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Intent judging, tool routing and arg extraction are classification/JSON calls, not the spoken answer, yet the stack aliased OLLAMA_INTENT_MODEL back to the big chat model — so each command paid the big model's cost 2-3 times for routing before the reply even ran. With the GPU on, that round-trip stacking is the main remaining per-turn latency. Default OLLAMA_INTENT_MODEL to qwen2.5:3b (the project's reference small model, clean Korean on classification) and have ollama-init pull it. The reply engine already routes these calls through intent_judge_model, so answer quality is untouched; set OLLAMA_INTENT_MODEL = OLLAMA_CHAT_MODEL to fold back onto one resident model. Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
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