What is the best approach to optimize GPU utilization across multiple AI project teams?

The optimal approach to enhance GPU utilization among different AI project teams involves implementing dynamic GPU resource allocation based on real-time workload demands. This strategy is effective because AI workloads can vary significantly in terms of resource requirements depending on the task at hand, such as training, inference, or data preprocessing.

Dynamic resource allocation allows the system to adjust the GPU resources assigned to each project team in real time, effectively responding to spikes in demand or fluctuations in workload. This means that if one team requires more resources temporarily, the system can allocate more GPUs to them while reallocating resources from teams or tasks that are currently less demanding. This flexibility maximizes the overall use of available GPU resources, reduces idle time, and makes sure that all teams can operate at peak efficiency based on their present needs.

In contrast, the other approaches have limitations. Prioritizing deep learning training tasks over inference tasks may lead to inefficiencies, as inference tasks can also be critical and require significant resources. Allocating fixed GPU resources may not adapt to the changing demands of the projects, potentially resulting in underutilization or overloading. Limiting the number of active tasks per team might prevent overloaded GPUs but does not effectively optimize overall resource usage across teams and can lead to unnecessary bottlenecks.