mlsanityData to 7 Oct 2026

Carbontracker: Tracking and Predicting the Carbon Footprint of Training Deep Learning Models

arXiv 2007.03051 ↗First posted 6 Jul 2020cs.CY cs.LG eess.SPcited by 168

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Abstract (arXiv)

Deep learning (DL) can achieve impressive results across a wide variety of tasks, but this often comes at the cost of training models for extensive periods on specialized hardware accelerators. This energy-intensive workload has seen immense growth in recent years. Machine learning (ML) may become a significant contributor to climate change if this exponential trend continues. If practitioners are aware of their energy and carbon footprint, then they may actively take steps to reduce it whenever possible. In this work, we present Carbontracker, a tool for tracking and predicting the energy and carbon footprint of training DL models. We propose that energy and carbon footprint of model development and training is reported alongside performance metrics using tools like Carbontracker. We hope this will promote responsible computing in ML and encourage research into energy-efficient deep neural networks.

Key facts. Cited by 168 papers in the map (data through 7 Oct 2026). First posted on arXiv 6 Jul 2020.

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Citing papers per year: 2021: 23; 2022: 24; 2023: 34; 2024: 30; 2025: 33; 2026: 24.

168 papers.

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  1. 2022Estimating the Carbon Footprint of BLOOM, a 176B Parameter Language Modelcited by 264
  2. 2021Training Spiking Neural Networks Using Lessons From Deep Learningcited by 251
  3. 2022Metrics reloaded: Recommendations for image analysis validationcited by 190
  4. 2023Change is Hard: A Closer Look at Subpopulation Shiftcited by 119
  5. 2022Measuring the Carbon Intensity of AI in Cloud Instancescited by 110
  6. 2023Art and the science of generative AI: A deeper divecited by 93
  7. 2025Efficient Reasoning Models: A Surveycited by 93
  8. 2023SpikeGPT: Generative Pre-trained Language Model with Spiking Neural Networkscited by 82
  9. 2022Zeus: Understanding and Optimizing GPU Energy Consumption of DNN Trainingcited by 78
  10. 2022Efficient Methods for Natural Language Processing: A Surveycited by 78
  11. 2023A Systematic Review of Green AIcited by 77
  12. 2023LLMCarbon: Modeling the end-to-end Carbon Footprint of Large Language Modelscited by 71
38 more
  1. 2021Compute and Energy Consumption Trends in Deep Learning Inferencecited by 61
  2. 2023FUTURE-AI: International consensus guideline for trustworthy and deployable artificial intelligence in healthcarecited by 59
  3. 2021Distributed Deep Learning in Open Collaborationscited by 58
  4. 2022Eco2AI: carbon emissions tracking of machine learning models as the first step towards sustainable AIcited by 58
  5. 2023Counting Carbon: A Survey of Factors Influencing the Emissions of Machine Learningcited by 42
  6. 2021How I failed machine learning in medical imaging -- shortcomings and recommendationscited by 37
  7. 2023How to estimate carbon footprint when training deep learning models? A guide and reviewcited by 34
  8. 2021Unraveling the Hidden Environmental Impacts of AI Solutions for Environmentcited by 32
  9. 2025Think Smarter not Harder: Adaptive Reasoning with Inference Aware Optimizationcited by 29
  10. 2022Spiking Graph Convolutional Networkscited by 26
  11. 2021Towards Green Automated Machine Learning: Status Quo and Future Directionscited by 25
  12. 2023Exploring the Carbon Footprint of Hugging Face's ML Models: A Repository Mining Studycited by 23
  13. 2024The Price of Prompting: Profiling Energy Use in Large Language Models Inferencecited by 22
  14. 2022Towards Climate Awareness in NLP Researchcited by 19
  15. 2022Carbon Footprint of Selecting and Training Deep Learning Models for Medical Image Analysiscited by 18
  16. 2022The Cost of Training Machine Learning Models over Distributed Data Sourcescited by 18
  17. 2023The Ethical Implications of Generative Audio Models: A Systematic Literature Reviewcited by 18
  18. 2021Shrinking Bigfoot: Reducing wav2vec 2.0 footprintcited by 16
  19. 2022An Energy and Carbon Footprint Analysis of Distributed and Federated Learningcited by 16
  20. 2023Watt For What: Rethinking Deep Learning's Energy-Performance Relationshipcited by 16
  21. 2021A Bregman Learning Framework for Sparse Neural Networkscited by 15
  22. 2022Federated Learning for 5G Base Station Traffic Forecastingcited by 15
  23. 2023An Interdisciplinary Outlook on Large Language Models for Scientific Researchcited by 15
  24. 2024MLPerf Power: Benchmarking the Energy Efficiency of Machine Learning Systems from Microwatts to Megawatts for Sustainable AIcited by 15
  25. 2023Towards energy-efficient Deep Learning: An overview of energy-efficient approaches along the Deep Learning Lifecyclecited by 13
  26. 2025Intelligence per Watt: Measuring Intelligence Efficiency of Local AIcited by 13
  27. 2021Energy Consumption of Deep Generative Audio Modelscited by 12
  28. 2021Edge-Native Intelligence for 6G Communications Driven by Federated Learning: A Survey of Trends and Challengescited by 12
  29. 2023Efficiency is Not Enough: A Critical Perspective of Environmentally Sustainable AIcited by 12
  30. 2025From Prompts to Power: Measuring the Energy Footprint of LLM Inferencecited by 12
  31. 2021IrEne: Interpretable Energy Prediction for Transformerscited by 11
  32. 2022EC-NAS: Energy Consumption Aware Tabular Benchmarks for Neural Architecture Searchcited by 11
  33. 2024The Responsible Foundation Model Development Cheatsheet: A Review of Tools & Resourcescited by 11
  34. 2025Towards Sustainable NLP: Insights from Benchmarking Inference Energy in Large Language Modelscited by 11
  35. 2021Dissecting FLOPs along input dimensions for GreenAI cost estimationscited by 10
  36. 2021Keys to Accurate Feature Extraction Using Residual Spiking Neural Networkscited by 10
  37. 2022MEDIAR: Harmony of Data-Centric and Model-Centric for Multi-Modality Microscopycited by 10
  38. 2023CatAlyst: Domain-Extensible Intervention for Preventing Task Procrastination Using Large Generative Modelscited by 10

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