Mac Mini vs Mac Studio for Local LLMs: What the Memory Really Buys

Apple silicon made large local models possible on a desk rather than in a rack, because the GPU can address the whole system memory pool. But the Mac mini vs Mac Studio for local LLMs decision is really two separate questions, and people keep collapsing them into one. Capacity decides which models you can load. Bandwidth decides how fast they run.

Capacity and bandwidth are different purchases

On a discrete GPU those two things arrive together: you buy a card, you get its VRAM and its bandwidth as a package. On a Mac they are decoupled. Memory capacity is a configuration option you pay for at checkout. Memory bandwidth is a property of the chip tier — and that is the axis the mini and the Studio actually sit on.

Roughly, and this moves generation to generation, the tiers look like this:

• Base chip (Mac mini entry) — around 100GB/s class
• Pro chip (Mac mini upper configs) — roughly double that
• Max chip (Mac Studio entry) — roughly double again
• Ultra chip (Mac Studio top) — roughly double the Max

Treat those as order-of-magnitude guides and check the current spec page before buying, because Apple revises them. The shape is the point: each step up is close to a doubling, and token generation is bandwidth-bound, so the steps translate almost directly into tokens per second for the same model.

system_profiler SPHardwareDataType | grep -E "Chip|Memory|Cores"

You don’t get all of the memory

macOS reserves a share of unified memory for the CPU and the system, and caps what the GPU may wire down. The default ceiling is in the region of three quarters of installed RAM. On a 16GB machine that is the difference between “the 4-bit 14B model fits” and “it does not”.

You can raise it, at your own risk, and it resets on reboot:

sysctl iogpu.wired_limit_mb            # 0 means "use the default"
sudo sysctl iogpu.wired_limit_mb=57344 # e.g. 56GB on a 64GB machine

Leave the OS at least 8GB or you will trade model speed for swapping, which is a far worse deal. And remember the KV cache comes out of the same pool — at long context it is not a rounding error.

What each memory tier actually unlocks

Assuming 4-bit quantisation and a sensible context window:

• 16GB — 7B and 8B models comfortably, a 14B if you keep the context modest. Fine for chat, autocomplete and summarising.
• 24-32GB — 32B-class models at usable context. This is the first tier where a local model holds its own on real coding tasks.
• 48-64GB — 70B-class at 4-bit with room for context, or a 32B with a very long window plus everything else you have open.
• 96GB+ — large mixture-of-experts models and multiple models resident at once. Capacity stops being the constraint and bandwidth becomes the only thing you feel.

The trap is buying capacity on a low-bandwidth chip. A base-tier mini with a lot of RAM will happily load a 70B model and then generate at a pace that makes you close the window. Capacity without bandwidth is a party trick.

Prompt processing is the other half

Generation is bandwidth-bound; reading your prompt is compute-bound, and this is where Apple silicon is relatively weaker against a discrete GPU. If you paste 30,000 tokens of source into an agent, the wait before the first token is compute, not bandwidth. It is very noticeable on base and Pro chips and much better on Max and Ultra, which have far more GPU cores.

Measure it on your own machine rather than trusting anyone’s numbers. Ollama reports both phases, and MLX is usually a little quicker than GGUF on Apple silicon:

ollama run qwen2.5-coder:14b --verbose "summarise this file" < big_file.py
ollama ps

pip install mlx-lm
mlx_lm.generate --model mlx-community/Qwen2.5-Coder-14B-Instruct-4bit   --prompt "write a bash script that rotates logs" --max-tokens 256

The things the spec sheet does not tell you

Memory is soldered. There is no adding 32GB in two years, so buy the capacity you will want then, not the one you need now — this is the single most common regret I hear from people who bought a Mac for local inference.

Against a tower, both win on power draw and noise by a wide margin. Sustained generation on a mini pulls a fraction of what a big discrete GPU does, and it will sit on a shelf as an always-on inference box without anyone noticing. The Studio is louder under load but still civilised.

Which one I’d buy

If local LLMs are a serious part of your workflow rather than an experiment, skip the base-tier mini. It is an excellent computer and a mediocre inference machine, because its bandwidth caps you at small models however much RAM you specify.

• Best value — a Pro-tier Mac mini with as much memory as you can stretch to. It runs 32B-class models at a speed you will tolerate daily and costs far less than the Studio.
• Best if you want 70B-class — a Max-tier Mac Studio with 64GB or more. The bandwidth step is what makes a large model feel responsive instead of merely possible.
• Ultra — only if you are running several large models concurrently or serving other people. For one developer it is more machine than the workload justifies.

Whichever you pick, size it against the real requirement rather than a model name — the weights plus KV cache arithmetic is quick, and if you are planning to drive it from an editor, check the context settings in the guides to using Claude Code with local LLM models and using Cursor with local LLM models before you choose a memory configuration you cannot change later.