Environmental Impacts

Mitigation strategy

1. Prioritize algorithmic and model optimization to reduce computational demand: Shift to smaller language models (SLMs) and employ simplified models or more efficient algorithms to minimize the requisite computational resources (energy and hardware) for both training and inference without compromising performance. 2. Integrate environmental metrics into hardware roadmapping and infrastructure planning: Embed carbon intensity as a key metric (alongside power and performance) in the semiconductor design process, and deploy AI compute systems on infrastructure powered by scaled renewable energy sources with effective process gas abatement (f-GHG). 3. Adopt circular economy principles for computing infrastructure: Increase the operational lifespan of existing computing hardware through systematic maintenance and upgrades, while designing AI-specific hardware with modular components to facilitate maximum reuse, remanufacturing, and advanced material recycling.