  We show that numerical computations based on tensor renormalization group
(TRG) methods can be significantly accelerated with PyTorch on graphics
processing units (GPUs) by leveraging NVIDIA's Compute Unified Device
Architecture (CUDA). We find improvement in the runtime and its scaling with
bond dimension for two-dimensional systems. Our results establish that the
utilization of GPU resources is essential for future precision computations
with TRG.
