Our quick guide “Cost, resource and energy savings for sustainable AI development” shows how AI can be aligned with sustainability and cost reduction goals. You will find out how big the carbon footprint of large language models is and learn methods for downsizing deep learning models to save energy and costs. We also introduce tools such as Eco2AI and CodeCarbon that monitor the environmental impact of AI. These tools provide you with best practices for reducing emissions in cloud computing, for example by choosing “greener” cloud locations and optimizing workflows. This guide is ideal for anyone looking to optimize the performance of their AI systems sustainably.
