Persistent KV Cache: Simplifying LLM Knowledge Access Beyond RAG

RAG has become the standard for augmenting LLM knowledge access, but production deployments reveal significant operational overhead: embedding pipelines, vector database maintenance, and retrieval failures. Persistent KV caching offers a simpler alternative by storing LLM's attention cache directly, eliminating embedding models and retrieval steps entirely. This approach could reshape how teams handle document-based LLM queries, trading context window constraints for operational simplicity and improved answer quality.