NVIDIA-Driver-DaemonSet kernel module build failed gcc version mismatch

The GPU Operator driver daemonset could not build the NVIDIA kernel module for the running kernel. Until it succeeds the node has no driver, so every GPU pod stays Pending while the node otherwise looks healthy. This entry explains how to confirm the cause, apply the fix, and separate it from adjacent k8s failures.

Symptom

NVIDIA-Driver-DaemonSet kernel module build failed gcc version mismatch

Root cause

The GPU Operator driver daemonset could not build the NVIDIA kernel module for the running kernel. Until it succeeds the node has no driver, so every GPU pod stays Pending while the node otherwise looks healthy. The decisive evidence is the first log line that precedes "NVIDIA-Driver-DaemonSet kernel module build failed gcc version mismatch" and differs from a healthy run.

Recommended fix

read the driver container logs (kubectl logs -n gpu-operator -l app=nvidia-driver-daemonset). The build error names the missing piece, usually kernel headers or a compiler version mismatch.

How Denpex helps

Denpex investigates NVIDIA-Driver-DaemonSet kernel module build failed gcc version mismatch using the evidence you provide or your connected workload collects. Earlier rank, host or application evidence is needed to distinguish an initiating failure from a downstream report.

The literal signature is "NVIDIA-Driver-DaemonSet kernel module build failed gcc version mismatch". It is a infrastructure failure associated with Kubernetes GPU nodes and the NVIDIA GPU Operator. The line identifies the failing operation or subsystem, while the surrounding evidence decides whether it is the initiating fault or a downstream symptom.

The GPU Operator driver daemonset could not build the NVIDIA kernel module for the running kernel. Until it succeeds the node has no driver, so every GPU pod stays Pending while the node otherwise looks healthy. The failure becomes visible at this call site because the operation first requires the missing resource, valid state, healthy peer, or correct result. Earlier log lines and a known-good control carry more causal value than the final wrapper exception.

• The workload stops or loses forward progress after emitting "NVIDIA-Driver-DaemonSet kernel module build failed gcc version mismatch".
• A retry on the same configuration reproduces the failure because the causal state has not changed.
• The outer framework exception can hide the rank, node, allocation, or dependency that failed first.
• Increasing timeouts or reducing workload size can suppress the symptom without correcting the cause.

• Kubernetes GPU nodes and the NVIDIA GPU Operator
• production-shaped multi-accelerator workloads
• containerized and bare-metal deployments of the same stack

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.

• ✓Find the first occurrence of "NVIDIA-Driver-DaemonSet kernel module build failed gcc version mismatch" and preserve at least 100 lines before it.
• ✓Identify which rank, node, device, or process emitted the first related warning.
• ✓the driver container must match the HOST kernel. A node that auto-upgraded its kernel without a matching driver image is the usual cause. Confirm: uname -r on the node vs the kernel the driver image targets. Precompiled driver images only cover specific kernel versions.
• ✓Repeat the same input after the targeted change and require the signature to disappear.
• ✓Resume from pod scheduling, once the driver is loaded only after the control passes.

• The GPU Operator driver daemonset could not build the NVIDIA kernel module for the running kernel. Until it succeeds the node has no driver, so every GPU pod stays Pending while the node otherwise looks healthy.
• The decisive evidence is the first log line that precedes "NVIDIA-Driver-DaemonSet kernel module build failed gcc version mismatch" and differs from a healthy run.
• A nearby failure remains a competing hypothesis until a control separates configuration, capacity, transport, and hardware causes.

NVIDIA-Driver-DaemonSet kernel module build failed gcc version mismatch

Use this text as a lookup key in logs and upstream issue trackers. It is not presented as a captured customer log. Confirm the cause from your own preceding events, versions, configuration and the cited references.

read the driver container logs (kubectl logs -n gpu-operator -l app=nvidia-driver-daemonset). The build error names the missing piece, usually kernel headers or a compiler version mismatch. This changes the condition in the causal diagnosis instead of hiding the outer exception. The repeated control proves ownership before recovery from pod scheduling, once the driver is loaded.

# Preserve evidence before restarting
kubectl describe pod <pod>
kubectl get events --sort-by=.lastTimestamp
kubectl logs <pod> --all-containers

# Find the exact signature in the complete log
rg -n -F -- "NVIDIA-Driver-DaemonSet kernel module build failed gcc version mismatch" <log-file>

Adapt the snippet to your framework. The same pattern holds for PyTorch Lightning, Hugging Face Trainer, DeepSpeed, Megatron-LM, and vLLM training wrappers. Where the wrapper exposes a config flag (for examplelr_scheduler_type in Trainer), prefer the flag over the imperative API to keep the schedule declarative and reproducible.

Select the traceback or open the failed terminal, then run Denpex locally to see the initiating rank, collateral failures, exact fix, and verification command without uploading the log.