RocketKV introduces a novel training-free compression strategy that reduces memory requirements during LLM inference, enabling faster processing of long contexts.

• Implements a two-stage compression approach specifically designed for the decode phase
• Significantly reduces both memory bandwidth and capacity demands
• Enables more efficient processing of extended context windows
• Requires no model retraining while maintaining performance

Engineering Impact: This optimization technique represents an important advancement for deploying large language models in memory-constrained environments and real-time applications, potentially improving user experience in interactive AI systems.

RocketKV: Accelerating Long-Context LLM Inference via Two-Stage KV Cache Compression