The paper "Effectively Compress KV Heads for LLM" addresses the challenge of reducing the memory footprint of Key-Value (KV) caches in large language models (LLMs), which is a significant bottleneck in LLM deployment. The authors propose a novel approach based on the low-rank property of KV caches, which allows for efficient compression of KV heads while maintaining comparable performance to the original models. They optimize the transformation from Multi-Head Attention (MHA) to Grouped-Query Attention (GQA) to minimize compression error and introduce specialized strategies for handling Rotary Position Embeddings (RoPE). The method can compress up to three-quarters of KV heads, significantly reducing memory usage and improving inference speed. Extensive experiments on various LLM models demonstrate the effectiveness and efficiency of the proposed approach, showing that it can achieve high accuracy with reduced KV cache sizes, making it a promising solution for resource-constrained environments.The paper "Effectively Compress KV Heads for LLM" addresses the challenge of reducing the memory footprint of Key-Value (KV) caches in large language models (LLMs), which is a significant bottleneck in LLM deployment. The authors propose a novel approach based on the low-rank property of KV caches, which allows for efficient compression of KV heads while maintaining comparable performance to the original models. They optimize the transformation from Multi-Head Attention (MHA) to Grouped-Query Attention (GQA) to minimize compression error and introduce specialized strategies for handling Rotary Position Embeddings (RoPE). The method can compress up to three-quarters of KV heads, significantly reducing memory usage and improving inference speed. Extensive experiments on various LLM models demonstrate the effectiveness and efficiency of the proposed approach, showing that it can achieve high accuracy with reduced KV cache sizes, making it a promising solution for resource-constrained environments.