Unverified60% confidenceFactExact time
vLLM'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%.
2
Sources
60%
Confidence
Long-term
Relevance
7/19/2026
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% similarVerifiedPagedAttention 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%.83% similarUnverifiedPagedAttention cuts the KV Cache into fixed-size blocks, allocated on demand and stored non-contiguously, raising VRAM utilization from the traditional 20%–40% to over 90%.78% similarUnverifiedvLLM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77% similarUnverifiedBy default, vLLM plans 90% of GPU VRAM into the budget via the --gpu-memory-utilization parameter, and only the remainder is used as KV cache.76% similar
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