How can you prioritize critical workloads in a shared GPU cluster when less critical workloads are consuming more resources?

Implementing GPU quotas with Kubernetes resource management is a strategic approach to ensure that critical workloads receive the necessary resources in a shared GPU cluster environment. By setting up these quotas, administrators can define the maximum GPU resources allocated to each workload or user. This allows for the controlled distribution of GPU resources, ensuring that less critical workloads do not monopolize the GPUs, which can happen in shared resources scenarios.

This strategy allows the system to enforce limits on resource consumption, preventing any single workload from consuming more than its allotted share, hence safeguarding the performance of critical applications that are time-sensitive or resource-intensive. In this way, critical workloads can execute efficiently without being hindered by competing, less essential processes that may demand more resources than they should.

In contrast, the other options, while useful in their own contexts, do not address the core problem of resource competition between critical and less critical workloads as effectively as implementing GPU quotas. Model optimization techniques and upgrading GPU hardware might improve overall efficiency or performance, but they don’t directly enforce a fair distribution of resources. Using CPU-based inference for less critical workloads could be a workaround, but it doesn’t resolve the underlying issue of GPU resource contention and may lead to sub-optimal performance for both workloads.