This research provides a unified framework for evaluating and implementing cross-layer key-value cache sharing techniques to make Large Language Models run more efficiently.

• Systematic evaluation of different KV sharing configurations and their performance trade-offs
• Improved inference throughput while maintaining acceptable model quality
• Practical optimization technique that can be implemented across various LLM architectures
• Performance benchmarks across language modeling and downstream tasks

For engineering teams, this research offers immediately applicable methods to reduce computational resource requirements during LLM inference, potentially lowering deployment costs.

A Systematic Study of Cross-Layer KV Sharing for Efficient LLM Inference