The Energy Consumption of Transformer Fine-Tuning: A Roofline-Inspired Scaling Model

• URL: http://arxiv.org/abs/2606.23546v1
• Date: Mon, 22 Jun 2026 16:18:06 GMT
• Title: The Energy Consumption of Transformer Fine-Tuning: A Roofline-Inspired Scaling Model
• Abstract summary: We present a framework for modeling the energy consumption of Transformer training on multiple GPUs.<n>Inspired by roofline models, our approach incorporates a speedup-based hardware-efficiency factor.<n>We derive a scaling law model that accurately predicts training energy across heterogeneous configurations.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Transformer-based models underpin modern natural language processing but incur rapidly growing computational and energy costs. As training scales in both model size and parallelism, accurately predicting energy consumption has become critical for sustainable and cost-aware system design. We present a framework for modeling the energy consumption of Transformer training on multiple GPUs. Using controlled architectural sweeps of BERT models, we relate measured energy to lightweight proxies for compute, memory traffic, and hardware efficiency. Inspired by roofline models, our approach incorporates a speedup-based hardware-efficiency factor that captures the effects of tensor parallelism and fully sharded data parallelism. We derive a scaling law model that accurately predicts training energy across heterogeneous configurations.

Related papers

• Unlocking Feature Learning in Gated Delta Networks at Scale [52.705562336559446]
  Training and scaling Large Language Models demand enormous computational resources.<n>We derive the scaling rules for Gated Delta Network.<n> Experiments on language-model pre-training confirm that our configurations enable stable learning-rate transfer.
  arXiv  Detail & Related papers  (2026-06-02T08:45:24Z)
• Energy Scaling Laws for Diffusion Models: Quantifying Compute and Carbon Emissions in Image Generation [50.21021246855702]
  We propose an adaptation of Kaplan scaling laws to predict GPU energy consumption for diffusion models based on computational complexity (FLOPs)<n>Our approach decomposes diffusion model inference into text encoding, iterative denoising, and decoding components, with the hypothesis that denoising operations dominate energy consumption due to their repeated execution across multiple inference steps.<n>Our results validate the compute-bound nature of diffusion inference and provide a foundation for sustainable AI deployment planning and carbon footprint estimation.
  arXiv  Detail & Related papers  (2025-11-21T08:12:47Z)
• Study Design and Demystification of Physics Informed Neural Networks for Power Flow Simulation [2.3641090634080064]
  Power flow simulators are commonly used to support operators by evaluating potential actions before implementation.<n>Traditional physical solvers, while accurate, are often too slow for near real-time use.<n>Machine learning models have emerged as fast surrogates, and to improve their adherence to physical laws.<n>This paper presents an ablation study to demystify hybridization strategies, ranging from incorporating physical constraints as regularization terms or unsupervised losses.
  arXiv  Detail & Related papers  (2025-09-23T16:55:13Z)
• Atleus: Accelerating Transformers on the Edge Enabled by 3D Heterogeneous Manycore Architectures [18.355570259898]
  We propose the design of a 3D heterogeneous architecture referred to as Atleus.<n>Atleus incorporates heterogeneous computing resources specifically optimized to accelerate transformer models.<n>We show that Atleus outperforms existing state-of-the-art by up to 56x and 64.5x in terms of performance and energy efficiency respectively.
  arXiv  Detail & Related papers  (2025-01-16T15:11:33Z)
• PearSAN: A Machine Learning Method for Inverse Design using Pearson Correlated Surrogate Annealing [66.27103948750306]
  PearSAN is a machine learning-assisted optimization algorithm applicable to inverse design problems with large design spaces.<n>It uses a Pearson correlated surrogate model to predict the figure of merit of the true design metric.<n>It achieves a state-of-the-art maximum design efficiency of 97%, and is at least an order of magnitude faster than previous methods.
  arXiv  Detail & Related papers  (2024-12-26T17:02:19Z)
• Beyond Scaling Laws: Understanding Transformer Performance with Associative Memory [11.3128832831327]
  Increasing the size of a Transformer does not always lead to enhanced performance.<n>We present a theoretical framework that sheds light on the memorization during pre-training of transformer-based language models.
  arXiv  Detail & Related papers  (2024-05-14T15:48:36Z)
• Improved particle-flow event reconstruction with scalable neural networks for current and future particle detectors [1.4609888393206634]
  We study scalable machine learning models for event reconstruction in electron-positron collisions based on a full detector simulation. We compare a graph neural network and kernel-based transformer and demonstrate that we can avoid operations while achieving realistic reconstruction. The best graph neural network model shows improvement in the jet transverse momentum resolution by up to 50% compared to the rule-based algorithm.
  arXiv  Detail & Related papers  (2023-09-13T08:16:15Z)
• TransCODE: Co-design of Transformers and Accelerators for Efficient Training and Inference [6.0093441900032465]
  We propose a framework that simulates transformer inference and training on a design space of accelerators. We use this simulator in conjunction with the proposed co-design technique, called TransCODE, to obtain the best-performing models. The obtained transformer-accelerator pair achieves 0.3% higher accuracy than the state-of-the-art pair.
  arXiv  Detail & Related papers  (2023-03-27T02:45:18Z)
• Learning to Grow Pretrained Models for Efficient Transformer Training [72.20676008625641]
  We learn to grow pretrained transformers, where we learn to linearly map the parameters of the smaller model to initialize the larger model. Experiments across both language and vision transformers demonstrate that our learned Linear Growth Operator (LiGO) can save up to 50% computational cost of training from scratch.
  arXiv  Detail & Related papers  (2023-03-02T05:21:18Z)
• Efficient pre-training objectives for Transformers [84.64393460397471]
  We study several efficient pre-training objectives for Transformers-based models. We prove that eliminating the MASK token and considering the whole output during the loss are essential choices to improve performance.
  arXiv  Detail & Related papers  (2021-04-20T00:09:37Z)
• Efficient Transformers in Reinforcement Learning using Actor-Learner Distillation [91.05073136215886]
  "Actor-Learner Distillation" transfers learning progress from a large capacity learner model to a small capacity actor model. We demonstrate in several challenging memory environments that using Actor-Learner Distillation recovers the clear sample-efficiency gains of the transformer learner model.
  arXiv  Detail & Related papers  (2021-04-04T17:56:34Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.