A multi-tenant GPU cluster frequently suffers from scheduling delays where smaller, critical model training jobs are delayed or evicted because larger research jobs have consumed all available capacity. Which administrative control should you implement to resolve this resource hogging?
Select an answer to reveal the explanation.
Short Explanation and Infographic
Here's the deal: if you run a shared cluster without boundaries, someone is going to submit a massive job that consumes every single GPU, leaving everyone else stuck in the queue. You can't just keep buying more GPUs to solve this—that's a money pit, and the new hardware will just get hogged too! The real solution is setting up resource quotas. Think of it like a strict budget for each department or user. By limiting how many GPUs any single team can use at once, you make sure there's always space for other jobs to run. It prevents one user from hogging the playground and keeps your model pipeline running smoothly. Got it? Sweet, let's keep going.
Full explanation below image
Full Explanation
In multi-tenant AI cluster environments, resource contention is inevitable if scheduling is left unmanaged. Resource quotas are administrative constraints enforced by cluster orchestrators (such as Kubernetes or Slurm) that limit the total amount of compute (CPU/GPU) and memory a specific namespace, user, or project can consume. By enforcing these boundaries, you prevent any single tenant from launching massive jobs that starve other pipelines of resources, thereby guaranteeing fair share distribution and preventing unexpected scheduling delays or evictions.
Why other approaches are less effective: - Disabling job preemption (Option A) can result in deadlock or extreme queue wait times. If a low-priority job starts running and cannot be preempted, a critical high-priority model update will be blocked indefinitely until the lower-priority job finishes. - Adding more hardware (Option B) is an expensive operational fix that does not address the root behavioral issue. Without quotas, the new capacity will eventually be saturated by larger workloads, recreating the original bottleneck. - Manually raising job priorities (Option C) leads to 'priority inflation.' If every user or delayed job is promoted to high priority, the scheduling queue loses its ordering logic, rendering priority levels useless.
Implementing quotas, combined with resource requests and limits, allows cluster administrators to run high-density environments while maintaining predictable service levels for critical production pipelines.