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More than carbon: cradle-to-grave environmental impacts of GenAI training on the Nvidia A100 GPU

Author

Listed:
  • Sophia Falk

    (Universität Bonn = University of Bonn)

  • David Ekchajzer

    (IMT-BS - DEFI - Département Data analytics, Économie et Finances - IMT-BS - Institut Mines-Télécom Business School - IMT - Institut Mines-Télécom [Paris], LITEM - Laboratoire en Innovation, Technologies, Economie et Management (EA 7363) - UEVE - Université d'Évry-Val-d'Essonne - Université Paris-Saclay - IMT-BS - Institut Mines-Télécom Business School - IMT - Institut Mines-Télécom [Paris])

  • Thibault Pirson

    (UCLouvain - Université Catholique de Louvain = Catholic University of Louvain)

  • Etienne Lees-Perasso

    (Toledo Institute for Development and Environment (TIDE))

  • Augustin Wattiez

    (UCLouvain - Université Catholique de Louvain = Catholic University of Louvain)

  • Lisa Biber-Freudenberger

    (Universität Bonn = University of Bonn)

  • Sasha Luccioni

    (Hugging Face)

  • Aimee van Wynsberghe

    (Universität Bonn = University of Bonn)

Abstract

The rapid expansion of AI has intensified concerns about its environmental sustainability. Current assessments focus on operational carbon emissions using secondary data, overlooking impacts in other life cycle stages. This study presents a comprehensive, multi-criteria life cycle assessment of AI training, building on an innovative life cycle inventory methodology for electronic products that combines physical teardown and multi-element composition analysis. Results for GPT-4 training show the use phase dominates 10 categories, contributing 96% to climate change and fossil fuel depletion. Manufacturing dominates 6 categories, including human toxicity (94%) and freshwater eutrophication (81%). The GPU chip is the largest contributor in 10 categories, particularly climate change (81%) and fossil resource use (80%). While primary data produces modest changes in carbon estimates, substantial variations emerge elsewhere, e.g. minerals and metals depletion increases by 33%. This analysis expands Sustainable AI discourse beyond carbon emissions, challenging current sustainability narratives.

Suggested Citation

  • Sophia Falk & David Ekchajzer & Thibault Pirson & Etienne Lees-Perasso & Augustin Wattiez & Lisa Biber-Freudenberger & Sasha Luccioni & Aimee van Wynsberghe, 2026. "More than carbon: cradle-to-grave environmental impacts of GenAI training on the Nvidia A100 GPU," Post-Print hal-05667182, HAL.
    • Handle: RePEc:hal:journl:hal-05667182
    DOI: 10.1016/j.eiar.2026.108525
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