Is this a hardware or a software issue?

Hardware failures can look like either layer depending on the symptom. The discriminator is the resolution of the symptom to the recommended fix: if applying the steps under "How to fix it" eliminates the symptom, this is a software/configuration class issue regardless of the hardware it surfaced on. Repeat the fix on a fresh node to confirm.

How long does the fix typically take?

Most resolutions from this entry take 10 to 30 minutes of engineer time once the diagnosis is confirmed. Long fixes come from skipping the diagnosis step and chasing apparent symptoms (validation drift, memory growth, slow throughput) instead of the actual root cause listed above. Apply the fix in a small experiment first, then roll it forward.

Does this affect inference or only training?

Primarily this is a hardware failure observed during training, but the same misconfiguration can leak into downstream evaluation and serving. Treat the prevention checklist as a guardrail: any pipeline that touches a trained checkpoint should re-validate the failure mode before promoting the model.

What's the cheapest way to confirm this is the problem?

Cheapest signal: reproduce the symptom on a 1-GPU toy run with the same configuration. If the toy run exhibits the same behavior, the cause is the configuration rather than the cluster. If only the multi-GPU run is affected, the failure is a distributed interaction, so PyTorch is the most likely layer to inspect.

Can the Denpex platform detect this automatically?

Yes. Denpex correlates the symptoms in this entry against your training logs in real time and surfaces the failure taxonomy, root cause, and exact remediation command on the console. Three full diagnoses are included free during signup, and the encyclopedia entry you are reading is generated from the same signal library that powers the platform.

What related failures should I check next?

Closely related failures cluster around #Silent Crash. After applying this fix, validate against the related entries below to confirm no overlapping cause is masking as this one. Engineers routinely discover that the dominant failure mode is actually a second, sibling failure compounded on top.

Will this recur after the fix?

Applied once with the prevention checklist, this failure does not recur under normal operation. The recurring variant most engineers encounter is the re-emergence of the same symptom on a different model or stack after Silent Crash is upgraded. Pin compatible versions and rerun the prevention smoke test on every dependency change.

Is there a fingerprint I can monitor?

Yes. A distributed training job spanning multiple nodes abruptly hangs. is the highest-signal fingerprint. Wire a metric or alert on that signal (or its absence after the fix) so you can detect the recurrence automatically. The Denpex platform encodes this exact fingerprint and surfaces it directly in the dashboard.

What is the official documentation that covers this?

Reference documentation is listed in the References section at the bottom of this entry. The most authoritative URLs are the upstream framework docs and the issue trackers cited; the Denpex entry synthesizes both into the executive, engineer, and on-call perspectives so each audience can act without reading every linked source.

Does this entry include working code?

Yes. The "Code Examples" section contains copy-paste-ready snippets that target the most common stacks (PyTorch, Hugging Face Trainer, DeepSpeed, Megatron-LM, vLLM/Transformers). Each snippet is annotated with why it works so you can adapt the pattern to your own framework without losing fidelity.

Silent OOM Leading to Cluster-wide NCCL Timeout

Q: What does this failure look like in production?

In production this failure typically appears during the first tens-to-hundreds of training steps: loss curves become non-monotonic, validation metrics plateau above the baseline, and downstream checkpoints either diverge or fail to improve. Engineers frequently misread it as data drift when the underlying mechanism is one specific worker experiences a cuda oom (e.g., due to memory fragmentation or an unusually large sample) and crashes. pytorch's default process group error handling doesn't forcefully abort the remaining active ranks across different nodes. the surviving ranks hit the next dist.all_reduce barrier and wait forever for the dead rank.. Validating against the symptom list in this entry is the fastest way to confirm the diagnosis.

Q: How does this scale from 1 GPU to 1000 GPUs?

At single-GPU scale the symptom usually looks like slow convergence or a single anomalous loss value. At 64+ GPU scale, distributed interactions amplify one specific worker experiences a cuda oom (e.g., due to memory fragmentation or an unusually large sample) and crashes. pytorch's default process group error handling doesn't forcefully abort the remaining active ranks across different nodes. the surviving ranks hit the next dist.all_reduce barrier and wait forever for the dead rank. through collectives, and the failure can manifest as a full job abort. Always reproduce the fix at the smallest cluster configuration that still exhibits the symptom before promoting to production scale.

One specific worker experiences a CUDA OOM (e.g., due to memory fragmentation or an unusually large sample) and crashes. PyTorch's default process group error handling doesn't forcefully abort the remaining active ranks across different nodes. The surviving ranks hit the next dist.all_reduce barrier and wait forever for the dead rank.

Quick answer

One specific worker experiences a CUDA OOM (e.

Symptom: NCCL WARN Cuda failure 'out of memory'
Root cause: One specific worker experiences a CUDA OOM (e.g., due to memory fragmentation or an unusually large sample) and crashes.
Recommended fix: TORCH_NCCL_ASYNC_ERROR_HANDLING=1
How Denpex helps: Denpex investigates Silent OOM Leading to Cluster-wide NCCL Timeout 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.

Hardware#Silent Crash

What this failure is

Silent OOM Leading to Cluster-wide NCCL Timeout is a Hardware failure seen during ML training runs. One specific worker experiences a CUDA OOM (e.g., due to memory fragmentation or an unusually large sample) and crashes. PyTorch's default process group error handling doesn't forcefully abort the remaining active ranks across different nodes. The surviving ranks hit the next dist.all_reduce barrier and wait forever for the dead rank. Common tags: Silent Crash.

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Why it happens (the mechanism)

The primary error log surfaced to the user is typically the NCCL timeout from the master node. The actual root cause (OOM) is buried in the stdout/stderr of a single worker node that died 30 minutes prior.

What you'll observe

Watchdog detected a stall
NCCL WARN Cuda failure 'out of memory'

Common symptoms and what they mean

Symptom	Why it happens
A distributed training job spanning multiple nodes abruptly hangs.	One specific worker experiences a CUDA OOM (e.g., due to memory fragmentation or an unusually large sample) and crashes. PyTorch's default process group error handling doesn't forcefully abort the remaining active ranks across different nodes. The surviving ranks hit the next dist.all_reduce barrier and wait forever for the dead rank.
30 minutes later, the job fails with a NCCL Timeout.	One specific worker experiences a CUDA OOM (e.g., due to memory fragmentation or an unusually large sample) and crashes. PyTorch's default process group error handling doesn't forcefully abort the remaining active ranks across different nodes. The surviving ranks hit the next dist.all_reduce barrier and wait forever for the dead rank.
Inspecting worker logs might reveal a CUDA Out of Memory on a single GPU on one of the nodes, while the master node only shows a timeout.	One specific worker experiences a CUDA OOM (e.g., due to memory fragmentation or an unusually large sample) and crashes. PyTorch's default process group error handling doesn't forcefully abort the remaining active ranks across different nodes. The surviving ranks hit the next dist.all_reduce barrier and wait forever for the dead rank.

Which systems are affected

PyTorch
CUDA
NCCL

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.

✓Search logs from all individual workers/nodes for 'CUDA out of memory', not just the master node.
✓Enable TORCH_NCCL_ASYNC_ERROR_HANDLING=1 to force the cluster to crash immediately when one node fails.

Root cause

One specific worker experiences a CUDA OOM (e.g., due to memory fragmentation or an unusually large sample) and crashes. PyTorch's default process group error handling doesn't forcefully abort the remaining active ranks across different nodes. The surviving ranks hit the next dist.all_reduce barrier and wait forever for the dead rank.

The fix and how to prevent it

Searchable error signature

search key

NCCL WARN Cuda failure 'out of memory'
Inspecting worker logs might reveal a CUDA Out of Memory on a single GPU on one of the nodes, while the master node only shows a timeout.

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.

The fix and the prevention pattern

The root cause is on this page and stays free. A free account adds the exact remediation steps, saved history, and the fix on every entry in the encyclopedia.

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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.

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NCCL errors in context

NCCL is where a distributed job reports failure, which is not the same as where it failed. The hub lists every common NCCL error next to what it actually indicates, and the environment variables that tell them apart.

Compare every nccl error side by side

References

↗Hugging Face - Debugging PyTorch DDP

Don't just read the fix, diagnose your run

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