How Much Energy Is Wasted in LLM operations? Evidence from Kernel-Level DVFS
Abstract
The rapid growth of AI has fueled the expansion of accelerator- or GPU-based data centers. However, the rising operational energy consumption has emerged as a critical bottleneck and a major sustainability concern. Dynamic Voltage and Frequency Scaling (DVFS) is a well-known technique used to reduce energy consumption, and thus improve energy-efficiency, since it requires little effort and works with existing hardware. Reducing the energy consumption of training and inference of Large Language Models (LLMs) through DVFS or power capping is feasible: related work has shown energy savings can be significant, but at the cost of significant slowdowns.
Fields of Study & Subjects
Advanced Neural Network Applications
Big Data and Digital Economy
Parallel Computing and Optimization Techniques
Publication details
- Journal / source
- Proceedings of the 23rd ACM International Conference on Computing Frontiers
- Publisher
- ACM
- Pages
- 90–100
- Publication date
- 19 May 2026
- Language
- EN