Training GAN models requires substantial computational resources, including high-powered GPUs, significant memory, and considerable storage capacity, to process large datasets and perform the complex calculations needed for the iterative adversarial training process. Businesses can optimize resource utilization by employing cloud computing services that offer scalable, on-demand access to powerful computing resources, thereby efficiently managing costs and adjusting to varying workload demands. Implementing more efficient neural network architectures, utilizing transfer learning, and applying GAN model pruning techniques can reduce the computational load, making the training process faster and more resource-efficient.