Rapid-MLX

Rapid-MLX

Run Codex, Claude Code, OpenCode, Pi, Hermes, Crush or Goose with a local model on Mac:
rapid-mlx launch <agent>

Install on a stock Apple Silicon Mac with curl -fsSL https://rapidmlx.com/install.sh | bash, restart your terminal (or run export PATH="$HOME/.local/bin:$PATH"), then install your chosen coding agent and run, for example, rapid-mlx launch codex. Run the same command tomorrow; rapid-mlx launch codex --remove removes Rapid's private profile without changing your existing Codex settings. One-command guides for each agent. A first reply can take longer while a model downloads, loads, or processes the agent's opening prompt.

Rapid-MLX is an open-source (Apache 2.0) OpenAI- and Anthropic-compatible LLM inference server for Apple Silicon, built on MLX, focused on reliable tool calling for coding agents.
Up to 4× faster than Apple's MLX (mlx-lm), 1.5× on a typical task — same model, same weights.
Decode speed, Qwen3.5-9B 4-bit on a Mac mini M4 Pro, greedy decoding, both servers at their defaults — per-task results, method, and raw data.

Faster decode than oMLX and LM Studio on Qwen3.5-9B, Qwen3.8-27B, and Qwen3.6-35B

Measured speed on a Mac Studio M5 Max (36 GB) — Rapid-MLX 0.16.1 vs oMLX 0.7.0 and LM Studio, using the same base 4-bit weights for each model.

_{Single run per engine; default serving modes with Rapid's MTP text mode, oMLX's SSD cache, and LM Studio's configured context and four parallel slots. Full results and raw data.}

_{rapidmlx.com · Docs · Model mirror · Desktop app · Discord}

How it compares

Feature facts only, taken mostly from each project's own documentation as of 2026-09-26 (sources below). For speed, see the measured results above.

_{Sources: oMLX README · Ollama OpenAI and Anthropic compatibility, FAQ, MLX engine · LM Studio Anthropic compatibility, parallel requests, 0.4.2 changelog, app terms, mlx-engine · mlx-lm server docs and server.py. Ollama prompt-cache reuse is from our benchmark notes. — = not verified for this table. Spot an error? Please open an issue.}

vs Apple's mlx-lm: per-task results and raw data at https://rapidmlx.com/compare/mlx-lm.

Works with your AI stack

Use Rapid-MLX as a local backend for agents, apps, or your own code. If a client accepts an OpenAI- or Anthropic-compatible endpoint, it can usually use Rapid-MLX without an adapter.

For Claude Desktop, set up its in-app Gateway.

Five Tier-1 agents are exercised end-to-end on real weights before release. See the tested compatibility matrix for exact API coverage and setup status.

Install

Desktop — macOS (Apple Silicon)

The easiest way to chat locally, manage models, and use vision, files, voice, and image generation from one app.

• Download Rapid-MLX Desktop
• Browse signed Desktop releases
• Requires an M-series Mac; Windows and Linux desktop builds are not available yet
• Share Compute setup and credit ledger

CLI and server — macOS (Apple Silicon)

# Homebrew — prebuilt bottle from homebrew-core
brew install rapid-mlx

# Or the guided installer — detects RAM and recommends a starter model
curl -fsSL https://rapidmlx.com/install.sh | bash

Both install the same rapid-mlx CLI. Prefer uv or pip, or want to verify the installer before running it? See alternative install methods and install security.

The guided installer prefers a runnable model already cached on this Mac when it fits the RAM tier. Otherwise its quick first-chat download is lfm2.5-1b-4bit below 16 GB and qwen3.5-4b-4bit at 16 GB or above; larger quality picks remain available through rapid-mlx recipe and the model picker.

Submit a Community Benchmark

Help other Mac users choose a model by sharing a standardized result from your own Apple Silicon Mac. Runs stay local until you explicitly choose to publish; you do not need a GitHub account or a benchmark JSON pull request.

• Desktop: Enable Community Benchmark in Settings → Experimental, then choose a model in the Community Benchmark tab, run it, review the result in My Results, and select Publish if you want to share it.
• CLI: Run rapid-mlx benchmark catalog, then rapid-mlx benchmark run <model>. Review the saved result and publish it with rapid-mlx benchmark share <run-id> only if you choose to.

Explore the Community Benchmark leaderboard or read the protocol and privacy details.

Quick Start (60 seconds)

1. Chat with a model right now:

rapid-mlx chat

A complete cached chat model that fits this Mac starts without another download. Otherwise Rapid-MLX selects a starter model and downloads its weights before opening the REPL. Type /help for slash commands, /exit to quit.

2. Or serve it for use from other apps:

rapid-mlx serve qwen3.5-4b-4bit

Starts an OpenAI-compatible HTTP server on the first free port in 8000–8009 when --port is omitted. An explicit --port never falls back. The examples below assume port 8000 was selected. Point any client that supports a local custom endpoint (Aider, LangChain, OpenCode, PydanticAI, your own scripts) at http://localhost:8000/v1; Claude Code / Anthropic SDK uses http://localhost:8000 (the Anthropic messages route lives at /v1/messages under the same host).

curl http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{"model":"default","messages":[{"role":"user","content":"Say hello"}]}'

from openai import OpenAI
client = OpenAI(base_url="http://localhost:8000/v1", api_key="not-needed")
print(client.chat.completions.create(
    model="default",
    messages=[{"role": "user", "content": "Say hello"}],
).choices[0].message.content)

3. Or wire up your coding agent — one command:

rapid-mlx launch claude-code

This saves a private Claude Code profile, starts a local server if needed, and launches Claude Code without editing ~/.claude/settings.json. The same command works after a restart. Swap in codex, opencode, pi, hermes, crush, or goose. For the original config-writing behavior, use --legacy-config; cline and continue-dev keep their existing setup flow.

Agent setup details.

Cursor: Cursor currently routes BYOK requests through its own servers, so its servers cannot reach a Rapid-MLX endpoint on localhost. Rapid-MLX therefore does not generate a Cursor localhost config. If you intentionally expose the server through a public HTTPS tunnel, set RAPID_MLX_API_KEY=your-secret for both rapid-mlx serve ... and rapid-mlx launch cursor --server-url https://your-public-host. This is no longer a fully local connection; never expose an unauthenticated server. Rapid-MLX rejects explicit local/private addresses but cannot verify reachability from Cursor's network, whose DNS view may differ from your Mac.

Vision / image / video models? They work out of the box with the official installer above (and Rapid-MLX Desktop), which include the vision, image-generation and video-generation runtimes (image and video generation need Python 3.11+). Audio (TTS, STT, voice cloning) and embeddings ship as opt-in extras. → Optional extras

Not into the terminal? Rapid-MLX Desktop bundles the same engine inside a one-click Mac app.

See it in action

_{Real session on a Mac mini (M2 Pro, 32 GB), rapid-mlx 0.15.6 from PyPI; idle waits shortened. Recording: docs/assets/demo.cast.}

Image generation

Generate images locally from the Desktop Images tab or the OpenAI-compatible Images API. The image runtime ships with the base install (Python 3.11+):

rapid-mlx serve flux2-klein-4b

curl http://localhost:8000/v1/images/generations \
  -H 'Content-Type: application/json' \
  -d '{"model":"flux2-klein-4b","prompt":"A red sailboat on an alpine lake","size":"1024x1024","seed":42}'

Edit an existing PNG or JPEG with the edit-specialized model:

In one terminal:

rapid-mlx serve qwen-image-edit

In another terminal:

curl http://localhost:8000/v1/images/edits \
  -F image=@input.png \
  -F model=qwen-image-edit \
  -F 'prompt=Change the sky to a warm sunset' \
  -F response_format=b64_json

flux2-klein-4b is the recommended starting point. Download sizes are the currently cataloged model payloads; minimum unified memory includes practical headroom for macOS and the serving process. Omit steps to use the validated family default shown below.

qwen-image-2.1 uses mflux 0.20 with a native MLX q4 transfor