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.
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.
| Model | Metric | Rapid-MLX | oMLX | LM Studio |
|---|
| Qwen3.5-9B | Decode (tok/s) | 115.5 | 77.9 | 81.0 |
| Qwen3.5-9B | 4 streams (tok/s) | 248.2 | 224.6 | 230.0 |
| Qwen3.8-27B | Decode (tok/s) | 37.9 | 25.3 | 26.0 |
| Qwen3.8-27B | 4 streams (tok/s) | 82.8 | 75.3 | 69.7 |
| Qwen3.6-35B | Decode (tok/s) | 173.4 | 145.8 | 124.5 |
| Qwen3.6-35B | 4 streams (tok/s) | 293.9 | 281.9 | 265.6 |
| Qwen3.6-35B | 2K-prompt first token (s, lower is better) | 0.55 | 1.14 | 1.49 |
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.
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.
| Rapid-MLX | oMLX | Ollama | LM Studio | mlx-lm (mlx_lm.server) |
|---|
| License | Apache-2.0 | Apache-2.0 | MIT | Closed-source app; its MLX engine and lms CLI are MIT | MIT |
| Inference engine on a Mac | MLX | MLX | GGML/GGUF engine, plus an MLX engine for safetensors models | llama.cpp (GGUF) and MLX engines | MLX |
| Local API | OpenAI (/v1/chat/completions, /v1/responses) and Anthropic Messages (/v1/messages) | OpenAI and Anthropic Messages | Ollama API, OpenAI-compatible, Anthropic-compatible (subset) | OpenAI-compatible, Anthropic-compatible, LM Studio REST API | OpenAI-style chat API |
| GUI | Rapid-MLX Desktop (macOS app) | macOS menu-bar app and web admin panel | Desktop app (macOS, Windows) | Desktop app | None (CLI / Python) |
| Concurrent requests | Continuous batching | Continuous batching (mlx-lm BatchGenerator) | Parallel requests per model (OLLAMA_NUM_PARALLEL, default 1) | Continuous batching (llama.cpp engine; MLX engine since 0.4.2) | Continuous batching (BatchGenerator); one request at a time with a quantized KV cache |
| Prompt / KV cache | Radix prefix cache in memory (with state snapshots for hybrid models), saved to disk on shutdown and restored at startup; quantized KV cache | Tiered KV cache: in-memory hot tier plus SSD cold tier, reused across restarts | Reuses the previous request's prompt KV cache; KV cache quantization (OLLAMA_KV_CACHE_TYPE) | — | In-memory prompt cache; quantized KV cache (--kv-bits) |
| Tool-call parsing | 27 parser modules, including an auto-detect fallback () |
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.
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
lfm2.5-2.6b-4bit at 8 GB or above, with lfm2.5-1b-4bit for smaller or
unknown RAM. 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.
This is the beginning of the README. Read the rest at the original. Read the full README ↗
