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youssofal/mtplx v2.12.2 — Local inference tool for Apple Silicon

youssofal/mtplx is a tool that runs local models on Apple Silicon with MTP speculative decoding, and its latest release is v2.12.2.

Repositoryyoussofal/mtplx ↗

youssofal/mtplx is a tool that runs local models on Apple Silicon with MTP speculative decoding, and its latest release is v2.12.2.

GitHub description: The fastest way to run Qwen 3.8 Flash Next, Qwen 3.8 27B and Ternary Bonsai 2 27B on a Mac: 125 tok/s in OpenCode on an M5 Max, and a 27B model on 16 GB Macs. Native MTP speculative decoding on Apple Silicon, exact at any temperature. OpenAI and Anthropic compatible local server.

stars
★ 2.6K
Forks
206
Language
Python
License
Apache-2.0
Latest release
v2.12.2
Read original source ↗View on GitHub ↗

youssofal/mtplx is a Python app and command-line tool that runs local models on Apple Silicon. The latest release is v2.12.2. The provided records do not show what was added or changed in this version. The tool performs speculative decoding using the multi-token prediction (MTP) head built into the model. Its targets are Qwen 3.8 Flash Next, Qwen 3.8 27B, and Ternary Bonsai 2 27B.

Speculative decoding is a way to speed up generation by validating draft tokens that the model produced first, all at once. The README states that it uses rejection sampling with residual correction for draft validation, and that the output is accurate at any temperature. The repository description cites 125 tok/s when using OpenCode on an M5 Max, and says a 27B model can run on a Mac with 16 GB of memory. Measurement conditions and record links are published for the figures. It also provides an OpenAI- and Anthropic-compatible local server, and the license is Apache-2.0.

The performance figures are results from the specific environment the repository presents; there is no ground for assuming they apply as-is to other hardware or usage patterns. The README also notes that equivalence checks for image requests have not been repeated yet. macOS 14 or later is required. Therefore the published performance claims should be read as records under those conditions rather than general comparison results, and the accuracy of image requests needs further confirmation.