Unverified50% confidenceBenchmarkExact time
vLLM通过PagedAttention技术使同等硬件下可并发服务用户数提升2-4倍，吞吐量提升最高24倍
1
Sources
50%
Confidence
Long-term
Relevance
7/16/2026
First Seen
Sources
Related Claims
UnverifiedCompared to traditional methods, PagedAttention enables vLLM to increase batch size by 2–4 times and achieve close to 100% memory utilization.80% similarUnverifiedPagedAttention 使显存利用率提升至接近 100%，实现同等硬件下并发吞吐量提升 2-4 倍73% 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71% 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 0.64% similarUnverifiedThe New API small-scale operation configuration (2 CPU cores, 4 GB RAM, 40 GB hard drive with MySQL and Redis) can handle approximately 30 to 100 concurrent users and tens to hundreds of requests per minute64% similar
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