DeepSeek、Kimi都在拼命砍缓存，英伟达开始让缓存跨模型复用

DeepSeek、Kimi都在拼命砍缓存，英伟达开始让缓存跨模型复用

2026-08-10 12:15

ChainThink 消息，8 月 10 日，英伟达最新论文提出跨模型复用 KV Cache 的方法，尝试解决缓存通常只能在同一模型内部复用的问题。

KV Cache 是模型读取上下文后的中间计算结果，长上下文会显著占用显存和带宽。

此前 DeepSeek-V2 通过 MLA 相比 DeepSeek 67B 将 KV Cache 减少 93.3%，Kimi Linear 相比全 MLA 最多减少 75%。

论文称，在同一家族、KV 结构匹配的不同大小模型之间，缓存存在明显线性关系。

团队使用 500 段、每段 1024 Token 的文本校准后，可拟合映射，将一个模型算好的 KV Cache 转给另一个模型继续使用。

在 Qwen3-14B 切换至 32B 的测试中，32K 上下文重新计算约需 7 秒，转换缓存约需 0.28 秒，速度提升约 25 倍。

若该方法成熟，模型路由中长上下文 prefill 可更多由小模型完成，大模型在需要时直接接续生成。

DeepSeek、Kimi都在拼命砍缓存，英伟达开始让缓存跨模型复用

Disclaimer: Contains third-party opinions, does not constitute financial advice

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