Structural stability diagnostics for interconnect-induced performance collapse in distributed AI training and HPC systems. Identifies coupling patterns that cause economic instability despite nominal hardware health.
Interconnect degradation manifests as throughput variance, not failure. Standard monitoring shows healthy hardware while economics deteriorate. The coupling between interconnect topology and training efficiency creates non-linear effects that only appear at scale thresholds specific to each system configuration.
These scenarios demonstrate how interconnect-level instabilities propagate into system-level economic effects. Each scenario isolates a different coupling mechanism between physical topology and computational economics.
Three diagnostic scenarios examining structural stability under different operational contexts. Each scenario provides pre-computed evidence artifacts for a specific system configuration.
Gradient synchronization efficiency degradation under interconnect variability in multi-thousand GPU training clusters.
View ScenarioTail latency amplification from interconnect jitter in latency-sensitive inference serving deployments.
View ScenarioCoupling instabilities in mixed-generation accelerator deployments with asymmetric interconnect capabilities.
View ScenarioSupporting materials for context and technical orientation.