More than Carbon: Cradle-to-Grave environmental impacts of GenAI training on the Nvidia A100 GPU
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arXiv
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| Format: | Preprint |
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2025
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| author | Falk, Sophia Ekchajzer, David Pirson, Thibault Lees-Perasso, Etienne Wattiez, Augustin Biber-Freudenberger, Lisa Luccioni, Sasha van Wynsberghe, Aimee |
| author_facet | Falk, Sophia Ekchajzer, David Pirson, Thibault Lees-Perasso, Etienne Wattiez, Augustin Biber-Freudenberger, Lisa Luccioni, Sasha van Wynsberghe, Aimee |
| contents | The rapid expansion of Artificial Intelligence (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, examining 16 environmental impact categories using primary data from the Nvidia A100 SXM 40 GB GPU. Results for GPT-4 training show that 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 the Sustainable AI discourse beyond carbon emissions, challenging current sustainability narratives. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_00093 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | More than Carbon: Cradle-to-Grave environmental impacts of GenAI training on the Nvidia A100 GPU Falk, Sophia Ekchajzer, David Pirson, Thibault Lees-Perasso, Etienne Wattiez, Augustin Biber-Freudenberger, Lisa Luccioni, Sasha van Wynsberghe, Aimee Computers and Society The rapid expansion of Artificial Intelligence (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, examining 16 environmental impact categories using primary data from the Nvidia A100 SXM 40 GB GPU. Results for GPT-4 training show that 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 the Sustainable AI discourse beyond carbon emissions, challenging current sustainability narratives. |
| title | More than Carbon: Cradle-to-Grave environmental impacts of GenAI training on the Nvidia A100 GPU |
| topic | Computers and Society |
| url | https://arxiv.org/abs/2509.00093 |