hardScenarioDesignSeniorStaff
How would you architect a Kubernetes cluster to manage a heterogeneous fleet of GPUs simultaneously running massive distributed training jobs and latency-sensitive inference services without catastrophic degradation?
2 min readUpdated 2026-08-15
Answer
Short answer
An in-depth look at designing a robust Kubernetes orchestration layer to optimise GPU utilisation across diverse, conflicting machine learning workloads.
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Likely follow-ups
- How would your design change if the cluster also needed to support a third workload type, such as batch data processing, on the same GPU nodes?
- What happens to in-flight inference requests when a MIG partition needs to be reconfigured?
- How do you decide when time-slicing is acceptable versus when you must insist on MIG or dedicated GPUs?
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