Summary
A user fine-tuning LLMs locally on an RTX 3090 discovered a significant lack of visibility into the actual electricity costs per job. By tracking power consumption, they found surprising expenses, such as wasted money from forgotten Jupyter kernels and inefficient hyperparameter sweeps. This highlights a practical challenge for local AI developers in accurately attributing power costs to specific tasks, leading to unexpected expenditures.
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