The article discusses the vAttention model for serving large language models (LLMs), focusing on its optimizations that enhance memory management. Unlike traditional models that require kernel trips for memory allocation, vAttention employs strategies to efficiently manage memory bandwidth during critical phases like prefill and decoding. It achieves an impressive memory allocation rate, outperforming conventional methods, thus ensuring better performance for LLM serving systems. The approach highlights the importance of avoiding latency caused by memory allocation by overlapping it with model execution, thereby addressing key challenges in fragmentation and responsiveness.
In contrast, vAttention needs to invoke CUDA's kernel driver while mapping a new physical page in a request's KV-cache.
With our optimizations, vAttention can effectively meet the requirements of both the prefill and decode phases in an LLM serving system.
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