The Environmental Impacts of Machine Learning Training Keep Rising Evidencing Rebound Effect

Abstract

Recent Machine Learning (ML) approaches have shown increased performance on benchmarks but at the cost of escalating computational demands. Hardware, algorithmic and carbon optimizations have been proposed to curb energy consumption and environmental impacts. Can these strategies lead to sustainable ML model training? Here, we estimate the environmental impacts associated with training notable AI systems over the last decade, including Large Language Models, with a focus on the life cycle of graphics cards. Our analysis reveals two critical trends: First, the impacts of graphics cards production have increased steadily over this period; Second, energy consumption and environmental impacts associated with training ML models have increased exponentially, even when considering reduction strategies such as location shifting to places with less carbon intensive electricity mixes. Optimization strategies do not mitigate the impacts induced by model training, evidencing rebound effect. We show that the impacts of hardware must be considered over the entire life cycle rather than the sole use phase in order to avoid impact shifting. Our study demonstrates that increasing efficiency alone cannot ensure sustainability in ML. Mitigating the environmental impact of AI also requires reducing AI activities and questioning the scale and frequency of resource-intensive training.