The utilization of supercomputers and large clusters for big-data processing has recently gained immense popularity, primarily due to the widespread adoption of Graphics Processing Units (GPUs) to execute iterative algorithms, such as Deep Learning and 2D/3D imaging applications. This trend is especially prominent in the context of large-scale datasets, which can range from hundreds of gigabytes to several terabytes in size. Similar to the field of Deep Learning, which deals with datasets of comparable or even greater sizes (e.g. LION-3B), these efforts encounter complex challenges related to data storage, retrieval, and efficient GPU utilization. In this work, we benchmarked a collection of high-performance general dataloaders used in Deep Learning with a dual focus on user-friendliness (Pythonic) and high-performance execution. These dataloaders have become crucial tools. Notably, advanced dataloading solutions such as Web-datasets, FFCV, and DALI have demonstrated significantly superior performance when compared to traditional PyTorch general data loaders. This work provides a comprehensive benchmarking analysis of high-performance general dataloaders tailored for handling extensive datasets within supercomputer environments. Our findings indicate that DALI surpasses our baseline PyTorch dataloader by up to 3.4x in loading times for datasets comprising one million images.