Question

A research organization runs two distinct workloads on the same GPU hardware pool. Team A submits large, tightly-coupled multi-node distributed training jobs that require MPI-style rank coordination, batch queuing, and fair-share accounting across competing academic groups. Team B deploys long-running, always-on inference microservices that must auto-scale based on request load and self-heal when a pod crashes. The platform team must choose the most appropriate orchestration approach for each workload. Which recommendation best fits both needs?

Accepted Answer

Use Slurm for Team A's batch training jobs and Kubernetes for Team B's inference microservices

Answer

Use Kubernetes for both workloads, since it natively provides batch queuing and fair-share scheduling equal to Slurm

Answer

Use Slurm for both workloads, since it handles auto-scaling and self-healing of long-running services better than Kubernetes

Answer

Use Kubernetes for Team A's training jobs and Slurm for Team B's inference microservices