Mac Studio vs AI PC for Local LLMs

This is usually framed as a platform preference, and it is not. It is a genuine engineering trade with a calculable crossover point, and the answer flips depending on which side of it you sit.

The three-way trade

Every machine that runs models locally is competing on three axes, and no platform currently wins all three:

Read that table and the decision becomes concrete. If your models fit in 32 GB, NVIDIA wins two of three and there is nothing to discuss. Above 32 GB, NVIDIA is not in the running at all on consumer hardware, and the question becomes whether you need CUDA badly enough to pay workstation-GPU prices for it.

The crossover is 32 GB. Everything below follows from that.

The specifications

Capacity: Apple wins outright above 32 GB

The fastest consumer graphics card in the world has 32 GB, and that ceiling is set by the memory bus width — 512 bits addressing sixteen 2 GB modules. It is a design decision, not a configuration, and no variant will exceed it this generation.

Apple’s unified memory has no such ceiling. A Mac Studio reaches 128 GB with an M4 Max and 512 GB with an M3 Ultra.

In model terms:

There is no configuration of consumer NVIDIA hardware that fills the second row without a second card, and none at all that fills the fourth.

Bandwidth: closer than the reputation suggests

The usual objection to Apple silicon is that unified memory is slow. That is true relative to a discrete card, and it is much less true relative to the other unified-memory options.

At each capacity tier, the fastest thing available:

Above 32 GB, Apple is not merely an option — it holds the bandwidth crown as well as the capacity one. An M4 Max at 546 GB/s is more than double a Strix Halo machine at 256, on the same 128 GB of memory.

What that means in practice, for a 70B model at 4-bit:

• Mac Studio M3 Ultra: roughly 19 tokens/second ceiling
• Mac Studio M4 Max: roughly 13 tokens/second
• Strix Halo, 128 GB: roughly 6 tokens/second
• RTX 5090: will not load it

Thirteen tokens per second is around reading pace — usable interactively, if not fast. Six is not.

Ecosystem: the CUDA question, specifically

This is where the PC wins, and it is worth being precise rather than hand-waving.

What works well on Apple silicon

• llama.cpp, Ollama and LM Studio all have mature Metal backends. Text generation is well served and actively optimised.
• MLX, Apple’s own array framework, is designed around unified memory and is frequently faster than llama.cpp for supported models. mlx-lm also supports LoRA fine-tuning.
• PyTorch runs through its Metal backend. Most operations work; some fall back to the CPU, which you notice.
• Diffusion models run, more slowly than on an equivalent NVIDIA card.

What does not

• CUDA-specific kernels. The fast attention implementations, Marlin and ExLlama quantisation kernels, and most performance work from research repositories.
• bitsandbytes, which underpins a great deal of quantised training and paged optimisers.
• TensorRT and the NVIDIA deployment stack entirely.
• A large share of ComfyUI custom nodes, which is where image-generation workflows actually live.
• Whatever appeared on GitHub this month, which is the real cost and the hardest to price.

The honest summary: if your work is running language models, Apple is excellent. If it involves image generation, fine-tuning at scale, or reproducing research, the PC is not merely better — it is the only one that works.

Power, over three years

Rarely in the comparison and genuinely material.

Assume four hours a day at sustained load and twenty hours idle:

That is a difference of roughly 3,700 kWh — around $590 at $0.16/kWh, or £925 at £0.25 — before accounting for the cooling load the extra heat imposes in summer.

Noise follows the same curve. A Mac Studio under sustained inference is close to inaudible. A 575 W graphics card is not, and cannot be.

Upgradeability and what happens in year four

The axis where the PC wins decisively, and it is easy to overlook when comparing day-one specifications.

A PC can be changed. The graphics card is the component that determines capability, and it is the one you can replace — which matters in a field where memory requirements have only grown. Memory and storage are also replaceable.

A Mac cannot. Memory and storage are soldered. The configuration you buy is the configuration you own for the machine’s life, which is why the sizing decision carries so much more weight. Buy more memory than you think you need; you cannot revisit it.

Set against that, Macs hold resale value notably well, and GPU resale is far less predictable.

Where Strix Halo fits

Worth naming because it is the option that makes the trade three-way rather than two.

An AMD Ryzen AI Max+ 395 machine — a Framework Desktop or similar — reaches 128 GB of unified memory on standard x86, which means Linux, containers, and every tool that assumes a normal PC.

What it gives up is bandwidth: 256 GB/s against the M4 Max’s 546. Same capacity, less than half the speed.

So the three-way version of the decision:

• Need CUDA, models fit in 32 GB → NVIDIA
• Need capacity and speed, do not need CUDA → Apple
• Need capacity and x86, can accept slow → Strix Halo

The winner, by workload

How to decide, in three questions

1. Do your models fit in 32 GB? If yes, build the PC. Nothing else is close on speed, and you keep the ecosystem.
2. Do you need CUDA for anything — image generation, fine-tuning, research code? If yes, build the PC and accept the 32 GB ceiling, or budget for a workstation GPU.
3. If both answers were no: buy the Mac. You need capacity, you are not tied to CUDA, and Apple has both the memory and the bandwidth that nothing else at that capacity offers.

The verdict

For most people running local models today, the AI PC is the right machine. Models up to 32B cover an enormous amount of genuinely useful work, the ecosystem is unmatched, and the machine can be upgraded.

The Mac Studio wins in one specific and increasingly common situation: you need models larger than 32 GB, and your work is language rather than images. In that case it is not a preference — it is the only desktop that does the job at a usable speed, and it does so silently and at a quarter of the power.

What we would avoid is buying a Mac for AI work that involves image generation or fine-tuning. The memory is not the constraint there, and the missing ecosystem is.

Continue your research

• Best AI Workstations — the full range on both platforms
• Best Mini PCs for Local LLMs — the Strix Halo option in detail
• NVIDIA vs AMD for Local AI — the other ecosystem question
• How Much VRAM Do You Need for Local AI? — establishing which side of 32 GB you are on