Mlx Python Packages

einops

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

mlx

MLX: An array framework for Apple silicon

mlx-metal

MLX: An array framework for Apple silicon

mlx-lm

Run LLMs with MLX

mlx-whisper

Examples in the MLX framework

mlx-audio

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.

mlx-vlm

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

mlx-cpu

MLX: An array framework for Apple silicon

veloxquant-mlx

Fast KV-cache quantization for Apple Silicon (MLX) — 43 research-adapted compression methods with Metal kernels

mflux

Apple MLX native implementations of state-of-the-art generative image & video models

flama

The production framework for Predictive and Generative AI. Serve any model as an API in one line, with OpenAI/Anthropic/Ollama-compatible endpoints, a built-in chat UI, and native MCP.

laya-mlx

Native MLX runtime for Laya typed decision models — 7–14 ms short decisions on M3 Max. No text generation, PyTorch, or cloud API.

metal-linalg

QR, symmetric eigendecomposition and SVD for batches of matrices on Apple Silicon GPUs, for MLX and PyTorch. 2x to 10x gain in performance. Metal kernels per regime, routed to the GPU or the CPU by policies measured per Mac. C++, Python (pip: metal-linalg, metal-linalg-torch), Swift and C.

rapid-mlx

Rapid-MLX is an open-source (Apache 2.0) OpenAI- and Anthropic-compatible LLM inference server for Apple Silicon, built on MLX, focused on reliable tool calling for coding agents. Up to 4× faster than Apple's MLX (mlx-lm) on the same weights.

llmfit

Hundreds of models & providers. One command to find what runs on your hardware.

vmlx

vMLX - Use MLX models easily - JANGQ (GGUF for MLX) - Not dependant on mlx_vlm

abstractframework

Write once. Generate everything. A modular, open-source ecosystem for building durable, observable, multimodal AI systems. Text, voice, image, video, music — one unified interface, any provider, any model, local or cloud.

mlx-kquant

Native K-quant support for MLX, with a quantization and fine-tuning toolchain for Apple Silicon

fermion-research

Phonon: open speech recognition models (Phonon-2, Phonon-1) — CLI, CPU and CUDA images

lucid-dl

Lumerico's Comprehensive Interface for Deep Learning

mlx-cuda-12

MLX: An array framework for Apple silicon

mtplx

The fastest way to run Qwen 3.8 Flash Next, Qwen 3.8 27B and Ternary Bonsai 2 27B on a Mac: 125 tok/s in OpenCode on an M5 Max, and a 27B model on 16 GB Macs. Native MTP speculative decoding on Apple Silicon, exact at any temperature. OpenAI and Anthropic compatible local server.

abstractvision

Model-agnostic generative vision for Python and AbstractCore — text/image/video generation via MLX-Gen, Diffusers, stable-diffusion.cpp, and OpenAI-compatible APIs.

mlx-cuda-13

MLX: An array framework for Apple silicon