This paper explores lightweight Generative AI (GenAI) models for network traffic synthesis to address data scarcity and privacy in Network Traffic Classification (NTC). It evaluates transformer-based, state-space, and diffusion models, demonstrating their effectiveness in generating high-fidelity synthetic traffic for training and augmenting NTC systems.