To improve energy efficiency while supporting a high-throughput deep learning inference service, which strategy is most effective?

Utilizing a workload management system to dynamically allocate GPU resources is particularly effective for several reasons when aiming to enhance energy efficiency in a high-throughput deep learning inference service.

First, dynamic resource allocation allows for the optimal use of available GPU resources depending on the demand at any given time. This means that during periods of high demand, more GPU resources can be allocated, and during low demand, resources can be scaled down to reduce energy consumption. By adapting to the workload in real-time, this approach minimizes waste, as it ensures that GPUs are only utilized when needed, thus leading to better overall efficiency.

In contrast, implementing a static high-performance GPU cluster does not offer the flexibility to adjust based on varying workloads, which can lead to periods of underutilization and thereby increased energy consumption. Scheduling inference tasks during off-peak hours could also help but does not address the immediate needs effectively and may not fully utilize the capable resources during peak times. Similarly, deploying all tasks on lower-power CPUs may not provide the necessary throughput or performance required for complex deep learning inference tasks, slowing down the service.

Thus, the dynamic workload management system is the most effective strategy as it ensures that the infrastructure operates at peak efficiency, balancing performance and energy use to meet the demands of