MLX review

Q: How do I install MLX?

Run pip install mlx. The mlx package needs Python 3.10 or newer. The install section above has the other options from the project's README.

MLX is an array framework for machine learning on Apple silicon, from Apple machine learning research, with a NumPy-like Python API.

Updated Nov 1, 2026. Review written Oct 22, 2026 from the project's own sources; usage data refreshed every Monday.

TL;DR

MLX is an array framework for machine learning on Apple silicon, built by Apple machine learning research. It is aimed at machine learning researchers who want to train and deploy models. Its unified memory model sets it apart: arrays live in shared memory, so operations run on CPU or GPU without transferring data.

29kGitHub stars
+82new stars, week of Oct 25
356.9kdownloads a week, +13% vs 4-week avg
#21in Inference and training this week

MLX at a glance

Category: Inference and training
Maintained by: ml-explore (organization, @ml-explore on GitHub)
License: MIT
Language: C++, Python, Cuda
Repository: github.com/ml-explore/mlx
Website: ml-explore.github.io/mlx
PyPI package: mlx 0.32.4
Latest release: v0.32.4 on GitHub, Oct 14, 2026
First published: Nov 28, 2023

What MLX does

MLX provides a Python API that closely follows NumPy, plus C++, C, and Swift APIs that mirror it.
MLX includes mlx.nn and mlx.optimizers packages with APIs that closely follow PyTorch for building complex models.
MLX supports composable function transformations for automatic differentiation, automatic vectorization, and computation graph optimization.
MLX uses lazy computation, so arrays are only materialized when needed.
MLX builds computation graphs dynamically, so changing function argument shapes does not trigger slow compilations.
MLX runs operations on the CPU or GPU using shared memory, without transferring data between devices.
MLX offers a CUDA backend on Linux, with CUDA support for FFT and quantized matmuls.

How it works

MLX is installed from PyPI and used as a library from Python, C++, C, or Swift. On macOS it runs on Apple silicon CPU and GPU with unified memory. On Linux you can install a CUDA backend or a CPU-only package. A separate examples repo covers transformer training, LLaMA text generation, LoRA finetuning, Stable Diffusion, and Whisper.

How to install and start using MLX

On macOS with pip

pip install mlx

CUDA backend on Linux

pip install mlx[cuda]

CPU-only on Linux

pip install mlx[cpu]

Requirements

Python 3.10 or newer
macOS on Apple silicon, or Linux for the CUDA or CPU-only packages

First steps

Install MLX with pip for your platform: macOS, Linux with CUDA, or Linux CPU-only.
Follow the quick start guide in the MLX documentation.
Browse the MLX examples repo for language model training, LLaMA generation, LoRA finetuning, Stable Diffusion, and Whisper.

Who MLX is for

Machine learning researchers who want to explore new ideas on Apple silicon
Developers familiar with NumPy or PyTorch who want similar APIs on a Mac
Running and finetuning LLMs, image generation, or speech recognition locally on Apple silicon
Swift or C++ developers who need an array framework for machine learning

Limitations to know

MLX is a low-level array framework. Model workflows such as LLM generation come from the separate examples repo.
On Linux, MLX requires choosing either the CUDA backend or the CPU-only package.

Is MLX growing? Usage data

In the week of Oct 25–31, 2026, MLX ranked #212 on the Devtoolwatch Board and #21 in Inference and training, with a momentum score of 43 out of 100. Its main package had 356.9k downloads that week, +13% against its own 4-week average.

New GitHub stars per week for the last 12 weeks, from GitHub's stargazer history (today's stargazers by the week they starred).

Show the weekly numbers

New GitHub stars per week for MLX
Week of	New stars
Aug 9, 2026	102
Aug 16, 2026	137
Aug 23, 2026	121
Aug 30, 2026	106
Sep 6, 2026	93
Sep 13, 2026	91
Sep 20, 2026	89
Sep 27, 2026	81
Oct 4, 2026	88
Oct 11, 2026	118
Oct 18, 2026	105
Oct 25, 2026	82

On Hacker News

Apple's MLX adding CUDA support, 548 points and 203 comments (Jul 14, 2025)
MLX: An array framework for Apple Silicon, 244 points and 46 comments (Dec 6, 2023)
MLX: NumPy like framework for Apple Silicon by Apple, 162 points and 16 comments (Dec 6, 2023)
Mistral LLM on Apple Silicon Using Apple's MLX Framework Runs Instantaneously, 79 points and 27 comments (Dec 7, 2023)
Mixtral on MLX, 5 points and 0 comments (Dec 21, 2023)

The Board ranks tools by four signals each week: new stars, new stars for the repo's size, added downloads and download growth beyond the market. How the ranking works.

MLX alternatives

Other LLM inference and training tools on the Board in the week of Oct 25–31, 2026, by momentum:

Inference and training ranked by momentum, the week of Oct 25–31, 2026
Rank	Tool	Stars	Downloads/wk
#1	dsh-our-free-modeldsh-our-free-model is a dsh plugin that provides DeepSeek V4.1 Flash, Kimi K3 and other models without login, sign-up or API key.	54.3k	–
#2	magpiemagpie is a local gateway and app that lets coding agents like Claude Code, Codex and Gemini CLI use any provider's model.	26.9k	–
#3	StrataStrata is a local inference engine and app that runs large open models like Qwen3.8-Flash-Next on consumer hardware with…	37.1k	–
#4	edge0edge0 is an open-source streaming inference framework that runs large sparse mixture-of-experts models on consumer hardware by streaming…	8.9k	–
#5	OmniRouteOmniRoute is an open-source AI gateway that routes requests across many model providers through one OpenAI-compatible endpoint, with…	83.2k	106k
#6	CLIProxyAPICLIProxyAPI is a Go proxy server that exposes CLI coding accounts like Codex, Claude Code and Gemini CLI through OpenAI, Gemini and…	57.1k	–

All LLM inference and training tools, ranked

Recent MLX releases

v0.32.4, Oct 14, 2026
v0.32.3, Sep 29, 2026
v0.32.2, Aug 25, 2026
v0.32.1, Aug 18, 2026
v0.32.0, Jul 7, 2026

MLX FAQ

Is MLX free and open source?

Yes. MLX's source code is on GitHub (ml-explore/mlx) under the MIT license, so you can use it for free.

How do I install MLX?

Run pip install mlx. The mlx package needs Python 3.10 or newer. The install section above has the other options from the project's README.

Is MLX still maintained?

Yes. The ml-explore/mlx repository was last updated on Oct 21, 2026, and the latest release, v0.32.4 on GitHub, came out on Oct 14, 2026.

How popular is MLX?

MLX has 29k GitHub stars as of Nov 1, 2026. It gained 82 new stars in the week of Oct 25–31, 2026, against a 12-week average of 101 a week. Its main package had 356.9k downloads that week, +13% against its own 4-week average. That made it #21 among LLM inference and training tools on the Devtoolwatch Board.

MLX vs Ollama: how do they compare?

MLX and Ollama are both tracked by Devtoolwatch as LLM inference and training tools. MLX has 29k GitHub stars and Ollama has 185.5k. In the week of Oct 25–31, 2026, MLX's main package had 356.9k downloads and Ollama's had 4M. Ollama is a tool for running and building with open AI models locally or in the cloud on macOS, Windows, and Linux. Ollama review.

MLX vs llama.cpp: how do they compare?

MLX and llama.cpp are both tracked by Devtoolwatch as LLM inference and training tools. MLX has 29k GitHub stars and llama.cpp has 133k. llama.cpp is a plain C/C++ library and set of tools for running LLM and VLM inference locally or in the cloud on many kinds of hardware. llama.cpp review.

What are the best alternatives to MLX?

Other LLM inference and training tools on the Devtoolwatch Board in the week of Oct 25–31, 2026, by momentum: dsh-our-free-model (#1), magpie (#2), Strata (#3), edge0 (#4), OmniRoute (#5). The alternatives table above has their numbers.

Is MLX for Mac?

MLX is designed for machine learning on Apple silicon, and the standard pip package installs on macOS. MLX also offers a CUDA backend and a CPU-only package for Linux.

Is MLX like CUDA?

MLX is not a GPU programming platform like CUDA. MLX is an array framework with a NumPy-like API. It runs on Apple silicon GPUs and, on Linux, can use a CUDA backend.

How do I use MLX on a Mac?

MLX installs on macOS with pip install mlx. Then use its NumPy-like Python API, or the mlx.nn and mlx.optimizers packages for models. The quick start guide in the documentation covers the basics.

How do I use MLX models for LLMs or Whisper?

MLX points to the MLX examples repo for these tasks. The repo includes LLaMA text generation, LoRA finetuning, Stable Diffusion image generation, and speech recognition with OpenAI's Whisper.

What makes MLX different from other frameworks?

MLX uses a unified memory model. Arrays live in shared memory, so operations can run on the CPU or GPU without transferring data. MLX also uses lazy computation and dynamic graph construction.

Sources

MLX README badge

Maintainers can show the tool's weekly Board rank in their README:

[![Devtoolwatch](https://index.devtoolwatch.com/badge/mlx.svg)](https://index.devtoolwatch.com/tools/mlx/)

How this page is made: Devtoolwatch writes each review from the project's README, releases, package metadata and website, with an AI model, then checks every claim against the source it cites and drops what the sources don't support. The numbers come from Devtoolwatch's daily GitHub snapshots, GitHub's stargazer history, and the npm and PyPI download APIs. Spotted an error? Email [email protected].