OSpark
KV Cache：让大模型生成又快又省算力的秘密

KV Cache：让大模型生成又快又省算力的秘密

6/9/2026LeoLeo

KV Cache（键值缓存）是在自回归生成时把历史 token 的 K/V 缓存起来，避免重复计算，显著提速但增加显存占用。

大模型为什么会越聊越快？🔥 KV Cache（键值缓存）把已生成 token 的 K/V 存起来，下一步只为新 token 计算 Q/K/V，旧的 K/V 直接复用。🧠⚡ 直观比喻：像写小说，把已写好的章节放到一旁参考，不用每次都重抄一遍。📚✍️ 好处很明显：生成速度大幅提升、重复计算减少、服务端吞吐量上升。🚀 代价也坦白：显存占用变大，尤其是长上下文时（KV Cache 随 seq_len 线性增长）。💾🔥 常见工程优化：量化 KV、分块/分页缓存、局部注意力、前缀共享等，都是为了解决显存和访问瓶颈。🛠️🔍 想看一个 2 层 Transformer 的示意伪代码或 PyTorch 风格示例吗？在评论写“代码”并把这篇分享给正在做 LLM 部署的朋友！🔁💬

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