Why Your Local LLM Setup Is Costing More Than You Think — And What Happens When It Breaks

You're three hours into debugging a model quantization issue. The GPU utilization is sitting at 12%. Your M2 Max is running hot, the fans sound like a small aircraft, and you've already burned through two days trying to get Llama 3 to run at acceptable token speeds. Meanwhile, your teammate just pushed code using the OpenAI API — it works, it's fast, and nobody is pagercalling at 2 AM about CUDA memory errors. This is the local LLM paradox. It looks free. It feels empowering. But somewhere betw...