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 #Xid Error. 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 Xid Error is upgraded. Pin compatible versions and rerun the prevention smoke test on every dependency change.

Is there a fingerprint I can monitor?

Yes. Distributed training halts. 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.

GPU Fallen Off Bus (Xid 79) causing Async NCCL Hang

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 a severe hardware fault (often power delivery or pcie signal integrity) causes the gpu to disconnect from the pcie bus entirely. the cuda runtime loses contact with the device, and nccl operations immediately fail, cascading into timeouts across other nodes waiting for this gpu.. 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 a severe hardware fault (often power delivery or pcie signal integrity) causes the gpu to disconnect from the pcie bus entirely. the cuda runtime loses contact with the device, and nccl operations immediately fail, cascading into timeouts across other nodes waiting for this gpu. 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.

A severe hardware fault (often power delivery or PCIe signal integrity) causes the GPU to disconnect from the PCIe bus entirely. The CUDA runtime loses contact with the device, and NCCL operations immediately fail, cascading into timeouts across other nodes waiting for this GPU.

Quick answer

A severe hardware fault (often power delivery or PCIe signal integrity) causes the GPU to disconnect from the PCIe bus entirely.

Symptom: RuntimeError: NCCL error in: ... unhandled system error
Root cause: A severe hardware fault (often power delivery or PCIe signal integrity) causes the GPU to disconnect from the PCIe bus entirely. The CUDA runtime loses contact with the device, and NCCL operations immediately fail, cascading into timeouts across other nodes waiting for this GPU.
Recommended fix: Hard reboot the node and potentially replace the GPU/motherboard sudo reboot A full power cycle is required to re-initialize the PCIe bus. Persistent Xid 79 usually means the GPU or riser cable needs replacement.
How Denpex helps: Denpex matches GPU Fallen Off Bus (Xid 79) causing Async NCCL Hang across every rank in a distributed run and reports which rank failed first, so you act on the initiating node instead of the loudest one.

Hardware#Xid Error

What this failure is

GPU Fallen Off Bus (Xid 79) causing Async NCCL Hang is a Hardware failure seen during ML training runs. A severe hardware fault (often power delivery or PCIe signal integrity) causes the GPU to disconnect from the PCIe bus entirely. The CUDA runtime loses contact with the device, and NCCL operations immediately fail, cascading into timeouts across other nodes waiting for this GPU. Common tags: Xid Error.

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Why it happens (the mechanism)

Other nodes just report an NCCL timeout because they are waiting. Only the node with the dead GPU will show the true root cause, but its logs might be buried or its monitoring agent might hang.

What you'll observe

CUDA error: unhandled system error
RuntimeError: NCCL error in: ... unhandled system error
NVRM: Xid (PCI:[0-9a-f:]+): 79, GPU has fallen off the bus.

Common symptoms and what they mean

Symptom	Why it happens
Distributed training halts.	A severe hardware fault (often power delivery or PCIe signal integrity) causes the GPU to disconnect from the PCIe bus entirely. The CUDA runtime loses contact with the device, and NCCL operations immediately fail, cascading into timeouts across other nodes waiting for this GPU.
One process reports a CUDA unhandled system error, while others hit NCCL timeout.	A severe hardware fault (often power delivery or PCIe signal integrity) causes the GPU to disconnect from the PCIe bus entirely. The CUDA runtime loses contact with the device, and NCCL operations immediately fail, cascading into timeouts across other nodes waiting for this GPU.
Running `nvidia-smi` on the affected node hangs or shows missing GPUs.	A severe hardware fault (often power delivery or PCIe signal integrity) causes the GPU to disconnect from the PCIe bus entirely. The CUDA runtime loses contact with the device, and NCCL operations immediately fail, cascading into timeouts across other nodes waiting for this GPU.

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.

✓Check system logs for Xid errors: `dmesg -T | grep -i xid` or `grep Xid /var/log/syslog`.
✓Run `lspci | grep NVIDIA` to see if the device is still enumerated.
✓Attempt a GPU reset or node reboot.

Searchable error signature

search key

RuntimeError: NCCL error in: ... unhandled system error
One process reports a CUDA unhandled system error, while others hit NCCL timeout.
CUDA error: unhandled system error

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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Xid 79 in context

Xid 79 is one of a small set of codes the NVIDIA driver uses to report GPU faults, and the number is most of the diagnosis: it tells you whether you are looking at your own code, the driver, or a board that needs replacing.

Compare every Xid code side by side

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Root cause

A severe hardware fault (often power delivery or PCIe signal integrity) causes the GPU to disconnect from the PCIe bus entirely. The CUDA runtime loses contact with the device, and NCCL operations immediately fail, cascading into timeouts across other nodes waiting for this GPU.

The fix and how to prevent it

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References

↗NVIDIA Docs: Xid Errors

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