My strong intuition at the moment is that the environmental impact is greatly exaggerated.
The energy cost of executing prompts has dropped enormously over the past two years - something that's reflected in this report when it says "Driven by increasingly capable small models, the inference cost for a system performing at the level of GPT-3.5 dropped over 280-fold between November 2022 and October 2024". I wrote a bit about that here: https://simonwillison.net/2024/Dec/31/llms-in-2024/#the-envi...
We still don't have great numbers on training costs for most of the larger labs, which are likely extremely high.
Llama 3.3 70B cost "39.3M GPU hours of computation on H100-80GB (TDP of 700W) type hardware" which they calculated as 11,390 tons CO2eq. I tried to compare that to fully loaded passenger jet flights between London and New York and got a number of between 28 and 56 flights, but I then completely lost confidence in my ability to credibly run those calculations because I don't understand nearly enough about how CO2eq is calculated in different industries.
The "LLMs are an environmental catastrophe" messaging has become so firmly ingrained in our culture that I think it would benefit the AI labs themselves enormously if they were more transparent about the actual numbers.