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.
Decision gist · record as of 2026-08-14
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.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python >=3.10 and MLX (Apple Silicon Mac recommended for optimal performance).
- 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 · maintenance · safety
MIT (permissive) — MIT license permits commercial and private use with minimal restrictions; attribution required.
last release 2026-08-12 (2 days) · last repo commit 2026-08-14 · 5,326 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 959,074 downloads/mo, #4,639 on PyPI
Alternatives
Verify before relying
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)- 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.
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 on this page. Verify before relying on it.
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
mlx-vlm on PyPI
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.
Requires Python >=3.10 and MLX (Apple Silicon Mac recommended for optimal performance).
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)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 the current Python release >=3.10 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 15 packagesmlxtransformersminiaudiotqdmPillowrequestsllguidancemlx-lmmlx-audioopencv-pythonfastapipython-multipartstarletteuvicornnumpy |
| Maintenance | Actively maintained 2 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 959,074 / month, #4,639 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 4 - BetaIntended Audience :: DevelopersLicense :: OSI Approved :: MIT LicenseProgramming Language :: Python :: 3Programming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12 |
Evidence: mlx_vlm-0.6.13-py3-none-any.whl
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See also mlx-lm · mlx-audio · mlx-whisper · leptonai · qwen-omni-utils · ms-swift · vllm-cpu · qwen-vl-utils · smg-grpc-servicer · mlx