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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.
DesktopOpen source
Install and connect
From the official MCP Registry, Homebrew, npm, PyPI, and crates.io. Matched by repository or domain once a day.
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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.
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.
Samosa Chat
Run Qwen3.6-35B-A3B Locally on a 16 GB Mac
Qwen3.8-Flash-Next
The open-weight preview of Qwen4
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.