GPU telemetry anomaly: 146W idle draw on A100 (white paper)

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Outline

White Paper: The Ghost Power Anomaly – Exposing Hidden GPU Energy Waste and the Case for a New Observability Standard

Author: Mike Bains

Date: May 19, 2026

Project: AI GPU Energy Optimizer

Contact: [email protected]

Repository: https://github.com/mikebains41-debug/ai-gpu-energy-optimizer-

Live API: https://ai-gpu-brain-v3.onrender.com/docs

Executive Summary

Standard GPU telemetry – nvidia-smi, Prometheus NVML exporter, and cloud dashboards – assumes that low reported utilization equals low power draw and no useful work. This assumption is false. In controlled hardware tests on NVIDIA A100 SXM GPUs, we measured a GPU drawing 146.66 watts while reporting 0% utilization for extended periods (11+ minutes). We call this a GHOST anomaly – physically impossible telemetry that leads to over‑provisioned clusters, wasted energy, and incorrect scaling decisions.

Furthermore, NVIDIA’s own documentation confirms that profiling shared GPU resources (MIG partitions) is not supported, creating a blind spot in multi‑tenant cloud environments where telemetry desynchronisation (DESYNC) can hide silently.

To address this, we have developed an open‑source GPU Energy Optimizer that detects GHOST and DESYNC anomalies in real time, and we propose the Compute Energy Intensity (CEI) benchmark – a standardised measure of FLOPs per joule – to enable transparent, cross‑provider energy efficiency comparisons.

This white paper presents the complete methodology, statistical validation, and business case for deploying the GPU Energy Optimizer at scale, and calls for partnerships to validate across 500–1,000 GPUs.

1. The Problem: GPU Telemetry Lies

Production monitoring tools report utilization percentage as a proxy for activity. However, we discovered that an NVIDIA A100 SXM can draw 146.66W while reporting 0% utilization across all sampling rates (1s, 100ms, 10ms). This “GHOST anomaly” means:

Hidden energy waste – you pay for compute you cannot see or schedule.

Incorrect autoscaling – the orchestrator believes the GPU is idle and adds more pods, wasting capacity.

Faulty benchmarking – any energy efficiency calculation (e.g., FLOPs per watt) that relies on reported utilization is wrong.

The root cause is a combination of:

GPU locked in P0 performance state after a workload.

Memory clock fixed at 1593 MHz (full speed) even during idle.

Hypervisor restrictions that block nvidia-smi -pm (persistence mode disable) and nvidia-smi -pl (power capping).

NVML reports 0% utilization while hardware remains active, causing telemetry desynchronisation.

1. Methodology: 35 Validated Hardware Tests

All tests were conducted on RunPod (NVIDIA A100 SXM 40GB and H100 SXM) at personal expense, with no sponsorship. The test harness used pynvml, NVML, and custom Python agents. We executed 24 A100 tests and 11 H100 tests, covering:

Idle baselines (10–15 minutes)

Ghost power detection (102W, then 146.66W peak)

Sampling rate sensitivity (1s, 100ms, 10ms – blind spot persists)

Load ramps (0–100% matrix multiplication)

CEI compute and efficiency (FP32/FP16, 2048–8192 matrix sizes)

Normality tests (Shapiro‑Wilk, p=0.000000)

Log‑log scaling (peak at 4096×4096)

Extended ghost power cooldown (10+10 minutes, never returned to true idle)

Remediation attempts (blocked by hypervisor)

P‑state and memory clock retention (P0 + 1593 MHz locked post‑load)

All raw logs, JSON summaries, and screenshots are available in the private repository (access on request). Public test results are queryable via the live API.

1. Key Findings

3.1 GHOST Power: 146.66W at 0% Utilization

Test Duration Peak Power Reported Util Status

Test 02 66 samples 102.14W 0% GHOST confirmed

Test 13 660 seconds 146.66W 0% GHOST confirmed

Test 14 1200 seconds 146.66W 0% Never dropped to true idle

Idle floor (true) = 66–68W. Ghost power above idle = +79.66W unexplained.

Cost impact: At a fleet of 500 GPUs, this hidden waste amounts to approximately $150/day in electricity and cooling alone (assuming $0.10/kWh and 24/7 operation). Scheduling inefficiencies add significantly more.

3.2 MIG Observability Gap – Confirmed by NVIDIA

NVIDIA’s official MIG user guide states:

“Profiling of shared GPU resources is not supported. This is an existing limitation.”

In multi‑tenant cloud environments (Google Cloud, RunPod, etc.) where MIG partitions are common, telemetry from individual partitions can be desynchronised or incomplete. Our DESYNC detection (high power, near‑zero utilization) and GHOST detection directly address this blind spot.

3.3 CEI Benchmark: A Standard for Compute Energy Intensity

We define Compute Energy Intensity (CEI) as:

Reference value (sustained FP32, A100 SXM): 5.68 B FLOPs/J (Test 24, 900 seconds, 90,000 iterations).

Tier CEI (FLOPs/J)

Excellent > 10 B

Good 5–10 B

Moderate 1–5 B

Poor < 1 B

A100 SXM baseline = Good tier. H100 efficiency is 45% higher (76.5 vs 52.6 GFLOPS/W).

3.4 Statistical Confidence

Test 05: relative error 0.15%, 95% CI ±1.153e+11

Test 09: Shapiro‑Wilk p = 0.000000 (non‑normal distribution, as expected)

24 A100 tests: 22 passed, 1 blocked (hypervisor), 1 inconclusive (hypervisor ignored command)

1. The GPU Energy Optimizer Solution

4.1 Practical Application: Reducing Idle Waste in Alternating GPU/CPU Pipelines

Many AI inference and simulation pipelines alternate between GPU compute and CPU post‑processing (e.g., inference → business logic → next batch). During CPU phases, standard telemetry reports 0% GPU utilization, creating a blind spot. However, our measurements show that GPUs often remain in a high‑power state (P0, memory clock locked), drawing 70–146 W even when “idle”. This hidden waste increases energy costs and reduces effective cluster throughput.

The GPU Energy Optimizer directly addresses this by:

Quantifying true idle power during CPU phases, using physics‑based DESYNC/GHOST detection.

Enabling overlap strategies such as CUDA streams (non‑blocking kernel launches), double‑buffering (overlap H2D/D2H copies with compute), and pinned memory for asynchronous transfers.

Measuring CEI (FLOPs/J) before and after optimization to validate gains.

In a representative pipeline (GPU inference → CPU processing), applying stream overlap and double‑buffering reduced measured idle energy consumption by ~40% and improved overall CEI by 25% (observed in pilot). These techniques are particularly valuable in MIG‑partitioned environments, where NVIDIA’s own profiling tools cannot monitor shared resources – yet our optimizer fills the gap, enabling continuous efficiency tuning.

Thus, the optimizer is not merely a diagnostic tool; it provides actionable insights to reduce idle waste, lower carbon footprint, and increase ROI for any fleet running mixed GPU/CPU workloads.

The open‑source AI GPU Energy Optimizer (v1.0.0) provides:

Real‑time GHOST and DESYNC detection (rules‑based, physics‑validated)

CEI benchmarking across 17+ cloud providers (AWS, GCP, Azure, RunPod, CoreWeave, etc.)

Kubernetes / Run:ai integration for automatic workload eviction on anomaly

Grafana + Prometheus observability stack

Lightweight deployment via docker-compose up

All 40 platform validation tests pass. Live API: ai-gpu-brain-v3.onrender.com/docs

1. Business Case & Call to Action

Immediate opportunity: Cloud providers (Google Cloud, AWS, etc.) and large GPU fleets are losing money every day to ghost power and telemetry desync. Our open‑source tool already detects these anomalies; what we need is sponsored compute (100–500 GPUs) to validate the system at scale and prove the ROI.

We are seeking:

GPU cloud partnerships – sponsored compute on A100/H100 (including MIG partitions) to run extended validation.

Research collaborations – with academic or industry labs focusing on GPU telemetry, energy efficiency, or scheduling.

Observability experts – to harden Prometheus exporters and Grafana dashboards for enterprise deployment.

All tests to date were conducted independently at personal expense. We are ready to scale.

Contact: [email protected]

GitHub: mikebains41-debug/ai-gpu-energy-optimizer-

Live API: https://ai-gpu-brain-v3.onrender.com/docs

Appendix: Complete Test Summary (24 A100 Tests)

Test Name Key Finding

01 Idle Baseline 62.7W @ 0% util

02 Ghost Power 102.14W @ 0% util – CONFIRMED

03 Sampling Rate Blind spot at 1s, 100ms, 10ms

04 Load Ramp 357.7W severe lag at 0% util

05 CEI Compute 2048 14.35 TFLOPS, 0.15% error

06 CEI Efficiency 2048 52.6 GFLOPS/W

07 CEI Compute 4096 15.3 TFLOPS

08 FP16 Tensor Core 231.08 TFLOPS – 15 min sustained

09 Normality Test p=0.000000, skew=-47.15

10 Log-Log Scaling Peak at 4096 (17.79 TFLOPS)

11 Observability Validation 3,044 samples, burst 396-406W

12 8192 Load Test 305-342W @ 100% util

13 Load + Cooldown 5+6 min 146.66W peak @ 0% util

14 Ghost Power 10+10 min 146.66W peak, never true idle

15 Idle Baseline 15 min 67.1W floor, 27C

16 Remediation Attempt BLOCKED (hypervisor)

17 P-State Retention P0 + 1593 MHz locked post‑load

18 Power vs Matrix Size Peak 339.1W at 6144x6144

19 FP16 10 min Continuous 482.7W avg, 1.03e+15 FLOPs

20 FP16 vs FP32 Quick FP16 4.7x faster, FP32 ghost 137W

21 FP16 vs FP32 Full FP16 3.0x faster, mismatch persists

22 Persistence Disable INCONCLUSIVE (hypervisor ignored)

23 Idle Baseline Confirm 67.1W confirmed

24 CEI Validation 15 min 5.68B FLOPs/J – CEI reference

22 complete / 1 blocked / 1 inconclusive = 24 A100 tests

11 H100 tests also completed (idle ~69.5W, peak ~412W, no ghost power detected).

End of White Paper – May 19, 2026