Many of the same apps, yes; the same models at the same speed, no. A NUC around S$2,000 can run Ollama, small language models of about 7 to 8 billion parameters, embeddings for document search and speech-to-text, which covers a lot of one-person office AI. What it cannot do is hold the large models that a GB10's 128GB of unified memory was built for, or serve many users at once. The ASUS NUC 16 Pro X7 358H kit with 32GB is S$1,947.65 at SourceIT (it still needs an SSD and Windows), while GB10 systems with 128GB currently start at S$7,955 for the Acer Altos BrainSphere GB10 F1, with the NVIDIA DGX Spark at S$9,300 (listed 3 Nov 2026). So the "S$7,000 GB10" in the title is nearer S$8,000 today.
How do they compare app by app?
| Workload | NUC 16 Pro X7 358H, 32GB | GB10, 128GB |
|---|---|---|
| Ollama | Runs; Ollama reaches Intel GPUs through its Vulkan backend, on by default | Runs on CUDA; Ollama tested GB10 at launch |
| 7–8B model, 4-bit | About 17–19 tokens/s in community llama.cpp tests on Panther Lake graphics; 26.18 tokens/s for Llama 3 in StorageReview's Procyon test | 38.0 tokens/s for Llama 3.1 8B (Ollama, q4_K_M) |
| 27–32B model | 3.6 tokens/s for Qwen3.6-27B Q4_K_M in one community test | 9.4 tokens/s for Qwen3 32B (Ollama); 61.1 tokens/s for Qwen3-Coder-30B-A3B Q8_0 (llama.cpp) |
| 70B model | A 4-bit 70B file is about 42.5 GB, more than 32GB | 4.4 tokens/s for Llama 3.1 70B (Ollama) |
| gpt-oss-120b (about 59 GiB) | Does not fit | 41.1–58.7 tokens/s (Ollama and llama.cpp) |
| Embeddings and RAG | Small embedding models run; OpenVINO Model Server offers an OpenAI-style API. No published RAG benchmark | NVIDIA playbooks for RAG and a multi-agent chatbot |
| Speech recognition | OpenVINO's Whisper pipeline runs on CPU or GPU. No published speed for the NUC | Whisper large-v3 about 25x real time; NVIDIA Parakeet up to about 458x (third-party forum tests) |
| Several users at once | Best for one person | Batches well: gpt-oss-20b reached 681 tokens/s total at 32 requests in llama.cpp's batched benchmark |
| Memory bandwidth | About 136.5 GB/s theoretical (our calculation for LPDDR5x-8533) | 273 GB/s (NVIDIA) |
NUC figures from StorageReview's NUC 16 Pro review (Oct 2026, UL Procyon on OpenVINO) and the llama.cpp SYCL community thread (May–Jun 2026), which says its data is not double-checked. GB10 figures from Ollama (Oct 2025), NVIDIA's developer blog (Oct 2025), llama.cpp's DGX Spark benchmark file (Feb 2026) and NVIDIA forum posts. Tools, quantisation and settings differ, so treat these as indications, not a head-to-head test. We have not tested either system ourselves.
What decides whether a model will run?
Memory first. A language model has to fit in memory before speed matters, and the NUC 16 Pro shares its 32GB between Windows, apps and the model. GB10 has 128GB, so 70B models and gpt-oss-120b fit with room to spare. SourceIT also lists NUC 16 Pro kits with 64GB, from S$2,921.96, which can load a 4-bit 70B model on paper, but we found no published speed for that. Bandwidth decides how fast tokens come out, and GB10 has about twice the NUC's theoretical figure.
A S$2,000 NUC can do personal AI. A GB10 is for models and workloads that would not fit on the NUC at all.
What should NUC owners watch out for?
- Old guides. Many tutorials for Intel GPUs used IPEX-LLM's Ollama "portable zip". Intel archived IPEX-LLM on 28 Jan 2026 and says it has known security issues, so don't use it for new set-ups. Use Ollama's built-in Vulkan support or OpenVINO.
- Uneven Vulkan results. In one community report on a Core Ultra 285HX, Vulkan crashed or ran very slowly on a large model, and the user went back to CPU. Test your own model before you commit.
- Memory configuration. Intel's Arc graphics need dual-channel memory of at least 16GB. Kits with soldered LPDDR5x, like the X7 358H 32GB, meet that automatically.
- Features that are still previews. OpenVINO's llama.cpp backend arrived as a preview in 2026.1.
For a hands-on guide to Ollama on the NUC 16 Pro's Arc B390, see Ollama on Intel Arc B390: Local LLMs on the ASUS NUC 16 Pro. How the NUC sits against RTX Spark and GB10 overall is covered in Three Tiny Computers, Three Different Brains.
When is the GB10 worth the extra money?
- You need 70B-class or larger models, or long context windows. See Can NVIDIA GB10 Analyse Thousands of Company Documents Without Sending Them to the Cloud?
- Several people will use the same model at once. See Can One GB10 Support 20 Employees Using AI at the Same Time?
- You want to fine-tune models or use NVIDIA's CUDA tools such as TensorRT-LLM and vLLM.
- You transcribe a lot of audio. See Can a GB10 Transcribe 100 Hours of Meetings?
If one person needs a Windows desktop that can also run a local assistant, meeting notes and document search on small models, the NUC is the sensible spend. The wider ladder of local-AI hardware is in Best PC to Run LLMs Locally.
Buying in Singapore
SourceIT supplies the ASUS NUC 16 Pro X7 358H 32GB kit (S$1,947.65, no SSD or OS), the ready-to-use NUC 16 Pro X7 358H, 32GB, 1TB, Windows 11 Pro (S$2,727.10) and the NUC 16 Pro X9 378H 64GB kit (S$2,921.96), plus GB10 systems including the NVIDIA DGX Spark 4TB (S$9,300) and ASUS Ascent GX10 128GB/1TB (S$10,350). The Acer Altos BrainSphere GB10 F1 128GB/4TB is S$7,955. Prices listed 3 Nov 2026. Browse the ASUS NUC and DGX Spark collections. For a quotation, use Request for Quotation on the product page, call (65) 6978 3502, email Sales@sourceit.com.sg or use our contact page.
Frequently asked questions
Can an ASUS NUC run Ollama? Yes. Ollama uses its Vulkan backend for Intel GPUs, enabled by default. Avoid older guides based on IPEX-LLM, which Intel archived in January 2026.
How fast is a 7–8B model on a NUC 16 Pro? Published figures range from about 17–19 tokens per second in community llama.cpp tests to 26.18 tokens per second for Llama 3 in StorageReview's Procyon test on the GPU.
Can a NUC run a 70B model? Not with 32GB, because a 4-bit 70B model is about 42.5 GB. A 64GB NUC 16 Pro kit could hold one, but we found no published speed.
Why is a GB10 so much more expensive? It has 128GB of unified memory, about twice the NUC's memory bandwidth, NVIDIA's CUDA software stack and the ability to serve several users at once.
How much does each cost at SourceIT? On 3 Nov 2026, the NUC 16 Pro X7 358H 32GB kit was S$1,947.65 and GB10 systems with 128GB started at S$7,955.
