Connect your NVIDIA DGX, HGX, and EGX AI infrastructure to iFactory AI's intelligent CMMS. Monitor GPU health in real-time via DCGM, predict hardware failures 7–21 days ahead, and maximize uptime for your AI workloads.
iFactory AI integrates directly with NVIDIA's Data Center GPU Manager (DCGM) to collect real-time telemetry from your entire GPU fleet. Our AI analyzes 100+ metrics per GPU to predict failures, automate maintenance, and maximize uptime.
DCGM Exporter container with Prometheus-compatible endpoints for K8s GPU clusters.
Direct DCGM API integration for standalone DGX systems and HPC clusters.
TLS encryption, RBAC, audit logging. SOC 2 Type II compliant infrastructure.
iFactory AI integrates with NVIDIA's Data Center GPU Manager (DCGM) for comprehensive health monitoring across your entire GPU fleet. Track 100+ metrics per GPU in real-time.
Core, memory & board thermal monitoring
Per-GPU watts & total rack power
HBM usage, bandwidth & allocation
Correctable & uncorrectable errors
Interconnect status & bandwidth
NVIDIA error codes decoded & alerted
DGX-02 GPU #3 shows progressive temperature increase (+2.5°C/week). Cooling system inspection recommended.
DGX-03 GPU #7 elevated correctable ECC errors (127 → 342 in 30 days). Memory approaching end of life.
DGX-01 approaching 10,000 GPU-hours. Firmware update and thermal paste refresh recommended.
iFactory AI analyzes historical GPU telemetry patterns to predict hardware failures 7–21 days in advance. Anticipate degradation before it impacts your AI workloads.
Detect declining performance patterns
ECC error trends predict HBM issues
Identify cooling system degradation
Predict PSU failures from patterns
Interconnect bandwidth trend analysis
Link AI jobs with hardware stress
Modern NVIDIA GPUs draw 700W+ each, with DGX systems pushing 6–10kW per node. iFactory AI monitors thermal conditions across your entire cooling infrastructure — from direct-to-chip liquid cooling to CRAC units.
CDU flow rates, coolant temp, pressure
AI identifies thermal anomalies early
CRAC/CRAH unit health tracking
Power Usage Effectiveness monitoring
iFactory AI integrates with the complete NVIDIA AI infrastructure ecosystem — from DGX SuperPOD clusters to EGX edge deployments.
Temperature exceeded 80°C threshold (currently 82°C)
When GPU anomalies are detected or failures predicted, iFactory AI automatically creates detailed work orders with full diagnostic context. Reduce mean time to repair by 60%.
GPU alerts create tickets automatically
DCGM logs, error codes, telemetry
Critical issues to senior GPU techs
Auto-suggest replacement components
iFactory AI connects GPU monitoring directly to maintenance — when DCGM detects an issue, the system automatically triggers corrective actions with full diagnostic context.
Work orders include DCGM diagnostics, error logs, and AI-suggested repair actions for immediate technician context.
Every GPU issue is linked to root cause, repair history, and verification — complete audit trail for compliance.
Historical data improves AI predictions and prevents recurring failures across your GPU fleet over time.
AI infrastructure teams using iFactory AI achieve measurable improvements in GPU uptime and operational efficiency.
"iFactory AI predicted a GPU memory failure 12 days before it happened on our DGX SuperPOD. Saved us $180K in potential downtime costs."
"DCGM integration gives us complete visibility into our GPU cluster. We went from reactive calls to proactive maintenance."
"We're a small team managing 3 DGX systems. Automated work orders mean we don't need dedicated operations staff."
"Thermal management alerts caught a cooling issue before any GPUs throttled. Our LLM training runs uninterrupted now."
Everything you need to know about iFactory AI's NVIDIA server integration and GPU infrastructure maintenance.
iFactory AI integrates with NVIDIA's Data Center GPU Manager (DCGM) via the DCGM Exporter, which exposes GPU metrics in Prometheus format. For Kubernetes environments, we use the official NVIDIA DCGM Exporter container. For bare-metal deployments, we support direct DCGM API integration or custom metric exporters. Setup typically takes 15–30 minutes per cluster with our guided configuration wizard.
iFactory AI monitors 100+ GPU metrics including: temperature (GPU core, memory, board), power consumption (current, peak, limits), memory utilization (used, free, bandwidth), clock speeds (SM, memory), ECC errors (correctable/uncorrectable), PCIe throughput, NVLink bandwidth and errors, compute utilization, encoder/decoder usage, XID errors, thermal throttling events, and fan speeds where applicable.
Yes, iFactory AI fully supports liquid-cooled DGX systems including the latest Blackwell-based DGX B200 and B300. We monitor coolant distribution unit (CDU) metrics including flow rates, inlet/outlet temperatures, pressure differentials, and pump status. For direct-to-chip cooling systems, we track per-GPU coolant temperatures and alert on thermal anomalies indicating cooling system degradation.
iFactory AI typically predicts GPU failures 7–21 days in advance depending on the failure mode. Thermal degradation patterns are usually detectable 2–3 weeks ahead. Memory issues (via ECC error trends) can be predicted 1–4 weeks out. Power supply problems often show patterns 7–10 days before failure. Prediction accuracy improves over time as the AI learns your specific workload patterns and infrastructure characteristics.
iFactory AI scales from a single DGX Station to enterprise DGX SuperPOD deployments with thousands of GPUs. Our architecture is designed for high-volume telemetry ingestion, processing millions of metrics per minute. Pricing is based on the number of GPU nodes (systems) rather than individual GPUs, making it cost-effective for dense 8-GPU DGX systems. There are no hard limits on GPU count.
Most NVIDIA infrastructure integrations are completed within 1–2 weeks. Day 1–2: DCGM Exporter deployment and iFactory AI connection. Day 3–5: Asset registration, threshold configuration, and alerting setup. Week 2: Team training, workflow optimization, and AI model calibration. Our team provides hands-on implementation support for enterprise deployments, including on-site assistance for large SuperPOD installations.
Yes. iFactory AI supports NVIDIA Multi-Instance GPU (MIG) monitoring on A100, H100, H200, and Blackwell GPUs. You can track metrics per MIG instance — including compute utilization, memory usage, and ECC errors — giving you granular visibility into partitioned GPU resources across multi-tenant or multi-workload environments.
iFactory AI is SOC 2 Type II compliant with TLS encryption for all data in transit, AES-256 encryption at rest, role-based access control (RBAC), and comprehensive audit logging. We support SSO/SAML integration and offer hybrid deployment options for organizations with data sovereignty requirements.
Stop losing GPU compute time to unexpected failures. iFactory AI connects NVIDIA DCGM telemetry to intelligent maintenance management for maximum uptime. Join AI infrastructure teams already protecting their GPU investments.