Severe internal fragmentation of GPU memory caused by static allocation strategies of the KV cache in LLM model services affects performance and limits the scalability of high-demand inferences
With vLLM's PagedAttention, this inefficiency is mitigated thanks to its dynamic memory allocation that optimizes GPU space usage and reduces internal fragmentation by dividing memory into adaptively managed pages
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