Every query typed into a large language model (LLM), such as ChatGPT, requires energy and produces CO2 emissions.
Every query typed into a large language model (LLM), such as ChatGPT, requires energy and produces CO2 emissions. Emissions, however, depend on the model, the subject matter, and the user. Researchers have now compared 14 models and found that complex answers cause more emissions than simple answers, and that models that provide more accurate answers produce more emissions. Users can, however, to an extent, control the amount of CO2 emissions caused by AI by adjusting their personal use of the technology, the researchers said.
No matter which questions we ask an AI, the model will come up with an answer. To produce this information – regardless of whether than answer is correct or not – the model uses tokens. Tokens are words or parts of words that are converted into a string of numbers that can be processed by the LLM.
This conversion, as well as other computing processes, produce CO2 emissions. Many users, however, are unaware of the substantial carbon footprint associated with these technologies. Now, researchers in Germany measured and compared CO2 emissions of different, already trained, LLMs using a set of standardized questions.
“The environmental impact of questioning trained LLMs is strongly determined by their reasoning approach, with explicit reasoning processes significantly driving up energy consumption and carbon emissions,” said first author Maximilian Dauner, a researcher at Hochschule München University of Applied Sciences and first author of the Frontiers in Communication study. “We found that reasoning-enabled models produced up to 50 times more CO₂ emissions than concise response models.”
Read more at Frontiers
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