MLX: An array framework for Apple silicon

Flexible and powerful tensor operations for readable and reliable code (for pytorch, jax, TF and others)

Examples in the MLX framework

A text-to-speech (TTS), speech-to-text (STT) and speech-to-speech (STS) library built on Apple's MLX framework, providing efficient speech analysis on Apple Silicon.

The open source research environment for AI researchers to seamlessly train, evaluate, and scale models from local hardware to GPU clusters.

Run LLMs with MLX

AI edge infrastructure for macOS. Run local or cloud models, share tools across apps via MCP, and power AI workflows with a native, always-on runtime.

Examples using MLX Swift

MLX-VLM is a package for inference and fine-tuning of Vision Language Models (VLMs) on your Mac using MLX.

MLX native implementations of state-of-the-art generative image models

Swift API for MLX

This repository provides the code and model checkpoints for AIMv1 and AIMv2 research projects.

AirPods as AI posture coach on iOS (launching 2026)

LM Studio Apple MLX engine

🤖✨ChatMLX is a modern, open-source, high-performance chat application for MacOS based on large language models.

Optimized Whisper models for streaming and on-device use

The open-source voice synthesis studio powered by Qwen3-TTS.

MLX Omni Server is a local inference server powered by Apple's MLX framework, specifically designed for Apple Silicon (M-series) chips. It implements OpenAI-compatible API endpoints, enabling seamless integration with existing OpenAI SDK clients while leveraging the power of local ML inference.

Implementation of F5-TTS in MLX

Solve Puzzles. Learn Metal 🤘