Comment on Black Mirror AI

• vivendi@programming.dev ⁨19⁩ ⁨hours⁩ ago

  This is actually misleading in the other direction: ChatGPT is a particularly intensive model. You can run a GPT-4o class model on a consumer mid to high end GPU which would then use something in the ballpark of gaming in terms od environmental impact.

  You can also run a cluster of 3090s or 4090s to train the model, which is what people do actually, in which case it’s still in the same range as gaming. (And more productive than 8 hours of WoW grind while chugging a warmed up Nutella glass as a drink).

  Models like Google’s Gemma (NOT Gemini these are two completely different things) are insanely power efficient.

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  • xthexder@l.sw0.com ⁨19⁩ ⁨hours⁩ ago

    I didn’t even say which direction it was misleading, it’s just not really a valid comparison to compare a single invocation of an LLM with a continuous task.

    You’re comparing Volume of Water with Flow Rate. Or if this was power, you’d be comparing Energy (Joules or kWh) with Power (Watts)

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    • vivendi@programming.dev ⁨18⁩ ⁨hours⁩ ago

      I can’t really provide any further insight without finding the damn paper again (academia is cooked) but Inference is famously low-cost, this is basically “average user damage to the environment” comparison, so for example if a user chats with ChatGPT they gobble less water comparatively than downloading 4K porn (at least according to this particular paper)

      As with any science, statistics are varied and to actually analyze this with rigor we’d need to sit down and really go down deep and hard on the data. Which is more than I intended when I made a passing comment lol

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