A Guide to Understanding GPUs and Maximizing GPU Utilization

GPU utilization is often bottlenecked not by compute but by data pipeline inefficiencies. This guide covers GPU architecture fundamentals (SMs, VRAM, PCIe bridge, Roofline Model) and then walks through practical PyTorch optimizations: tuning DataLoader parameters (num_workers, pin_memory, prefetch_factor), increasing batch size, using mixed precision (FP16/BF16/TF32), gradient accumulation, and kernel fusion via torch.compile() or the Hugging Face kernels library. A Hugging Face Trainer example ties all settings together in one place.