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As we push the boundaries of AI, especially with generative models, we are confronted with a pressing question that is forecasted to only become more urgent: What is the environmental cost of our progress? Training, hosting, and running these models arent just compute-intensive they require substantial natural resources, leading to significant carbon and water footprints that often fly under the radar. This discussion has become even more timely with Googles recent report on July 2, 2024, highlighting the challenges in meeting their ambitious climate goals. The report revealed a 13% increase in emissions in 2023 compared to the previous year and a 48% rise compared to their baseline year of 2019. The demand for AI has significantly strained data centers, a trend reflected in Microsofts environmental sustainability report from May, which noted a 29% increase in emissions above their 2020 baseline due to data center usage. Additionally, the International Energy Agency predicts that global data center and AI electricity demand could double by 2026, underscoring the urgent need for sustainable practices. For everyone

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Eco-Friendly AI: How to Reduce the Carbon and Water Footprints of Your ML Models - Towards Data Science