Rapid-MLX

Quick Start (60 seconds)

1. Install — pick one path (run only one of these):

One-liner — detects your RAM, picks a starter model (recommended):

curl -fsSL https://rapidmlx.com/install.sh | bash

or Homebrew — prebuilt bottle straight from homebrew-core:

brew install rapid-mlx

Both land the same rapid-mlx CLI. The curl installer additionally installs Python 3.10+ if missing, creates an isolated venv at ~/.rapid-mlx/, symlinks the rapid-mlx CLI into ~/.local/bin/, and prints a serve command sized to your Mac (8–23 GB → qwen3.5-4b-4bit; 24–47 GB → gpt-oss-20b-mxfp4-q8; 48–95 GB → qwen3.6-35b-8bit; 96 GB+ → gpt-oss-120b-mxfp4-q8).

Install security. install.sh is served over HTTPS (HSTS-preload) from rapidmlx.com and is a byte-identical mirror of install.sh at the release commit — read it before running if you like. If you want a cryptographically verified installer rather than trusting the website pipe, don't curl | bash the URL above: instead download the release's install.sh asset, verify it against the cosign-signed SHA256SUMS.txt shipped alongside it, and run that verified copy — full recipe in SECURITY.md. PyPI artifacts additionally carry Sigstore attestations (PEP 740). Two more low-trust paths:

• Pin to a commit hash — curl -fsSL https://raw.githubusercontent.com/raullenchai/Rapid-MLX/<commit>/install.sh -o install.sh && shasum -a 256 install.sh && bash install.sh
• Skip the shell script entirely — use Homebrew, uv, or pip below.

See Alternative install methods for the non-curl paths.

2. Chat with a model right now:

rapid-mlx chat

Defaults to qwen3.5-4b-4bit. First run downloads the weights (~2.5 GB) with a progress bar and drops you into a REPL. Type /help for slash commands, /exit to quit.

3. Or serve it for use from other apps:

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

Starts an OpenAI-compatible HTTP server bound to http://localhost:8000. Point any OpenAI SDK / client (Cursor, 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)

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

rapid-mlx launch claude-code

With a server running (step 3), this patches Claude Code's local config (~/.config/claude/settings.json) to route at http://localhost:8000 — no manual env vars, no editing JSON by hand. You get a fully local Claude Code: $0 per token, nothing leaves your Mac. Swap in cursor, cline, or continue-dev for the other IDE clients, or run rapid-mlx launch list to see what's detected on this machine.

Vision / audio / video / diffusion models? Base install is text-only (~460 MB). Vision, audio (TTS, STT, voice cloning), video generation, embeddings, and DFlash speculative decoding ship as opt-in extras. → Optional extras

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

Video generation

Run text-to-video or image-to-video locally through the OpenAI-compatible Videos API. Three backends ship — Wan 2.1 / 2.2, CogVideoX-Fun and LTX-2.3 — across 8 registered checkpoints. wan2.2-ti2v-5b-q8 is the recommended starting point: smallest of the Wan set, and TI2V means one checkpoint does both text-to-video and image-to-video.

Requires Python 3.11+ (the video runtime does not support 3.10; core text and audio still do) and ffmpeg for the final MP4 mux.

pip install 'rapid-mlx[video]'
brew install ffmpeg
rapid-mlx serve wan2.2-ti2v-5b-q8

Create and download a clip:

curl http://localhost:8000/v1/videos \
  -F model=wan2.2-ti2v-5b-q8 \
  -F 'prompt=A fox running through fresh snow, cinematic tracking shot' \
  -F seconds=1 \
  -F size=832x512

# Poll until GET /v1/videos/VIDEO_ID reports "status": "completed", then:
curl http://localhost:8000/v1/videos/VIDEO_ID/content -o output.mp4

The create call returns a job immediately. Poll GET /v1/videos/VIDEO_ID until status is completed. Add -F input_reference=@start.png for image-to-video.

Generation is serialized — one clip at a time — because two diffusion pipelines resident at once will exhaust unified memory. Expect minutes of compute per second of footage, not real time.

→ Every checkpoint, RAM requirement and tuning knob

Audio: speech, transcription, voice cloning

41 audio aliases behind the OpenAI-compatible /v1/audio/* endpoints — any OpenAI SDK works unchanged.

pip install 'rapid-mlx[audio]'

# Text to speech
rapid-mlx serve kokoro
curl http://localhost:8000/v1/audio/speech \
  -H "Content-Type: application/json" \
  -d '{"model":"kokoro","input":"hello from rapid-mlx"}' --output hello.wav

# Transcription (Whisper / Parakeet / SenseVoice)
rapid-mlx serve whisper-large-v3-turbo
curl http://localhost:8000/v1/audio/transcriptions \
  -F file=@hello.wav -F model=whisper-large-v3-turbo

Beyond the basics, three things you may not expect to run locally:

• Zero-shot voice cloning from a reference clip. indextts is the only one that takes the clip alone; qwen3-tts-clone, f5-tts-zh and chatterbox all require ref_text (the clip's exact transcript) paired with ref_audio, and the request is rejected before generation if it is missing.
• Voice design — qwen3-tts-voicedesign has no named speakers at all. Describe the voice you want in natural language via instructions (timbre, gender, age, accent, emotion, prosody) and it synthesises it.
• Forced alignment — qwen3-aligner takes audio plus the transcript you already have and returns per-character timings. It never guesses at the words, so it cannot mis-hear them; that is what karaoke captions and beat-synced editing need.

Also: word-level timestamps on transcription, and local text-to-music at /v1/audio/music.

→ All 41 aliases across 12 families

Why Rapid-MLX

→ Full feature breakdown

Use Cases