Beyond Batch Sizes: Cracking the Code of Low GPU Utilization in PyTorch

Achieving high GPU utilization requires moving beyond intuitive fixes like increasing batch sizes to understanding specific hardware bottlenecks. By profiling arithmetic intensity and identifying memory-bandwidth constraints, developers can transform underperforming training loops into efficient workloads that justify high-end hardware costs.