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

arXiv: https://arxiv.org/abs/2306.08323 · HTML: https://mlsanity.com/paper/2306.08323/

## 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 (most cited first, 17 of 17)

- [Energy and Policy Considerations for Deep Learning in NLP](https://mlsanity.com/paper/1906.02243/) (2019)
- [Quantifying the Carbon Emissions of Machine Learning](https://mlsanity.com/paper/1910.09700/) (2019)
- [Tackling Climate Change with Machine Learning](https://mlsanity.com/paper/1906.05433/) (2019)
- [Estimating the Carbon Footprint of BLOOM, a 176B Parameter Language Model](https://mlsanity.com/paper/2211.02001/) (2022)
- [Towards the Systematic Reporting of the Energy and Carbon Footprints of Machine Learning](https://mlsanity.com/paper/2002.05651/) (2020)
- [Sustainable AI: Environmental Implications, Challenges and Opportunities](https://mlsanity.com/paper/2111.00364/) (2021)
- [Plug-and-Play Methods Provably Converge with Properly Trained Denoisers](https://mlsanity.com/paper/1905.05406/) (2019)
- [The Computational Limits of Deep Learning](https://mlsanity.com/paper/2007.05558/) (2020)
- [Carbontracker: Tracking and Predicting the Carbon Footprint of Training Deep Learning Models](https://mlsanity.com/paper/2007.03051/) (2020)
- [Measuring the Carbon Intensity of AI in Cloud Instances](https://mlsanity.com/paper/2206.05229/) (2022)
- [The role of artificial intelligence in achieving the Sustainable Development Goals](https://mlsanity.com/paper/1905.00501/) (2019)
- [Chasing Carbon: The Elusive Environmental Footprint of Computing](https://mlsanity.com/paper/2011.02839/) (2020)
- [Eco2AI: carbon emissions tracking of machine learning models as the first step towards sustainable AI](https://mlsanity.com/paper/2208.00406/) (2022)
- [Energy Usage Reports: Environmental awareness as part of algorithmic accountability](https://mlsanity.com/paper/1911.08354/) (2019)
- [Unraveling the Hidden Environmental Impacts of AI Solutions for Environment](https://mlsanity.com/paper/2110.11822/) (2021)
- [SECure: A Social and Environmental Certificate for AI Systems](https://mlsanity.com/paper/2006.06217/) (2020)
- [Green Algorithms: Quantifying the carbon footprint of computation](https://mlsanity.com/paper/2007.07610/) (2020)

## Built on by (most cited first, 30 of 34)

- [HiMAE: Hierarchical Masked Autoencoders Discover Resolution-Specific Structure in Wearable Time Series](https://mlsanity.com/paper/2510.25785/) (2025)
- [Mitigating Memorization In Language Models](https://mlsanity.com/paper/2410.02159/) (2024)
- [Towards Sustainable NLP: Insights from Benchmarking Inference Energy in Large Language Models](https://mlsanity.com/paper/2502.05610/) (2025)
- [Life-Cycle Emissions of AI Hardware: A Cradle-To-Grave Approach and Generational Trends](https://mlsanity.com/paper/2502.01671/) (2025)
- [Brevity is the soul of sustainability: Characterizing LLM response lengths](https://mlsanity.com/paper/2506.08686/) (2025)
- [Optical Generative Models](https://mlsanity.com/paper/2410.17970/) (2024)
- [Greening AI-enabled Systems with Software Engineering: A Research Agenda for Environmentally Sustainable AI Practices](https://mlsanity.com/paper/2506.01774/) (2025)
- [CATransformers: Carbon Aware Transformers Through Joint Model-Hardware Optimization](https://mlsanity.com/paper/2505.01386/) (2025)
- [How green is continual learning, really? Analyzing the energy consumption in continual training of vision foundation models](https://mlsanity.com/paper/2409.18664/) (2024)
- [Estimating Deep Learning energy consumption based on model architecture and training environment](https://mlsanity.com/paper/2307.05520/) (2023)
- [Edge Deployment of Small Language Models, a comprehensive comparison of CPU, GPU and NPU backends](https://mlsanity.com/paper/2511.22334/) (2025)
- [Latent Pollution Model: The Hidden Carbon Footprint in 3D Image Synthesis](https://mlsanity.com/paper/2407.14892/) (2024)
- [How Green Can AI Be? A Study of Trends in Machine Learning Environmental Impacts](https://mlsanity.com/paper/2412.17376/) (2024)
- [How Green are Neural Language Models? Analyzing Energy Consumption in Text Summarization Fine-tuning](https://mlsanity.com/paper/2501.15398/) (2025)
- [An Attention-based Feature Memory Design for Energy-Efficient Continual Learning](https://mlsanity.com/paper/2510.04660/) (2025)
- [The Environmental Impacts of Language Model Training Keep Rising Now is the Time to Catch Impacts on the Rebound](https://mlsanity.com/paper/2510.09022/) (2025)
- [Light over Heavy: Automated Performance Requirements Quantification with Linguistic Inducement](https://mlsanity.com/paper/2511.03421/) (2025)
- [Energy-Efficient Federated Learning for AIoT using Clustering Methods](https://mlsanity.com/paper/2505.09704/) (2025)
- [Guidelines for the Quality Assessment of Energy-Aware NAS Benchmarks](https://mlsanity.com/paper/2505.15631/) (2025)
- [First-of-its-kind AI model for bioacoustic detection using a lightweight associative memory Hopfield neural network](https://mlsanity.com/paper/2507.10642/) (2025)
- [Comparing energy consumption and accuracy in text classification inference](https://mlsanity.com/paper/2508.14170/) (2025)
- [Improving AI Efficiency in Data Centres by Power Dynamic Response](https://mlsanity.com/paper/2510.11119/) (2025)
- [CloudTracks: A Dataset for Localizing Ship Tracks in Satellite Images of Clouds](https://mlsanity.com/paper/2401.14486/) (2024)
- [Improve Machine Learning carbon footprint using Parquet dataset format and Mixed Precision training for regression models -- Part II](https://mlsanity.com/paper/2409.11071/) (2024)
- [Local Differences, Global Lessons: Insights from Organisation Policies for International Legislation](https://mlsanity.com/paper/2503.05737/) (2025)
- [Topology-Aware Knowledge Propagation in Decentralized Learning](https://mlsanity.com/paper/2505.11760/) (2025)
- [Self-Supervised Learning at the Edge: The Cost of Labeling](https://mlsanity.com/paper/2507.07033/) (2025)
- [Energy-Efficient Federated Learning via Adaptive Encoder Freezing for MRI-to-CT Conversion: A Green AI-Guided Research](https://mlsanity.com/paper/2512.03054/) (2025)
- [Towards eco friendly cybersecurity: machine learning based anomaly detection with carbon and energy metrics](https://mlsanity.com/paper/2601.00893/) (2025)
- [Optimising for Energy Efficiency and Performance in Machine Learning](https://mlsanity.com/paper/2601.08991/) (2026)

## 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.