# Install mlx with Homebrew, MacPorts

Array framework for Apple silicon. Version 0.32.0 via Homebrew; verified 2026-07-08. Also installable with macports: sudo port install mlx.

## Install

```sh
sudo av install brew:mlx
```

Additional install commands:

### macOS

- Homebrew (100%):

```sh
brew install mlx
```

  Evidence: local Homebrew formula metadata

- MacPorts (94%):

```sh
sudo port install mlx
```

  Evidence: MacPorts ports tree: llm/mlx/Portfile from https://api.github.com/repos/macports/macports-ports/git/trees/master?recursive=1

## Package facts

- **Package key:** brew:mlx
- **Package manager:** Homebrew
- **Version:** 0.32.0
- **Source summary:** Array framework for Apple silicon
- **Homepage:** <https://ml-explore.github.io/mlx/build/html/index.html>
- **Repository:** <https://github.com/ml-explore/mlx>
- **Last updated:** 2026-07-08T14:26:04Z
- **Generated:** 2026-08-03T19:37:03+00:00

## Executables

- mlx.distributed_config (alias)
- mlx.launch (alias)

## Install behavior

- Bottle: not available

## Freshness

- Page generated: 2026-08-03
- Package-manager version: 0.32.0
## Project history and usage

MLX is Apple's open source array framework for machine learning on Apple silicon. The project is published under the ml-explore organization and is described by Apple and the upstream README as an array framework optimized for Apple silicon and its unified memory architecture. The MLX examples citation credits the initial software suite to Awni Hannun, Jagrit Digani, Angelos Katharopoulos, and Ronan Collobert, with a 2023 citation entry.

### Project history

Technically, MLX sits in the NumPy, PyTorch, JAX, and ArrayFire family rather than being only a model runner. Its README emphasizes familiar NumPy-like APIs, C, C++, and Swift bindings, PyTorch-like higher-level neural-network and optimizer packages, composable transformations for automatic differentiation and vectorization, lazy computation, dynamic graph construction, CPU/GPU execution, and unified memory so arrays can be operated on across supported devices without explicit transfers.

### How it is used

The framework quickly became the base layer for a cluster of Apple-silicon ML tools: MLX LM for language models, MLX examples for reference implementations across text, image, audio, video, and multimodal models, and community-converted model weights on Hugging Face. LM Studio also shipped an MLX engine for on-device LLM use on Apple-silicon Macs. In package managers, the `mlx` package is the foundation dependency; users install higher-level packages such as `mlx-lm` when they want complete model workflows.

### Sources

- <https://formulae.brew.sh/formula/mlx>
- <https://github.com/ml-explore/mlx>
- <https://github.com/ml-explore/mlx-examples>
- <https://lmstudio.ai/blog/lmstudio-v0.3.4>
- <https://ml-explore.github.io/mlx/build/html/usage/unified_memory.html>
- <https://opensource.apple.com/projects/mlx>


## Security Notes

narrow executable package without higher-risk signals.

- **Geiger risk:** green / low
- narrow executable package without higher-risk signals

## Other Package-Manager Records

- MacPorts - mlx: normalized package name match | MacPorts ports tree: llm/mlx/Portfile from https://api.github.com/repos/macports/macports-ports/git/trees/master?recursive=1


## Combined YAML source

View the package source record on GitHub. [combined/mlx.yml](https://github.com/mxcl/pkgdb/blob/main/combined/mlx.yml)


## Sources

- pkg.so package database
- Geiger risk classifier
- curated package history
- pkgdb category and tag curation
- external package-manager database matches
- cross-ecosystem install command graph