The Energy Blind Spot: NVIDIA's Flagship Edge AI Hardware Cannot Support Process-Level Energy Attribution

[2605.27599] The Energy Blind Spot: NVIDIA's Flagship Edge AI Hardware Cannot Support Process-Level Energy Attribution Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Computer Science > Machine Learning arXiv:2605.27599 (cs) [Submitted on 26 May 2026 ( v1 ), last revised 14 Aug 2026 (this version, v3)] Title: The Energy Blind Spot: NVIDIA's Flagship Edge AI Hardware Cannot Support Process-Level Energy Attribution Authors: Deepak Panigrahy , Aakash Tyagi View a PDF of the paper titled The Energy Blind Spot: NVIDIA's Flagship Edge AI Hardware Cannot Support Process-Level Energy Attribution, by Deepak Panigrahy and 1 other authors View PDF HTML (experimental) 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. Prior work shows orchestration structure dominates agentic energy cost and CPU-side processing accounts for up to 44% of total dynamic energy. We report a systematic energy-observability audit of the ASUS Ascent GX10 (GB10 SoC) and find that the platform exposes no CPU energy counter, no INA power-rail monitor, no IPMI/BMC, and no SCMI powercap protocol through any supported software interface. The only on-device energy telemetry is instantaneous GPU power via NVML. We further discover that the MediaTek firmware already computes per-rail energy internally via an undocumented ACPI interface (SPBM), but NVIDIA states there are "no plans to expose CPU rail information." On-device per-process energy attribution - as performed on x86 via RAPL - is therefore not reproducible on this platform through supported interfaces. We formalize a hardware requirements specification for energy-attributed AI, propose an interim calibration bridge for per-domain energy decomposition - confirmed on the Acer Veriton GN100 where CPU energy accumulators are live - and identify a standards-track path via SCMI powercap. Our findings motivate the low-carbon computing community to demand energy observability as a first-class hardware requirement. Comments: 5 pages, 0 figures. Accepted at the 2nd International Workshop on Low Carbon Computing (LOCO 2026), Lancaster University, United Kingdom, 10-11 September 2026. Part of the LOCO 2026 proceedings, arXiv:2608.02072 Subjects: Machine Learning (cs.LG) ; Artificial Intelligence (cs.AI); Hardware Architecture (cs.AR); Distributed, Parallel, and Cluster Computing (cs.DC); Performance (cs.PF) Report number: LOCO2026/P03 Cite as: arXiv:2605.27599 [cs.LG] (or arXiv:2605.27599v3 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2605.27599 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Deepak Panigrahy [ view email ] [v1] Tue, 26 May 2026 19:15:21 UTC (16 KB) [v2] Mon, 15 Jun 2026 07:12:23 UTC (16 KB) [v3] Fri, 14 Aug 2026 23:46:55 UTC (14 KB) Full-text links: Access Paper: View a PDF of the paper titled The Energy Blind Spot: NVIDIA's Flagship Edge AI Hardware Cannot Support Process-Level Energy Attribution, by Deepak Panigrahy and 1 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.LG < prev | next > new | recent | 2026-05 Change to browse by: cs cs.AI cs.AR cs.DC cs.PF References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... 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