MLX

Quickstart | Installation | Documentation | Examples

CircleCI

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:

  • Familiar APIs: MLX has a Python API that closely follows NumPy. MLX also has fully featured C++, C, and Swift APIs, which closely mirror the Python API. MLX has higher-level packages like mlx.nn and mlx.optimizers with APIs that closely follow PyTorch to simplify building more complex models.

  • Composable function transformations: MLX supports composable function transformations for automatic differentiation, automatic vectorization, and computation graph optimization.

  • Lazy computation: Computations in MLX are lazy. Arrays are only materialized when needed.

  • Dynamic graph construction: Computation graphs in MLX are constructed dynamically. Changing the shapes of function arguments does not trigger slow compilations, and debugging is simple and intuitive.

  • Multi-device: Operations can run on any of the supported devices (currently the CPU and the GPU).

  • Unified memory: A notable difference from MLX and other frameworks is the unified memory model. Arrays in MLX live in shared memory. Operations on MLX arrays can be performed on any of the supported device types without transferring data.

MLX is designed by machine learning researchers for machine learning researchers. The framework is intended to be user-friendly, but still efficient to train and deploy models. The design of the framework itself is also conceptually simple. We intend to make it easy for researchers to extend and improve MLX with the goal of quickly exploring new ideas.

The design of MLX is inspired by frameworks like NumPy, PyTorch, Jax, and ArrayFire.

Examples

The MLX examples repo has a variety of examples, including:

Quickstart

See the quick start guide in the documentation.

Installation

MLX is available on PyPI. To install MLX on macOS, run:

pip install mlx

To install the CUDA backend on Linux, run:

pip install mlx[cuda]

To install a CPU-only Linux package, run:

pip install mlx[cpu]

Checkout the documentation for more information on building the C++ and Python APIs from source.

Contributing

Check out the contribution guidelines for more information on contributing to MLX. See the docs for more information on building from source, and running tests.

We are grateful for all of our contributors. If you contribute to MLX and wish to be acknowledged, please add your name to the list in your pull request.

Citing MLX

The MLX software suite was initially developed with equal contribution by Awni Hannun, Jagrit Digani, Angelos Katharopoulos, and Ronan Collobert. If you find MLX useful in your research and wish to cite it, please use the following BibTex entry:

@software{mlx2023,
  author = {Awni Hannun and Jagrit Digani and Angelos Katharopoulos and Ronan Collobert},
  title = {{MLX}: Efficient and flexible machine learning on Apple silicon},
  url = {https://github.com/ml-explore},
  version = {0.0},
  year = {2023},
}

Project links

Data verified by PyPI on Aug 18, 2026
Data provided by the project maintainers, verified at the time the release was uploaded to PyPI.

Key dates

PyPI data
Data sourced directly from PyPI's database.
  • Released:
Newer release available (0.32.2)

1 maintainer

PyPI data
Data sourced directly from PyPI's database.
Avatar for awni from gravatar.com awni

Credits

Author: MLX Contributors

GitHub Statistics

Data verified by PyPI on Aug 18, 2026
The GitHub source repository was provided by the project maintainers and verified by PyPI at the time of upload. Stars, forks, and open issues/PRs are derived from that repository and have not been independently verified.

License

MIT

Requires

Python >=3.10

Report project as malware

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distributions

No source distribution files available for this release.See tutorial on generating distribution archives.

Built Distributions

If you're not sure about the file name format, learn more about wheel file names.

mlx_cuda_13-0.32.1-py3-none-win_amd64.whl (58.9 MB view details)

Uploaded Python 3Windows x86-64

mlx_cuda_13-0.32.1-py3-none-manylinux_2_35_x86_64.whl (87.2 MB view details)

Uploaded Python 3manylinux: glibc 2.35+ x86-64

mlx_cuda_13-0.32.1-py3-none-manylinux_2_35_aarch64.whl (84.3 MB view details)

Uploaded Python 3manylinux: glibc 2.35+ ARM64

File details

Details for the file mlx_cuda_13-0.32.1-py3-none-win_amd64.whl.

File metadata

  • Download URL: mlx_cuda_13-0.32.1-py3-none-win_amd64.whl
  • Upload date:
  • Size: 58.9 MB
  • Tags: Python 3, Windows x86-64
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/7.0.0 CPython/3.13.14

File hashes

Hashes for mlx_cuda_13-0.32.1-py3-none-win_amd64.whl
Algorithm Hash digest
SHA256 1c68e7ddaafb6de886aa5edfc95fb7d00c48c8476cdeaf2782d4335a7038cfd4
MD5 4b801edbe2afaf13c34dcb632c2d6039
BLAKE2b-256 5d66b6e7f0843b594785dbdb874fc76d42011559e86fc563e7a4d5726ff66ea0

See more details on using hashes here.

Provenance

The following attestation bundles were made for mlx_cuda_13-0.32.1-py3-none-win_amd64.whl:

Publisher: release.yml on ml-explore/mlx

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

File details

Details for the file mlx_cuda_13-0.32.1-py3-none-manylinux_2_35_x86_64.whl.

File metadata

File hashes

Hashes for mlx_cuda_13-0.32.1-py3-none-manylinux_2_35_x86_64.whl
Algorithm Hash digest
SHA256 f3187d76a3e90ab4357dc092a979a95679a3d02874cd8586536a40784fc1aefc
MD5 4d01be213069c1694e66adf8f274228a
BLAKE2b-256 75348bc7fb06b225b4c1bcd1c6bf53be98799bd2dfc085cdfbe976ce781acc60

See more details on using hashes here.

Provenance

The following attestation bundles were made for mlx_cuda_13-0.32.1-py3-none-manylinux_2_35_x86_64.whl:

Publisher: release.yml on ml-explore/mlx

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

File details

Details for the file mlx_cuda_13-0.32.1-py3-none-manylinux_2_35_aarch64.whl.

File metadata

File hashes

Hashes for mlx_cuda_13-0.32.1-py3-none-manylinux_2_35_aarch64.whl
Algorithm Hash digest
SHA256 a73b57b60ae987ed561c2b484d2b9497025934f4d9f0ebf9cfdae09f08510787
MD5 cf4b53a6894a0e456244b150f75531d2
BLAKE2b-256 38ee955e143afe122c7d31c890c3ef8a6d39d308ea01062cede266da2de68e31

See more details on using hashes here.

Provenance

The following attestation bundles were made for mlx_cuda_13-0.32.1-py3-none-manylinux_2_35_aarch64.whl:

Publisher: release.yml on ml-explore/mlx

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

Release history Release notifications | RSS feed

0.32.2

3 files

This release

0.32.1 This release

3 files

0.32.0

3 files

0.31.1

2 files

0.30.6

2 files

0.30.5

2 files

0.30.4

2 files

0.30.3

2 files

0.30.1

2 files

Anthropic, PBC Visionary sponsor Bloomberg Visionary sponsor Hudson River Trading Visionary sponsor Meta Visionary sponsor NVIDIA Visionary sponsor Microsoft Sustainability sponsor Depot Continuous Integration AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page