Verified65% confidenceBenchmarkExact time
PagedAttention 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%.
3
Sources
65%
Confidence
Long-term
Relevance
7/10/2026
First Seen
Sources
LangChain Hands-On Guide: From LLM Calls to Agent Development
bilibili今天AI了吗7/8/2026
Related Claims
UnverifiedvLLM'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%.83% 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% 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% similarVerifiedPagedAttention improves KV Cache memory utilization from below 40% to close to 100%.74% 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.74% similar
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