Not yet, but the ground is shifting under one part of it. For running AI models on a desk, CUDA is no longer the only practical choice: llama.cpp, Ollama, LM Studio and Apple's own MLX framework all run well on an Apple Mac Studio, and a Metal version of vLLM arrived in September 2026. For training, fine-tuning at scale and deploying to servers, CUDA still dominates, because the libraries, kernels and data-centre hardware are built around it. Apple's opportunity is real, but it is an inference opportunity, not a takeover.
How does software support compare across local AI platforms?
| Software (as of Nov 2026) | Mac Studio (macOS, Metal/MLX) | NVIDIA GB10 / DGX Spark (Linux, CUDA) | NVIDIA RTX Spark (Windows on Arm) | x86 PC with RTX GPU (CUDA) |
|---|---|---|---|---|
| llama.cpp | Yes (Metal) | Yes | Yes (unsigned builds may be blocked by Smart App Control) | Yes |
| Ollama | Yes; MLX backend in preview | Yes | Yes | Yes |
| LM Studio | Yes (MLX and llama.cpp engines) | Yes | Yes | Yes |
| vLLM | vllm-metal plugin, first release 22 Sep 2026 | Yes (CUDA) | No native support; WSL2 or community port | Yes (CUDA) |
| TensorRT-LLM | No | Yes | No (Linux-only install docs) | Yes (Linux) |
| PyTorch GPU | MPS backend; some ops fall back to CPU | Yes | CUDA build for Windows on Arm, developer preview | Yes |
| GPU inside Docker containers | No (no Metal passthrough) | Yes | Not validated | Yes |
| bitsandbytes quantisation | CPU backend only | Yes | Not confirmed | Yes |
Source: vLLM blog (22 Sep 2026); Ollama blog (30 Mar 2026); LM Studio 0.4.0 notes (28 Jan 2026); Hugging Face Transformers "Apple Silicon" and bitsandbytes install docs (accessed 8 Nov 2026); Docker blog (26 Feb 2026); XDA (26 Oct 2026) and NVIDIA briefings (Oct 2026) for RTX Spark. Status changes quickly; check each project's documentation before you buy.
Why has CUDA been so hard to dislodge?
CUDA is NVIDIA's programming platform for its GPUs, and it has had nearly two decades to accumulate libraries, tutorials and tooling. NVIDIA's annual report for the year to January 2026 says "over 7.5 million developers worldwide" use CUDA and its other software tools. That installed base matters more than any single benchmark. Research code is usually written and tested on NVIDIA hardware first, optimised kernels such as FlashAttention arrive for CUDA first, and the servers a company will eventually deploy to almost certainly run NVIDIA GPUs.
The result is a kind of gravity. A developer who prototypes on CUDA can move the same code to a cloud GPU or an on-premises server with little change. A developer on any other platform has to check whether each library, quantisation format and inference engine works there, and sometimes convert models first.
What has Apple actually built to compete?
Apple's answer is MLX, an open-source array framework designed for Apple silicon's unified memory, and mlx-lm, which adds text generation, a server, benchmarks, and low-rank (LoRA) and full fine-tuning, including on quantised models. MLX also supports distributed inference across several Macs. Since macOS 26.2 it can use RDMA over Thunderbolt 5, which cuts the latency between machines sharply.
Around MLX, the third-party ecosystem has filled in quickly:
- Ollama 0.19 (30 Mar 2026) moved Apple silicon to an MLX backend, still labelled a preview, and it needs more than 32 GB of unified memory.
- LM Studio runs both MLX and llama.cpp engines. Apple's own Mac Studio performance claims were measured in LM Studio.
- vllm-metal, a plugin under the vLLM project, had its first official release on 22 Sep 2026, with continuous batching, prefix caching and an OpenAI-compatible API. It is a separate Metal plugin, not the CUDA build of vLLM.
Hardware has helped too. In published tests, the M5 Ultra processes prompts about 2.3 to 4.4 times faster than the M3 Ultra, thanks to new Neural Accelerators in each GPU core, and generates tokens about 1.5 to 1.6 times faster. That narrows part of the gap on the compute-heavy side of inference. (Our guide to unified memory versus GPU power explains why that matters.)
Inference is becoming portable. Training, deployment and the long tail of research code are still written for CUDA first.
Where does the Mac still fall short?
The gaps are mostly outside chat-style inference, and they are specific:
- No CUDA at all. That rules out TensorRT-LLM, NCCL, cuDNN and CUDA-only kernels. Models released in NVIDIA-specific quantised formats may need converting before MLX can use them.
- PyTorch on Mac is incomplete. The MPS backend doesn't support every operation yet, so some fall back to the CPU, and it can't offload part of a model to CPU memory the way CUDA can. FP8 on MPS only appears in the PyTorch 2.15 release candidate.
- Containers can't use the GPU. Docker on macOS has no Metal passthrough, so inference has to run directly on the host. That breaks the common pattern of shipping a model server as a GPU container.
- Quantisation tools lag. bitsandbytes runs only on the CPU on macOS.
- Multi-user serving is young. vllm-metal is weeks old, and LM Studio's MLX engine lacked request batching as of its 0.4.0 notes. Our post on running a whole company on one Mac Studio looks at what that means for teams.
None of this stops a developer from chatting with a 120B model on a Mac. It does mean that a team whose work ends in a CUDA data centre will meet friction at the boundary. Our post on the developer mistakes to avoid when buying a Mac for AI covers that boundary in detail.
Is NVIDIA's own desktop story as smooth as CUDA's reputation?
Not everywhere. The NVIDIA DGX Spark and other GB10 systems run Linux with the full CUDA stack, so code moves from desk to data centre with little change. That is their strongest argument. NVIDIA's Windows-based RTX Spark PCs, such as the upcoming ASUS ProArt GR1X, are a different case. CUDA on Windows on Arm is still a developer preview, vLLM has no native Windows support, and Docker GPU use hasn't been validated (as of November 2026). For that platform, the software advantage over a Mac is smaller than the NVIDIA badge suggests. Our Windows on Arm compatibility guide has the app-by-app detail.
So is CUDA's dominance ending?
On the evidence, the honest answer has three parts:
- Local inference is now genuinely cross-platform. llama.cpp, Ollama and LM Studio run on Apple Metal, NVIDIA CUDA and other backends, and they use common model formats. A buyer who only needs to run open models has real choice, and the Mac's large memory options make it a strong one. See why some developers choose a Mac Studio over a DGX Spark.
- Training and production serving remain CUDA territory. The tooling for fine-tuning, high-concurrency serving and deployment parity with cloud GPUs is years more mature on NVIDIA.
- Apple's opportunity is real but bounded. It is strongest for individual developers, creators and small teams who value capacity, low power and a quiet desk, and who don't need to match a CUDA server environment exactly.
If you are choosing a platform, start with where your work will end up. Code that must run on NVIDIA servers is easiest to build on NVIDIA. Work that lives on the desk can go wherever the memory and price suit you best.
Buying in Singapore
SourceIT carries both ecosystems. The Mac Studio M5 Max with 128 GB and the Mac Studio M5 Ultra with 256 GB are price on request; ask us for a quotation. Apple Singapore's list price starts at S$3,499 for the M5 Max and S$7,999 for the M5 Ultra. For a Linux CUDA desk machine, the NVIDIA DGX Spark 4TB is S$9,300 and the ASUS Ascent GX10 128GB/1TB is S$10,350 (listed 8 Nov 2026). For a CUDA workstation card, the RTX PRO 6000 Blackwell Workstation Edition is S$29,100. Prices change; ask for a current 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 I run CUDA code on a Mac Studio? No. Apple silicon has no CUDA support. Code has to use Metal, MLX or a cross-platform engine such as llama.cpp, and CUDA-only libraries such as TensorRT-LLM won't run.
Is MLX a replacement for CUDA? For running and lightly fine-tuning models on a Mac, MLX covers a lot of ground. It isn't a drop-in replacement: code written for CUDA has to be ported, and much of the wider training and serving ecosystem still targets NVIDIA first.
Does vLLM work on a Mac now? Yes, through the vllm-metal plugin, whose first official release came on 22 Sep 2026. It's newer and supports fewer model families than CUDA vLLM, so check that your models are listed.
Can I run AI models inside Docker on a Mac with GPU acceleration? Not inside the container. Docker on macOS can't pass the Metal GPU through, so GPU inference has to run natively on the host.
Which platform should a company standardise on? If models will be deployed to NVIDIA servers or cloud GPUs, NVIDIA desk machines make the move easiest. If the work stays on the desk and memory capacity matters most, a Mac Studio is a strong option.
