Hugging Face Trending Papers July 2, 2026

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

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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.

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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

Hugging Face Trending Papers

Jul 29

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

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.

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

arXiv AI

Jul 8

Is Your NPU Ready for LLMs? Dissecting the Hidden Efficiency Bottlenecks in Mobile LLM Inference

arXiv:2607. 05475v1 Announce Type: cross Abstract: Deploying Large Language Models (LLMs) on mobile devices enhances privacy and reduces latency, but is severely bottlenecked by hardware inefficiency.

By Guanyu Cai, Ruiming Tian, Lang Yang, Zhouhong Ren, Jinliang Yuan, Lingkun Li, Jiliang Wang

llms efficiency safety

arXiv AI

Jul 7

Data Driven Optimization of GPU efficiency for Distributed LLM-Adapter Serving

arXiv:2602. 24044v2 Announce Type: replace-cross Abstract: Large Language Model (LLM) adapters enable low-cost model specialization, but introduce complex caching and scheduling challenges in distributed serving systems where hundreds of adapters must be hosted concurrently.

By Ferran Agullo, Joan Oliveras, Chen Wang, Alberto Gutierrez-Torre, Olivier Tardieu, Alaa Youssef, Jordi Torres, Josep Ll. Berral

llms efficiency benchmarks

arXiv Machine Learning

Jun 9

LLM Inference at the Edge: Mobile, NPU, and GPU Performance Efficiency Trade-offs Under Sustained Load

arXiv:2603. 23640v2 Announce Type: replace-cross Abstract: Deploying large language models on-device for always-on personal agents demands sustained inference from hardware tightly constrained in power, thermal envelope, and memory.

By Pranay Tummalapalli, Sahil Arayakandy, Ritam Pal, Kautuk Kundan

llms agents efficiency benchmarks

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

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

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

Is Your NPU Ready for LLMs? Dissecting the Hidden Efficiency Bottlenecks in Mobile LLM Inference

Data Driven Optimization of GPU efficiency for Distributed LLM-Adapter Serving

LLM Inference at the Edge: Mobile, NPU, and GPU Performance Efficiency Trade-offs Under Sustained Load