# Installer mlx avec Homebrew, MacPorts

Consultez les chemins d'installation, exécutables, métadonnées et notes de sécurité de mlx pour les workflows d'agents IA.

## installation

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

Commandes d'installation supplémentaires:

### macOS

- Homebrew (100%):

```sh
brew install mlx
```

  Preuve: local Homebrew formula metadata

- MacPorts (94%):

```sh
sudo port install mlx
```

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

## Faits du paquet

- **Clé du paquet:** brew:mlx
- **Gestionnaire de paquets:** Homebrew
- **Version:** 0.32.0
- **Résumé source:** Array framework for Apple silicon
- **Page d'accueil:** <https://ml-explore.github.io/mlx/build/html/index.html>
- **Dépôt:** <https://github.com/ml-explore/mlx>
- **Dernière mise à jour:** 2026-07-08T14:26:04Z
- **Généré:** 2026-08-03T19:37:03+00:00

## exécutables

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

## Comportement d'installation

- Bouteille: non disponible

## Version et fraîcheur

- page générée: 2026-08-03
- version du gestionnaire: 0.32.0
## Historique du projet et usages

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.

### Historique du projet

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.

### Modes d'utilisation

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>


## Notes de sécurité

narrow executable package without higher-risk signals.

- **Risque Geiger:** vert / faible
- narrow executable package without higher-risk signals

## Autres enregistrements de gestionnaires de paquets

- 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