Guides · 4 min read

Local AI on a Mac: unified memory, MLX and the GPU limit

How much of a Mac’s memory a model can use, why MLX helps, and how to raise the limit safely.

A Mac’s processor and GPU share one pool of memory (unified memory), so a Mac with a lot of memory can hold models that no consumer graphics card can. Three things to know.

1. Not all of it is available to the GPU

macOS limits how much unified memory the GPU may use. Reports for recent Apple silicon give roughly two-thirds on smaller-memory machines and about three-quarters on larger ones. Our estimates plan around 67% up to 36 GB of memory and 75% above that, and keep a little in reserve. A 16 GB Mac is realistically a 10 to 11 GB model machine.

2. You can raise the limit, with care

On recent versions of macOS you can raise the cap with a Terminal command, for example sudo sysctl iogpu.wired_limit_mb=<megabytes>. The setting does not survive a restart. Leave several gigabytes for macOS and your other apps: if the system runs short of memory it will slow to a crawl or become unstable. The setting name and behavior have changed between macOS versions, so check current instructions for yours before using it. We do not recommend it unless a model misses by a small margin.

3. Use MLX when you can

MLX is Apple’s array framework, built for this hardware. MLX versions of models (formats) are often faster than GGUF on a Mac. LM Studio runs both; MLX LM is the command-line route. Ollama uses GGUF.

What to expect

See every Mac we cover under Hardware, or press Detect on My hardware for yours.

Estimates come from published hardware specs and our open formulas, not from running the model on your machine. Real speed varies with runtime, drivers and settings. Numbers marked “Measured” come from a linked benchmark.