A new technical guide explores identifying and resolving data transfer bottlenecks in distributed AI/ML training. The article, part three in a series, focuses on using NVIDIA Nsight™ Systems to diagnose performance issues. It addresses common slowdowns that occur when moving data across nodes during large-scale model training.

These bottlenecks often stem from network latency, inefficient I/O, or GPU synchronization delays. For engineers running multi-GPU or multi-node jobs, pinpointing the exact source is critical. The guide provides a methodology for using Nsight's tracing capabilities to visualize data movement and isolate inefficiencies in the pipeline.

Resolving these issues can lead to faster training cycles and better resource utilization. As models grow larger, efficient data pipelines become as important as compute power. This work offers a practical framework for optimizing distributed training workloads, a key challenge for teams scaling AI infrastructure.