Unverified50% confidenceFactExact time
vLLM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
1
Sources
50%
Confidence
Long-term
Relevance
7/20/2026
First Seen
Sources
Related Claims
UnverifiedvLLM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85% 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%.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.82% similarUnverifiedvLLM引入PagedAttention技术将KV Cache分割为固定大小物理块按需分配，而llama.cpp采用预分配策略在模型加载时一次性保留完整KV Cache空间81% similarUnverifiedPagedAttention 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%.81% similar
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