AI infrastructure engineers, storage developers, and cloud service providers need fast and secure access to high-capacity file and object storage to support AI workloads.

AI workloads increasingly require high-speed data access for training, fine-tuning, inference context, tool calls, searches, and database lookups. Much of this data lies in files and objects stored both on-premises and in the cloud. Compute accelerators—including GPUs, TPUs, and XPUs—need remote direct memory access (RDMA) that uses NIC- or DPU-accelerated data transfers (like NVIDIA ConnectX NIC or NVIDIA BlueField DPU) and doesn’t copy the data through the server’s CPU-controlled memory. The need for RDMA-accelerated, zero-copy data transfers grows with faster GPU architectures.

Developers building direct access to file and object storage have had to navigate different APIs and protocols across providers.…