# Ground-Truthing AI Energy Consumption: Validating CodeCarbon Against External Measurements arXiv: https://arxiv.org/abs/2509.22092 ยท HTML: https://mlsanity.com/paper/2509.22092/ ## Key facts - Cited by 8 papers in the map (data through 14 Oct 2026). - Builds on 34 of its 77 references that are in the map. - First posted on arXiv 26 Sep 2025. ## Builds on (most cited first, 30 of 34) - [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015) - [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014) - [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024) - [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016) - [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019) - [Rethinking the Inception Architecture for Computer Vision](https://mlsanity.com/paper/1512.00567/) (2015) - [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://mlsanity.com/paper/1801.04381/) (2018) - [MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications](https://mlsanity.com/paper/1704.04861/) (2017) - [A ConvNet for the 2020s](https://mlsanity.com/paper/2201.03545/) (2022) - [Xception: Deep Learning with Depthwise Separable Convolutions](https://mlsanity.com/paper/1610.02357/) (2016) - [Searching for MobileNetV3](https://mlsanity.com/paper/1905.02244/) (2019) - [Identity Mappings in Deep Residual Networks](https://mlsanity.com/paper/1603.05027/) (2016) - [Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone](https://mlsanity.com/paper/2404.14219/) (2024) - [Gemma 3 Technical Report](https://mlsanity.com/paper/2503.19786/) (2025) - [Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning](https://mlsanity.com/paper/1602.07261/) (2016) - [EfficientNetV2: Smaller Models and Faster Training](https://mlsanity.com/paper/2104.00298/) (2021) - [Learning Transferable Architectures for Scalable Image Recognition](https://mlsanity.com/paper/1707.07012/) (2017) - [Green AI](https://mlsanity.com/paper/1907.10597/) (2019) - [TinyLlama: An Open-Source Small Language Model](https://mlsanity.com/paper/2401.02385/) (2024) - [Quantifying the Carbon Emissions of Machine Learning](https://mlsanity.com/paper/1910.09700/) (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) - [Power Hungry Processing: Watts Driving the Cost of AI Deployment?](https://mlsanity.com/paper/2311.16863/) (2023) - [The Values Encoded in Machine Learning Research](https://mlsanity.com/paper/2106.15590/) (2021) - [Carbontracker: Tracking and Predicting the Carbon Footprint of Training Deep Learning Models](https://mlsanity.com/paper/2007.03051/) (2020) - [Compute Trends Across Three Eras of Machine Learning](https://mlsanity.com/paper/2202.05924/) (2022) - [Magistral](https://mlsanity.com/paper/2506.10910/) (2025) - [Eco2AI: carbon emissions tracking of machine learning models as the first step towards sustainable AI](https://mlsanity.com/paper/2208.00406/) (2022) - [Hype, Sustainability, and the Price of the Bigger-is-Better Paradigm in AI](https://mlsanity.com/paper/2409.14160/) (2024) ## Built on by (most cited first, 8 of 8) - [Energy Scaling Laws for Diffusion Models: Quantifying Compute in Image Generation](https://mlsanity.com/paper/2511.17031/) (2025) - [EnviroLLM: Resource Tracking and Optimization for Local AI](https://mlsanity.com/paper/2512.12004/) (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) - [Pruning Extensions and Efficiency Trade-Offs for Sustainable Time Series Classification](https://mlsanity.com/paper/2604.07953/) (2026) - [Beyond Parameter Aggregation: Semantic Consensus for Federated Fine-Tuning of LLMs](https://mlsanity.com/paper/2605.11857/) (2026) - [The Right Information Extraction Pipeline Depends on the Document: Accuracy-Energy Trade-offs for Small, Local Models](https://mlsanity.com/paper/2609.31341/) (2026) - [From Grey-Box to Green-Box: When can Physics-Informed Machine Learning Reduce Carbon Footprints in Structural Health Monitoring?](https://mlsanity.com/paper/2609.33387/) (2026) ## Abstract (arXiv) Although machine learning (ML) and artificial intelligence (AI) present fascinating opportunities for innovation, their rapid development is also significantly impacting our environment. In response to growing resource-awareness in the field, quantification tools such as the ML Emissions Calculator and CodeCarbon were developed to estimate the energy consumption and carbon emissions of running AI models. They are easy to incorporate into AI projects, however also make pragmatic assumptions and neglect important factors, raising the question of estimation accuracy. This study systematically evaluates the reliability of static and dynamic energy estimation approaches through comparisons with ground-truth measurements across hundreds of AI experiments. Based on the proposed validation framework, investigative insights into AI energy demand and estimation inaccuracies are provided. While generally following the patterns of AI energy consumption, the established estimation approaches are shown to consistently make errors of up to 40%. By providing empirical evidence on energy estimation quality and errors, this study establishes transparency and validates widely used tools for sustainable AI development. It moreover formulates guidelines for improving the state-of-the-art and offers code for extending the validation to other domains and tools, thus making important contributions to resource-aware ML and AI sustainability research.