CacheBlend introduces a novel caching technique that significantly improves LLM serving efficiency for Retrieval-Augmented Generation (RAG) systems.

• 40-50% reduction in end-to-end serving latency for RAG applications
• Enables knowledge fusion by reusing KV caches even when chunks appear in different positions
• Improves throughput without affecting output quality through attention approximation
• Particularly valuable for resource-constrained scenarios where RAG is essential but speed is critical

This engineering breakthrough matters because it directly addresses a key bottleneck in RAG systems, making knowledge-intensive AI applications more responsive and cost-effective for real-world deployment.

CacheBlend: Fast Large Language Model Serving for RAG with Cached Knowledge Fusion