The use of deep learning for automatic seismic data interpretation is gaining the attention of many researchers across the oil and gas industry. The integration of high-performance computing (HPC) AI workflows in seismic data interpretation brings the challenge of moving and processing large amounts of data from HPC to AI computing solutions and vice-versa. We illustrate this challenge via a case study using a public deep learning model for salt identification applied on a 3D seismic survey from the F3 Dutch block in the North Sea. We present a workflow to address this challenge and perform accelerated AI on seismic data. Intel Distribution of OpenVINO toolkit was used to increase the inference performance of a pre-trained model on an Intel CPU. OpenVINO allows CPU users to get significant improvement in AI inference performance for high memory capacity deep learning models used on large datasets without any significant loss in accuracy.