 with ECC\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e900-5G147-2550-000\u003c\/td\u003e\n\u003ctd\u003e\n\u003ca href=\"\/products\/nvidia-rtx-pro%E2%84%A2-4500-blackwell-generation-900-5g147-2550-000\"\u003eRTX PRO 4500 Blackwell\u003c\/a\u003e — 32 GB GDDR7 with ECC\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e900-5G147-2570-000\u003c\/td\u003e\n\u003ctd\u003e\n\u003ca href=\"\/products\/nvidia-rtx-pro%E2%84%A2-4000-blackwell-generation-900-5g147-2570-000\"\u003eRTX PRO 4000 Blackwell\u003c\/a\u003e — 24 GB GDDR7 with ECC\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e900-5G195-2501-000\u003c\/td\u003e\n\u003ctd\u003e\n\u003ca href=\"\/products\/nvidia-rtx-pro%E2%84%A2-4000-blackwell-generation-sff-edition-900-5g195-2501-000\"\u003eRTX PRO 4000 Blackwell SFF Edition\u003c\/a\u003e — 24 GB GDDR7 with ECC\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e900-5G195-2551-000\u003c\/td\u003e\n\u003ctd\u003e\n\u003ca href=\"\/products\/nvidia-rtx-pro%E2%84%A2-2000-blackwell-generation-900-5g195-2551-000\"\u003eRTX PRO 2000 Blackwell\u003c\/a\u003e — 16 GB GDDR7 with ECC\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/table\u003e\n\u003cp\u003eChoosing between them: memory capacity is usually the deciding factor for AI work and scene size, while form factor and power decide what will physically fit. The \u003cstrong\u003eMax-Q\u003c\/strong\u003e variant exists to give full 96 GB capacity at 300 W for chassis that cannot take a 600 W card or that need more than one GPU. The \u003cstrong\u003eServer Edition\u003c\/strong\u003e is passively cooled and belongs in a server, not a workstation. The \u003cstrong\u003eSFF Edition\u003c\/strong\u003e trades bandwidth and PCIe lanes for a 70 W low-profile board.\u003c\/p\u003e\n\u003c\/div\u003e\u003c\/details\u003e\u003cdetails class=\"pd-acc\"\u003e\u003csummary style=\"cursor: pointer; font-weight: 700;\"\u003eDatasheet \u0026amp; Downloads\u003c\/summary\u003e\u003cdiv class=\"pd-acc-body\"\u003e\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https:\/\/www.nvidia.com\/en-us\/data-center\/rtx-pro-6000-blackwell-server-edition\/\" target=\"_blank\" rel=\"noopener\"\u003eNVIDIA RTX PRO™ 6000 Blackwell Server Edition official NVIDIA datasheet\u003c\/a\u003e\u003c\/li\u003e\n\u003cli\u003e\u003ca href=\"https:\/\/www.nvidia.com\/en-us\/data-center\/rtx-pro-6000-blackwell-server-edition\/\" target=\"_blank\" rel=\"noopener\"\u003eNVIDIA RTX PRO™ 6000 Blackwell Server Edition product page on NVIDIA.com\u003c\/a\u003e\u003c\/li\u003e\n\u003cli\u003e\u003ca href=\"https:\/\/www.nvidia.com\/content\/dam\/en-zz\/Solutions\/design-visualization\/quadro-product-literature\/NVIDIA-RTX-Blackwell-PRO-GPU-Architecture-v1.0.pdf\" target=\"_blank\" rel=\"noopener\"\u003eNVIDIA RTX PRO Blackwell GPU Architecture Whitepaper (PDF)\u003c\/a\u003e\u003c\/li\u003e\n\u003cli\u003e\u003ca href=\"https:\/\/www.nvidia.com\/en-us\/products\/workstations\/product-literature\/\" target=\"_blank\" rel=\"noopener\"\u003eNVIDIA professional workstation product literature library\u003c\/a\u003e\u003c\/li\u003e\n\u003c\/ul\u003e\u003c\/div\u003e\u003c\/details\u003e\u003cdetails class=\"pd-acc\"\u003e\u003csummary style=\"cursor: pointer; font-weight: 700;\"\u003eFrequently Asked Questions — RTX PRO 6000 Blackwell Server Edition\u003c\/summary\u003e\u003cdiv class=\"pd-acc-body\"\u003e\n\u003cp\u003e\u003cstrong\u003eHow much GPU memory does it have, and why does that matter?\u003c\/strong\u003e\u003cbr\u003e96 GB GDDR7 with ECC. For AI work, VRAM capacity decides which models fit on the card at all — a model that does not fit runs an order of magnitude slower or not at all. For visualisation it decides scene and texture budget. ECC means single-bit memory errors are corrected rather than silently corrupting a long job.\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eCan I put the Server Edition in a workstation?\u003c\/strong\u003e\u003cbr\u003eNo. It is passively cooled with no fans of its own and depends entirely on server chassis airflow. Installing it in a desktop workstation will overheat it. For workstations use the \u003ca href=\"\/products\/nvidia-rtx-pro%E2%84%A2-6000-blackwell-workstation-edition-900-5g144-2500-000\"\u003eRTX PRO 6000 Blackwell Workstation Edition\u003c\/a\u003e or the \u003ca href=\"\/products\/nvidia-rtx-pro%E2%84%A2-6000-blackwell-max-q-workstation-edition-900-5g153-2500-000\"\u003eMax-Q Workstation Edition\u003c\/a\u003e.\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eWhat power supply and chassis clearance do I need?\u003c\/strong\u003e\u003cbr\u003eThis card is up to 600 w (configurable) in a 4.4in (H) x 10.5in (L), dual slot (air-cooled, FHFL) form factor with passive cooling, powered via 1x PCIe CEM5 16-pin. Check both physical clearance and PSU headroom against your workstation before ordering — send us your machine model and we will confirm fit.\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eDoes it support Multi-Instance GPU?\u003c\/strong\u003e\u003cbr\u003eYes — up to 4 MIG instances @ 24 GB. Each instance gets isolated memory and compute, so several users, containers or services can share one card with hard boundaries instead of contending for it.\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eIs this a professional card or a gaming card?\u003c\/strong\u003e\u003cbr\u003eProfessional. RTX PRO Blackwell cards carry ECC memory, NVIDIA RTX Enterprise drivers with ISV application certification, longer driver support branches and a professional warranty. Consumer GeForce cards have none of these, which is why they are not accepted in most enterprise and regulated deployments regardless of raw frame rate.\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eDoes it come with local warranty in Singapore?\u003c\/strong\u003e\u003cbr\u003eYes. Every RTX PRO 6000 Blackwell Server Edition sold by SourceIT comes with 3 years local Singapore warranty and a GST invoice.\u003c\/p\u003e\n\u003c\/div\u003e\u003c\/details\u003e\n\u003c\/div\u003e","brand":"Nvidia","offers":[{"title":"Default Title","offer_id":51335995359396,"sku":"900-2G153-0000-000","price":27150.0,"currency_code":"SGD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0605\/0136\/0804\/files\/nvidia-rtx-pro-6000-blackwell-server-edition-3259490.webp?v=1779693427"},{"product_id":"altos-brainsphere™-gb10-f1-ai-desktop-4tb","title":"Altos BrainSphere™ GB10 F1 AI Desktop 4TB","description":"\u003ch2\u003e\n\u003cstrong\u003eAltos BrainSphere™ GB10 F1 AI Desktop 4TB (NVIDIA GB10 \/ 128GB LPDDR5x \/ NVIDIA DGX™ OS + Altos aiGeni Platform) (DT.L1BSM.001) - 1 Year Local Warranty \u003c\/strong\u003e\n\u003c\/h2\u003e\n\u003cp data-is-only-node=\"\" data-is-last-node=\"\" data-end=\"2325\" data-start=\"1731\"\u003e\u003ciframe width=\"560\" height=\"315\" src=\"https:\/\/www.youtube.com\/embed\/Sd2Ie68OD_o?si=s0psw3fnKrfzFmAM\" title=\"YouTube video player\"\u003e\u003c\/iframe\u003e\u003c\/p\u003e\n\u003ch3 data-start=\"171\" data-end=\"224\"\u003e\u003cstrong data-start=\"171\" data-end=\"222\"\u003eEnterprise AI Desktop for Advanced AI Computing\u003c\/strong\u003e\u003c\/h3\u003e\n\u003cp data-start=\"226\" data-end=\"672\"\u003e\u003cspan class=\"hover:entity-accent entity-underline inline cursor-pointer align-baseline\"\u003e\u003cspan class=\"whitespace-normal\"\u003eNVIDIA\u003c\/span\u003e\u003c\/span\u003e Altos BrainSphere™ GB10 F1 AI Desktop 4TB (DT.L1BSM.001) is a high-performance AI workstation designed for generative AI, machine learning, large language models (LLMs), simulation, and accelerated computing workloads. Powered by the NVIDIA GB10 platform and integrated with NVIDIA DGX™ OS and Altos aiGeni Platform, this AI desktop delivers enterprise-grade AI processing in a compact desktop environment.\u003c\/p\u003e\n\u003ch3 data-start=\"674\" data-end=\"725\"\u003e\u003cstrong data-start=\"674\" data-end=\"723\"\u003eOptimized for Generative AI and LLM Workloads\u003c\/strong\u003e\u003c\/h3\u003e\n\u003cp data-start=\"727\" data-end=\"1096\"\u003eDesigned for developers, enterprises, AI labs, and research institutions, the Altos BrainSphere GB10 F1 supports demanding AI applications including LLM inference, AI model fine-tuning, simulation, analytics, and generative AI development. The combination of 128GB LPDDR5x memory and 4TB storage enables efficient handling of large AI datasets and advanced workflows.\u003c\/p\u003e\n\u003ch3 data-start=\"1098\" data-end=\"1149\"\u003e\u003cstrong data-start=\"1098\" data-end=\"1147\"\u003ePowered by NVIDIA GB10 and DGX Software Stack\u003c\/strong\u003e\u003c\/h3\u003e\n\u003cp data-start=\"1151\" data-end=\"1484\"\u003eBuilt on NVIDIA GB10 accelerated computing architecture, the system leverages NVIDIA DGX™ OS together with the Altos aiGeni Platform to provide an optimized AI development and deployment environment. This enables organizations to accelerate AI adoption while maintaining local AI processing performance and operational flexibility.\u003c\/p\u003e\n\u003ch3 data-start=\"1486\" data-end=\"1543\"\u003e\u003cstrong data-start=\"1486\" data-end=\"1541\"\u003eCompact AI Infrastructure for Enterprise Deployment\u003c\/strong\u003e\u003c\/h3\u003e\n\u003cp data-start=\"1545\" data-end=\"1893\"\u003eThe Altos BrainSphere™ GB10 F1 AI Desktop provides a compact alternative to traditional AI server infrastructure while supporting powerful local AI computing capabilities. It is suitable for enterprises, educational institutions, AI developers, and organizations seeking on-premises AI infrastructure with lower latency and improved data privacy.\u003c\/p\u003e\n\u003ch3 data-start=\"1895\" data-end=\"1937\"\u003e\u003cstrong data-start=\"1895\" data-end=\"1935\"\u003eTechnical Specifications\u003c\/strong\u003e\u003c\/h3\u003e\n\u003cp data-start=\"72\" data-end=\"610\"\u003e\u003cstrong data-start=\"72\" data-end=\"83\"\u003eGeneral\u003c\/strong\u003e\u003cbr data-start=\"83\" data-end=\"86\"\u003eModel: GB10 F1\u003cbr data-start=\"100\" data-end=\"103\"\u003ePart Number: DT.L1BSM.001\u003cbr data-start=\"128\" data-end=\"131\"\u003eProduct Name: Altos BrainSphere™ GB10 F1 AI Desktop 4TB\u003cbr data-start=\"186\" data-end=\"189\"\u003eManufacturer: \u003cspan class=\"hover:entity-accent entity-underline inline cursor-pointer align-baseline\"\u003e\u003cspan class=\"whitespace-normal\"\u003eAltos Computing\u003c\/span\u003e\u003c\/span\u003e\u003cbr data-start=\"240\" data-end=\"243\"\u003eProduct Type: AI Desktop Supercomputer \/ AI Workstation\u003cbr data-start=\"298\" data-end=\"301\"\u003eDeployment: Generative AI, LLM Development, AI Inferencing, AI Training, Data Science, Edge AI\u003cbr data-start=\"395\" data-end=\"398\"\u003eArchitecture: NVIDIA Grace Blackwell Platform\u003cbr data-start=\"443\" data-end=\"446\"\u003eTarget Workloads: AI Agents, LLM Fine-Tuning, Generative AI, Deep Learning, AI Prototyping\u003cbr data-start=\"536\" data-end=\"539\"\u003eOperating System: NVIDIA DGX™ OS\u003cbr data-start=\"571\" data-end=\"574\"\u003eAI Platform: Altos aiGeni Platform\u003c\/p\u003e\n\u003cp data-start=\"612\" data-end=\"829\"\u003e\u003cstrong data-start=\"612\" data-end=\"640\"\u003eProcessor Specifications\u003c\/strong\u003e\u003cbr data-start=\"640\" data-end=\"643\"\u003eCPU Architecture: NVIDIA Grace Blackwell Superchip\u003cbr data-start=\"693\" data-end=\"696\"\u003eCPU Type: Arm-Based Processor\u003cbr data-start=\"725\" data-end=\"728\"\u003eCPU Core Count: 20 Cores\u003cbr data-start=\"752\" data-end=\"755\"\u003eCPU Configuration:\u003cbr data-start=\"773\" data-end=\"776\"\u003e• 10 x Cortex-X925 Cores\u003cbr data-start=\"800\" data-end=\"803\"\u003e• 10 x Cortex-A725 Cores\u003c\/p\u003e\n\u003cp data-start=\"831\" data-end=\"934\"\u003eProcessor Optimization: AI Accelerated Computing\u003cbr data-start=\"879\" data-end=\"882\"\u003eCPU-GPU Interconnect: NVIDIA NVLink-C2C Technology\u003c\/p\u003e\n\u003cp data-start=\"936\" data-end=\"1264\"\u003e\u003cstrong data-start=\"936\" data-end=\"958\"\u003eGPU Specifications\u003c\/strong\u003e\u003cbr data-start=\"958\" data-end=\"961\"\u003eGPU Architecture: NVIDIA Grace Blackwell\u003cbr data-start=\"1001\" data-end=\"1004\"\u003eCUDA Cores: 6144\u003cbr data-start=\"1020\" data-end=\"1023\"\u003eTensor Core Generation: 5th Generation Tensor Cores\u003cbr data-start=\"1074\" data-end=\"1077\"\u003eRT Core Generation: 4th Generation RT Cores\u003cbr data-start=\"1120\" data-end=\"1123\"\u003eAI Performance: Up to 1 PetaFLOP AI Performance\u003cbr data-start=\"1170\" data-end=\"1173\"\u003eAI Optimization: Supported\u003cbr data-start=\"1199\" data-end=\"1202\"\u003eGenerative AI Support: Supported\u003cbr data-start=\"1234\" data-end=\"1237\"\u003eLLM Processing: Supported\u003c\/p\u003e\n\u003cp data-start=\"1266\" data-end=\"1473\"\u003e\u003cstrong data-start=\"1266\" data-end=\"1291\"\u003eMemory Specifications\u003c\/strong\u003e\u003cbr data-start=\"1291\" data-end=\"1294\"\u003eMemory Type: LPDDR5x Unified System Memory\u003cbr data-start=\"1336\" data-end=\"1339\"\u003eMemory Capacity: 128GB\u003cbr data-start=\"1361\" data-end=\"1364\"\u003eMemory Architecture: Unified CPU-GPU Shared Memory\u003cbr data-start=\"1414\" data-end=\"1417\"\u003eMemory Interface: 256-bit\u003cbr data-start=\"1442\" data-end=\"1445\"\u003eMemory Bandwidth: 273 GB\/s\u003c\/p\u003e\n\u003cp data-start=\"1475\" data-end=\"1649\"\u003e\u003cstrong data-start=\"1475\" data-end=\"1501\"\u003eStorage Specifications\u003c\/strong\u003e\u003cbr data-start=\"1501\" data-end=\"1504\"\u003eStorage Capacity: 4TB\u003cbr data-start=\"1525\" data-end=\"1528\"\u003eStorage Type: NVMe M.2 SSD\u003cbr data-start=\"1554\" data-end=\"1557\"\u003eSelf Encryption Support: Supported\u003cbr data-start=\"1591\" data-end=\"1594\"\u003eStorage Architecture: High Speed AI Optimized Storage\u003c\/p\u003e\n\u003cp data-start=\"1651\" data-end=\"1828\"\u003e\u003cstrong data-start=\"1651\" data-end=\"1680\"\u003eNetworking Specifications\u003c\/strong\u003e\u003cbr data-start=\"1680\" data-end=\"1683\"\u003eNetwork Technology: NVIDIA ConnectX®-7\u003cbr data-start=\"1721\" data-end=\"1724\"\u003eHigh Speed Networking: Supported\u003cbr data-start=\"1756\" data-end=\"1759\"\u003eLow Latency Networking: Supported\u003cbr data-start=\"1792\" data-end=\"1795\"\u003eAI Cluster Expansion: Supported\u003c\/p\u003e\n\u003cp data-start=\"1830\" data-end=\"1982\"\u003eMulti-System Scaling:\u003cbr data-start=\"1851\" data-end=\"1854\"\u003e• Supports dual system interconnect\u003cbr data-start=\"1889\" data-end=\"1892\"\u003e• High-bandwidth AI scaling support\u003cbr data-start=\"1927\" data-end=\"1930\"\u003e• Supports larger AI model deployment environments\u003c\/p\u003e\n\u003cp data-start=\"1984\" data-end=\"2046\"\u003e\u003cstrong data-start=\"1984\" data-end=\"2000\"\u003eConnectivity\u003c\/strong\u003e\u003cbr data-start=\"2000\" data-end=\"2003\"\u003eUSB Ports:\u003cbr data-start=\"2013\" data-end=\"2016\"\u003e• 3 x USB 3.2 Gen 2x2 Type-C\u003c\/p\u003e\n\u003cp data-start=\"2048\" data-end=\"2136\"\u003eUSB Speed: Up to 20Gbps\u003cbr data-start=\"2071\" data-end=\"2074\"\u003eDisplay Output Support: DisplayPort Alternate Mode Supported\u003c\/p\u003e\n\u003cp data-start=\"2138\" data-end=\"2250\"\u003e\u003cstrong data-start=\"2138\" data-end=\"2164\"\u003eSoftware \u0026amp; AI Platform\u003c\/strong\u003e\u003cbr data-start=\"2164\" data-end=\"2167\"\u003eOperating System: NVIDIA DGX™ OS\u003cbr data-start=\"2199\" data-end=\"2202\"\u003eAI Development Platform: Altos aiGeni Platform\u003c\/p\u003e\n\u003cp data-start=\"2252\" data-end=\"2412\"\u003eSupported AI Functions:\u003cbr data-start=\"2275\" data-end=\"2278\"\u003e• AI Model Development\u003cbr data-start=\"2300\" data-end=\"2303\"\u003e• AI Fine-Tuning\u003cbr data-start=\"2319\" data-end=\"2322\"\u003e• AI Inferencing\u003cbr data-start=\"2338\" data-end=\"2341\"\u003e• AI Agent Development\u003cbr data-start=\"2363\" data-end=\"2366\"\u003e• Generative AI Workflows\u003cbr data-start=\"2391\" data-end=\"2394\"\u003e• LLM Deployment\u003c\/p\u003e\n\u003cp data-start=\"2414\" data-end=\"2510\"\u003eAI Resource Management: Supported\u003cbr data-start=\"2447\" data-end=\"2450\"\u003eAutomatic Backup: Supported\u003cbr data-start=\"2477\" data-end=\"2480\"\u003eSystem Monitoring: Supported\u003c\/p\u003e\n\u003cp data-start=\"2512\" data-end=\"2980\"\u003e\u003cstrong data-start=\"2512\" data-end=\"2538\"\u003ePerformance \u0026amp; Features\u003c\/strong\u003e\u003cbr data-start=\"2538\" data-end=\"2541\"\u003eSupports up to 1 PetaFLOP AI performance\u003cbr data-start=\"2581\" data-end=\"2584\"\u003eUnified memory architecture for accelerated AI workloads\u003cbr data-start=\"2640\" data-end=\"2643\"\u003eOptimized for local LLM deployment and fine-tuning\u003cbr data-start=\"2693\" data-end=\"2696\"\u003eDesigned for generative AI and AI agent development\u003cbr data-start=\"2747\" data-end=\"2750\"\u003eSupports enterprise AI prototyping environments\u003cbr data-start=\"2797\" data-end=\"2800\"\u003eCompact desktop AI supercomputer architecture\u003cbr data-start=\"2845\" data-end=\"2848\"\u003eHigh-speed CPU-GPU communication via NVLink-C2C\u003cbr data-start=\"2895\" data-end=\"2898\"\u003eSupports large parameter AI model processing \u003c\/p\u003e\n\u003cp data-start=\"2982\" data-end=\"3141\"\u003e\u003cstrong data-start=\"2982\" data-end=\"3010\"\u003eCooling \u0026amp; Thermal Design\u003c\/strong\u003e\u003cbr data-start=\"3010\" data-end=\"3013\"\u003eCooling Type: Advanced Active Cooling System\u003cbr data-start=\"3057\" data-end=\"3060\"\u003eThermal Optimization: AI Workload Optimized\u003cbr data-start=\"3103\" data-end=\"3106\"\u003eCompact Airflow Design: Supported\u003c\/p\u003e\n\u003cp data-start=\"3143\" data-end=\"3284\"\u003e\u003cstrong data-start=\"3143\" data-end=\"3164\"\u003eSecurity Features\u003c\/strong\u003e\u003cbr data-start=\"3164\" data-end=\"3167\"\u003eSelf Encrypting Storage: Supported\u003cbr data-start=\"3201\" data-end=\"3204\"\u003eEnterprise AI Data Privacy: Supported\u003cbr data-start=\"3241\" data-end=\"3244\"\u003eLocal AI Processing Support: Supported\u003c\/p\u003e\n\u003cp data-start=\"3286\" data-end=\"3496\"\u003e\u003cstrong data-start=\"3286\" data-end=\"3311\"\u003eDesign \u0026amp; Construction\u003c\/strong\u003e\u003cbr data-start=\"3311\" data-end=\"3314\"\u003eForm Factor: Compact Desktop AI Workstation\u003cbr data-start=\"3357\" data-end=\"3360\"\u003eDeployment Environment: Enterprise AI Lab \/ Research \/ AI Development \/ Edge AI\u003cbr data-start=\"3439\" data-end=\"3442\"\u003eConstruction: Enterprise Grade AI Computing Platform\u003c\/p\u003e\n\u003cp data-start=\"3498\" data-end=\"3653\"\u003e\u003cstrong data-start=\"3498\" data-end=\"3518\"\u003ePackage Contents\u003c\/strong\u003e\u003cbr data-start=\"3518\" data-end=\"3521\"\u003eAltos BrainSphere™ GB10 F1 AI Desktop\u003cbr data-start=\"3558\" data-end=\"3561\"\u003ePower Adapter\u003cbr data-start=\"3574\" data-end=\"3577\"\u003eDocumentation\u003cbr data-start=\"3590\" data-end=\"3593\"\u003ePreloaded NVIDIA DGX™ OS\u003cbr data-start=\"3617\" data-end=\"3620\"\u003ePreloaded Altos aiGeni Platform\u003c\/p\u003e\n\u003cp data-start=\"3655\" data-end=\"3989\"\u003e\u003cstrong data-start=\"3655\" data-end=\"3671\"\u003eKey Features\u003c\/strong\u003e\u003cbr data-start=\"3671\" data-end=\"3674\"\u003eNVIDIA GB10 Grace Blackwell Superchip\u003cbr data-start=\"3711\" data-end=\"3714\"\u003e128GB LPDDR5x unified memory\u003cbr data-start=\"3742\" data-end=\"3745\"\u003e4TB NVMe SSD storage\u003cbr data-start=\"3765\" data-end=\"3768\"\u003eUp to 1 PetaFLOP AI performance\u003cbr data-start=\"3799\" data-end=\"3802\"\u003eNVIDIA DGX™ OS preloaded\u003cbr data-start=\"3826\" data-end=\"3829\"\u003eAltos aiGeni AI platform included\u003cbr data-start=\"3862\" data-end=\"3865\"\u003eConnectX®-7 high-speed networking\u003cbr data-start=\"3898\" data-end=\"3901\"\u003eDesigned for generative AI and LLM workloads\u003cbr data-start=\"3945\" data-end=\"3948\"\u003eCompact desktop AI supercomputer design\u003c\/p\u003e\n\u003cp data-start=\"3991\" data-end=\"4259\"\u003e\u003cstrong data-start=\"3991\" data-end=\"4012\"\u003eTypical Use Cases\u003c\/strong\u003e\u003cbr data-start=\"4012\" data-end=\"4015\"\u003eLarge Language Model (LLM) fine-tuning\u003cbr data-start=\"4053\" data-end=\"4056\"\u003eGenerative AI development\u003cbr data-start=\"4081\" data-end=\"4084\"\u003eAI inferencing\u003cbr data-start=\"4098\" data-end=\"4101\"\u003eAI agent deployment\u003cbr data-start=\"4120\" data-end=\"4123\"\u003eDeep learning workloads\u003cbr data-start=\"4146\" data-end=\"4149\"\u003eAI research environments\u003cbr data-start=\"4173\" data-end=\"4176\"\u003eEnterprise AI prototyping\u003cbr data-start=\"4201\" data-end=\"4204\"\u003eEdge AI computing\u003cbr data-start=\"4221\" data-end=\"4224\"\u003eData science and machine learning\u003c\/p\u003e","brand":"Acer","offers":[{"title":"Default Title","offer_id":51338553884836,"sku":"DT.L1BSM.001","price":7955.0,"currency_code":"SGD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0605\/0136\/0804\/files\/altos-brainsphere-gb10-f1-ai-desktop-4tb-3338031.jpg?v=1779966968"},{"product_id":"gigabyte-ai-top-atom-personal-ai-computer-4tb-pcie4-0","title":"Gigabyte AI TOP ATOM Personal AI Computer 4TB (PCIe4.0)","description":"\u003ch2\u003e\n\u003cstrong\u003eGigabyte AI TOP ATOM Personal AI Computer 4TB (GB10 \/ 128GB LPDDR5x \/ \u003cspan style=\"color: rgb(255, 42, 0);\"\u003ePCIe4.0\u003c\/span\u003e 4TB NVMe M.2 SSD \/ NVIDIA DGX™ OS, Ubuntu Linux (ATAGB10-9000) - 3 Year Local Warranty \u003c\/strong\u003e\n\u003c\/h2\u003e\n\u003cp data-start=\"1731\" data-end=\"2325\" data-is-last-node=\"\" data-is-only-node=\"\"\u003e\u003ciframe title=\"YouTube video player\" src=\"https:\/\/www.youtube.com\/embed\/Sd2Ie68OD_o?si=s0psw3fnKrfzFmAM\" height=\"315\" width=\"560\"\u003e\u003c\/iframe\u003e\u003c\/p\u003e\n\u003ch3 data-start=\"203\" data-end=\"265\"\u003e\u003cstrong data-start=\"203\" data-end=\"263\"\u003ePersonal AI Computing Platform for Advanced AI Workloads\u003c\/strong\u003e\u003c\/h3\u003e\n\u003cp data-start=\"267\" data-end=\"717\"\u003eThe Gigabyte AI TOP ATOM Personal AI Computer 4TB (ATAGB10-9000) is a powerful desktop AI system built for developers, researchers, educators, and enterprises seeking local AI computing capabilities. Powered by the NVIDIA GB10 platform and equipped with 128GB LPDDR5x unified memory, this compact AI workstation delivers the performance needed for artificial intelligence development, machine learning, generative AI, and data science applications.\u003c\/p\u003e\n\u003ch3 data-start=\"719\" data-end=\"777\"\u003e\u003cstrong data-start=\"719\" data-end=\"775\"\u003eDesigned for Large Language Models and Generative AI\u003c\/strong\u003e\u003c\/h3\u003e\n\u003cp data-start=\"779\" data-end=\"1183\"\u003eBuilt to support modern AI workflows, the Gigabyte AI TOP ATOM enables users to run large language models (LLMs), AI inference workloads, deep learning frameworks, retrieval-augmented generation (RAG), and AI-assisted software development locally. Organizations can accelerate AI innovation while maintaining greater control over sensitive datasets and reducing dependence on cloud computing resources.\u003c\/p\u003e\n\u003ch3 data-start=\"1185\" data-end=\"1239\"\u003e\u003cstrong data-start=\"1185\" data-end=\"1237\"\u003eHigh-Speed Memory and Storage for AI Development\u003c\/strong\u003e\u003c\/h3\u003e\n\u003cp data-start=\"1241\" data-end=\"1599\"\u003eFeaturing 128GB LPDDR5x unified memory and a 4TB PCIe 4.0 NVMe M.2 SSD, the system provides ample capacity for AI models, datasets, development environments, and enterprise applications. The high-speed storage subsystem ensures rapid data access and smooth operation when working with large AI workloads, research projects, and complex computational tasks.\u003c\/p\u003e\n\u003ch3 data-start=\"1601\" data-end=\"1651\"\u003e\u003cstrong data-start=\"1601\" data-end=\"1649\"\u003eOptimized AI Environment with NVIDIA DGX™ OS\u003c\/strong\u003e\u003c\/h3\u003e\n\u003cp data-start=\"1653\" data-end=\"2015\"\u003ePreloaded with NVIDIA DGX™ OS based on Ubuntu Linux, the Gigabyte AI TOP ATOM offers a professional AI development environment optimized for machine learning frameworks, AI toolkits, and data science applications. This enables developers and researchers to deploy, test, and scale AI projects efficiently using industry-standard software and development tools.\u003c\/p\u003e\n\u003ch3 data-start=\"2017\" data-end=\"2059\"\u003e\u003cstrong data-start=\"2017\" data-end=\"2057\"\u003eTechnical Specifications\u003c\/strong\u003e\u003c\/h3\u003e\n\u003cp data-start=\"0\" data-end=\"625\"\u003e\u003cstrong data-start=\"0\" data-end=\"11\"\u003eGeneral\u003c\/strong\u003e\u003cbr data-start=\"11\" data-end=\"14\"\u003eModel: AI TOP ATOM Personal AI Computer 4TB\u003cbr data-start=\"57\" data-end=\"60\"\u003ePart Number: ATAGB10-9000\u003cbr data-start=\"85\" data-end=\"88\"\u003eProduct Name: Gigabyte AI TOP ATOM Personal AI Computer 4TB\u003cbr data-start=\"147\" data-end=\"150\"\u003eManufacturer: \u003cspan class=\"hover:entity-accent entity-underline inline cursor-pointer align-baseline\"\u003e\u003cspan class=\"whitespace-normal\"\u003eGigabyte Technology\u003c\/span\u003e\u003c\/span\u003e\u003cbr data-start=\"201\" data-end=\"204\"\u003eProduct Type: Personal AI Supercomputer\u003cbr data-start=\"243\" data-end=\"246\"\u003ePlatform: NVIDIA GB10 Grace Blackwell Superchip\u003cbr data-start=\"293\" data-end=\"296\"\u003eForm Factor: Compact Desktop AI Computer\u003cbr data-start=\"336\" data-end=\"339\"\u003eOperating System: NVIDIA DGX™ OS, Ubuntu Linux\u003cbr data-start=\"385\" data-end=\"388\"\u003eTarget Users: AI Developers, Researchers, Data Scientists, Universities, Enterprise AI Teams\u003cbr data-start=\"480\" data-end=\"483\"\u003eDeployment Type: AI Development, Generative AI, Machine Learning, LLM Inference, Edge AI Computing\u003cbr data-start=\"581\" data-end=\"584\"\u003eWarranty: Manufacturer Limited Warranty\u003c\/p\u003e\n\u003cp data-start=\"627\" data-end=\"1008\"\u003e\u003cstrong data-start=\"627\" data-end=\"655\"\u003eProcessor Specifications\u003c\/strong\u003e\u003cbr data-start=\"655\" data-end=\"658\"\u003eProcessor Platform: NVIDIA GB10 Grace Blackwell Superchip\u003cbr data-start=\"715\" data-end=\"718\"\u003eCPU Architecture: NVIDIA Grace ARM Architecture\u003cbr data-start=\"765\" data-end=\"768\"\u003eCPU Configuration: 20-Core CPU\u003cbr data-start=\"798\" data-end=\"801\"\u003eCPU Core Layout: 10× Cortex-X925 + 10× Cortex-A725\u003cbr data-start=\"851\" data-end=\"854\"\u003eProcessor Performance: Optimized for AI, Data Science, and High-Performance Computing Workloads\u003cbr data-start=\"949\" data-end=\"952\"\u003eSystem Architecture: Unified CPU and GPU Memory Design\u003c\/p\u003e\n\u003cp data-start=\"1010\" data-end=\"1434\"\u003e\u003cstrong data-start=\"1010\" data-end=\"1032\"\u003eGPU Specifications\u003c\/strong\u003e\u003cbr data-start=\"1032\" data-end=\"1035\"\u003eGPU Architecture: NVIDIA Blackwell GPU\u003cbr data-start=\"1073\" data-end=\"1076\"\u003eCUDA Technology: Supported\u003cbr data-start=\"1102\" data-end=\"1105\"\u003eTensor Cores: Latest Generation Tensor Cores\u003cbr data-start=\"1149\" data-end=\"1152\"\u003eRay Tracing Cores: Supported\u003cbr data-start=\"1180\" data-end=\"1183\"\u003eAI Compute Performance: Up to 1 PetaFLOP FP4 AI Performance\u003cbr data-start=\"1242\" data-end=\"1245\"\u003eNVIDIA AI Software Stack: Supported\u003cbr data-start=\"1280\" data-end=\"1283\"\u003eGenerative AI Processing: Supported\u003cbr data-start=\"1318\" data-end=\"1321\"\u003eLarge Language Model (LLM) Support: Supported\u003cbr data-start=\"1366\" data-end=\"1369\"\u003eNVIDIA TensorRT: Supported\u003cbr data-start=\"1395\" data-end=\"1398\"\u003eNVIDIA CUDA-X Libraries: Supported\u003c\/p\u003e\n\u003cp data-start=\"1436\" data-end=\"1711\"\u003e\u003cstrong data-start=\"1436\" data-end=\"1461\"\u003eMemory Specifications\u003c\/strong\u003e\u003cbr data-start=\"1461\" data-end=\"1464\"\u003eInstalled Memory: 128 GB\u003cbr data-start=\"1488\" data-end=\"1491\"\u003eMemory Type: LPDDR5x Unified Memory\u003cbr data-start=\"1526\" data-end=\"1529\"\u003eMemory Architecture: Shared CPU-GPU Unified Memory\u003cbr data-start=\"1579\" data-end=\"1582\"\u003eMemory Bandwidth: Up to 273 GB\/s\u003cbr data-start=\"1614\" data-end=\"1617\"\u003eECC Protection: Supported\u003cbr data-start=\"1642\" data-end=\"1645\"\u003eAI Model Optimization: Designed for Large AI Models and Datasets\u003c\/p\u003e\n\u003cp data-start=\"1713\" data-end=\"1935\"\u003e\u003cstrong data-start=\"1713\" data-end=\"1739\"\u003eStorage Specifications\u003c\/strong\u003e\u003cbr data-start=\"1739\" data-end=\"1742\"\u003eInstalled Storage: 4 TB\u003cbr data-start=\"1765\" data-end=\"1768\"\u003eStorage Type: NVMe M.2 SSD\u003cbr data-start=\"1794\" data-end=\"1797\"\u003eSSD Interface: PCIe 4.0\u003cbr data-start=\"1820\" data-end=\"1823\"\u003eStorage Expansion: Configuration Dependent\u003cbr data-start=\"1865\" data-end=\"1868\"\u003eHigh-Speed Data Access: Supported\u003cbr data-start=\"1901\" data-end=\"1904\"\u003eAI Dataset Storage: Supported\u003c\/p\u003e\n\u003cp data-start=\"1937\" data-end=\"2367\"\u003e\u003cstrong data-start=\"1937\" data-end=\"1971\"\u003eAI \u0026amp; Machine Learning Features\u003c\/strong\u003e\u003cbr data-start=\"1971\" data-end=\"1974\"\u003eLarge Language Model (LLM) Development: Supported\u003cbr data-start=\"2023\" data-end=\"2026\"\u003eAI Inference: Supported\u003cbr data-start=\"2049\" data-end=\"2052\"\u003eAI Fine-Tuning: Supported\u003cbr data-start=\"2077\" data-end=\"2080\"\u003eRetrieval-Augmented Generation (RAG): Supported\u003cbr data-start=\"2127\" data-end=\"2130\"\u003eGenerative AI Applications: Supported\u003cbr data-start=\"2167\" data-end=\"2170\"\u003eAI Agent Development: Supported\u003cbr data-start=\"2201\" data-end=\"2204\"\u003eComputer Vision Workloads: Supported\u003cbr data-start=\"2240\" data-end=\"2243\"\u003eNatural Language Processing (NLP): Supported\u003cbr data-start=\"2287\" data-end=\"2290\"\u003eDeep Learning Framework Support: Supported\u003cbr data-start=\"2332\" data-end=\"2335\"\u003eLocal AI Processing: Supported\u003c\/p\u003e\n\u003cp data-start=\"2369\" data-end=\"2633\"\u003e\u003cstrong data-start=\"2369\" data-end=\"2403\"\u003eSoftware \u0026amp; Development Support\u003c\/strong\u003e\u003cbr data-start=\"2403\" data-end=\"2406\"\u003eOperating System: NVIDIA DGX™ OS\u003cbr data-start=\"2438\" data-end=\"2441\"\u003eLinux Distribution: Ubuntu Linux\u003cbr data-start=\"2473\" data-end=\"2476\"\u003eCUDA Toolkit: Supported\u003cbr data-start=\"2499\" data-end=\"2502\"\u003eNVIDIA TensorRT: Supported\u003cbr data-start=\"2528\" data-end=\"2531\"\u003eNVIDIA NIM Microservices: Supported\u003cbr data-start=\"2566\" data-end=\"2569\"\u003eDocker Support: Supported\u003cbr data-start=\"2594\" data-end=\"2597\"\u003eContainerized Workloads: Supported\u003c\/p\u003e\n\u003cp data-start=\"2635\" data-end=\"2724\"\u003eSupported AI Frameworks:\u003cbr data-start=\"2659\" data-end=\"2662\"\u003e• PyTorch\u003cbr data-start=\"2671\" data-end=\"2674\"\u003e• TensorFlow\u003cbr data-start=\"2686\" data-end=\"2689\"\u003e• JAX\u003cbr data-start=\"2694\" data-end=\"2697\"\u003e• ONNX Runtime\u003cbr data-start=\"2711\" data-end=\"2714\"\u003e• RAPIDS\u003c\/p\u003e\n\u003cp data-start=\"2726\" data-end=\"2932\"\u003e\u003cstrong data-start=\"2726\" data-end=\"2755\"\u003eNetworking Specifications\u003c\/strong\u003e\u003cbr data-start=\"2755\" data-end=\"2758\"\u003eEthernet: 10 Gigabit Ethernet (10GbE)\u003cbr data-start=\"2795\" data-end=\"2798\"\u003eWireless Connectivity: Wi-Fi 7\u003cbr data-start=\"2828\" data-end=\"2831\"\u003eBluetooth: Bluetooth 5.x\u003cbr data-start=\"2855\" data-end=\"2858\"\u003eRemote Access Support: Supported\u003cbr data-start=\"2890\" data-end=\"2893\"\u003eHigh-Speed Data Networking: Supported\u003c\/p\u003e\n\u003cp data-start=\"2934\" data-end=\"3146\"\u003e\u003cstrong data-start=\"2934\" data-end=\"2958\"\u003eConnectivity \u0026amp; Ports\u003c\/strong\u003e\u003cbr data-start=\"2958\" data-end=\"2961\"\u003eUSB Type-C Ports: Supported\u003cbr data-start=\"2988\" data-end=\"2991\"\u003eUSB Type-A Ports: Supported\u003cbr data-start=\"3018\" data-end=\"3021\"\u003eHDMI Output: Supported\u003cbr data-start=\"3043\" data-end=\"3046\"\u003eDisplay Connectivity: Supported\u003cbr data-start=\"3077\" data-end=\"3080\"\u003eRJ-45 Ethernet Port: Supported\u003cbr data-start=\"3110\" data-end=\"3113\"\u003ePeripheral Expansion: Supported\u003c\/p\u003e\n\u003cp data-start=\"3148\" data-end=\"3324\"\u003e\u003cstrong data-start=\"3148\" data-end=\"3169\"\u003eSecurity Features\u003c\/strong\u003e\u003cbr data-start=\"3169\" data-end=\"3172\"\u003eSecure Boot: Supported\u003cbr data-start=\"3194\" data-end=\"3197\"\u003eOperating System Security Updates: Supported\u003cbr data-start=\"3241\" data-end=\"3244\"\u003eEncrypted Storage Support: Supported\u003cbr data-start=\"3280\" data-end=\"3283\"\u003eEnterprise Security Features: Supported\u003c\/p\u003e\n\u003cp data-start=\"3326\" data-end=\"3467\"\u003e\u003cstrong data-start=\"3326\" data-end=\"3350\"\u003ePower Specifications\u003c\/strong\u003e\u003cbr data-start=\"3350\" data-end=\"3353\"\u003ePower Efficiency: Optimized for AI Computing\u003cbr data-start=\"3397\" data-end=\"3400\"\u003ePower Supply: Included\u003cbr data-start=\"3422\" data-end=\"3425\"\u003eEnergy Efficient Architecture: Supported\u003c\/p\u003e\n\u003cp data-start=\"3469\" data-end=\"3638\"\u003e\u003cstrong data-start=\"3469\" data-end=\"3495\"\u003eCooling Specifications\u003c\/strong\u003e\u003cbr data-start=\"3495\" data-end=\"3498\"\u003eCooling System: Active Cooling Design\u003cbr data-start=\"3535\" data-end=\"3538\"\u003eThermal Optimization: Supported\u003cbr data-start=\"3569\" data-end=\"3572\"\u003eAI Workload Cooling: Supported\u003cbr data-start=\"3602\" data-end=\"3605\"\u003eContinuous Operation: Supported\u003c\/p\u003e\n\u003cp data-start=\"3640\" data-end=\"3998\"\u003e\u003cstrong data-start=\"3640\" data-end=\"3663\"\u003eSupported Workloads\u003c\/strong\u003e\u003cbr data-start=\"3663\" data-end=\"3666\"\u003eLarge Language Models (LLMs)\u003cbr data-start=\"3694\" data-end=\"3697\"\u003eGenerative AI Applications\u003cbr data-start=\"3723\" data-end=\"3726\"\u003eAI Model Inference\u003cbr data-start=\"3744\" data-end=\"3747\"\u003eMachine Learning Development\u003cbr data-start=\"3775\" data-end=\"3778\"\u003eData Science Workloads\u003cbr data-start=\"3800\" data-end=\"3803\"\u003eComputer Vision Applications\u003cbr data-start=\"3831\" data-end=\"3834\"\u003eNatural Language Processing\u003cbr data-start=\"3861\" data-end=\"3864\"\u003eAI Agent Development\u003cbr data-start=\"3884\" data-end=\"3887\"\u003eResearch and Academic Computing\u003cbr data-start=\"3918\" data-end=\"3921\"\u003eEdge AI Deployments\u003cbr data-start=\"3940\" data-end=\"3943\"\u003eLocal AI Computing\u003cbr data-start=\"3961\" data-end=\"3964\"\u003eSoftware Development and Testing\u003c\/p\u003e\n\u003cp data-start=\"4000\" data-end=\"4234\"\u003e\u003cstrong data-start=\"4000\" data-end=\"4025\"\u003eDesign \u0026amp; Construction\u003c\/strong\u003e\u003cbr data-start=\"4025\" data-end=\"4028\"\u003eForm Factor: Compact Desktop AI Workstation\u003cbr data-start=\"4071\" data-end=\"4074\"\u003eChassis Design: AI Computing Optimized\u003cbr data-start=\"4112\" data-end=\"4115\"\u003eDeployment Flexibility: Office, Lab, Research, Enterprise Environment\u003cbr data-start=\"4184\" data-end=\"4187\"\u003eConstruction: Enterprise-Grade Compact Design\u003c\/p\u003e\n\u003cp data-start=\"4236\" data-end=\"4610\"\u003e\u003cstrong data-start=\"4236\" data-end=\"4248\"\u003eFeatures\u003c\/strong\u003e\u003cbr data-start=\"4248\" data-end=\"4251\"\u003eNVIDIA GB10 Grace Blackwell Superchip\u003cbr data-start=\"4288\" data-end=\"4291\"\u003e128 GB LPDDR5x Unified Memory\u003cbr data-start=\"4320\" data-end=\"4323\"\u003e4 TB PCIe 4.0 NVMe M.2 SSD\u003cbr data-start=\"4349\" data-end=\"4352\"\u003eNVIDIA Blackwell GPU Architecture\u003cbr data-start=\"4385\" data-end=\"4388\"\u003eUp to 1 PetaFLOP FP4 AI Performance\u003cbr data-start=\"4423\" data-end=\"4426\"\u003eNVIDIA DGX OS Preloaded\u003cbr data-start=\"4449\" data-end=\"4452\"\u003eUbuntu Linux Support\u003cbr data-start=\"4472\" data-end=\"4475\"\u003e10GbE Networking\u003cbr data-start=\"4491\" data-end=\"4494\"\u003eWi-Fi 7 Connectivity\u003cbr data-start=\"4514\" data-end=\"4517\"\u003eCompact Desktop Form Factor\u003cbr data-start=\"4544\" data-end=\"4547\"\u003eEnterprise AI Ready Platform\u003cbr data-start=\"4575\" data-end=\"4578\"\u003eLocal AI Processing Capability\u003c\/p\u003e\n\u003cp data-start=\"4612\" data-end=\"5174\"\u003e\u003cstrong data-start=\"4612\" data-end=\"4638\"\u003ePerformance \u0026amp; Features\u003c\/strong\u003e\u003cbr data-start=\"4638\" data-end=\"4641\"\u003eDesigned for local AI development and inference workloads\u003cbr data-start=\"4698\" data-end=\"4701\"\u003eSupports advanced generative AI applications without cloud dependency\u003cbr data-start=\"4770\" data-end=\"4773\"\u003eEnables experimentation and deployment of large language models\u003cbr data-start=\"4836\" data-end=\"4839\"\u003eProvides high-bandwidth unified memory architecture for AI workloads\u003cbr data-start=\"4907\" data-end=\"4910\"\u003eOptimized for developers, researchers, and enterprise AI teams\u003cbr data-start=\"4972\" data-end=\"4975\"\u003eSupports modern AI frameworks and NVIDIA software ecosystem\u003cbr data-start=\"5034\" data-end=\"5037\"\u003eOffers workstation-class AI performance in a compact desktop footprint\u003cbr data-start=\"5107\" data-end=\"5110\"\u003eBuilt for continuous AI computing and development environments\u003c\/p\u003e\n\u003cp data-start=\"5176\" data-end=\"5470\"\u003e\u003cstrong data-start=\"5176\" data-end=\"5203\"\u003ePhysical Specifications\u003c\/strong\u003e\u003cbr data-start=\"5203\" data-end=\"5206\"\u003eForm Factor: Compact Desktop AI Computer\u003cbr data-start=\"5246\" data-end=\"5249\"\u003eProcessor: NVIDIA GB10 Grace Blackwell Superchip\u003cbr data-start=\"5297\" data-end=\"5300\"\u003eMemory: 128 GB LPDDR5x Unified Memory\u003cbr data-start=\"5337\" data-end=\"5340\"\u003eStorage: 4 TB PCIe 4.0 NVMe M.2 SSD\u003cbr data-start=\"5375\" data-end=\"5378\"\u003eNetworking: 10GbE, Wi-Fi 7, Bluetooth 5.x\u003cbr data-start=\"5419\" data-end=\"5422\"\u003eOperating System: NVIDIA DGX™ OS, Ubuntu Linux\u003c\/p\u003e\n\u003cp data-start=\"5472\" data-end=\"5662\"\u003e\u003cstrong data-start=\"5472\" data-end=\"5492\"\u003ePackage Contents\u003c\/strong\u003e\u003cbr data-start=\"5492\" data-end=\"5495\"\u003eGigabyte AI TOP ATOM Personal AI Computer\u003cbr data-start=\"5536\" data-end=\"5539\"\u003ePower Adapter and Power Cable\u003cbr data-start=\"5568\" data-end=\"5571\"\u003eQuick Start Guide\u003cbr data-start=\"5588\" data-end=\"5591\"\u003eDocumentation\u003cbr data-start=\"5604\" data-end=\"5607\"\u003ePreloaded NVIDIA DGX™ OS and Ubuntu Linux Environment\u003c\/p\u003e\n\u003cp data-start=\"5664\" data-end=\"6104\"\u003e\u003cstrong data-start=\"5664\" data-end=\"5680\"\u003eKey Features\u003c\/strong\u003e\u003cbr data-start=\"5680\" data-end=\"5683\"\u003eNVIDIA GB10 Grace Blackwell Superchip\u003cbr data-start=\"5720\" data-end=\"5723\"\u003e128 GB LPDDR5x Unified Memory\u003cbr data-start=\"5752\" data-end=\"5755\"\u003e4 TB PCIe 4.0 NVMe M.2 SSD\u003cbr data-start=\"5781\" data-end=\"5784\"\u003eUp to 1 PetaFLOP FP4 AI Performance\u003cbr data-start=\"5819\" data-end=\"5822\"\u003eNVIDIA Blackwell GPU Architecture\u003cbr data-start=\"5855\" data-end=\"5858\"\u003eNVIDIA DGX™ OS Preinstalled\u003cbr data-start=\"5885\" data-end=\"5888\"\u003e10GbE Network Connectivity\u003cbr data-start=\"5914\" data-end=\"5917\"\u003eWi-Fi 7 and Bluetooth Support\u003cbr data-start=\"5946\" data-end=\"5949\"\u003eGenerative AI and LLM Ready\u003cbr data-start=\"5976\" data-end=\"5979\"\u003eCompact Desktop AI Workstation Design\u003cbr data-start=\"6016\" data-end=\"6019\"\u003eEnterprise and Research AI Development Platform\u003cbr data-start=\"6066\" data-end=\"6069\"\u003eOptimized for Local AI Processing\u003c\/p\u003e\n\u003cp data-start=\"6106\" data-end=\"6509\"\u003e\u003cstrong data-start=\"6106\" data-end=\"6127\"\u003eTypical Use Cases\u003c\/strong\u003e\u003cbr data-start=\"6127\" data-end=\"6130\"\u003eLarge Language Model (LLM) Inference\u003cbr data-start=\"6166\" data-end=\"6169\"\u003eAI Model Development\u003cbr data-start=\"6189\" data-end=\"6192\"\u003eGenerative AI Applications\u003cbr data-start=\"6218\" data-end=\"6221\"\u003eMachine Learning Research\u003cbr data-start=\"6246\" data-end=\"6249\"\u003eData Science Projects\u003cbr data-start=\"6270\" data-end=\"6273\"\u003eUniversity and Academic Research\u003cbr data-start=\"6305\" data-end=\"6308\"\u003eAI Agent Development\u003cbr data-start=\"6328\" data-end=\"6331\"\u003eComputer Vision Applications\u003cbr data-start=\"6359\" data-end=\"6362\"\u003eNatural Language Processing Workloads\u003cbr data-start=\"6399\" data-end=\"6402\"\u003eEnterprise AI Prototyping\u003cbr data-start=\"6427\" data-end=\"6430\"\u003eEdge AI Computing Deployments\u003cbr data-start=\"6459\" data-end=\"6462\"\u003eLocal AI Development Without Cloud Dependency\u003c\/p\u003e\n\u003cp data-start=\"6511\" data-end=\"6827\" data-is-last-node=\"\" data-is-only-node=\"\"\u003e\u003cstrong data-start=\"6511\" data-end=\"6535\"\u003eOrdering Information\u003c\/strong\u003e\u003cbr data-start=\"6535\" data-end=\"6538\"\u003eModel: Gigabyte AI TOP ATOM Personal AI Computer 4TB\u003cbr data-start=\"6590\" data-end=\"6593\"\u003ePart Number: ATAGB10-9000\u003cbr data-start=\"6618\" data-end=\"6621\"\u003eConfiguration: NVIDIA GB10 \/ 128GB LPDDR5x \/ PCIe 4.0 4TB NVMe M.2 SSD \/ NVIDIA DGX™ OS \/ Ubuntu Linux\u003c\/p\u003e","brand":"Nvidia","offers":[{"title":"Default Title","offer_id":51373622362276,"sku":"ATAGB10-9001","price":8450.0,"currency_code":"SGD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0605\/0136\/0804\/files\/gigabyte-ai-top-atom-personal-ai-computer-4tb-3819840.jpg?v=1767711611"},{"product_id":"gigabyte-ai-top-atom-personal-ai-computer-1tb-pcie4-0","title":"Gigabyte AI TOP ATOM Personal AI Computer 1TB (PCIe4.0)","description":"\u003ch2\u003e\n\u003cstrong\u003eGigabyte AI TOP ATOM Personal AI Computer 1TB (GB10 \/ 128GB LPDDR5x \/ \u003cspan style=\"color: rgb(255, 42, 0);\"\u003ePCIe4.0\u003c\/span\u003e 1TB NVMe M.2 SSD \/ NVIDIA DGX™ OS, Ubuntu Linux (ATAGB10-9000) - 3 Year Local Warranty \u003c\/strong\u003e\n\u003c\/h2\u003e\n\u003cp data-start=\"1731\" data-end=\"2325\" data-is-last-node=\"\" data-is-only-node=\"\"\u003e\u003ciframe title=\"YouTube video player\" src=\"https:\/\/www.youtube.com\/embed\/Sd2Ie68OD_o?si=s0psw3fnKrfzFmAM\" height=\"315\" width=\"560\"\u003e\u003c\/iframe\u003e\u003c\/p\u003e\n\u003ch3 data-start=\"209\" data-end=\"263\"\u003e\u003cstrong data-start=\"209\" data-end=\"261\"\u003eCompact AI Workstation for Modern AI Development\u003c\/strong\u003e\u003c\/h3\u003e\n\u003cp data-start=\"265\" data-end=\"702\"\u003eThe Gigabyte AI TOP ATOM Personal AI Computer 1TB (ATAGB10-9000) is a powerful AI workstation designed for developers, researchers, educators, and enterprises seeking a dedicated platform for artificial intelligence and machine learning workloads. Powered by the NVIDIA GB10 architecture and featuring 128GB LPDDR5x unified memory, this compact system delivers exceptional AI performance while maintaining a desktop-friendly footprint.\u003c\/p\u003e\n\u003ch3 data-start=\"704\" data-end=\"759\"\u003e\u003cstrong data-start=\"704\" data-end=\"757\"\u003eBuilt for Generative AI and Large Language Models\u003c\/strong\u003e\u003c\/h3\u003e\n\u003cp data-start=\"761\" data-end=\"1163\"\u003eDesigned to support advanced AI applications, the Gigabyte AI TOP ATOM enables users to develop, test, and deploy large language models (LLMs), generative AI solutions, retrieval-augmented generation (RAG) systems, and machine learning projects locally. Running AI workloads on-premises helps improve data privacy, reduce latency, and provide greater control over valuable business and research data.\u003c\/p\u003e\n\u003ch3 data-start=\"1165\" data-end=\"1218\"\u003e\u003cstrong data-start=\"1165\" data-end=\"1216\"\u003eHigh-Speed Unified Memory and Fast NVMe Storage\u003c\/strong\u003e\u003c\/h3\u003e\n\u003cp data-start=\"1220\" data-end=\"1599\"\u003eEquipped with 128GB LPDDR5x unified memory and a 1TB PCIe 4.0 NVMe M.2 SSD, the system delivers the responsiveness required for AI model inference, data science projects, software development, and analytics workloads. The high-speed storage enables quick access to datasets, development environments, AI frameworks, and project files while ensuring smooth day-to-day operation.\u003c\/p\u003e\n\u003ch3 data-start=\"1601\" data-end=\"1651\"\u003e\u003cstrong data-start=\"1601\" data-end=\"1649\"\u003eAI-Optimized Environment with NVIDIA DGX™ OS\u003c\/strong\u003e\u003c\/h3\u003e\n\u003cp data-start=\"1653\" data-end=\"2037\"\u003ePreloaded with NVIDIA DGX™ OS based on Ubuntu Linux, the Gigabyte AI TOP ATOM provides a professional AI development environment optimized for machine learning frameworks, deep learning libraries, and modern AI toolchains. Developers can accelerate innovation using industry-standard AI software while benefiting from an environment specifically designed for AI computing workloads.\u003c\/p\u003e\n\u003ch3 data-start=\"2039\" data-end=\"2081\"\u003e\u003cstrong data-start=\"2039\" data-end=\"2079\"\u003eTechnical Specifications\u003c\/strong\u003e\u003c\/h3\u003e\n\u003cp data-start=\"0\" data-end=\"625\"\u003e\u003cstrong data-start=\"0\" data-end=\"11\"\u003eGeneral\u003c\/strong\u003e\u003cbr data-start=\"11\" data-end=\"14\"\u003eModel: AI TOP ATOM Personal AI Computer 1TB\u003cbr data-start=\"57\" data-end=\"60\"\u003ePart Number: ATAGB10-9000\u003cbr data-start=\"85\" data-end=\"88\"\u003eProduct Name: Gigabyte AI TOP ATOM Personal AI Computer 1TB\u003cbr data-start=\"147\" data-end=\"150\"\u003eManufacturer: \u003cspan class=\"hover:entity-accent entity-underline inline cursor-pointer align-baseline\"\u003e\u003cspan class=\"whitespace-normal\"\u003eGigabyte Technology\u003c\/span\u003e\u003c\/span\u003e\u003cbr data-start=\"201\" data-end=\"204\"\u003eProduct Type: Personal AI Supercomputer\u003cbr data-start=\"243\" data-end=\"246\"\u003ePlatform: NVIDIA GB10 Grace Blackwell Superchip\u003cbr data-start=\"293\" data-end=\"296\"\u003eForm Factor: Compact Desktop AI Computer\u003cbr data-start=\"336\" data-end=\"339\"\u003eOperating System: NVIDIA DGX™ OS, Ubuntu Linux\u003cbr data-start=\"385\" data-end=\"388\"\u003eTarget Users: AI Developers, Researchers, Data Scientists, Universities, Enterprise AI Teams\u003cbr data-start=\"480\" data-end=\"483\"\u003eDeployment Type: AI Development, Generative AI, Machine Learning, LLM Inference, Edge AI Computing\u003cbr data-start=\"581\" data-end=\"584\"\u003eWarranty: Manufacturer Limited Warranty\u003c\/p\u003e\n\u003cp data-start=\"627\" data-end=\"1008\"\u003e\u003cstrong data-start=\"627\" data-end=\"655\"\u003eProcessor Specifications\u003c\/strong\u003e\u003cbr data-start=\"655\" data-end=\"658\"\u003eProcessor Platform: NVIDIA GB10 Grace Blackwell Superchip\u003cbr data-start=\"715\" data-end=\"718\"\u003eCPU Architecture: NVIDIA Grace ARM Architecture\u003cbr data-start=\"765\" data-end=\"768\"\u003eCPU Configuration: 20-Core CPU\u003cbr data-start=\"798\" data-end=\"801\"\u003eCPU Core Layout: 10× Cortex-X925 + 10× Cortex-A725\u003cbr data-start=\"851\" data-end=\"854\"\u003eProcessor Performance: Optimized for AI, Data Science, and High-Performance Computing Workloads\u003cbr data-start=\"949\" data-end=\"952\"\u003eSystem Architecture: Unified CPU and GPU Memory Design\u003c\/p\u003e\n\u003cp data-start=\"1010\" data-end=\"1434\"\u003e\u003cstrong data-start=\"1010\" data-end=\"1032\"\u003eGPU Specifications\u003c\/strong\u003e\u003cbr data-start=\"1032\" data-end=\"1035\"\u003eGPU Architecture: NVIDIA Blackwell GPU\u003cbr data-start=\"1073\" data-end=\"1076\"\u003eCUDA Technology: Supported\u003cbr data-start=\"1102\" data-end=\"1105\"\u003eTensor Cores: Latest Generation Tensor Cores\u003cbr data-start=\"1149\" data-end=\"1152\"\u003eRay Tracing Cores: Supported\u003cbr data-start=\"1180\" data-end=\"1183\"\u003eAI Compute Performance: Up to 1 PetaFLOP FP4 AI Performance\u003cbr data-start=\"1242\" data-end=\"1245\"\u003eNVIDIA AI Software Stack: Supported\u003cbr data-start=\"1280\" data-end=\"1283\"\u003eGenerative AI Processing: Supported\u003cbr data-start=\"1318\" data-end=\"1321\"\u003eLarge Language Model (LLM) Support: Supported\u003cbr data-start=\"1366\" data-end=\"1369\"\u003eNVIDIA TensorRT: Supported\u003cbr data-start=\"1395\" data-end=\"1398\"\u003eNVIDIA CUDA-X Libraries: Supported\u003c\/p\u003e\n\u003cp data-start=\"1436\" data-end=\"1711\"\u003e\u003cstrong data-start=\"1436\" data-end=\"1461\"\u003eMemory Specifications\u003c\/strong\u003e\u003cbr data-start=\"1461\" data-end=\"1464\"\u003eInstalled Memory: 128 GB\u003cbr data-start=\"1488\" data-end=\"1491\"\u003eMemory Type: LPDDR5x Unified Memory\u003cbr data-start=\"1526\" data-end=\"1529\"\u003eMemory Architecture: Shared CPU-GPU Unified Memory\u003cbr data-start=\"1579\" data-end=\"1582\"\u003eMemory Bandwidth: Up to 273 GB\/s\u003cbr data-start=\"1614\" data-end=\"1617\"\u003eECC Protection: Supported\u003cbr data-start=\"1642\" data-end=\"1645\"\u003eAI Model Optimization: Designed for Large AI Models and Datasets\u003c\/p\u003e\n\u003cp data-start=\"1713\" data-end=\"1935\"\u003e\u003cstrong data-start=\"1713\" data-end=\"1739\"\u003eStorage Specifications\u003c\/strong\u003e\u003cbr data-start=\"1739\" data-end=\"1742\"\u003eInstalled Storage: 1 TB\u003cbr data-start=\"1765\" data-end=\"1768\"\u003eStorage Type: NVMe M.2 SSD\u003cbr data-start=\"1794\" data-end=\"1797\"\u003eSSD Interface: PCIe 4.0\u003cbr data-start=\"1820\" data-end=\"1823\"\u003eStorage Expansion: Configuration Dependent\u003cbr data-start=\"1865\" data-end=\"1868\"\u003eHigh-Speed Data Access: Supported\u003cbr data-start=\"1901\" data-end=\"1904\"\u003eAI Dataset Storage: Supported\u003c\/p\u003e\n\u003cp data-start=\"1937\" data-end=\"2367\"\u003e\u003cstrong data-start=\"1937\" data-end=\"1971\"\u003eAI \u0026amp; Machine Learning Features\u003c\/strong\u003e\u003cbr data-start=\"1971\" data-end=\"1974\"\u003eLarge Language Model (LLM) Development: Supported\u003cbr data-start=\"2023\" data-end=\"2026\"\u003eAI Inference: Supported\u003cbr data-start=\"2049\" data-end=\"2052\"\u003eAI Fine-Tuning: Supported\u003cbr data-start=\"2077\" data-end=\"2080\"\u003eRetrieval-Augmented Generation (RAG): Supported\u003cbr data-start=\"2127\" data-end=\"2130\"\u003eGenerative AI Applications: Supported\u003cbr data-start=\"2167\" data-end=\"2170\"\u003eAI Agent Development: Supported\u003cbr data-start=\"2201\" data-end=\"2204\"\u003eComputer Vision Workloads: Supported\u003cbr data-start=\"2240\" data-end=\"2243\"\u003eNatural Language Processing (NLP): Supported\u003cbr data-start=\"2287\" data-end=\"2290\"\u003eDeep Learning Framework Support: Supported\u003cbr data-start=\"2332\" data-end=\"2335\"\u003eLocal AI Processing: Supported\u003c\/p\u003e\n\u003cp data-start=\"2369\" data-end=\"2633\"\u003e\u003cstrong data-start=\"2369\" data-end=\"2403\"\u003eSoftware \u0026amp; Development Support\u003c\/strong\u003e\u003cbr data-start=\"2403\" data-end=\"2406\"\u003eOperating System: NVIDIA DGX™ OS\u003cbr data-start=\"2438\" data-end=\"2441\"\u003eLinux Distribution: Ubuntu Linux\u003cbr data-start=\"2473\" data-end=\"2476\"\u003eCUDA Toolkit: Supported\u003cbr data-start=\"2499\" data-end=\"2502\"\u003eNVIDIA TensorRT: Supported\u003cbr data-start=\"2528\" data-end=\"2531\"\u003eNVIDIA NIM Microservices: Supported\u003cbr data-start=\"2566\" data-end=\"2569\"\u003eDocker Support: Supported\u003cbr data-start=\"2594\" data-end=\"2597\"\u003eContainerized Workloads: Supported\u003c\/p\u003e\n\u003cp data-start=\"2635\" data-end=\"2724\"\u003eSupported AI Frameworks:\u003cbr data-start=\"2659\" data-end=\"2662\"\u003e• PyTorch\u003cbr data-start=\"2671\" data-end=\"2674\"\u003e• TensorFlow\u003cbr data-start=\"2686\" data-end=\"2689\"\u003e• JAX\u003cbr data-start=\"2694\" data-end=\"2697\"\u003e• ONNX Runtime\u003cbr data-start=\"2711\" data-end=\"2714\"\u003e• RAPIDS\u003c\/p\u003e\n\u003cp data-start=\"2726\" data-end=\"2932\"\u003e\u003cstrong data-start=\"2726\" data-end=\"2755\"\u003eNetworking Specifications\u003c\/strong\u003e\u003cbr data-start=\"2755\" data-end=\"2758\"\u003eEthernet: 10 Gigabit Ethernet (10GbE)\u003cbr data-start=\"2795\" data-end=\"2798\"\u003eWireless Connectivity: Wi-Fi 7\u003cbr data-start=\"2828\" data-end=\"2831\"\u003eBluetooth: Bluetooth 5.x\u003cbr data-start=\"2855\" data-end=\"2858\"\u003eRemote Access Support: Supported\u003cbr data-start=\"2890\" data-end=\"2893\"\u003eHigh-Speed Data Networking: Supported\u003c\/p\u003e\n\u003cp data-start=\"2934\" data-end=\"3146\"\u003e\u003cstrong data-start=\"2934\" data-end=\"2958\"\u003eConnectivity \u0026amp; Ports\u003c\/strong\u003e\u003cbr data-start=\"2958\" data-end=\"2961\"\u003eUSB Type-C Ports: Supported\u003cbr data-start=\"2988\" data-end=\"2991\"\u003eUSB Type-A Ports: Supported\u003cbr data-start=\"3018\" data-end=\"3021\"\u003eHDMI Output: Supported\u003cbr data-start=\"3043\" data-end=\"3046\"\u003eDisplay Connectivity: Supported\u003cbr data-start=\"3077\" data-end=\"3080\"\u003eRJ-45 Ethernet Port: Supported\u003cbr data-start=\"3110\" data-end=\"3113\"\u003ePeripheral Expansion: Supported\u003c\/p\u003e\n\u003cp data-start=\"3148\" data-end=\"3324\"\u003e\u003cstrong data-start=\"3148\" data-end=\"3169\"\u003eSecurity Features\u003c\/strong\u003e\u003cbr data-start=\"3169\" data-end=\"3172\"\u003eSecure Boot: Supported\u003cbr data-start=\"3194\" data-end=\"3197\"\u003eOperating System Security Updates: Supported\u003cbr data-start=\"3241\" data-end=\"3244\"\u003eEncrypted Storage Support: Supported\u003cbr data-start=\"3280\" data-end=\"3283\"\u003eEnterprise Security Features: Supported\u003c\/p\u003e\n\u003cp data-start=\"3326\" data-end=\"3467\"\u003e\u003cstrong data-start=\"3326\" data-end=\"3350\"\u003ePower Specifications\u003c\/strong\u003e\u003cbr data-start=\"3350\" data-end=\"3353\"\u003ePower Efficiency: Optimized for AI Computing\u003cbr data-start=\"3397\" data-end=\"3400\"\u003ePower Supply: Included\u003cbr data-start=\"3422\" data-end=\"3425\"\u003eEnergy Efficient Architecture: Supported\u003c\/p\u003e\n\u003cp data-start=\"3469\" data-end=\"3638\"\u003e\u003cstrong data-start=\"3469\" data-end=\"3495\"\u003eCooling Specifications\u003c\/strong\u003e\u003cbr data-start=\"3495\" data-end=\"3498\"\u003eCooling System: Active Cooling Design\u003cbr data-start=\"3535\" data-end=\"3538\"\u003eThermal Optimization: Supported\u003cbr data-start=\"3569\" data-end=\"3572\"\u003eAI Workload Cooling: Supported\u003cbr data-start=\"3602\" data-end=\"3605\"\u003eContinuous Operation: Supported\u003c\/p\u003e\n\u003cp data-start=\"3640\" data-end=\"3998\"\u003e\u003cstrong data-start=\"3640\" data-end=\"3663\"\u003eSupported Workloads\u003c\/strong\u003e\u003cbr data-start=\"3663\" data-end=\"3666\"\u003eLarge Language Models (LLMs)\u003cbr data-start=\"3694\" data-end=\"3697\"\u003eGenerative AI Applications\u003cbr data-start=\"3723\" data-end=\"3726\"\u003eAI Model Inference\u003cbr data-start=\"3744\" data-end=\"3747\"\u003eMachine Learning Development\u003cbr data-start=\"3775\" data-end=\"3778\"\u003eData Science Workloads\u003cbr data-start=\"3800\" data-end=\"3803\"\u003eComputer Vision Applications\u003cbr data-start=\"3831\" data-end=\"3834\"\u003eNatural Language Processing\u003cbr data-start=\"3861\" data-end=\"3864\"\u003eAI Agent Development\u003cbr data-start=\"3884\" data-end=\"3887\"\u003eResearch and Academic Computing\u003cbr data-start=\"3918\" data-end=\"3921\"\u003eEdge AI Deployments\u003cbr data-start=\"3940\" data-end=\"3943\"\u003eLocal AI Computing\u003cbr data-start=\"3961\" data-end=\"3964\"\u003eSoftware Development and Testing\u003c\/p\u003e\n\u003cp data-start=\"4000\" data-end=\"4234\"\u003e\u003cstrong data-start=\"4000\" data-end=\"4025\"\u003eDesign \u0026amp; Construction\u003c\/strong\u003e\u003cbr data-start=\"4025\" data-end=\"4028\"\u003eForm Factor: Compact Desktop AI Workstation\u003cbr data-start=\"4071\" data-end=\"4074\"\u003eChassis Design: AI Computing Optimized\u003cbr data-start=\"4112\" data-end=\"4115\"\u003eDeployment Flexibility: Office, Lab, Research, Enterprise Environment\u003cbr data-start=\"4184\" data-end=\"4187\"\u003eConstruction: Enterprise-Grade Compact Design\u003c\/p\u003e\n\u003cp data-start=\"4236\" data-end=\"4611\"\u003e\u003cstrong data-start=\"4236\" data-end=\"4248\"\u003eFeatures\u003c\/strong\u003e\u003cbr data-start=\"4248\" data-end=\"4251\"\u003eNVIDIA GB10 Grace Blackwell Superchip\u003cbr data-start=\"4288\" data-end=\"4291\"\u003e128 GB LPDDR5x Unified Memory\u003cbr data-start=\"4320\" data-end=\"4323\"\u003e1 TB PCIe 4.0 NVMe M.2 SSD\u003cbr data-start=\"4349\" data-end=\"4352\"\u003eNVIDIA Blackwell GPU Architecture\u003cbr data-start=\"4385\" data-end=\"4388\"\u003eUp to 1 PetaFLOP FP4 AI Performance\u003cbr data-start=\"4423\" data-end=\"4426\"\u003eNVIDIA DGX™ OS Preloaded\u003cbr data-start=\"4450\" data-end=\"4453\"\u003eUbuntu Linux Support\u003cbr data-start=\"4473\" data-end=\"4476\"\u003e10GbE Networking\u003cbr data-start=\"4492\" data-end=\"4495\"\u003eWi-Fi 7 Connectivity\u003cbr data-start=\"4515\" data-end=\"4518\"\u003eCompact Desktop Form Factor\u003cbr data-start=\"4545\" data-end=\"4548\"\u003eEnterprise AI Ready Platform\u003cbr data-start=\"4576\" data-end=\"4579\"\u003eLocal AI Processing Capability\u003c\/p\u003e\n\u003cp data-start=\"4613\" data-end=\"5175\"\u003e\u003cstrong data-start=\"4613\" data-end=\"4639\"\u003ePerformance \u0026amp; Features\u003c\/strong\u003e\u003cbr data-start=\"4639\" data-end=\"4642\"\u003eDesigned for local AI development and inference workloads\u003cbr data-start=\"4699\" data-end=\"4702\"\u003eSupports advanced generative AI applications without cloud dependency\u003cbr data-start=\"4771\" data-end=\"4774\"\u003eEnables experimentation and deployment of large language models\u003cbr data-start=\"4837\" data-end=\"4840\"\u003eProvides high-bandwidth unified memory architecture for AI workloads\u003cbr data-start=\"4908\" data-end=\"4911\"\u003eOptimized for developers, researchers, and enterprise AI teams\u003cbr data-start=\"4973\" data-end=\"4976\"\u003eSupports modern AI frameworks and NVIDIA software ecosystem\u003cbr data-start=\"5035\" data-end=\"5038\"\u003eOffers workstation-class AI performance in a compact desktop footprint\u003cbr data-start=\"5108\" data-end=\"5111\"\u003eBuilt for continuous AI computing and development environments\u003c\/p\u003e\n\u003cp data-start=\"5177\" data-end=\"5471\"\u003e\u003cstrong data-start=\"5177\" data-end=\"5204\"\u003ePhysical Specifications\u003c\/strong\u003e\u003cbr data-start=\"5204\" data-end=\"5207\"\u003eForm Factor: Compact Desktop AI Computer\u003cbr data-start=\"5247\" data-end=\"5250\"\u003eProcessor: NVIDIA GB10 Grace Blackwell Superchip\u003cbr data-start=\"5298\" data-end=\"5301\"\u003eMemory: 128 GB LPDDR5x Unified Memory\u003cbr data-start=\"5338\" data-end=\"5341\"\u003eStorage: 1 TB PCIe 4.0 NVMe M.2 SSD\u003cbr data-start=\"5376\" data-end=\"5379\"\u003eNetworking: 10GbE, Wi-Fi 7, Bluetooth 5.x\u003cbr data-start=\"5420\" data-end=\"5423\"\u003eOperating System: NVIDIA DGX™ OS, Ubuntu Linux\u003c\/p\u003e\n\u003cp data-start=\"5473\" data-end=\"5663\"\u003e\u003cstrong data-start=\"5473\" data-end=\"5493\"\u003ePackage Contents\u003c\/strong\u003e\u003cbr data-start=\"5493\" data-end=\"5496\"\u003eGigabyte AI TOP ATOM Personal AI Computer\u003cbr data-start=\"5537\" data-end=\"5540\"\u003ePower Adapter and Power Cable\u003cbr data-start=\"5569\" data-end=\"5572\"\u003eQuick Start Guide\u003cbr data-start=\"5589\" data-end=\"5592\"\u003eDocumentation\u003cbr data-start=\"5605\" data-end=\"5608\"\u003ePreloaded NVIDIA DGX™ OS and Ubuntu Linux Environment\u003c\/p\u003e\n\u003cp data-start=\"5665\" data-end=\"6105\"\u003e\u003cstrong data-start=\"5665\" data-end=\"5681\"\u003eKey Features\u003c\/strong\u003e\u003cbr data-start=\"5681\" data-end=\"5684\"\u003eNVIDIA GB10 Grace Blackwell Superchip\u003cbr data-start=\"5721\" data-end=\"5724\"\u003e128 GB LPDDR5x Unified Memory\u003cbr data-start=\"5753\" data-end=\"5756\"\u003e1 TB PCIe 4.0 NVMe M.2 SSD\u003cbr data-start=\"5782\" data-end=\"5785\"\u003eUp to 1 PetaFLOP FP4 AI Performance\u003cbr data-start=\"5820\" data-end=\"5823\"\u003eNVIDIA Blackwell GPU Architecture\u003cbr data-start=\"5856\" data-end=\"5859\"\u003eNVIDIA DGX™ OS Preinstalled\u003cbr data-start=\"5886\" data-end=\"5889\"\u003e10GbE Network Connectivity\u003cbr data-start=\"5915\" data-end=\"5918\"\u003eWi-Fi 7 and Bluetooth Support\u003cbr data-start=\"5947\" data-end=\"5950\"\u003eGenerative AI and LLM Ready\u003cbr data-start=\"5977\" data-end=\"5980\"\u003eCompact Desktop AI Workstation Design\u003cbr data-start=\"6017\" data-end=\"6020\"\u003eEnterprise and Research AI Development Platform\u003cbr data-start=\"6067\" data-end=\"6070\"\u003eOptimized for Local AI Processing\u003c\/p\u003e\n\u003cp data-start=\"6107\" data-end=\"6510\"\u003e\u003cstrong data-start=\"6107\" data-end=\"6128\"\u003eTypical Use Cases\u003c\/strong\u003e\u003cbr data-start=\"6128\" data-end=\"6131\"\u003eLarge Language Model (LLM) Inference\u003cbr data-start=\"6167\" data-end=\"6170\"\u003eAI Model Development\u003cbr data-start=\"6190\" data-end=\"6193\"\u003eGenerative AI Applications\u003cbr data-start=\"6219\" data-end=\"6222\"\u003eMachine Learning Research\u003cbr data-start=\"6247\" data-end=\"6250\"\u003eData Science Projects\u003cbr data-start=\"6271\" data-end=\"6274\"\u003eUniversity and Academic Research\u003cbr data-start=\"6306\" data-end=\"6309\"\u003eAI Agent Development\u003cbr data-start=\"6329\" data-end=\"6332\"\u003eComputer Vision Applications\u003cbr data-start=\"6360\" data-end=\"6363\"\u003eNatural Language Processing Workloads\u003cbr data-start=\"6400\" data-end=\"6403\"\u003eEnterprise AI Prototyping\u003cbr data-start=\"6428\" data-end=\"6431\"\u003eEdge AI Computing Deployments\u003cbr data-start=\"6460\" data-end=\"6463\"\u003eLocal AI Development Without Cloud Dependency\u003c\/p\u003e\n\u003cp data-start=\"6512\" data-end=\"6828\" data-is-last-node=\"\" data-is-only-node=\"\"\u003e\u003cstrong data-start=\"6512\" data-end=\"6536\"\u003eOrdering Information\u003c\/strong\u003e\u003cbr data-start=\"6536\" data-end=\"6539\"\u003eModel: Gigabyte AI TOP ATOM Personal AI Computer 1TB\u003cbr data-start=\"6591\" data-end=\"6594\"\u003ePart Number: ATAGB10-9000\u003cbr data-start=\"6619\" data-end=\"6622\"\u003eConfiguration: NVIDIA GB10 \/ 128GB LPDDR5x \/ PCIe 4.0 1TB NVMe M.2 SSD \/ NVIDIA DGX™ OS \/ Ubuntu Linux\u003c\/p\u003e","brand":"Nvidia","offers":[{"title":"Default Title","offer_id":51373623804068,"sku":"ATAGB10-9002","price":7495.0,"currency_code":"SGD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0605\/0136\/0804\/files\/gigabyte-ai-top-atom-personal-ai-computer-4tb-3819840.jpg?v=1767711611"},{"product_id":"hp-zbook-x-g1i-16-u7-255h-32gb-1tb-ssd-rtx-pro-1000-mobile-workstation","title":"HP ZBook X G1i 16 U7-255H\/ 32GB\/ 1TB SSD RTX PRO 1000 Mobile Workstation","description":"\u003ch2\u003e\u003cstrong\u003eHP ZBook X G1i 16 U7-255H\/ 32GB\/ 1TB SSD RTX PRO 1000 Mobile Workstation (DF7Q8AT) - 3 Year HP Limited Warranty \u003c\/strong\u003e\u003c\/h2\u003e\n\u003cp data-start=\"211\" data-end=\"278\" class=\"PDq2pG_selectionAnchorContainer\"\u003e\u003cstrong data-start=\"211\" data-end=\"276\"\u003eProfessional Mobile Workstation for AI and Creative Workflows\u003c\/strong\u003e\u003cspan aria-hidden=\"true\" class=\"PDq2pG_selectionAnchor\"\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp data-start=\"280\" data-end=\"883\"\u003eThe HP ZBook X G1i 16 is a powerful mobile workstation engineered for engineers, architects, designers, developers, and creative professionals who require workstation-class performance on the move. Powered by the Intel Core Ultra 7 255H processor, 32GB of DDR5 memory, a 1TB PCIe Gen4 NVMe SSD, and the NVIDIA RTX PRO 1000 Laptop GPU, it delivers exceptional performance for CAD, BIM, 3D modelling, AI development, rendering, simulation, video editing, and professional content creation. Running Windows 11 Pro, it offers the security, stability, and performance expected from HP's Z workstation family.\u003c\/p\u003e\n\u003cp data-start=\"885\" data-end=\"954\"\u003e\u003cstrong data-start=\"885\" data-end=\"952\"\u003eNVIDIA RTX PRO Graphics for Certified Professional Applications\u003c\/strong\u003e\u003c\/p\u003e\n\u003cp data-start=\"956\" data-end=\"1379\"\u003eEquipped with the NVIDIA RTX PRO 1000 Laptop GPU, the ZBook X G1i accelerates graphics-intensive workloads and AI-enhanced applications while supporting ISV-certified software commonly used in engineering, architecture, manufacturing, media production, and scientific computing. The dedicated professional GPU delivers reliable performance for demanding workflows, ensuring compatibility with industry-leading applications.\u003c\/p\u003e\n\u003cp data-start=\"1381\" data-end=\"1433\"\u003e\u003cstrong data-start=\"1381\" data-end=\"1431\"\u003eLarge 16-Inch Display with Enterprise Mobility\u003c\/strong\u003e\u003c\/p\u003e\n\u003cp data-start=\"1435\" data-end=\"1828\"\u003eThe 16-inch display provides an expansive workspace with sharp visuals and accurate colour reproduction, allowing professionals to work efficiently whether in the office, on-site, or remotely. Despite its workstation-class capabilities, the HP ZBook X G1i maintains a portable design, making it ideal for hybrid professionals who require desktop-level performance without sacrificing mobility.\u003c\/p\u003e\n\u003cp data-start=\"1830\" data-end=\"1881\"\u003e\u003cstrong data-start=\"1830\" data-end=\"1879\"\u003eEnterprise Security and Advanced Connectivity\u003c\/strong\u003e\u003c\/p\u003e\n\u003cp data-start=\"1883\" data-end=\"2278\"\u003eBuilt for modern enterprises, the HP ZBook X G1i includes Windows 11 Pro, HP Wolf Security for Business, TPM 2.0, and advanced management features to protect sensitive business data. Comprehensive connectivity including Thunderbolt 4, USB-C, USB-A, HDMI, Wi-Fi 7, and Bluetooth ensures seamless integration with docking stations, multiple displays, storage devices, and professional peripherals.\u003c\/p\u003e\n\u003cp data-start=\"2280\" data-end=\"2322\"\u003e\u003cstrong data-start=\"2280\" data-end=\"2320\"\u003eTechnical Specifications\u003c\/strong\u003e\u003c\/p\u003e\n\u003cp data-start=\"0\" data-end=\"625\"\u003e\u003cstrong data-start=\"0\" data-end=\"11\"\u003eGeneral\u003c\/strong\u003e\u003cbr data-start=\"11\" data-end=\"14\"\u003eModel: HP ZBook X G1i 16 Mobile Workstation\u003cbr data-start=\"57\" data-end=\"60\"\u003ePart Number: DF7Q8AT\u003cbr data-start=\"80\" data-end=\"83\"\u003eProduct Name: HP ZBook X G1i 16 U7-255H \/ 32GB \/ 1TB SSD RTX PRO 1000 Mobile Workstation\u003cbr data-start=\"171\" data-end=\"174\"\u003eManufacturer: HP\u003cbr data-start=\"190\" data-end=\"193\"\u003eProduct Type: AI Mobile Workstation\u003cbr data-start=\"228\" data-end=\"231\"\u003eSeries: HP ZBook X G1i 16\u003cbr data-start=\"256\" data-end=\"259\"\u003eForm Factor: 16-inch Mobile Workstation\u003cbr data-start=\"298\" data-end=\"301\"\u003eOperating System: Windows 11 Pro\u003cbr data-start=\"333\" data-end=\"336\"\u003eProcessor Platform: Intel® Core™ Ultra Series 2 (Arrow Lake-H)\u003cbr data-start=\"398\" data-end=\"401\"\u003eAI PC Category: AI Workstation\u003cbr data-start=\"431\" data-end=\"434\"\u003eDeployment: CAD, BIM, AI Development, Engineering, Media Creation, Data Science, Enterprise\u003cbr data-start=\"525\" data-end=\"528\"\u003eWarranty: 3 Years Onsite Warranty (Configuration Dependent) \u003cspan class=\"\" data-state=\"closed\"\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp data-start=\"627\" data-end=\"1060\"\u003e\u003cstrong data-start=\"627\" data-end=\"655\"\u003eProcessor Specifications\u003c\/strong\u003e\u003cbr data-start=\"655\" data-end=\"658\"\u003eProcessor: Intel® Core™ Ultra 7 255H\u003cbr data-start=\"694\" data-end=\"697\"\u003eProcessor Architecture: Arrow Lake-H\u003cbr data-start=\"733\" data-end=\"736\"\u003eCPU Cores: 16 Cores (6 Performance + 8 Efficient + 2 Low Power Efficient)\u003cbr data-start=\"809\" data-end=\"812\"\u003eThreads: 16\u003cbr data-start=\"823\" data-end=\"826\"\u003eMaximum Turbo Frequency: Up to 5.1 GHz\u003cbr data-start=\"864\" data-end=\"867\"\u003eIntel® Smart Cache: 24MB\u003cbr data-start=\"891\" data-end=\"894\"\u003eIntel® AI Boost NPU: Up to 13 TOPS\u003cbr data-start=\"928\" data-end=\"931\"\u003eIntel® Turbo Boost Technology: Supported\u003cbr data-start=\"971\" data-end=\"974\"\u003eIntel® vPro® Enterprise: Configuration Dependent \u003cspan class=\"\" data-state=\"closed\"\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp data-start=\"1062\" data-end=\"1529\"\u003e\u003cstrong data-start=\"1062\" data-end=\"1089\"\u003eGraphics Specifications\u003c\/strong\u003e\u003cbr data-start=\"1089\" data-end=\"1092\"\u003eGraphics Controller: NVIDIA RTX PRO™ 1000 Blackwell Laptop GPU\u003cbr data-start=\"1154\" data-end=\"1157\"\u003eGraphics Memory: 8GB GDDR7 Dedicated Memory\u003cbr data-start=\"1200\" data-end=\"1203\"\u003eIntegrated Graphics: Intel® Arc™ Graphics\u003cbr data-start=\"1244\" data-end=\"1247\"\u003eRay Tracing: Supported\u003cbr data-start=\"1269\" data-end=\"1272\"\u003eCUDA Technology: Supported\u003cbr data-start=\"1298\" data-end=\"1301\"\u003eTensor Cores: Supported\u003cbr data-start=\"1324\" data-end=\"1327\"\u003eAI-Accelerated Workflows: Supported\u003cbr data-start=\"1362\" data-end=\"1365\"\u003eISV Certified Graphics: Supported\u003cbr data-start=\"1398\" data-end=\"1401\"\u003eOpenGL: Supported\u003cbr data-start=\"1418\" data-end=\"1421\"\u003eDirectX 12 Ultimate: Supported\u003cbr data-start=\"1451\" data-end=\"1454\"\u003eMultiple External Displays: Supported \u003cspan class=\"\" data-state=\"closed\"\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp data-start=\"1531\" data-end=\"1778\"\u003e\u003cstrong data-start=\"1531\" data-end=\"1556\"\u003eMemory Specifications\u003c\/strong\u003e\u003cbr data-start=\"1556\" data-end=\"1559\"\u003eInstalled Memory: 32GB DDR5-5600\u003cbr data-start=\"1591\" data-end=\"1594\"\u003eMemory Configuration: 1 × 32GB\u003cbr data-start=\"1624\" data-end=\"1627\"\u003eMemory Slots: 2 × DDR5 SODIMM\u003cbr data-start=\"1656\" data-end=\"1659\"\u003eMaximum Memory Capacity: 64GB\u003cbr data-start=\"1688\" data-end=\"1691\"\u003eDual Channel Memory: Supported\u003cbr data-start=\"1721\" data-end=\"1724\"\u003eUpgradeable: Yes \u003cspan class=\"\" data-state=\"closed\"\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp data-start=\"1780\" data-end=\"2080\"\u003e\u003cstrong data-start=\"1780\" data-end=\"1806\"\u003eStorage Specifications\u003c\/strong\u003e\u003cbr data-start=\"1806\" data-end=\"1809\"\u003eInstalled Storage: 1TB PCIe Gen4 NVMe SSD\u003cbr data-start=\"1850\" data-end=\"1853\"\u003eSSD Interface: PCIe Gen4 x4 NVMe\u003cbr data-start=\"1885\" data-end=\"1888\"\u003eSSD Form Factor: M.2 2280\u003cbr data-start=\"1913\" data-end=\"1916\"\u003eStorage Expansion: Dual M.2 PCIe NVMe Slots (Configuration Dependent)\u003cbr data-start=\"1985\" data-end=\"1988\"\u003eSelf-Encrypting OPAL SSD: Supported on Selected Models \u003cspan class=\"\" data-state=\"closed\"\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp data-start=\"2082\" data-end=\"2459\"\u003e\u003cstrong data-start=\"2082\" data-end=\"2108\"\u003eDisplay Specifications\u003c\/strong\u003e\u003cbr data-start=\"2108\" data-end=\"2111\"\u003eDisplay Size: 16 Inches\u003cbr data-start=\"2134\" data-end=\"2137\"\u003eResolution: WUXGA (1920 × 1200)\u003cbr data-start=\"2168\" data-end=\"2171\"\u003ePanel Type: IPS UWVA Anti-Glare Touch Display\u003cbr data-start=\"2216\" data-end=\"2219\"\u003eAspect Ratio: 16:10\u003cbr data-start=\"2238\" data-end=\"2241\"\u003eBrightness: 300 nits\u003cbr data-start=\"2261\" data-end=\"2264\"\u003eRefresh Rate: 60Hz\u003cbr data-start=\"2282\" data-end=\"2285\"\u003eTouchscreen: Yes\u003cbr data-start=\"2301\" data-end=\"2304\"\u003eColor Gamut: 62.5% sRGB\u003cbr data-start=\"2327\" data-end=\"2330\"\u003eLow Blue Light: Supported\u003cbr data-start=\"2355\" data-end=\"2358\"\u003eOptional DreamColor Displays: Available on Other Configurations \u003cspan class=\"\" data-state=\"closed\"\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp data-start=\"2461\" data-end=\"2675\"\u003e\u003cstrong data-start=\"2461\" data-end=\"2485\"\u003eAudio Specifications\u003c\/strong\u003e\u003cbr data-start=\"2485\" data-end=\"2488\"\u003eAudio by Poly Studio\u003cbr data-start=\"2508\" data-end=\"2511\"\u003eDual Stereo Speakers\u003cbr data-start=\"2531\" data-end=\"2534\"\u003eDual Array Microphones\u003cbr data-start=\"2556\" data-end=\"2559\"\u003eAI Noise Reduction\u003cbr data-start=\"2577\" data-end=\"2580\"\u003eIntegrated Digital Microphones\u003cbr data-start=\"2610\" data-end=\"2613\"\u003ePremium Conference Audio \u003cspan class=\"\" data-state=\"closed\"\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp data-start=\"2677\" data-end=\"2861\"\u003e\u003cstrong data-start=\"2677\" data-end=\"2702\"\u003eCamera Specifications\u003c\/strong\u003e\u003cbr data-start=\"2702\" data-end=\"2705\"\u003e5MP IR Webcam\u003cbr data-start=\"2718\" data-end=\"2721\"\u003eWindows Hello Facial Recognition\u003cbr data-start=\"2753\" data-end=\"2756\"\u003ePrivacy Shutter\u003cbr data-start=\"2771\" data-end=\"2774\"\u003eTemporal Noise Reduction\u003cbr data-start=\"2798\" data-end=\"2801\"\u003eAI Camera Enhancements \u003cspan class=\"\" data-state=\"closed\"\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp data-start=\"2863\" data-end=\"3039\"\u003e\u003cstrong data-start=\"2863\" data-end=\"2888\"\u003eWireless Connectivity\u003c\/strong\u003e\u003cbr data-start=\"2888\" data-end=\"2891\"\u003eWi-Fi 7 (Intel® BE201)\u003cbr data-start=\"2913\" data-end=\"2916\"\u003eBluetooth® 5.4\u003cbr data-start=\"2930\" data-end=\"2933\"\u003eOptional 5G WWAN (Configuration Dependent)\u003cbr data-start=\"2975\" data-end=\"2978\"\u003eGigabit Ethernet (RJ45) \u003cspan class=\"\" data-state=\"closed\"\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp data-start=\"3041\" data-end=\"3331\"\u003e\u003cstrong data-start=\"3041\" data-end=\"3065\"\u003ePorts \u0026amp; Connectivity\u003c\/strong\u003e\u003cbr data-start=\"3065\" data-end=\"3068\"\u003e2 × Thunderbolt™ 4 (USB-C, 40Gbps)\u003cbr data-start=\"3102\" data-end=\"3105\"\u003e2 × USB Type-A 10Gbps\u003cbr data-start=\"3126\" data-end=\"3129\"\u003e1 × HDMI 2.1\u003cbr data-start=\"3141\" data-end=\"3144\"\u003e1 × RJ45 Gigabit Ethernet\u003cbr data-start=\"3169\" data-end=\"3172\"\u003e1 × 3.5mm Headphone\/Microphone Combo Jack\u003cbr data-start=\"3213\" data-end=\"3216\"\u003e1 × Smart Card Reader (Configuration Dependent)\u003cbr data-start=\"3263\" data-end=\"3266\"\u003e1 × Nano Security Lock Slot \u003cspan class=\"\" data-state=\"closed\"\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp data-start=\"3333\" data-end=\"3564\"\u003e\u003cstrong data-start=\"3333\" data-end=\"3353\"\u003eKeyboard \u0026amp; Input\u003c\/strong\u003e\u003cbr data-start=\"3353\" data-end=\"3356\"\u003eFull-Size Spill-Resistant Keyboard\u003cbr data-start=\"3390\" data-end=\"3393\"\u003eBacklit Keyboard\u003cbr data-start=\"3409\" data-end=\"3412\"\u003eNumeric Keypad\u003cbr data-start=\"3426\" data-end=\"3429\"\u003eLarge Precision Touchpad\u003cbr data-start=\"3453\" data-end=\"3456\"\u003eFingerprint Reader (Configuration Dependent)\u003cbr data-start=\"3500\" data-end=\"3503\"\u003eMicrosoft Copilot Key \u003cspan class=\"\" data-state=\"closed\"\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp data-start=\"3566\" data-end=\"3866\"\u003e\u003cstrong data-start=\"3566\" data-end=\"3587\"\u003eSecurity Features\u003c\/strong\u003e\u003cbr data-start=\"3587\" data-end=\"3590\"\u003eHP Wolf Security for Business\u003cbr data-start=\"3619\" data-end=\"3622\"\u003eHP Sure Start\u003cbr data-start=\"3635\" data-end=\"3638\"\u003eHP Sure Click\u003cbr data-start=\"3651\" data-end=\"3654\"\u003eHP Sure Recover\u003cbr data-start=\"3669\" data-end=\"3672\"\u003eHP Sure Sense\u003cbr data-start=\"3685\" data-end=\"3688\"\u003eTrusted Platform Module (TPM 2.0)\u003cbr data-start=\"3721\" data-end=\"3724\"\u003eWindows Hello IR Camera\u003cbr data-start=\"3747\" data-end=\"3750\"\u003eFingerprint Reader (Configuration Dependent)\u003cbr data-start=\"3794\" data-end=\"3797\"\u003eBIOS Protection\u003cbr data-start=\"3812\" data-end=\"3815\"\u003eSecure Boot \u003cspan class=\"\" data-state=\"closed\"\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp data-start=\"3868\" data-end=\"4150\"\u003e\u003cstrong data-start=\"3868\" data-end=\"3894\"\u003eBattery Specifications\u003c\/strong\u003e\u003cbr data-start=\"3894\" data-end=\"3897\"\u003eBattery Type: 6-Cell Lithium-Ion Polymer\u003cbr data-start=\"3937\" data-end=\"3940\"\u003eBattery Capacity: 83Wh\u003cbr data-start=\"3962\" data-end=\"3965\"\u003eHP Fast Charge: Up to 50% in Approximately 30 Minutes\u003cbr data-start=\"4018\" data-end=\"4021\"\u003ePower Adapter: 150W HP Smart AC Adapter\u003cbr data-start=\"4060\" data-end=\"4063\"\u003eUSB-C Charging Support: Configuration Dependent \u003cspan class=\"\" data-state=\"closed\"\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp data-start=\"4152\" data-end=\"4391\"\u003e\u003cstrong data-start=\"4152\" data-end=\"4175\"\u003eManagement Features\u003c\/strong\u003e\u003cbr data-start=\"4175\" data-end=\"4178\"\u003eHP Image Assistant\u003cbr data-start=\"4196\" data-end=\"4199\"\u003eHP Client Management Script Library\u003cbr data-start=\"4234\" data-end=\"4237\"\u003eHP Connect for Microsoft Endpoint Manager\u003cbr data-start=\"4278\" data-end=\"4281\"\u003eMicrosoft Autopilot Ready\u003cbr data-start=\"4306\" data-end=\"4309\"\u003eHP Smart Support\u003cbr data-start=\"4325\" data-end=\"4328\"\u003eFirmware Update Support \u003cspan class=\"\" data-state=\"closed\"\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp data-start=\"4393\" data-end=\"4585\"\u003e\u003cstrong data-start=\"4393\" data-end=\"4405\"\u003eSoftware\u003c\/strong\u003e\u003cbr data-start=\"4405\" data-end=\"4408\"\u003eOperating System: Windows 11 Pro\u003cbr data-start=\"4440\" data-end=\"4443\"\u003eMicrosoft Copilot Support\u003cbr data-start=\"4468\" data-end=\"4471\"\u003eHP Support Assistant\u003cbr data-start=\"4491\" data-end=\"4494\"\u003eHP PC Hardware Diagnostics\u003cbr data-start=\"4520\" data-end=\"4523\"\u003eHP Wolf Security Suite \u003cspan class=\"\" data-state=\"closed\"\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp data-start=\"4587\" data-end=\"4797\"\u003e\u003cstrong data-start=\"4587\" data-end=\"4619\"\u003eEnvironmental Specifications\u003c\/strong\u003e\u003cbr data-start=\"4619\" data-end=\"4622\"\u003eMIL-STD-810H Certified\u003cbr data-start=\"4644\" data-end=\"4647\"\u003eENERGY STAR® Certified\u003cbr data-start=\"4669\" data-end=\"4672\"\u003eEPEAT® Registered (Configuration Dependent)\u003cbr data-start=\"4715\" data-end=\"4718\"\u003eRecycled Materials Used in Construction \u003cspan class=\"\" data-state=\"closed\"\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp data-start=\"4799\" data-end=\"5010\"\u003e\u003cstrong data-start=\"4799\" data-end=\"4824\"\u003eDesign \u0026amp; Construction\u003c\/strong\u003e\u003cbr data-start=\"4824\" data-end=\"4827\"\u003eColor: Meteor Silver\u003cbr data-start=\"4847\" data-end=\"4850\"\u003eChassis Material: Premium All-Aluminum Chassis\u003cbr data-start=\"4896\" data-end=\"4899\"\u003eMIL-STD-810H Durability Tested\u003cbr data-start=\"4929\" data-end=\"4932\"\u003eProfessional Mobile Workstation Design \u003cspan class=\"\" data-state=\"closed\"\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp data-start=\"5012\" data-end=\"5182\"\u003e\u003cstrong data-start=\"5012\" data-end=\"5039\"\u003ePhysical Specifications\u003c\/strong\u003e\u003cbr data-start=\"5039\" data-end=\"5042\"\u003eDimensions (W × D × H): 359.4 × 251 × 22.9 mm\u003cbr data-start=\"5087\" data-end=\"5090\"\u003eWeight: Starting from 2.04 kg\u003cbr data-start=\"5119\" data-end=\"5122\"\u003eColor: Meteor Silver \u003cspan class=\"\" data-state=\"closed\"\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp data-start=\"5184\" data-end=\"5368\"\u003e\u003cstrong data-start=\"5184\" data-end=\"5204\"\u003ePackage Contents\u003c\/strong\u003e\u003cbr data-start=\"5204\" data-end=\"5207\"\u003eHP ZBook X G1i 16 Mobile Workstation\u003cbr data-start=\"5243\" data-end=\"5246\"\u003e150W HP Smart AC Adapter\u003cbr data-start=\"5270\" data-end=\"5273\"\u003ePower Cord\u003cbr data-start=\"5283\" data-end=\"5286\"\u003eQuick Setup Guide\u003cbr data-start=\"5303\" data-end=\"5306\"\u003eWarranty Documentation \u003cspan class=\"\" data-state=\"closed\"\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp data-start=\"5370\" data-end=\"5857\"\u003e\u003cstrong data-start=\"5370\" data-end=\"5386\"\u003eKey Features\u003c\/strong\u003e\u003cbr data-start=\"5386\" data-end=\"5389\"\u003eIntel® Core™ Ultra 7 255H Processor\u003cbr data-start=\"5424\" data-end=\"5427\"\u003eNVIDIA RTX PRO™ 1000 Blackwell Laptop GPU (8GB GDDR7)\u003cbr data-start=\"5480\" data-end=\"5483\"\u003e32GB DDR5-5600 Memory\u003cbr data-start=\"5504\" data-end=\"5507\"\u003e1TB PCIe Gen4 NVMe SSD\u003cbr data-start=\"5529\" data-end=\"5532\"\u003e16-inch WUXGA IPS Touch Display\u003cbr data-start=\"5563\" data-end=\"5566\"\u003eWindows 11 Pro\u003cbr data-start=\"5580\" data-end=\"5583\"\u003eIntel® AI Boost NPU\u003cbr data-start=\"5602\" data-end=\"5605\"\u003eWi-Fi 7 \u0026amp; Bluetooth® 5.4\u003cbr data-start=\"5629\" data-end=\"5632\"\u003eThunderbolt™ 4 Connectivity\u003cbr data-start=\"5659\" data-end=\"5662\"\u003eRJ45 Gigabit Ethernet\u003cbr data-start=\"5683\" data-end=\"5686\"\u003eHP Wolf Security Suite\u003cbr data-start=\"5708\" data-end=\"5711\"\u003e5MP IR Camera with Windows Hello\u003cbr data-start=\"5743\" data-end=\"5746\"\u003eMIL-STD-810H Certified Chassis\u003cbr data-start=\"5776\" data-end=\"5779\"\u003eISV Certified Professional Workstation \u003cspan class=\"\" data-state=\"closed\"\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp data-start=\"5859\" data-end=\"6718\"\u003e\u003cstrong data-start=\"5859\" data-end=\"5885\"\u003ePerformance \u0026amp; Features\u003c\/strong\u003e\u003cbr data-start=\"5885\" data-end=\"5888\"\u003eThe HP ZBook X G1i 16 is engineered for professionals who require certified workstation performance in a portable form factor. Powered by the Intel® Core™ Ultra 7 255H processor and NVIDIA RTX PRO™ 1000 Blackwell GPU with 8GB GDDR7 memory, it delivers exceptional performance for CAD, BIM, 3D modeling, AI development, data science, engineering simulation, video editing, and content creation. ISV certifications ensure compatibility with leading professional software such as Autodesk, SOLIDWORKS, Siemens NX, Adobe Creative Cloud, and Dassault Systèmes applications. The premium aluminum chassis, Thunderbolt™ 4 connectivity, Wi-Fi 7, enterprise-grade HP Wolf Security, and upgradeable DDR5 memory make it an ideal workstation for professionals working both in the office and on the move. \u003cspan class=\"\" data-state=\"closed\"\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp data-start=\"6720\" data-end=\"6743\"\u003e\u003cstrong data-start=\"6720\" data-end=\"6741\"\u003eTypical Use Cases\u003c\/strong\u003e\u003c\/p\u003e\n\u003cul data-start=\"6744\" data-end=\"7261\" data-is-last-node=\"\" data-is-only-node=\"\"\u003e\n\u003cli data-section-id=\"15igxkl\" data-start=\"6744\" data-end=\"6764\"\u003eCAD \u0026amp; CAM Design\u003c\/li\u003e\n\u003cli data-section-id=\"1e8hfme\" data-start=\"6765\" data-end=\"6804\"\u003eBIM \u0026amp; Architecture (Autodesk Revit)\u003c\/li\u003e\n\u003cli data-section-id=\"1bdon6q\" data-start=\"6805\" data-end=\"6831\"\u003eEngineering Simulation\u003c\/li\u003e\n\u003cli data-section-id=\"19rxe4c\" data-start=\"6832\" data-end=\"6859\"\u003e3D Modeling \u0026amp; Rendering\u003c\/li\u003e\n\u003cli data-section-id=\"10obqcc\" data-start=\"6860\" data-end=\"6884\"\u003eAI Model Development\u003c\/li\u003e\n\u003cli data-section-id=\"1gals8r\" data-start=\"6885\" data-end=\"6920\"\u003eMachine Learning \u0026amp; Data Science\u003c\/li\u003e\n\u003cli data-section-id=\"9evbvs\" data-start=\"6921\" data-end=\"6956\"\u003eVideo Editing \u0026amp; Motion Graphics\u003c\/li\u003e\n\u003cli data-section-id=\"93fs0f\" data-start=\"6957\" data-end=\"6991\"\u003eAdobe Creative Cloud Workflows\u003c\/li\u003e\n\u003cli data-section-id=\"12la75g\" data-start=\"6992\" data-end=\"7016\"\u003eSoftware Development\u003c\/li\u003e\n\u003cli data-section-id=\"28uf8g\" data-start=\"7017\" data-end=\"7047\"\u003eGIS \u0026amp; Scientific Computing\u003c\/li\u003e\n\u003cli data-section-id=\"1cg2m9a\" data-start=\"7048\" data-end=\"7070\"\u003eFinancial Modeling\u003c\/li\u003e\n\u003cli data-section-id=\"1wxoin\" data-start=\"7071\" data-end=\"7115\"\u003eEnterprise Mobile Workstation Deployment\u003c\/li\u003e\n\u003cli data-section-id=\"1op7uxx\" data-start=\"7116\" data-end=\"7154\"\u003eGovernment \u0026amp; Research Institutions\u003c\/li\u003e\n\u003cli data-section-id=\"vu0fr\" data-start=\"7155\" data-end=\"7189\"\u003eProduct Design \u0026amp; Manufacturing\u003c\/li\u003e\n\u003cli data-section-id=\"ufarb2\" data-start=\"7190\" data-end=\"7261\" data-is-last-node=\"\"\u003eProfessional Content Creation\u003c\/li\u003e\n\u003c\/ul\u003e","brand":"HP","offers":[{"title":"Default Title","offer_id":51407771500708,"sku":"DF7Q8AT","price":5215.0,"currency_code":"SGD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0605\/0136\/0804\/files\/hp-zbook-x-g1i-16-u7-255h-32gb-1tb-ssd-rtx-pro-1000-mobile-workstation-2108831.jpg?v=1784784371"},{"product_id":"asus-expertcenter-pro-et900n-g3-gb300-ai-supercomputer","title":"ASUS ExpertCenter Pro ET900N G3 GB300 AI Supercomputer","description":"\u003ch2\u003e\u003cstrong\u003eASUS ExpertCenter Pro ET900N G3 GB300 AI Supercomputer with NVIDIA Grace Blackwell Ultra, 748GB Memory \u0026amp; Dual 400G Networking - Local Warranty \u003c\/strong\u003e\u003c\/h2\u003e\n\u003ch3\u003e\u003cstrong\u003eEnterprise AI Supercomputer Powered by NVIDIA Grace Blackwell Ultra\u003c\/strong\u003e\u003c\/h3\u003e\n\u003cp\u003eThe ASUS ExpertCenter Pro ET900N G3 is ASUS's flagship AI supercomputer engineered to accelerate next-generation artificial intelligence, machine learning, and high-performance computing workloads. Powered by the NVIDIA Grace Blackwell Ultra (GB300) Superchip, it delivers exceptional AI performance with up to \u003cstrong\u003e20 PFLOPS\u003c\/strong\u003e of AI compute, enabling organisations to train, fine-tune, and deploy large language models (LLMs), generative AI applications, AI agents, computer vision, and scientific simulations directly within their own infrastructure. It is purpose-built for enterprises, research institutions, government agencies, and AI innovators seeking uncompromising performance with complete data sovereignty.\u003c\/p\u003e\n\u003ch3\u003e\u003cstrong\u003eMassive 748GB Coherent Memory for Large AI Models\u003c\/strong\u003e\u003c\/h3\u003e\n\u003cp\u003eDesigned for today's most demanding AI workloads, the ExpertCenter Pro ET900N G3 features an incredible \u003cstrong\u003e748GB of coherent unified memory\u003c\/strong\u003e, allowing significantly larger AI models and datasets to be processed without the memory bottlenecks associated with conventional GPU systems. This unified memory architecture dramatically improves performance for inference, retrieval-augmented generation (RAG), AI agent frameworks, digital twins, engineering simulations, and data-intensive research applications.\u003c\/p\u003e\n\u003ch3\u003e\u003cstrong\u003eUltra-Fast Dual 400G Networking for AI Clusters\u003c\/strong\u003e\u003c\/h3\u003e\n\u003cp\u003eThe system is equipped with \u003cstrong\u003edual NVIDIA ConnectX®-8 400GbE QSFP networking\u003c\/strong\u003e, enabling ultra-low latency communication between multiple AI nodes for distributed training and large-scale inference. Whether deployed as a standalone AI workstation or as part of an enterprise AI cluster, the ET900N G3 provides the networking bandwidth required for high-performance computing environments, research laboratories, universities, and private AI clouds.\u003c\/p\u003e\n\u003ch3\u003e\u003cstrong\u003eEnterprise-Class Reliability and Future-Ready AI Infrastructure\u003c\/strong\u003e\u003c\/h3\u003e\n\u003cp\u003eBuilt for continuous enterprise operation, the ASUS ExpertCenter Pro ET900N G3 includes integrated Baseboard Management Controller (BMC) for secure remote monitoring and administration, support for NVIDIA Multi-Instance GPU (MIG) technology for multi-tenant AI environments, and enterprise-grade reliability for mission-critical deployments. It provides organisations with a scalable AI platform capable of supporting generative AI, foundation models, cybersecurity, healthcare, financial modelling, engineering, autonomous systems, and advanced scientific research while keeping sensitive data securely on-premises.\u003c\/p\u003e\n\u003cp\u003e\u003ciframe width=\"560\" height=\"315\" src=\"https:\/\/www.youtube.com\/embed\/WNmTKjAAAgs?si=l5sZhGkt2_G4TtHZ\" title=\"YouTube video player\"\u003e\u003c\/iframe\u003e\u003c\/p\u003e\n\u003ch3\u003e\u003cstrong\u003eTechnical Specifications\u003c\/strong\u003e\u003c\/h3\u003e\n\u003cp\u003e\u003cstrong\u003eGeneral\u003c\/strong\u003e\u003c\/p\u003e\n\u003cp\u003eModel: ExpertCenter Pro ET900N G3\u003cbr\u003eProduct Name: ASUS ExpertCenter Pro ET900N G3 GB300 AI Supercomputer with NVIDIA Grace Blackwell Ultra, 748GB Memory \u0026amp; Dual 400G Networking\u003cbr\u003eManufacturer: ASUS\u003cbr\u003eProduct Series: ASUS ExpertCenter Pro\u003cbr\u003eProduct Type: Enterprise AI Supercomputer \/ AI Server\u003cbr\u003eCompute Platform: NVIDIA Grace Blackwell Ultra\u003cbr\u003eSuperchip Platform: NVIDIA GB300\u003cbr\u003eProcessor Architecture: NVIDIA Grace CPU + NVIDIA Blackwell Ultra GPU\u003cbr\u003eSystem Architecture: Unified CPU-GPU Accelerated Computing Platform\u003cbr\u003eForm Factor: Enterprise Rackmount AI System\u003cbr\u003eTarget Deployment: Enterprise Datacentres, AI Factories, Research Laboratories, Universities, Government Agencies and Cloud Infrastructure\u003cbr\u003ePrimary Workloads: AI Training, AI Fine-Tuning, AI Inferencing, AI Agents, Generative AI, Machine Learning, Deep Learning, Scientific Computing and High-Performance Computing\u003cbr\u003eOperating System: Enterprise Linux Distribution, Configuration Dependent\u003cbr\u003eRemote Management: Integrated Baseboard Management Controller\u003cbr\u003eMulti-Tenant Support: NVIDIA Multi-Instance GPU\u003cbr\u003eWarranty: ASUS Enterprise Warranty, Configuration 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Performance\u003c\/strong\u003e\u003c\/p\u003e\n\u003cp\u003eMaximum AI Performance: Up to 20 PFLOPS\u003cbr\u003eAI Accelerator Architecture: NVIDIA Blackwell Ultra\u003cbr\u003eTransformer Engine: NVIDIA Blackwell Transformer Engine\u003cbr\u003eTensor Core Acceleration: Supported\u003cbr\u003eMixed-Precision AI Computing: Supported\u003cbr\u003eFP4 AI Computing: Supported\u003cbr\u003eFP8 AI Computing: Supported\u003cbr\u003eFP16 AI Computing: Supported\u003cbr\u003eBF16 AI Computing: Supported\u003cbr\u003eINT8 AI Inferencing: Supported\u003cbr\u003eDynamic Precision Management: Supported\u003cbr\u003eSparsity Acceleration: Supported\u003cbr\u003eLarge Language Model Acceleration: Supported\u003cbr\u003eFoundation Model Training: Supported\u003cbr\u003eFoundation Model Fine-Tuning: Supported\u003cbr\u003eGenerative AI Acceleration: Supported\u003cbr\u003eAI Agent Development: Supported\u003cbr\u003eReasoning Model Processing: Supported\u003cbr\u003eMultimodal AI Processing: 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Access: Supported\u003cbr\u003eReduced CPU-GPU Data Movement: Supported\u003cbr\u003eLarge AI Model Loading: Supported\u003cbr\u003eLarge Context Window Processing: Supported\u003cbr\u003eMulti-Billion Parameter Model Support: Supported\u003cbr\u003eLarge Dataset Processing: Supported\u003cbr\u003eAI Model Fine-Tuning Support: Supported\u003cbr\u003eEnterprise Inferencing Support: Supported\u003cbr\u003eRetrieval-Augmented Generation Support: Supported\u003cbr\u003eVector Database Processing: Supported\u003cbr\u003eMemory Virtualisation: Supported\u003cbr\u003eMulti-Tenant Memory Allocation: Supported\u003cbr\u003eContinuous AI Pipeline Processing: Supported\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eStorage Specifications\u003c\/strong\u003e\u003c\/p\u003e\n\u003cp\u003eStorage Technology: NVMe Solid-State Drive\u003cbr\u003eStorage Interface: PCI Express 5.0\u003cbr\u003eDrive Form Factor: M.2 2280\u003cbr\u003eStorage Slot Capacity: Up to 4 × M.2 2280 NVMe PCIe 5.0 SSD Slots\u003cbr\u003eMaximum Internal Storage Capacity: Up to 8TB Total Capacity When Fully Populated\u003cbr\u003eMaximum Capacity per Drive: Configuration Dependent\u003cbr\u003eBoot Drive Support: Supported\u003cbr\u003eEnterprise NVMe SSD Support: Supported\u003cbr\u003ePCIe Gen5 Storage Performance: Supported\u003cbr\u003eHigh-Speed AI Dataset Loading: Supported\u003cbr\u003eLarge AI Model Repository Support: Supported\u003cbr\u003eAI Model Checkpoint Storage: Supported\u003cbr\u003eVector Database Storage: Supported\u003cbr\u003eTraining Dataset Storage: Supported\u003cbr\u003eHigh-Speed Data Preprocessing: Supported\u003cbr\u003eContinuous Read and Write Workloads: Supported\u003cbr\u003eStorage Expansion: Supported\u003cbr\u003eRAID Support: Configuration Dependent\u003cbr\u003eHot-Swap Support: Configuration Dependent\u003cbr\u003eSelf-Encrypting Drive Support: Configuration Dependent\u003cbr\u003eStorage Health Monitoring: Supported\u003cbr\u003eRemote Storage Status 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GPU Workload Isolation: Supported\u003cbr\u003eMulti-Tenant GPU Allocation: Supported\u003cbr\u003eEnterprise GPU Virtualisation: Supported\u003cbr\u003eMixed-Precision Computing: Supported\u003cbr\u003eLarge Language Model Acceleration: Supported\u003cbr\u003eScientific Simulation Acceleration: Supported\u003cbr\u003eHigh-Performance Data Analytics: Supported\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eNetworking Specifications\u003c\/strong\u003e\u003c\/p\u003e\n\u003cp\u003eIntegrated Network Adapter: NVIDIA ConnectX-8\u003cbr\u003eNetwork Interface Type: High-Speed Enterprise AI Networking\u003cbr\u003eNetwork Ports: 2 × 400GbE QSFP Ports\u003cbr\u003eConnector Type: QSFP\u003cbr\u003eMaximum Per-Port Bandwidth: Up to 400Gbps\u003cbr\u003eMaximum Aggregate Network Bandwidth: Up to 800Gbps\u003cbr\u003eEthernet Support: Supported\u003cbr\u003eInfiniBand Support: Platform and Configuration Dependent\u003cbr\u003eRDMA Support: Supported\u003cbr\u003eRoCE Support: Supported\u003cbr\u003eRoCE v2 Support: Supported\u003cbr\u003eGPUDirect RDMA: Supported\u003cbr\u003eGPUDirect Storage: Supported\u003cbr\u003eUltra-Low-Latency Networking: Supported\u003cbr\u003eDistributed AI Training: Supported\u003cbr\u003eAI Cluster Interconnect: Supported\u003cbr\u003eHigh-Speed Storage Fabric: Supported\u003cbr\u003eScale-Out AI Infrastructure: Supported\u003cbr\u003eEnterprise AI Fabric Integration: Supported\u003cbr\u003eMulti-Node Model Training: Supported\u003cbr\u003eHigh-Performance Computing Cluster Support: Supported\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003ePorts \u0026amp; Expansion\u003c\/strong\u003e\u003c\/p\u003e\n\u003cp\u003e2 × NVIDIA ConnectX-8 400GbE QSFP Network Ports\u003cbr\u003eUp to 4 × M.2 2280 NVMe PCIe 5.0 SSD Slots\u003cbr\u003ePCI Express 5.0 Expansion Support\u003cbr\u003eEnterprise Network Expansion Support\u003cbr\u003eHigh-Speed Storage Expansion Support\u003cbr\u003eAI Accelerator Expansion: Configuration 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Monitoring\u003cbr\u003eNetwork Status Monitoring\u003cbr\u003eTemperature Monitoring\u003cbr\u003eFan Speed Monitoring\u003cbr\u003eVoltage Monitoring\u003cbr\u003ePower Consumption Monitoring\u003cbr\u003eSystem Event Logging\u003cbr\u003eHardware Inventory Management\u003cbr\u003eAsset Management\u003cbr\u003eEnterprise Fleet Management\u003cbr\u003eAlert and Notification Support\u003cbr\u003e24×7 Remote Administration\u003cbr\u003eDatacentre Management Integration\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eSoftware \u0026amp; AI Framework Support\u003c\/strong\u003e\u003c\/p\u003e\n\u003cp\u003eNVIDIA AI Enterprise\u003cbr\u003eNVIDIA CUDA Toolkit\u003cbr\u003eNVIDIA cuDNN\u003cbr\u003eNVIDIA TensorRT\u003cbr\u003eNVIDIA Triton Inference Server\u003cbr\u003eNVIDIA NeMo\u003cbr\u003eNVIDIA RAPIDS\u003cbr\u003eNVIDIA NGC Containers\u003cbr\u003ePyTorch\u003cbr\u003eTensorFlow\u003cbr\u003eJAX\u003cbr\u003eONNX Runtime\u003cbr\u003eHugging Face Transformers\u003cbr\u003eDeepSpeed\u003cbr\u003eMegatron-LM\u003cbr\u003eKubernetes\u003cbr\u003eDocker\u003cbr\u003eRed Hat OpenShift\u003cbr\u003eSlurm Workload Manager\u003cbr\u003eNVIDIA Base Command Manager\u003cbr\u003eMLflow\u003cbr\u003eRay\u003cbr\u003eApache Spark\u003cbr\u003eVector Database Platforms\u003cbr\u003eEnterprise MLOps Platforms\u003cbr\u003eAI Model Serving Frameworks\u003cbr\u003eDistributed Training Frameworks\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eOperating System Compatibility\u003c\/strong\u003e\u003c\/p\u003e\n\u003cp\u003eUbuntu Server\u003cbr\u003eRed Hat Enterprise Linux\u003cbr\u003eRocky Linux\u003cbr\u003eSUSE Linux Enterprise Server\u003cbr\u003eNVIDIA-Certified Linux Environments\u003cbr\u003eEnterprise Linux Distributions\u003cbr\u003eContainer-Optimised Operating Environments\u003cbr\u003eOperating System Support: Configuration and Certification Dependent\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eCooling \u0026amp; Reliability\u003c\/strong\u003e\u003c\/p\u003e\n\u003cp\u003eEnterprise Air-Cooled Architecture\u003cbr\u003eOptimised Datacentre Airflow\u003cbr\u003eHigh-Efficiency Thermal Management\u003cbr\u003eContinuous AI Workload Cooling\u003cbr\u003eCPU Thermal Monitoring\u003cbr\u003eGPU Thermal Monitoring\u003cbr\u003eMemory Thermal Monitoring\u003cbr\u003eStorage Thermal Monitoring\u003cbr\u003eVariable-Speed Fan Control\u003cbr\u003eSystem Fan Redundancy: Configuration Dependent\u003cbr\u003eThermal Protection\u003cbr\u003eAutomated Fan Management\u003cbr\u003eHardware Health Alerts\u003cbr\u003eHigh-Availability Design\u003cbr\u003eEnterprise-Grade Components\u003cbr\u003eContinuous 24×7 Operation\u003cbr\u003eLong-Duration AI Training Support\u003cbr\u003eDatacentre Rack Deployment Ready\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003ePower Specifications\u003c\/strong\u003e\u003c\/p\u003e\n\u003cp\u003ePower Supply Type: Enterprise High-Efficiency Power Supply\u003cbr\u003ePower Supply Configuration: Configuration Dependent\u003cbr\u003eRedundant Power Supply Support: Configuration Dependent\u003cbr\u003eHot-Plug Power Supply Support: Configuration Dependent\u003cbr\u003ePower Input: Datacentre AC Power, Configuration Dependent\u003cbr\u003ePower Consumption: Workload and Configuration Dependent\u003cbr\u003eRemote Power Monitoring: Supported\u003cbr\u003eRemote Power Control: Supported\u003cbr\u003ePower Usage Reporting: Supported\u003cbr\u003ePower Fault Monitoring: Supported\u003cbr\u003eContinuous High-Performance Operation: Supported\u003cbr\u003eDatacentre Power Optimisation: Supported\u003cbr\u003eEnergy-Efficient Workload Processing: Supported\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompatibility\u003c\/strong\u003e\u003c\/p\u003e\n\u003cp\u003eCompatible AI Workloads:\u003c\/p\u003e\n\u003cp\u003eLarge Language Models\u003cbr\u003eFoundation Models\u003cbr\u003eGenerative AI\u003cbr\u003eAI Agents\u003cbr\u003eAgentic AI\u003cbr\u003eRetrieval-Augmented Generation\u003cbr\u003eReasoning Models\u003cbr\u003eMultimodal AI\u003cbr\u003eComputer Vision\u003cbr\u003eNatural Language Processing\u003cbr\u003eSpeech AI\u003cbr\u003eRecommendation Engines\u003cbr\u003eScientific Machine Learning\u003cbr\u003ePredictive Analytics\u003cbr\u003eDigital Twins\u003cbr\u003eAutonomous Systems\u003cbr\u003eEnterprise AI Inferencing\u003c\/p\u003e\n\u003cp\u003eCompatible Development Frameworks:\u003c\/p\u003e\n\u003cp\u003ePyTorch\u003cbr\u003eTensorFlow\u003cbr\u003eJAX\u003cbr\u003eONNX Runtime\u003cbr\u003eHugging Face Transformers\u003cbr\u003eDeepSpeed\u003cbr\u003eNVIDIA NeMo\u003cbr\u003eNVIDIA TensorRT\u003cbr\u003eNVIDIA Triton Inference Server\u003cbr\u003eNVIDIA RAPIDS\u003c\/p\u003e\n\u003cp\u003eCompatible Deployment Platforms:\u003c\/p\u003e\n\u003cp\u003eDocker\u003cbr\u003eKubernetes\u003cbr\u003eRed Hat OpenShift\u003cbr\u003eSlurm\u003cbr\u003eNVIDIA Base Command Manager\u003cbr\u003eEnterprise MLOps Platforms\u003cbr\u003ePrivate Cloud Infrastructure\u003cbr\u003eHybrid Cloud Infrastructure\u003cbr\u003eAI Factory Infrastructure\u003c\/p\u003e\n\u003cp\u003eCompatible Network Environments:\u003c\/p\u003e\n\u003cp\u003e400GbE Ethernet Fabrics\u003cbr\u003eNVIDIA ConnectX Networking\u003cbr\u003eRDMA Networks\u003cbr\u003eRoCE Networks\u003cbr\u003eHigh-Performance Storage Networks\u003cbr\u003eDistributed AI Clusters\u003cbr\u003eHigh-Performance Computing Clusters\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eDesign \u0026amp; Construction\u003c\/strong\u003e\u003c\/p\u003e\n\u003cp\u003eEnterprise Rackmount Chassis\u003cbr\u003eDatacentre-Optimised Mechanical Design\u003cbr\u003eProfessional AI Server Construction\u003cbr\u003eHigh-Density Compute Architecture\u003cbr\u003eOptimised Front-to-Rear Airflow\u003cbr\u003eRack Installation Support\u003cbr\u003eEnterprise Cable Management\u003cbr\u003eServiceable Internal Components\u003cbr\u003eContinuous 24×7 Operational Design\u003cbr\u003eRemote Serviceability\u003cbr\u003eHigh-Availability Architecture\u003cbr\u003eDatacentre Integration Ready\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eEnvironmental Specifications\u003c\/strong\u003e\u003c\/p\u003e\n\u003cp\u003eOperating Environment: Enterprise Datacentre\u003cbr\u003eOperating Temperature: Configuration Dependent\u003cbr\u003eStorage Temperature: Configuration Dependent\u003cbr\u003eOperating Humidity: Configuration Dependent\u003cbr\u003eStorage Humidity: Configuration Dependent\u003cbr\u003eMaximum Operating Altitude: Configuration Dependent\u003cbr\u003eThermal Monitoring: Supported\u003cbr\u003eEnvironmental Monitoring: Supported\u003cbr\u003eAirflow Requirement: Datacentre Front-to-Rear Airflow\u003cbr\u003eContinuous Operation: Supported\u003cbr\u003eCompliance and Certifications: Region and Configuration Dependent\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003ePhysical Specifications\u003c\/strong\u003e\u003c\/p\u003e\n\u003cp\u003eProduct Type: Enterprise AI Supercomputer\u003cbr\u003eForm Factor: Rackmount AI Server\u003cbr\u003eRack Unit Height: Configuration Dependent\u003cbr\u003eChassis Colour: Black\u003cbr\u003eDimensions: Configuration Dependent\u003cbr\u003eSystem Weight: Configuration Dependent\u003cbr\u003eInstalled Weight: Configuration Dependent\u003cbr\u003eRack Mounting: Supported\u003cbr\u003eRail Kit: Configuration Dependent\u003cbr\u003eCable Management Arm: Configuration Dependent\u003cbr\u003eService Access: Enterprise Rack Service Design\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003ePackage Contents\u003c\/strong\u003e\u003c\/p\u003e\n\u003cp\u003eASUS ExpertCenter Pro ET900N G3 AI Supercomputer\u003cbr\u003eNVIDIA Grace Blackwell Ultra GB300 Platform\u003cbr\u003eEnterprise Power Cables\u003cbr\u003eRack Mounting Rail Kit: Configuration Dependent\u003cbr\u003eCable Management Accessories: Configuration Dependent\u003cbr\u003eQuick Installation Guide\u003cbr\u003eSafety Documentation\u003cbr\u003eWarranty Documentation\u003cbr\u003eEnterprise Support Information\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eKey Features\u003c\/strong\u003e\u003c\/p\u003e\n\u003cp\u003eNVIDIA Grace Blackwell Ultra GB300 Superchip\u003cbr\u003e72-Core NVIDIA Grace CPU\u003cbr\u003eArm Neoverse V2 CPU Architecture\u003cbr\u003eNVIDIA Blackwell Ultra GPU\u003cbr\u003eUp to 20 PFLOPS AI Performance\u003cbr\u003e748GB Unified Coherent Memory\u003cbr\u003eShared CPU-GPU Memory Architecture\u003cbr\u003eUp to 4 × M.2 2280 NVMe PCIe 5.0 SSD Slots\u003cbr\u003eUp to 8TB Total Internal NVMe Storage\u003cbr\u003eDual NVIDIA ConnectX-8 400GbE QSFP Networking\u003cbr\u003eUp to 800Gbps Aggregate Network Bandwidth\u003cbr\u003eNVIDIA Multi-Instance GPU Support\u003cbr\u003eSecure Multi-Tenant AI Workloads\u003cbr\u003eIntegrated Baseboard Management Controller\u003cbr\u003eOut-of-Band Remote Management\u003cbr\u003eNVIDIA AI Enterprise Ready\u003cbr\u003eCUDA and TensorRT Optimised\u003cbr\u003eDistributed AI Training Ready\u003cbr\u003eEnterprise AI Factory Deployment\u003cbr\u003eLarge Language Model Training and Inferencing\u003cbr\u003eHigh-Performance Computing Support\u003cbr\u003eEnterprise Rackmount Architecture\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003ePerformance \u0026amp; Features\u003c\/strong\u003e\u003c\/p\u003e\n\u003cp\u003eThe ASUS ExpertCenter Pro ET900N G3 is an enterprise AI supercomputer powered by the NVIDIA Grace Blackwell Ultra GB300 platform. It combines a 72-core NVIDIA Grace CPU based on the Arm Neoverse V2 architecture with an NVIDIA Blackwell Ultra GPU to deliver up to 20 PFLOPS of AI performance for demanding model training, fine-tuning and production inferencing workloads.\u003c\/p\u003e\n\u003cp\u003eThe system features 748GB of coherent unified memory, allowing the Grace CPU and Blackwell Ultra GPU to access a shared memory pool through a unified address space. This architecture reduces unnecessary data transfers between separate CPU and GPU memory pools, improves processing efficiency and enables the system to handle larger language models, longer context windows and memory-intensive scientific applications.\u003c\/p\u003e\n\u003cp\u003eInternal storage supports up to four M.2 2280 NVMe PCIe 5.0 SSD slots, with a maximum total capacity of up to 8TB when fully populated. The PCIe Gen5 storage architecture provides high-speed access to training datasets, model checkpoints, vector databases, AI application containers and production inference assets.\u003c\/p\u003e\n\u003cp\u003eDual NVIDIA ConnectX-8 400GbE QSFP network interfaces provide up to 800Gbps of aggregate network bandwidth. Support for RDMA, RoCE, GPUDirect RDMA and GPUDirect Storage enables low-latency communication between compute nodes, storage systems and GPU resources in distributed AI and high-performance computing environments.\u003c\/p\u003e\n\u003cp\u003eNVIDIA Multi-Instance GPU technology allows the GPU to be partitioned into isolated processing instances for different users, departments or AI services. This enables organisations to run multiple workloads securely on the same platform while improving overall hardware utilisation and maintaining predictable resource allocation.\u003c\/p\u003e\n\u003cp\u003eThe integrated Baseboard Management Controller provides out-of-band access for remote administration, system monitoring, firmware management, diagnostics and power control. Administrators can monitor processor, GPU, memory, storage, networking, thermal and power conditions without requiring direct operating system access.\u003c\/p\u003e\n\u003cp\u003eSupport for NVIDIA AI Enterprise, CUDA, TensorRT, Triton Inference Server, NeMo, RAPIDS, PyTorch, TensorFlow, JAX, Kubernetes and Docker provides a comprehensive environment for developing, training, optimising and deploying enterprise AI applications.\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eTypical Use Cases\u003c\/strong\u003e\u003c\/p\u003e\n\u003cp\u003eLarge Language Model Training\u003cbr\u003eLarge Language Model Fine-Tuning\u003cbr\u003eFoundation Model Development\u003cbr\u003eGenerative AI Applications\u003cbr\u003eAI Agent Development\u003cbr\u003eAgentic AI Deployment\u003cbr\u003eRetrieval-Augmented Generation\u003cbr\u003eReasoning Model Processing\u003cbr\u003eMultimodal AI\u003cbr\u003eProduction AI Inferencing\u003cbr\u003eMachine Learning\u003cbr\u003eDeep Learning\u003cbr\u003eComputer Vision\u003cbr\u003eNatural Language Processing\u003cbr\u003eSpeech AI\u003cbr\u003eRecommendation Systems\u003cbr\u003eEnterprise Knowledge Assistants\u003cbr\u003eCybersecurity AI\u003cbr\u003eFinancial Services AI\u003cbr\u003eHealthcare AI Research\u003cbr\u003eDrug Discovery\u003cbr\u003eMolecular Simulation\u003cbr\u003eScientific Computing\u003cbr\u003eHigh-Performance Computing\u003cbr\u003eDigital Twin Simulation\u003cbr\u003eManufacturing AI\u003cbr\u003eAutonomous Systems Development\u003cbr\u003eRobotics Research\u003cbr\u003eUniversity Research Clusters\u003cbr\u003eGovernment AI Infrastructure\u003cbr\u003ePrivate AI Datacentres\u003cbr\u003eHybrid Cloud AI Infrastructure\u003cbr\u003eCloud Service Provider Infrastructure\u003cbr\u003eMulti-Tenant AI Platforms\u003cbr\u003eEnterprise AI Factories\u003c\/p\u003e","brand":"Asus","offers":[{"title":"Default Title","offer_id":51421362847908,"sku":"ET900N G3","price":170000.0,"currency_code":"SGD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0605\/0136\/0804\/files\/asus-expertcenter-pro-et900n-g3-gb300-ai-supercomputer-6195459.png?v=1785247990"},{"product_id":"msi-xpertstation-ws300-nvidia-dgx-station-ai-supercomputer","title":"MSI XpertStation WS300 NVIDIA DGX Station AI Supercomputer","description":"\u003cdiv\u003e\n\u003cstyle\u003e\n.pd-acc { border: 1px solid #e2e2e2; border-radius: 8px; margin: 10px 0; overflow: hidden; }\n.pd-acc summary { cursor: pointer; padding: 14px 16px; font-weight: 700; font-size: 1.05em; list-style: none; display: flex; justify-content: space-between; align-items: center; background: #f7f7f7; }\n.pd-acc[open] \u003e summary { border-bottom: 1px solid #e2e2e2; }\n.pd-acc summary::-webkit-details-marker { display: none; }\n.pd-acc summary::after { content: \"+\"; font-size: 1.4em; font-weight: 400; line-height: 1; margin-left: 12px; }\n.pd-acc[open] \u003e summary::after { content: \"\\2212\"; }\n.pd-acc .pd-acc-body { padding: 8px 16px 14px; }\n.pd-spec, .pd-spec tr, .pd-spec td { border: none !important; background: transparent !important; }\n.pd-spec { width: 100%; border-collapse: collapse !important; margin: 6px 0 18px; }\n.pd-spec td { padding: 7px 10px 7px 0 !important; border-bottom: 1px solid #ececec !important; vertical-align: top; font-size: 0.95em; text-align: left; }\n.pd-spec td:first-child { width: 38%; font-weight: 600; color: #333; }\n.pd-spec tr:last-child td { border-bottom: none !important; }\n\u003c\/style\u003e\n\u003ch2\u003e\u003cstrong\u003eMSI XpertStation WS300 NVIDIA DGX Station with Grace Blackwell Ultra, 748GB Coherent Memory, 1.92TB RAID 1 \u0026amp; Dual 400G ConnectX-8 Networking - Local Warranty\u003c\/strong\u003e\u003c\/h2\u003e\n\u003ch3\u003e\u003cstrong\u003eMSI XpertStation WS300 — Datacentre-Class AI in a Workstation\u003c\/strong\u003e\u003c\/h3\u003e\n\u003cp\u003eThe MSI XpertStation WS300 is an enterprise AI supercomputer built on the NVIDIA DGX Station GB300 platform. The NVIDIA Grace Blackwell Ultra Desktop Superchip delivers up to 20 petaFLOPS of FP4 AI performance with 748 GB of coherent unified memory — enough to work with AI models up to 1 trillion parameters — without a specialised liquid-cooled rack installation.\u003c\/p\u003e\n\u003ch3\u003e\u003cstrong\u003e748 GB Coherent Memory: One Memory Space for Giant Models\u003c\/strong\u003e\u003c\/h3\u003e\n\u003cp\u003e252 GB of HBM3e GPU memory at 7.1 TB\/s pairs with 496 GB of LPDDR5X CPU memory over NVLink-C2C at 900 GB\/s, so models and datasets live in a single 748 GB coherent memory space — accelerating LLM fine-tuning, RAG pipelines, simulations and multi-team AI workloads.\u003c\/p\u003e\n\u003ch3\u003e\u003cstrong\u003eEnterprise Storage and 800G-Class Networking\u003c\/strong\u003e\u003c\/h3\u003e\n\u003cp\u003e1.92 TB of enterprise NVMe storage in RAID 1 protects your work from day one, with expansion slots for growth. Dual 400G QSFP112 ports on the NVIDIA ConnectX-8 SuperNIC (up to 800 Gb\/s) connect the WS300 into private AI clusters and distributed training environments.\u003c\/p\u003e\n\u003ch3\u003e\u003cstrong\u003eAvailable in Singapore Through SourceIT\u003c\/strong\u003e\u003c\/h3\u003e\n\u003cp\u003eEnterprise procurement with GST invoice and local warranty support. For desktop-scale AI, see the \u003ca href=\"\/products\/nvidia-dgx-spark-ai-supercomputer-4tb-940-54242-0007-000\"\u003eNVIDIA DGX Spark\u003c\/a\u003e; for an alternative GB300 workstation, see the \u003ca href=\"\/products\/hp-zgx-fury-g1n-gb300-ai-workstation-with-nvidia-blackwell-ultra\"\u003eHP ZGX Fury G1n\u003c\/a\u003e.\u003c\/p\u003e\n\u003cdetails class=\"pd-acc\"\u003e\n\u003csummary style=\"cursor: pointer; font-weight: 700;\"\u003eMSI XpertStation WS300 Datasheet — Technical Specifications\u003c\/summary\u003e\n\u003cdiv class=\"pd-acc-body\"\u003e\n\u003cp\u003e\u003cstrong\u003eGeneral\u003c\/strong\u003e\u003c\/p\u003e\n\u003ctable class=\"pd-spec\"\u003e\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eModel\u003c\/td\u003e\n\u003ctd\u003eMSI XpertStation WS300 (NVIDIA DGX Station GB300 Platform)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eProduct Type\u003c\/td\u003e\n\u003ctd\u003eEnterprise Desktop AI Supercomputer \/ AI Workstation\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eWarranty\u003c\/td\u003e\n\u003ctd\u003eMSI Enterprise Warranty, Local Support via SourceIT (Configuration Dependent)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/tbody\u003e\u003c\/table\u003e\n\u003cp\u003e\u003cstrong\u003eProcessor — NVIDIA GB300 Grace Blackwell Ultra Desktop Superchip\u003c\/strong\u003e\u003c\/p\u003e\n\u003ctable class=\"pd-spec\"\u003e\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eGPU\u003c\/td\u003e\n\u003ctd\u003e1 × NVIDIA Blackwell Ultra\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eCPU\u003c\/td\u003e\n\u003ctd\u003e1 × NVIDIA Grace — 72-Core Arm Neoverse V2\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eInterconnect\u003c\/td\u003e\n\u003ctd\u003eNVLink-C2C, 900 GB\/s\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eAI Performance\u003c\/td\u003e\n\u003ctd\u003eUp to 20 PFLOPS FP4 Tensor Core\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eModel Support\u003c\/td\u003e\n\u003ctd\u003eAI Models Up to 1 Trillion Parameters\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eGPU Partitioning\u003c\/td\u003e\n\u003ctd\u003eMulti-Instance GPU (MIG) — Up to 7 Instances\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/tbody\u003e\u003c\/table\u003e\n\u003cp\u003e\u003cstrong\u003eMemory\u003c\/strong\u003e\u003c\/p\u003e\n\u003ctable class=\"pd-spec\"\u003e\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eGPU Memory\u003c\/td\u003e\n\u003ctd\u003e252 GB HBM3e, 7.1 TB\/s\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eCPU Memory\u003c\/td\u003e\n\u003ctd\u003e496 GB LPDDR5X, 396 GB\/s\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eTotal Coherent Memory\u003c\/td\u003e\n\u003ctd\u003e748 GB Unified\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/tbody\u003e\u003c\/table\u003e\n\u003cp\u003e\u003cstrong\u003eStorage\u003c\/strong\u003e\u003c\/p\u003e\n\u003ctable class=\"pd-spec\"\u003e\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eInstalled Storage\u003c\/td\u003e\n\u003ctd\u003e1.92 TB Enterprise NVMe (2 × M.2 2280 PCIe 5.0) in RAID 1\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eExpansion\u003c\/td\u003e\n\u003ctd\u003e2 × PCIe 6.0 x4 M.2 2280 Slots Available (4 × M.2 Gen 5 Platform Slots)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/tbody\u003e\u003c\/table\u003e\n\u003cp\u003e\u003cstrong\u003eNetworking\u003c\/strong\u003e\u003c\/p\u003e\n\u003ctable class=\"pd-spec\"\u003e\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eSuperNIC\u003c\/td\u003e\n\u003ctd\u003eNVIDIA ConnectX-8 — Up to 800 Gb\/s Ethernet\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eHigh-Speed Ports\u003c\/td\u003e\n\u003ctd\u003e2 × QSFP112 (400 Gb\/s Each)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eStandard Ethernet\u003c\/td\u003e\n\u003ctd\u003e10 GbE + 1 GbE Management\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/tbody\u003e\u003c\/table\u003e\n\u003cp\u003e\u003cstrong\u003eSoftware \u0026amp; Power\u003c\/strong\u003e\u003c\/p\u003e\n\u003ctable class=\"pd-spec\"\u003e\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eOperating System\u003c\/td\u003e\n\u003ctd\u003eUbuntu with NVIDIA AI Developer Tools (DGX Software Stack)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eAI Stack\u003c\/td\u003e\n\u003ctd\u003eCUDA, NVIDIA AI Enterprise, TensorRT, NVIDIA NIM, PyTorch\/TensorFlow\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ePower\u003c\/td\u003e\n\u003ctd\u003e1,600 W Enterprise High-Efficiency Power Supply\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eCooling\u003c\/td\u003e\n\u003ctd\u003eEnterprise Workstation Cooling Architecture — No Liquid-Cooled Rack Required\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eOptional\u003c\/td\u003e\n\u003ctd\u003eSupports Additional NVIDIA RTX PRO Blackwell GPU\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/tbody\u003e\u003c\/table\u003e\n\u003c\/div\u003e\n\u003c\/details\u003e\n\u003cdetails class=\"pd-acc\"\u003e\n\u003csummary style=\"cursor: pointer; font-weight: 700;\"\u003eOrdering Information — AI Supercomputers at SourceIT\u003c\/summary\u003e\n\u003cdiv class=\"pd-acc-body\"\u003e\n\u003ctable class=\"pd-spec\"\u003e\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eMSI XpertStation WS300 (DGX Station GB300)\u003c\/td\u003e\n\u003ctd\u003eThis Product — In Stock\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eHP ZGX Fury G1n GB300 AI Workstation\u003c\/td\u003e\n\u003ctd\u003e\u003ca href=\"\/products\/hp-zgx-fury-g1n-gb300-ai-workstation-with-nvidia-blackwell-ultra\"\u003eEnquire\u003c\/a\u003e\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eNVIDIA DGX Spark 4TB (Desktop, 1 PFLOP)\u003c\/td\u003e\n\u003ctd\u003e\u003ca href=\"\/products\/nvidia-dgx-spark-ai-supercomputer-4tb-940-54242-0007-000\"\u003e940-54242-0007-000 — In Stock\u003c\/a\u003e\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eASUS Ascent GX10 (GB10 Platform, from 1TB)\u003c\/td\u003e\n\u003ctd\u003e\u003ca href=\"\/products\/asus-ascent-gx10-compact-desktop-ai-supercomputer-1tb-gx10-gg0007bn\"\u003eIn Stock\u003c\/a\u003e\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/tbody\u003e\u003c\/table\u003e\n\u003c\/div\u003e\n\u003c\/details\u003e\n\u003cdetails class=\"pd-acc\"\u003e\n\u003csummary style=\"cursor: pointer; font-weight: 700;\"\u003eDatasheet \u0026amp; Downloads\u003c\/summary\u003e\n\u003cdiv class=\"pd-acc-body\"\u003e\n\u003cp\u003e• \u003ca href=\"https:\/\/www.nvidia.com\/en-us\/products\/workstations\/dgx-station\/\" rel=\"noopener\" target=\"_blank\"\u003eNVIDIA DGX Station GB300 — Official Platform Specifications\u003c\/a\u003e\u003c\/p\u003e\n\u003c\/div\u003e\n\u003c\/details\u003e\n\u003cdetails class=\"pd-acc\"\u003e\n\u003csummary style=\"cursor: pointer; font-weight: 700;\"\u003eFrequently Asked Questions — MSI XpertStation WS300\u003c\/summary\u003e\n\u003cdiv class=\"pd-acc-body\"\u003e\n\u003cp\u003e\u003cstrong\u003eHow does the WS300 compare to the NVIDIA DGX Spark?\u003c\/strong\u003e\u003cbr\u003e\nDifferent class entirely: the WS300 delivers up to 20 PFLOPS FP4 and 748 GB coherent memory for models up to 1 trillion parameters, versus the \u003ca href=\"\/products\/nvidia-dgx-spark-ai-supercomputer-4tb-940-54242-0007-000\"\u003eDGX Spark's\u003c\/a\u003e 1 PFLOP and 128 GB (200B parameters). Choose the Spark for individual developers; the WS300 for teams and production-scale AI.\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eCan multiple team members share one WS300?\u003c\/strong\u003e\u003cbr\u003e\nYes — Multi-Instance GPU (MIG) partitions the Blackwell Ultra GPU into up to 7 isolated instances, so several users or workloads run concurrently with guaranteed resources.\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eDoes it need a server room?\u003c\/strong\u003e\u003cbr\u003e\nNo liquid-cooled rack is required — the WS300 uses enterprise workstation cooling and a 1,600 W power supply. Plan a dedicated power circuit as you would for a high-end workstation.\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eWhat storage does it ship with?\u003c\/strong\u003e\u003cbr\u003e\n1.92 TB enterprise NVMe in RAID 1 for data protection, with two additional PCIe 6.0 M.2 slots free for expansion.\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eHow do I purchase in Singapore?\u003c\/strong\u003e\u003cbr\u003e\nSourceIT supplies the WS300 with GST invoice, local warranty support and enterprise procurement assistance — contact us for configuration options and volume pricing.\u003c\/p\u003e\n\u003c\/div\u003e\n\u003c\/details\u003e\n\u003cp\u003e\u003ciframe title=\"YouTube video player\" src=\"https:\/\/www.youtube.com\/embed\/LaTQE00kCvA?si=TJemSJ2OFA7YdBpz\" height=\"315\" width=\"560\"\u003e\u003c\/iframe\u003e\u003c\/p\u003e\n\u003c\/div\u003e","brand":"Nvidia","offers":[{"title":"Default Title","offer_id":51421413441700,"sku":"XpertStation WS300","price":150000.0,"currency_code":"SGD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0605\/0136\/0804\/files\/msi-xpertstation-ws300-nvidia-dgx-station-ai-supercomputer-4865915.png?v=1785247991"},{"product_id":"gigabyte-w775-v10-l01-deskside-ai-supercomputer-with-nvidia-gb300-blackwell","title":"Gigabyte W775-V10-L01 Deskside AI Supercomputer with NVIDIA GB300 Blackwell","description":"\u003cstyle\u003e\n.sitw775{position:relative;overflow:hidden;background:radial-gradient(ellipse at 50% -20%,rgba(118,185,0,.18),transparent 55%),radial-gradient(ellipse at 85% 110%,rgba(34,211,238,.12),transparent 50%),#0a0e1a;border-radius:18px;padding:38px 24px 30px;margin:0 0 30px;color:#e6edf6;text-align:center}\n.sitw775 *{box-sizing:border-box}\n.sitw775-badge{display:inline-block;padding:6px 16px;border:1px solid rgba(118,185,0,.55);border-radius:999px;font-size:12px;letter-spacing:2px;text-transform:uppercase;color:#9be32e;animation:sitw775pulse 2.8s ease-in-out infinite}\n.sitw775-title{font-size:clamp(24px,4vw,40px);font-weight:800;line-height:1.15;margin:16px auto 8px;max-width:760px;background:linear-gradient(90deg,#76b900,#22d3ee,#ff7a00,#76b900);background-size:300% 100%;-webkit-background-clip:text;background-clip:text;color:transparent;-webkit-text-fill-color:transparent;animation:sitw775grad 7s linear infinite}\n.sitw775-sub{color:#9fb0c7;font-size:15px;line-height:1.55;max-width:640px;margin:0 auto 26px}\n.sitw775-grid{display:grid;grid-template-columns:repeat(6,1fr);gap:12px;max-width:1100px;margin:0 auto}\n@media (max-width:900px){.sitw775-grid{grid-template-columns:repeat(3,1fr)}}\n@media (max-width:520px){.sitw775-grid{grid-template-columns:repeat(2,1fr)}}\n.sitw775-tile{position:relative;overflow:hidden;background:rgba(255,255,255,.045);border:1px solid rgba(255,255,255,.11);border-radius:14px;padding:18px 8px;opacity:0;animation:sitw775rise .8s ease forwards}\n.sitw775-tile:nth-child(1){animation-delay:.1s}.sitw775-tile:nth-child(2){animation-delay:.25s}.sitw775-tile:nth-child(3){animation-delay:.4s}.sitw775-tile:nth-child(4){animation-delay:.55s}.sitw775-tile:nth-child(5){animation-delay:.7s}.sitw775-tile:nth-child(6){animation-delay:.85s}\n.sitw775-tile::after{content:\"\";position:absolute;top:0;left:-130%;width:60%;height:100%;background:linear-gradient(105deg,transparent,rgba(255,255,255,.17),transparent);animation:sitw775shine 4.2s ease-in-out infinite}\n.sitw775-tile:nth-child(2)::after{animation-delay:.5s}.sitw775-tile:nth-child(3)::after{animation-delay:1s}.sitw775-tile:nth-child(4)::after{animation-delay:1.5s}.sitw775-tile:nth-child(5)::after{animation-delay:2s}.sitw775-tile:nth-child(6)::after{animation-delay:2.5s}\n.sitw775-num{font-size:clamp(18px,1.8vw,24px);font-weight:800;color:#fff;line-height:1.1}\n.sitw775-num em{font-style:normal;color:#9be32e}\n.sitw775-lab{font-size:10px;letter-spacing:1px;text-transform:uppercase;color:#8fa1b8;margin-top:6px;line-height:1.4}\n@keyframes sitw775grad{0%{background-position:0% 50%}100%{background-position:300% 50%}}\n@keyframes sitw775pulse{0%,100%{box-shadow:0 0 0 rgba(118,185,0,0)}50%{box-shadow:0 0 18px rgba(118,185,0,.4)}}\n@keyframes sitw775rise{from{opacity:0;transform:translateY(18px)}to{opacity:1;transform:translateY(0)}}\n@keyframes sitw775shine{0%{left:-130%}55%{left:135%}100%{left:135%}}\n@media (prefers-reduced-motion:reduce){.sitw775,.sitw775 *,.sitw775 .sitw775-tile{animation:none!important;opacity:1!important}}\n\u003c\/style\u003e\n\u003cdiv class=\"sitw775\"\u003e\n\u003cspan class=\"sitw775-badge\"\u003eGIGABYTE AI TOP · Deskside AI Supercomputer\u003c\/span\u003e\n\u003cdiv class=\"sitw775-title\"\u003eNVIDIA GB300 Grace Blackwell Ultra\u003c\/div\u003e\n\u003cp class=\"sitw775-sub\"\u003eDatacenter-class AI at your desk. Train, fine-tune and deploy trillion-parameter models fully on-premises — with closed-loop liquid cooling and enterprise-grade 24×7 reliability.\u003c\/p\u003e\n\u003cdiv class=\"sitw775-grid\"\u003e\n\u003cdiv class=\"sitw775-tile\"\u003e\n\u003cdiv class=\"sitw775-num\"\u003e20 \u003cem\u003ePFLOPS\u003c\/em\u003e\n\u003c\/div\u003e\n\u003cdiv class=\"sitw775-lab\"\u003eFP4 AI Compute\u003c\/div\u003e\n\u003c\/div\u003e\n\u003cdiv class=\"sitw775-tile\"\u003e\n\u003cdiv class=\"sitw775-num\"\u003e748 \u003cem\u003eGB\u003c\/em\u003e\n\u003c\/div\u003e\n\u003cdiv class=\"sitw775-lab\"\u003eUnified Coherent Memory\u003c\/div\u003e\n\u003c\/div\u003e\n\u003cdiv class=\"sitw775-tile\"\u003e\n\u003cdiv class=\"sitw775-num\"\u003e72 \u003cem\u003eCores\u003c\/em\u003e\n\u003c\/div\u003e\n\u003cdiv class=\"sitw775-lab\"\u003eNVIDIA Grace CPU\u003c\/div\u003e\n\u003c\/div\u003e\n\u003cdiv class=\"sitw775-tile\"\u003e\n\u003cdiv class=\"sitw775-num\"\u003e7.1 \u003cem\u003eTB\/s\u003c\/em\u003e\n\u003c\/div\u003e\n\u003cdiv class=\"sitw775-lab\"\u003eHBM3E GPU Bandwidth\u003c\/div\u003e\n\u003c\/div\u003e\n\u003cdiv class=\"sitw775-tile\"\u003e\n\u003cdiv class=\"sitw775-num\"\u003e800 \u003cem\u003eGb\/s\u003c\/em\u003e\n\u003c\/div\u003e\n\u003cdiv class=\"sitw775-lab\"\u003eConnectX-8 Networking\u003c\/div\u003e\n\u003c\/div\u003e\n\u003cdiv class=\"sitw775-tile\"\u003e\n\u003cdiv class=\"sitw775-num\"\u003e1 \u003cem\u003eTrillion\u003c\/em\u003e\n\u003c\/div\u003e\n\u003cdiv class=\"sitw775-lab\"\u003eParameter Model Support\u003c\/div\u003e\n\u003c\/div\u003e\n\u003c\/div\u003e\n\u003c\/div\u003e\n\u003ch2\u003e\u003cstrong\u003eGigabyte W775-V10-L01 NVIDIA GB300 AI Supercomputer, 72-Core Grace CPU, 748GB Memory \u0026amp; Dual 400Gb Networking - Local Warranty \u003c\/strong\u003e\u003c\/h2\u003e\n\u003ch3 class=\"PDq2pG_selectionAnchorContainer\"\u003e\n\u003cstrong\u003eEnterprise AI Supercomputer Powered by NVIDIA GB300 Grace Blackwell Ultra\u003c\/strong\u003e\u003cspan class=\"PDq2pG_selectionAnchor\"\u003e\u003c\/span\u003e\n\u003c\/h3\u003e\n\u003cp\u003eThe GIGABYTE W775-V10-L01 is a next-generation AI supercomputer built to accelerate the most demanding artificial intelligence, machine learning, and high-performance computing workloads. Powered by the NVIDIA GB300 Grace Blackwell Ultra Superchip, the system delivers up to \u003cstrong\u003e20 PFLOPS of AI compute\u003c\/strong\u003e, enabling enterprises to train, fine-tune, and deploy large language models (LLMs), generative AI applications, AI agents, computer vision models, and scientific simulations entirely on-premises. Designed for organisations requiring maximum performance and complete data sovereignty, it is an ideal solution for enterprise AI, research, and advanced engineering environments.\u003c\/p\u003e\n\u003ch3\u003e\u003cstrong\u003e748GB Coherent Unified Memory for Massive AI Models\u003c\/strong\u003e\u003c\/h3\u003e\n\u003cp\u003eThe GIGABYTE W775-V10-L01 features \u003cstrong\u003e748GB of coherent unified memory\u003c\/strong\u003e, allowing AI models and large datasets to reside within a single high-speed memory space. This advanced architecture significantly reduces data transfer bottlenecks between the CPU and GPU, improving performance for AI inferencing, retrieval-augmented generation (RAG), foundation model fine-tuning, digital twins, engineering simulations, cybersecurity analytics, and large-scale scientific research.\u003c\/p\u003e\n\u003ch3\u003e\u003cstrong\u003e72-Core NVIDIA Grace CPU with Dual 400Gb Networking\u003c\/strong\u003e\u003c\/h3\u003e\n\u003cp\u003eAt the heart of the system is the \u003cstrong\u003e72-core NVIDIA Grace Arm Neoverse V2 CPU\u003c\/strong\u003e, tightly integrated with the NVIDIA Blackwell Ultra GPU for exceptional memory bandwidth and processing efficiency. Dual \u003cstrong\u003eNVIDIA ConnectX®-8 400GbE networking\u003c\/strong\u003e provides ultra-low latency, high-bandwidth connectivity, making the workstation ideal for distributed AI training, clustered deployments, and enterprise-scale high-performance computing environments.\u003c\/p\u003e\n\u003ch3\u003e\u003cstrong\u003eBuilt for Private AI Infrastructure and Enterprise Deployment\u003c\/strong\u003e\u003c\/h3\u003e\n\u003cp\u003eDesigned for continuous enterprise operation, the GIGABYTE W775-V10-L01 includes advanced liquid cooling, enterprise-grade reliability, high-speed PCIe Gen5 expansion, and remote management capabilities for mission-critical deployments. Whether deployed as a standalone AI workstation or integrated into a private AI cloud, it delivers the scalability, performance, and security required for healthcare, financial services, manufacturing, autonomous systems, government agencies, and AI research laboratories.\u003c\/p\u003e\n\u003ch3\u003e\u003cstrong\u003eTechnical Specifications\u003c\/strong\u003e\u003c\/h3\u003e\n\u003cp\u003e\u003cstrong\u003eGeneral\u003c\/strong\u003e\u003c\/p\u003e\n\u003cp\u003eModel: W775-V10-L01\u003cbr\u003eProduct Name: GIGABYTE W775-V10-L01 NVIDIA GB300 AI Supercomputer, 72-Core Grace CPU, 748GB Memory \u0026amp; Dual 400Gb Networking\u003cbr\u003eManufacturer: GIGABYTE\u003cbr\u003eProduct Series: GIGABYTE AI TOP Workstation\u003cbr\u003eProduct Type: Enterprise AI Supercomputer \/ AI Workstation\u003cbr\u003eCompute Platform: NVIDIA GB300 Grace Blackwell Ultra Desktop Superchip\u003cbr\u003eProcessor Architecture: NVIDIA Grace CPU + NVIDIA Blackwell Ultra GPU\u003cbr\u003eCPU Platform: NVIDIA Grace CPU Superchip\u003cbr\u003eGPU Platform: NVIDIA Blackwell Ultra GPU\u003cbr\u003eSystem Architecture: Unified Coherent CPU-GPU Computing Platform\u003cbr\u003eCooling System: Closed-Loop Liquid Cooling Solution\u003cbr\u003ePower Supply: 1600W ATX 80 PLUS Platinum Power Supply\u003cbr\u003eOperating System: Ubuntu Linux AI Environment (Configuration Dependent)\u003cbr\u003eDeployment: AI Training, AI Inferencing, AI Agents, Machine Learning, Deep Learning, HPC, Scientific Research and Enterprise AI Development\u003cbr\u003eTarget Users: AI Developers, Research Institutions, Universities, Government Agencies, Enterprises, Data Scientists and Engineering Teams\u003cbr\u003eWarranty: GIGABYTE Enterprise Warranty (Configuration Dependent)\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eProcessor Specifications\u003c\/strong\u003e\u003c\/p\u003e\n\u003cp\u003eProcessor Platform: NVIDIA GB300 Grace Blackwell Ultra Desktop Superchip\u003cbr\u003eProcessor Quantity: 1\u003cbr\u003eCPU Model: NVIDIA Grace CPU\u003cbr\u003eCPU Architecture: Arm\u003cbr\u003eCPU Core Architecture: Arm Neoverse V2\u003cbr\u003eCPU Core Count: 72 Cores\u003cbr\u003eGPU Architecture: NVIDIA Blackwell Ultra\u003cbr\u003eCPU-GPU Interconnect: NVIDIA NVLink-C2C\u003cbr\u003eUnified CPU-GPU Architecture: Supported\u003cbr\u003eUnified Address Space: Supported\u003cbr\u003eCoherent CPU-GPU Memory Access: Supported\u003cbr\u003eLarge-Scale Parallel Computing: Supported\u003cbr\u003eHigh-Speed CPU-GPU Data Exchange: Supported\u003cbr\u003eScientific Computing: Supported\u003cbr\u003eHigh-Performance Computing: Supported\u003cbr\u003eContinuous AI Processing: Supported\u003cbr\u003eOptimized for Enterprise AI Workloads\u003cbr\u003eOptimized for Foundation Models\u003cbr\u003eOptimized for Large Language Models\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eAI Performance\u003c\/strong\u003e\u003c\/p\u003e\n\u003cp\u003eMaximum AI Compute Performance: Up to 20 PetaFLOPS FP4\u003cbr\u003eAI Accelerator: NVIDIA Blackwell Ultra GPU\u003cbr\u003eTransformer Engine: Second-Generation NVIDIA Transformer Engine\u003cbr\u003eTensor Cores: Fifth-Generation NVIDIA Tensor Cores\u003cbr\u003eFP4 Computing: Supported\u003cbr\u003eFP6 Computing: Supported\u003cbr\u003eFP8 Computing: Supported\u003cbr\u003eFP16 Computing: Supported\u003cbr\u003eBF16 Computing: Supported\u003cbr\u003eINT8 Inferencing: Supported\u003cbr\u003eMixed Precision Computing: Supported\u003cbr\u003eLarge Language Model Acceleration: Supported\u003cbr\u003eFoundation Model Training: Supported\u003cbr\u003eFoundation Model Fine-Tuning: Supported\u003cbr\u003eGenerative AI Processing: Supported\u003cbr\u003eAI Agent Development: Supported\u003cbr\u003eReasoning Model Processing: Supported\u003cbr\u003eRetrieval-Augmented Generation (RAG): Supported\u003cbr\u003eMultimodal AI: Supported\u003cbr\u003eScientific AI Computing: Supported\u003cbr\u003eDistributed AI Training: Supported\u003cbr\u003eProduction AI Inferencing: Supported\u003cbr\u003eSupports AI Models Up to 1 Trillion Parameters\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eAI Features\u003c\/strong\u003e\u003c\/p\u003e\n\u003cp\u003eLarge Language Model Development\u003cbr\u003eLarge Language Model Training\u003cbr\u003eLarge Language Model Fine-Tuning\u003cbr\u003eFoundation Model Development\u003cbr\u003eFoundation Model Optimization\u003cbr\u003eGenerative AI\u003cbr\u003eAgentic AI\u003cbr\u003eAI Agent Development\u003cbr\u003eRetrieval-Augmented Generation\u003cbr\u003eReasoning Models\u003cbr\u003eMultimodal AI\u003cbr\u003eComputer Vision\u003cbr\u003eNatural Language Processing\u003cbr\u003eSpeech AI\u003cbr\u003eRecommendation Systems\u003cbr\u003ePredictive Analytics\u003cbr\u003eDigital Twin Simulation\u003cbr\u003eScientific Machine Learning\u003cbr\u003eDrug Discovery\u003cbr\u003eMolecular Modelling\u003cbr\u003eFinancial AI\u003cbr\u003eCybersecurity AI\u003cbr\u003eManufacturing AI\u003cbr\u003eHealthcare AI\u003cbr\u003eEnterprise Knowledge Assistants\u003cbr\u003ePrivate AI Deployment\u003cbr\u003eHybrid AI Infrastructure\u003cbr\u003eDistributed AI Computing\u003cbr\u003eEnterprise AI Factory Deployment\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eMemory Specifications\u003c\/strong\u003e\u003c\/p\u003e\n\u003cp\u003eTotal System Memory: 748GB Coherent Unified Memory\u003cbr\u003eGPU Memory: 252GB HBM3E\u003cbr\u003eGPU Memory Bandwidth: Up to 7.1TB\/s\u003cbr\u003eCPU Memory: 496GB LPDDR5X\u003cbr\u003eCPU Memory Bandwidth: Up to 396GB\/s\u003cbr\u003eMemory Architecture: Unified Coherent CPU-GPU Memory\u003cbr\u003eUnified Address Space: Supported\u003cbr\u003eCPU-GPU Shared Memory Pool: Supported\u003cbr\u003eMemory Coherency: Supported\u003cbr\u003eHigh-Bandwidth Memory Architecture: Supported\u003cbr\u003eLow-Latency Memory Access: Supported\u003cbr\u003eLarge AI Model Loading: Supported\u003cbr\u003eLarge Context Window Processing: Supported\u003cbr\u003eTrillion-Parameter Model Inferencing: Supported\u003cbr\u003eLarge Dataset Processing: Supported\u003cbr\u003eMemory Intensive AI Applications: Supported\u003cbr\u003eScientific Simulation Support: Supported\u003cbr\u003eVector Database Processing: Supported\u003cbr\u003eEnterprise AI Pipeline Support: Supported\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eStorage Specifications\u003c\/strong\u003e\u003c\/p\u003e\n\u003cp\u003eStorage Technology: NVMe Solid-State Drive\u003cbr\u003eDrive Form Factor: M.2 2280\u003cbr\u003eStorage Interface: PCI Express NVMe\u003c\/p\u003e\n\u003cp\u003eInstalled Storage Slots:\u003c\/p\u003e\n\u003cp\u003e2 × M.2 PCIe Gen6 x4\u003c\/p\u003e\n\u003cp\u003e2 × M.2 PCIe Gen5 x4\u003c\/p\u003e\n\u003cp\u003eMaximum Storage Configuration: Configuration Dependent\u003c\/p\u003e\n\u003cp\u003eBoot Drive Support: Supported\u003c\/p\u003e\n\u003cp\u003eEnterprise NVMe SSD Support\u003c\/p\u003e\n\u003cp\u003ePCIe Gen6 High-Speed Storage\u003c\/p\u003e\n\u003cp\u003ePCIe Gen5 High-Speed Storage\u003c\/p\u003e\n\u003cp\u003eAI Dataset Storage\u003c\/p\u003e\n\u003cp\u003eModel Checkpoint Storage\u003c\/p\u003e\n\u003cp\u003eVector Database Storage\u003c\/p\u003e\n\u003cp\u003eContainer Repository Storage\u003c\/p\u003e\n\u003cp\u003eTraining Dataset Storage\u003c\/p\u003e\n\u003cp\u003eContinuous Enterprise Read\/Write Workloads\u003c\/p\u003e\n\u003cp\u003eStorage Expansion Supported\u003c\/p\u003e\n\u003cp\u003eStorage Health Monitoring\u003c\/p\u003e\n\u003cp\u003eRemote Storage Status Monitoring\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eGraphics Specifications\u003c\/strong\u003e\u003c\/p\u003e\n\u003cp\u003eGPU Model: NVIDIA Blackwell Ultra\u003c\/p\u003e\n\u003cp\u003eGPU Quantity: 1\u003c\/p\u003e\n\u003cp\u003eGPU Architecture: NVIDIA Blackwell Ultra\u003c\/p\u003e\n\u003cp\u003eTensor Cores: Fifth-Generation NVIDIA Tensor Cores\u003c\/p\u003e\n\u003cp\u003eTransformer Engine: Second Generation\u003c\/p\u003e\n\u003cp\u003eCUDA Support\u003c\/p\u003e\n\u003cp\u003eCUDA-X Libraries\u003c\/p\u003e\n\u003cp\u003eTensorRT\u003c\/p\u003e\n\u003cp\u003eTensorRT-LLM\u003c\/p\u003e\n\u003cp\u003ecuDNN\u003c\/p\u003e\n\u003cp\u003eRAPIDS\u003c\/p\u003e\n\u003cp\u003eNVIDIA AI Enterprise\u003c\/p\u003e\n\u003cp\u003eNVIDIA NeMo\u003c\/p\u003e\n\u003cp\u003eNVIDIA NGC Containers\u003c\/p\u003e\n\u003cp\u003eGPUDirect RDMA\u003c\/p\u003e\n\u003cp\u003eGPUDirect Storage\u003c\/p\u003e\n\u003cp\u003eMixed Precision Computing\u003c\/p\u003e\n\u003cp\u003eLarge Language Model Acceleration\u003c\/p\u003e\n\u003cp\u003eScientific Computing Acceleration\u003c\/p\u003e\n\u003cp\u003eEnterprise AI Development\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eNetworking Specifications\u003c\/strong\u003e\u003c\/p\u003e\n\u003cp\u003eHigh-Speed Network Controller:\u003c\/p\u003e\n\u003cp\u003eNVIDIA ConnectX-8 SuperNIC\u003c\/p\u003e\n\u003cp\u003eNetwork Ports:\u003c\/p\u003e\n\u003cp\u003e2 × 400Gb\/s QSFP112\u003c\/p\u003e\n\u003cp\u003eMaximum Aggregate Network Bandwidth:\u003c\/p\u003e\n\u003cp\u003eUp to 800Gbps\u003c\/p\u003e\n\u003cp\u003eAdditional Ethernet:\u003c\/p\u003e\n\u003cp\u003e1 × 10GbE RJ45 (Marvell)\u003c\/p\u003e\n\u003cp\u003eDedicated Management Port:\u003c\/p\u003e\n\u003cp\u003e1 × Management LAN\u003c\/p\u003e\n\u003cp\u003eRDMA Support\u003c\/p\u003e\n\u003cp\u003eRoCE Support\u003c\/p\u003e\n\u003cp\u003eGPUDirect RDMA\u003c\/p\u003e\n\u003cp\u003eGPUDirect Storage\u003c\/p\u003e\n\u003cp\u003eDistributed AI Training\u003c\/p\u003e\n\u003cp\u003eAI Cluster Networking\u003c\/p\u003e\n\u003cp\u003eScale-Out AI Infrastructure\u003c\/p\u003e\n\u003cp\u003eEnterprise AI Fabric Integration\u003c\/p\u003e\n\u003cp\u003eHigh-Speed Storage Networking\u003c\/p\u003e\n\u003cp\u003eHigh-Performance Computing Cluster Support\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003ePorts \u0026amp; Expansion\u003c\/strong\u003e\u003c\/p\u003e\n\u003cp\u003e2 × 400Gb\/s QSFP112\u003c\/p\u003e\n\u003cp\u003e1 × 10GbE RJ45 LAN\u003c\/p\u003e\n\u003cp\u003e1 × Dedicated Management LAN\u003c\/p\u003e\n\u003cp\u003e2 × M.2 PCIe Gen6 x4\u003c\/p\u003e\n\u003cp\u003e2 × M.2 PCIe Gen5 x4\u003c\/p\u003e\n\u003cp\u003e1 × PCIe Gen5 x16 Expansion Slot\u003c\/p\u003e\n\u003cp\u003e2 × PCIe Gen5 x8 Expansion Slots\u003c\/p\u003e\n\u003cp\u003eUSB Connectivity: Configuration Dependent\u003c\/p\u003e\n\u003cp\u003eDisplay Connectivity: Configuration Dependent\u003c\/p\u003e\n\u003cp\u003eFuture Enterprise Expansion Supported\u003c\/p\u003e\n\u003cp\u003eRack Integration Supported\u003c\/p\u003e\n\u003cp\u003eHigh-Speed Storage Expansion Supported\u003c\/p\u003e\n\u003cp\u003eAdditional Enterprise Networking Expansion Supported\u003c\/p\u003e\n\u003cp\u003eDedicated Management Interface Supported\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eCooling System\u003c\/strong\u003e\u003c\/p\u003e\n\u003cp\u003eClosed-Loop Liquid Cooling\u003c\/p\u003e\n\u003cp\u003eOptimized CPU Cooling\u003c\/p\u003e\n\u003cp\u003eOptimized GPU Cooling\u003c\/p\u003e\n\u003cp\u003eHigh-Efficiency Thermal Design\u003c\/p\u003e\n\u003cp\u003eContinuous AI Training Support\u003c\/p\u003e\n\u003cp\u003eContinuous AI Inferencing Support\u003c\/p\u003e\n\u003cp\u003eLow Acoustic Enterprise Design\u003c\/p\u003e\n\u003cp\u003eThermal Monitoring\u003c\/p\u003e\n\u003cp\u003eFan Speed Monitoring\u003c\/p\u003e\n\u003cp\u003eAutomatic Thermal Protection\u003c\/p\u003e\n\u003cp\u003e24×7 Enterprise Operation\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003ePower Specifications\u003c\/strong\u003e\u003c\/p\u003e\n\u003cp\u003ePower Supply Capacity: 1600W\u003c\/p\u003e\n\u003cp\u003ePower Supply Type: ATX\u003c\/p\u003e\n\u003cp\u003eEfficiency Rating: 80 PLUS Platinum\u003c\/p\u003e\n\u003cp\u003eEnterprise High-Efficiency Design\u003c\/p\u003e\n\u003cp\u003eContinuous AI Workloads Supported\u003c\/p\u003e\n\u003cp\u003eRemote Power Monitoring: Configuration Dependent\u003c\/p\u003e\n\u003cp\u003eEnterprise Power Management\u003c\/p\u003e\n\u003cp\u003eHigh-Load GPU Processing Support\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompatibility\u003c\/strong\u003e\u003c\/p\u003e\n\u003cp\u003eCompatible AI Frameworks:\u003c\/p\u003e\n\u003cp\u003ePyTorch\u003c\/p\u003e\n\u003cp\u003eTensorFlow\u003c\/p\u003e\n\u003cp\u003eJAX\u003c\/p\u003e\n\u003cp\u003eONNX Runtime\u003c\/p\u003e\n\u003cp\u003eHugging Face Transformers\u003c\/p\u003e\n\u003cp\u003eDeepSpeed\u003c\/p\u003e\n\u003cp\u003eMegatron-LM\u003c\/p\u003e\n\u003cp\u003eTensorRT\u003c\/p\u003e\n\u003cp\u003eTensorRT-LLM\u003c\/p\u003e\n\u003cp\u003eNVIDIA NeMo\u003c\/p\u003e\n\u003cp\u003eNVIDIA Triton Inference Server\u003c\/p\u003e\n\u003cp\u003eCUDA Toolkit\u003c\/p\u003e\n\u003cp\u003ecuDNN\u003c\/p\u003e\n\u003cp\u003eRAPIDS\u003c\/p\u003e\n\u003cp\u003eCompatible Deployment Platforms:\u003c\/p\u003e\n\u003cp\u003eDocker\u003c\/p\u003e\n\u003cp\u003eKubernetes\u003c\/p\u003e\n\u003cp\u003eNVIDIA NGC\u003c\/p\u003e\n\u003cp\u003eUbuntu Linux\u003c\/p\u003e\n\u003cp\u003eEnterprise AI Enterprise\u003c\/p\u003e\n\u003cp\u003ePrivate AI Infrastructure\u003c\/p\u003e\n\u003cp\u003eHybrid AI Infrastructure\u003c\/p\u003e\n\u003cp\u003eEnterprise MLOps Platforms\u003c\/p\u003e\n\u003cp\u003eCompatible AI Workloads:\u003c\/p\u003e\n\u003cp\u003eLarge Language Models\u003c\/p\u003e\n\u003cp\u003eFoundation Models\u003c\/p\u003e\n\u003cp\u003eGenerative AI\u003c\/p\u003e\n\u003cp\u003eAI Agents\u003c\/p\u003e\n\u003cp\u003eRetrieval-Augmented Generation\u003c\/p\u003e\n\u003cp\u003eMachine Learning\u003c\/p\u003e\n\u003cp\u003eDeep Learning\u003c\/p\u003e\n\u003cp\u003eComputer Vision\u003c\/p\u003e\n\u003cp\u003eNatural Language Processing\u003c\/p\u003e\n\u003cp\u003eSpeech AI\u003c\/p\u003e\n\u003cp\u003eScientific Computing\u003c\/p\u003e\n\u003cp\u003eHigh-Performance Computing\u003c\/p\u003e\n\u003cp\u003eDigital Twins\u003c\/p\u003e\n\u003cp\u003eEnterprise AI\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eDesign \u0026amp; Construction\u003c\/strong\u003e\u003c\/p\u003e\n\u003cp\u003eEnterprise AI Workstation Design\u003c\/p\u003e\n\u003cp\u003eProfessional AI Supercomputer Chassis\u003c\/p\u003e\n\u003cp\u003eClosed-Loop Liquid Cooling Chassis\u003c\/p\u003e\n\u003cp\u003eHigh-Density Compute Platform\u003c\/p\u003e\n\u003cp\u003eEnterprise Cable Management\u003c\/p\u003e\n\u003cp\u003eServiceable Internal Components\u003c\/p\u003e\n\u003cp\u003eContinuous 24×7 Operational Design\u003c\/p\u003e\n\u003cp\u003eDatacenter-Class Internal Architecture\u003c\/p\u003e\n\u003cp\u003eProfessional Engineering Design\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eEnvironmental Specifications\u003c\/strong\u003e\u003c\/p\u003e\n\u003cp\u003eOperating Environment: Enterprise Office or AI Laboratory\u003c\/p\u003e\n\u003cp\u003eContinuous Operation Supported\u003c\/p\u003e\n\u003cp\u003eThermal Monitoring\u003c\/p\u003e\n\u003cp\u003eEnvironmental Monitoring\u003c\/p\u003e\n\u003cp\u003eHigh Reliability Components\u003c\/p\u003e\n\u003cp\u003eConfiguration Dependent Certifications\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003ePhysical Specifications\u003c\/strong\u003e\u003c\/p\u003e\n\u003cp\u003eProduct Type: Enterprise AI Supercomputer\u003c\/p\u003e\n\u003cp\u003eForm Factor: Deskside AI Supercomputer\u003c\/p\u003e\n\u003cp\u003eCooling: Closed-Loop Liquid Cooling\u003c\/p\u003e\n\u003cp\u003ePower Supply: 1600W Platinum\u003c\/p\u003e\n\u003cp\u003eColour: Black\u003c\/p\u003e\n\u003cp\u003eDimensions: Configuration Dependent\u003c\/p\u003e\n\u003cp\u003eWeight: Configuration Dependent\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003ePackage Contents\u003c\/strong\u003e\u003c\/p\u003e\n\u003cp\u003eGIGABYTE W775-V10-L01 AI Supercomputer\u003c\/p\u003e\n\u003cp\u003e1600W Platinum Power Supply\u003c\/p\u003e\n\u003cp\u003ePower Cable\u003c\/p\u003e\n\u003cp\u003eQuick Installation Guide\u003c\/p\u003e\n\u003cp\u003eWarranty Documentation\u003c\/p\u003e\n\u003cp\u003eEnterprise Support Documentation\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eKey Features\u003c\/strong\u003e\u003c\/p\u003e\n\u003cp\u003eNVIDIA GB300 Grace Blackwell Ultra Desktop Superchip\u003c\/p\u003e\n\u003cp\u003e72-Core NVIDIA Grace CPU\u003c\/p\u003e\n\u003cp\u003eArm Neoverse V2 Architecture\u003c\/p\u003e\n\u003cp\u003eSingle NVIDIA Blackwell Ultra GPU\u003c\/p\u003e\n\u003cp\u003e748GB Unified Coherent Memory\u003c\/p\u003e\n\u003cp\u003e252GB HBM3E GPU Memory\u003c\/p\u003e\n\u003cp\u003e496GB LPDDR5X CPU Memory\u003c\/p\u003e\n\u003cp\u003eUp to 20 PFLOPS AI Performance\u003c\/p\u003e\n\u003cp\u003eSupports AI Models Up to 1 Trillion Parameters\u003c\/p\u003e\n\u003cp\u003eDual NVIDIA ConnectX-8 400Gb Networking\u003c\/p\u003e\n\u003cp\u003e800Gbps Aggregate Network Bandwidth\u003c\/p\u003e\n\u003cp\u003e1 × 10GbE LAN\u003c\/p\u003e\n\u003cp\u003eDedicated Management LAN\u003c\/p\u003e\n\u003cp\u003e2 × PCIe Gen6 M.2 SSD Slots\u003c\/p\u003e\n\u003cp\u003e2 × PCIe Gen5 M.2 SSD Slots\u003c\/p\u003e\n\u003cp\u003ePCIe Gen5 Expansion\u003c\/p\u003e\n\u003cp\u003eClosed-Loop Liquid Cooling\u003c\/p\u003e\n\u003cp\u003e1600W 80 PLUS Platinum Power Supply\u003c\/p\u003e\n\u003cp\u003eEnterprise AI Development Ready\u003c\/p\u003e\n\u003cp\u003eCUDA, TensorRT \u0026amp; NVIDIA AI Enterprise Ready\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003ePerformance \u0026amp; Features\u003c\/strong\u003e\u003c\/p\u003e\n\u003cp\u003eThe GIGABYTE W775-V10-L01 is a next-generation deskside AI supercomputer powered by the NVIDIA GB300 Grace Blackwell Ultra Desktop Superchip. It combines a 72-core NVIDIA Grace CPU based on the Arm Neoverse V2 architecture with the latest NVIDIA Blackwell Ultra GPU to deliver up to \u003cstrong\u003e20 petaFLOPS\u003c\/strong\u003e of FP4 AI compute performance for enterprise AI development, model training, fine-tuning and inferencing.\u003c\/p\u003e\n\u003cp\u003eThe system integrates \u003cstrong\u003e748GB of coherent unified memory\u003c\/strong\u003e, consisting of \u003cstrong\u003e252GB HBM3E GPU memory\u003c\/strong\u003e delivering up to \u003cstrong\u003e7.1TB\/s\u003c\/strong\u003e bandwidth and \u003cstrong\u003e496GB LPDDR5X CPU memory\u003c\/strong\u003e providing up to \u003cstrong\u003e396GB\/s\u003c\/strong\u003e bandwidth. 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