Dense Model

Dense models, while powerful, face limitations in scalability and efficiency for very large language models. Current research focuses on Mixture-of-Experts (MoE) architectures as a more efficient alternative, leveraging sparse computation to activate only a subset of model parameters depending on the input. This approach aims to achieve comparable or superior performance to dense models while significantly reducing computational costs and memory requirements. The resulting improvements in efficiency and scalability have significant implications for deploying large language models in resource-constrained environments and for advancing research in areas like federated learning and multimodal modeling.