Low-Resource Generative AI: Model Optimization for Edge and Mobile Devices

The effective deployment of generative AI models in real-time applications has been impeded by the computational and memory requirements of Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and transformer-based models. We investigate the deployment of generative models on low-resource hardware (edge devices, mobile devices) using optimization techniques such as pruning, quantization, and knowledge distillation. In this study, we defined a detailed experimental framework to measure the performance of the studied methods against standard benchmarks, including CIFAR-10, CelebA, and OpenWebText, across heterogeneous hardware platforms ranging from the Raspberry Pi 4 and Jetson Nano to the Google Pixel 6. The results demonstrate that applying pruning techniques reduces model parameters by approximately 50 percent without statistically significant degradation in output quality. In contrast, quantization significantly decreases both inference latency and power consumption by 70.3 ± 3.2% and 61.7 ± 4.1%, respectively. Additionally, knowledge distillation methods compress transformer architectures while maintaining acceptable perplexity values. Collectively, these optimizations reduce inference time by up to 70 percent and energy consumption by more than 60 percent, supporting the feasibility of deploying generative artificial intelligence on devices with constrained processing and energy resources. Practically, these findings have implications for the successful deployment of useful, privacy-preserving, and portable AI across a wide range of application domains such as health, communications, and education.