We've been asked the question: "Is using LLMs bad for the environment?" It's an important question, since it's a new technology that many are trying to find uses for -- yet it clearly uses a nontrivial amount of energy to run LLMs, which translates into impacts such as CO2 emissions. As machine learning researchers, I think we have a duty to be able to give a decent answer.
Here's a beginning, from my Tilburg colleague Nikos: "One way to answer the question whether using LLMs is bad for the environment is to take a comparative approach. There are tasks that LLMs can do that other technologies can do (e.g., search), and there we can compare the resource intensity of the technologies. There are other capabilities that are unique to the LLM technology, and for those cases, the best available reference is how much resources humans would use to perform the same task." -- Good start. What to add?
LLMs are a general-purpose technology which, when used for a specific task such as web search, will always be much less efficient, simply because "classic" web search can be heavily optimised for the single task.
Some early estimates of the carbon footprint of LLMs (Strubell et al 2019) were too pessimistic and created some very bad headlines. Improved estimates are more accurate. Here's a recent research paper that tries to make accurate and precise estimates: Faiz et al (2024)
The biggest and most impressive LLMs are undoubtedly highly carbon-intensive as part of their drive to outperform their competitors. The difference can certainly be a factor of 100. However, there is also a push to create efficient "small" LLMs, even ones that could run on your own computer.
So, the exact footprint of an LLM depends on which LLM it is, but also on other factors such as how clean is the energy used for the data centre. These factors can easily change the footprint by a factor of 10 or 100 - they are not to be ignored. (Just the same way as, when deciding to take a flight or a train, the difference in carbon footprint can be x10 to x100. These "multipliers" are important to take care of.)
It is important to note that the developers of the most well-known LLMs (GPT4) refuse to publish the information that would give a clear answer to exactly how bad they are for the environment. -- Thus, I would recommend only using LLMs that make clear numerical statements about their carbon footprint. We must not incentivise bad practice.