The energy behind AI: Why power efficiency matters

Training and running AI models require substantial compute capacity, specialized hardware, and energy-intensive data centers, which has made managing the energy footprint of AI systems a critical concern. As AI adoption increases, the energy demand from data centers has surged, driven by power-dense hardware and complex infrastructures, leading to electricity consumption projections in the U.S. reaching up to 580 TWh annually by 2028. This growing energy usage places responsibility on various stakeholders, including end users, hardware engineers, and cloud providers, to optimize energy efficiency across different levels of the AI production process. Nebius, a vertically integrated AI cloud provider, examines these processes through a four-layer efficiency framework, which includes model, cluster, fabric, and data center stages, to identify optimization levers and ensure that energy usage is purposeful. By dissecting the energy-to-AI process, Nebius aims to clarify where efficiency gains can be made and how sustainability impacts can be tracked, highlighting the importance of innovation and an engineering-led approach to responsibly scale AI infrastructure.

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