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Stars gained: From May 8, 2026 to Nov 12, 2026, each from zeroStars gainedFrom May 8, 2026 to Nov 12, 2026, each from zero01K2KMay 8Nov 12, 2026youssofal/MTPLXraullenchai/Rapid-MLX
Stars: The star count at the end of each dayStarsThe star count at the end of each day02K4KMay 8Nov 12, 2026youssofal/MTPLXraullenchai/Rapid-MLX

Facts

Fact youssofal/MTPLX

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.

raullenchai/Rapid-MLX

Rapid-MLX is an open-source (Apache 2.0) OpenAI- and Anthropic-compatible LLM inference server for Apple Silicon, built on MLX, focused on reliable tool calling for coding agents. Up to 4× faster than Apple's MLX (mlx-lm) on the same weights.

Stars 2,7014,134
Net stars, last 30 days +131+171
Largest star day +87 on May 16, 2026+79 on May 8, 2026
Milestone reached in the record 1,000 on Jul 13, 2026None
Forks 212442
Language PythonPython
License APACHE-2.0Other license
Created May 2, 2026Feb 25, 2026
Last push Nov 7, 2026Nov 12, 2026
Archived NoNo
Health facts: Maintenance 2 of 4 checks (2 without data)2 of 4 checks (2 without data)
Health facts: Releases 2 of 3 checks (1 without data)2 of 3 checks (1 without data)
Health facts: Security practice 0 of 1 check (1 without data)0 of 1 check (1 without data)
Health facts: Community files 1 of 3 checks (2 without data)1 of 3 checks (2 without data)
Commits, last 13 weeks No dataNo data
Weeks with commits, last 52 No dataNo data
In the Repository Index since Nov 11, 2026Nov 10, 2026

Topics that all of them have on the Repository Host: apple-silicon, local-ai, mlx, openai-compatible, speculative-decoding, claude-code, llm-inference, local-llm, macos. The facts come from the Repository Host and from the Star History that AwesomeRepos records. A shared topic does not mean that one repository can replace another. Each Health Facts row is one dimension alone. AwesomeRepos does not add them into one score. How each check is defined

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