What action would best ensure the scalability of AI infrastructure to support increased workloads?

Adopting a multi-cloud strategy for distributing AI workloads is the best action to ensure scalability of AI infrastructure to handle increased workloads effectively. This approach allows organizations to take advantage of the diverse resources available across multiple cloud service providers. It facilitates efficient resource allocation, enabling the distribution of workloads based on demand and availability.

By leveraging multiple cloud environments, organizations can scale out their infrastructure dynamically, borrowing resources from different providers as needed. This not only enhances flexibility but also reduces the risk of downtime or bottlenecks that may occur if all workloads are concentrated on a single cloud provider or on-premises infrastructure. Furthermore, multi-cloud strategies promote redundancy and can optimize costs by allowing the organization to choose the most cost-effective resources across providers.

The other options, while they may have benefits in specific contexts, do not directly enhance scalability in the same comprehensive manner. Reducing the number of models being trained simultaneously may limit the potential of AI development and operational efficiencies. Using a single large server, even if powerful, creates a single point of failure and limits scalability, as it cannot handle increased loads beyond its capacity. Upgrading CPUs across servers may improve performance but does not address the need for flexibility and resource distribution necessary for scalable infrastructure.