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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.

Our tips

For long contexts, adjust the key-value cache size with --max-kv-size: a value of 4096 or higher improves quality but uses more RAM, while 512 saves memory at the cost of lower quality.
Use mlx_lm.cache_prompt to cache a long prompt and reuse it across different queries without recomputing it each time, which noticeably speeds up multi-turn conversations.
If a model is slow because it exceeds available RAM, increase the system wired memory limit with sudo sysctl iogpu.wired_limit_mb=N (requires macOS 15 or higher).
Reduce the prefill step size with --prefill-step-size to lower peak memory usage when reading long prompts, at a slight cost to processing speed.
To convert and quantize a model without writing code, the Hugging Face Space mlx-my-repo allows you to do it directly from a browser.

Watch out

Models whose size exceeds the machine's total RAM will be slow, even with available optimizations. A warning is displayed in this case, but the tool does not block execution.
Some models require enabling the --trust-remote-code option to load their tokenizer, which involves running remote code: it is advisable to verify the model's source before accepting.

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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