Native macOS app for the vMLX inference server on Apple Silicon

Looking for a native Swift macOS app or Swift inference engine? See osaurus.ai.

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MLX Studio is a full-featured macOS app that wraps the vMLX inference server — large language models, vision models, and image generation served via an OpenAI-compatible HTTP API on Apple Silicon. Built on Apple's MLX framework for GPU-accelerated inference, self-hosted on the user's machine.

Screenshots

Chat Interface	Image Generation
Developer Tools	Menu Bar Tray

Download

Download the latest DMG from Releases

1. Download vMLX-X.Y.Z-tahoe-arm64.dmg for macOS Tahoe (recommended), or vMLX-X.Y.Z-sequoia-arm64.dmg for macOS Sequoia
2. Open the DMG and drag vMLX to your Applications folder
3. Launch from Applications or Spotlight

All releases are signed and notarized by Apple for Gatekeeper compatibility.

Quick Start

1. Launch MLX Studio from Applications
2. Pick a model -- browse and download from Hugging Face directly in the app
3. Chat -- start a conversation, attach images, or use tool calling
4. That's it. No Python setup, no terminal, no configuration files.

Features

• Chat -- Multi-turn conversations with streaming, markdown rendering, and code highlighting
• Vision -- Send images to multimodal models (Qwen-VL, LLaVA, Pixtral, and more)
• Reasoning -- Native support for thinking/reasoning models (DeepSeek-R1, QwQ, GLM-Z1)
• Tool Calling -- Function calling with structured output for agent workflows
• Image Generation -- Text-to-image with MLX-powered diffusion models
• Text-to-Speech -- TTS via Kokoro with multiple voices
• Speech-to-Text -- Whisper-based transcription
• OpenAI-Compatible API -- HTTP server on localhost for any compatible client
• Menu Bar Mode -- Runs in the tray for quick access
• Developer Tools -- Model inspection, conversion, quantization, and diagnostics
• Session Management -- Save, restore, and organize conversations with SQLite persistence
• JANG Quantization -- First-class support for JANG mixed-precision quantization formats

Engine Highlights

• Continuous Batching with PagedAttention for efficient memory use
• Speculative Decoding for faster generation on supported models
• KV Cache Quantization to fit larger contexts in limited memory
• Prefix Caching for faster repeated prompts
• Hybrid Model Support (Mamba/SSM + Transformer architectures)

System Requirements

Requirement	Minimum
macOS	14.0 Sonoma or later
Chip	Apple Silicon (M1, M2, M3, M4 -- any variant)
RAM	8 GB (16 GB+ recommended for larger models)
Disk	~500 MB for the app; models vary (1--50 GB each)

What's Included

MLX Studio bundles everything needed to run the inference server:

Mode	Description
Chat	Conversational UI with vision, reasoning, and tool support
Server	OpenAI-compatible API server (/v1/chat/completions, /v1/responses)
Image	Text-to-image generation with diffusion models
Tools	Model conversion, quantization, diagnostics, and inspection
API	Interactive API documentation and testing playground

A bundled Python 3.12 environment with all dependencies is included -- no system Python or virtual environments required.

Build from Source

If you prefer to build MLX Studio yourself:

# Clone the source repository
git clone https://github.com/jjang-ai/vmlx.git
cd vmlx

# Set up the Python engine
python3 -m venv .venv
source .venv/bin/activate
pip install -e .

# Build the Electron app
cd panel
npm install
npm run build
npx electron-builder --mac --dir

# The app is in panel/release/mac-arm64/MLX Studio.app

For a distributable DMG:

npx electron-builder --mac dmg

Also Available

Resource	Link
PyPI (CLI / Python library)	pip install vmlx -- pypi.org/project/vmlx
Source Code	github.com/jjang-ai/vmlx
JANG Quantization	Mixed-precision quantization for MLX -- docs
Models on Hugging Face	huggingface.co/jjang-ai
Website	vmlx.net

License

Apache License 2.0. See the source repository license for details.

Contact

Jinho Jang -- eric@jangq.ai

Built by JANGQ AI