High performance computing (HPC) based visual question and answering is the contemporary research in visual question answering. This study introduces the Image Patch-Question feature fusion approach for HPC-based visual question and answering. The proposed framework divides the input image into four patches. Subsequently, each image patch, accompanied by the input question, is processed using a vision-and-language transformer utilizing an HPC cluster to produce a feature vector. Finally, the answer will be produced with a fusion-based voting algorithm that employs four feature vectors obtained from the HPC clusters. The proposed model has been assessed using the low-resolution Visual Question Answering for Remote Sensing (RSVQA) dataset. Our model demonstrates higher performance compared to the base models with 86.4% accuracy.