The fastest local AI engine for Apple Silicon.
Drop-in OpenAI / Anthropic API · up to 3× Ollama's throughput (measured) · Runs on any M-series Mac.

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

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.

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

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.

Quick Start (60 seconds)

1. Chat with a model right now:

rapid-mlx chat

Defaults to qwen3.5-4b-4bit. First run downloads the weights (~3 GB) with a progress bar and drops you into a 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 bound to http://localhost:8000. 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

With a server running (step 2), this patches Claude Code's local 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 cline or continue-dev for the other IDE clients, or run rapid-mlx launch list to see what's detected on this machine.

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 / 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.

Image generation

Generate images locally from the Desktop Images tab or the OpenAI-compatible Images API. Install the image runtime when using the CLI:

pip install 'rapid-mlx[image]'
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.

At 1024×1024 with the four-step Klein default, measured warm generation was about 9.2 seconds per image on an M3 Ultra. On a 32 GB M2 Pro, the q4 path measured a 52.7-second median; explicitly selecting flux2-klein-4b-bf16 reduced that median to 41.8 seconds (1.26× throughput). The eight-step z-image-turbo baseline was about 34 seconds on the M3 Ultra and 150 seconds on the M2 Pro. The q4 alias remains the predictable default; on the measured 32 GB M2 Pro, use the larger 14.9 GiB BF16 alias when latency matters. Exact results depend on resolution, power/thermal state, and other workloads. The server completion log reports total time and, when the backend exposes exact loop boundaries, measured denoise s/step. For the qualified 1024-square Klein shape it also reports estimated achieved TFLOPS; unavailable or unqualified values are never fabricated.

→ Reproducible precision benchmark and methodology

Image work is single-flight: one generation runs at a time so two diffusion pipelines cannot exhaust unified memory. Model weights retain their own licenses and use restrictions; review the upstream model card before commercial deployment.

→ Release dogfood coverage and reproducible acceptance contract

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

44 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 44 aliases across 13 families

Why Rapid-MLX

→ Full feature breakdown

Use Cases

→ One-shot IDE setup with rapid-mlx launch <claude-code|cline|continue-dev>

Agent Support

All 12 agents below are wire-verified against real weights every release via their own integration-test cell. Of these, five are Tier-1 — Claude Code, Codex CLI, Hermes, Aider, and DeepSeek Harness — re-verified end-to-end against the current client binary every release, with one guardian per API wire (Anthropic /v1/messages, OpenAI /v1/responses, and /v1/chat/completions covered for tool-calling depth, reach, and DeepSeek's own harness protocol). The other seven are Tier-2: wire-verified in the matrix and configured on-demand. The first nine agents each ship a rapid-mlx agents <name> --setup config template (except Claude Code, which is one env-var), and Continue.dev gets the same one-command setup via rapid-mlx agents continue --setup (ten setup-capable clients in all, though Continue.dev is not part of the wire-verified matrix below); GitHub Copilot, Factory Droid, and Moonshot Kimi Code plug in through their own documented BYOK config (auth-gated, so the matrix cell is a wire smoke).

Tier-1 is not a label — it is a job that blocks the release. tests/integrations/agent_smoke.sh drives each of the five through the same real multi-step bug-fix task against a local 35B model and asserts the repo's own test suite goes green afterwards; if any one of them fails, the version cannot tag or publish.

Tier-1 (5): Claude Code · Codex CLI · Hermes · Aider — last re-verified end-to-end 2026-07-28 on current binaries (claude 2.1.211, codex 0.145.0, hermes 0.9.0, aider 0.86.2). DeepSeek Harness — promoted 2026-08-17, verified on dsh 0.1.0-rc.7 against qwen3.6-35b-8bit. Tier-2 (7): OpenCode · Qwen Code · OpenHands · Kilo Code · GitHub Copilot · Factory Droid · Moonshot Kimi Code.

Also compatible with OpenAI-compatible clients that allow direct local endpoints via http://localhost:8000/v1 — LibreChat, Open WebUI, and more plug in with a single URL change.

→ Full 12-agent + 3-framework matrix (test cells + xfail reasons) → Codex CLI · Claude Code · OpenCode · Qwen Code · OpenHands · Hermes · Aider · Kilo Code · DeepSeek Harness · Copilot · Droid · Kimi Code

Latest Large-Model Benchmarks

Single-request (B=1) serving on a 256 GB M3 Ultra, with one 4-bit model resident. Each row is the median of three requests after server readiness, with the prefix cache cleared before every run. These are local serving measurements, not model quality scores. All three rows target an 8K prompt and 256-token decode; after chat templating the Qwen prompts contain 8,156 tokens and the GLM prompt contains 8,192.

The biggest 0.13.4 gain is Qwen3.8-27B's verified MTP path. On the same model, Mac, dependency versions, prompts, and cache-clear procedure:

¹ Flash-Next comprises a 125B language model, 51B n-gram embedding, and 4B MTP head. Its default autoregressive path was effectively flat between the two builds; separately qualified MTP and prompt-lookup modes remain workload-dependent.

Flash-Next prefill reaches 262 / 875 / 868 / 716 tok/s at 128 / 2K / 8K / 32K; median TTFT is 0.35 / 2.30 / 9.40 / 45.73s. Its 99 GB quantized weights make 192 GB the practical recommended tier; 128 GB is tight and was not physically tested. GLM-5.3-Flash reached 27.2 tok/s decode at 32K, but its TTFT was 118.34s and its 195.6 GB allocator peak leaves no safe system headroom on a 192 GB Mac. Use a 256 GB Mac for this measured 32K workload; 192 GB remains the catalog floor for shorter contexts. Qwen3.8-27B remains the 32 GB recommendation for the standard approximately 8K workload; its 32K automatic MTP path was not physically qualified on a 32 GB Mac.

→ Environment, exact methods, full context curves, and qualification notes

Choose Your Model

The installer and desktop app use the same RAM-tier recommendation catalog. Run rapid-mlx recipe to see its Smart and Fast picks for this Mac (--max-ram 32 simulates another tier; --json is machine-readable). If you want to shop the full catalog: rapid-mlx models lists every alias, rapid-mlx info <alias> shows the per-alias profile (parser, MoE / hybrid flags, KV codec eligibility, speculative-decoding gates).

This table is the same one the desktop app's picker reads, and the installer prints the matching line for your Mac — a CI test parses both files and fails if they drift apart. Measured rows use the standard ~8K prompt peak of the complete rapid-mlx serve process tree on an M2 Pro 32 GB Mac mini (the 32 GB+ row: M3 Ultra, 2026-08-18 — footprint is config-bound, speed reads lower on smaller chips).

Every Mac from 32 GB up gets the same pick, and that is the point: Qwen3.8-27B scores 52 on the Artificial Analysis Intelligence Index (2026-08-18) — GPT-5.6-class, the highest of any open-weights model we serve, ahead of the much larger 122B (33) and 35B (32) it replaces (the index scores the full-precision release; our 4-bit build's deltas are unmeasured — the standing caveat for every quantized pick here). On the measured 8K workload it prefills at 330.8 tok/s and decodes at 43.4 tok/s, with zero new swap. The verified 4-bit artifact automatically enables its MTP path when the selected cache and serving lane are compatible; --no-spec-decode remains the explicit opt-out. The 32 GB recommendation is based on the approximately 8K qualification (about 20 GB for the complete process tree). The 32K Studio run reached a 27.1 GB MLX allocator peak before non-MLX process and macOS memory; on a 32 GB Mac, reduce context/cache use or choose a larger-memory machine.

→ Full RAM tier map + serve flags per tier → Every alias, quant, and family (186 text + 10 image + 10 video + 44 audio aliases, 250 total) · interactive at models.rapidmlx.com

Alternative install methods

The two paths above cover most users — reach for these only if you already manage Python yourself.

brew install rapid-mlx

uv tool install rapid-mlx@latest

python3.12 -m pip install rapid-mlx

Command Reference

rapid-mlx --help                    # top-level command list
rapid-mlx <subcommand> --help       # per-subcommand flags

Covers chat, serve, share, agents (setup / test), bench, recipe, models, ls, pull, rm, alias, ps, info, connect, doctor, upgrade, telemetry, and launch.

→ Full CLI reference with every flag

Troubleshooting

Run the built-in self-check first:

rapid-mlx doctor

Top three things that go wrong:

• Much slower than expected. Qwen3.5 / 3.6 default to thinking-on — add --no-think to skip chain-of-thought. → Slow tok/s
• Out of memory. Model too big for your RAM — pick a smaller quant from Choose Your Model or the full tier map. → OOM guide
• Tool calls arriving as plain text. Auto-recovery handles most cases; if not, set --tool-call-parser explicitly for your model. → Tool-call recovery

→ All troubleshooting entries (OOM, empty responses, slow TTFT, port taken, shell completion, HF cache, and more)

See it in action

Community & Support

• Discord: Join the Rapid-MLX community for live help and local-AI discussion.
• Twitter / X: Follow @rapidmlx for releases, benchmarks, and project updates.
• Questions & builds: Ask or share in GitHub Discussions.
• Feedback & ideas: Report a bug, request a model, or propose a feature.
• Security: Send sensitive reports through a private advisory; see SECURITY.md.
• Contributing: Start with CONTRIBUTING.md. Explore compatible benchmark models with rapid-mlx benchmark catalog; runs stay local unless you explicitly share one in the internal beta.
• Show support: Star this repository to follow releases and help others discover the project.

Privacy: Anonymous telemetry is off by default and requires an explicit rapid-mlx telemetry enable. Prompts, completions, paths, IP addresses, and API keys are never collected. See what we do and don't collect.

Contributors

Every avatar here shipped something in rapid-mlx — model support, tool-call parsers, fixes, docs, and benchmark submissions. Thank you.

Star History

Acknowledgements

Rapid-MLX began as vLLM-MLX by Wayner Barrios, which is where this repository's history starts and where the engine's paged KV cache, prefix cache, and continuous batching were first built. It was renamed to Rapid-MLX in March 2026 and has been heavily modified since. Thank you.

It stands on Apple's MLX stack and the runtimes built around it:

• MLX — Apple's array framework for Apple Silicon
• mlx-lm — LLM inference, KV cache, quantization
• mlx-vlm — vision-language models
• mlx-audio — speech and audio models

Vendored third-party components and their licenses are listed in NOTICE; what the macOS app ships is enumerated in apps/rapid-mac/THIRD_PARTY.md.

License

Apache 2.0 — see LICENSE and NOTICE.