A developer explored the impact of various caching strategies on LLM performance and cost. On a local Apple MacBook Air running a 4B model via Ollama, retrieval and prompt caching reduced the time to first token by approximately 10x, from 3-4 seconds to 0.3 seconds, by reusing previously processed prompt segments and passages. However, when using Anthropic's Claude Sonnet 5.5, prompt caching increased costs by 18-25% unless prompts were intentionally clustered around the same passages, which then reduced costs by 23%. The developer also noted that similarity caches can sometimes return confidently incorrect answers. AI
IMPACT Caching strategies can significantly improve local LLM performance but may increase costs for API-based models if not carefully managed.
RANK_REASON Developer's exploration of caching strategies for LLMs.
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