MTPLX
SHA-256Run 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.
Smart Download
Download Download Version
v2.10.2 · 56.1 MB
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
Getting Started
Install the software
Open the downloaded dmg file, then drag the app to Applications
Download the DMG from mtplx.com/download and double-click to open
Follow the built-in wizard to check hardware, download a recommended model; the app configures everything automatically
Press the play button in the app to launch the local server and start chatting or making API calls
- Download the DMG from mtplx.com/download and double-click to open
- Follow the built-in wizard to check hardware, download a recommended model; the app configures everything automatically
- Press the play button in the app to launch the local server and start chatting or making API calls
SHA-256 checksum verified
Checksum extracted from GitHub official Release page
SHA256 Checksum
9156224c402c60bdd08bc82ee3a1567f1b9c50f58f4bc21130b1bcb264d0af60This checksum is extracted from the GitHub Release page. Verify file integrity after download.
All SHA-256 checksums on this platform are extracted from the project's official GitHub Release page, without any modification. You can independently verify them on the GitHub Releases page.
Open Source Transparency
View GitHub SourceUninstall 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.
Having issues? Check the FAQ below
5 FAQs
Similar Projects
ragflow
RAGFlow is a leading open-source RAG engine that fuses deep document understanding with agentic workflows, offering precise, citation-grounded answers. Unlike LangChain's DIY approach, RAGFlow provides a turnkey, enterprise-ready platform with a visual interface and template-based chunking, reducing hallucinations and setup complexity.
unsloth
Unsloth is the first desktop app to run and train AI models locally. It supports a wide range of models including Qwen3.8, Kimi K3, MiniMax-H3, Gemma 4, DeepSeek-V4, and FLUX. Unlike most local model tools that focus only on inference, Unsloth adds a full training stack: fine-tune LLMs, diffusion, TTS, and embedding models 2× faster with 70% less VRAM. It also supports LoRA, QLoRA, RL, GRPO, DPO, and FP8. The app includes built-in Agents & Tools integration (Claude Code, Codex, MCP), private search and RAG, image/video diffusion, and audio support. With hardware support for CPU, NVIDIA, AMD, Intel, macOS, and multi-GPU setups, plus remote access via Cloudflare, Unsloth is a comprehensive local AI studio. It can export to GGUF, NVFP4, FP8 and offers an OpenAI-compatible API for easy deployment.
ollama
Ollama lets you download, run, and manage large language models locally. One command, multiple platforms, endless possibilities.