Prompt Caching for Local LLMs: Cut Latency by Reusing Prefixes

Prompt caching allows local large language models to deliver faster responses by reusing the precomputed key-value cache of lengthy shared system prompts instead of reprocessing those fixed instructions from scratch on every incoming request.

In short: Prompt caching skips the repeated prefill work on shared prefixes, making local LLM chats and agents feel much more responsive without any loss in reasoning power.

Why recompute the same prefix every time?#

In typical local LLM usage patterns a detailed system prompt that encodes rules of behavior, persona characteristics, safety constraints, and illustrative few-shot examples remains constant throughout an entire conversation or across many separate sessions. Nevertheless most inference setups recompute the full prefill pass over this fixed prefix plus whatever new user message arrives, for every single request that comes in. The prefill stage involves running the entire prompt through the transformer layers to build up the initial key-value cache and hidden representations, and this work is repeated even though the system portion has not changed at all since the last interaction. The waste grows worse as system prompts become longer and more elaborate, which is increasingly common when users craft specialized assistants that must follow complex guidelines or maintain a consistent character across many turns. Local hardware feels the impact directly because each recomputation consumes GPU memory bandwidth and compute cycles that could otherwise serve actual new content generation. A helpful beginner analogy here is to imagine studying from a thick textbook where the first several chapters form a shared preface that explains all the core concepts and rules; you would not reread those opening chapters from scratch before tackling each new practice question at the end of the book. Instead, you simply bookmark the end of the shared preface and jump straight to the fresh question material. Prompt caching applies the same principle inside the model by saving the intermediate key-value states after the common prefix has been processed once, so only the novel user input needs fresh attention calculations.

How does prompt caching save work?#

Prefix caching detects when an incoming prompt begins with a token sequence that exactly matches a previously processed prefix. Once a match is confirmed the runtime retrieves the stored key and value tensors that were computed during the earlier prefill of that prefix. It then continues the forward pass solely on the remaining new tokens, extending the KV cache from that point onward. This reuse eliminates the quadratic-cost attention work over the shared prefix tokens on every subsequent request. The technique proves especially valuable in chat interfaces, agent frameworks, and retrieval-augmented systems where the same instructional block precedes user queries repeatedly.

Prompt prefix caching behavior (qualitative, 2026-07) · columns: Situation, What happens, Speed, Typical support · 출처 Hax hax.moche.ai/en/p/1249?ref=ai_answer

Situation | What happens | Speed | Typical support
No cache (prefill every time) | Recompute the whole system prompt + input each request | Baseline (slow) | Practically all
Prefix cache hit | Reuse shared-prefix KV, compute only new tokens | Fast (skips repeated prefill) | Ollama, vLLM, llama.cpp
Prefix cache miss | Any change in the prefix forces a full recompute | Baseline (no gain) | -

Consider the three situations in turn. When no cache is active the engine must tokenize and prefill the complete prompt including the long system section on every request, performing full self-attention across all tokens from the start. This produces the baseline slow speed experienced in most naive local setups. A prefix cache hit changes the picture dramatically: after the first request populates the cache for the system prompt, later requests that open with the identical prefix load the saved KV state and compute attention only for the fresh tokens that follow. The model therefore generates replies much faster because it avoids re-deriving the internal representations of the rules and examples it already processed moments earlier. The KV cache can be pictured as a compact digital summary or bookmark of everything the model learned while reading the prefix; instead of forcing the model to reread and re-understand the entire preface each time, the cache hands it the ready-made understanding so attention can focus immediately on the user's actual question. A prefix cache miss occurs as soon as the prefix tokens differ in any way from the cached version. Even minor edits such as rephrasing one sentence in the persona, inserting a timestamp, or altering example formatting break the exact match and force the runtime to discard the old cache entry and recompute the prefix in full, returning latency to the slow baseline level.

How do you raise the hit rate locally?#

Achieving consistent speedups from prompt caching on local machines requires careful prompt design and request handling so that the shared prefix remains byte-for-byte identical across requests. Small changes anywhere in the opening section destroy cache utility, therefore developers must separate stable instructions from variable data. The following practices reliably increase cache hit rates in local deployments.

Keep the fixed system prompt identical at the very front so that the common instructions always occupy the exact same token positions at the start of every request.

Put dynamic parts like date or user input at the end of the prompt rather than interleaving them inside the stable system section where they would alter the prefix.

Batch requests that share the same prefix together when processing multiple queries in parallel or in quick succession, allowing the runtime to leverage the cached state across the batch.

Applying these rules means the costly prefill of the system prompt happens only once per distinct prefix rather than on every turn. In a typical chat application the system message is prepended automatically by the framework on each call; by ensuring the system text never varies and by appending any per-turn variables after it, the prefix stays stable and cache hits become the common case. Over a long session or across many users who share the same agent configuration the cumulative savings in compute time and energy become substantial. Monitoring tools in modern inference servers can report hit versus miss statistics, letting developers verify that their structuring efforts are paying off and adjust prompt templates when hit rates remain unexpectedly low.

Note: As of 2026-07-12 KST prompt caching behavior and auto-enablement differ across local LLM runtimes and versions, therefore users should consult the documentation of their specific framework and perform direct latency measurements on representative workloads to verify the expected improvements under their own hardware and prompt patterns.

Reference links

vLLM (automatic prefix caching)

llama.cpp

Ollama