Object storage is increasingly attractive for deep learning (DL) applications due to its cost-effectiveness and high scalability. However, it exacerbates CPU burdens in DL clusters due to intensive object storage processing and multiple data movements. Data processing unit (DPU) offloading is a promising solution, but naively offloading the existing object storage client leads to severe performance degradation. Besides, only offloading the object storage client still involves redundant data movements, as data must first transfer from the DPU to the host and then from the host to the GPU, which continues to consume valuable host resources.

The NVMe-over-Fabrics (NVMe-oF) is gaining popularity in cloud data centers as a remote storage protocol for accessing NVMe storage devices across servers. With the rapid increase in throughput of the NVMe storage devices, the NVMe-oF stack consumes a significant amount of valuable CPU resources. To release these valuable computing power to other tasks, many smartNICs now support NVMe-oF Target offloading. However, this emerging NVMe-oF offloading scheme's performance has not been fully investigated.