Applying virtual memory and paging techniques to GPU workloads can be effective for managing the KV cache in LLM serving, as these workloads require dynamic memory allocation.
vLLM optimizes memory management with techniques like an all-or-nothing swap-out policy, leveraging application-specific semantics to improve performance in LLMs.
#large-language-models #gpu-memory-management #virtual-memory #paging-techniques #model-optimization
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