Beyond Efficiency: Scaling AI Sustainably

Barroso’s seminal contributions in energy proportional warehouse-scale computing launched an era that has led to modern datacenters being extremely energy efficient and significantly more cost-effective today. At the same time, modern AI applications have driven ever-increasing demands in computing, highlighting the opportunity and importance of optimizing efficiency across the entire deep learning model development cycle. This work characterizes the carbon impact of AI, including both operational carbon emissions from
Training
and
Inference
as well as embodied carbon emissions from datacenter construction and hardware manufacturing. We highlight key efficiency optimization opportunities for cuttingedge AI technologies, from deep learning recommendation models to multi-modal generative AI tasks. To scale AI sustainably, we need to make AI and computing, more broadly, efficient and flexible. We must also go beyond efficiency and optimize across the life cycle of computing infrastructures, from hardware manufacturing to datacenter operation and end-of-life processing for the hardware.