The paper demonstrates that an external observer can identify the type of workload running on an NVIDIA H200 GPU by analyzing its power draw, distinguishing training, inference, and non‑AI tasks with high accuracy. Using 930 recorded traces, the authors achieve 97% accuracy and a macro‑averaged F1 score of 0.955 on unseen model families. They also test four evasion strategies to disguise training as inference, showing that a hardened detector can catch most attacks, though one strategy (LoRA) remains partially detectable.
By Simone Gargiulo, Gabriel Kulp
arXiv:2606. 00279v1 Announce Type: cross Abstract: Verifying claims about AI workloads is a pre- requisite for credible AI governance of covert adversaries (who comply with monitoring only when detection likelihood is high), yet the ap- parent non-determinism of GPU floating-point arithmetic forces auditors to accept approximate output matches.
By Naci Cankaya
The paper proposes a method to detect large AI model training runs on a data‑centre power grid using Viterbi tracking of iteration cadence. On simulated data, the tracker identifies at least 96 % of training runs with a 1 % false‑alarm rate when the operator does not hide activity, and remains effective even against several adversarial hiding strategies. The approach requires only a modest passive power meter sampling at 2 Hz or faster, making hidden training both detectable and costly for the operator.
By Tom Kimpson, Mauricio Baker, Emlyn Graham
arXiv:2605. 27599v2 Announce Type: replace-cross Abstract: Agentic AI workloads - where a single user goal triggers multi-step orchestration, tool calls, retries, and failure recovery - are being targeted for edge deployment, with NVIDIA, Dell, HP, ASUS, MSI, Acer, and Gigabyte all shipping GB10-based desktop AI systems in 2026.
By Deepak Panigrahy, Aakash Tyagi
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.
By Jiansheng Wang, Jaskeerat Singh, Haochen Xu, Xinyi Li, Mingyuan Yan, Shaohui Liu, Yuzhuo Li, Yize Chen, Wenqi Cui
arXiv:2607. 13221v1 Announce Type: cross Abstract: Real-time N-1 contingency screening in an energy management system trades assurance against cost: verifying every credible outage with full power flow is too slow, while fast linear-sensitivity screening gives no statistical guarantee and can silently pass unsafe operating points, especially when a controller drives the system into unfamiliar regimes.
By Jayakumar Manoharan
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.
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
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
arXiv:2606. 19262v1 Announce Type: new Abstract: Hardware-enabled monitoring of GPU workloads underpins many proposals for AI compute governance, but if developers can defeat monitoring mechanisms, such schemes are unworkable.
By Robi Rahman, Sabiha Tajdari
arXiv:2608. 05944v1 Announce Type: cross Abstract: We report operational experience full-fine-tuning a 32.
By Seon Ho Kim, Ui Jeong Jeon, Su Hyeon Kim, Min Tae Hwang
The paper introduces a phase‑decoupled, model‑calibrated power controller for disaggregated large‑language‑model (LLM) serving, addressing the mismatch between GPU power settings and the distinct hardware regimes of prefill and decode stages. By calibrating separate power caps for each lane based on measured throughput‑latency cliffs, the authors achieve a 20.4% increase in tokens per joule with only a 3.5% rise in mean end‑to‑end latency on an 8‑node B200 cluster, outperforming NVIDIA’s Max‑Q profile. The approach also demonstrates consistent meeting of ITL‑p99 service‑level objectives across multiple MoE models and yields a 32.3% electricity savings over a sustained three‑day run.
By Jae Gon Kim, Donghoon Yoo, Hanyul Ryu, Sungho Ha, Juyeon Lee, Soojung Ryu