AI PC · mini-pc
Apple Mac Mini (M4 Pro)
The cheap, fast way into Apple local AI: 64GB of quick unified memory in a box the size of a sandwich.
- Memory
- 64 GB
- Bandwidth
- 273 GB/s
- AI compute
- 38 TOPS
- £ / GB
- £31
What it runs
With 64 GB of memory you can load, at 4-bit, a model up to roughly
~124B params
Verdict
The cheap, fast entry to Apple local AI: quick 273 GB/s memory, but capped at 64GB.
BEST FOR
- + running small-to-mid models fast
- + a cheap, silent always-on box
NOT FOR
- - 70B and larger models
- - anyone who needs CUDA
The Mac Mini with the M4 Pro is the cheap way into fast local AI. Not the most memory, not the biggest models - but 64GB of quick unified memory in a box the size of a sandwich, and it sips power doing it. If your models fit, this is the value entry to the Apple side of the fence.
The take
This is the budget bandwidth pick, and that’s the clever bit. The M4 Pro moves memory at 273 GB/s, the same figure as a DGX Spark that costs twice as much, so for small and mid models it punches well above its price. The catch is capacity. 64GB is the ceiling, so you’re not loading a dense 70B at any sensible quant. Think of it as the machine for people whose models fit in 64GB and who want them answering quickly, quietly, and cheaply. For that person it’s brilliant. For anyone chasing the very biggest models, it’s the wrong Mac.
What it’ll actually run
With 64GB of unified memory you’ve room for models up to the 30B-class at a decent quant, with context to spare. A 32B at Q4 sits comfortably, an 8B to 14B flies, and you can keep a couple loaded at once for different jobs. What you can’t do is squeeze a dense 70B in - that wants more memory than this tops out at, so it’s off the table.
Speed is the pleasant surprise. That 273 GB/s of bandwidth means a 14B answers fast and a 32B stays usable, and owners of the 64GB version report a 70B-class MoE ticking over around 11 tokens a second when it fits. MLX is the tool to reach for on Apple silicon, it’s quicker than llama.cpp for the same model, and the whole thing runs near silent and barely warms up. No CUDA, mind. It’s macOS and Apple’s own stack, so anything wedded to Nvidia won’t run.
Who should buy it
Buy it if your models fit in 64GB and you want the cheapest way to run them fast on a Mac. It’s a lovely little always-on assistant box, silent, tiny, and cheap to leave running, and it doubles as a proper desktop for everything else.
Look past it if you need to load big models. 64GB caps what you can hold, so a 70B is out, and if that’s the goal you want a 128GB machine or the Mac Studio instead. But for quick small-to-mid models on a budget, the M4 Pro mini is the sensible pick.
Settings people actually run
The configs owners land on, pulled from the community. A sensible starting point, not gospel - tune to your own kit.
Everyday sweet spot
8B-32B Q4/Q8 via MLX (Qwen2.5 14B, Llama 8B)
Quick and comfortable; keep one or two loaded for different jobs.
Biggest sensible model
32B Q4 (~20GB), or a 70B-class MoE if it fits in 64GB
Owners of the 64GB mini report a large MoE around 11 tokens/s; a dense 70B won't fit.
Fast inference
MLX quants in LM Studio, not GGUF
MLX beats llama.cpp on Apple silicon for the same model.
What owners report
Real first-hand experience gathered from owners and the community.
- “
An M4 Pro mini with 64GB runs mid-size models at a decent clip, with owners reporting around 11 tokens/s on suitable models. The 64GB ceiling is the real limit here, not the speed.
- “
273 GB/s is the M4 Pro bandwidth from Apple's own tech specs; it matches the DGX Spark and beats the Strix Halo mini-PCs on paper, which is why small models feel quick on this box.
3 claim(s) we couldn't fully verify
- · compute_tops 38 - Apple does not headline a TOPS figure on the Mac mini spec page; 38 TOPS is the M4-generation 16-core Neural Engine class figure, and the NPU isn't used for LLM inference anyway - the GPU does the work.
- · power_w 155 - 155W is Apple's maximum continuous whole-unit rating; real LLM-load draw is far lower, typically 40-65W.
- · Around 11 tokens/s on a 70B-class model - Owner-reported on a 64GB M4 Pro mini, not a first-party benchmark, and it depends heavily on the model and quant.
Hands-on reviews we drew on
We don't just copy the spec sheet. These are the teardowns and hands-on reviews behind this page - worth watching in their own right.
- Mac mini (2024) - Tech Specs (vendor spec)Apple · primary source
- So happy with the M4 Pro - using AI locally (64GB owner thread)MacRumors owner thread · forum
Common questions
How big a model can the Mac Mini M4 Pro run?+
The ceiling is 64GB of unified memory, so a 30B-class model at Q4 fits comfortably with room for context, and smaller 8B to 14B models leave plenty spare. A dense 70B wants more memory than this tops out at, so it's off the menu here. For big models you want a 128GB machine or the Mac Studio.
Is the M4 Pro Mac Mini fast for local LLMs?+
For its price, yes. The M4 Pro feeds memory at 273 GB/s, the same figure as a DGX Spark that costs twice as much, so small and mid models answer quickly. Owners of the 64GB version report a 70B-class MoE around 11 tokens a second when it fits, and smaller models feel snappy.
Can it run CUDA models?+
No. There's no Nvidia CUDA on Apple silicon. You run models through MLX or llama.cpp, both of which work well here, but anything that specifically needs CUDA won't run.