CUDA Out of Memory
CUDA out of memory is the most common training error, occurring when GPU memory is exhausted.
CUDA out of memory is the most common training error, occurring when GPU memory is exhausted.
What this failure is
CUDA Out of Memory is a Memory failure seen during ML training runs. CUDA out of memory is the most common training error, occurring when GPU memory is exhausted. Common tags: Cuda, Oom, Memory, Critical.
Is this what broke your run? Paste your log.
You're reading about CUDA Out of Memory. Paste your own crash log or traceback below and get the real root cause for YOUR run, not this generic entry. No account, no card. Logs are masked at ingress and never saved to account history.
Want 14 days on the Scale plan?
Request a work-email trial for up to 50 diagnoses a day, alerts, history, and follow-up questions. No credit card or automatic subscription.
Why it happens (the mechanism)
Model + activations + optimizer state exceed GPU memory. Activation memory peaks at specific layers. Batch size too large. Memory leak. Gradient accumulation memory spike. 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
- CUDA out of memory error
- RuntimeError: CUDA OOM
- Training crashes with OOM at specific batch
Common symptoms and what they mean
| Symptom | Why it happens |
|---|---|
| torch.cuda.OutOfMemoryError | Model + activations + optimizer state exceed GPU memory |
| CUDA error: out of memory | Activation memory peaks at specific layers |
| OOM at forward or backward pass | Batch size too large |
| OOM at specific step (not always first) | Memory leak |
Which systems are affected
- Any large model training
- Limited GPU memory scenarios
- Memory-intensive operations
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: torch.cuda.OutOfMemoryError
- ✓Verified signal present: CUDA error: out of memory
- ✓Verified signal present: OOM at forward or backward pass
- ✓Verified signal present: OOM at specific step (not always first)
- ✓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
The root cause is on this page and stays free. A free account adds the exact remediation steps, keeps your diagnoses instead of discarding them, and unlocks the fix on every entry in the encyclopedia.
Sign up free. Unlock the full analysisNo credit card · 3 free diagnoses · Instant access
Diagnose this failure in VS Code
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.
Install the free VS Code extensionCUDA errors in context
CUDA reports errors asynchronously, so the traceback usually points at whatever line synchronised next rather than the one at fault. The hub covers every common CUDA error and how to make it report honestly.
Compare every cuda error side by sideRelated failures to investigate next
Root cause
- Model + activations + optimizer state exceed GPU memory
- Activation memory peaks at specific layers
- Batch size too large
- Memory leak
- Gradient accumulation memory spike
The fix and how to prevent it
Unlock the full remediation runbook
14 days on the Scale plan, up to 50 diagnoses a day. Step-by-step remediation, the RMA evidence payload, and multi-node correlation on your own logs. No card, and it does not roll into a subscription.
Don't just read the fix, diagnose your run
The encyclopedia tells you what went wrong. Denpex tells you what went wrong in YOUR training run. With your logs, your config, and your stack.