Implementing a distributed architecture with multiple GPU servers and a load balancer is the best approach for a highly available AI data center supporting training and inference with minimal downtime. This design, exemplified by NVIDIA's DGX SuperPOD, uses redundancy across GPU nodes, allowing workloads to shift dynamically if a server fails. A load balancer ensures even distribution and failover, maintaining performance.
NVIDIA's "DGX SuperPOD Reference Architecture" emphasizes distributed systems for high availability and fault tolerance in AI workloads.
A single GPU server (A) is a single point of failure despite redundancies. A warm standby (C) involves manual intervention, increasing downtime. CPU-based clusters (D) lack GPU optimization for AI. Distributed GPU architecture is NVIDIA's recommended solution.