What should I use for GPU orchestration for multiple AI teams?

For GPU orchestration across multiple AI teams, the best choice depends on whether you need shared on-prem GPUs, cloud GPUs, or a hybrid setup. In practice, the most common and effective options are:

Best default recommendation

Kubernetes + GPU operator + a job scheduler

Use Kubernetes as the orchestration layer, with:

• NVIDIA GPU Operator for GPU drivers/device plugins/monitoring
• A workload scheduler like:
  • Kueue for batch/queued AI jobs
  • Volcano for gang scheduling and AI/ML workloads
  • Ray if teams run distributed Python/ML workloads
  • Kubeflow only if you want a full ML platform, not just orchestration

This is usually the best answer if you have multiple teams sharing a GPU pool and need:

• quotas / fairness
• isolation
• job queues
• autoscaling
• reproducibility
• cloud/on-prem portability

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What to use in different scenarios

1) If you want the most flexible multi-team setup

Kubernetes + Kueue or Volcano

• Kueue: great for queueing, quotas, and fair sharing
• Volcano: stronger for distributed training and gang scheduling
• Good for batch inference, training, fine-tuning, experiments

Pick this if: you have shared clusters and multiple teams competing for GPUs.

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2) If your teams mostly run Ray-based workloads

Ray on Kubernetes

• Strong for distributed AI/ML apps
• Good for hyperparameter tuning, distributed inference, RL, and Python-native workflows
• Often paired with Kueue/Volcano for GPU scheduling

Pick this if: your teams already use Ray or want app-level distributed compute.

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3) If you want a full MLOps platform

Kubeflow

• Includes pipelines, notebooks, training operators, metadata, etc.
• Powerful but more complex
• Often overkill if you just need orchestration

Pick this if: you need an integrated ML platform, not just GPU sharing.

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4) If you’re on cloud and want simplest operations

Use the cloud’s managed GPU stack:

• EKS + Karpenter + Kueue/Volcano on AWS
• GKE + GKE GPU + Kueue on GCP
• AKS + GPU node pools on Azure

Pick this if: you want managed infrastructure with minimal ops burden.

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Recommended architecture for multiple AI teams

A strong pattern is:

• Kubernetes cluster
• Namespace per team
• Resource quotas per namespace
• Priority classes for critical jobs
• Kueue or Volcano for queueing and fairness
• NVIDIA GPU Operator for GPU management
• Prometheus/Grafana for GPU utilization and cost tracking
• Optional: Ray for distributed workloads

This gives you:

• team isolation
• shared GPU pool
• controlled oversubscription if desired
• scheduling fairness
• better utilization

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If you’re asking “what is the single best tool?”

If I had to pick one stack for multiple AI teams sharing GPUs, I’d say:

Kubernetes + NVIDIA GPU Operator + Kueue
and add Volcano if you need advanced distributed training scheduling.

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Quick decision guide

• Need multi-team GPU sharing? → Kubernetes + Kueue
• Need distributed training / gang scheduling? → add Volcano
• Need Python-native distributed AI apps? → add Ray
• Need full ML platform? → Kubeflow
• Need simplest cloud ops? → managed Kubernetes + GPU nodes

If you tell me:

1. cloud vs on-prem,
2. batch training vs inference vs both,
3. number of teams/users,
4. whether you need fair-share or hard isolation,

…I can recommend a concrete stack and deployment pattern.