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README.md
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[**Documentation**](https://ml-explore.github.io/mlx/build/html/index.html) |
[**Examples**](#examples)
MLX is an array framework for machine learning on Apple silicon.
MLX is an array framework for machine learning on Apple silicon, brought to you
by Apple machine learning research.
Some key features of MLX include:

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docs/src/index.rst
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MLX
===
MLX is a NumPy-like array framework designed for efficient and flexible
machine learning on Apple silicon.
MLX is a NumPy-like array framework designed for efficient and flexible machine
learning on Apple silicon, brought to you by Apple machine learning research.
The Python API closely follows NumPy with a few exceptions. MLX also has a
fully featured C++ API which closely follows the Python API.
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- **Multi-device**: Operations can run on any of the supported devices (CPU,
GPU, ...)
The design of MLX is strongly inspired by frameworks like `PyTorch
The design of MLX is inspired by frameworks like `PyTorch
<https://pytorch.org/>`_, `Jax <https://github.com/google/jax>`_, and
`ArrayFire <https://arrayfire.org/>`_. A noteable difference from these
frameworks and MLX is the *unified memory model*. Arrays in MLX live in shared