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A queued cloud GPU job and a CUDA out-of-memory (OOM) error are different failures. A queue means the platform has not scheduled or provisioned the workload; OOM means a memory allocation failed after (or during) startup. Read the exact state, event, and error first, then follow the matching path below.
Do not repeatedly resubmit an unchanged job. Retries can occasionally outlast a temporary capacity shortage, but they cannot fix an exceeded quota, an unsupported GPU type, an impossible resource request, a missing device plugin, or a workload that genuinely needs more memory than the GPU provides.
First identify which failure you have
| What you see | What it means | First evidence to collect |
|---|---|---|
| Queued, Pending, or RUNNABLE | The scheduler has not assigned a suitable GPU or the service is still provisioning one. | Provider, region or zone, requested GPU type and count, CPU and host-memory requests, image, scheduler events, quota messages, and timestamps. |
| CUDA out of memory | An allocation failed in a particular workload phase. The failing pool may be tensor memory, allocator cache, device memory used by another process, or memory outside the framework allocator. | Full traceback, failing operation, GPU model and total memory, allocated and reserved memory, batch and input dimensions, precision, placement, concurrent processes, and framework version. |
“Pending” and “queued” are symptoms, not diagnoses. Likewise, an OOM message does not prove that the provider has a capacity problem. Keep the two investigations separate until the logs show a connection.
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1. Capture scheduler evidence before changing anything
Save the exact event text and the configuration that produced it. In Kubernetes, inspect the Pod description and events, then review its resource requests and limits. In Google Kubernetes Engine (GKE), cluster-autoscaler events can distinguish zone resource unavailability, exceeded quota, timeouts, IP exhaustion, missing GPU limits, an unsupported accelerator, and unsatisfied scheduling predicates. GKE notes that noScaleUp events are best-effort, so their absence does not prove that no problem exists.
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- Record the provider, service, project or account, region and zone, and submission time.
- Record the accelerator name, number of GPUs, CPU, system memory, ephemeral storage, and any maximum-run or priority setting.
- Save the container image, driver or CUDA requirements, node selectors, affinity rules, taints and tolerations, and the job definition.
- Copy the first scheduler event and any quota, capacity, networking, or provisioning error verbatim.
This snapshot lets you tell a transient shortage from a request that can never fit. It also prevents a retry from silently changing the evidence.
2. Separate quota from live GPU capacity
A quota error means your project or account is not allowed to consume more of a specific resource. Find the named Compute Engine or provider quota, check current usage and the requested increase, and apply for a higher limit when the provider allows it. A capacity error is different: the requested GPU may simply be unavailable in that zone at that time. Google’s resource-availability guidance treats zone capacity and quota as separate conditions and recommends trying another supported zone for a capacity shortage.
Moving zones can change price, network latency, data-transfer cost, and available accelerator models. If the workload cannot move, a different supported GPU configuration or a later retry may help, but no documentation establishes a guaranteed wait time. Keep the original request details so you can tell whether the error class changed.
3. Check whether one eligible node can fit the request
Autoscaling cannot solve an impossible placement. If a Pod requests more CPU, memory, or GPUs than any single eligible node can provide, a new node would still be unable to host it. Compare the Pod’s aggregate requests with the allocatable resources of nodes that satisfy its selectors, affinity, taints, and GPU type.
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- If the request is accurate, choose a node or instance size with enough capacity for the entire Pod.
- If the request is inflated, correct it rather than lowering it blindly; under-requesting can cause contention or eviction.
- Include system-reserved resources when comparing requests with allocatable capacity.
- Check that a multi-GPU request can be placed together if the application requires a single host.
A smaller request may schedule sooner, but it can also reduce throughput or make the workload fail later. Treat it as a deliberate capacity and performance trade-off.
4. Validate GPU scheduling configuration
Kubernetes schedules a GPU only when the node has a working vendor driver and device plugin exposing a schedulable resource. The resource name in the Pod must match what the plugin advertises. Kubernetes documents GPU resources as limits; when you specify a GPU limit, the corresponding request is handled according to the platform’s GPU resource rules.
resources:
limits:
nvidia.com/gpu: 1The snippet is illustrative; use the resource name exposed by your cluster. Verify the following before retrying:
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- The requested accelerator type is supported in the selected region and node pool.
- Node selectors and affinity do not exclude every GPU node.
- Taints have matching tolerations.
- GPU limits are present in the job or Pod specification.
- The image’s CUDA and framework versions are compatible with the installed driver.
5. Investigate provisioning, networking, and service-specific mismatches
A managed notebook or workbench can remain pending because private networking is invalid, even when GPUs are available. Inspect the instance and serial logs and use the service’s diagnostic events; do not assume every pending state is a shortage. Workbench troubleshooting lists GPU quota exhaustion as a separate failure from networking.
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In AWS Batch, a job can remain in RUNNABLE when its definition requests GPU resources that the compute environment cannot supply. GPU jobs need supported NVIDIA GPU instance families, and incompatible compute-environment families can prevent placement. Compare the job definition, requested GPU count, architecture, and the compute environment’s supported instance families.
A controlled queue-resolution procedure
- Freeze the evidence. Export the Pod or job description, events, logs, resource specification, and provider diagnostics.
- Classify the message. Label it as quota, zone capacity, impossible fit, scheduling configuration, provisioning or networking, or service-specific instance mismatch.
- Correct configuration errors first. Fix resource names, GPU limits, selectors, affinity, tolerations, unsupported types, and invalid networking before changing zones.
- Choose one variable to change. Depending on the error, try one supported zone, GPU type, node size, or parallelism setting. Recheck memory capacity, software support, data locality, quota impact, and cost.
- Resubmit once and compare events. A changed event confirms that the request reached a different constraint; an identical event indicates that the changed variable did not address the cause.
- Escalate with a complete case. Include the timestamp, region, exact resource request, event text, account or project identifiers required by the provider, and the changes already tested.
Protect data before you re-provision
Before deleting a machine or Pod, locate checkpoints, datasets, logs, environment files, and job metadata. Ephemeral GPU assignments are not a storage strategy. A provider may release the particular physical GPU when you stop a Pod; restarting later may return no GPU on that machine.
Runpod’s documented approach is to keep working data on a network volume that can be attached to a redeployed Pod. Its guidance also lists accessing data with zero GPUs, waiting and retrying, or terminating and redeploying on another machine. This behavior is specific to that product; other providers differ in whether volumes persist, detach cleanly, and can be reattached in another zone. Confirm those rules before termination and test that a fresh instance can read the checkpoint.
When the workload fails with CUDA out of memory
1. Find the failing phase
Read the complete traceback rather than reacting to the final line alone. Identify whether the failure occurs while loading weights, creating inputs, producing activations, computing gradients, reserving inference or key-value cache, compiling or capturing a graph, or later during execution.
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- Weight-loading OOM often points to model size, precision, placement, or a second process already using the device.
- Activation or gradient OOM usually scales with batch size, sequence length, image dimensions, and training checkpoints.
- Inference OOM can come from a large context or key-value cache even when the model weights fit.
- Compilation and graph capture may require temporary peak memory beyond the steady-state footprint.
Record the GPU model and total memory, model precision, device map, batch and input dimensions, concurrent processes, and framework version. An allocation failure alone does not show that the GPU is defective.
2. Measure the memory owner
For PyTorch, compare tensor memory with allocator memory. memory_allocated() and peak allocated memory describe live tensors; memory_reserved() and peak reserved memory include blocks held by the PyTorch caching allocator. memory_summary() provides a readable snapshot. For difficult cases, capture a memory history or snapshot around the failure.
print(torch.cuda.memory_allocated())
print(torch.cuda.max_memory_allocated())
print(torch.cuda.memory_reserved())
print(torch.cuda.max_memory_reserved())
print(torch.cuda.memory_summary())Compare these values with device-level usage from the provider or NVIDIA monitoring tools. PyTorch’s standard profiler sees allocations managed by its allocator, but not every allocation made directly through CUDA APIs or by third-party bindings. A large difference between device usage and PyTorch’s numbers can therefore belong to another process or an allocation outside PyTorch’s normal visibility.
3. Understand what empty_cache() can and cannot do
torch.cuda.empty_cache() releases unused cached blocks so other applications can use them and so that the released memory becomes visible in tools such as nvidia-smi. It does not free memory occupied by live tensors, and clearing the cache does not increase the amount of memory available for tensors merely by itself. Use it only after identifying genuinely unused cached blocks; do not treat it as a substitute for reducing the workload’s peak demand.
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4. Reduce or redistribute peak demand
If measurements show that the workload really exceeds the GPU’s usable memory, change the factor that creates the peak. Test one change at a time and record throughput and output quality.
- Lower batch size: reduces activation and gradient peaks, but may lower throughput.
- Reduce input or context dimensions: lowers attention, activation, or image-processing memory, with a possible quality or coverage cost.
- Use lower precision or quantization: can reduce weight and activation memory when the model and hardware support it; validate numerical stability, operators, and output quality.
- Enable activation checkpointing for training: stores fewer activations and recomputes them during backpropagation, trading memory for extra compute time.
- Use a smaller model: changes capability and accuracy but may be the cleanest single-GPU solution.
- Split placement across GPUs: use model or pipeline parallelism only when the framework, interconnect, and communication pattern support it; network overhead can erase the benefit.
Do not combine several changes before measuring. Otherwise you will not know which constraint was removed or which trade-off caused a quality or performance regression.
5. Investigate fragmentation only when the evidence supports it
A much larger reserved value than allocated can motivate an allocator investigation, but it is not proof of fragmentation. Review allocator statistics and snapshots, check for long-lived tensors or accidental references, and confirm the framework and backend version before changing allocator settings. PyTorch allocator options are version- and backend-specific, and non-PyTorch allocations may not appear in its standard profiler.
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| Action | Best fit | What to verify first | Main trade-off |
|---|---|---|---|
| Retry unchanged | Documented transient provisioning or capacity event | That the event is temporary rather than quota, fit, or configuration failure | Unknown wait and repeated queue time |
| Change zone or provider region | GPU unavailable in the current zone | Supported accelerator, quota in the destination, data location, network path, and price | Data movement, latency, and a different capacity situation |
| Change GPU type or node size | Current request cannot fit or the type is unsupported | Framework and driver compatibility, memory capacity, quota, and cost | Different performance, availability, and software behavior |
| Reduce workload size | Measured OOM from batch, context, activations, or weights | Which phase owns the peak and whether quality requirements remain met | Lower throughput, quality, or model capability |
| Use multiple GPUs | Model exceeds one device and parallelism is supported | Placement method, interconnect, communication cost, and aggregate quota | More operational complexity and potentially higher cost |
Evaluate every option against six axes: whether it addresses the diagnosed error class, GPU memory and accelerator support, availability uncertainty, quota and cost, region and persistent-storage behavior, and software compatibility.
Prevent the next incident
- Keep a tested job specification with explicit GPU, CPU, host-memory, and storage requests.
- Validate the image, driver, CUDA, and framework combination on the target accelerator before a long run.
- Check quota and supported GPU types in the intended region before submitting a large batch.
- Use checkpoints and external persistent storage so a lost GPU slot does not erase progress.
- Log batch size, input dimensions, precision, device placement, and peak memory for each run.
- Alert on queue age separately from OOM errors; they require different responders and remedies.
- After a successful run, retain the scheduler events and memory summary as a baseline for future changes.
Provider quotas, GPU inventories, supported instance families, product labels, and APIs change over time. Verify the exact region, service edition, and framework version when you apply these steps. The PyTorch CUDA memory documentation cited here was updated July 7, 2026; the documented Runpod network-volume page was last modified July 20, 2026.
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