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Outbyte PC Repair FREEClear out junk files and repair common Windows errorsFree Scan →Outbyte Driver Updater FREEFix the driver behind crashes, sound loss and screen glitchesFind Drivers →Use Kubernetes as the foundation, then add an AI workload control layer only when you have a measured need that Kubernetes alone does not meet. Kubernetes runs the cluster control plane and places Pods on nodes. An additional layer can control when jobs start, enforce shared quotas and fairness, schedule gangs of Pods, share GPUs, apply tenant policy, or expose AI-specific monitoring. In a Ray deployment, there is another scheduler again: Ray places tasks and actors on Ray nodes that Kubernetes runs as Pods.
These are complementary layers rather than mutually exclusive products. The right choice depends on your workload mix, queue contention, GPU topology, tenancy model, framework, supported Kubernetes version, and the operational team that will own upgrades and incidents.
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What “AI workload control plane” means
“AI workload control plane” is not a single standardized Kubernetes component. In practice, the phrase covers one or more control layers above (or alongside) the Kubernetes cluster control plane:
- Cluster scheduling: Kubernetes chooses a node for each Pod and reconciles the desired state of the cluster. Its control plane exposes the API and coordinates controllers, as described in the Kubernetes cluster architecture documentation.
- Batch admission and quota management: A queueing system decides which jobs may start, which quota they consume, and whether a running job should be preempted. Kueue is an example of this Kubernetes-native layer.
- AI resource and policy management: A broader platform can add GPU-aware placement or sharing, gang scheduling, tenant policy, workload submission, analytics, and centralized administration. NVIDIA Run:ai documents this model.
- Framework scheduling: Distributed frameworks such as Ray schedule application tasks and actors on their own worker nodes. Those worker nodes are usually Kubernetes Pods, so both schedulers remain involved.
Calling all of these “the scheduler” causes design and troubleshooting mistakes. A Pod in Pending state is a Kubernetes-level problem; a Ray task waiting for an available worker is a Ray-level problem. Queue admission can delay the Pod before either scheduler gets a chance to place it.
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What Kubernetes already provides
Kubernetes is normally the substrate on which the other layers run. Its standard mechanisms are relevant to AI workloads even when no AI-specific platform is installed.
Placement and resource requests
Pods declare resource requests and limits, and the Kubernetes scheduler uses those requests with node labels, affinity rules, taints, and tolerations to select a node. This is the right layer for machine-level constraints such as accelerator presence, CPU and memory capacity, zones, or dedicated node pools.
Policy and tenancy primitives
Namespaces, resource quotas, priority classes, admission controllers, service accounts, and RBAC provide the basic isolation and authorization model. Controllers manage the lifecycle of Deployments, Jobs, StatefulSets, and custom resources supplied by workload operators.
Where the baseline can stop being sufficient
These primitives do not automatically provide every AI scheduling policy. A cluster can place individual Pods successfully while still lacking all-or-nothing admission for a distributed training job, fair sharing across teams, fractional GPU allocation, topology-aware gang placement, or a view of queue wait time and GPU utilization by experiment. Whether a particular requirement is met depends on your Kubernetes version, device plugins, operators, and policies; it should be demonstrated with your workloads rather than assumed.
How the scheduling layers fit together
The same training run may pass through several decisions:
- Admission: A queue or policy layer determines whether the job is allowed to consume a team’s quota now or must wait.
- Pod creation: The workload controller creates one or more Pods, possibly with a gang or all-or-nothing requirement.
- Node placement: Kubernetes selects nodes that satisfy the Pods’ resource requests and placement constraints.
- Framework placement: Once workers are running, Ray or another distributed framework assigns application tasks and actors to those workers.
- Runtime observation: Metrics and events from each layer explain whether time is spent waiting for quota, a node, a worker, or application resources.
Changing one layer does not remove the others. For example, a queue can admit a job, but Kubernetes can still leave a Pod pending because no node satisfies its request. Conversely, all Pods can be running while Ray waits for an actor placement group.
Kubernetes alone: when it is enough
A Kubernetes-only design is often the simplest and most reliable starting point when the following conditions hold:
- Your workloads are mostly services or small batch jobs rather than large, competing distributed experiments.
- Teams can request resources accurately and existing quotas and priority classes prevent one namespace from monopolizing the cluster.
- You do not need fractional GPU sharing, centralized queue fairness, or a job-level all-or-nothing start condition.
- Operators already provide the lifecycle and retry behavior you need for training and inference.
- Cluster, namespace, and application metrics answer your on-call questions.
Keeping one control plane reduces components to upgrade, secure, monitor, and debug. It also avoids paying an operational cost for features no team actually uses. The burden is that your platform team must assemble and maintain admission policies, queue semantics, GPU accounting, and dashboards from Kubernetes-native components and workload operators.
Kueue: an additive queue and admission layer
Kueue is designed to sit on a Kubernetes cluster rather than replace it. Its project documentation states: “Kueue is a kubernetes-native system that manages quotas and how jobs consume them.” Kueue decides when jobs wait, are admitted, or are preempted; Kubernetes components continue to schedule Pods onto nodes and manage their lifecycle. See the Kueue overview for the current architecture and integrations.
What Kueue adds
- Queues with priorities and quota accounting.
- Resource flavors for selecting among classes of capacity.
- Fair sharing and preemption policies.
- Integrations for Kubernetes Batch Jobs, Kubeflow training jobs, and Ray workloads.
- Prometheus metrics for queue and admission visibility.
What Kueue deliberately leaves to Kubernetes
Kueue does not become the Pod-to-node scheduler, autoscaler, or general-purpose job lifecycle controller. Those responsibilities remain with the established Kubernetes components and operators. This separation is useful when your missing capability is admission or fair sharing, not a wholesale replacement of cluster operations.
When Kueue is a good fit
Choose this pattern when you want an open Kubernetes-native queue and quota layer, already operate Kubernetes confidently, and can accept assembling the remaining GPU, topology, and observability pieces. Validate the exact workload integrations and behavior against the Kueue release you plan to run.
Run:ai: a broader AI workload platform
NVIDIA’s self-hosted installation documentation describes two components installed over Kubernetes: a control plane for resource management, workload submission, monitoring, and analytics, plus one or more cluster components that extend Kubernetes scheduling and workload management. Both components require Kubernetes. A typical deployment places the control plane and first cluster on the same Kubernetes cluster, while connected and air-gapped self-hosted arrangements are also documented. Check the current release requirements before committing to an architecture: Run:ai self-hosted installation.
Capabilities and boundaries
The published feature matrix lists fairness, priority and preemption, quotas, node pools, bin-packing or spread policies, GPU fractions, gang scheduling, monitoring, and RBAC for various workload categories. Some capabilities are limited to distributed workloads, and the documentation warns that support varies by version and deployment: Run:ai supported features.
Externally submitted Kubernetes workloads may receive only a subset of the platform’s scheduling and management features. Do not assume that applying a Run:ai component to an existing workload gives it the same behavior as a workload submitted through the platform’s native workflow.
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Supported workload objects
The supported workload-type documentation includes standard Kubernetes objects, Kubeflow jobs, RayService, RayCluster, RayJob, and other ecosystem integrations: Run:ai supported workload types. It also draws an important Slurm boundary: Slurm NodeSet support is for Slurm compute-node Pods on Kubernetes. Run:ai does not submit or manage Slurm jobs, partitions, accounts, or the Slurm controller.
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When a broader platform is justified
A platform of this scope can make sense when several teams need consistent GPU policy, centralized submission and monitoring, multi-cluster governance, or specialized scheduling that would otherwise require substantial internal engineering. The trade-off is another control plane, its agents or cluster components, a release-compatibility matrix, and a separate operational ownership model.
Ray and framework-level scheduling
Ray’s KubeRay scheduling guide makes the layer distinction explicit: Kubernetes places Pods on nodes, while Ray places tasks or actors on Ray nodes. Kubernetes constraints express machine requirements, quota, and admission; Ray constraints express application resources and placement. Read the current guide at Ray scheduling on Kubernetes.
Diagnosing a two-layer wait
- Pod is Pending: inspect Kubernetes events, resource requests, taints, affinity, quotas, and any queue admission condition.
- Ray task or actor is waiting while workers are Ready: inspect Ray resource labels, placement groups, actor requirements, and Ray cluster autoscaling.
- Pods never appear: investigate the Ray operator or admission layer before debugging the Kubernetes node scheduler.
Running Ray does not make Kubernetes optional. You configure and troubleshoot both schedulers, and a queueing layer may sit in front of the Ray custom resources as well.
Comparison of the main architecture choices
| Architecture | Primary decision maker | Queue, quota, priority | GPU sharing and topology | Workload scope | Operational trade-off |
|---|---|---|---|---|---|
| Kubernetes alone | Kubernetes scheduler and workload controllers | Native quotas and priorities; job-level queueing depends on your controllers and policies | Depends on device plugins, scheduler configuration, and operators; no universal AI policy is implied | Standard Kubernetes workloads and any installed operators | Fewest components; more platform engineering may be required for advanced AI policy |
| Kubernetes + Kueue | Kueue admits or preempts jobs; Kubernetes places Pods and manages lifecycle | Queues, resource flavors, fair sharing, priorities, and preemption are documented | Not supplied by Kueue alone; combine with Kubernetes and workload-specific components | BatchJob, Kubeflow training, Ray, and other documented integrations | Kubernetes-native additive layer; you retain responsibility for cluster scheduling and lifecycle |
| Kubernetes + Run:ai | Run:ai control and cluster components extend Kubernetes management | Feature matrix includes quotas, fairness, priority, and preemption, subject to version and workload | Feature matrix includes GPU fractions, gang scheduling, node pools, and placement policies for supported cases | Standard Kubernetes, Kubeflow, Ray, and listed ecosystem objects; externally submitted workloads may have reduced coverage | Broader platform and centralized tooling; additional components and release constraints |
| Kubernetes + Ray | Kubernetes schedules Ray Pods; Ray schedules tasks and actors | Kubernetes admission and quota plus Ray’s application-level resources | Depends on both Kubernetes constraints and Ray placement requirements | RayService, RayCluster, RayJob, and Ray applications | Powerful distributed execution, but two distinct schedulers must be configured and debugged |
Licensing and total cost are not stated in the cited project and product pages. Obtain current commercial terms, support scope, and any per-cluster or per-accelerator charges directly from the supplier before comparing budgets. Kueue is an open-source Kubernetes project, but operating cost still includes engineering time, observability, upgrades, and incident response.
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1. Inventory the workloads
List interactive notebooks, conventional Deployments, batch training, distributed training, inference services, and framework-managed task graphs. Record the owning team, namespace, framework, expected run duration, accelerator type, number of workers, restart behavior, and whether all workers must start together.
2. Measure the actual bottleneck
For at least one representative busy period, collect queue wait, time from admission to Pod scheduling, unschedulable reasons, startup and image-pull time, GPU idle time, preemption or retry counts, and application-level waiting. A Pod that cannot find a node needs a different fix from a job that is intentionally waiting for quota.
3. Write explicit policies
Define per-team quotas, borrowing rules, priority classes, preemption limits, maximum run time, and what “fair” means. Decide whether a distributed job may start with only part of its requested capacity. If you cannot state the policy, adding a control plane will not resolve the governance problem.
4. Check feature and version coverage
Map each required policy to the exact Kubernetes, operator, Kueue, Run:ai, device-plugin, and framework versions you will run. Run:ai explicitly notes that feature support varies by version and deployment. Treat a feature as available only after a small test proves it for your workload object.
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5. Compare operating models
Assign ownership for upgrades, certificates, webhooks, agents, dashboards, backups, and incident escalation. Kueue leaves core scheduling and lifecycle with your Kubernetes team. A broader platform adds its own control-plane and cluster components. Include air-gapped or network-bound environments, data locality, and change-approval requirements in this review.
6. Pilot under contention
Use a test cluster or isolated queues with realistic mixtures: short interactive jobs, long training runs, distributed jobs, and inference services. Exercise quota exhaustion, priority inversion, preemption, node failure, controller restart, image-pull failure, and a rolling upgrade. Compare observed behavior with the written policy, not with a feature checklist.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Implementation and troubleshooting checklists
Kubernetes-only baseline
- Define namespaces and resource quotas for each tenant or team.
- Set resource requests that reflect actual CPU, memory, and accelerator needs.
- Use labels, affinity, taints, and tolerations to isolate specialized nodes.
- Configure priority classes conservatively and document preemption expectations.
- Install workload operators only where they are required, and define retry and checkpoint behavior.
- Expose Kubernetes events, controller logs, node conditions, and workload metrics to the on-call team.
Adding Kueue
- Install the Kueue release supported by your Kubernetes version on a non-production cluster first.
- Create queues, resource flavors, and cohort or borrowing policies that match your written quota model.
- Start with one Batch Job or training integration and verify the transition from waiting to admitted to running.
- Test fair sharing and preemption with two teams competing for the same scarce resource.
- Confirm that autoscaling, Pod-to-node scheduling, and job cleanup still behave as expected; Kueue delegates these functions to Kubernetes components.
- Connect its Prometheus metrics to your existing dashboards and alert on prolonged admission waits.
Evaluating Run:ai
- Choose connected or air-gapped deployment according to your network and data-boundary requirements.
- Confirm the supported Kubernetes version and the installation prerequisites for both the control plane and cluster components.
- Map each workload object to the current supported-features matrix; test externally submitted Kubernetes workloads separately from natively submitted ones.
- Exercise GPU fractions, gang scheduling, node pools, fairness, RBAC, and monitoring only where your workload type is listed as supported.
- Document which team owns the platform components, upgrades, credentials, and incident escalation.
Ray on Kubernetes
- Verify that the Ray custom resource creates the expected worker Pods and that Kubernetes can place them.
- Express machine constraints in Kubernetes and application resources or placement groups in Ray.
- When a task waits, inspect Ray’s view of available resources; when a Pod waits, inspect Kubernetes events and admission status.
- Test worker loss, autoscaling, checkpoint recovery, and shutdown so that a queue or preemption event does not corrupt a training run.
Common failure modes
“The queue is fair, but GPUs are still idle”
Admission fairness does not guarantee node-level packing or application efficiency. Check resource requests, fragmentation, topology constraints, image startup time, and whether a gang requirement is blocking a large job. A queue can be functioning correctly while capacity remains unusable for the requested shape.
“The Pod is Pending even though the job was admitted”
Admission and placement are separate decisions. Read the Pod’s Kubernetes events, then check node labels, taints, available capacity, affinity, and topology constraints. Fix the node-level constraint or change the request; do not increase queue priority blindly.
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Compare the resources Ray advertises with the task or actor requirements. Placement groups, custom resource labels, or an incorrect worker template can prevent assignment even when Kubernetes reports healthy Pods.
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“A feature works for one object but not another”
Support is workload- and version-specific. Recheck the current feature matrix and supported-workload documentation, then test the exact custom resource or submission path used in production.
“A distributed job starts partially”
Decide whether partial startup is acceptable. If it is not, configure a gang or all-or-nothing policy at the layer that owns admission, and verify that the framework does not independently create workers outside that policy.
How to evaluate performance claims
NVIDIA’s public Run:ai overview displays “10x GPU Availability,” “20x Workloads Running,” “5x GPU Utilization,” and “0 Manual Intervention.” The page does not provide a dated study method or an independent comparator alongside those figures, so they are vendor claims rather than neutral benchmarks: NVIDIA Run:ai overview. No like-for-like published benchmark establishing that Kubernetes alone is always slower or less utilized was identified in the cited material.
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Start with Kubernetes and make its resource requests, quotas, priorities, placement rules, and observability explicit. Add Kueue when the missing capability is queue admission, quota consumption, fair sharing, or preemption and you want to keep those decisions Kubernetes-native. Evaluate a broader platform such as Run:ai when you need a coordinated set of GPU-aware policies, workload submission, analytics, RBAC, and possibly multi-cluster governance, and you are prepared to operate its additional components. If Ray is part of the stack, retain a clear contract between Kubernetes placement and Ray task scheduling.
The decision is successful when every waiting job has an explainable reason, every tenant’s policy is enforceable, and the platform team can upgrade and recover the system without guesswork. Choose the smallest additional layer that satisfies those requirements, then validate it with representative contention and failure tests in the target environment.
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