Unverified50% confidenceBenchmarkExact time
PagedAttention将KV Cache的内存利用率从不足40%提升至接近100%
1
Sources
50%
Confidence
Long-term
Relevance
7/11/2026
First Seen
Sources
vLLM推理框架详解:吞吐优化核心原理与面试攻略
bilibiliAI大模型升升6/9/2026
Related Claims
UnverifiedvLLM通过PagedAttention技术将KV Cache的内存碎片率从60%以上压缩至接近零,在高并发场景下可将推理吞吐量提升数倍77% similarUnverifiedPagedAttention 使显存利用率提升至接近 100%,实现同等硬件下并发吞吐量提升 2-4 倍75% similarUnverifiedPagedAttention将KV Cache拆分为固定大小的物理块通过块表映射,GPU利用率可从低于40%提升至90%以上74% similarUnverified传统LLM推理中KV Cache的内存管理导致60%-80%的显存被浪费73% similarUnverified传统LLM推理中,KV Cache的内存管理导致60%-80%的GPU显存被浪费73% similar
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