HackerPapers

arXiv:2204.05149cs.LG

The Carbon Footprint of Machine Learning Training Will Plateau, Then Shrink

David Patterson, Joseph Gonzalez, Urs Hölzle, Quoc Le, Chen Liang, Lluis-Miquel Munguia, Daniel Rothchild, David So, Maud Texier, Jeff Dean

Abstract

Machine Learning (ML) workloads have rapidly grown in importance, but raised concerns about their carbon footprint. Four best practices can reduce ML training energy by up to 100x and CO2 emissions up to 1000x. 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 were to adopt best practices, total carbon emissions from training would reduce. Hence, we recommend that ML papers include emissions explicitly to foster competition on more than just model quality. Estimates of emissions in papers that omitted them have been off 100x-100,000x, so publishing emissions has the added benefit of ensuring accurate accounting. Given the importance of climate change, we must get the numbers right to make certain that we work on its biggest challenges.

arXiv comments
Journal reference
DOI
Subjects
cs.LGMachine Learning cs.AIArtificial Intelligence cs.GLGeneral Literature

On Hacker News

Linked by 3 identifiable commenters in 4 comments across 4 threads, from Jan 2023 to Aug 2025. Found in 1 HN submission from Apr 2022 to Apr 2022. Open submission discussions.

Comments linking this paper, per year.

Discussed in comments4 threads

  1. In a first, Google has released data on how much energy an AI prompt uses 1 comment

    • My gosh you're right! The paper in question is https://arxiv.org/pdf/2204.05149, "The Carbon Footprint of Machine Learning Training Will Plateau, Then Shrink"

      gcrView on HNParent context

  2. Google agrees to pause AI workloads when power demand spikes 1 comment

    • I'm not sure if that's true of not. As the article indicates, training for AI is naturally demand responsive. Training can move on the clock, and it can move around the world to minimize carbon footprint. See the PDF I just love to share: "Revamped Happy CO2e Paper"…

      jeffbeeView on HNParent context

  3. Mistral reports on the environmental impact of LLMs 1 comment

    • These conclusions are broadly compatible with "The Carbon Footprint of Machine Learning Training Will Plateau, Then Shrink" or, as I prefer, the PDF metadata title that they left in there, "Revamped Happy CO2e Paper". Despite the incredible focus by the press on this topic…

      jeffbeeView on HNParent context

  4. On the dangers of stochastic parrots: Can language models be too big? (2021) 1 comment

    • People seem to miss the specific thing that was controversial about this paper: The paper gives inaccurate estimates of the carbon dioxide emissions that result from machine learning. My understanding is that this is the main reason why Google wanted the paper pulled. You can't…

      kinstonView on HNParent context

Submitted to Hacker News

Connected through shared HN threads, not inferred similarity or a recommendation.