Non-volatile storage (NVM) technologies provide faster data access compared to traditional hard disk drives and can benefit applications executing on accelerators like general purpose graphics processing units (GPGPUs). Many contemporary GPU-friendly applications process huge volumes of data residing in the secondary storage. Several research work propose techniques to optimize data transfer overheads between devices connected to the same bus e.g., peer-to-peer data transfer between NVMe-SSD and GPU connected to a PCI bus. The applicability of these techniques, extent of their benefit and associated costs in virtualized systems is the scope of this paper. In this paper, we present a comprehensive empirical analysis of different combinations of NVMe-SSD virtualization techniques and data transfer mechanisms between NVMe-SSDs and GPUs. Further, the impact of different data transfer parameters and, root-cause analysis of the resulting performance in terms of data transfer throughput and CPU utilization for different combinations of techniques is presented. Based on the empirical analysis, we provide insights to address several bottlenecks related to different GPU data transfer techniques in different virtualization setups and motivate an alternate design by extending the VirtIO framework for efficient peer-to-peer data transfer.