English(EN) A hot-expert cache made a 181 GiB model faster on one 96 GiB GPU

优化GLM-5.3-Flash以适应单GPU，速度提升1.5倍

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-21 19:22

一篇技术博文详细介绍了在单个96 GiB GPU上运行大型GLM-5.3-Flash模型的优化方法，实现了高达每秒16.5个token的生成速度。作者实现了一个“热点专家缓存”，将频繁使用的模型组件保留在GPU上，显著加快了token生成和提示读取的速度。这种方法结合了用于高效数据加载的专用Triton内核以及用于长提示的专家分组技术，将写入性能提高了高达1.5倍，提示读取性能提高了2.9倍，同时保持了输出质量。 AI

影响展示了在有限硬件上高效部署LLM的技术，可能使更多用户能够访问大型模型。

排序理由技术博文，详细介绍了在特定硬件上运行特定LLM的优化方法，并非新模型发布或重大行业事件。

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Signal score: 0 / 100; Composite score across the factors below. Higher = stronger signal that this story matters right now.
Newsworthiness bucket: Tool; 技术博文，详细介绍了在特定硬件上运行特定LLM的优化方法，并非新模型发布或重大行业事件。
Source corroboration: Single-source cluster; Only one publisher covered this so far. Single-source stories can still rank when the publisher is high-authority, but they lack cross-source corroboration.
Topics: infra, model release; Editorial topic classification. Feeds into how the story surfaces on /topic/<slug> hub pages and into the per-entity coverage mix.
AI-industry relevance: High; Clearly on-topic for AI-industry coverage.
Story freshness: 2 days old; Coverage has settled into its steady-state source set.

完整方法见我们的编辑标准。

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dev.to — LLM tag TIER_1 English(EN) · Rahul Kumar · 2026-10-21 19:22

A hot-expert cache made a 181 GiB model faster on one 96 GiB GPU

<p>The build of GLM-5.3-Flash I run is 181 GiB, and my GPU has 96 GiB. It still writes up to 16.5 tokens a second on one desktop. Offloading more on its own made it slower; keeping each layer's hot experts on the card made it faster.</p> <p>One of the eight bugs on the way gave n…

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A hot-expert cache made a 181 GiB model faster on one 96 GiB GPU

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