Efficiency of State-Space Models and Mixture of Experts in AI Architecture

State-space models offer linear time inference with respect to context length, making them more computationally feasible compared to transformers. By combining state-space models with mixture of experts technique, the recent Mamba model showcased improved performance compared to both original Mamba model and transformers. This combination increases the model's power without significantly increasing computational costs, making it a promising approach for more efficient AI architecture. The integration of these two techniques could potentially lead to better models and scalability in AI architecture, providing a cost-effective solution for model development and training.