KV Cache

KV cache, a crucial component in large language model (LLM) inference, aims to accelerate processing by storing previously computed key-value pairs, thereby reducing computational complexity. Current research focuses on optimizing KV cache efficiency through various compression techniques, including quantization, low-rank projection, and selective token eviction, often guided by attention weight analysis and adaptive budget allocation strategies. These advancements are vital for enabling efficient inference of LLMs with expanding context windows, impacting both the scalability of LLM applications and the resource requirements for deploying these powerful models.