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What can fail during a GPU cloud outage?
“GPU cloud outage” can describe several different failures. A provider’s management console, API, scheduler, worker-management service, GPU instance, network, or storage may be affected independently or together. An upstream dependency can also disrupt a service without taking every GPU machine offline.
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The visible symptom is not enough to determine whether a job is still running or whether its data is safe. A failed API request, a dashboard that will not load, a job waiting for a worker, a dropped connection, slower distributed training, or missing data each points to a different possible failure domain.
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- Scheduling or worker management: New work may wait for capacity, or workers may be unable to accept and process requests normally.
- Compute: A GPU instance can become unreachable or stop running, interrupting the workload.
- Network or storage: A running job may lose access to other workers, datasets, checkpoints, or outputs; distributed training can also slow if interconnects are impaired.
Will a running AI job keep going if the dashboard is down?
Sometimes, but it depends on the provider and the failed component. In its account of an AWS-region outage, Runpod said existing Pod workloads stayed operational even while its console and Pod provisioning or access were affected. Runpod also reported that workers could not process requests normally when its worker-management microservice was impacted. That is Runpod’s description of a particular incident, not a guarantee for other providers or future outages. Read Runpod’s incident account.
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“Pod workloads remained operational during the AWS outage, and even when the Runpod UI was unavailable, your Pods, endpoints, and clusters remained intact and secure.”
This is Runpod’s engineering team describing its own service during that incident. It should not be treated as a general rule for GPU clouds.
A separate example illustrates why console availability is not the same as compute availability. CoreWeave’s status history records a global cloud-console incident on October 6, 2026: console requests returned 404, and dependent services including Grafana were affected. The provider marked it resolved at 7:22 PM UTC. The incident entry does not establish whether GPU compute was affected. See CoreWeave’s incident history.
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How can you tell whether your job or data is affected?
- Record what you know. Note the time, region, affected service or component, job and resource IDs, error messages, and last known checkpoint. Preserve logs and request evidence in case you need them for a post-incident review or an SLA claim.
- Check the right incident channel. Review the provider’s status history and any customer-specific health or support channel. Identify whether the reported issue concerns capacity, the control plane, compute, networking, storage, or an upstream dependency.
- Verify the job before retrying. Do not launch a duplicate or stop a possibly active job until you can determine whether it is still running. Where possible, check checkpoint and output state through a path independent of the affected console.
- Decide whether to wait or recover elsewhere. If the outage exceeds the workload’s recovery objective, use an alternate region or provider only if the required capacity, data, credentials, software environment, and deployment inputs are ready.
- Reconcile after service returns. Compare outputs and checkpoints, identify duplicate work, record actual recovery time, and review any applicable SLA claim requirements and deadlines.
Status pages may not show every customer-specific issue. Microsoft says the public Azure status page covers defined broad-impact scenarios and directs customers to personalized Azure Service Health for customer-specific incidents, maintenance, and advisories. Microsoft’s Azure status overview explains the distinction.
Will a training job resume automatically?
Do not assume that it will. Whether a workload can resume depends on what survived, whether the application saved a usable checkpoint, and whether the job’s inputs and runtime environment can be restored. An SLA credit addresses a contractual remedy; it does not restart a job, recreate its state, or make an alternate GPU available.
Make recoverability an engineering property of the workload. Keep checkpoints and deployment inputs recoverable outside the failure domain you are preparing for. Document where the data, model weights, container images, code, dependencies, and secrets live, and test restoring the job in the alternate environment. A checkpoint that exists only on an unavailable instance or storage service may not help during that failure.
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How should you prepare an alternate GPU environment?
A backup region or provider is useful only if it is genuinely independent and can run the workload. Lambda documents on-demand GPU virtual machines as tied to a geographic region, so a different region is not automatically equivalent or available when needed. Lambda’s on-demand GPU documentation describes its regional instances.
- Failure-domain independence: Check whether the backup shares the same control plane, identity system, network, storage, DNS, or upstream provider as the primary environment.
- GPU fit and capacity: Confirm the alternate option can supply the required GPU model, memory, interconnect, and quota. Do not assume capacity will be immediately available during a regional event.
- Portable data and environment: Ensure that checkpoints, datasets, model weights, images, code, dependencies, and credentials can be restored there.
- Recovery objective and cost: Decide how quickly the workload needs to return and what duplicate capacity, data-transfer, and storage costs are acceptable.
- Operational access: Know how to receive customer-specific notices, inspect component status, retrieve logs, and reach support if the normal console is unavailable.
Runpod says that after the incident it deployed core services across multiple AWS regions within 72 hours and enabled workers to use cached configurations during some control-plane disruptions. Those are provider-reported changes for its service; they do not guarantee future availability or establish behavior at other GPU providers. Runpod’s account describes those changes.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What do GPU-cloud SLAs cover?
Read the SLA for the exact service and account rather than treating a headline uptime figure as a promise that every job will survive an outage. Availability definitions, exclusions, evidence requirements, claim deadlines, and remedies vary by offering and contract.
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| Provider document | What it specifies | What to keep in mind |
|---|---|---|
| AWS EC2 SLA | Defines region-level unavailability using running instances across two or more Availability Zones in the same region, or the specified cross-region condition for a single-AZ region. A claim must include dates and times, affected region, resource IDs, and request logs. | The claim deadline is the end of the second billing cycle after the incident. Credits are the remedy, subject to the SLA’s terms and exclusions. |
| NVIDIA Cloud Services SLA | Availability is calculated monthly and tracked every 15 minutes; capacity availability is tracked hourly. Targets and remedies depend on the covered offering. | The 2025 SLA lists a 99% service availability target for specified offerings, including Omniverse Cloud, NVIDIA Cloud Functions, and Attestation Service. For NVIDIA DGX Cloud, it lists a 99% service availability target and a 95% capacity availability target. These are offering-specific contractual targets, not an industry-wide uptime statistic. Claims for covered offerings must be received within two months; exclusions apply. |
For both providers, the signed agreement and applicable service terms control eligibility. A credit claim is separate from the engineering work of restoring a workload.
Can anyone estimate how often GPU cloud outages happen?
The provider incidents and SLA terms described above do not establish an industry-wide outage rate. A single provider’s incident cannot show how often GPU clouds fail overall, and a contractual availability target is not a measured cross-provider performance statistic. Use each provider’s component and region status information to understand a specific incident, and plan recovery around the workload’s needs rather than an assumed outage probability.
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