ArXiv

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

2026-06-22 · 16:18 UTC ·Mansour Zoubeirou a Mayaki ·1 min read
Authors
Mansour Zoubeirou a Mayaki
Categories
cs.LG, cs.AI, cs.AR, cs.CL, cs.DC
arXiv
https://arxiv.org/abs/2606.23546v1
PDF
https://arxiv.org/pdf/2606.23546v1

Brief

Transformer fine-tuning energy modeling: the author proposes a roofline-inspired scaling model that maps measured energy to compute and memory-traffic proxies and a speedup-based hardware-efficiency term capturing tensor parallelism and fully sharded data parallelism. The law is validated on controlled BERT architectural sweeps across multiple GPUs and claims accurate predictions for heterogeneous training setups (arXiv 2606.23546v1).

Why it matters

Introduces a roofline-inspired scaling law that accurately predicts Transformer (BERT) fine-tuning energy across heterogeneous multi-GPU configurations, validated via controlled architectural sweeps of BERT models.

Key details

  • Energy predictions use lightweight proxies for compute and memory traffic plus a speedup-based hardware-efficiency factor that captures effects of tensor parallelism and fully sharded data parallelism.
  • Paper by Mansour Zoubeirou a Mayaki, posted on arXiv 2026-06-22 as 2606.23546v1 (cs.LG, cs.AI, cs.AR, cs.CL, cs.DC).
Source evidence

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.