The article discusses the advancements in serving large language models (LLMs), focusing on the limitations of the PagedAttention model, including its need for a rewritten attention kernel and associated performance overhead. It introduces vAttention as a solution that leverages low-level CUDA support to improve efficiency by reducing memory fragmentation and allocating physical memory dynamically. The evaluation includes comparisons between vLLM and FlashAttention under long-context scenarios, highlighting vAttention's advantages in decode performance, showcasing its optimized architecture for serving LLMs effectively.
To evaluate decode performance, we focus on long-context scenarios (16K) because the latency of attention kernel becomes significant only for long contexts.
We evaluate the following configurations: vLLM: We use vLLM v0.2.7 as the primary baseline. vLLM pioneered PagedAttention and uses a custom paged kernel for decodes.
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