mlx

mlx is a local-AI tool - MLX: An array framework for Apple silicon.

Overview

MLX is an array framework for machine learning on Apple silicon, brought to you by Apple machine learning research.

• 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 open-source, written primarily in C++, with 28,727 GitHub stars under the MIT license. The latest release is v0.32.3 (2026-09-29).

Key capabilities

From the project's documentation:

• Familiar APIs: MLX has a Python API that closely follows NumPy. MLX
• Composable function transformations: MLX supports composable function
• Lazy computation: Computations in MLX are lazy. Arrays are only
• Dynamic graph construction: Computation graphs in MLX are constructed
• Multi-device: Operations can run on any of the supported devices
• Unified memory: A notable difference from MLX and other frameworks

Install

A quick way to get started (always check the official docs for the latest):

pip install mlx

How it fits a local-AI stack

mlx runs on your own hardware, so pair it with a model and a GPU sized to your needs. Use the VRAM calculator to pick a model that fits your card, and see what you can run for hardware guidance. Related local-AI tools in the directory:

• "algernon"
• "blinko"
• Continue
• "exo"

Sources

• Source code & docs: ml-explore/mlx
• Official website: https://ml-explore.github.io/mlx/

Stats from GitHub, 2026-10-12.