Prompt caching is an optimization technique that can significantly reduce the cost of using large language models by reusing previously computed key/value tensors. This method is effective when subsequent requests share an identical prefix with a prior request, allowing the system to bypass recomputation for that portion. The cost savings are substantial, potentially reducing prefix costs by up to 89%, but the hit rate is highly sensitive to prompt structure, with even minor changes like including a timestamp in the system prompt leading to a 0% hit rate. AI
IMPACT Enables significant cost reductions for LLM inference by optimizing token usage through KV tensor caching.
RANK_REASON The item discusses a technical optimization for LLM usage, not a new model release or core research.
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