How do you implement prompt caching for offline LLM deployments?
Prompt caching stores the computed key-value pairs for frequently used prompt prefixes, eliminating redundant computation for repeated system prompts or context templates. When a new request shares a cached prefix, only the unique portion needs processing, dramatically reducing time-to-first-token. Implementation involves maintaining a prefix tree of cached KV states, using hash-based lookup for cache hits, and managing cache eviction based on usage frequency and memory constraints. llama.cpp supports prompt caching natively. For multi-user offline deployments, a shared system prompt cache benefits all users simultaneously. This is especially valuable for RAG applications where the system prompt and retrieval context template remain constant across queries, reducing per-query compute by 30-70% depending on prefix length.