This research explores economic benefits of reusing key-value (KV) caches in large language models to reduce both latency and operational costs.

• Cost-performance tradeoffs analyzed for storing and reusing KV caches in cloud environments
• Potential for significant savings when processing repeated input contexts across different LLM requests
• Technical design considerations for optimally placing and reusing cached intermediate representations
• Practical implications for developers using cloud-based LLM services

For engineering teams, this research offers valuable insights into optimizing LLM deployment architectures, particularly when scaling applications that process similar contexts across multiple requests.

Towards More Economical Context-Augmented LLM Generation by Reusing Stored KV Cache