Accelerated AI Storage Access with RDMA

This feature thread tracks the evolution of accelerated storage access for AI workloads, focusing on enabling direct, RDMA-based data transfers between compute accelerators (GPUs, TPUs, XPUs) and high-capacity file and object storage. Initial efforts focused on addressing the need for zero-copy, CPU-offloaded data transfers to support the increasing demands of AI training, fine-tuning, and inference. The development of standardized APIs and wire protocols, such as NVIDIA cuObject and the SCADA Server SDK, aims to improve interoperability across storage providers and cloud services. The xio-sig consortium plays a key role in defining these open industry standards. Subsequent developments will likely focus on broader adoption, performance optimizations, and integration with emerging storage technologies.