MTPLX
What it is
MTPLX is a native Apple Silicon framework and application for running local LLMs using Multi-Token Prediction (MTP). It implements the Leviathan and Chen rejection sampling theorem with residual correction to achieve significantly faster inference speeds without requiring a separate draft model.
The problem it solves
Slow inference speeds and high memory requirements for local LLM deployment on consumer hardware.
What you could use it for
- Running local LLMs on Apple Silicon with high-speed inference
- Developing and testing MTP-enabled models via 'Forge'
- Building AI agents or applications against a local OpenAI-compatible API
- Benchmarking model performance using the built-in AIME runner
Read the source ↗Back to the library →
Summary written by an AI model from the source; check the source before you rely on it.