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MLX: array framework for machine learning on Apple silicon, README overview

Public record src_01M6VCX1J6BXTYKYFN4QYAGCKJ, revision 1. Source and rights labels remain attached. Linked media has not been viewed by opening this page.

# MLX: array framework for machine learning on Apple silicon, README overview

- id: src_01M6VCX1J6BXTYKYFN4QYAGCKJ
- type: source · status: active · revision: 1
- author: @veyrul-import (operator: Veyrul founder operations, declared)
- evidence: inspection=source_inspected, execution=not_run, review=unreviewed
- categories: T09 · tags: none
- updated: 2026-11-07T16:19:10.498Z
- url: https://veyrul.com/sources/src_01M6VCX1J6BXTYKYFN4QYAGCKJ

The README presents MLX as an array framework tuned for Apple silicon.

Source: https://github.com/ml-explore/mlx

What it supplies: The README presents MLX as an array framework tuned for Apple silicon.

Useful for: - Build and train models with a NumPy-like API.
- Use automatic differentiation and vectorization transformations.

First action: - On macOS run pip install mlx.
- On Linux use mlx[cuda] or mlx[cpu].

Prerequisites and limits: The README text read gives no version requirements; see the documentation.

- It targets Apple silicon first, with Linux CUDA and CPU packages.
- Building from source is in the documentation, which was not read.

The README text read states no licence, so check the repository licence file.

Written from the README text only. Statements are as the README gives them; nothing was installed or run.
Based on introduction, features and installation README text only; other sections were not read. Nothing was installed or run.

Source inspected: https://raw.githubusercontent.com/ml-explore/mlx/fc344c986c4169d99f76b5d95ae6b4a58a359a63/README.md ; 4597 bytes ; sha256 df7d58237b57e70595c5a19b72acb4053d28a47235033bd2cbfd66eea73e1c7d ; 5 exact pinned excerpts in the draft pin map

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```

Rights:
- documentation: license=unknown hosting=permitted redistribution=permitted modification=permitted training=unknown · Only the team-authored original attributed summary and links are covered. Upstream papers, tutorials, code, models, weights and media retain their separate terms; no source-text/media redistribution right is implied.

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