English(EN) Why the KV cache, not the weights, decides how many users an LLM server can hold

KV缓存，而非权重，限制了LLM服务器的用户容量

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-25 11:37

KV缓存，而不是模型权重，是决定LLM服务器支持多少用户的主要因素。虽然LLaMA-2 7B模型的模型权重占用13.5 GB，但一个4096个token序列的KV缓存可能消耗0.5 MiB，并且随着上下文长度和用户数量的增加而迅速增加。多查询注意力、分组查询注意力、缓存量化、滑动窗口和PagedAttention等技术被用来减少KV缓存的内存占用，从而提高服务器吞吐量。 AI

影响优化KV缓存使用对于扩展LLM推理和降低服务成本至关重要。

排序理由对LLM推理优化技术的技术解释。[lever_c_demoted from research: ic=1 ai=1.0]

在 dev.to — LLM tag 阅读 →

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Signal score: 23 / 100; Composite score across the factors below. Higher = stronger signal that this story matters right now.
Newsworthiness bucket: Tool; 对LLM推理优化技术的技术解释。[lever_c_demoted from research: ic=1 ai=1.0]
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dev.to — LLM tag TIER_1 English(EN) · Andika Rachman · 2026-10-25 11:37

为什么是KV缓存，而不是权重，决定了LLM服务器能容纳多少用户

<p>A 7B model in fp16 takes 13.5 GB of GPU memory for its weights. On an 80 GB card that leaves about 66 GB free, which sounds like plenty. It runs out at around 30 users with 4,096-token contexts, and the weights aren't what fills it. The KV cache is.</p> <p>This post works thro…

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为什么是KV缓存，而不是权重，决定了LLM服务器能容纳多少用户

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