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Kubernetes GPU Pod Pending

Kubernetes GPU pods can stay in Pending state when GPU resources are not available or configured incorrectly.

Quick answer

Kubernetes GPU pods can stay in Pending state when GPU resources are not available or configured incorrectly.

Infrastructure#kubernetes#k8s#gpu#scheduling#pending#infrastructure

What this failure is

Kubernetes GPU Pod Pending is a Infrastructure failure seen during ML training runs. Kubernetes GPU pods can stay in Pending state when GPU resources are not available or configured incorrectly. Common tags: Kubernetes, K8s, Gpu, Scheduling.

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Why it happens (the mechanism)

GPU resource requests exceed cluster capacity. NVIDIA device plugin not installed or not detecting GPUs. Node selectors or taints prevent scheduling. GPU time-slicing not configured. PersistentVolume not bound. Taken together, these mechanisms explain why the failure is reproducible, why it tends to surface on specific workloads or scales, and why generic mitigation attempts often fall short without addressing the underlying cause.

What you'll observe

  • Pod stays in Pending state indefinitely
  • kubectl describe pod shows insufficient resources
  • GPU pods are not scheduled

Common symptoms and what they mean

SymptomWhy it happens
0/N nodes are available: insufficient nvidia.com/gpuGPU resource requests exceed cluster capacity
Pod has unbound PersistentVolumeClaimsNVIDIA device plugin not installed or not detecting GPUs
Pod scheduling failed due to node affinityNode selectors or taints prevent scheduling

Which systems are affected

  • Kubernetes training jobs with GPU
  • Kubeflow training operators
  • Multi-tenant GPU clusters

How to confirm this is the problem

Use this checklist to test the hypothesis against a small reproduction. No single line proves the root cause, so preserve the preceding events and compare one variable at a time.

  • ✓Reproduce the failure from a clean checkpoint/seed: the symptom must appear without warm-up state from a previous run.
  • ✓Verified signal present: 0/N nodes are available: insufficient nvidia.com/gpu
  • ✓Verified signal present: Pod has unbound PersistentVolumeClaims
  • ✓Verified signal present: Pod scheduling failed due to node affinity
  • ✓A targeted fix from the "How to fix it" section eliminates or substantially reduces the symptom within one validation pass.

The fix and the prevention pattern

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Related failures to investigate next

Lambda Bgp Route Withdrawal Region Outage often co-occurs with this failure because both share underlying assumptions about infrastructure; if you fix this entry but still see drift, validate the related primary cause next. lambda bgp route withdrawal region outage →
Slurm Node Oom often co-occurs with this failure because both share underlying assumptions about infrastructure; if you fix this entry but still see drift, validate the related neighboring cause next. slurm node oom →

Root cause

  • GPU resource requests exceed cluster capacity
  • NVIDIA device plugin not installed or not detecting GPUs
  • Node selectors or taints prevent scheduling
  • GPU time-slicing not configured
  • PersistentVolume not bound

The fix and how to prevent it

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References

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