mlsanityData to 14 Oct 2026

How to estimate carbon footprint when training deep learning models? A guide and review

arXiv 2306.08323 ↗First posted 14 Jun 2023cs.LG cs.AI cs.CYcited by 34

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Abstract (arXiv)

Machine learning and deep learning models have become essential in the recent fast development of artificial intelligence in many sectors of the society. It is now widely acknowledge that the development of these models has an environmental cost that has been analyzed in many studies. Several online and software tools have been developed to track energy consumption while training machine learning models. In this paper, we propose a comprehensive introduction and comparison of these tools for AI practitioners wishing to start estimating the environmental impact of their work. We review the specific vocabulary, the technical requirements for each tool. We compare the energy consumption estimated by each tool on two deep neural networks for image processing and on different types of servers. From these experiments, we provide some advice for better choosing the right tool and infrastructure.

Key facts. Cited by 34 papers in the map (data through 14 Oct 2026). Builds on 17 of its 51 references that are in the map. First posted on arXiv 14 Jun 2023.

Builds on

17 of 51 references.

References · 17

  1. 2019Energy and Policy Considerations for Deep Learning in NLPcited by 1,254
  2. 2019Quantifying the Carbon Emissions of Machine Learningcited by 521
  3. 2019Tackling Climate Change with Machine Learningcited by 265
  4. 2022Estimating the Carbon Footprint of BLOOM, a 176B Parameter Language Modelcited by 265
  5. 2020Towards the Systematic Reporting of the Energy and Carbon Footprints of Machine Learningcited by 249
  6. 2021Sustainable AI: Environmental Implications, Challenges and Opportunitiescited by 242
  7. 2019Plug-and-Play Methods Provably Converge with Properly Trained Denoiserscited by 198
  8. 2020The Computational Limits of Deep Learningcited by 193
  9. 2020Carbontracker: Tracking and Predicting the Carbon Footprint of Training Deep Learning Modelscited by 169
  10. 2022Measuring the Carbon Intensity of AI in Cloud Instancescited by 111
  11. 2019The role of artificial intelligence in achieving the Sustainable Development Goalscited by 100
  12. 2020Chasing Carbon: The Elusive Environmental Footprint of Computingcited by 72
5 more
  1. 2022Eco2AI: carbon emissions tracking of machine learning models as the first step towards sustainable AIcited by 58
  2. 2019Energy Usage Reports: Environmental awareness as part of algorithmic accountabilitycited by 44
  3. 2021Unraveling the Hidden Environmental Impacts of AI Solutions for Environmentcited by 32
  4. 2020SECure: A Social and Environmental Certificate for AI Systemscited by 7
  5. 2020Green Algorithms: Quantifying the carbon footprint of computationcited by 7

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 1; 2024: 7; 2025: 21; 2026: 5.

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  2. 2024Mitigating Memorization In Language Modelscited by 12
  3. 2025Towards Sustainable NLP: Insights from Benchmarking Inference Energy in Large Language Modelscited by 11
  4. 2025Life-Cycle Emissions of AI Hardware: A Cradle-To-Grave Approach and Generational Trendscited by 10
  5. 2025Brevity is the soul of sustainability: Characterizing LLM response lengthscited by 9
  6. 2024Optical Generative Modelscited by 8
  7. 2025Greening AI-enabled Systems with Software Engineering: A Research Agenda for Environmentally Sustainable AI Practicescited by 8
  8. 2025CATransformers: Carbon Aware Transformers Through Joint Model-Hardware Optimizationcited by 7
  9. 2024How green is continual learning, really? Analyzing the energy consumption in continual training of vision foundation modelscited by 5
  10. 2023Estimating Deep Learning energy consumption based on model architecture and training environmentcited by 4
  11. 2025Edge Deployment of Small Language Models, a comprehensive comparison of CPU, GPU and NPU backendscited by 4
  12. 2024Latent Pollution Model: The Hidden Carbon Footprint in 3D Image Synthesiscited by 2
22 more
  1. 2024How Green Can AI Be? A Study of Trends in Machine Learning Environmental Impactscited by 2
  2. 2025How Green are Neural Language Models? Analyzing Energy Consumption in Text Summarization Fine-tuningcited by 2
  3. 2025An Attention-based Feature Memory Design for Energy-Efficient Continual Learningcited by 2
  4. 2025The Environmental Impacts of Language Model Training Keep Rising Now is the Time to Catch Impacts on the Reboundcited by 2
  5. 2025Light over Heavy: Automated Performance Requirements Quantification with Linguistic Inducementcited by 2
  6. 2025Energy-Efficient Federated Learning for AIoT using Clustering Methodscited by 1
  7. 2025Guidelines for the Quality Assessment of Energy-Aware NAS Benchmarkscited by 1
  8. 2025First-of-its-kind AI model for bioacoustic detection using a lightweight associative memory Hopfield neural networkcited by 1
  9. 2025Comparing energy consumption and accuracy in text classification inferencecited by 1
  10. 2025Improving AI Efficiency in Data Centres by Power Dynamic Responsecited by 1
  11. 2024CloudTracks: A Dataset for Localizing Ship Tracks in Satellite Images of Cloudscited by 0
  12. 2024Improve Machine Learning carbon footprint using Parquet dataset format and Mixed Precision training for regression models -- Part IIcited by 0
  13. 2025Local Differences, Global Lessons: Insights from Organisation Policies for International Legislationcited by 0
  14. 2025Topology-Aware Knowledge Propagation in Decentralized Learningcited by 0
  15. 2025Self-Supervised Learning at the Edge: The Cost of Labelingcited by 0
  16. 2025Energy-Efficient Federated Learning via Adaptive Encoder Freezing for MRI-to-CT Conversion: A Green AI-Guided Researchcited by 0
  17. 2025Towards eco friendly cybersecurity: machine learning based anomaly detection with carbon and energy metricscited by 0
  18. 2026Optimising for Energy Efficiency and Performance in Machine Learningcited by 0
  19. 2026Benchmarking the Energy Savings with Speculative Decoding Strategiescited by 0
  20. 2026Towards Resource-Efficient LLMs: End-to-End Energy Accounting of Distillation Pipelinescited by 0
  21. 2026Leveraging AutoML for Sustainable Deep Learning: A Multi-Objective HPO Approach on Deep Shift Neural Networkscited by 0
  22. 2026From Grey-Box to Green-Box: When can Physics-Informed Machine Learning Reduce Carbon Footprints in Structural Health Monitoring?cited by 0