What Is Prompt Caching for Local LLMs?

How Caching Actually Speeds Things Up

Processing a prompt means the model computes a key-value (KV) representation for every input token before it can start generating a response. This step — prompt processing — happens before generation begins, and its cost scales with prompt length. Without caching, a 2,000-token system prompt gets fully reprocessed on every single request, even if it's byte-for-byte identical each time.

Caching stores that computed KV representation for a given prefix, keyed to the exact token sequence. When a new request starts with the same tokens, the cached KV values are reused directly, and only the new, non-cached portion of the prompt needs processing. The practical effect is that time-to-first-token drops sharply for cache hits, since most of the input no longer needs recomputation.

This is why multi-turn conversations benefit almost automatically: turn 3 of a conversation includes turns 1 and 2 as its prefix, so a caching-aware serving setup only processes the newly added turn, not the entire conversation history over again.

Structuring Prompts to Actually Benefit

• ▸**Put shared, unchanging content first.** System prompts, instructions, and reference documents that stay identical across requests should come before the variable, request-specific content — caching only works on an exact-match prefix, so anything before the first point of difference is what gets reused.
• ▸**Keep the shared prefix byte-identical across requests.** Even a single-character difference in a system prompt (a timestamp, a random ID) breaks the prefix match and forces full reprocessing — move any per-request variation to the end of the prompt, after the reusable content.
• ▸**Don't expect caching to help unrelated, one-off requests.** If your workload is mostly single-shot questions with no shared setup, there's nothing for caching to reuse — the technique's value is proportional to how much of your traffic actually shares a common prefix.
• ▸**Long-running conversations benefit most from cache retention settings.** Check how long your serving setup keeps cached prefixes before evicting them — a cache that's evicted too aggressively won't help conversations with gaps between turns.