Mac Studio vs PC Build for AI: Which Runs 70B Locally?

Published April 23, 2026 · Updated August 23, 2026

Short answer: a Mac Studio with 96 GB of unified memory can hold a 70B model in memory; an RTX 4090 with 24 GB of VRAM cannot, and has to spill most of it to system RAM. For anything that fits in 24 GB, the 4090 is the faster machine. The whole decision comes down to whether your model list crosses 24 GB — and that is a question you can answer with arithmetic before you spend anything.

This page does that arithmetic in the open. Every number below is either a figure the vendor publishes or a calculation from those figures with the formula shown, so you can check it against your own shortlist.

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Quick Verdict: Who Should Buy Which

Lean Mac Studio (96 GB or more) if:

• 70B-class models are on your list, at any quantisation
• You run several models resident at once — a chat model, an embedder, a transcriber
• You also do video editing, music production, or Xcode work
• You want one machine for five years with no upgrade path to plan

Lean RTX 4090 PC if:

• Image and video generation is a daily workflow (FLUX, Wan, SDXL)
• You fine-tune with LoRA or QLoRA
• Everything you run fits inside 24 GB, and you want maximum speed within that limit
• You expect to swap the GPU in two or three years

The PC is faster on everything that fits. The Mac is the one that can hold what does not fit. That single distinction decides most of these purchases.

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The Two Architectures

Mac Studio, M3 Ultra

The defining property is that the GPU and CPU address the same memory pool, so "VRAM" is whatever you have not spent on everything else. Confirm the configuration you are pricing on Apple's Mac Studio tech specs page, since Apple revises core counts and memory tiers between refreshes.

Custom RTX 4090 desktop

NVIDIA's figures for the card are on the official RTX 4090 product page. Component prices move weekly in both directions, so price this list in your own market rather than trusting any number written months ago. Our AI PC build guide walks the assembly if you have not built one before.

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How These Numbers Are Derived

There are no stopwatch results on this page. What follows is published vendor specification plus two formulas, both stated so you can recompute them for any model you care about.

Model size at Q4_K_M  ~= 0.6 GB per billion parameters
Model size at Q5_K_M  ~= 0.75 GB per billion parameters
Model size at Q8       ~= 1.0 GB per billion parameters

Throughput ceiling (tok/s) = memory bandwidth (GB/s) / model size (GB)

The second formula is an upper bound, not a prediction. Producing one token requires reading every active weight out of memory at least once, so bandwidth divided by size is the fastest the hardware could possibly go. Real output lands well below it — KV cache traffic, attention, sampling, and framework overhead all take a cut. The ceiling is still the right tool for this comparison, because it isolates the one thing that genuinely separates these two machines: how much memory they have and how fast they can read it.

Three published bandwidth figures do all the work below:

• RTX 4090: 1008 GB/s (384-bit GDDR6X at 21 Gbps)
• M3 Ultra unified memory: 819 GB/s (Apple's published figure)
• DDR5-6000 dual channel: 96 GB/s (6000 MT/s x 8 bytes x 2 channels)

That third one is the important one. It is the speed the PC falls back to the moment a model stops fitting in VRAM, and it is roughly a tenth of the GPU's.

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Round 1: 7B and 8B Models

The bread-and-butter category — chat, summarisation, drafting, code — all runs on a 7B to 13B class model, and everything here fits in 24 GB with room to spare.

The interesting result is the ratio: 1008 / 819 = 1.23. On memory bandwidth alone, the 4090 should be about 23% ahead in this category — not the two-to-one gap that gets quoted around. Where the 4090 pulls further ahead is prompt processing, which is compute-bound rather than bandwidth-bound: prefill runs dense matrix multiplies across the whole prompt at once, and that is what dedicated tensor cores and a mature cuBLAS stack are for. So expect the PC to feel considerably snappier on long prompts and only moderately faster on generation.

Both machines are far past the point where a 7B model feels instant. Neither one is the reason to choose the other.

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Round 2: 13B to 34B Models

This is where the 4090's 24 GB starts to bite, and it happens sooner than most people expect because context and KV cache also live in VRAM.

Two rows deserve a note. Mixtral is a mixture-of-experts model: all 46.7B parameters must be resident, but only about 12.9B are read per token, so its bandwidth ceiling is set by 7.7 GB of active weights rather than the full 28 GB. That is why an MoE model can feel faster than its size suggests — provided it fits, which on a 24 GB card it does not.

And Qwen 2.5 Coder 32B at Q5 needs 24 GB exactly. A card with 24 GB of VRAM has less than 24 GB available once the display, the OS, and the KV cache take their share. "It technically fits" and "it runs" are different claims.

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Round 3: 70B Models — Where the Arithmetic Decides

Here the PC does not lose on speed. It stops being able to hold the model.

When a model does not fit, llama.cpp splits it: some layers stay on the GPU, the rest are read from system RAM every single token. The cost is straightforward to compute, because the two reads happen at different speeds and both must complete:

Per-token time = (GB on GPU / GPU bandwidth) + (GB in system RAM / RAM bandwidth)

RTX 4090 PC, Llama 3.1 70B Q4 (42 GB total),
assuming ~22 GB of weights on the GPU and ~20 GB in DDR5-6000:

  22 GB / 1008 GB/s = 0.022 s
  20 GB /   96 GB/s = 0.208 s
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  total 0.230 s per token  ->  ~4.3 tok/s ceiling

Mac Studio M3 Ultra 96 GB, same model, nothing offloaded:

  42 GB / 819 GB/s  = 0.051 s per token  ->  ~19.5 tok/s ceiling

Note what dominates that first calculation: the GPU portion contributes 0.022 seconds and the system-RAM portion contributes 0.208 — roughly ten times as much, for less than half the weights. Once you are offloading, the fast GPU is almost irrelevant; you are running at DDR5 speed with extra steps. That is the entire 70B story in two lines of division.

If 70B-class quality is on your roadmap — agents, long-context reasoning, technical drafting — this is the single benchmark that decides the purchase. A 48 GB NVIDIA card would close the gap, and costs multiples of a whole Mac Studio.

For per-model sizes at every quantisation level, our Ollama model RAM and VRAM table has the full grid, and best local AI models for 8GB RAM covers the other end of the range.

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Round 4: Image and Video Generation

Now the PC takes it back, and for a different reason: diffusion is compute-bound, not bandwidth-bound. Every denoising step runs dense convolutions and attention over the full latent, so the deciding factor is FP16/BF16 throughput and kernel maturity rather than how fast memory can be read. That is exactly where NVIDIA's tensor cores and the CUDA ecosystem are strongest, and where Apple's Metal path has the least optimisation history.

We have not measured seconds per image on either machine and will not guess at them. What is computable is whether a model loads at all, at roughly 2 GB per billion parameters at FP16 and 1 GB per billion at FP8:

So the Mac can hold FLUX at full precision while the 4090 cannot — and the 4090 will still very likely finish the image first, because this workload is not about memory. If image and video generation is core to your week, that is the build to make.

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Round 5: Transcription and Speech

Whisper large-v3 is roughly 1.55B parameters, about 3.1 GB at FP16. It fits on anything in this comparison with room to spare, so memory is not the question — throughput is, and transcription is another compute-bound encoder-decoder workload where CUDA's optimisation history favours the PC.

We have no measured transcription times to publish. The practical guidance that does not require a stopwatch: for a few hours of audio a week both machines are comfortably faster than listening to it, and for a transcription pipeline running continuously the NVIDIA path has both faster kernels and better-maintained tooling.

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Round 6: Power and Running Cost

Rather than quote wall-meter readings, here is the formula and the one published figure that matters.

Annual cost = watts / 1000 x hours per day x 365 x tariff per kWh

Every sustained 100 W difference, at 8 h/day and $0.18/kWh:
  100 / 1000 x 8 x 365 = 292 kWh per year
  292 x 0.18 = ~$53 per year  ->  ~$263 over five years

NVIDIA rates the RTX 4090 at 450 W TGP for the card alone, before CPU, drives, and fans. A Mac Studio's entire system draw is far below that — Apple publishes maximum continuous power per configuration in its documentation, so use the figure for the exact model you are pricing.

Work it through with your own tariff. At a 400 W sustained difference and 8 hours a day, the formula gives roughly $210 a year, or around $1,050 over five years — real money, though rarely the deciding factor on its own.

The acoustic difference is the more commonly cited one, and it is genuinely structural rather than incidental: a 450 W card has to move that heat through fans, while a machine drawing a fraction of that does not. We have not taken decibel measurements and are not going to publish numbers we did not record — but if you record audio or share a room, this is worth testing in a shop before committing.

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Round 7: Running Several Models at Once

Both machines can serve an internal endpoint through Ollama's HTTP API. The difference is again capacity, not speed.

Every concurrent request needs its own KV cache on top of the shared weights. KV cache size scales with context length, layer count, and batch size, and it comes out of the same pool as the model. On a 24 GB card running a 13B model at 7.8 GB, you have roughly 16 GB left for cache, display, and overhead. On a 96 GB Mac running the same model, you have roughly 88 GB. That ratio is the whole story for concurrency, and it is why the Mac can also keep a chat model, an embedding model, and a transcription model resident simultaneously while the PC has to unload one to load another.

For a small team wanting one private AI endpoint on one box, the larger memory pool is the simpler operational answer.

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Five-Year Cost of Ownership

The purchase price is the number people compare, and it is the smaller half of the decision. The structure looks like this:

The honest framing: the PC usually starts cheaper and stays cheaper if you never upgrade it, and ends more expensive if you do the year-three GPU swap that is the entire reason to buy a modular machine. The Mac cannot be upgraded at all, which is a real disadvantage and also the reason its five-year cost is easy to predict. Pick the failure mode you prefer.

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Which Should You Buy?

If your model list crosses 24 GB: Mac Studio, at whatever memory tier covers your largest model plus context. Nothing else at this price holds a 70B model in memory.

If you generate images or video, or fine-tune: RTX 4090 PC. The gap is compute, CUDA's ecosystem lead is years wide, and no amount of unified memory closes it.

If everything you run fits in 24 GB: RTX 4090 PC. You get the higher bandwidth ceiling, the faster prefill, and the mature tooling, and you are not paying for memory you will not use.

If you want one quiet machine that also does video editing and Xcode: Mac Studio, and the AI capability is a bonus rather than the justification.

For narrower breakdowns, see our Apple Silicon AI buying guide, best GPUs for AI, and the Mac local AI setup guide for getting Ollama tuned on Apple Silicon.

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How To Decide in 7 Steps

1. Write down every model you actually intend to run. Not aspirationally — the ones you will use this month.
2. Multiply each parameter count by 0.6 for Q4, 0.75 for Q5, 1.0 for Q8. That is your memory requirement in GB.
3. Add headroom for context. KV cache and the OS both take a share; leave a few GB above the largest model.
4. Compare the largest number to 24 GB. If it exceeds 24, the 4090 will offload, and the offload arithmetic above tells you what that costs.
5. Estimate your image and video generation hours. Daily or client-facing work points at the PC regardless of the memory answer.
6. Check your electricity tariff and run the annual-cost formula on the wattage difference you expect.
7. Account for adjacent work. Video editing, Xcode and Final Cut favour the Mac; gaming and training favour the PC.

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Pitfalls and Gotchas

Mac Studio:

• Low unified-memory tiers are a false economy for AI. The memory is the reason to buy the machine, and it cannot be added later.
• There is no CUDA on macOS and there will not be. You will use MLX, llama.cpp with the Metal backend, and Ollama.
• Many open-source AI projects ship CUDA-first and add Metal support months later, or never. Expect a lag on brand-new tools.
• Unified memory is shared with everything else running. A browser with forty tabs is competing with your model.

RTX 4090 PC:

• 24 GB is the whole ceiling, and context eats into it. Size your models against ~22 GB of usable space, not 24.
• A 1000W PSU is the sensible minimum for transient spikes on a 450 W card. This is not the place to save money.
• Native Linux generally beats Windows plus WSL2 for Ollama throughput. Dual-boot if the workload is serious.
• Used cards from mining rigs can be fine, but check fans, thermal pads, and warranty transferability before buying.

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What This Page Does Not Cover

To keep the scope honest:

• Multi-GPU PC builds, which change both the price and the memory ceiling substantially
• Other Apple Silicon tiers — see our Apple M4 for AI guide for the mid-range picture
• RTX 50-series cards, whose 32 GB tier changes the Round 2 and Round 3 tables materially
• Linux on Apple Silicon (Asahi works, but loses Metal acceleration)
• Delivered tokens per second on either machine, which nobody should quote without naming the hardware, driver version, quantisation and context length

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Frequently Asked Questions

Is a Mac Studio really better than an RTX 4090 for local AI?

Only above 24 GB. For anything that fits in the 4090's VRAM, the PC has both the higher bandwidth ceiling (1008 GB/s versus Apple's published 819 GB/s) and much stronger compute for prompt processing and diffusion. Above 24 GB the 4090 has to offload to system RAM at roughly 96 GB/s, and a large unified pool wins by a wide margin. Your model list decides it, not the brand.

Can a Mac Studio run a 70B model without quantising it into the ground?

Yes, if you buy enough memory. At 0.6 GB per billion parameters, a 70B model is about 42 GB at Q4, 52.5 GB at Q5, and 70 GB at Q8 — all of which a 96 GB unified pool can hold outright. A 24 GB card holds none of them, and partial offload drops per-token time onto DDR5 bandwidth, which the arithmetic above works through in full.

How much slower is CPU offload, really?

Compute it rather than guessing. Per-token time is (GB on GPU / GPU bandwidth) + (GB in RAM / RAM bandwidth). For a 42 GB model split roughly 22 GB on a 4090 and 20 GB in DDR5-6000, that is 0.022 s + 0.208 s = 0.230 s per token. The system-RAM half is about ten times the GPU half despite holding fewer weights — offload does not slow you down a little, it changes which component you are bottlenecked on.

What about power consumption and electricity costs?

NVIDIA rates the RTX 4090 at 450 W TGP for the card alone; a Mac Studio's whole system draw is far below that, and Apple publishes maximum continuous power per configuration. Use the formula: watts / 1000 x hours per day x 365 x your tariff. Every sustained 100 W of difference costs roughly $53 a year at 8 h/day and $0.18/kWh.

Does the Mac Studio support CUDA libraries?

No, and it never will. macOS AI work runs on Metal Performance Shaders, Apple's MLX framework, llama.cpp with the Metal backend, or Ollama (which uses llama.cpp underneath). Most major projects do support Metal, but CUDA-first releases typically arrive there months later.

Can the Mac Studio fine-tune models?

LoRA and small QLoRA jobs run via MLX or PyTorch with the Metal backend. Training is compute-bound rather than memory-bound, which is the regime where NVIDIA's tensor cores and the CUDA tooling ecosystem are strongest — so if fine-tuning is your primary workload rather than an occasional one, build the PC.

Would an RTX 5090 change the answer?

For the sub-24 GB categories, only marginally — both machines are already far past interactive speed on a 7B model. The change that matters is the larger VRAM tier, which moves 32B models at Q5 back inside the card and shifts the Round 2 table. It does not reach 70B at Q4 (42 GB), so the crossover point moves rather than disappearing.

Should I buy a used RTX 4090 from a mining rig?

It can be a reasonable buy — mining cards are often run undervolted at steady temperatures — but insist on a fan and thermal-pad inspection, proof of transferable warranty, and a return window. Walk away from visible PCB damage or a seller who will not let you test it. Our used GPU buying guide covers the checks in detail.

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The Short Version

The comparison is usually framed as Apple versus NVIDIA. It is really memory capacity versus memory speed, and the crossover point is sharp:

1. Under 24 GB, the RTX 4090 wins on both bandwidth ceiling and prefill compute.
2. Over 24 GB, the 4090 falls back to DDR5 at roughly a tenth of its bandwidth, and a large unified-memory pool wins by a factor of four or more.
3. For diffusion and fine-tuning, compute decides and CUDA wins regardless of memory.

Work out which side of 24 GB your model list sits on. That answers the question more reliably than any benchmark table, including this one.