Unverified50% confidenceBenchmarkExact time
vLLM的PagedAttention将KV Cache碎片化率从传统方案的60-80%降至不足4%，吞吐量提升可达24倍
1
Sources
50%
Confidence
Long-term
Relevance
7/20/2026
First Seen
Sources
Related Claims
UnverifiedvLLM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85% similarUnverifiedvLLM compresses the KV Cache memory fragmentation rate from above 60% to near zero through PagedAttention technology, and can increase inference throughput by several times in high-concurrency scenarios.79% similarUnverifiedvLLM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%75% similarUnverifiedPagedAttention was proposed by the vLLM team in 2023; drawing on the virtual memory paging mechanism of operating systems, it reduced the KV Cache memory waste rate from over 60% to close to 4%.73% similarUnverifiedvLLM's PagedAttention draws on the operating system concept of virtual memory paging to manage KV caches, reducing GPU memory fragmentation overhead from approximately 30% to near 0.73% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/566984API
curl https://kongchang.com/api/v1/knowledge/claims/566984MCP
get_claim(id=566984)