# More than Carbon: Cradle-to-Grave environmental impacts of GenAI training on the Nvidia A100 GPU

arXiv: https://arxiv.org/abs/2509.00093 · HTML: https://mlsanity.com/paper/2509.00093/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Aug 2025.

## Built on by (most cited first, 10 of 10)

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- [How Hyper-Datafication Impacts the Sustainability Costs in Frontier AI](https://mlsanity.com/paper/2602.00056/) (2026)
- [BIRDS: Characterizing and Understanding Biodiversity Impact of Large Language Model Serving](https://mlsanity.com/paper/2605.27480/) (2026)
- [Is Knowledge Distillation Actually Greener? A Case Study in Machine Translation](https://mlsanity.com/paper/2602.09691/) (2026)
- [The Global Landscape of Environmental AI Regulation: From the Cost of Reasoning to a Right to Green AI](https://mlsanity.com/paper/2603.00068/) (2026)
- [Environmental Footprint of GenAI Research: Insights from the Moshi Foundation Model](https://mlsanity.com/paper/2604.11154/) (2026)
- [The Rising Unsustainability of AI Graphics Cards Production](https://mlsanity.com/paper/2607.01258/) (2026)
- [Environmental Slow AI: Design Principles for Generative Systems](https://mlsanity.com/paper/2608.20398/) (2026)
- [The Battery Price of edge AI: A study of the Environmental Impact of LLM Inference on Mobile Devices](https://mlsanity.com/paper/2609.11940/) (2026)

## Abstract (arXiv)

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.