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 Jul 7, 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 (0.32.1)

Owner

PyPI data
Data sourced directly from PyPI's database.

2 maintainers

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

Credits

Author: MLX Contributors

GitHub Statistics

Data verified by PyPI on Jul 7, 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_cpu-0.32.0-py3-none-win_arm64.whl (7.4 MB view details)

Uploaded Python 3Windows ARM64

mlx_cpu-0.32.0-py3-none-win_amd64.whl (15.3 MB view details)

Uploaded Python 3Windows x86-64

mlx_cpu-0.32.0-py3-none-manylinux_2_35_x86_64.whl (10.4 MB view details)

Uploaded Python 3manylinux: glibc 2.35+ x86-64

mlx_cpu-0.32.0-py3-none-manylinux_2_35_aarch64.whl (8.8 MB view details)

Uploaded Python 3manylinux: glibc 2.35+ ARM64

File details

Details for the file mlx_cpu-0.32.0-py3-none-win_arm64.whl.

File metadata

  • Download URL: mlx_cpu-0.32.0-py3-none-win_arm64.whl
  • Upload date:
  • Size: 7.4 MB
  • Tags: Python 3, Windows ARM64
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/6.1.0 CPython/3.13.12

File hashes

Hashes for mlx_cpu-0.32.0-py3-none-win_arm64.whl
Algorithm Hash digest
SHA256 e59dcc61b36620767431c927791ffddfde12266851f5558d7128c84ebcae4911
MD5 c8b1bc6843f276986a513f32be30591c
BLAKE2b-256 2bc0de8e2c29f0927df9156c797ae20ef57f5e7f5e3fc103c325348ba5cc5f4a

See more details on using hashes here.

Provenance

The following attestation bundles were made for mlx_cpu-0.32.0-py3-none-win_arm64.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_cpu-0.32.0-py3-none-win_amd64.whl.

File metadata

  • Download URL: mlx_cpu-0.32.0-py3-none-win_amd64.whl
  • Upload date:
  • Size: 15.3 MB
  • Tags: Python 3, Windows x86-64
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/6.1.0 CPython/3.13.12

File hashes

Hashes for mlx_cpu-0.32.0-py3-none-win_amd64.whl
Algorithm Hash digest
SHA256 ebb670029ed47cfd1cbaa136ff4ad35e5577d5f75c00d731d3fb78f57c45b8ac
MD5 99e11db5e9204d6158b1fa6b245d4c6e
BLAKE2b-256 5494095afc93d09f825c01263c046098f2cd44c1a51ad050f833f42d3a053f03

See more details on using hashes here.

Provenance

The following attestation bundles were made for mlx_cpu-0.32.0-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_cpu-0.32.0-py3-none-manylinux_2_35_x86_64.whl.

File metadata

File hashes

Hashes for mlx_cpu-0.32.0-py3-none-manylinux_2_35_x86_64.whl
Algorithm Hash digest
SHA256 d5052fa8e1cbc135cc4b6fc09745f9a57a2cbd1b4def17449e965ef263239d75
MD5 14232898ec0421dfeeb0a91c85909298
BLAKE2b-256 0a0216c07a41e382312d6b86f1df5ee2975246b94f88a289c0468a9d9a5cd368

See more details on using hashes here.

Provenance

The following attestation bundles were made for mlx_cpu-0.32.0-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_cpu-0.32.0-py3-none-manylinux_2_35_aarch64.whl.

File metadata

File hashes

Hashes for mlx_cpu-0.32.0-py3-none-manylinux_2_35_aarch64.whl
Algorithm Hash digest
SHA256 b23f19d34073445b32a724358ba21bfb114a84021c9507be700479a191a9ae08
MD5 7bcbb3f7ec54559d18a86786b8ed0dcb
BLAKE2b-256 58f75d9b53ad219d2ed31f66a103eba097e07a77bb5b0b0ea4dbfb12a8bfd224

See more details on using hashes here.

Provenance

The following attestation bundles were made for mlx_cpu-0.32.0-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.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page