This paper proposes Multi-Head Mixture-of-Experts (MH-MoE), a method to enhance model capacity and fine-grained understanding by enabling denser expert activation. MH-MoE addresses two key issues in Sparse Mixtures of Experts (SMoE): low expert activation and limited fine-grained analytical capabilities for multiple semantic concepts within individual tokens. The method splits each input token into multiple sub-tokens, which are then processed by a diverse set of experts in parallel and seamlessly reintegrated into the original token form. This approach increases expert activation and enables the model to collectively attend to information from various representation spaces within different experts, leading to deeper context understanding. MH-MoE is straightforward to implement and decouples from other SMoE frameworks, making it easy to integrate with these frameworks for enhanced performance. Extensive experimental results across three tasks—English-focused language modeling, multi-lingual language modeling, and masked multi-modality modeling—demonstrate the effectiveness of MH-MoE. The model achieves higher expert activation, finer-grained understanding, and seamless integration with other frameworks. The paper also presents ablation studies and analysis of expert activation and fine-grained understanding. Results show that MH-MoE outperforms existing models in terms of performance and scalability. The method is effective in capturing diverse and intricate semantic information, particularly for polysemous and false cognate words in languages and semantically-rich areas in images. The model's performance is validated across multiple tasks, demonstrating its effectiveness in both upstream and downstream tasks. MH-MoE is implemented with a multi-head mechanism that splits each token into sub-tokens and routes them to different experts. This approach increases the average volume of data routed to a specific expert, leading to denser expert activation. The model's performance is further enhanced by the inclusion of a token-splitting-merging (TSM) operation, which allows for more efficient integration of information from different expert representation spaces. The paper concludes that MH-MoE is an effective method for enhancing model capacity and fine-grained understanding, with the potential to be integrated with other SMoE frameworks to further improve performance. The results demonstrate the effectiveness of MH-MoE in various tasks, highlighting its potential for future research and applications.This paper proposes Multi-Head Mixture-of-Experts (MH-MoE), a method to enhance model capacity and fine-grained understanding by enabling denser expert activation. MH-MoE addresses two key issues in Sparse Mixtures of Experts (SMoE): low expert activation and limited fine-grained analytical capabilities for multiple semantic concepts within individual tokens. The method splits each input token into multiple sub-tokens, which are then processed by a diverse set of experts in parallel and seamlessly reintegrated into the original token form. This approach increases expert activation and enables the model to collectively attend to information from various representation spaces within different experts, leading to deeper context understanding. MH-MoE is straightforward to implement and decouples from other SMoE frameworks, making it easy to integrate with these frameworks for enhanced performance. Extensive experimental results across three tasks—English-focused language modeling, multi-lingual language modeling, and masked multi-modality modeling—demonstrate the effectiveness of MH-MoE. The model achieves higher expert activation, finer-grained understanding, and seamless integration with other frameworks. The paper also presents ablation studies and analysis of expert activation and fine-grained understanding. Results show that MH-MoE outperforms existing models in terms of performance and scalability. The method is effective in capturing diverse and intricate semantic information, particularly for polysemous and false cognate words in languages and semantically-rich areas in images. The model's performance is validated across multiple tasks, demonstrating its effectiveness in both upstream and downstream tasks. MH-MoE is implemented with a multi-head mechanism that splits each token into sub-tokens and routes them to different experts. This approach increases the average volume of data routed to a specific expert, leading to denser expert activation. The model's performance is further enhanced by the inclusion of a token-splitting-merging (TSM) operation, which allows for more efficient integration of information from different expert representation spaces. The paper concludes that MH-MoE is an effective method for enhancing model capacity and fine-grained understanding, with the potential to be integrated with other SMoE frameworks to further improve performance. The results demonstrate the effectiveness of MH-MoE in various tasks, highlighting its potential for future research and applications.