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 25, 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:
Latest release

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 25, 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_13-0.32.2-py3-none-win_amd64.whl (59.1 MB view details)

Uploaded Python 3Windows x86-64

mlx_cuda_13-0.32.2-py3-none-manylinux_2_35_x86_64.whl (87.4 MB view details)

Uploaded Python 3manylinux: glibc 2.35+ x86-64

mlx_cuda_13-0.32.2-py3-none-manylinux_2_35_aarch64.whl (84.5 MB view details)

Uploaded Python 3manylinux: glibc 2.35+ ARM64

File details

Details for the file mlx_cuda_13-0.32.2-py3-none-win_amd64.whl.

File metadata

  • Download URL: mlx_cuda_13-0.32.2-py3-none-win_amd64.whl
  • Upload date:
  • Size: 59.1 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_13-0.32.2-py3-none-win_amd64.whl
Algorithm Hash digest
SHA256 7dcd71e30b8d6a1bc6543f8e94422f00de680f9713857a36c286a11b6135d5ee
MD5 a2b1c057fdb032594f8b114565f27044
BLAKE2b-256 ff76b44e8adfc883c6452c491346dd634c0408bf495c52b9fb36f409259febaf

See more details on using hashes here.

Provenance

The following attestation bundles were made for mlx_cuda_13-0.32.2-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_13-0.32.2-py3-none-manylinux_2_35_x86_64.whl.

File metadata

File hashes

Hashes for mlx_cuda_13-0.32.2-py3-none-manylinux_2_35_x86_64.whl
Algorithm Hash digest
SHA256 335094ccd1c0c6dc3e09c8caba207c8180602bacc0c38ba20c1d81e9456ab394
MD5 8f933e698cb518d6a2c76ed9f641ef48
BLAKE2b-256 f1595bcbee69a4425214bd25b7a4c102ddcbb91e9975a0b22613ddcac87de282

See more details on using hashes here.

Provenance

The following attestation bundles were made for mlx_cuda_13-0.32.2-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_13-0.32.2-py3-none-manylinux_2_35_aarch64.whl.

File metadata

File hashes

Hashes for mlx_cuda_13-0.32.2-py3-none-manylinux_2_35_aarch64.whl
Algorithm Hash digest
SHA256 49b1a25df9021f3b519a534fd39a32a5cdbf93a1b265a31e59671c2d9fc9f4a2
MD5 189ff8fb563ec2b9e6b3a8b860d70135
BLAKE2b-256 ead238a801b9726d31c71dc7cbfb5f3bac22fbde5b66e7892f4c453a5e6069cf

See more details on using hashes here.

Provenance

The following attestation bundles were made for mlx_cuda_13-0.32.2-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

This release

0.32.2 This release

3 files

0.32.1

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