Nice one
this post was submitted on 22 Dec 2024
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The environmental cost of training is a bit of a meme. The details are spread around, but basically, Alibaba trained a GPT-4 level-ish model on a relatively small number of GPUs... probably on par with a steel mill running for a long time, a comparative drop in the bucket compared to industrial processes. OpenAI is extremely inefficient, probably because they don't have much pressure to optimize GPU usage.
Inference cost is more of a concern with crazy stuff like o3, but this could dramatically change if (hopefully when) bitnet models come to frutition.
Still, I 100% agree with this. Closed LLM weights should be public domain, as many good models already are.
With current kWh/token it's 100x of a regular google search query. That's where the environmental meme came from. Also, Nvidia plans to manufacture enough chips to require global electricity production to increase by 20-30%.
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(2 replies)
Delete them. Wipe their databases. Make the companies start from scratch with new, ethically acquired training data.
Mmm yes so all that electricity is pure waste
Genuine question, does anyone know how much of the electricity is used for training the model vs using it to generate responses?
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Correct
Only if they were trained on public material.