GPU-accelerated libraries enable deep learning frameworks like TensorFlow and PyTorch to run on multi-GPUs, thereby dramatically enhancing the performance of running models. GPU-accelerated libraries are really powerful compared to CPU-accelerated libraries. Because of the super-low latency, unmatched processing speed, and support for parallel computing, GPU libraries are very powerful for deep learning applications. Although there are multiple GPU-accelerated libraries, the main ones are CUDA, cuDNN, DALI, and NCCL.