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MLX provides an array computing framework designed for Apple silicon devices.
About MLX
MLX is an array framework for machine learning on Apple silicon from Apple machine learning research. It provides a Python API that follows NumPy, plus C, C++, and Swift APIs, with mlx.nn and mlx.optimizers packages that follow PyTorch for building models.
It uses lazy computation, dynamic graph construction, and composable transformations for automatic differentiation, vectorization, and graph optimization. Operations run on CPU and GPU using shared unified memory without transferring data between devices.
Key features
- Python API that closely follows NumPy
- C, C++, and Swift APIs mirroring Python API
- mlx.nn and mlx.optimizers packages for building models
- Composable transforms for differentiation, vectorization and optimization
- Lazy computation with arrays materialized when needed
- CPU and GPU execution using shared unified memory
Best for: Machine learning researchers building and experimenting with models on Apple silicon using familiar NumPy-like workflows.
Good for
- Offers Python, C, C++, and Swift APIs with familiar NumPy and PyTorch patterns
- Uses shared unified memory so arrays run on CPU or GPU without data transfer
- Includes example workflows for language modeling, image generation, and speech recognition
Watch out for
- Supported operation devices are currently limited to CPU and GPU
MLX at a glance
- Website
- github.com/ml-explore/mlx
- Type
- Open-source project
- Pricing
- Not confirmed. Check the site
- Open source
- Yes · Source code
- Language
- English
- Good for
- Researchers
- Last checked
- Oct 5, 2026 · Online when checked
- Listed
- Oct 5, 2026
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MLX FAQ
How do I install MLX on macOS?
MLX is available on PyPI. On macOS, run: pip install mlx
How do I install MLX on Linux?
For the CUDA backend run pip install mlx[cuda]. For a CPU-only package run pip install mlx[cpu].
What devices can MLX operations run on?
Operations can run on any of the supported devices, currently the CPU and the GPU.
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