Mac vs NVIDIA for AI: the M5 Mac Studio changed the math on local LLMs

For two years the Mac vs NVIDIA question had a tidy answer. NVIDIA was faster on anything that fit in its VRAM, and the Mac held bigger models but chewed through long prompts slowly. The M5 generation broke that. On the llama.cpp maintainers' own Apple Silicon scoreboard, an M5 Ultra Mac Studio processes a 7B model's prompt at 4,945 tokens/sec and generates at 179 tokens/sec. Those numbers beat a used RTX 3090 on a similar test.

So the decision moved. It is no longer "speed vs capacity." It is now price of entry, software and upgrades on one side, and memory per box on the other. We read the benchmark sources and vendor specs on 4 October 2026. We did not run our own benchmarks.

Is a Mac or NVIDIA better for AI?

It depends on model size and software. If your models fit in 24GB and you want CUDA tooling, NVIDIA is better and cheaper to start. If you want 70B-class or larger models in one quiet box, a Mac Studio is better. The M5 chips removed the Mac's old prompt-speed penalty on small models.

The two designs still make opposite bets. Generating each token means reading the model's weights from memory, so bandwidth sets the speed. Reading your prompt is mostly compute. NVIDIA pairs fast compute with a small, fast pool of VRAM. Apple pairs a huge pool of unified memory with high bandwidth. Apple's spec page lists 614 GB/s for the 40-core M5 Max and 1.2 TB/s for the M5 Ultra. An RTX 3090 has 24GB at 936 GB/s.

*Mac figures come from the llama.cpp Apple Silicon scoreboard: LLaMA 7B, Q4_0, short prompt (pp512, tg128), top GPU-core config of each chip. RTX 3090 figures come from hardware-corner.net: Qwen3 8B, Q4_K, CUDA with flash attention, at 4k context. Same llama.cpp family and quant class, but different models and settings. Read small gaps as a tie. The RTX 3090 power figures are NVIDIA's card power and recommended system power. Apple's is the Mac Studio's maximum continuous power, not typical draw.

What a Mac Studio does that no single GPU can

Hold a very large model in one box. The M5 Ultra is configurable to 256GB, and Apple lists a 512GB option as "coming late October." No consumer NVIDIA card comes close to that in one slot, and stacking enough 24GB cards gets loud, hot and complicated.

The catch has always been long prompts on giant models. On the previous M3 Ultra with 512GB, hardware-corner measured DeepSeek V3 671B at Q4_K_M taking 14.8 minutes to process an 8,001-token prompt (9.01 t/s), then generating at 6.21 t/s. Apple says the M5 Max delivers up to 10.7x faster time to first token in LM Studio than an M1 Max. We have not seen an equivalent 671B test on M5 yet, so treat the M3 figure as the worst case.

Read more on running DeepSeek R1 locally and on which quantization to use.

What an NVIDIA PC does that no Mac can

• Start cheap. A used 24GB card in a PC you may already own is the lowest-cost entry to serious local AI. Check what fits in 24GB and our used RTX 3090 buying guide for current prices.
• Use CUDA tools. LoRA and QLoRA fine-tuning recipes, vLLM serving, and most image and video generation tools target CUDA first. Apple's MLX runs LoRA fine-tuning, but the wider tooling is smaller.
• Upgrade. A Mac's memory is fixed at checkout. A PC takes a second GPU or a bigger card later, and a 4090 is a straight step up.

Mac Studio 96GB vs four Intel Arc Pro B60s

Both give you 96GB of memory, but they spend it differently. The base M5 Ultra Mac Studio has 96GB in one pool at 1.2 TB/s, for $5,499. Four Intel Arc Pro B60 cards also total 96GB, but as four separate 24GB pools at 456 GB/s each, per Intel's spec sheet.

That split matters. With the common way of spreading a model across cards, each card holds a slice of the layers and they take turns. Each token then moves at roughly one card's bandwidth, not the sum. Tensor-parallel serving can use the cards together, but it needs software that supports it on Intel GPUs.

Power and noise differ too. Intel rates each B60 at 200W board power, so four cards alone are rated 800W before the CPU. Apple rates the whole Mac Studio at 480W maximum continuous power. We could not find a published like-for-like benchmark of the two setups, so we are not printing a speed comparison.

Which should you buy at each budget?

At the low end, a used RTX 3090 PC wins on cost and tools, and the M5 Max at $2,499 wins on silence and simplicity. At $5,499, the 96GB M5 Ultra competes with a dual-GPU 48GB build. Above that, only the Mac offers 256GB or more in one box; the alternative is renting.

At around $2,500, the base M5 Max Studio (32-core GPU, 36GB, 460 GB/s) is the cheapest new Mac Studio. A used-3090 PC is faster per dollar on anything that fits in 24GB. At around $5,500, the base M5 Ultra (96GB) faces a dual-3090 build with 48GB. The deciding question is whether your models sit above or below 48GB. Above that, the realistic NVIDIA alternative is renting by the hour.

Whichever side you pick, size the machine to a real model list. The VRAM calculator shows what fits in any memory budget, and for smaller budgets the Mac mini local AI buying guide and best Mac for local AI cover the cheaper Apple options.