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Data-distributed training across multiple GPUs requires constant gradient transfer between devices, which can bottleneck performance. Using NVIDIA Nsight Systems profiler on a Vision Transformer model, the analysis reveals that GPU interconnect type dramatically impacts throughput—NVLink-equipped instances maintain near-baseline performance while PCIe-based instances suffer 6x slowdowns. Four optimization techniques are evaluated: static graph declaration, memory-efficient gradients, gradient compression (BF16 and PowerSGD), and parallelized reduction via bucket tuning. On PCIe instances, PowerSGD compression achieves over 5x speedup, while NVLink instances see modest 4% gains, demonstrating that optimal strategies depend heavily on hardware topology.

Optimizing Data Transfer in Distributed AI/ML Training Workloads

Instance Selection for Distributed Training

Optimization 2: Increase Memory Efficiency

Optimization 4: Parallelize Gradient Reduction