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Estimating the Carbon Footprint of BLOOM, a 176B Parameter Language Model

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arxiv 2211.02001 v1 pith:ZNPOGCSZ submitted 2022-11-03 cs.LG

Alexandra Sasha Luccioni , Sylvain Viguier , Anne-Laure Ligozat This is my paper
classification cs.LG
keywords carbonbloomfootprintconsumptionemissionsenergyestimatinglanguage
verification ladder T0 review T1 audit T2 compute T3 formal
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Progress in machine learning (ML) comes with a cost to the environment, given that training ML models requires significant computational resources, energy and materials. In the present article, we aim to quantify the carbon footprint of BLOOM, a 176-billion parameter language model, across its life cycle. We estimate that BLOOM's final training emitted approximately 24.7 tonnes of~\carboneq~if we consider only the dynamic power consumption, and 50.5 tonnes if we account for all processes ranging from equipment manufacturing to energy-based operational consumption. We also study the energy requirements and carbon emissions of its deployment for inference via an API endpoint receiving user queries in real-time. We conclude with a discussion regarding the difficulty of precisely estimating the carbon footprint of ML models and future research directions that can contribute towards improving carbon emissions reporting.

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Cited by 10 Pith papers

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