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.
Whether you're designing next-generation AI pipelines, optimizing multi-node training or tackling federal AI-specific challenges, this session provided a blueprint for building storage architectures that deliver both performance and resilience at scale.