Storage Backend Overview

The AI storage backend for AI encompasses the hardware and software components for storing, retrieving, and managing the vast amounts of data involved in AI workloads, and the infrastructure that allows the GPUs to communicate with these storage components.

The key aspects of the storage backend include:

• High-Performance Storage Devices: optimized for high I/O throughput, which is essential for handling the intensive data processing requirements of the AI tasks such as deep learning. This includes high-performance storage devices designed to facilitate fast access to data during model training and to accommodate the storage needs of large datasets. These storage devices must provide:
  • Data Management Capabilities: which support efficient data querying, indexing, and retrieval and are crucial for minimizing preprocessing and feature extraction times in AI workflows, as well as for facilitating quick data access during inference.
  • Scalability: which accommodates growing data volumes and efficiently manages and stores massive amounts of data over time, to support AI workloads often involving large-scale datasets.
• Storage Backend Fabric: routing and switching infrastructure that provides the connectivity between the GPU and the storage devices. This integration ensures that data can be efficiently transferred between storage and computational resources, optimizing overall AI workflow performance. The performance of the storage backend significantly impacts the efficiency and JCT of AI/ML workflows. A storage backend that provides quick access to data can significantly reduce the amount of time for training AI/ML models.