As Artificial Intelligence (AI) models grow in power and usage, they present both environmental costs and opportunities. The computing power needed for AI to work – especially Large Language Models (LLMs) – requires significant amounts of energy. The scope and size of the energy usage, however, depends on what phase of the ML lifecycle you measure: training or deployment.

In this blog post, we provide an overview of the environmental implications of AI, focusing on LLMs. We also discuss the importance of understanding the energy usage in different phases of the ML lifecycle.