AI Carbon Footprint: Efficiency & Emissions
- Large language models, or LLMs, are increasingly prevalent, but their energy demands raise environmental concerns.
- Researchers at Hochschule München University of Applied Sciences evaluated 14 LLMs, ranging from 7 billion to 72 billion parameters, using 1,000 benchmark questions.The study focused on how LLMs...
- Maximilian Dauner, a researcher at Hochschule München University of Applied Sciences, said the environmental impact depends heavily on the reasoning approach.He added that reasoning-enabled models produced up to...
Uncover the critical link between AI’s rapid advancement and its growing carbon footprint. Cutting-edge studies reveal a stark trade-off: increased accuracy in Large Language Models (LLMs) frequently enough means significantly higher energy consumption, leading to increased carbon emissions. Research highlights that some LLMs produce up to 50 times more emissions than others. Reasoning-enabled models contribute disproportionately to CO2 output compared to concise models. The subject matter itself influences the emissions, too. For deeper insights, explore the latest findings from the Hochschule München University of Applied Sciences. News Directory 3 keeps you informed on pressing developments. Discover what’s next in AI’s journey towards sustainability.
Large Language Models‘ Carbon Footprint Shows Accuracy-Sustainability Tradeoff
Updated June 19, 2025
Large language models, or LLMs, are increasingly prevalent, but their energy demands raise environmental concerns. A recent study indicates some LLMs generate up to 50 times more carbon emissions than others when processing queries. The research, published in Frontiers in Communication, suggests that higher accuracy in LLMs often correlates with greater energy consumption, creating a carbon footprint dilemma.
Researchers at Hochschule München University of Applied Sciences evaluated 14 LLMs, ranging from 7 billion to 72 billion parameters, using 1,000 benchmark questions.The study focused on how LLMs convert prompts into tokens-numerical representations of words-and the energy used in subsequent computations. Reasoning models, which insert “thinking tokens” for internal processing, averaged 543.5 tokens per question, while concise models used only 37.7.
Maximilian Dauner, a researcher at Hochschule München University of Applied Sciences, said the environmental impact depends heavily on the reasoning approach.He added that reasoning-enabled models produced up to 50 times more carbon dioxide emissions than concise response models.
The study also revealed a trade-off between accuracy and sustainability. The Cogito model, with 70 billion parameters, achieved 84.9% accuracy but produced three times more carbon dioxide emissions than similar models. Dauner noted that models with emissions below 500 grams of carbon dioxide equivalent struggled to exceed 80% accuracy.
Subject matter also plays a role. Complex questions, such as those involving abstract algebra or philosophy, resulted in up to six times higher emissions compared to straightforward topics.
What’s next
while acknowledging that emissions depend on local energy grids and specific models, the study’s authors hope their findings will encourage users to be more selective in their LLM use. Dauner suggests that users can significantly reduce emissions by prompting AI for concise answers or reserving high-capacity models for tasks that genuinely require their power.