Top 3 Gpu Interview Questions
Browse 3 real-world technical and behavioral interview questions about Gpu. Review scenarios, edge cases, and architectural best practices.
How would you autoscale a GPU inference service?
Scale on queue depth or in-flight concurrency rather than request rate, because a GPU replica that takes minutes to load weights cannot be added after the queue has already formed. Then decide deliberately how much warm capacity to keep, since that is what you are paying to avoid cold starts.
How would you cut the cost of a GPU training fleet without slowing the team down?
Measure utilisation first, because most fleets pay for idle accelerators rather than for too few. Then right-size to whatever each job is bound by, move interruptible work onto spot capacity behind tested checkpointing, and pool the fleet behind one queue instead of reserving per team.
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?
An in-depth look at designing a robust Kubernetes orchestration layer to optimise GPU utilisation across diverse, conflicting machine learning workloads.