Use Case · Infrastructure AI

GPU rack onboarding: weeks to hours.

EdgeTelemetry automates GPU rack onboarding end-to-end — hardware discovery, telemetry ingestion, schema normalization, readiness validation, and signed operational handoff. What previously required weeks of manual SRE effort completes in hours.

→ The problem

When a new GPU rack lands in a data center, the clock is already running. Customers are waiting. SLAs are in effect. And the typical process is: SREs manually pull telemetry from multiple vendor tools, check configurations against a spec document, coordinate with networking and storage teams, wait for each team to confirm their part, and then — if everything checks out — declare the rack operational.

Each step is manual, each handoff adds delay, and when something fails the diagnostic cycle starts over. The result is weeks between rack landing and operational status. For operators standing up AI infrastructure at scale, this is a capital efficiency problem: hardware that cost millions of dollars is sitting unproductive for weeks.

How EdgeTelemetry fixes it

Step 1 · Hardware discovery and ingestion

On rack arrival, EdgeTelemetry automatically discovers all hardware components — GPUs, host systems, cooling, power, and network — and begins real-time telemetry ingestion via NVML, IPMI/BMC, and vendor APIs. No manual inventory step.

Step 2 · Schema normalization

Raw telemetry from heterogeneous vendors is normalized to a unified operational schema with validation and lineage tracking. The data becomes queryable immediately, regardless of source format.

Step 3 · Automated readiness validation

Validation checks run automatically against configured readiness criteria: driver versions, firmware levels, thermal thresholds, network connectivity, power supply health, inter-rack dependencies. Failed checks generate specific, actionable remediation guidance with telemetry context.

Step 4 · Signed operational handoff

When all checks pass, EdgeTelemetry generates a signed onboarding record with full telemetry lineage. The rack enters the operational fleet with a documented baseline — not just a verbal "it looks good."

[ FAQ ]

Questions about GPU rack onboarding automation.

Why does GPU rack onboarding currently take weeks?
Manual onboarding requires SREs to inventory hardware, pull telemetry from multiple vendor tools in different formats, manually check configuration against a spec, and coordinate handoffs between networking, storage, and platform teams. Each step requires human coordination and each coordination delay adds days. When a check fails, the back-and-forth diagnostic cycle adds more. EdgeTelemetry automates every step that can be automated — which is most of them.
What checks does EdgeTelemetry run during automated rack validation?
Hardware presence and health (all expected components detected), driver and firmware versions against spec, GPU thermal and power state within operating ranges, NVLINK/InfiniBand connectivity, network interface configuration and reachability, cooling system telemetry within thresholds, power supply redundancy status, and inter-rack dependencies. Failed checks generate specific, actionable remediation guidance rather than generic failure notifications.
What happens when a validation check fails?
EdgeTelemetry generates a structured failure report with the specific failed check, the observed vs. expected state, and recommended remediation steps. The Claude reasoning layer can interpret telemetry context around the failure to generate more specific root-cause hypotheses. The rack stays in pre-operational status until all required checks pass, preventing partially-validated infrastructure from entering the operational fleet.

Standing up GPU clusters and tired of weeks-long onboarding?

EdgeTelemetry is deployed with select operators today. Tell us about your environment — rack count, vendor mix, current onboarding process — and we'll show you what automated validation looks like for your setup.