SLURM Node Out of Memory

SLURM node OOM kills training when the requested memory exceeds the SLURM memory limit.

SLURM Node Out of Memory is a Infrastructure failure seen during ML training runs. SLURM node OOM kills training when the requested memory exceeds the SLURM memory limit. Common tags: Slurm, Node, Oom, Memory.

Memory request too low for training workload. Memory leak during training. Other processes on node consuming memory. 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.

• Job is killed before completion
• Sacct shows OOM in job state
• Job exits with non-zero status

• Training with high memory usage
• Datasets that don't fit in memory
• CPU offloading for large models

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: slurmstepd: error: job X killed by OOM
• ✓Verified signal present: Job exceeded memory limit
• ✓Verified signal present: Dmesg shows OOM killer
• ✓A targeted fix from the "How to fix it" section eliminates or substantially reduces the symptom within one validation pass.

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.

Slurm GPU failures can occur before allocation, during cgroup creation or inside the workload. The hub joins job reason, GRES, memory, node-health and distributed-launch evidence.

• Memory request too low for training workload
• Memory leak during training
• Other processes on node consuming memory