Mac Studio vs. RTX PC for Local AI: The Real 2026 Comparison

The RTX 5090 boasts 1,792 GB/s of bandwidth, compared to 546 GB/s for the M4 Max and 819 GB/s for the M3 Ultra. For models that fit within its 32 GB, it generates 3 to 4 times more tokens per second than a Mac Studio: we measure beyond 200 tok/s on 8B models, approximately 50 to 65 tok/s on dense 30B models. For interactive uses — chat, code assistant, agents — the difference is felt with every response.

Honest verdict: a 70B model in Q4 weighs about 40 GB. It does not fit in the 32 GB of an RTX 5090. The card must then spill over into system RAM, and speed collapses to an unusable 1 to 3 tok/s. A 128 GB Mac Studio loads the entire model and runs it at 8-11 tok/s: slow, but functional. For very large models in single-user mode on a controlled budget, the Mac scores the point.

Nuance on the PC side: this ceiling is for a single consumer card. An RTX 6000 Blackwell (96 GB) or a dual-GPU setup eliminates the limit at a higher budget. And the NVIDIA GB10 mini-server offers 128 GB unified memory on the CUDA side, we'll come back to that.

We always forget: before generating, the model must read your prompt, and this phase depends on computational power. This is the Achilles' heel of Apple Silicon: the Mac generates creditably, but ingests long contexts much more slowly. For an agent that re-reads thousands of tokens at each step, RAG on large documents, or a code assistant that swallows an entire codebase, the NVIDIA advantage is clear and compounds with each iteration.

Serving a team changes the game: requests must be processed in parallel. The NVIDIA GPU architecture absorbs concurrent streams much better than Apple's unified memory, which does not scale well in this scenario. The measured difference reaches 1.5 to 2 times in multi-user throughput. For an enterprise server, the question is quickly settled.

Flows, video models, ComfyUI, LoRA, TensorRT: the entire AI visual creation ecosystem is built CUDA first. Many tools run poorly or not at all on Mac, and rendering times are incomparably longer. If your local AI includes image or video, there is no debate.

PyTorch, training libraries, LoRA/QLoRA techniques: the tooling primarily targets CUDA. Seriously fine-tuning a model on Mac remains marginal and slow. As soon as you want to adapt a model to your data, an NVIDIA PC is the only viable path.

Second point honestly conceded: a Mac Studio operates under 100 W, almost silently, in a tiny enclosure. A single RTX 5090 can draw 575 W. For a machine running continuously, electricity bills and acoustics favor Apple. However, it should be noted that a well-designed PC with adequate ventilation and a suitable case remains discreet in real use, and the GB10 mini-server (approx. 240 W, 15 cm format) falls into the same category of energy efficiency.

A Mac Studio is ordered fixed: soldered memory, no possible expansion, requiring resale or repurchase to upgrade. A PC can grow: more RAM, more storage, a second card, a GPU replaced when a more powerful generation comes out. For an investment of several thousand euros intended to last, this is foundational. Let's add a point of context: Apple has removed the highest capacity M3 Ultra configurations from its catalog, and the M4 Ultra never saw the light of day. Apple's memory roadmap is not controllable, but a modular PC's is.