Which strategy is most effective in addressing uneven GPU utilization in a multi-node AI deployment?

Enabling GPU affinity in the job scheduler is the most effective strategy for addressing uneven GPU utilization in a multi-node AI deployment. This approach allows the job scheduler to assign tasks specifically to certain GPUs based on their availability and current workload. By controlling which tasks are sent to which GPUs, it helps in optimizing resource usage, ensuring that each GPU is utilized more evenly and efficiently. This results in improved overall performance and reduced idle time across the GPUs in the deployment.

Focusing on GPU affinity ensures that tasks are not randomly assigned to GPUs, which could otherwise lead to cases where some GPUs are overburdened while others are underutilized. This targeted approach allows for a finer level of control over the utilization of GPU resources, addressing the core issue of uneven utilization effectively.

The other strategies do not directly address the problem of uneven GPU utilization. Using a CPU-based load balancer primarily manages CPU resources, which may not translate effectively to GPU workloads. Mixed precision training optimizes the training process but does not inherently solve GPU workload distribution issues. Increasing the batch size might lead to more data being processed at once but does not equalize the workload across GPUs, which is the root of the problem in uneven utilization.