Having 8–16 GPUs doesn't mean you're using them: distributed training monitoring tips
goyalshaliniuk · x · 2026-11-10
Part 7 of a series on GPU utilization in distributed training: owning 8–16 GPUs doesn't mean efficiency — some GPUs may stay idle, memory usage varies across devices, communication overhead can dominate computation, and bottlenecks stay hidden without distributed monitoring. The author recommends tracking utilization, memory, communication time, throughput and training performance, pointing to NVIDIA's open-source DCGM (Data Center GPU Manager) for telemetry and GPU health checks. The preceding post covers fault tolerance: as clusters grow, a single GPU or network failure can kill the job, so checkpointing and recovery are essential to avoid wasting GPU time.
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