One of the most important challenges facing U-Net architecture performance is the method design of its components and how to choose the suitable hardware computing device to deal with the training labelled datasets. Convolution is the most process that requires computations and memory costs, which is needs to minimize. Thus, one of the suitable selection is to change the type of the convolution. Other suggested solutions are to reduce the size of image, number of bits, and, stride value, in addition to number of filters, and image batches. Therefore, in this paper the roofline model will used as performance guide in analyzing the FLOPs and the memory bandwidth boundaries of a U-Net model with different configurations. The cost has been assessed with compared to the limitation of three computing devices, GPU230MX, GPU940MX and GPU2060rtx super. 128 × 128 image dataset has been used during the U-Net costperformance evaluation process. Based on the analysis, the evaluation results show that the solution that achieves a balance between memory and computations is to implement a U-Net model in parallel using RTX2060 super card with the configurations of batch size is 16, image size of 128×128, number of bits is 32, shared memory management.