Verified65% confidenceBenchmarkExact time
PagedAttention splits the KV Cache into fixed-size physical blocks mapped via a block table, and can improve GPU utilization from below 40% to above 90%.
3
Sources
65%
Confidence
Long-term
Relevance
7/10/2026
First Seen
Sources
LangChain Hands-On Guide: From LLM Calls to Agent Development
bilibili今天AI了吗7/8/2026
Related Claims
UnverifiedvLLM的PagedAttention将KV缓存切分为固定大小物理块（通常16个token），使显存利用率从不足40%提升至接近90%83% similarUnverifiedvLLM的PagedAttention将KV Cache划分为固定大小物理块，通过块表实现地址映射，将碎片化率从60-80%降至不足4%，吞吐量提升可达24倍81% similarVerifiedPagedAttention improves KV Cache memory utilization from below 40% to close to 100%.74% similarUnverifiedvLLM引入PagedAttention技术，将KV Cache分割为固定大小物理块按需动态分配，多并发场景下显存利用率可比朴素实现提升3倍以上71% similarVerifiedPagedAttention divides the KV cache into fixed-size pages allocated on demand, and can improve VRAM utilization by 2–4×.71% similar
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