KV Cache Compression

KV cache compression aims to reduce the substantial memory footprint of key-value caches used in large language models (LLMs) during inference, thereby improving efficiency and enabling longer context windows. Current research focuses on techniques like low-rank matrix decomposition, adaptive merging of key-value states, and selective token dropping based on attention patterns, often incorporating algorithms such as FlashAttention for speed improvements. These advancements are crucial for deploying LLMs on resource-constrained hardware and scaling up applications requiring long-context processing, such as complex question answering and code generation.