Which strategy is best for a data center platform to handle unpredictable spikes in AI workload demand?

The chosen strategy of utilizing a hybrid cloud model with on-premises GPUs for steady workloads and cloud GPUs to scale during demand spikes is optimal for handling unpredictable spikes in AI workload demand. This approach allows for a flexible infrastructure that can efficiently manage varying workloads over time.

Using on-premises resources ensures that consistent demands are met without incurring additional costs associated with cloud usage. This setup provides reliability and lower latency since the data doesn't have to be transmitted to and from the cloud for routine processing. However, when an unexpected spike in workload occurs—common in AI workloads that may fluctuate significantly—the hybrid model allows for the seamless addition of cloud resources. This capability ensures that the data center can maintain performance and responsiveness during high-demand periods without being restricted to the capacity of on-site equipment.

Moreover, the hybrid model balances cost-effectiveness with performance. It allows for scaling resources up or down depending on the fluctuating needs, ensuring that users are not over-provisioning or under-provisioning resources. This responsiveness to demand further enhances operational efficiency, as organizations can allocate resources as needed rather than maintaining a static infrastructure that may be underutilized during off-peak times.

In contrast, relying solely on round-robin scheduling or migrating all workloads to a single large cloud