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Large-scale distributed AI training is highly sensitive to infrastructure failures, where even a single node disruption can halt progress and waste substantial compute. This talk presents Nebius’s approach to fault-tolerant training, combining reliability metrics such as goodput, MTBF, and MTTR with automated infrastructure practices including health checks, workload isolation, node replacement, state recovery, and observability. Drawing on production cluster results, the presentation shows how these techniques reduce interruptions, accelerate recovery, and improve the stability and efficiency of long-running AI workloads.
