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MTPLX

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1.9k stars·AI Productivity·SHA-256 checksum verified

Run local LLMs on Apple Silicon around 2-3x faster. PowerShell: leverages built-in MTP heads for exact speculative decoding, no separate draft model, same output distribution.

Native Mac tool for 2-3x faster local LLMs on Apple Silicon using built-in multitoken prediction, without an external drafter.

Core Features

  • Leverages model's native MTP heads for speculative decoding (Qwen 3.5/3.6/3.8 etc.)
  • Exact rejection sampling with residual correction ensures same output distribution as normal decoding
  • No external draft model, saving precious RAM on Macs
  • Measured 1.6x faster on M4 Mac mini and 2.24x on M5 Max
  • Auto-detects your hardware, recommends fitting models, and tunes optimal decoding depth

What It Can't Do

  • •Only Apple Silicon (M1 or newer) with macOS 14+ is supported; Intel Macs will not work.
  • •Sufficient unified memory is required: at least 16 GB for 4B/9B models, 32 GB+ for 27B models recommended.
  • •Not every model has native MTP heads — only Qwen 3.5/3.6/3.8 and Gemma 4 show clear benefits. Attaching a separate MTP sidecar to an arbitrary MLX trunk is not supported.
  • •Speedup depends on chip, memory bandwidth, and thermals; run auto-tune to find whether MTP depth helps on your specific Mac.

Use Cases

  • Run coding/chat assistants (Qwen, Gemma) locally on your Mac without cloud dependency
  • Serve local OpenAI/Anthropic-compatible API for clients like Claude Code, Cline, Open WebUI
  • Combine LLM serving with MLX embedding/reranker in one daemon for RAG pipelines

MTPLX is a native macOS app and CLI for running local language models on Apple Silicon with multi-token prediction. Modern LLMs such as Qwen 3.5/3.6/3.8 include MTP heads, but almost no runtime uses them. MTPLX activates these heads: the model drafts several future tokens, verifies the block in one batched pass, and commits tokens using exact rejection sampling with residual correction. The result is identical output distribution to normal decoding, but measured 1.6-2.24x faster on real hardware. No separate draft model eats your RAM, and no greedy approximation changes the sampling quality. The app auto-detects your chip, recommends a fitting model, downloads it, and auto-tunes the optimal decoding depth. It includes a built-in chat UI, an OpenAI/Anthropic compatible API server, embedded embedding/reranking endpoints, and Forge, a tool to convert any Hugging Face model into an MTP-optimized artifact.

Tags

Apple SiliconLLMMLXSpeculative DecodingMTPQwenLocal AI

Getting Started

1

Download installer

Click the button above to download the installer for your system

2

Install the software

Open the downloaded dmg file, then drag the app to Applications

3

Download the DMG from mtplx.com/download and double-click to open

4

Follow the built-in wizard to check hardware, download a recommended model; the app configures everything automatically

5

Press the play button in the app to launch the local server and start chatting or making API calls

Install Guide
  1. Download the DMG from mtplx.com/download and double-click to open
  2. Follow the built-in wizard to check hardware, download a recommended model; the app configures everything automatically
  3. Press the play button in the app to launch the local server and start chatting or making API calls
File Integrity

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SHA256 Checksum

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Open Source Transparency

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Environment Guide

Uninstall Info

To uninstall, drag the MTPLX app from Applications to the Trash. If you installed the CLI, run 'brew uninstall mtplx' or 'pip uninstall mtplx'. For full cleanup, delete the ~/.mtplx cache directory if no longer needed.

No Extra Dependencies

Ready to use after download. No additional runtime required.

Project Info
LicenseApache-2.0
Last Updated2026-10-18 16:01:25
GitHub RepositoryOfficial Website

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