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What Is Prompt Caching for Local LLMs?

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Quick Answer

Prompt caching stores the computed key-value (KV) cache for a prompt's prefix so it doesn't need to be recomputed when a later request shares that same prefix β€” a fixed system prompt, a reused document, or earlier conversation turns. It cuts prompt-processing time significantly for repeated-prefix workloads, but provides no benefit when every request is unrelated.

  • β–ΈCaches the KV cache for a prompt prefix, not the model's output
  • β–ΈBiggest win: long fixed system prompts, reused documents, multi-turn conversations
  • β–ΈNo benefit for one-off requests with no shared prefix across calls

Updated: July 15, 2026

Technique & Concept ExplainersIntermediate

Key Takeaways

  • βœ“Prompt caching stores the KV cache computed for a prompt's prefix, letting a later request reuse it instead of reprocessing the same tokens
  • βœ“The bigger and more repeated the shared prefix, the bigger the speedup β€” a long, unchanging system prompt reused across thousands of requests benefits enormously
  • βœ“Multi-turn conversations benefit automatically in most local serving setups, since each new turn shares the entire prior conversation as its prefix
  • βœ“Caching only helps prompt processing (the input), not token generation (the output) β€” it reduces time-to-first-token, not tokens-per-second once generation starts

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 β€” prompt processing β€” 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

  • β–Έ**Put shared, unchanging content first.** System prompts, instructions, and reference documents that stay identical across requests should come before the variable, request-specific content β€” caching only works on an exact-match prefix, so anything before the first point of difference is what gets reused.
  • β–Έ**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 β€” move any per-request variation to the end of the prompt, after the reusable content.
  • β–Έ**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 β€” the technique's value is proportional to how much of your traffic actually shares a common prefix.
  • β–Έ**Long-running conversations benefit most from cache retention settings.** Check how long your serving setup keeps cached prefixes before evicting them β€” a cache that's evicted too aggressively won't help conversations with gaps between turns.

Frequently Asked Questions

Does prompt caching change the model's output?β–Ύ
No. Caching only affects how the shared prefix is processed internally β€” it produces mathematically identical results to full reprocessing, just faster. It's a performance optimization, not a change to model behavior or quality.
Does prompt caching help token generation speed, or just the initial response time?β–Ύ
Only the initial response time (time-to-first-token). Once generation starts, tokens are still produced one at a time at the same generation speed regardless of caching β€” caching eliminates redundant prompt-processing work, not the generation step itself.
How much VRAM does caching use?β–Ύ
Cached KV data lives in VRAM alongside the model itself, and its size scales with how much context is being cached and for how many concurrent conversations. Caching many long conversations simultaneously can meaningfully add to VRAM usage β€” this is a real tradeoff against caching depth on VRAM-constrained hardware.
Does every local serving tool support prompt caching?β–Ύ
Support and default behavior vary by tool β€” some cache automatically for multi-turn use within a session, others require explicit configuration, and cache retention (how long a prefix stays cached before eviction) is also tool-specific. Check your specific serving tool's documentation for its caching behavior and any settings that control it.

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