TL;DR
An array framework designed for efficient machine learning development and execution on Apple silicon.
Key features
Familiar APIs: NumPy-like Python API and PyTorch-style mlx.nn, mlx.optimizers.
Function transformations: Supports automatic differentiation, automatic vectorization, and computation graph optimization.
Lazy computation: Arrays are materialized only when needed.
Dynamic graph: Graphs are constructed dynamically, making debugging easy and avoiding slow compilations.
Multi-device: Operations can run on CPU and GPU.
Unified memory: Arrays live in shared memory, enabling operations across devices without data copying.
When to use it
Use MLX for machine learning research and model development on Apple silicon (M1/M2/M3 etc.) running macOS or Linux. It is especially suitable for researchers familiar with PyTorch or JAX who want efficient training and inference on Apple hardware.