Worried about one corner of its business, yes. Threatened overall, no. Apple's M5 Ultra Mac Studio, on sale from 22 September 2026, gives one person up to 512 GB of unified memory at about 1.2 TB/s, and independent reviewers found it ahead of NVIDIA's DGX Spark on both prompt processing and text generation. That makes it a real rival for the desk-side local AI machine. But NVIDIA's AI business is overwhelmingly data-centre GPUs, its CUDA software is used by millions of developers, and on raw GPU work its discrete cards still beat the Mac. The competition is real, but narrow: the private, single-user AI box.
Where does each side lead today?
| Area | Apple Mac Studio (M5 Ultra) | NVIDIA | Who leads |
|---|---|---|---|
| Memory in one desktop box | 96, 256 or 512 GB unified | 128 GB (DGX Spark / GB10); 96 GB on one RTX PRO 6000 card | Apple |
| Memory bandwidth | About 1.2 TB/s | 273 GB/s (GB10); 1,792 GB/s (RTX 5090, RTX PRO 6000) | Apple over GB10; NVIDIA cards over Apple |
| Prompt processing, Qwen3.8-27B (MacStories) | 1,701 tok/s | 3,031 tok/s on an RTX 5090 PC | NVIDIA |
| Text generation, same test | 48 tok/s | 59 tok/s on the RTX 5090 | NVIDIA, when the model fits |
| Text generation vs DGX Spark (Tom's Hardware) | "Almost four times higher" | DGX Spark | Apple |
| Software ecosystem | MLX, Metal, llama.cpp, Ollama (MLX preview), vllm-metal | CUDA, TensorRT-LLM, vLLM, SGLang; "over 7.5 million developers" | NVIDIA |
| Power at the wall | 385 W max (Apple); about 300 W peak in LLM inference (Apple ML Research) | An RTX 5090 PC peaked at 567–577 W (MacStories) | Apple |
Source: Apple Mac Studio specifications and Apple Support power figures (accessed 8 Nov 2026); Apple ML Research (15 Oct 2026); MacStories M5 Ultra review (~21 Sep 2026; MLX on the Mac, GGUF Q4_K_M on the RTX 5090, 6,091-token prompt); Tom's Hardware (21 Sep 2026, chart figures); NVIDIA FY2026 Form 10-K. Engines and quantisation differ between rows, so compare within a row.
Why is the Mac Studio suddenly an AI machine?
Two things changed with the M5 generation. The first is memory. A large language model has to sit in memory to run quickly, and the Mac's memory is shared between the CPU and GPU. The M5 Ultra offers 96 GB as standard and up to 512 GB, which Apple opened for ordering on 28 October 2026. No single NVIDIA desktop GPU comes close to that capacity, which is why the Mac became a favourite for running very large open models at home or in the office. Back in 2025, a 512 GB M3 Ultra ran DeepSeek R1 671B at 4-bit at around 17–18 tokens per second, drawing under 200 watts.
The second change is speed where the Mac used to be weakest. Apple added Neural Accelerators to each GPU core. Apple's own research team measured the M5 Ultra reaching the first token 4.4 times faster than the M3 Ultra on a 27B model, while token generation improved about 1.6 times, roughly in line with the higher memory bandwidth. Independent testers saw similar patterns: big gains in prompt processing, smaller gains in generation.
Apple now markets this directly. The Mac Studio product page talks about running "frontier-class models on device — no cloud tokens needed", clustering several Macs with exo, and agent tools in Xcode. Reviewers have leaned in too: MacStories called the M5 Ultra "a dream machine for local AI agents", and Tom's Hardware gave it an Editor's Choice award.
Where does NVIDIA still hold the advantage?
Raw GPU throughput. When a model fits in VRAM, a 1,792 GB/s NVIDIA card is faster. In MacStories' test the RTX 5090 PC processed the prompt about 1.8 times faster and reached the first token in 2.0 seconds against the Mac's 4.0 seconds. Hardware Corner found an RTX PRO 6000 two to almost five times faster than an M5 Max at prompt processing on gpt-oss-120b. For long documents, coding agents that resend large contexts and image or video generation, that compute gap matters.
Software. NVIDIA's latest annual report counts "over 7.5 million developers" using CUDA and its tools. Training frameworks, serving engines such as vLLM and SGLang, TensorRT-LLM and most research code assume CUDA first. On the Mac, PyTorch's Metal backend still lacks some operations, FP8 support is only in a release candidate, Docker containers can't use the GPU and bitsandbytes runs on the CPU only. Apple's MLX is good and improving fast, but it's one ecosystem against a much larger one.
Deployment parity. Code built on a DGX Spark or an RTX workstation runs on NVIDIA servers in a data centre or the cloud with few changes. Code tuned for MLX doesn't move to production GPUs as easily. For teams that develop locally and deploy at scale, that matters more than desk-side speed.
Scale. NVIDIA's data-centre segment brought in US$89.0 billion of its US$96.2 billion revenue in the second quarter of fiscal 2027, about 93%. The desk-side local AI market is a small slice of that. Even a big Mac Studio success would barely register in NVIDIA's results.
The Mac Studio doesn't compete with NVIDIA's data-centre business. It competes for the single developer or small team who wants a quiet box with a lot of memory, and there it has become a serious choice.
So should NVIDIA be worried?
About its desk-side products, a little. NVIDIA's answer to the "big memory on the desk" buyer is the GB10 family (DGX Spark and partner boxes such as the ASUS Ascent GX10) and the new RTX Spark Windows PCs. On generation speed, the M5 Ultra's 1.2 TB/s memory gives it a large lead over GB10's 273 GB/s, about 4.5 times on paper, and the measured gap was in the same range. The M5 Ultra also scales to 256 or 512 GB in one box, which GB10 can't.
NVIDIA still wins on what most of its customers buy for: CUDA compatibility, multi-user serving, fine-tuning tools and a straight path from desk to data centre. Our posts on the M5 Ultra versus GB10 and CUDA versus Apple's MLX look at both in more detail.
There are also limits on Apple's side. The M5 Ultra's memory can't be upgraded after purchase. Rumours from Bloomberg's Mark Gurman suggest Apple will skip an M6 Ultra and bring an M7 Ultra Mac Studio around 2028, so this machine may be Apple's top desktop for a while. And Apple has published no tokens-per-second claims of its own; its "4.3x" figure is peak AI compute against the M3 Ultra, and its "4x" figure is time to first token on a 14B model in LM Studio.
What does this mean for buyers?
Competition is good for buyers. For a single user who wants to run very large open models privately, quietly and with low power, the Mac Studio is now one of the strongest options available, especially at 256 or 512 GB. For developers who need CUDA, plan to deploy on NVIDIA servers, fine-tune models or serve many users at once, an NVIDIA platform is still the safer pick. Many teams will end up with both: a Mac for large-model experiments and an NVIDIA machine for development that has to match production.
If you're weighing the two, our guides on whether the Mac replaces high-VRAM GPUs and the developer mistakes to avoid when buying a Mac for AI cover the trade-offs. Our earlier comparison of NVIDIA GB10 and the Mac Studio is also worth a read.
Buying in Singapore
SourceIT supplies both platforms. The Mac Studio M5 Ultra (96 GB, 1 TB), which is S$7,999 at Apple Singapore's list price, and the M5 Ultra with 256 GB are available on price on request; ask us for a quotation. On the NVIDIA side, the NVIDIA DGX Spark 4TB is S$9,300 and the ASUS Ascent GX10 128 GB / 1 TB is S$10,350 (both listed 8 Nov 2026). For maximum GPU throughput, the RTX PRO 6000 Blackwell Workstation Edition is S$29,100. Prices change, so 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
Is the Mac Studio faster than NVIDIA for AI? It depends on the NVIDIA product. The M5 Ultra was faster than a DGX Spark in Tom's Hardware's tests, but an RTX 5090 PC was faster than the M5 Ultra in MacStories' test when the model fitted in the card's 32 GB.
Why do AI users like the Mac Studio? Mainly memory. Up to 512 GB of unified memory in one quiet desktop lets it hold models that would need several NVIDIA cards, at much lower power.
Can a Mac Studio run CUDA? No. Macs use Apple's Metal and MLX instead. Tools that need CUDA, such as TensorRT-LLM, don't run on a Mac.
Is Apple competing with NVIDIA in data centres? Not with the Mac Studio. It is a desktop computer; NVIDIA's AI revenue comes overwhelmingly from data-centre GPUs.
Which should a developer buy? If you need CUDA or plan to deploy on NVIDIA servers, an NVIDIA machine. If you mainly want to run large models privately for yourself or a small team, the Mac Studio is a strong option.
