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MLX Studio

MLX Studio is a native macOS application that wraps the vMLX inference server to run language and vision models and generate images on Apple Silicon, using Apple’s MLX framework and serving inference through an OpenAI-compatible HTTP API on the user’s machine.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

MLX Studio — OpenViST project profile in Local & Self-hosted AI.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use MLX Studio when the target is local inference on an Apple Silicon Mac, whether through its desktop chat interface or a compatible client connecting to the local HTTP API.

Who it is for

  • Mac users who want to chat with locally hosted language models.
  • Developers who want compatible clients to call a local inference API.
  • Users exploring local vision models, image generation, or model conversion and quantization tools.

Documented uses

  • Browse and download models from Hugging Face, then chat with them locally.
  • Send images to multimodal models and use tool calling in conversations.
  • Expose local model inference to compatible applications through an OpenAI-compatible HTTP API.
  • Generate images, transcribe speech, or synthesize speech using the app’s listed capabilities.
  • Inspect, convert, quantize, and diagnose models.
Before you choose

Requirements & limits

The documented application targets macOS 14 or later on Apple Silicon. The repository provides release DMGs and states a minimum of 8 GB RAM, recommending 16 GB or more for larger models.

  • Apple Silicon and macOS 14 or later are required.
  • The bundled app includes Python 3.12 and dependencies; the documented quick start does not require system Python or terminal configuration.
  • Model storage varies substantially, with the repository listing approximately 1–50 GB per model.
  • The local server documents OpenAI-compatible /v1/chat/completions and /v1/responses routes.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Install MLX Studio from a release DMG

Install the macOS application using the release instructions.

Before you start

  • macOS 14 or later
  • An Apple Silicon Mac
  • At least 8 GB RAM; 16 GB or more is recommended for larger models

Steps

  1. Download the DMG matching the macOS version from the repository’s Releases page.

  2. Open the DMG and drag vMLX to the Applications folder.

  3. Launch the app from Applications or Spotlight.

Expected outcome

The application is installed and launched on the Mac.

Guide limitations

  • The app is for Apple Silicon Macs.
  • Model storage requirements vary; the repository gives a range of 1–50 GB per model.
Documentation 1 ↗
Source-based practical guide

Start a local model chat

Select a model and begin a conversation in MLX Studio.

Before you start

  • MLX Studio launched on a supported Apple Silicon Mac
  • Disk space for the chosen model

Steps

  1. Browse and download a model from Hugging Face inside the app.

  2. Start a chat with the selected model.

  3. Attach images or use tool calling when appropriate.

Expected outcome

A conversation runs through a model hosted on the user’s Mac.

Guide limitations

  • Model files vary in size, and larger models benefit from 16 GB or more of RAM.
Documentation 1 ↗
COMPARE OPTIONS

Compare options for Model inference and serving

These projects document a common task and share a project role. This is a source-based comparison, not an execution benchmark or a guarantee of interchangeability.

ProjectDocumented purposePlatformAccess and requirements
MLX StudioBrowse and download models from Hugging Face, then chat with them locally.macOS, Self-hostedThe documented application targets macOS 14 or later on Apple Silicon. The repository provides release DMGs and states a minimum of 8 GB RAM, recommending 16 GB or more for larger models.
Bodhi AppDiscover and download GGUF models from Hugging Face that suit local hardware.Linux, macOS, Self-hosted, WindowsThe README documents a Mac desktop release and Docker images for CPU, CUDA, ROCm and Vulkan configurations. The project identifies itself as open source.
OfflinellmImport a GGUF model and use it for local multi-turn chat.AndroidThe documented release is an APK available from GitHub Releases. It requires Android 13 or later and arm64-v8a; the Vanilla APK requires users to supply and import their own GGUF model.
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
Active
Sources checked
16 October 2026
Original website title
jjang-ai/mlxstudio · Official homepage title · retained in its source language
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