Pre-computing & reusing KV caches to accelerate RAG inference

How can pre-computing and reusing Key-Value (KV) caches accelerate inference for Retrieval-Augmented Generation and other long-context LLM tasks? The provided sources identify the same core problem—high latency in Large Language Model (LLM) inference due to processing long, repetitive contexts—and converge on a unified solution: leveraging pre-computed Key-Value (KV) caches. Each source then contributes a unique perspective on *how* to implement this solution effectively, addressing specific challenges that arise from this approach. The unified answer proposed by all sources is to avoid redundant computation by pre-computing, storing, and reusing the KV caches of recurring text segments (referred to as chunks, documents, or prompt modules).