How to measure the energy and environmental impact of AI models? Challenges and practical solutions

Abstract

The energetic and environmental impact of AI is becoming significant in all domains of applications. Specific to Document Engineering, AI models have become the predominant components of modern processing pipelines, ranging from document information extraction, transformation, interpretation, translation, etc. As AI workloads are projected to potentially double global data center energy consumption by 2030, driven largely by generative models, there is an urgent need to accurately quantify and mitigate this footprint. However, measuring this impact is challenged by the “black box” nature of massive model training, opaque hardware-software interactions, and a historical prioritization of accuracy over environmental efficiency. This tutorial addresses these challenges by exploring Lifecycle Assessment (LCA) frameworks and telemetry tools to track operational energy, carbon intensity, and embodied carbon. Participants will gain practical experience in auditing AI pipelines with open-source tools, implementing efficiency techniques such as quantization and distillation, and making strategic deployment decisions to minimize environmental harm. The session includes a live demonstration using a physical benchmark server to measure power drainage in real-time.