NVIDIA Merlin Dataloader accelerates training of recommender models by loading data directly into GPU memory and providing zero-copy transfers to deep learning frameworks via dlpack. It claims over 10x speedup over native framework dataloaders, handles datasets larger than memory, provides per-epoch shuffling, and supports distributed training across TensorFlow, PyTorch, and JAX.