GPU Fleet AIOps: 7 LLM Backends, 6 Failure Scenarios
Abstract After measuring a 292x cost gap between a rented B200 and frontier API providers on a batch inference workload, the logical next question was whether the same pattern held for operational intelligence: could a smaller, dedicated model handle the continuous judgment calls required to run a GPU fleet? The batch inference test had exposed KV cache contention as the dominant bottleneck on shared API infrastructure. Processing similar structured data at scale, but this time continuously rather than in batch, seemed like a valid test of whether that contention would degrade operational quality the same way it degraded throughput. ...