Hugging Face Trending Papers July 29, 2026

From Tokens to Watt-hours: Analytical Energy Estimation for LLM Inference on Modern GPUs

Read the original on Hugging Face Trending Papers →

The operational energy consumption of large language model (LLM) inference is becoming an increasingly important component of the environmental footprint of deployed AI systems. However, direct measurement of inference energy often requires hardware telemetry, power instrumentation, or infrastructure-specific monitoring, limiting its applicability in comparative studies, early-stage system design, and sustainability reporting.

Summary generated by The Flow from the publisher's feed. The full article lives at Hugging Face Trending Papers.

llms

arXiv Machine Learning

Jul 30

From Tokens to Watt-hours: Analytical Energy Estimation for LLM Inference on Modern GPUs

arXiv:2607. 26571v1 Announce Type: new Abstract: The operational energy consumption of large language model (LLM) inference is becoming an increasingly important component of the environmental footprint of deployed AI systems.

By Tina Vartziotis, Rodopi Kosteli, Elli Vartziotis, George Dasoulas, Michael Keckeisen, Konstantinos Skianis, Sotirios Kotsopoulos, Francesca Dominici

llms

Hugging Face Trending Papers

Jul 2

WattGPU: Predicting Inference Power and Latency on Unseen GPUs and LLMs

Large Language Model (LLM) inference workloads are a rapidly growing contributor to data center energy consumption. Optimizing these deployments requires matching specific LLMs to the most efficient GPUs, but operators currently lack the tools to do so without exhaustively profiling each combination.

llms

arXiv Machine Learning

Jul 3

WattGPU: Predicting Inference Power and Latency on Unseen GPUs and LLMs

arXiv:2607. 02391v1 Announce Type: cross Abstract: Large Language Model (LLM) inference workloads are a rapidly growing contributor to data center energy consumption.

By Mauricio Fadel Argerich, Jonathan F\"urst, Marta Pati\~no-Mart\'inez

llms

arXiv Machine Learning

Jun 15

BigPower: Hierarchical Source-Level Module Power Estimation for CPUs with Large Language Models

arXiv:2606. 13747v1 Announce Type: cross Abstract: Accurate power estimation is important for understanding and optimizing CPU power behavior, yet practical workflows often rely on simulation-derived information or post-silicon analysis.

By Honghua Zhu, Chunjie Luo, Jianfeng Zhan

llms

arXiv AI

Aug 10

Multi-Level Modeling of Large Language Model Inference Latency and Energy via Hybrid Analytical--Machine-Learning Predictors

arXiv:2608. 06723v1 Announce Type: cross Abstract: The rapid scaling of Large Language Models (LLMs) has significantly increased computational cost, energy consumption, and inference latency, making accurate estimation essential for sustainable artificial intelligence deployment and hardware-aware design.

By Saeid Shokoufa, Mohammad Erfan Sadeghi, Mehdi Kamal, Massoud Pedram

llms efficiency

arXiv Machine Learning

Jun 24

EnerInfer: Energy-Aware On-Device LLM Inference

arXiv:2606. 23001v1 Announce Type: cross Abstract: On-device LLM inference is increasingly attractive for privacy-preserving, reliable, and cost-effective deployment, yet its energy and thermal costs remain a critical bottleneck.

By Bohua Zou, Nian Liu, Binqi Sun, Matteo Mascherin, Debayan Roy, Yutao Liu, Yu Peng, Ning Jia, Haibo Chen

llms efficiency safety

Related stories

From Tokens to Watt-hours: Analytical Energy Estimation for LLM Inference on Modern GPUs

WattGPU: Predicting Inference Power and Latency on Unseen GPUs and LLMs

WattGPU: Predicting Inference Power and Latency on Unseen GPUs and LLMs

BigPower: Hierarchical Source-Level Module Power Estimation for CPUs with Large Language Models

Multi-Level Modeling of Large Language Model Inference Latency and Energy via Hybrid Analytical--Machine-Learning Predictors

EnerInfer: Energy-Aware On-Device LLM Inference