The research introduces Cross-Layer Attention (CLA) as a novel architectural modification designed to mitigate the substantial memory overhead associated with the Key-Value (KV) cache during the decoding phase of large language models (LLMs). Unlike established methods such as Multi-Query Attention (MQA) and Grouped-Query Attention (GQA), which reduce the cache size by sharing heads within a layer, CLA achieves memory savings by sharing key and value activations across adjacent layers. Extensive experiments conducted on 1B- and 3B-parameter models show that combining CLA with MQA achieves a 2× reduction in KV cache size with minimal impact on accuracy metrics like perplexity. The authors argue that this new technique provides a significant improvement on the accuracy/memory Pareto frontier compared to existing transformer designs. By making LLM serving more memory-efficient, CLA promises to enable practitioners to use models supporting both longer sequence lengths and larger batch sizes