Abstract
The development of large language models (LLMs) has significantly expanded model sizes, resulting in substantial GPU memory requirements during inference. The key and value storage of the attention map in the KV (key-value) cache accounts for more than 80% of this memory consumption. Nowadays, most existing KV cache compression methods focus on intra-layer compression within a single Transformer l