---
id: mlx-vlm
version: "0.6.13"
license: MIT
license_treatment: permissive
maintenance: active
---

# mlx-vlm — MLX-VLM is a package for inference and fine-tuning of Vision Language Models (VLMs) and Omni Models (VLMs with audio and video support) on your Mac using MLX.

License: permissive · Maintenance: active · Downloads: 959.1K/mo

## What it is and what it does

MLX-VLM is a framework for running vision language models on Apple Silicon Macs using the MLX framework. It supports inference and fine-tuning of models that combine vision, language, and optionally audio and video inputs. The package provides multiple interfaces: a command-line tool for quick inference, a Python API for programmatic use, a FastAPI server for deployment with features like continuous batching and KV cache optimization, and a Gradio web UI for interactive chat.

The package is built on a stack of 15 runtime dependencies including transformers for model loading, FastAPI and Uvicorn for the server, Pillow and OpenCV for image processing, and miniaudio for audio support. It targets developers working on Mac hardware who want to run or fine-tune multimodal models without cloud infrastructure. The framework includes quantization support, speculative decoding, and model-specific documentation for OCR and reasoning models.

Use it for:

- Run vision-language inference locally on Mac via CLI or Python without cloud API calls.
- Deploy a multimodal model as a local FastAPI service with continuous batching and KV cache optimization.
- Build an interactive chat interface with Gradio for image, audio, and text input on a single machine.
- Fine-tune a vision language model on your own Mac hardware for domain-specific tasks.
- Process images and audio together in a single inference call using omni models.

## Worth the install?

AI-flagged interpretation of the facts above — verify before relying.

MLX-VLM runs vision language models and omni models (with audio and video support) for inference and fine-tuning on Mac hardware using MLX, with CLI, Python, FastAPI server, and Gradio UI interfaces.

Yes. MLX-VLM is actively maintained, has no known vulnerabilities, uses a permissive MIT license, and offers low-friction installation. It fills a clear niche for developers with Mac hardware who want local multimodal inference without cloud dependencies. The Beta status and broad feature set (CLI, server, fine-tuning, multiple model architectures) suggest a maturing project worth adopting for Mac-based ML workflows.

## Install

pip install mlx-vlm
uv add mlx-vlm
poetry add mlx-vlm

## Installing mlx-vlm

Before you install: Low friction install with a pure-Python wheel and 15 runtime dependencies. Actively maintained with a recent release and 5326 repository stars; last commit 2026-08-14.

License in practice: MIT license permits commercial and private use with minimal restrictions; attribution required.

Quickstart:

pip install -U mlx-vlm

from mlx_vlm.generate import generate_text
from mlx_vlm.utils import load_config, load_model_and_processor_from_hub

model, processor = load_model_and_processor_from_hub('mlx-community/Qwen2-VL-2B-Instruct-4bit')
output = generate_text(model, processor, prompt='Hello, how are you?', max_tokens=100)

Requires Python >=3.10 and MLX (Apple Silicon Mac recommended for optimal performance).

Verify before relying:

- Whether all 15 runtime dependencies are required for basic CLI usage or if some are optional for specific features.
- Performance characteristics and memory requirements for different model sizes on Mac hardware.
- Whether fine-tuning is production-ready or still experimental given the Beta development status.

## Package facts

- License: MIT (permissive)
- Python support: supports_current
- Install friction: low
- Maintenance: active
- Downloads: 959.1K/month (top 5,000 on PyPI)
- Known vulnerabilities: none known

## Tags

vision language model inference mac, multimodal llm on apple silicon, vlm with audio video support, mlx model serving fastapi, fine-tune vision models mac, mac-native, multimodal-inference, quantization

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