PipeGAN uses concepts of pipelining in microprocessors and applies it to the area of Machine Learning. The model, implemented using PyTorch and Threading, achieves good quality images from the MNIST and FashionMNIST dataset with a speedup of up to 30% compared to similar serial implementations.