Key, Value, Compress: A Systematic Exploration of KV Cache Compression Techniques
This work addresses efficiency challenges for developers and researchers using LLMs in long-context scenarios, but it is incremental as it systematically explores existing compression techniques.
The paper tackled the computational inefficiency of attention in large language models as context length grows by analyzing various Key-Value cache compression strategies, evaluating their impact on performance and inference latency to provide insights for more efficient implementations.
Large language models (LLMs) have demonstrated exceptional capabilities in generating text, images, and video content. However, as context length grows, the computational cost of attention increases quadratically with the number of tokens, presenting significant efficiency challenges. This paper presents an analysis of various Key-Value (KV) cache compression strategies, offering a comprehensive taxonomy that categorizes these methods by their underlying principles and implementation techniques. Furthermore, we evaluate their impact on performance and inference latency, providing critical insights into their effectiveness. Our findings highlight the trade-offs involved in KV cache compression and its influence on handling long-context scenarios, paving the way for more efficient LLM implementations.