MLX Model Format Explained: Supported Engines and Models
What Is MLX?
MLX is a machine-learning framework for Apple Silicon (M-series chips), developed by Apple’s machine learning research team. Its companion package mlx-lm runs LLMs, and Hugging Face hosts many models converted and quantized for MLX (for example in the mlx-community organization). When we say “MLX format", we mean weights prepared to be loaded by MLX.
Why It Matters
- It uses the Mac’s unified memory directly. CPU and GPU share one pool of memory on Apple Silicon, so a Mac with plenty of memory can run models too large for a consumer GPU.
- It is optimized for Apple Silicon. On the same Mac it can generate faster than GGUF (llama.cpp) in some cases; which one wins depends on the model and quantization.
- 4-bit, 8-bit and other quantized builds are common. A repository name ending in
-4bitor-8bittells you the level.
Tips for Running It Locally
- Mac (Apple Silicon) only. It does not run on NVIDIA GPUs under Windows or Linux; choose GGUF or AWQ there.
- LM Studio loads MLX models directly. From the command line,
pip install mlx-lmand usemlx_lm.generateormlx_lm.server. - Not all memory is available to the GPU. macOS caps how much memory the GPU can use, so leave headroom rather than picking a model that barely fits your installed memory.
- The files use the
.safetensorsextension. Tell MLX builds apart by the repository name or themlxtag.
Sources: the ml-explore/mlx README, the ml-explore/mlx-lm README and the mlx-community organization on Hugging Face (all as of 2026-09-27).
Our Coverage and Data
Local Model Watch has published 12 article(s) on models available in MLX: 3 where the repository itself is in MLX, and 9 where we found a MLX build of the model. The lists below only include builds we have checked (the publisher’s organization and well-known quantizers); a model missing here may still have a MLX build elsewhere. Part of our model format index.
Main Engines That Load This Format
| Engine | Overview |
|---|---|
| exo | Framework that clusters everyday devices (Macs, PCs, phones) to run a single model across them. |
Models Available in MLX
→ Scroll horizontally to see all columns
“Smallest VRAM tier" is the smallest tier in the requirements table of each article (for other builds, of those builds; for image, video and audio models, always the article’s own table, which counts every component). Leave headroom for context length.
Last updated 2026-10-02 (JST). The explanation at the top of this page was written with the help of AI from the primary sources it cites. The tables under “Our Coverage and Data" are assembled by code from our article log.