Unverified50% confidenceFactExact time
Traditional inference frameworks pre-allocate contiguous KV Cache memory blocks for each request, resulting in an average waste rate as high as 60–80%.
1
Sources
50%
Confidence
Long-term
Relevance
7/22/2026
First Seen
Sources
Related Claims
Unverified传统LLM推理中KV Cache的显存浪费率可达60-80%83% similarUnverified传统推理框架中每个请求的 KV Cache 需预分配等长连续显存，显存利用率通常仅有 20%-40%，PagedAttention 可将其提升到接近 100%83% similarUnverified传统KV Cache分配方案因按最大序列长度预分配连续显存块，导致平均显存利用率仅20%-40%75% similarUnverified推理时的KV缓存、激活值缓冲区和中间计算状态会额外占用30%-50%显存68% similarUnverifiedAccording to empirical data, approximately 50–70% of production requests can be completed by local models at acceptable quality, hybrid routing can typically reduce total inference costs by 40–60%, and compress average response latency to less than one-third of a pure API solution.64% similar
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