AI BriefingPagedAttention: Virtual Memory for KV Cache
Key point
PagedAttention applies virtual memory concepts to significantly reduce KV cache memory waste during LLM inference.
Details
In LLM inference systems, the KV cache grows linearly with sequence length, becoming the largest consumer of GPU memory. Traditional methods use contiguous memory allocation, pre-allocating the maximum context length for each request, which leads to internal fragmentation (wasted memory even when actual usage is low) and external fragmentation (fragmented free space after requests complete).
PagedAttention solves these issues by applying the operating system's virtual memory concept to KV cache management. By dividing memory into fixed-size blocks and allocating them only when needed, it significantly improves KV cache utilization rates, which were reported to be 20–40% in real-world workloads in the vLLM paper. This achieves efficiency gains that increase the number of users served per GPU by 2–4x.
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