PyTorch provides a flexible and dynamic computational graph, making it well-suited for research and rapid prototyping of deep learning models. It combines the ease of use of Python with high performance through its CUDA support (for NVIDIA GPUs). Key features include automatic differentiation (autograd) which simplifies gradient calculation for training neural networks, a rich ecosystem of pre-built modules and functions for common tasks like convolutional layers, recurrent networks, and optimizers, and strong community support. PyTorch is widely used in academia and industry for various applications including computer vision, natural language processing, and reinforcement learning. Its dynamic graph allows for more flexible model architectures compared to static graphs found in some other frameworks, enabling features like debugging and conditional execution within the network.