arXiv AI October 31, 2026 By Jiansheng Wang, Jaskeerat Singh, Haochen Xu, Xinyi Li, Mingyuan Yan, Shaohui Liu, Yuzhuo Li, Yize Chen, Wenqi Cui

LLM-PowerTrace: A Dataset of Time-Resolved GPU Power Traces for AI Compute

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LLM-PowerTrace is an open dataset that provides time‑resolved, per‑GPU power traces for large language model fine‑tuning and inference. The traces are sampled every 100 ms across various NVIDIA platforms and include detailed workload and system metadata. The dataset covers a wide range of model families, sizes, and controlled sweeps over batch size, sequence length, precision, parallelism, checkpointing, concurrency, GPU frequency, KV‑cache and weight quantization, prompt and output lengths, and request‑arrival patterns, and it defines power‑grid‑facing metrics and an AI‑compute power & energy scaling law.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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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

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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.

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Hugging Face Trending Papers

Jul 2

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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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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

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arXiv Machine Learning

Aug 31

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By Prabhu Vellaisamy, Vanessa Lam, Shawn Blanton, John Paul Shen

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arXiv AI

Sep 28

Energy Efficiency of Locally Deployed LLMs: A Preliminary Quantitative GPU Power Benchmark on Consumer Hardware

The paper reports a reproducible GPU power benchmark for 18 open‑source LLMs (0.5B–7B parameters) run on a single consumer RTX 4060ti GPU using the Ollama inference engine. Energy metrics such as mean/peak power, total energy per prompt, energy per output token, and throughput were measured, revealing that model architecture and quantization strategy, rather than parameter count alone, drive energy efficiency. The most efficient models were qwen2.5:0.5b and tinyllama:1.1b, while the 7B‑Mistral model consumed up to 8.6× more energy per token, and qwen3.5:0.8b(on) showed unusually high per‑prompt energy due to extended internal reasoning.

By Philipp M. Z\"ahl, Elja Dalipaj, Anika Hennig, Timon Bayer

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