Abstract
Machine Learning (ML) workloads have rapidly grown in importance, but
raised concerns about their carbon footprint. We show four best
practices to reduce ML training energy by up to 100x and CO2 emissions
up to 1000x, and that recent papers overestimated the cost and carbon
footprint of ML training by 100x–100,000x. Finally, we show that by
following best practices, overall ML energy use (across research,
development, and production) held steady at <15% of Google’s
total energy use for the past three years. If the whole ML field adopts
best practices, we predict that by 2030 total carbon emissions from
training will reduce.