Solving the AI Bottleneck: Storage Architectures that Keep GPUs Fed

In this technical session, we explored how VDURA's V5000 and VPOD architectures addresses the unique performance, scalability and compliance challenges of modern AI workloads. From metadata-intensive language model training to multi-model defense applications, we broke down how unified namespaces, dynamic data acceleration and parallel I/O paths eliminate traditional constraints on AI pipelines.

Attendees gained insight into: 

• How to overcome metadata bottlenecks in large-scale training.
• Strategies for sustaining GPU saturation with 1 TB/s throughout per rack.
• Balancing training, interference and preprocessing workloads in a single infrastructure.
• Architecting for edge-to-core AI deployments in federal environments.
• Meeting governance and compliance requirements while scaling AI models.