Running LLM models locally for AI agents involves significant tradeoffs. Unlike simple chatbots, agents require large context windows (64k+) because they inject system prompts, tool descriptions, and persistent information into every message, causing token usage to spike 10-40k tokens for simple inputs. This overhead is necessary for agent functionality but makes local inference impractical for most users.