Key Value Cache Compression

Key-value (KV) cache compression aims to reduce the substantial memory footprint of large language models (LLMs) during inference, a critical bottleneck hindering the deployment of long-context generation. Current research focuses on developing efficient compression techniques, including low-rank approximations, uncertainty-aware compression, and variable compression rates across attention heads, often integrated within existing transformer architectures. These advancements enable significant memory savings and throughput improvements, paving the way for more efficient and scalable LLM deployment in resource-constrained environments.