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Cake day: June 30th, 2023

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  • I’m not so sure that power usage should be dismissed so easily just because it is distributed instead of centralized. The slop per watt rate may even be worse than at a datacenter. Fundamentally, we should care more about efficiency.

    Imagine a panel of 20 standard LED light bulbs. That’s 180 watts, roughly the equivalent of GPU usage while a local LLM is doing any work. If you keep that in mind, then you have to ask yourself if the benefit you’re getting out of your local LLM is really worth that energy cost. Now, monetarily speaking, that’s not a ton of money, because electricity is cheap, but would you flip that switch for the duration of the task you’re performing? What if you could use conventional non-LLM methods to do it instead? Would that be more efficient? And where is your electricity coming from? Is it a solar farm, or a coal plant?

    How was your local LLM trained? Was there copyrighted material in its training data set? Were low-wage workers asked to sift through horrendous content to clean up the data?

    We need to consider the externalities, even when using local LLMs. We moved so quickly from the initial release of ChatGPT to now, that we never stopped to ask those questions. They remain unanswered until someone cares enough to think.