From Prompts to Power: Measuring the Energy Footprint of LLM Inference

• URL: http://arxiv.org/abs/2511.05597v1
• Date: Wed, 05 Nov 2025 15:06:46 GMT
• Title: From Prompts to Power: Measuring the Energy Footprint of LLM Inference
• Authors: Francisco Caravaca, Ángel Cuevas, Rubén Cuevas,
• Abstract summary: We present a large-scale measurement-based study comprising over 32,500 measurements across 21 GPU configurations and 155 model architectures.<n>Using the vLLM inference engine, we quantify energy usage at the prompt level and identify how architectural and operational factors shape energy demand.<n>We develop a predictive model that accurately estimates inference energy consumption across unseen architectures and hardware, and implement it as a browser extension to raise awareness of the environmental impact of generative AI.
• Score: 1.9116784879310027
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: The rapid expansion of Large Language Models (LLMs) has introduced unprecedented energy demands, extending beyond training to large-scale inference workloads that often dominate total lifecycle consumption. Deploying these models requires energy-intensive GPU infrastructure, and in some cases has even prompted plans to power data centers with nuclear energy. Despite this growing relevance, systematic analyses of inference energy consumption remain limited. In this work, we present a large-scale measurement-based study comprising over 32,500 measurements across 21 GPU configurations and 155 model architectures, from small open-source models to frontier systems. Using the vLLM inference engine, we quantify energy usage at the prompt level and identify how architectural and operational factors shape energy demand. Building on these insights, we develop a predictive model that accurately estimates inference energy consumption across unseen architectures and hardware, and implement it as a browser extension to raise awareness of the environmental impact of generative AI.

Related papers

• 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)
• Predicting Large-scale Urban Network Dynamics with Energy-informed Graph Neural Diffusion [51.198001060683296]
  Networked urban systems facilitate the flow of people, resources, and services.<n>Current models such as graph neural networks have shown promise but face a trade-off between efficacy and efficiency.<n>This paper addresses this trade-off by drawing inspiration from physical laws to inform essential model designs.
  arXiv  Detail & Related papers  (2025-07-31T01:24:01Z)
• Green MLOps to Green GenOps: An Empirical Study of Energy Consumption in Discriminative and Generative AI Operations [2.2765705959685234]
  This study investigates the energy consumption of Discriminative and Generative AI models within real-world MLOps pipelines.<n>We employ software-based power measurements to ensure ease of replication across diverse configurations, models, and datasets.
  arXiv  Detail & Related papers  (2025-03-31T10:28:04Z)
• From Computation to Consumption: Exploring the Compute-Energy Link for Training and Testing Neural Networks for SED Systems [9.658615045493734]
  We study several neural network architectures that are key components of sound event detection systems. We measure the energy consumption for training and testing small to large architectures. We establish complex relationships between the energy consumption, the number of floating-point operations, the number of parameters, and the GPU/memory utilization.
  arXiv  Detail & Related papers  (2024-09-08T12:51:34Z)
• Fine-Tuning and Deploying Large Language Models Over Edges: Issues and Approaches [64.42735183056062]
  Large language models (LLMs) have evolved from specialized deep models to versatile foundation models.<n>LLMs require fine-tuning on local datasets and substantial memory for deployment over the network edges.<n>LLMs have been expanded beyond text generation to create images, audio, video, and multi-modal content.<n>Model fine-tuning and model-compression techniques have been developed to support the sustainable growth of LLMs.
  arXiv  Detail & Related papers  (2024-08-20T09:42:17Z)
• Computing Within Limits: An Empirical Study of Energy Consumption in ML Training and Inference [2.553456266022126]
  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.
  arXiv  Detail & Related papers  (2024-06-20T13:59:34Z)
• Power Hungry Processing: Watts Driving the Cost of AI Deployment? [74.19749699665216]
  generative, multi-purpose AI systems promise a unified approach to building machine learning (ML) models into technology. This ambition of generality'' comes at a steep cost to the environment, given the amount of energy these systems require and the amount of carbon that they emit. We measure deployment cost as the amount of energy and carbon required to perform 1,000 inferences on representative benchmark dataset using these models. We conclude with a discussion around the current trend of deploying multi-purpose generative ML systems, and caution that their utility should be more intentionally weighed against increased costs in terms of energy and emissions
  arXiv  Detail & Related papers  (2023-11-28T15:09:36Z)
• Energy Transformer [64.22957136952725]
  Our work combines aspects of three promising paradigms in machine learning, namely, attention mechanism, energy-based models, and associative memory. We propose a novel architecture, called the Energy Transformer (or ET for short), that uses a sequence of attention layers that are purposely designed to minimize a specifically engineered energy function.
  arXiv  Detail & Related papers  (2023-02-14T18:51:22Z)
• Energy Efficiency of Training Neural Network Architectures: An Empirical Study [11.325530936177493]
  The evaluation of Deep Learning models has traditionally focused on criteria such as accuracy, F1 score, and related measures. The computations needed to train such models entail a large carbon footprint. We study the relations between DL model architectures and their environmental impact in terms of energy consumed and CO$$ emissions produced during training.
  arXiv  Detail & Related papers  (2023-02-02T09:20:54Z)
• Precise Energy Consumption Measurements of Heterogeneous Artificial Intelligence Workloads [0.534434568021034]
  We present measurements of the energy consumption of two typical applications of deep learning models on different types of compute nodes. One advantage of our approach is that the information on energy consumption is available to all users of the supercomputer.
  arXiv  Detail & Related papers  (2022-12-03T21:40:55Z)
• Power Modeling for Effective Datacenter Planning and Compute Management [53.41102502425513]
  We discuss two classes of statistical power models designed and validated to be accurate, simple, interpretable and applicable to all hardware configurations and workloads. We demonstrate that the proposed statistical modeling techniques, while simple and scalable, predict power with less than 5% Mean Absolute Percent Error (MAPE) for more than 95% diverse Power Distribution Units (more than 2000) using only 4 features.
  arXiv  Detail & Related papers  (2021-03-22T21:22:51Z)
• Energy Drain of the Object Detection Processing Pipeline for Mobile Devices: Analysis and Implications [77.00418462388525]
  This paper presents the first detailed experimental study of a mobile augmented reality (AR) client's energy consumption and the detection latency of executing Convolutional Neural Networks (CNN) based object detection. Our detailed measurements refine the energy analysis of mobile AR clients and reveal several interesting perspectives regarding the energy consumption of executing CNN-based object detection.
  arXiv  Detail & Related papers  (2020-11-26T00:32:07Z)

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.