Which resource management strategy would be most effective for fluctuating workloads in an AI data center?

The most effective resource management strategy for fluctuating workloads in an AI data center is to implement NVIDIA MIG (Multi-Instance GPU) for resource partitioning. This approach allows data centers to allocate GPU resources dynamically, providing a flexible way to handle varying demands.

NVIDIA MIG enables a single GPU to be partitioned into multiple smaller instances, which can be assigned to different tasks or workloads. This means that during peak periods, more instances can be allocated to handle increased demand, while during quieter periods, the resources can be scaled down to conserve power and optimize costs. This elasticity is particularly beneficial in an AI data center where workloads can change rapidly and unpredictably.

In contrast, using round-robin scheduling does not account for the specific needs of workloads and may result in uneven distribution of resources, leading to inefficiencies. Manually scheduling workloads can be time-consuming and may not adequately adapt to real-time changes in demand. Upgrading all GPUs may lead to unnecessary costs without guaranteeing that resource allocation will meet the dynamic needs of fluctuating workloads. The ability of MIG to create multiple instances from a single GPU makes it a powerful solution for efficiently managing changing workloads.