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MTPLX

github.com

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.

데스크톱오픈소스

github.com 방문외부 사이트GitHub2,691 스타외부 사이트

설치·연결

공식 MCP Registry, Homebrew, npm, PyPI, crates.io에서 저장소나 도메인으로 맞춰 하루 한 번 가져옵니다.

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비슷한 프로덕트

설명이 비슷한 순

  1. Running 104GB Qwen3.8-Flash-Next on 48GB Mac with at ~12 tok/s

    Run a 105 GB AI model on a Mac that can't hold it. Qwen3.8-Flash-Next 4-bit, streamed from SSD on 16–64 GB Apple Silicon Macs. Fully native Swift, MLX and Metal. Offline, no Python. CLI, Swift library, and Ollama/OpenAI/Anthropic APIs. Works with Claude Code and Codex.

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  2. Bonsai 1.7B ternary model at 442T/s on M4 Max

    A drop-in optimized inference build of Bonsai 1.7B for Apple Silicon. ~42% faster decode (tg128) and ~9% faster prefill (pp512) on M-series GPUs. All Metal kernels written and tuned by ata, our autonomous engineering agent.

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  3. Samosa Chat

    Run Qwen3.6-35B-A3B Locally on a 16 GB Mac

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  4. Qwen3.8-Flash-Next

    The open-weight preview of Qwen4

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  5. Run an 80B Qwen in 4.3 GB of RAM on a Mac, and a 35B on an iPhone

    Swiftlet is a Swift and Metal runtime that runs large Qwen Mixture-of-Experts models locally on Apple devices by streaming expert weights from storage, enabling 35B and 80B models to run with low RAM, including on iPhone.

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