A queued GPU job and a CUDA out-of-memory (OOM) failure are different problems. A queue means the platform has not scheduled or provisioned the requested resources; OOM means a memory allocation failed after (or during) startup. Read the exact state, event, and error first, then follow the matching path below. Retrying an unchanged request rarely fixes an invalid configuration, impossible node fit, exhausted quota, or a workload whose peak memory genuinely exceeds the GPU.
Start by identifying the failure class
Save the complete status message, event text, and traceback before changing anything. Record the provider and service, region or zone, requested accelerator type and count, CPU and host-memory requests, container image, framework version, model precision, input or batch dimensions, and the time of failure. Those details distinguish a temporary capacity shortage from a request that can never be scheduled, and a tensor-allocation failure from a driver or external-process problem.
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| What you see | What it means so far | What it does not prove |
|---|---|---|
Pending, Queued, or RUNNABLE |
The scheduler has not placed the job on a usable GPU resource. | It does not prove that GPUs are sold out; quota, affinity, taints, networking, an unsupported accelerator, or an impossible resource fit can produce the same symptom. |
CUDA out of memory or an allocation traceback |
A requested allocation failed in a particular workload phase. | It does not prove the GPU is defective or that the cloud provider lacks capacity. |
| Provisioning timeout or notebook startup failure | Instance creation, networking, or initialization failed or stalled. | It does not automatically indicate a GPU-memory problem. |
When the job is queued or a Pod is pending
1. Capture scheduler evidence instead of repeatedly resubmitting
For Kubernetes, inspect the Pod’s description and events, then check the declared requests and limits:
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kubectl describe pod <pod-name> -n <namespace>
kubectl get events -n <namespace> --sort-by=.lastTimestamp
kubectl get pod <pod-name> -n <namespace> -o yamlLook for messages about unsatisfied scheduling predicates, insufficient CPU or memory, an unavailable GPU resource, taints, affinity, quota, or failed provisioning. In Google Kubernetes Engine (GKE), cluster-autoscaler noScaleUp events can identify zone resource unavailability, exceeded quota, timeouts, IP exhaustion, missing GPU limits, unsupported GPU types, and scheduling predicates. Google notes that these events are best-effort, so the absence of a noScaleUp event is not proof that no constraint exists.
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For a managed service, copy the provider’s event identifier and error text, not just the user-interface label. A message such as “insufficient resources” is actionable only when you know whether it refers to quota, live capacity, node fit, or networking.
2. Separate quota from live GPU capacity
Quota is an account or project limit; capacity is whether the requested hardware is available in the selected location at that moment. They require different responses.
- Quota exceeded: identify the exact Compute Engine or provider quota named in the event. Remove unnecessary reservations or request an increase if your account and region support one. GKE guidance recommends finding the specific exceeded quota before submitting a limit-increase request.
- GPU unavailable in the zone: try another supported zone or, if the workload can tolerate it, another compatible accelerator configuration. Google Cloud’s resource-availability guidance treats this as distinct from quota. Availability changes over time, so no fixed waiting period can be promised.
Do not infer that a quota increase creates physical capacity, or that changing zones fixes an account limit. Check both independently.
3. Verify that one eligible node can actually fit the Pod
Schedulers compare the Pod’s aggregate CPU, RAM, ephemeral-storage, and GPU requests with the allocatable resources of a single eligible node. System-reserved resources reduce what the workload can use. If your request exceeds every eligible node, autoscaling cannot help by creating another node of the same size. Google states this directly in its GKE troubleshooting guidance: “If a Pod’s resource requests exceed the resources of a single node, cluster autoscaler doesn’t scale up because the Pod still can’t fit on a new node.”
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Check the requested GPU count and type, CPU and host-memory values, and any sidecars that consume resources. If the request is accurate, select a node shape that can hold it. If it is inflated, correct the request rather than lowering limits blindly; under-requesting can lead to eviction or contention after scheduling.
4. Check GPU drivers, device plugins, and resource names
Kubernetes does not discover a GPU by itself. The node needs the vendor driver and a device plugin that advertises a schedulable extended resource. Your manifest must use the exact resource name exposed by that plugin and a supported accelerator type.
resources:
limits:
nvidia.com/gpu: 1GPU requests and limits follow Kubernetes’ device-resource rules; in common configurations the GPU appears under limits, with request and limit values kept consistent. Confirm the node labels, selectors, required affinity, taints, and matching tolerations. A selector for a GPU family that is not present in the chosen zone can leave a Pod pending even when other GPU models are idle.
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5. Investigate provisioning and networking failures separately
A managed notebook or workbench can remain pending because private networking is invalid, an IP range is exhausted, or initialization cannot reach required services. Inspect the service’s instance and serial logs and its provider-specific diagnostics. Workbench troubleshooting lists GPU quota exhaustion as a separate condition, so do not treat every startup timeout as accelerator scarcity.
AWS Batch provides another distinct example: GPU jobs require supported NVIDIA GPU instance types. An incompatible compute-environment family can leave a job in RUNNABLE even when the job definition asks for a GPU. Compare the job definition’s accelerator requirement with the compute environment’s supported instance families and architecture.
6. Change one scheduling variable at a time
After recording the original request, make a controlled change and observe the new event:
- Try a supported alternate zone if the event identifies location capacity.
- Choose a compatible GPU type or count with a realistic memory capacity.
- Adjust CPU, RAM, or parallelism so the Pod fits an eligible node.
- Correct selectors, affinity, taints, tolerations, or the GPU resource key.
- Retry only after checking quota, image compatibility, and network prerequisites.
For each option, recheck price, quota consumption, software support, data locality, and persistent-storage behavior. A different zone or provider may have a different queue and different accelerator availability; none is guaranteed to succeed.
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7. Protect checkpoints and data before terminating anything
Determine whether checkpoints, datasets, logs, and configuration are on durable storage or only on the machine’s local workspace. Provider behavior is not universal. Runpod documents that stopping a Pod releases its particular GPU; restarting may not return the same machine or any GPU immediately. Its documented choices include accessing data with zero GPUs, waiting and retrying, or terminating and redeploying elsewhere. It recommends a network volume so /workspace data can attach to a replacement Pod. Treat that as Runpod-specific behavior and verify the detach, reattach, and persistence rules for your own provider before deleting a workload.
When the workload fails with CUDA out of memory
1. Locate the failing phase and memory owner
Read the full traceback and identify whether the failure happens while loading weights, creating inputs, producing activations, computing gradients, reserving an inference or key-value cache, compiling or capturing a graph, or during a later operation. Record the GPU model and total memory, model placement, precision, batch and context dimensions, concurrent processes, and framework version. The same message can require different fixes in each phase.
- Weights or initialization: the model may not fit in the selected precision or placement.
- Inputs, activations, or gradients: batch size, sequence length, image resolution, or training intermediates may create the peak.
- Inference cache: concurrent requests or long contexts can reserve more memory than expected.
- Later execution: a leak, retained tensor, graph capture, fragmentation, or an external CUDA allocation may be responsible.
2. Compare tensor memory with allocator memory
In PyTorch, inspect both live tensor usage and the allocator’s reservations:
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import torch
print(torch.cuda.memory_summary())
print("allocated:", torch.cuda.memory_allocated() / 2**30, "GiB")
print("reserved:", torch.cuda.memory_reserved() / 2**30, "GiB")
print("peak allocated:", torch.cuda.max_memory_allocated() / 2**30, "GiB")
print("peak reserved:", torch.cuda.max_memory_reserved() / 2**30, "GiB")memory_allocated describes memory occupied by live tensors; memory_reserved includes blocks held by PyTorch’s caching allocator. Compare these values with device-level usage from your platform’s GPU monitor. A gap can reflect cached blocks, CUDA libraries, another process, or an allocation made outside PyTorch.
For a deeper investigation, capture PyTorch’s allocator history and memory snapshot around the failure. The PyTorch memory guide (updated July 7, 2026) cautions that the standard profiler sees allocations managed by the PyTorch allocator but not every allocation made directly through CUDA APIs or by third-party bindings. Device-level telemetry is therefore necessary when external allocations are plausible.
3. Do not mistake empty_cache() for extra VRAM
torch.cuda.empty_cache() releases unused cached blocks so other GPU applications can use them and makes that unused portion visible to tools such as nvidia-smi. It does not free memory occupied by live tensors, and clearing the cache alone does not increase the capacity available for those tensors. Use it only when you have evidence that unused cached blocks or interaction with another process is relevant; it is not a general cure for a workload whose live allocation is too large.
4. Reduce or redistribute peak demand
| Change | When it helps | Trade-off to validate |
|---|---|---|
| Lower batch size or request parallelism | Activations, gradients, or concurrent inference requests create the peak. | Lower throughput and potentially more steps or latency. |
| Reduce input, image, or context length | Memory scales with sequence, resolution, or token count. | Less detail or shorter context; application quality may change. |
| Use lower precision or supported quantization | Weights and many intermediate tensors can use fewer bytes. | Compatibility and numerical quality must be tested for the exact model and kernels. |
| Enable activation checkpointing | Training activations dominate memory. | Extra recomputation increases training time. |
| Use a smaller model or split placement across GPUs | The model itself exceeds one GPU’s usable memory. | Multi-GPU execution needs framework and interconnect support; a smaller model can change results. |
Apply one change at a time and log the peak allocated and reserved values after each run. A smaller batch can avoid an OOM while leaving a memory leak untouched; a different precision can fit weights but still fail on a long-context cache.
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5. Investigate fragmentation only when measurements support it
Reserved memory substantially above allocated memory can justify examining allocator statistics and snapshots, but it is not proof of fragmentation. First rule out another process, CUDA libraries, third-party bindings, and a retained reference to tensors. Allocator configuration options depend on the PyTorch version and backend, so consult the memory guide for the exact release before changing them. Do not copy a setting from an unrelated CUDA or PyTorch version and assume it is valid.
How to choose between retrying, resizing, or moving
| Action | Best evidence for it | Questions to check first |
|---|---|---|
| Retry the same request | A transient provisioning or capacity event with an otherwise valid configuration. | Is the request known to fit, within quota, and supported in that zone? |
| Change zone or provider | Explicit zone-capacity unavailability. | Will the accelerator, driver, image, quota, data path, and persistent volume work there? |
| Choose a larger node or GPU | The Pod cannot fit on any eligible node or the model’s live memory exceeds one GPU. | What is the new cost, quota impact, and software support? |
| Reduce workload size | Peak tensor allocation exceeds usable memory. | Which dimension causes the peak, and what quality or throughput loss is acceptable? |
| Inspect allocator or external usage | Snapshots show a large reserved/allocated gap or device usage not explained by tensors. | Are non-PyTorch allocations visible, and is the diagnostic compatible with your framework version? |
Use six decision axes for any alternative: whether it addresses the observed error class, GPU-memory capacity and accelerator support, availability and queue uncertainty, cost and quota impact, region and data movement, and software compatibility and operational effort.
Prevent the next incident
- Keep a deployable manifest with explicit GPU type, count, CPU, RAM, selectors, tolerations, image tag, and region assumptions.
- Test the smallest representative workload before requesting the full batch or context size.
- Emit peak allocated and reserved memory, device-level usage, batch dimensions, and framework version into job logs.
- Store checkpoints and datasets on storage whose persistence and reattachment behavior you have verified.
- Set alerts for quota consumption, queue age, failed provisioning, and repeated OOMs rather than relying on a manual retry loop.
- When capacity is uncertain, make zone or accelerator selection configurable, but validate every alternative’s driver, CUDA, framework, and data requirements.
The fastest recovery comes from matching the remedy to the evidence: scheduler events for queued work, allocator and device measurements for OOM, and provider-specific persistence rules before re-provisioning. Current quotas, accelerator inventories, supported instance families, product labels, and APIs change; verify the exact documentation for your provider, region, and framework release before applying a configuration change.
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