Generative AI impact assessment through a life cycle analysis of multiple data center typologies

Data centers are energy-intensive infrastructures that generate, manage, and store information for our interconnected society. Models based on Artificial Intelligence (AI), such as ChatGPT, are increasingly accessible, leading to significant energy consumption and associated carbon emissions. Assessing the environmental footprint of Generative AI (GenAI) is essential for evaluating its sustainability and promoting responsible AI development. In this work, a comprehensive environmental assessment of GenAI systems was performed – which includes both training and inference phases – using a life cycle assessment (LCA) approach. Prior studies have primarily focused on server-level assessments or energy consumption analyses. In contrast, this work considers the full lifecycle of data centers and evaluates environmental impacts across complete architectural configurations, offering a broader and more integrated perspective. Finally, multiple data center architectures are compared, from edge systems to AI dedicated infrastructures. Two simulation-based use cases are presented: (1) A 20-year simulation comparing different data center architectures across three indicators – total emissions, emissions per year, and emissions per installed IT MW. For a subset of these architectures, emissions per Floating-Point Operations Per Second (FLOPS) are also included to assess performance efficiency – considering that FLOPS estimations can only be done on GPU based data center architectures; (2) A focused simulation comparing the environmental footprint of three large language models – GPT-4o, LLaMA 3.1405B, and DeepSeek V3 – to quantify trade-offs between benchmark performance and environmental impact. By expanding the scope of assessment and incorporating varied use cases, this work aims to inform strategies for minimizing the environmental costs of GenAI while advancing sustainable AI development. • Full architecture LCA reveals GenAI impacts extend far beyond servers alone. • High-density, liquid-cooled designs yield lowest environmental cost per eFLOP. • Model choice (GPT-4o, Llama, DeepSeek) significantly alters AI footprint. • Geographic location and grid carbon intensity influences GenAI impacts.