What should I use for a private GPU cluster?

It depends on what you mean by “private GPU cluster” and what you want to run on it. Here’s the practical short version:

Best default choice

Kubernetes + NVIDIA GPU Operator
Use this if you want:

• multiple users / teams
• containerized workloads
• easier scheduling and scaling
• long-term flexibility

Typical stack:

• Linux servers with NVIDIA GPUs
• NVIDIA drivers
• Kubernetes for orchestration
• NVIDIA GPU Operator to manage drivers, device plugins, monitoring, etc.
• Container registry for private images
• Shared storage like NFS, Ceph, or Lustre if needed

If you want the simplest setup

Single-node or small cluster with Docker + NVIDIA Container Toolkit
Good for:

• development
• prototyping
• one team
• a few machines

This is much easier to get running than Kubernetes, but less powerful for scheduling and multi-user control.

If this is for AI/ML training at scale

Consider:

• Slurm if you want HPC-style job scheduling
• Kubernetes if you want cloud-native workflows
• Ray if your workloads are Python/distributed ML-centric

For many ML labs, the combo is:

• Slurm on bare metal
• NVIDIA drivers/toolkit
• shared filesystem
• optionally Kubernetes for some services

If you want “private cloud” style management

You might look at:

• Proxmox or OpenStack for infrastructure virtualization
• then pass GPUs through to VMs or containers

But this is more complex and usually only worth it if you need VM isolation.

Recommended choices by use case

1. Small private cluster, easiest

• Ubuntu Server or Rocky Linux
• NVIDIA drivers
• Docker + NVIDIA Container Toolkit
• NFS for shared files
• Optional: Portainer or Rancher for management

2. Medium cluster, multi-user AI workloads

• Kubernetes
• NVIDIA GPU Operator
• Ceph or NFS for storage
• Prometheus + Grafana for monitoring

3. Research/HPC cluster

• Slurm
• NVIDIA drivers/toolkit
• Lustre/Ceph/NFS
• InfiniBand if networking matters

Hardware notes

For a private cluster, use:

• enterprise or datacenter GPUs if you need reliability and ECC
• NVLink only if your workloads benefit from tight multi-GPU communication
• strong CPU/RAM balance
• fast networking: 10/25/100 GbE depending on scale

My recommendation

If you didn’t give more constraints, I’d say:

Use Ubuntu Server + NVIDIA drivers + Kubernetes + NVIDIA GPU Operator.
If you want the least complexity, start with Docker + NVIDIA Container Toolkit and move to Kubernetes later.

If you want, I can suggest a stack based on:

1. your budget,
2. number of GPUs/nodes,
3. whether it’s for ML training, inference, or rendering,
4. whether you need multi-user access.