• sushibowl@feddit.nl
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    8 months ago

    If you amortize training costs over all inference uses, I don’t think 1000MW is too crazy. For a model like GPT3 there’s likely millions of inference calls to split that cost between.

    • AliasAKA@lemmy.world
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      8 months ago

      Sure, and I think that these may even be useful and it warrants the cost. But it is to just say that this still isn’t simply running a couple light bulbs or something. This is a major draw on the grid (but likely still pales in comparison to crypto farms).

      Note that most people would be better off using a model that’s trained for a specific task. For example, training image recognition uses vastly less energy because the models are vastly smaller, but they’re exceedingly excellent at image recognition.

      • Zaktor@sopuli.xyz
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        8 months ago

        The article claims 200M ChatGPT requests per day. Assuming they make a new version yearly, that’s 73B requests per training. Spreading 1000MW across 73B requests yields a per-request amortized cost of 0.01 watt. It’s nothing.

        47 more households-worth of electricity just isn’t a major draw on anything. We add ~500,000 households a year from natural growth.