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
This claim has expired. It is kept for the record and no longer describes the current state.
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 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%.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's PagedAttention splits the KV cache into fixed-size physical blocks (typically 16 tokens), improving GPU memory utilization from below 40% to close to 90%.78% similar
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