Artificial Intelligence is revolutionizing industries, but its environmental cost is significant. Training and deploying large models like GPT-3 and T5 require immense computational power. For instance, if everyone in France or Germany generated five pages daily with ChatGPT, it could demand the energy equivalent of two nuclear plants. Geographic location also impacts AI's carbon footprint. Models trained in areas reliant on fossil fuels can emit up to 30 times more carbon than those in regions using renewable energy. This highlights the urgency for adopting greener AI strategies to mitigate escalating environmental effects.
Why Large Organizations Can’t Ignore AI & Sustainabily
Governments and EU regulators are tightening environmental impact reporting requirements, soon extending to AI emissions. The Corporate Sustainability Reporting Directive (CSRD) mandates nearly 50,000 companies to disclose their environmental footprint, including AI-related emissions, by 2026, with non-EU companies affected by 2028. Non-compliance could result in fines and reputational damage.
The AI Act further demands robust governance and reporting of AI emissions, focusing on safety, trust, and minimizing environmental impacts. Together, CSRD and AI Act create a framework requiring companies to track and reduce their AI carbon footprint to avoid penalties.
Up to 91% Carbon Emissions Reduction - We Already Did It!
Pruna’s optimization engine compresses machine learning models, cutting carbon emissions by up to 91%—as shown with this Computer Vision model. It integrates seamlessly into your existing AI systems, delivering immediate benefits without infrastructure changes. Optimizing models boosts speed, lowers costs, and enhances user experiences. Faster models reduce time-to-market, while cutting energy use lowers operational costs—benefiting both your business and the environment.