Unverified50% confidenceBenchmarkExact time
vLLM can keep GPU memory waste below 4%.
1
Sources
50%
Confidence
Long-term
Relevance
8/7/2026
First Seen
Sources
vLLM架构深度解析：PagedAttention如何实现高吞吐LLM推理
Related Claims
UnverifiedvLLM的PagedAttention将KV Cache碎片化率从传统方案的60-80%降至不足4%，吞吐量提升可达24倍70% similarUnverifiedPagedAttention was proposed by the vLLM team in 2023; drawing on the virtual memory paging mechanism of operating systems, it reduced the KV Cache memory waste rate from over 60% to close to 4%.68% 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 0.68% similarUnverified4-bit quantization can reduce model size by approximately 75%.67% similarUnverifiedCompared to a pure LLM solution, a hybrid architecture can reduce token consumption by 40%–70%.67% similar
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