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A Run:ai workload usually remains Pending because the scheduler cannot satisfy the job’s complete request on an eligible node at the same time. An idle-looking GPU is not enough: quota, limits, CPU and memory, node-pool rules, labels, affinities, taints, topology, gang scheduling and fair-share policy can all make that GPU unusable for this workload. Start with the workload’s event history and structured scheduler reason, then correct the specific quota, policy, placement or resource-shape constraint it reports.
What a Pending Run:ai workload actually means
Pending is a scheduling result, not a diagnosis. The scheduler has accepted the workload but has not found a legal placement that satisfies all of its requirements. Those requirements include the number and type of GPUs, GPU memory, CPU, system memory, project and department policy, node-pool selection, labels, affinities, taints, topology and, for multi-pod jobs, the gang-scheduling condition that enough pods can start together.
This is why a dashboard can show unused GPUs while your job cannot obtain one. The free devices may be in a node pool your workload cannot use, may not have the requested GPU type or memory, may be split across nodes when the job needs several on one node, or may be reserved by quota and fair-share rules. Treat utilization as a capacity clue only; treat the scheduler reason and events as the explanation.
Separate a normal queue wait from a scheduling block
A newly submitted workload can be waiting for capacity. A workload that has accumulated substantial pending time with the same reason is more likely blocked by a constraint or policy. The workload API exposes totalPendingTimeSeconds; record it with the current reason so an administrator can tell a fresh queue wait from a persistent placement failure.
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The first five minutes: a diagnostic runbook
- Open the workload and its event history. In the Run:ai Workload table, open the job, inspect its pod status and event history, and check the project’s available quota. Confirm that every service required by the workload is bound. NVIDIA’s workload troubleshooting guidance specifically calls out required quota, project available quota, bound services and event history as the first checks.
- Capture the structured scheduling fields. From the workload UI or API, record
phaseReason, every entry inpendingSchedulingMessages, requested GPU, CPU and memory, selected or requested node pool, pod count, andtotalPendingTimeSeconds. Keep the values exactly as displayed; they are more useful than a screenshot that only says “unable to obtain GPUs.” - Follow the reason to one category. A quota or limit message requires a policy check. A node-pool, type, affinity, taint or topology message requires a placement check. A fragmentation, gang or insufficient-resource message requires a resource-shape check. Do not reduce the GPU request or change policy until the event identifies the constraint.
Read the scheduler reason instead of guessing
The Run:ai API exposes machine-readable and human-readable scheduling information. phaseReason identifies the broad condition, while pendingSchedulingMessages provides the explanation attached to the current scheduling attempt. The documented examples include:
phaseReason |
Displayed message | What it indicates |
|---|---|---|
NonPreemptibleOverQuota |
Non-preemptible over quota | The workload is non-preemptible and its request exceeds the quota it can use without relying on preemption. |
OverLimit |
You have reached the limit of non-preemptible resources | The project or department limit for non-preemptible resources has been reached. |
Save the reason before making changes. If the reason changes after a quota, node-pool or request edit, save the new message as well; the change shows which constraint was removed and which one remains.
Quota, limits and preemptibility
Run:ai separates the quota a project deserves from the spare capacity it may temporarily borrow. A non-preemptible workload must fit its deserved quota. An over-quota workload can run only when spare capacity, fair-share ranking and department rules permit it. A project can therefore show quota remaining while the department, node pool or non-preemptible limit prevents this particular allocation.
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Check project and department quota
Open the project dialog and compare the workload’s requested GPUs with the project’s available quota. Then check the department-level allocation and the node pool that the workload selected. A project-level value cannot override a tighter department or pool constraint. If the workload requests multiple GPUs, compare the complete request rather than multiplying a single-GPU allowance: the scheduler must be able to grant the whole allocation under the applicable policy.
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Check the non-preemptible limit
The project or department limit caps the sum of deserved and over-quota GPUs that may run as non-preemptible work. The OverLimit reason means that limit, not necessarily physical GPU exhaustion, is stopping admission. An administrator must raise the limit, free non-preemptible capacity, or approve a different workload policy when that is permitted by the cluster’s operating rules.
Check whether the workload may be preempted
Over-quota work depends on spare capacity and fair-share. Preemptible work can be displaced when higher-priority or better-ranked workloads need the GPUs; non-preemptible work cannot rely on that mechanism. If policy allows it and interruption is acceptable, changing the workload’s preemptibility can make an over-quota request schedulable. If the job must not be interrupted, the durable fixes are sufficient deserved quota, a higher approved limit, a smaller request or a pool with capacity.
Understand fair-share effects
Fair-share uses project rank or weight and current consumption to decide who receives spare capacity. A project with apparent spare quota can wait behind another project with a stronger fair-share position, or behind workloads that already consume the available pool. This is a policy outcome, not evidence that the scheduler has failed.
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Verify node-pool selection
A prioritized node-pool list is evaluated in order. When no pool in the list can satisfy the request, the selector cycles through the configured choices. Inspect the selected pool and each fallback pool for the exact GPU type, GPU count, CPU and memory requested by the workload.
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Common node-pool mistakes
- The pool contains GPUs, but not the requested GPU type or GPU memory.
- The pool has enough total GPUs, but not the requested number available together on an eligible node.
- The workload’s CPU or system-memory request is larger than the remaining capacity beside the GPUs.
- The prioritized list excludes the pool that actually has capacity.
- A pool-level policy or label makes its otherwise idle devices ineligible.
Change the pool only after checking the event reason. Selecting another pool is a useful fix when that pool satisfies the same request and policy; it is not a remedy for an over-limit project or an invalid node label.
Check node type labels and affinity
Project scheduling rules can require a run.ai/type label. A mismatch between that required value and the labels on eligible nodes prevents placement even when their GPUs are idle.
Inspect labels
Run:
kubectl get nodes --show-labelsCompare the output with the node type or selector named in the workload and project configuration. Look for spelling, capitalization and value differences, not only for the presence of a label.
Apply a missing label
An administrator can label an intended node with:
kubectl label node <node-name> run.ai/type=<value>Use the value required by the project rule. Do not add a label merely because a node has an available GPU; changing labels changes which workloads can reach that node and should follow the cluster’s ownership and change-control process.
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Review other placement constraints
Inspect required and preferred affinities, node selectors, taints and tolerations, and any topology rules attached to the workload or project. A required affinity or an untolerated taint can exclude every node in a pool. If the constraint is intentional, wait for a matching node; if it is obsolete, remove or revise it and resubmit or restart the workload according to your platform procedure.
Resource shape, fragmentation and topology
The scheduler must find the requested resources together, in a legal arrangement. Total free capacity across the cluster can be misleading.
Fragmented GPUs
Suppose a workload requests several GPUs on one node. Free GPUs scattered across multiple nodes do not satisfy that request. The same problem occurs when free GPUs are paired with too little CPU or system memory on every eligible node. This is fragmentation: capacity exists, but not in the shape the workload needs.
GPU memory, CPU and system memory
Record all resource dimensions, not just GPU count. A node can have the right number of devices but insufficient GPU memory for the requested type, or enough GPU memory but too little CPU or system memory for the pod. Reducing an oversized CPU or memory request, choosing a compatible GPU type, or using a pool with the required combination can resolve this class of Pending state.
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Topology constraints
Topology rules can require devices to be in a particular zone, rack, NUMA arrangement or other locality domain. The exact rule depends on the Kubernetes configuration and Run:ai version. When an event cites topology, choose a pool or request shape that satisfies the locality requirement; do not infer that any free device is interchangeable.
Gang scheduling
For a multi-pod workload, gang scheduling can hold every pod in Pending until the minimum group is placeable. Starting one pod would not make progress if the remaining pods could never join the gang. Check the requested pod count and the minimum group condition in the workload specification. If the event confirms a gang shortage, reduce the group or GPU count, select a pool that can place the whole group, or wait for enough capacity.
Background workloads may be Pending by design
Run:ai documents background workloads as preemptible. When AI workloads fully use the cluster, background workloads are expected to remain in Pending until resources become free. That state is intentional for this workload class. Verify the workload category before treating a long wait as an incident; changing it to non-preemptible may require quota or limit approval and changes the protection and fairness behavior.
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| Confirmed condition | Policy-oriented fix | Placement or request fix | When to use it |
|---|---|---|---|
| Non-preemptible request is over deserved quota | Raise approved quota or limit, or use an allowed preemptible policy. | Reduce the GPU, CPU or memory request if the workload can run smaller. | Use an administrative change when the job must keep its size and protection; resize when the workload is flexible. |
| Project or department non-preemptible limit reached | Free capacity or raise the applicable limit. | Select a pool only if the limit is pool-specific; otherwise a pool change will not solve it. | Escalate with the OverLimit reason. |
| Fair-share delay or scarce spare capacity | Wait for capacity or adjust approved priority, rank or weight. | Use another eligible pool or a smaller request. | Choose waiting when the job is not urgent and policy should remain unchanged. |
| Node-pool mismatch | Update the allowed pool list through the platform owner. | Select a pool containing the requested GPU type, count, CPU and memory. | Use when events identify pool selection or no feasible pool. |
| Label, affinity or taint mismatch | Correct the project rule or obtain an appropriately labeled node. | Remove an unnecessary required selector, add the required toleration, or target a matching pool. | Change constraints only after verifying the event and ownership of the rule. |
| Fragmentation, topology or gang shortage | Wait for a placement that satisfies the group or locality policy. | Reduce group or GPU count, relax an unnecessary constraint, or select a pool with the required shape. | Use when total capacity exists but the requested arrangement does not. |
Escalate with an actionable evidence bundle
If the workload remains Pending after correcting an identified request or placement issue, send the platform administrator a compact record:
- Workload ID and project.
- Submission time and
totalPendingTimeSeconds. - Requested GPU type and count, CPU and system memory.
- Requested or selected node pool and pod count.
- Current
phaseReason. - All
pendingSchedulingMessages. - Relevant event-history entries.
- Project available quota, department or limit information that the UI exposes.
- Relevant node labels, including the
run.ai/typevalue, and any affinity, taint or topology rule.
Ask the administrator to confirm which constraint is authoritative and whether the proposed change affects other projects. This evidence lets them distinguish quota policy from physical capacity, and a label mismatch from fragmentation, without asking you to resubmit blindly.
Prevent repeat Pending incidents
Preflight the request
- Verify the project’s available quota and the department’s applicable limit.
- Confirm whether the workload is preemptible, non-preemptible or a background workload.
- Choose a node pool that contains the requested GPU type and enough GPUs together.
- Check CPU, system-memory and GPU-memory requirements alongside GPU count.
- Validate required labels, affinities, taints, tolerations and topology rules.
- For multi-pod jobs, confirm that the requested group can be placed as a gang.
Monitor the right signals
Alert on increasing pending time combined with an unchanged scheduling message, not on GPU utilization alone. Keep the first event history and the structured reason so an operator can see whether the workload is waiting normally, cycling through pools or blocked by a policy that will never resolve without intervention.
When no immediate change is safe
Waiting is the correct action when the reason shows a normal fair-share queue, a preemptible background workload is behind active AI workloads, or the cluster is temporarily full and the request is valid. Do not weaken quota, labels, affinity or preemptibility simply to make the status change. Make a change only when it addresses the recorded reason and the owner of that policy has approved it.
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