The article discusses the challenge of using PagedAttention in Large Language Models (LLMs), noting that it deviates from traditional demand paging by requiring extensive changes to application code. The need to rewrite the attention kernel complicates performance optimization, as the conventional designs presume contiguous memory layouts. This highlights the complexities in effectively implementing attention mechanisms within LLM architectures. The discussion also points to broader implications for LLM serving systems and solutions like vAttention that strive to address fragmentation and optimize memory allocation to improve performance.
PagedAttention's approach diverges from conventional demand paging by demanding application code adjustments for physical memory allocation, complicating the implementation of the attention kernel.
The performance optimizations in the attention operator are crucial for the transformer architecture, indicating the need for innovations in LLM serving models.
#large-language-models #pagedattention #attention-mechanisms #performance-optimization #memory-management
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