Large scientific images and spectral-learning workloads often require two-dimensional Fast Fourier Transforms (2D FFTs) that exceed single-GPU memory. This matters for Fourier Neural Operators (FNOs) and Transform Once (T1)-style models, where frequency-domain computation is central to the learning workflow. PyTorch provides convenient FFT APIs, but scaling these transforms across multiple GPUs requires lower-level libraries and careful memory-layout management.