PoinTramba is a novel hybrid framework that combines the strengths of Transformer and Mamba for point cloud analysis. The framework segments point clouds into groups, where the Transformer captures intra-group dependencies and generates group embeddings, while the Mamba architecture efficiently models inter-group relationships. A key innovation is the bi-directional importance-aware ordering (BIO) strategy, which reorders group embeddings based on importance scores to enhance Mamba's performance. This approach significantly improves the handling of unordered point clouds and optimizes the overall analytical process. Extensive experiments on datasets like ScanObjectNN, ModelNet40, and ShapeNetPart demonstrate PoinTramba's superior balance between computational efficiency and analytical performance, establishing a new state-of-the-art benchmark in point cloud analysis. The framework's effectiveness is validated through comprehensive experiments, showcasing its potential for future advancements in point cloud tasks.PoinTramba is a novel hybrid framework that combines the strengths of Transformer and Mamba for point cloud analysis. The framework segments point clouds into groups, where the Transformer captures intra-group dependencies and generates group embeddings, while the Mamba architecture efficiently models inter-group relationships. A key innovation is the bi-directional importance-aware ordering (BIO) strategy, which reorders group embeddings based on importance scores to enhance Mamba's performance. This approach significantly improves the handling of unordered point clouds and optimizes the overall analytical process. Extensive experiments on datasets like ScanObjectNN, ModelNet40, and ShapeNetPart demonstrate PoinTramba's superior balance between computational efficiency and analytical performance, establishing a new state-of-the-art benchmark in point cloud analysis. The framework's effectiveness is validated through comprehensive experiments, showcasing its potential for future advancements in point cloud tasks.