EPIC introduces a novel approach to LLM context caching that significantly improves serving performance without requiring exact prefix matches.

• Position-independent caching: Enables flexible reuse of KV cache across diverse requests
• AttnLink and KVSplit: Technical innovations that maintain model quality while improving efficiency
• Performance gains: Reduces time-to-first-token (TTFT) by up to 38.5% in real-world scenarios
• Versatile applications: Particularly valuable for few-shot learning and multi-document QA use cases

This research matters because it addresses a critical engineering challenge in LLM deployment, making advanced AI more responsive and cost-effective at scale without compromising on quality.

EPIC: Efficient Position-Independent Context Caching for Serving Large Language Models