Unverified50% confidenceFactExact time
vLLM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
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50%
Confidence
Long-term
Relevance
7/16/2026
First Seen
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Related Claims
UnverifiedvLLM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84% 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技术借鉴操作系统虚拟内存的分页思想，将KV Cache切分为固定大小的物理块动态分配79% similarUnverifiedCompared to traditional methods, PagedAttention enables vLLM to increase batch size by 2–4 times and achieve close to 100% memory utilization.78% 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.78% similar
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