Reducing computational complexity while maintaining competitive performance has significant implications for efficiency and scalability in various applications. In the context of ParFormer, this reduction in computational complexity allows for faster processing times, lower resource requirements, and potentially lower energy consumption. By optimizing the model to achieve comparable or even superior performance with fewer parameters and FLOPs, ParFormer demonstrates a more efficient approach to vision transformer architectures. This efficiency can lead to cost savings in terms of hardware resources needed for training and inference processes.