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AppRisingofficial publisher

MLX (mlx-lm)

App : A product you use directly

Apple framework for running LLMs on M-series chips using unified memory.

The fastest path on Apple Silicon Macs, reserved for users comfortable with Python.

GitHub · 7,467 ★
Overall score
8.3 /10
Security: nothing to flag
Utility
7.0
Reliability
10.0
Security
No caveat on record
Ease
Technical · Technical

Security reflects where data goes, requested permissions and incidents on record at the verification date. It is not a guarantee of zero risk: check the deployment mode and the connections you enable.

What it does, concretely

Running a large language model locally usually requires a dedicated graphics card with substantial video memory. On Apple Silicon Macs, memory is shared between the CPU and GPU, which changes the equation: MLX (mlx-lm) leverages this architecture to run LLMs directly on the machine, without cloud services or additional hardware.

In practice, the mlx-lm Python package installs with a single command and connects to the Hugging Face Hub to download thousands of compatible models. Users can then generate text, open an interactive chat session in the terminal, fine-tune an existing model, or quantize a model to reduce its size, all from the command line or through the Python API.

The tool is aimed at developers and users comfortable with Python and the command line who want to experiment with LLMs locally on an Apple Silicon Mac. It also suits those who want to fine-tune models or prepare them for sharing on Hugging Face.

It is not suited for people looking for a graphical interface or using an Intel Mac: mlx-lm is exclusively designed for Apple Silicon and is used only via the command line or Python code.

What it offers

  • ·Text generation and interactive chat from the command line with thousands of models available on the Hugging Face Hub.
  • ·Model fine-tuning (LoRA and full fine-tuning) with support for quantized models.
  • ·Model quantization to reduce size, with the option to publish directly to the Hugging Face Hub.
  • ·Distributed inference and fine-tuning via mx.distributed across multiple machines.
  • ·Long context handling through a rotating key-value cache, prompt caching, and a configurable prefill step size.
  • ·Full Python API with streaming generation, customizable sampling, and batch processing.
  • ·Built-in local server (mlx_lm.server) to expose a model as an API endpoint.

In practice

Price
Free
Commercial use
Commercial use: yes
Available on
macos, cli
Licence
MIT

Security and data

Rating
Security: nothing to flag
Your data
Data stays local
Maintenance
Active
Last release
16 October 2026

Links and repository checked on 25 October 2026 · added on 1 December 2023

How to install MLX (mlx-lm)

1. Make sure you have an Apple Silicon Mac (M-series chip) running macOS. 2. Ensure Python is installed on your machine. 3. Install mlx-lm via pip: pip install mlx-lm (or via conda: conda install -c conda-forge mlx-lm). 4. Run a text generation with: mlx_lm.generate --prompt "Your question here" (the default model is downloaded automatically from Hugging Face). 5. To open an interactive chat session, use: mlx_lm.chat 6. If a model requests --trust-remote-code, verify its source on Hugging Face before accepting.
pip install mlx-lm

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