expert-streaming

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Running 95.5 GiB Qwen3.8-Flash-Next at 41–52 tok/s on a 64GB Mac (1.76x faster than llama.cpp): Slipstream release, 130k context scaling, + Swift variant

Reddit r/LocalLLaMA ↗ · 2026-10-01

作者发布 Slipstream——一款针对 Apple Silicon 的 C++ Metal 推理引擎,支持原生 SSD 专家流式加载与投机草稿解码,可在 64GB Mac 上以 41–52 tok/s 运行 95.5 GiB 的 Qwen3.8-Flash-Next 模型,比 llama.cpp 快 1.76 倍,且 130k 长上下文下速度不衰减。

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#expert-streaming

Qwen3.8-Flash-Next (95.5 GiB) on a 64GB Mac at ~27 tok/s, checkpoint + fork

Reddit r/LocalLLaMA ↗ · 2026-09-18

The author optimized the Qwen3.8-Flash-Next model to run on a 64GB Mac using expert streaming and other techniques, achieving ~27 tok/s by publishing a checkpoint and a llama.cpp fork.

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#expert-streaming

Faster than Light in Air: 8-22 tg/s Qwen3.8-Flash-Next (Q4/Q4ish) on a 32GB M4 MacBook Air

Reddit r/LocalLLaMA ↗ · 2026-09-10

Cherenkov is a new inference engine for Apple Silicon that enables efficient memory-constrained inference of large AI models like Qwen3.8-Flash-Next using predictive expert streaming and mixed-precision execution.

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#expert-streaming

@ErickSky: Forget about vLLM, llama.cpp, and expensive GPUs. [colibri] This runs GLM-5.2 (744B MoE) on ~25 GB of RAM with pure C a…

X AI KOLs Timeline ↗ · 2026-07-10 Cached

colibri is a pure C inference tool that runs the GLM-5.2 744B MoE model on ~25 GB RAM by streaming experts from disk, eliminating the need for expensive GPUs.

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