Unverified50% confidenceFactExact time
PagedAttention is a memory management mechanism introduced by the vLLM inference engine that splits the KV cache into fixed-size blocks allocated on demand, raising memory utilization from about 60% to over 90% and supporting multiple requests sharing base weights.
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50%
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Long-term
Relevance
10/16/2026
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Related Claims
VerifiedPaged Attention (PagedAttention) is the core technology of vLLM, managing KV cache in blocks, while Flash Attention optimizes the memory access pattern of attention computation.79% similarUnverifiedvLLM and SGLang are service-oriented inference frameworks that support technologies such as Continuous Batching and PagedAttention to maximize concurrent throughput.73% similarUnverifiedvLLM introduced PagedAttention as an optimization technique72% similarUnverifiedvLLM is a Python framework designed for high-throughput inference scenarios, using PagedAttention technology to improve GPU memory utilization and concurrent processing capability.71% similar
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