... and neither does the author (or so I believe - I made them both up).
On the other hand, AI is definitely good at creative writing.
this post was submitted on 29 Sep 2024
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The energy usage is mainly on the training side with LLMs. Generating afterwards is fairly cheap. Maybe what you want is to have fewer companies trying to train their own models from scratch and encourage collaborating instead?
Indeed. Though what we should be thinking about is not just the cost in absolute terms, but in relation to the benefit. GPT-4 is one of the more expensive models to run right now, and you can accomplish very good results with their smaller GPT-4o mini at 0.5% of the energy cost^[1]^. That's the cost of running 0.07 LED bulbs over an hour, or running 1 LED bulb over 0.07 hours (i.e. 5min). If that saves you 5min of time writing an email while the room is lit with a single LED bulb and your computer is drawing energy, that might just be worth it, right?
[1] Estimated by using https://huggingface.co/spaces/genai-impact/ecologits-calculator and the pricing difference between GPT-4o, 4o mini, and 3.5 (https://openai.com/api/pricing/). The assumption I'm making is that the total hardware and energy cost scales linearly with the API pricing.
Yeah, they operate very opaquely, so we can't know the true cost, but based on what I can know with certainty given models I can run on my own machines, the numbers seem reasonable. In any case, that's not really relevant to this discussion. Treat it as a hypothetical, then work out the math later to figure out where we want to be and what threshold we should be setting.
It sounds like you don't like how LLMs are currently used, not their power consumption.
I agree that they're a dead end. But I also don't think they need much improvement over what we currently have. We just need to stop jamming them where they don't belong and leave them be where they shine.
Weren't you just telling me that the environmental cost has no impact on your stance?