Here's a fun way to start interacting with it (this loads and runs Llama 3.2 3B in a terminal chat UI):
uv run --isolated --with mlx-lm python -m mlx_lm.chatThis is typical of what happens any time I try to run something written in Python. It may be easier than setting up an NVIDIA GPU, but that's a low bar.
For 4 bit deepseek-r1-distill-llama-70b on a Macbook Pro M4 Max with the MLX version on LM Studio: 10.2 tok/sec on power and 4.2 tok/sec on battery / low power
For 4 bit gemma-3-27b-it-qat I get: 26.37 tok/sec on power and on battery low power 9.7
It'd be nice to know all the possible power tweaks to get the value higher and get additional insight on how llm's work and interact with the cpu and memory.
What have you used those models for, and how would you rate them in those tasks?
Any other resources like that you could share?
Also, what kind of models do you run with mlx and what do you use them for?
Lately I’ve been pretty happy with gemma3:12b for a wide range of things (generating stories, some light coding, image recognition). Sometimes I’ve been surprised by qwen2.5-coder:32b. And I’m really impressed by the speed and versatility, at such tiny size, of qwen2.5:0.5b (playing with fine tuning it to see if I can get it to generate some decent conversations roleplaying as a character)
I mainly use MLX for LLMs (with https://github.com/ml-explore/mlx-lm and my own https://github.com/simonw/llm-mlx which wraps that), vision LLMs (via https://github.com/Blaizzy/mlx-vlm) and running Whisper (https://github.com/ml-explore/mlx-examples/tree/main/whisper)
I haven't tried mlx-audio yet (which can synthesize speech) but it looks interesting too: https://github.com/Blaizzy/mlx-audio
The two best people to follow for MLX stuff are Apple's Awni Hannun - https://twitter.com/awnihannun and https://github.com/awni - and community member Prince Canuma who's responsible for both mlx-vlm and mlx-audio: https://twitter.com/Prince_Canuma and https://github.com/Blaizzy
pj_mukh•4h ago
But as a measure for what you can achieve with a course like this: does anyone know what the max tok/s vs iPhone model plot look like, and how does MLX change that plot?