Simulation Active

AI Power Governance in Action

Watch Spark-XC sit above a simulated fleet of 8 GPUs to validate, authorize, and prove power actions in real time. Select your use case. Trigger real scenarios. Every governed action commits a Power Event Record. Mission Control executes. Spark-XC validates.

8
GPUs Governed
5
Validation Paths
0
Actions Blocked

See SPARK-XC from your perspective

Choose your role, then trigger scenarios tailored to your environment. Spark-XC validates, authorizes, and proves each power action — and the GPU fleet below responds in real time.

Protect AI training & inference

Training runs last days. A single thermal spike can corrupt weights or damage hardware. Inference demands consistent latency. Spark-XC validates each power action against live GPU telemetry and workload context, and authorizes it through policy gates that catch scheduler misconfiguration before it reaches hardware — Mission Control executes, Spark-XC validates.

Every governed power action during a run becomes a Power Event Record — when something goes wrong, the evidence chain tells you exactly what happened, and proves it.

🔥
Training Run Thermal Spike
GPU-3 overheats mid-epoch 142/500 — telemetry path flags it, policy authorizes an emergency limit, action proven in a Power Event Record
Inference SLA Guard
Scheduler over-allocates GPU-6 to 600W — policy / approval gates block it to protect latency SLAs
🔍
Training Run Forensics
Audit verification of GPU power timeline during a completed training run

Hyperscale fleet management

Data centers running thousands of GPUs face ambient temperature events, PUE pressure, carbon cost optimization, and rogue workloads. Spark-XC validates fleet-wide power actions against telemetry and facility correlation (DCIM, BMS, PDU/UPS), and authorizes them through policy gates — sitting above Mission Control, DCGM, and the schedulers that execute.

Every governed action is committed as a Power Event Record for compliance reporting and operational audit.

🌡️
Ambient Temperature Event
Facility cooling stressed — all 8 GPUs warm up; telemetry + facility paths correlate, policy authorizes a fleet-wide limit
🌱
PUE & Carbon Optimization
Rebalance fleet power — improve PUE from 1.4→1.2, reduce carbon cost at $52/MWh
🚨
Rogue Rack Power
Unauthorized workload exceeds rack power allocation — policy / approval gates block it, facility correlation proves the breach

Quantified savings & ROI

GPU fleets are the largest CapEx in AI infrastructure. SPARK-XC delivers measurable OpEx reduction through ML-driven power optimization while protecting $25–40K per GPU from thermal damage. Every dollar saved is logged and auditable.

Patent-pending architecture: an AI power governance layer that sits above Mission Control, DCGM, schedulers, DCIM, and BMS — validating, authorizing, and proving every power action.

💰
OpEx Savings Projection
Fleet optimization with $/hour, $/month, $/year projection — built on the 38.8% B200 power delta Spark-XC measured on hardware (validation evidence, not a savings guarantee)
🛡️
Hardware Protection ROI
Thermal event flagged by the telemetry path — emergency limit authorized and proven, preventing $35K GPU replacement + downtime
📊
TCO Efficiency Report
Before/after fleet metrics — power down, utilization maintained, audit chain verified

Audit trail & compliance evidence

Enterprise GPU infrastructure must be safe AND auditable. Spark-XC's policy / approval gates map to your power policies, every governed action becomes a Power Event Record on a tamper-evident chain, and the evidence stream integrates with SIEM systems.

Power Event Records map directly to SOC 2 Type II, ISO 27001, and NIST SP 800-53 controls.

Policy Enforcement
Business unit exceeds approved power budget — policy / approval gates block it, captured in a Power Event Record
🔎
Incident Forensics
Reconstruct a thermal event — complete timeline from detection to recovery
SOC 2 Audit Export
Chain verification mapped to CC6.1, CC7.2, CC8.1 — exportable compliance evidence
GPU Fleet — 8 Devices
Validation Paths
1
GPU Telemetry Validation
NVML/DCGM live
2
Workload / Scheduler Context
Slurm · K8s · Run:ai
3
Facility Power Correlation
DCIM · BMS · PDU
4
Policy / Approval Gates
Authority · rate · scope
5
Tamper-Evident Evidence Chain
PER chain: 14,820
Fleet Power
2,460W
0WBudget: 3,400W
Thermal Overview
SPARK-XC AUDIT STREAM
--:--:--.---[OK]  System initialized
_

Real hardware, measured power deltas

These are GPU-side validation results — power deltas Spark-XC measured and proved on real hardware, each captured inside a Power Event Record. They are validation evidence, not guaranteed or promised savings.

38.8%
B200 Power Delta (Measured)
26.8%
H100 Power Delta (Measured)
<1s
Policy Response (HW throttle as backstop)
5,500+
Automated Tests
B200 — 38.8% measured delta
From a single ~3-minute, 8-GPU A/B run (949W→581W per GPU) with utilization essentially unchanged (93.9%→95.7%). A single run — the magnitude is not yet established as a guarantee, only that Spark-XC measured and proved the delta on hardware.
H100 — 26.8% avg across 2 runs
Achieved via clock scaling. The ~117–119W baseline on a 700W-TDP part means the MatMul workload was near idle — so this demonstrates the control and validation layer working end-to-end, not the savings to expect on a saturated production workload.

The takeaway: this is the GPU-telemetry-validation path proving a measured power delta inside a Power Event Record — not a savings promise.

See a real power action
validated and proven

We're onboarding a select group of design partners — data center operators, AI labs, energy teams, and AI infrastructure leaders. Request a replay of a real Power Event Record from hardware-validated runs.

Patent Pending5 Validation PathsPower Event RecordsMission Control–Compatible