Incident Library
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Cases3 cases

1 to 3 of 3

  1. Symptoms

    From about 21:00 UTC on March 4, 2026, customers using Linodes backed by NVIDIA RTX 4000 Ada GPUs saw the GPUs lock up. At first the problem seemed limited to worker nodes of the Linode Kubernetes Engine (LKE); later Linode confirmed that every Linode with this hardware was affected, while Standard Compute and other GPU instances were not.

    Trigger

    Deployment of a repaired legacy GPU monitoring script, fixed during a routine improvement effort to restore a missing metric on internal observability dashboards.

    Root cause
    Component
    Contributing factor
    Detection

    Customers saw the lockups from about 21:00 UTC; the first status post went up at 02:07 UTC on March 5, about 5 hours after the lockups began.

    Remediation
    • Disabled the monitoring script across the GPU fleet
    • Rebooted the affected nodes
    Action items
    • Take improvement and prevention measures to stop a recurrence; the postmortem does not list them
    Lessons

    A monitoring script can take down the hardware it watches. A fix meant only to restore a missing metric brought back a firmware query written for an earlier GPU generation, and on the RTX 4000 Ada architecture that query against an active GPU triggers a lockup. The script ran across the GPU fleet, so every Linode with this GPU was affected in several regions, and the investigation first suspected a regression in the host hypervisor or the GPU firmware.

  2. Symptoms

    GPU nodes for an AI video upscaling job were created normally and reached Ready, and then about 10 to 11 minutes later Karpenter treated each one as unhealthy and replaced it. Every new node went the same way, so the GPU pods kept restarting and the upscaling work could not make progress.

    Trigger

    Enabling Karpenter's node auto-repair feature gate (nodeRepair: true) and then running GPU pods on nodes from the Bottlerocket NVIDIA AMI.

    Root cause
    Component
    Contributing factor
    Detection

    The team noticed GPU nodes being replaced repeatedly after running GPU pods, then found the DCGMError message in the node events.

    Remediation
    • Disabled Karpenter node auto-repair by setting the nodeRepair feature gate to false
    Lessons

    Automatic node repair acts on every unhealthy node condition, including AcceleratedHardwareReady, so an image defect that only breaks GPU health monitoring is enough to have every GPU node terminated and recreated in a loop. Before turning on auto-repair, check which conditions it watches and whether the node image reports them correctly. Disabling auto-repair stops the loop at once but is only a temporary measure; installing the missing library or changing the AMI address the cause but cost more on an immutable OS such as Bottlerocket.

  3. Symptoms

    From 08:00 US/Pacific on 12 March 2024, GKE GPU nodes running Container-Optimized OS (COS) that were newly created or recreated came up with GPU drivers that did not work, so workloads that used the GPUs could not run. GKE showed elevated errors.

    Root cause
    Component
    Contributing factor
    Detection

    The report does not say how Google detected the issue. Affected users saw "Failed to download GPU driver installer, status: 403 Forbidden" on the GPU node when installing the driver.

    Remediation
    • Halted automatic node recreations, which attempt GPU driver downloads, until the issue was mitigated
    • Worked with the driver partner to resolve access to the driver download bucket
    Lessons

    The status page report states no lessons or follow-up actions.

Check your cluster for this

These 3 cases involve GPU driver and Node operating system image. kubegap audits a Kubernetes cluster for gaps like these, read-only, from Claude Code or Codex; its checks cite public reports like the ones in this library.

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