Advanced Caching Strategies for High-Throughput Large Language Model Serving

Abstract

The deployment of Large Language Models (LLM) in enterprise applications faces significant computational and economic challenges due to their substantial resource requirements and inference latency. This technical review explores innovative caching strategies that transcend traditional methods to enhance LLM serving efficiency through prompt caching techniques. Prompt caching represents a paradigm shift from output-based to process-based optimization by storing intermediate computational states generated during transformer inference, enabling the reuse of cached states for subsequent requests with similar prompt patterns. The implementation involves sophisticated state management mechanisms that handle multi-dimensional transformer computations, including attention weights, hidden representations, and positional encodings across hierarchical cache structures. Cache invalidation logic addresses the probabilistic nature of LLM generation while managing dependencies across transformer layers, requiring advanced dependency tracking mechanisms to maintain cache integrity. Memory management strategies employ dynamic compression techniques and predictive allocation algorithms to handle variable-length cached states efficiently. Distributed serving integration demands sophisticated coherence protocols, intelligent load balancing, and fault tolerance mechanisms to maintain consistency across multiple serving nodes. Performance optimization demonstrates substantial improvements in latency reduction, computational cost savings, and memory efficiency while supporting sustainable AI deployment through reduced energy consumption and carbon footprint.