# MLX review

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

Devtoolwatch Board, the week of Oct 25–31, 2026. Canonical page: https://index.devtoolwatch.com/tools/mlx/

## 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.

## MLX at a glance

- **Category:** [Inference and training](https://index.devtoolwatch.com/categories/inference-and-training/)
- **Maintained by:** ml-explore (organization, [@ml-explore](https://github.com/ml-explore) on GitHub)
- **License:** MIT
- **Language:** C++, Python, Cuda
- **Repository:** [github.com/ml-explore/mlx](https://github.com/ml-explore/mlx)
- **Website:** [ml-explore.github.io/mlx](https://ml-explore.github.io/mlx/)
- **PyPI package:** [mlx](https://pypi.org/project/mlx/) 0.32.4
- **Latest release:** [v0.32.4](https://github.com/ml-explore/mlx/releases/tag/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 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

1. Install MLX with pip for your platform: macOS, Linux with CUDA, or Linux CPU-only.
2. Follow the quick start guide in the MLX documentation.
3. 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

- 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.

## Usage data (the week of Oct 25–31, 2026)

- Board rank: #212 overall, #21 in Inference and training, momentum score 43
- Downloads that week: 356.9k (+13% vs 4-week average)
- New GitHub stars per week (last 12 weeks): Aug 9: 102; Aug 16: 137; Aug 23: 121; Aug 30: 106; Sep 6: 93; Sep 13: 91; Sep 20: 89; Sep 27: 81; Oct 4: 88; Oct 11: 118; Oct 18: 105; Oct 25: 82

## MLX alternatives

| Rank | Tool | Stars | Downloads/wk |
|---|---|---|---|
| #1 | [dsh-our-free-model](https://index.devtoolwatch.com/tools/ebony-vinyl-dsh-our-free-model/) | 54.3k | – |
| #2 | [magpie](https://index.devtoolwatch.com/tools/yetone-magpie/) | 26.9k | – |
| #3 | [Strata](https://index.devtoolwatch.com/tools/niko1221-strata/) | 37.1k | – |
| #4 | [edge0](https://index.devtoolwatch.com/tools/edge0-ai-edge0/) | 8.9k | – |
| #5 | [OmniRoute](https://index.devtoolwatch.com/tools/diegosouzapw-omniroute/) | 83.2k | 106k |
| #6 | [CLIProxyAPI](https://index.devtoolwatch.com/tools/router-for-me-cliproxyapi/) | 57.1k | – |

## 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](https://index.devtoolwatch.com/tools/ollama/)

### 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](https://index.devtoolwatch.com/tools/llama-cpp/)

### 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

1. [ml-explore/mlx on GitHub](https://github.com/ml-explore/mlx)
2. [MLX README](https://github.com/ml-explore/mlx/blob/main/README.md)
3. [mlx on PyPI](https://pypi.org/project/mlx/)
4. [MLX website](https://ml-explore.github.io/mlx/)
5. [Release v0.32.4 (Oct 14, 2026)](https://github.com/ml-explore/mlx/releases/tag/v0.32.4)
6. [Release v0.32.3 (Sep 29, 2026)](https://github.com/ml-explore/mlx/releases/tag/v0.32.3)
7. [Release v0.32.2 (Aug 25, 2026)](https://github.com/ml-explore/mlx/releases/tag/v0.32.2)
8. [Hacker News: Apple's MLX adding CUDA support](https://news.ycombinator.com/item?id=44565668)
9. [Hacker News: MLX: An array framework for Apple Silicon](https://news.ycombinator.com/item?id=38539153)
10. [Hacker News: MLX: NumPy like framework for Apple Silicon by Apple](https://news.ycombinator.com/item?id=38539020)
11. [Devtoolwatch Board, the week of Oct 25–31, 2026](https://index.devtoolwatch.com/)