arXiv:2610.08502v1 Announce Type: cross Abstract: Proactive power management systems reduce processor dynamic power through runtime power prediction and power-aware scheduling. Accurate, stable and l...
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.
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:2609.09662v1 Announce Type: cross Abstract: Deploying Large Language Models (LLMs) directly on mobile platforms at the edge is gaining traction due to a myriad of benefits, such as increased pr...
arXiv:2607. 02391v1 Announce Type: cross Abstract: Large Language Model (LLM) inference workloads are a rapidly growing contributor to data center energy consumption.
arXiv:2605.31478v2 Announce Type: replace-cross Abstract: Large language models (LLMs) can turn grid-analysis requests into executable programs for power-system simulation, but utilities and research...
arXiv:2308. 07867v4 Announce Type: replace-cross Abstract: The absence of formal performance guarantees in machine learning (ML) has limited its adoption for safety-critical power system applications, where confidence and interpretability are as vital as accuracy.
arXiv:2606. 11348v1 Announce Type: new Abstract: Clock Tree Synthesis (CTS) is a computationally expensive stage in the physical design flow, requiring iterative EDA tool invocations to navigate a vast configuration space for optimal power, wirelength, and timing skew.
The paper explores energy-aware knowledge distillation for large language models (LLMs) used in software engineering tasks such as clone detection, vulnerability prediction, and code summarization. It shows that the commonly used FLOPs metric does not reliably reflect actual energy consumption, and that using energy-surrogate models during distillation can reduce inference energy by up to 90% and memory usage by 86% with only modest accuracy loss. The study demonstrates that guiding distillation with direct energy estimates improves the sustainability and deployability of LLMs on consumer hardware.
arXiv:2610.14389v1 Announce Type: new Abstract: Benchmark scores tell us what an LLM can do, but not how much energy is required to deliver that capability. We directly measure this trade-off for 86...
The paper introduces the Power Flexibility Index (PFI) to measure how large language model (LLM) training performance changes when GPU power is reduced. Using 131 training runs on H200 and H100 GPUs, the study finds that LLM jobs have significant but variable power elasticity and identifies telemetry signals that can predict PFI during runtime. The authors demonstrate that allocating power based on PFI maximizes overall token throughput, recovering about 1.5k tokens/s per job under a 30% power reduction, which represents 63% of the gap between equal-weight and perfect-information allocations.
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.