Mac vs PC for Local LLMs (2026)
Updated July 2026
Neither side of this debate is wrong, and neither is complete on its own — a PC wins on speed under about 34B parameters, and a Mac wins on capacity above it. Here’s the honest breakdown with real tokens-per-second numbers.
The one-line answer
For models up to roughly 34B parameters, a PC with an NVIDIA GPU is faster — often significantly so — and usually cheaper. For models at 70B parameters and above, a Mac with enough unified memory is often the more practical single-machine option, because it can hold the model at all, which a discrete GPU’s fixed VRAM frequently cannot without a painful slowdown. Neither platform "wins" outright; they’re optimized for different sides of the model-size curve.
This guide exists because most online debates on the topic pick a side rather than reporting the trade-off honestly. The numbers below are drawn from independent 2026 benchmarks on both platforms — treat them as representative ranges, not guarantees, since exact figures vary by quantization, software version, and specific model.
Why unified memory changes the equation
A discrete GPU has a fixed, dedicated pool of VRAM (24GB on an RTX 3090, 32GB on an RTX 5090) connected to the GPU chip at very high bandwidth. If a model’s weights and its working memory (the KV cache) fit inside that pool, generation is fast. If they don’t, the excess has to be pushed across the PCIe bus to system RAM at roughly 64GB/s — a fraction of VRAM bandwidth — and throughput collapses, often by an order of magnitude.
Apple Silicon takes a different architectural approach: the CPU and GPU share one physical pool of memory, so a Mac Studio configured with 128GB gives the GPU access to the full 128GB at 400–800GB/s, depending on the chip. There’s no VRAM ceiling to hit and no PCIe fallback penalty — the trade-off is that this shared-memory bandwidth is generally lower than a high-end discrete GPU’s dedicated VRAM bandwidth, which is why Apple Silicon tends to lose on raw speed for models that would fit comfortably in a discrete GPU’s VRAM anyway.
That’s the whole debate in one paragraph: discrete GPUs win when the model fits, because dedicated VRAM is faster than shared unified memory; Macs win when it doesn’t, because they simply have more addressable memory to work with in one machine.
Model-size-by-model-size: who wins
At 7–8B parameters, both platforms are fast enough that the difference is mostly academic, but the PC still leads on raw numbers: independent testing puts a Mac Studio M5 Max around 95–110 tokens/sec versus roughly 130–150 on an RTX 5090. Either is comfortably fast for interactive use.
At 13–14B, the gap widens in the PC’s favor: an RTX 4090 has been measured around 40–60 tokens/sec on a 13B model at 4-bit, versus roughly 20–40 tokens/sec on a Mac mini M4 Pro with 48GB. This is squarely the PC’s territory — the model fits easily in discrete VRAM, so discrete bandwidth wins outright.
At 70B, the picture flips. Reported figures put a Mac Studio M5 Max around 25–32 tokens/sec, versus roughly 14–22 tokens/sec on an RTX 5090 that has to offload part of the model to system RAM because it doesn’t fit in 32GB of VRAM. The Mac isn’t just competitive here — it’s the faster single-consumer-device option, because it’s not paying the PCIe-offload penalty the GPU is.
The crossover, in short: somewhere in the 34–50B range, depending on your exact quantization and VRAM, is where "biggest single discrete GPU" stops being the fast option and a large-memory Mac (or a multi-GPU PC pool, if you’re willing to build one) takes over.
Software ecosystem: CUDA vs MLX
The PC side runs on NVIDIA’s CUDA, which is where the local-AI tooling ecosystem — inference engines, fine-tuning frameworks, quantization libraries — is built first and optimized hardest. New models and techniques typically land CUDA support before anything else, and the majority of tutorials, forks, and community troubleshooting assume it.
Apple’s MLX framework has matured quickly and is genuinely good for inference on Apple Silicon, with strong support in popular local-inference tools. It’s a smaller ecosystem than CUDA’s, though, so expect to occasionally hit a brand-new model or technique that’s CUDA-only for its first few weeks, or a tool that assumes an NVIDIA GPU exists somewhere in the setup instructions.
For inference on well-established model families, this gap rarely matters in practice. For chasing day-one support of the newest research or fine-tuning workflows, CUDA’s head start is a real, if narrowing, advantage.
Cost comparison
For the sub-34B tier, a PC is usually the cheaper path to the same speed: a used RTX 3090 (roughly $700–900, 24GB) or a new RTX 5080 (roughly $1,100–1,350, 16GB) covers this range for well under the price of any Mac with comparable memory. This is the range where recommending a Mac for local AI specifically is hard to justify on cost.
For the 70B-and-up tier, the calculus is closer. A Mac Studio configured with 128GB of unified memory runs several thousand dollars, but it’s a complete, quiet, power-efficient single machine that simply holds the model — the PC alternative is a dual-GPU pool (two used RTX 3090s, roughly $1,400–1,800 combined, plus a platform built to house them) or a single RTX 5090 at $4,300+ that still doesn’t comfortably clear the model size. Depending on exactly what you value — one quiet box versus a build project with a lower parts cost — either can come out ahead.
Buyers who already own a Mac for other work should weigh how much of that 70B-class capability they can get for free before pricing a dedicated PC build at all.
Who should buy which
Buy a PC if your target models sit at or below roughly 34B parameters, you want the fastest tokens-per-second for the money, you plan to fine-tune, or you want first-day access to new models and techniques. This describes the large majority of local-AI hobbyists and developers, which is why a discrete-GPU PC remains the default recommendation for most people starting out.
Buy a Mac (or lean toward one) if your target models are consistently at 70B and above, you want one quiet, power-efficient machine rather than a multi-GPU build project, or you already use Apple Silicon for other work and want to extend it rather than buy a second machine. It’s a genuinely strong choice at the large end of the model-size range — not a compromise.
If you’re not sure which camp you’re in, run your actual target models through the model-fit calculator — it’ll show whether they fit comfortably on a realistic PC GPU budget, which is the single fact that should decide this for you.
Frequently asked questions
Is a Mac or PC better for running LLMs locally?
It depends on model size. A PC with an NVIDIA GPU is faster for models up to roughly 34B parameters, because they fit comfortably in discrete VRAM. A Mac with enough unified memory is often better for 70B-and-up models, because it can hold them at all without the slowdown a discrete GPU suffers once it has to offload to system RAM.
Why is a Mac faster than a PC for 70B models?
Because a Mac’s unified memory lets the GPU address the whole memory pool (up to 128GB on a Mac Studio) without a capacity ceiling, while a discrete GPU with less VRAM than the model needs has to offload the excess across a much slower PCIe connection to system RAM, which sharply cuts throughput.
Do I need CUDA for local AI, or does Apple’s MLX work fine?
MLX works well for inference on well-established models. CUDA’s advantage is a larger ecosystem and typically faster support for brand-new models and fine-tuning workflows. For running popular, established models, the gap rarely matters day to day.
What Mac do I need to run a 70B model?
A Mac Studio configured with enough unified memory to comfortably hold the model and its context — 128GB is a comfortable target for a 70B model at a reasonable quantization — reportedly delivers around 25–32 tokens/sec, ahead of a single RTX 5090 that has to offload part of the model to system RAM.
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