Unverified60% confidenceFactExact time
与传统方法相比，PagedAttention使vLLM能够将批处理大小提升2-4倍，并实现接近100%的内存利用率
2
Sources
60%
Confidence
Medium-term (~90 days)
Relevance
7/2/2026
First Seen
Valid until: 9/30/2026
Sources
vLLM Deep Dive: How PagedAttention Enables High-Throughput LLM Inference
githubvllm-project6/6/2026
Related Claims
UnverifiedvLLM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80% 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78% similarUnverifiedvLLM通过PagedAttention技术将KV Cache的内存碎片率从60%以上压缩至接近零，在高并发场景下可将推理吞吐量提升数倍78% similarUnverifiedvLLM的PagedAttention将KV缓存切分为固定大小物理块（通常16个token），使显存利用率从不足40%提升至接近90%74% similar
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