{"product_id":"supermicro-super-ai-station-ars-511gd-nb-lcc-nvidia-dgx-station-gb300","title":"Supermicro Super AI Station ARS-511GD-NB-LCC NVIDIA DGX Station GB300 AI Workstation","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\u003eSupermicro Super AI Station ARS-511GD-NB-LCC NVIDIA DGX Station GB300 Grace Blackwell Ultra AI Workstation, 748GB Coherent Memory, 20 PFLOPS, 4 × 1.92TB NVMe, Dual 400G ConnectX-8, Closed-Loop Liquid Cooling (ARS-511GD-NB-LCC) - 3 Years Local Warranty\u003c\/h2\u003e\n\u003ch3\u003eSupermicro Super AI Station — NVIDIA DGX Station GB300 in a Tower or 5U Rack Chassis\u003c\/h3\u003e\n\u003cp\u003eThe Supermicro Super AI Station ARS-511GD-NB-LCC is Supermicro's build of the NVIDIA DGX Station GB300 platform: an NVIDIA GB300 Grace Blackwell Ultra Desktop Superchip pairing a 72-core NVIDIA Grace CPU (Arm Neoverse V2) with an NVIDIA Blackwell Ultra B300 GPU over NVLink-C2C, sharing 748 GB of coherent memory and delivering up to 20 petaFLOPS of FP4 AI performance for training, fine-tuning and serving AI models of up to 1 trillion parameters — on premises, under your own data governance.\u003c\/p\u003e\n\u003cp\u003eSupermicro brings its data-centre server engineering to the deskside: closed-loop direct-to-chip liquid cooling, a 1600 W Titanium power supply, IPMI 2.0 and Redfish remote management, a Silicon Root of Trust and a chassis that works as a tower today and converts to a 5U rack unit with an optional rail kit when your AI deployment grows. SourceIT supplies it in Singapore with Supermicro's 3-year parts-and-service warranty with next-business-day onsite hardware support, a full GST invoice and enterprise procurement assistance.\u003c\/p\u003e\n\u003ch3\u003e748 GB ECC Coherent Memory for Trillion-Parameter Models\u003c\/h3\u003e\n\u003cp\u003e496 GB of ECC LPDDR5X CPU memory (four 128 GB SOCAMM modules) and 252 GB of ECC HBM3e GPU memory unite over NVLink-C2C into a single 748 GB coherent memory space, so frontier-scale LLMs, long-context RAG pipelines, fine-tuning runs and simulation datasets stay resident without CPU–GPU offloading. Multi-Instance GPU (MIG) partitions the Blackwell Ultra GPU into up to seven isolated instances so several users or workloads share one system with guaranteed resources.\u003c\/p\u003e\n\u003ch3\u003e4 × 1.92 TB Enterprise NVMe, Ubuntu 24.04 LTS Preinstalled\u003c\/h3\u003e\n\u003cp\u003eThe SourceIT configuration ships with four 1.92 TB enterprise NVMe M.2 SSDs (1 DWPD): two in the PCIe 5.0 x4 slots as an OS RAID 1 pair and two in the PCIe 6.0 x4 slots as a RAID 0 data volume, both configured through the host OS. Ubuntu 24.04 LTS with NVIDIA drivers, CUDA and cuDNN is preinstalled alongside the NVIDIA AI Developer Tools, so the system is ready for vLLM, NVIDIA NeMo, NIM microservices and the wider CUDA ecosystem on delivery. An M.2 E-key slot takes an optional Wi-Fi \/ Bluetooth module.\u003c\/p\u003e\n\u003ch3\u003eDual 400 GbE ConnectX-8 Networking, PCIe 5.0 Expansion and an Optional Second GPU\u003c\/h3\u003e\n\u003cp\u003eTwo QSFP 400 GbE ports on the NVIDIA ConnectX-8 SuperNIC (up to 800 Gb\/s aggregate) connect the Super AI Station into private AI clusters, distributed training fabrics and NVMe-over-fabric storage, with a 10 GbE RJ45 port for site networking and a dedicated 1 GbE BMC port for out-of-band management. A PCIe 5.0 x16 full-height double-width slot accepts an optional NVIDIA RTX PRO Blackwell display GPU (RTX PRO 2000, 4000 SFF, 6000 Max-Q or 6000 Workstation Edition), and two PCIe 5.0 x8 slots take additional NICs or accelerators.\u003c\/p\u003e\n\u003ch3\u003eEnterprise Management, Security and Serviceability\u003c\/h3\u003e\n\u003cp\u003eAn onboard BMC provides IPMI 2.0 with KVM-over-LAN and virtual media plus a Redfish API, so the system slots into existing data-centre management tooling. TPM 2.0 and a Silicon Root of Trust compliant with NIST 800-193 protect firmware integrity, and chassis intrusion detection is built in. Closed-loop direct-to-chip liquid cooling with five system fans keeps the Superchip at full performance through sustained 24 × 7 training and inference.\u003c\/p\u003e\n\u003ch3\u003e1600 W Titanium Power, Tower or 5U, 40 kg\u003c\/h3\u003e\n\u003cp\u003eA single 1600 W 80 PLUS Titanium (94 %) power supply feeds the system on 115–240 V AC. The chassis measures 218.4 mm high, 454.7 mm wide and 701 mm deep, weighs 40 kg net (51 kg gross), and is rated for 15–32 °C operation at 20–80 % non-condensing humidity. The optional MCP-290-75905-0B rail kit converts it to a 5U rackmount unit.\u003c\/p\u003e\n\u003cdetails class=\"pd-acc\"\u003e\u003csummary style=\"cursor: pointer; font-weight: 700;\"\u003eSupermicro Super AI Station ARS-511GD-NB-LCC Datasheet — Technical Specifications\u003c\/summary\u003e\u003cdiv class=\"pd-acc-body\"\u003e\n\u003ch3\u003eGeneral\u003c\/h3\u003e\n\u003ctable class=\"pd-spec\"\u003e\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eProduct Name\u003c\/td\u003e\n\u003ctd\u003eSupermicro Super AI Station\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eModel \/ Part Number\u003c\/td\u003e\n\u003ctd\u003eARS-511GD-NB-LCC (motherboard Super GPU-NVGB300-WSHPM; chassis CSE-759TS-R0CNBP)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eCategory\u003c\/td\u003e\n\u003ctd\u003eEnterprise AI workstation \/ deskside AI supercomputer (NVIDIA DGX Station GB300 platform)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eForm Factor\u003c\/td\u003e\n\u003ctd\u003eTower, convertible to 5U rackmount with optional rail kit MCP-290-75905-0B\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eWarranty\u003c\/td\u003e\n\u003ctd\u003e3 years parts and service with next-business-day onsite hardware support by Supermicro (OSNBD3)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eLocal Warranty (Singapore)\u003c\/td\u003e\n\u003ctd\u003e3 Years Supermicro NBD onsite; procurement and GST invoice by SourceIT\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/tbody\u003e\u003c\/table\u003e\n\u003ch3\u003eProcessor \u0026amp; GPU\u003c\/h3\u003e\n\u003ctable class=\"pd-spec\"\u003e\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eCompute Platform\u003c\/td\u003e\n\u003ctd\u003eNVIDIA DGX Station architecture — NVIDIA GB300 Grace Blackwell Ultra Desktop Superchip\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eCPU\u003c\/td\u003e\n\u003ctd\u003e1 × NVIDIA Grace CPU, 72 Arm Neoverse V2 cores\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eGPU\u003c\/td\u003e\n\u003ctd\u003e1 × NVIDIA Blackwell Ultra GPU (fifth-generation Tensor Cores, second-generation Transformer Engine)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eCPU–GPU Interconnect\u003c\/td\u003e\n\u003ctd\u003eNVIDIA NVLink-C2C, 900 GB\/s (per NVIDIA DGX Station datasheet)\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 (with sparsity), per NVIDIA — 10 PFLOPS FP8\/FP6, 5 PFLOPS FP16\/BF16\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, per NVIDIA\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\u003ctr\u003e\n\u003ctd\u003eOptional Second GPU\u003c\/td\u003e\n\u003ctd\u003e1 × double-width PCIe 5.0 x16 slot for NVIDIA RTX PRO 2000 \/ 4000 SFF \/ 6000 Max-Q \/ 6000 Workstation Edition Blackwell (quoted separately)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/tbody\u003e\u003c\/table\u003e\n\u003ch3\u003eMemory\u003c\/h3\u003e\n\u003ctable class=\"pd-spec\"\u003e\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eTotal Coherent Memory\u003c\/td\u003e\n\u003ctd\u003e748 GB unified CPU + GPU memory space\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eGPU Memory\u003c\/td\u003e\n\u003ctd\u003e252 GB ECC HBM3e\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eCPU Memory\u003c\/td\u003e\n\u003ctd\u003e496 GB ECC LPDDR5X, 4 × 128 GB SOCAMM\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/tbody\u003e\u003c\/table\u003e\n\u003ch3\u003eStorage\u003c\/h3\u003e\n\u003ctable class=\"pd-spec\"\u003e\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eOS Storage\u003c\/td\u003e\n\u003ctd\u003e2 × M.2 2280 PCIe 5.0 x4 NVMe slots — fitted with 2 × 1.92 TB enterprise NVMe Gen4 M.2 SSD (1 DWPD), RAID 1 via OS\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eData Storage\u003c\/td\u003e\n\u003ctd\u003e2 × M.2 2280 PCIe 6.0 x4 NVMe slots — fitted with 2 × 1.92 TB enterprise NVMe Gen4 M.2 SSD (1 DWPD), RAID 0 via OS\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eWireless Slot\u003c\/td\u003e\n\u003ctd\u003e1 × M.2 2230 E-key PCIe 2.0 x1 (Wi-Fi \/ Bluetooth module optional)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/tbody\u003e\u003c\/table\u003e\n\u003ch3\u003eExpansion\u003c\/h3\u003e\n\u003ctable class=\"pd-spec\"\u003e\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003ePCIe Slots\u003c\/td\u003e\n\u003ctd\u003e1 × PCIe 5.0 x16 FHFL double-width; 2 × PCIe 5.0 x8 (in x16) FHHL\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/tbody\u003e\u003c\/table\u003e\n\u003ch3\u003eNetworking\u003c\/h3\u003e\n\u003ctable class=\"pd-spec\"\u003e\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eHigh-Speed Networking\u003c\/td\u003e\n\u003ctd\u003e2 × QSFP 400 GbE, NVIDIA ConnectX-8 SuperNIC (up to 800 Gb\/s)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eGeneral Ethernet\u003c\/td\u003e\n\u003ctd\u003e1 × RJ45 10 GbE\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eManagement LAN\u003c\/td\u003e\n\u003ctd\u003e1 × RJ45 1 GbE dedicated BMC port\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/tbody\u003e\u003c\/table\u003e\n\u003ch3\u003eI\/O Ports\u003c\/h3\u003e\n\u003ctable class=\"pd-spec\"\u003e\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eFront I\/O\u003c\/td\u003e\n\u003ctd\u003e2 × USB 3.0 Type-A\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eRear I\/O\u003c\/td\u003e\n\u003ctd\u003e4 × USB 3.0 Gen2 Type-A, 1 × mini-DisplayPort, audio\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/tbody\u003e\u003c\/table\u003e\n\u003ch3\u003eManagement \u0026amp; Security\u003c\/h3\u003e\n\u003ctable class=\"pd-spec\"\u003e\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eBMC\u003c\/td\u003e\n\u003ctd\u003eIPMI 2.0 with KVM-over-LAN and virtual media over LAN; Redfish API\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eSecurity\u003c\/td\u003e\n\u003ctd\u003eTPM 2.0; Silicon Root of Trust (NIST 800-193 compliant); chassis intrusion detection\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eBIOS\u003c\/td\u003e\n\u003ctd\u003eAMI, 64 MB SPI flash\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/tbody\u003e\u003c\/table\u003e\n\u003ch3\u003eOperating System \u0026amp; Software\u003c\/h3\u003e\n\u003ctable class=\"pd-spec\"\u003e\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eOperating System\u003c\/td\u003e\n\u003ctd\u003eUbuntu 24.04 LTS with NVIDIA AI Developer Tools — preinstalled with NVIDIA drivers, CUDA and cuDNN\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/tbody\u003e\u003c\/table\u003e\n\u003ch3\u003ePower, Cooling, Physical \u0026amp; Environmental\u003c\/h3\u003e\n\u003ctable class=\"pd-spec\"\u003e\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003ePower Supply\u003c\/td\u003e\n\u003ctd\u003e1 × 1600 W 80 PLUS Titanium (94 %); input 115–240 V AC, 50–60 Hz\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eCooling\u003c\/td\u003e\n\u003ctd\u003eClosed-loop direct-to-chip liquid cooling; fans 2 × 140 mm, 2 × 80 mm, 1 × 60 mm\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eDimensions (H × W × D)\u003c\/td\u003e\n\u003ctd\u003e218.4 × 454.7 × 701 mm (8.6 × 17.9 × 27.6 in)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eWeight\u003c\/td\u003e\n\u003ctd\u003e40 kg net; 51 kg gross\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eOperating Environment\u003c\/td\u003e\n\u003ctd\u003e15 – 32 °C, 20 – 80 % non-condensing\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eColour\u003c\/td\u003e\n\u003ctd\u003eBlack\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/tbody\u003e\u003c\/table\u003e\n\u003c\/div\u003e\u003c\/details\u003e\n\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\u003ch3\u003eNVIDIA AI Software Stack\u003c\/h3\u003e\n\u003cp\u003eShips with Ubuntu Linux and the NVIDIA AI Developer Tools preinstalled — the same NVIDIA software foundation used across the DGX family. CUDA, cuDNN, TensorRT and TensorRT-LLM, NVIDIA NeMo, NVIDIA NIM microservices, Triton Inference Server, RAPIDS and NGC containers run natively on the Grace Blackwell Ultra platform, alongside PyTorch, TensorFlow, JAX, vLLM, Hugging Face Transformers, DeepSpeed and ONNX Runtime. Docker and Kubernetes deployment are supported for containerised MLOps pipelines. NVIDIA AI Enterprise is available as a separate subscription for supported production deployments.\u003c\/p\u003e\n\u003ch3\u003eArm64 Architecture — Plan Your Software Accordingly\u003c\/h3\u003e\n\u003cp\u003eThe Grace CPU is an Arm Neoverse V2 processor, so the operating system, drivers and container images are arm64 (aarch64) builds. Mainstream AI frameworks and NVIDIA NGC containers are published for arm64, but proprietary x86-only applications will not run natively — check any commercial software you rely on before ordering. NVIDIA has announced a separate DGX Station for Windows SKU (Windows plus Windows Subsystem for Linux) for a later release; the units SourceIT supplies today ship with Ubuntu.\u003c\/p\u003e\n\u003ch3\u003eClustering and Networking Compatibility\u003c\/h3\u003e\n\u003cp\u003eThe two NVIDIA ConnectX-8 SuperNIC QSFP112 ports run at 400 Gb\/s each (up to 800 Gb\/s aggregate) with RDMA over Converged Ethernet, GPUDirect RDMA and GPUDirect Storage support, so two or more systems can be linked for distributed training, or connected to an NVMe-over-fabric storage array. QSFP112 transceivers or direct-attach cables are ordered separately to match your switch fabric — SourceIT will quote compatible optics alongside the system. The 10GbE RJ45 port covers everyday site networking, and the dedicated management LAN keeps out-of-band BMC traffic isolated from production data.\u003c\/p\u003e\n\u003ch3\u003eSupermicro Enterprise Platform\u003c\/h3\u003e\n\u003cp\u003eThe Super AI Station uses the same BMC, IPMI 2.0 and Redfish management foundation as Supermicro's rack servers, so it can be monitored and serviced alongside existing Supermicro infrastructure, and the optional rail kit lets it move from deskside to a 5U rack slot without a chassis change. Supermicro also offers the system as a Gold Series ready-to-ship configuration; SourceIT quotes the configuration described on this page and can adjust storage or add a display GPU to order.\u003c\/p\u003e\n\u003c\/div\u003e\u003c\/details\u003e\n\u003cdetails class=\"pd-acc\"\u003e\u003csummary style=\"cursor: pointer; font-weight: 700;\"\u003ePackage Contents \u0026amp; Ordering Information\u003c\/summary\u003e\u003cdiv class=\"pd-acc-body\"\u003e\n\u003ch3\u003eWhat Ships With the System\u003c\/h3\u003e\n\u003cul\u003e\n\u003cli\u003eSupermicro Super AI Station ARS-511GD-NB-LCC tower with NVIDIA GB300 Grace Blackwell Ultra Desktop Superchip\u003c\/li\u003e\n\u003cli\u003e2 × 1.92 TB NVMe Gen4 enterprise M.2 SSD (1 DWPD) configured as OS RAID 1\u003c\/li\u003e\n\u003cli\u003e2 × 1.92 TB NVMe Gen4 enterprise M.2 SSD (1 DWPD) configured as data RAID 0\u003c\/li\u003e\n\u003cli\u003eUbuntu 24.04 LTS preinstalled with NVIDIA drivers, CUDA and cuDNN\u003c\/li\u003e\n\u003cli\u003e1600 W Titanium power supply (installed) and power cord\u003c\/li\u003e\n\u003cli\u003eSupermicro 3-year parts-and-service warranty with next-business-day onsite hardware support (OSNBD3)\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cp\u003eQSFP transceivers or DAC cables, the 5U rackmount rail kit (MCP-290-75905-0B), a Wi-Fi \/ Bluetooth module and an optional NVIDIA RTX PRO Blackwell display GPU are quoted separately.\u003c\/p\u003e\n\u003ch3\u003eNVIDIA DGX Station GB300 Systems at SourceIT Singapore\u003c\/h3\u003e\n\u003cp\u003eThe Supermicro Super AI Station is quoted by SourceIT Singapore with GST invoice and Supermicro's 3-year NBD onsite warranty; a dedicated 20 A power circuit is recommended for the 1600 W platform.\u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ca href=\"\/products\/asus-expertcenter-pro-et900n-g3-gb300-ai-supercomputer\"\u003eASUS ExpertCenter Pro ET900N G3\u003c\/a\u003e — NVIDIA DGX Station GB300 platform, same 748 GB \/ 20 PFLOPS core compute\u003c\/li\u003e\n\u003cli\u003e\n\u003ca href=\"\/products\/msi-xpertstation-ws300-nvidia-dgx-station-ai-supercomputer\"\u003eMSI XpertStation WS300\u003c\/a\u003e — NVIDIA DGX Station GB300 platform, same 748 GB \/ 20 PFLOPS core compute\u003c\/li\u003e\n\u003cli\u003e\n\u003ca href=\"\/products\/hp-zgx-fury-g1n-gb300-ai-workstation-with-nvidia-blackwell-ultra\"\u003eHP ZGX Fury AI Station G1n\u003c\/a\u003e — NVIDIA DGX Station GB300 platform, same 748 GB \/ 20 PFLOPS core compute\u003c\/li\u003e\n\u003cli\u003e\n\u003ca href=\"\/products\/gigabyte-w775-v10-l01-deskside-ai-supercomputer-with-nvidia-gb300-blackwell\"\u003eGIGABYTE W775-V10-L01\u003c\/a\u003e — NVIDIA DGX Station GB300 platform, same 748 GB \/ 20 PFLOPS core compute\u003c\/li\u003e\n\u003cli\u003e\n\u003ca href=\"\/products\/nvidia-dgx-spark-ai-supercomputer-4tb-940-54242-0007-000\"\u003eNVIDIA DGX Spark\u003c\/a\u003e — desktop-scale GB10 system (128 GB unified memory, 1 PFLOP FP4) for individual developers\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cp\u003eBrowse the full \u003ca href=\"\/collections\/nvidia-dgx-spark\"\u003eNVIDIA DGX Spark \u0026amp; DGX Station collection\u003c\/a\u003e. For volume, project, research-grant or education pricing, GST invoicing and current lead times, email \u003ca href=\"mailto:sales@sourceit.com.sg\"\u003esales@sourceit.com.sg\u003c\/a\u003e.\u003c\/p\u003e\n\u003c\/div\u003e\u003c\/details\u003e\n\u003cdetails class=\"pd-acc\"\u003e\u003csummary style=\"cursor: pointer; font-weight: 700;\"\u003eDatasheet \u0026amp; Downloads\u003c\/summary\u003e\u003cdiv class=\"pd-acc-body\"\u003e\n\u003cp\u003eOfficial Supermicro documentation for the Super AI Station ARS-511GD-NB-LCC:\u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https:\/\/www.supermicro.com\/datasheet\/datasheet_Supermicro_Super_AI_Station.pdf\" target=\"_blank\" rel=\"noopener\"\u003eSupermicro Super AI Station datasheet (PDF)\u003c\/a\u003e\u003c\/li\u003e\n\u003cli\u003e\u003ca href=\"https:\/\/www.supermicro.com\/en\/products\/system\/gpu\/tower%20or%205u\/ars-511gd-nb-lcc\" target=\"_blank\" rel=\"noopener\"\u003eSupermicro ARS-511GD-NB-LCC — product page\u003c\/a\u003e\u003c\/li\u003e\n\u003cli\u003e\u003ca href=\"https:\/\/www.supermicro.com\/en\/products\/system\/datasheet\/ars-511gd-nb-lcc\" target=\"_blank\" rel=\"noopener\"\u003eSupermicro ARS-511GD-NB-LCC — full specifications\u003c\/a\u003e\u003c\/li\u003e\n\u003cli\u003e\u003ca href=\"https:\/\/www.supermicro.com\/en\/accelerators\/nvidia\/super-ai-station\" target=\"_blank\" rel=\"noopener\"\u003eSupermicro Super AI Station — overview\u003c\/a\u003e\u003c\/li\u003e\n\u003cli\u003e\u003ca href=\"https:\/\/www.nvidia.com\/en-sg\/products\/workstations\/dgx-station\/\" target=\"_blank\" rel=\"noopener\"\u003eNVIDIA DGX Station (GB300) — official platform page (Singapore)\u003c\/a\u003e\u003c\/li\u003e\n\u003cli\u003e\u003ca href=\"https:\/\/www.nvidia.com\/content\/dam\/en-zz\/Solutions\/products\/workstations\/dgx-station\/workstation-datasheet-dgx-station-nvidia-us-5543600-web.pdf\" target=\"_blank\" rel=\"noopener\"\u003eNVIDIA DGX Station datasheet (PDF)\u003c\/a\u003e\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003c\/div\u003e\u003c\/details\u003e\n\u003cdetails class=\"pd-acc\"\u003e\u003csummary style=\"cursor: pointer; font-weight: 700;\"\u003eFrequently Asked Questions — Supermicro Super AI Station ARS-511GD-NB-LCC\u003c\/summary\u003e\u003cdiv class=\"pd-acc-body\"\u003e\n\u003ch3\u003eWhat is the Supermicro Super AI Station?\u003c\/h3\u003e\n\u003cp\u003eSupermicro's build of the NVIDIA DGX Station GB300 platform (model ARS-511GD-NB-LCC): a liquid-cooled tower with the GB300 Grace Blackwell Ultra Superchip, 748 GB ECC coherent memory and up to 20 PFLOPS FP4 for training, fine-tuning and serving AI models of up to 1 trillion parameters on premises — with the option to rack-mount it as a 5U unit.\u003c\/p\u003e\n\u003ch3\u003eWhat storage does it ship with?\u003c\/h3\u003e\n\u003cp\u003eFour 1.92 TB enterprise NVMe Gen4 M.2 SSDs rated at 1 DWPD: two as an OS RAID 1 pair on the PCIe 5.0 slots and two as a RAID 0 data volume on the PCIe 6.0 slots, both configured through the host OS.\u003c\/p\u003e\n\u003ch3\u003eIs the operating system preinstalled?\u003c\/h3\u003e\n\u003cp\u003eYes. Ubuntu 24.04 LTS is preinstalled with NVIDIA drivers, CUDA and cuDNN, together with the NVIDIA AI Developer Tools.\u003c\/p\u003e\n\u003ch3\u003eCan I rack-mount it?\u003c\/h3\u003e\n\u003cp\u003eYes. The chassis is a tower by default and converts to a 5U rackmount unit with Supermicro's optional MCP-290-75905-0B rail and handle kit, quoted separately.\u003c\/p\u003e\n\u003ch3\u003eCan I add a display graphics card?\u003c\/h3\u003e\n\u003cp\u003eYes. The PCIe 5.0 x16 double-width slot accepts an NVIDIA RTX PRO 2000, RTX PRO 4000 SFF, RTX PRO 6000 Max-Q or RTX PRO 6000 Workstation Edition Blackwell card — quoted separately.\u003c\/p\u003e\n\u003ch3\u003eHow does it compare to the NVIDIA DGX Spark?\u003c\/h3\u003e\n\u003cp\u003eDifferent class entirely: the Super AI Station delivers up to 20 PFLOPS FP4 and 748 GB coherent memory for models up to 1 trillion parameters, versus the DGX Spark's 1 PFLOP and 128 GB (about 200 billion parameters). Choose the Spark for individual developers; the Super AI Station for teams and production-scale AI.\u003c\/p\u003e\n\u003ch3\u003eCan multiple team members share one system?\u003c\/h3\u003e\n\u003cp\u003eYes — Multi-Instance GPU (MIG) partitions the Blackwell Ultra GPU into up to seven isolated instances for concurrent users and workloads.\u003c\/p\u003e\n\u003ch3\u003eDoes it need a server room?\u003c\/h3\u003e\n\u003cp\u003eNo rack is required — cooling is a closed-loop direct-to-chip liquid loop inside the chassis. Plan an air-conditioned space within Supermicro's 15–32 °C operating range and a dedicated 20 A power circuit for the 1600 W Titanium power supply.\u003c\/p\u003e\n\u003ch3\u003eWhat warranty does it come with?\u003c\/h3\u003e\n\u003cp\u003eSupermicro's 3-year parts-and-service warranty with next-business-day onsite hardware support (OSNBD3), supplied through SourceIT with local support in Singapore.\u003c\/p\u003e\n\u003ch3\u003eHow do I purchase the Supermicro Super AI Station in Singapore?\u003c\/h3\u003e\n\u003cp\u003eSourceIT is an authorised IT reseller in Singapore and supplies the ARS-511GD-NB-LCC with GST invoice, Supermicro onsite warranty and enterprise procurement assistance — contact sales@sourceit.com.sg for configuration options, volume pricing and lead times.\u003c\/p\u003e\n\u003c\/div\u003e\u003c\/details\u003e\n\u003ch3\u003eProduct Video\u003c\/h3\u003e\n\u003cp\u003e\u003ciframe width=\"560\" height=\"315\" src=\"https:\/\/www.youtube.com\/embed\/7YhgDWpedJA\" title=\"The Future of AI Development Solutions - Super AI Station | Supermicro\" frameborder=\"0\" allow=\"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture\" allowfullscreen\u003e\u003c\/iframe\u003e\u003c\/p\u003e\n\u003c\/div\u003e","brand":"Supermicro","offers":[{"title":"Default Title","offer_id":51487892799652,"sku":"ARS-511GD-NB-LCC","price":138000.0,"currency_code":"SGD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0605\/0136\/0804\/files\/ARS-511GD-NB-LCC_main.jpg?v=1790258695","url":"https:\/\/sourceit.com.sg\/products\/supermicro-super-ai-station-ars-511gd-nb-lcc-nvidia-dgx-station-gb300","provider":"SourceIT ","version":"1.0","type":"link"}