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Counting Carbon

This exhibit gives participants the chance to explore the costs and the impacts of energy consumption on model performance as analyzed in Dr. Luccioni’s  and Dr. Hernandez-Garcia’s research

AI/ML requires using energy to carry out computations during the model training process. The generation of this energy comes with an environmental cost in terms of greenhouse gas emissions, depending on quantity used and the energy source.

What are the main sources of energy used to train the top 95 AI/ML models?

Estimating the carbon footprint of training AI/ML models requires examination of the main sources of energy used for training, the order of magnitude of CO2 emissions produced, and the evolution of these emissions over time.

How much equivalent CO2 is produced
per kilowatt hour (gCO

The energy source that powers the hardware to train AI/ML models can result in differences of up to 60 times more CO2eq in terms of total emissions.

Which types of model cost the most to train?

As production uses of AI/ML demand higher quality predictions, training to higher-degrees of precision, energy costs grow exponentially.   

We need to take action sooner than later. 

As models perform more complex tasks and we train larger models, we are also increasing emissions faster than we are making advancements in energy conservation.

Dr. Sasha Luccioni

Sasha is a researcher in ethical and sustainable artificial intelligence at HuggingFace, as well as a Founding Member of Climate Change AI and a Board Member of Women in Machine Learning. Her work focuses on having a better understanding of the societal and environmental impacts of AI models, datasets and systems.

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