Which strategy is most effective in addressing GPU utilization imbalances across multiple workloads?

Implementing dynamic GPU load balancing across the infrastructure is the most effective strategy for addressing GPU utilization imbalances across multiple workloads. This approach involves actively monitoring GPU usage and automatically redistributing workloads based on current demand and resource availability. By dynamically allocating tasks to the most suitable GPUs, this strategy optimizes resource utilization, minimizes idle times, and enhances overall system performance.

This method responds to the varying demands of different workloads, ensuring that GPUs are not overburdened while others remain underutilized. This proactive adjustment allows for better handling of resource contention and can adapt in real-time to workload changes, leading to a more balanced and efficient computational environment.

In contrast, disabling GPU overclocking might limit performance and not directly address utilization imbalances. Increasing the power limit on underutilized GPUs could lead to inefficiencies without ensuring that workloads are appropriately assigned based on actual performance needs. Reducing the batch size for all AI workloads may help in specific scenarios but does not inherently solve the underlying issue of workload distribution and could lead to decreased performance if not aligned with the capabilities of the GPUs. Thus, dynamic load balancing stands out as a comprehensive solution.