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Engineering Case File · AI Infrastructure
GPU / RoCE Network Validation
A GPU cluster network validation program focused on lossless Ethernet behavior, congestion visibility, and production readiness.
This case file draws on real-world infrastructure work. Identifying details and protected implementation specifics are intentionally excluded.
Challenge
An AI platform team needed to validate that a new GPU fabric would support training workloads with predictable network behavior before cluster production cutover.
Constraints
- RoCEv2 sensitivity to congestion and misconfigured buffer behavior
- Tight coordination required between network and compute teams
- Limited window for stress testing before tenant onboarding
- Need for operational telemetry beyond basic interface statistics
Approach
- Reviewed fabric design against workload communication patterns
- Defined validation test plan covering baseline, congestion, and failure cases
- Executed structured pre-production checks and traffic validation
- Identified observability gaps and recommended operational signals
Engineering Decisions
- Prioritized end-to-end validation over component-level sign-off alone
- Mapped test cases to operational alerts for ongoing monitoring
- Documented expected vs. anomalous fabric behavior for NOC handoff
- Separated tuning recommendations from production acceptance criteria
Outcome
- Fabric validated against defined acceptance criteria before go-live
- Compute and network teams aligned on operational expectations
- Observability recommendations integrated into monitoring roadmap
- Production cutover proceeded with documented rollback plan
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