Barroso's seminal contributions in energy-proportional warehouse-scale computing launched an era where modern datacenters have become more energy efficient and cost effective than ever before. At the same time, modern AI applications have driven ever-increasing demands in computing, highlighting the importance of optimizing efficiency across the entire deep learning model development cycle. This paper 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 cutting-edge AI technologies, from deep learning recommendation models to multi-modal generative AI tasks. To scale AI sustainably, we must also go beyond efficiency and optimize across the life cycle of computing infrastructures, from hardware manufacturing to datacenter operations and end-of-life processing for the hardware.

翻译：Barroso在能量比例仓库级计算领域的开创性贡献开启了一个新时代，使现代数据中心比以往任何时候都更加节能且经济高效。与此同时，现代人工智能应用持续推升计算需求，凸显了在整个深度学习模型开发周期中优化效率的重要性。本文刻画了人工智能的碳排放影响，包括训练和推理产生的运营碳排放，以及数据中心建设和硬件制造带来的隐含碳排放。我们重点介绍了前沿人工智能技术（从深度学习推荐模型到多模态生成式AI任务）的关键效率优化机会。为可持续地扩展人工智能，我们必须超越效率，在整个计算基础设施生命周期（从硬件制造、数据中心运营到硬件报废处理）进行优化。