Prefix Caching

Prefix caching is an LLM inference optimization that reuses the computed KV cache for shared prompt prefixes across multiple requests, eliminating redundant computation when many requests share the same system prompt, instructions, or document context. The technique is especially impactful for RAG applications where many queries share the same retrieved passages, for chatbot applications where every request includes the same system prompt, and for few-shot learning where many requests share the same example block. vLLM implements automatic prefix caching with content-hash-based identification, while TensorRT-LLM supports KV cache reuse with explicit prefix specification. OpenAI's API offers prompt caching with a separate cached-token billing rate (50% of standard input rate) for prompts cached on their infrastructure. Anthropic's Claude also supports prompt caching with 90% discounted rates for cached portions. AI governance teams document prefix caching configuration because it can significantly reduce production costs and improve latency for prompt-heavy workloads. The technique is one of the highest-impact production optimizations available for LLM serving in 2024-2025.

Prefix-cached generation with Centralpoint: Centralpoint coordinates prefix caching across whatever inference backend you operate, exploiting OpenAI, Anthropic, and self-hosted prefix-cache features. Tokens are metered with cached-rate awareness, prompts stay local, and chatbots deploy through one line of JavaScript.


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