An Introduction to Life-Cycle Emissions of AI Hardware

Specialized hardware accelerators aid the rapid advancement of artificial intelligence (AI), and their efficiency impacts AI’s environmental sustainability. This study presents the first publication of a comprehensive AI accelerator life-cycle assessment (LCA) of greenhouse gas emissions, including the first publication of manufacturing emissions of an AI accelerator. Our analysis of five Tensor Processing Units (TPUs) encompasses all stages of the hardware lifespan—from material extraction and manufacturing, to energy consumption during training and serving of AI models. Using first-party data, it offers the most comprehensive evaluation of AI hardware’s environmental impact. We introduce a new metric, compute carbon intensity (CCI), that will help evaluate AI hardware sustainability and estimate the carbon footprint of training and inference. We show that CCI improves 3x from TPU v4i to TPU v6e. Moreover, while this paper’s focus is on hardware, software advancements leverage and amplify these gains.