Not across the board. Intel's software for Arc graphics has improved, with upstream PyTorch support, OpenVINO and Windows ML, and Ollama now reaches Intel GPUs through Vulkan. But CUDA is still what most AI tools target first, and NVIDIA's RTX Spark brings CUDA, an RTX GPU and up to 128GB of shared memory to Windows. Where Intel wins is the platform around the GPU: the ASUS NUC 16 Pro is a standard x86 Windows PC, so every developer tool, container and x64 app runs natively, while RTX Spark runs Windows on Arm and translates x64 apps. At SourceIT the NUC 16 Pro X9 388H with 32GB and 1TB is S$2,775.81, and the cheapest RTX Spark machine, the MSI Prestige N16 Flip AI+ laptop with 32GB and 1TB, is S$4,974.08 (listed 3 Nov 2026).
How do the two software stacks compare?
| Tool or task | NUC 16 Pro (Intel Arc B390, x86 Windows) | RTX Spark (NVIDIA RTX, Windows on Arm) |
|---|---|---|
| Ollama | Runs through Ollama's Vulkan backend, on by default | Runs on CUDA; StorageReview ran Ollama and llama.cpp Arm64 CUDA builds |
| llama.cpp | SYCL and Vulkan backends; OpenVINO backend in preview since OpenVINO 2026.1 | CUDA backend |
| PyTorch | Upstream XPU support lists Panther Lake on Windows 11, WSL2 and Ubuntu 26.04 | CUDA is PyTorch's main GPU target; Windows-on-Arm builds are not covered in our sources |
| vLLM and TensorRT-LLM | CUDA-first tools | vLLM does not support Windows natively (needs WSL); TensorRT-LLM install docs are Linux only |
| Windows ML | OpenVINO execution provider, downloaded on demand (Windows 11 24H2 or later) | Microsoft lists NVIDIA's TensorRT for RTX execution provider for RTX GPUs; Windows-on-Arm support not confirmed in our sources |
| Retired Intel tools | Intel Extension for PyTorch archived 30 Mar 2026; IPEX-LLM archived 28 Jan 2026 | Not applicable |
| x64 Windows apps | Native | Translated by Microsoft's Prism emulation; some games reported incompatible |
| Memory for models | 32GB or 64GB soldered at SourceIT; about 136.5 GB/s theoretical (our calculation) | Up to 128GB shared; about 300 GB/s (reviewers' calculation). GPU allocation is capped by default, for example 64GB on a 128GB ASUS ProArt P16 |
From PyTorch, Ollama, OpenVINO and Microsoft Learn documentation (2026), Intel's GitHub archive notices, vLLM's documentation, and StorageReview, Notebookcheck and ServeTheHome reviews of RTX Spark machines (Oct 2026). We have not tested either system ourselves.
Is the performance comparable?
We do not assume it is, and the published numbers cannot settle it, because they use different benchmarks. StorageReview measured the NUC 16 Pro X7 358H at 26.18 tokens per second for Llama 3 on its GPU using UL Procyon on OpenVINO. On an RTX Spark Surface Laptop Ultra with 128GB, it measured 56.4 tokens per second for Llama 3.1 8B in MLPerf and 89.2 tokens per second for Qwen3.6-35B-A3B. RTX Spark's roughly double memory bandwidth and its larger memory pool point the same way: for local language models, it is the faster and more capable machine. The question is whether you need that.
Intel Arc does not beat CUDA at its own game. It makes a cheaper x86 Windows desktop good enough for the AI most offices actually run.
Where does the NUC's x86 platform matter?
- Developer tooling. Docker's documentation notes you cannot run linux/amd64 containers on an arm64 host without emulation, which "can be much slower". On an x86 NUC, amd64 containers run natively.
- Line-of-business software and older Windows drivers that have no Arm version.
- Office deployment. A NUC images, manages and patches like any other company PC.
- Price and form. SourceIT lists RTX Spark only in laptops today. If a desk-bound PC is what you need, the NUC avoids paying for a screen and battery.
Where does RTX Spark pull ahead?
- Bigger models. With 128GB it can load 70B to 120B-class models that will not fit in the NUC's memory.
- CUDA-only apps. Many AI, rendering and video tools are developed and tested on CUDA first.
- Portability. It is a laptop, with long reported battery life for video playback.
For RTX Spark against NVIDIA's Linux GB10, see NVIDIA RTX Spark vs GB10: Is Windows Finally Ready to Challenge Linux for Local AI?, and for app compatibility on Arm, our Windows on Arm compatibility guide for RTX Spark. For the three-way view including GB10, read Three Tiny Computers, Three Different Brains; for why AI TOPS are hard to compare, see Intel NPU vs NVIDIA GPU.
Buying in Singapore
SourceIT supplies the ASUS NUC 16 Pro X9 388H, 32GB, 1TB, Windows 11 Pro (S$2,775.81), the 64GB version (S$4,281.65) and the X7 358H, 32GB, 1TB (S$2,727.10), and RTX Spark laptops including the MSI Prestige N16 Flip AI+ 32GB/1TB (S$4,974.08), Lenovo Yoga Pro 9n 64GB (S$5,897.20) and ASUS ProArt P16 128GB (S$10,773.20). Prices listed 3 Nov 2026. Browse the ASUS NUC and RTX 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 Intel Arc run the same AI software as an NVIDIA RTX GPU? Some of it. Ollama, llama.cpp, PyTorch (XPU), OpenVINO and Windows ML support Intel Arc, but many tools, including vLLM and TensorRT-LLM, are built for CUDA first.
Is the ASUS NUC 16 Pro faster than RTX Spark for AI? No published like-for-like test exists. StorageReview's figures, from different benchmarks, put RTX Spark well ahead on language models, helped by about twice the memory bandwidth.
Should I still use IPEX-LLM on an Intel NUC? No. Intel archived IPEX-LLM in January 2026 and says it has known security issues. Use Ollama's Vulkan support or OpenVINO instead.
Does RTX Spark run normal Windows apps? Most, through Windows on Arm, with x64 apps translated by Prism. Some games were reported incompatible, and vLLM needs WSL.
What do they cost at SourceIT? On 3 Nov 2026 the NUC 16 Pro X9 388H 32GB/1TB was S$2,775.81 and RTX Spark laptops started at S$4,974.08.
