What architecture is best suited for a highly available AI data center that must support training and inference workloads?

The selection of a distributed architecture with multiple GPU servers as the best option for a highly available AI data center that must support both training and inference workloads is grounded in several factors intrinsic to the requirements of AI tasks.

AI workloads, particularly in training and inference, often demand substantial computational power and the ability to handle large datasets efficiently. A distributed architecture allows for the scaling of resources according to workload requirements. Multiple GPU servers can work in parallel to accelerate the training process, enabling the handling of larger models and datasets without the bottleneck that may be caused by a single machine.

Additionally, this architecture supports redundancy and fault tolerance. If one GPU server fails, the workload can be redistributed among the remaining servers, maintaining high availability and minimizing downtime. This resilience is crucial for production environments where consistent access to AI services is necessary.

Moreover, by utilizing multiple servers, it benefits from optimized resource allocation and can cater to simultaneous requests for inference processing, providing the necessary responsiveness and performance. This flexibility is essential for various users or applications interacting with the AI system concurrently.

In contrast, the other options fall short in various aspects: a warm standby system requires manual activation, which is not ideal for dynamic AI workloads; a cluster of CPU-based servers may not provide the necessary computational power