Mistral AI (Artificial Intelligence) Startup has released Mixtral-Transformers based on a Mixture of Experts layer (MoE). MoE architectures have gained prominence in both Large Language Modeling (LLM) and Computer Vision due to their ability to scale efficiently by dynamically selecting an ensemble of specialized sub-models (a group of experts) for different inputs rather than using the entire model for every input to optimize the overall performance with a constant computational cost. Following Mixtral Transformers, GSHard, GLaM, and Switch Transformer, we propose the “Parallel Mixture-of-Experts Transformer (PMoET)” that coupled with Spatial Linear Layer (SLL) and use parallel Mixture of Experts (MoE) layers for going wider instead of deeper in traditional Transformer. PMoET model could be scaled along width using parallel MoE layers instead of a single Feed-Forward Network (FFN) layer in traditional Transformer. Using MoE architecture for graph learning of 3D dynamic skeleton-data for 3D hand gesture recognition can provide significant benefits due to the model’s ability to handle complex and varied 3D skeleton data efficiently. For this purpose, Decoupling spatial and temporal graph learning using a PMoET model by integrating parallel MoE layers into parallel configuration is an advanced approach for recognizing 3D hand gestures. By focusing on both the spatial configuration of the hand and the temporal dynamics of gestures, the PMoET architecture provides a robust and efficient solution for complex gesture recognition tasks. Finally, to evaluate the PMoET performance, conducting extensive experiments on SHREC’17 dataset provides strong evidence of PMoET effectiveness. PMoET improves the overall performance, the training stability and reduces the computational cost. Experiments show the efficiency and superiority of the PMoET model variants, particularly PMoET Model 4, which surpasses the state-of-the-art (SOTA) performance achieved by DG-STA method.