Expired50% confidenceFactExact time
vLLM 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.
1
Sources
50%
Confidence
Medium-term (~90 days)
Relevance
7/13/2026
First Seen
Valid until: 10/11/2026(expired)
Sources
AI课程怎么选？避坑与进阶实战指南
redditr/learnmachinelearning7/12/2026
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82% 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 0.81% similarUnverifiedPagedAttention 由 vLLM 团队于 2023 年提出，借鉴操作系统虚拟内存分页机制，将 KV Cache 内存浪费率从超过 60% 降低至接近 4%80% similarUnverifiedvLLM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%78% similar
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