From Detection to Recovery: Operational Analysis on LLM Pre-training with 504 GPUs
A Lablup Technical Report on operating a 63-node NVIDIA B200 production cluster
Large-scale AI training is now fundamentally a distributed systems problem, where hardware failures are a routine operating condition rather than rare exceptions. This technical report presents an empirical analysis of a 63-node NVIDIA B200 production cluster (504 GPUs), drawing on 55 days of Prometheus time-series data and operational logs across 224 multi-node training sessions.
Every analysis here runs on the Backend.AI production infrastructure: session-level workload management, GPU-centric scheduling, and unified observability.
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