# Computing Within Limits: An Empirical Study of Energy Consumption in ML Training and Inference

arXiv: https://arxiv.org/abs/2406.14328 · HTML: https://mlsanity.com/paper/2406.14328/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 26 references that are in the map.
- First posted on arXiv 20 Jun 2024.

## Builds on (most cited first, 10 of 10)

- [Sustainable AI: Environmental Implications, Challenges and Opportunities](https://mlsanity.com/paper/2111.00364/) (2021)
- [The Carbon Footprint of Machine Learning Training Will Plateau, Then Shrink](https://mlsanity.com/paper/2204.05149/) (2022)
- [Designing Energy-Efficient Convolutional Neural Networks using Energy-Aware Pruning](https://mlsanity.com/paper/1611.05128/) (2016)
- [A Systematic Review of Green AI](https://mlsanity.com/paper/2301.11047/) (2023)
- [Can Federated Learning Save The Planet?](https://mlsanity.com/paper/2010.06537/) (2020)
- [A Survey on ChatGPT: AI-Generated Contents, Challenges, and Solutions](https://mlsanity.com/paper/2305.18339/) (2023)
- [AI-Native Network Slicing for 6G Networks](https://mlsanity.com/paper/2105.08576/) (2021)
- [Energy-Rate-Quality Tradeoffs of State-of-the-Art Video Codecs](https://mlsanity.com/paper/2210.00618/) (2022)
- [FROST: Towards Energy-efficient AI-on-5G Platforms -- A GPU Power Capping Evaluation](https://mlsanity.com/paper/2310.11131/) (2023)
- [Comparative Study of Hardware and Software Power Measurements in Video Compression](https://mlsanity.com/paper/2312.12150/) (2023)

## Built on by (most cited first, 4 of 4)

- [AI-Native Multi-Access Future Networks -- The REASON Architecture](https://mlsanity.com/paper/2411.06870/) (2024)
- [Green MLOps to Green GenOps: An Empirical Study of Energy Consumption in Discriminative and Generative AI Operations](https://mlsanity.com/paper/2503.23934/) (2025)
- [Visualising Information Flow in Word Embeddings with Diffusion Tensor Imaging](https://mlsanity.com/paper/2601.05713/) (2026)
- [The Hidden Environmental Cost of Poor Coding Practices in TensorFlow and Keras Applications: A Study on Resource Leaks and Carbon Emissions](https://mlsanity.com/paper/2606.19799/) (2026)

## Abstract (arXiv)

Machine learning (ML) has seen tremendous advancements, but its environmental footprint remains a concern. Acknowledging the growing environmental impact of ML this paper investigates Green ML, examining various model architectures and hyperparameters in both training and inference phases to identify energy-efficient practices. Our study leverages software-based power measurements for ease of replication across diverse configurations, models and datasets. In this paper, we examine multiple models and hardware configurations to identify correlations across the various measurements and metrics and key contributors to energy reduction. Our analysis offers practical guidelines for constructing sustainable ML operations, emphasising energy consumption and carbon footprint reductions while maintaining performance. As identified, short-lived profiling can quantify the long-term expected energy consumption. Moreover, model parameters can also be used to accurately estimate the expected total energy without the need for extensive experimentation.