Optimizing LLM Memory with EMS
A novel adaptive approach to KV cache compression
This research introduces a head-wise KV cache compression technique that balances memory efficiency and processing speed for large language models.
- Combines eviction and merging strategies based on global-local importance metrics
- Achieves up to 60% memory reduction with minimal performance impact
- Dynamically adapts compression based on content importance, preserving critical information
- Outperforms existing compression methods while maintaining retrieval accuracy
This engineering advancement enables more efficient deployment of LLMs in resource-constrained environments, making long-context processing more practical for real-world applications.