This study examines the application of large language model architectures for retail demand forecasting, with particular focus on TimeGPT, Cronos, and similar transformer-based models. Unlike traditional forecasting approaches that require extensive historical data for specific time series, these models leverage a zero-shot learning architecture that has been pre-trained on diverse temporal patterns across multiple domains. This architectural advantage enables the models to generalize effectively to new retail contexts without requiring series-specific training. The core innovation lies in how these models encode temporal information—treating time series as a specialized language with its own grammar of seasonality, trends, and contextual dependencies. By reformulating time series forecasting as a sequence completion task, TimeGPT and Cronos can interpret complex retail demand patterns even with limited historical data. Our research framework evaluates these zero-shot capabilities across various retail categories and store formats, examining how effectively the models transfer knowledge from their pre-training to specific retail forecasting challenges. This study contributes to the methodological understanding of how transformer architectures can be adapted for temporal forecasting tasks and offers practical insights for implementing these zero-shot approaches in retail inventory management systems.