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 CUDA 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 #ECC Page Retirement. 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 ECC Page Retirement is upgraded. Pin compatible versions and rerun the prevention smoke test on every dependency change.

Is there a fingerprint I can monitor?

Yes. System logs (dmesg) are flooded with hundreds of Xid 63 messages. 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.

Xid 63 Page Retirement Storm causing severe throttling

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 the hbm is rapidly degrading, triggering single-bit ecc errors at an extremely high frequency. the gpu's self-healing mechanism dynamically retires and remaps these bad memory pages (xid 63). doing this hundreds of times per second stalls the memory controller and destroys memory bandwidth.. 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 the hbm is rapidly degrading, triggering single-bit ecc errors at an extremely high frequency. the gpu's self-healing mechanism dynamically retires and remaps these bad memory pages (xid 63). doing this hundreds of times per second stalls the memory controller and destroys memory bandwidth. 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.

The HBM is rapidly degrading, triggering single-bit ECC errors at an extremely high frequency. The GPU's self-healing mechanism dynamically retires and remaps these bad memory pages (Xid 63). Doing this hundreds of times per second stalls the memory controller and destroys memory bandwidth.

Quick answer

The HBM is rapidly degrading, triggering single-bit ECC errors at an extremely high frequency.

Symptom: System logs (dmesg) are flooded with hundreds of Xid 63 messages.
Root cause: The HBM is rapidly degrading, triggering single-bit ECC errors at an extremely high frequency. The GPU's self-healing mechanism dynamically retires and remaps these bad memory pages (Xid 63). Doing this hundreds of times per second stalls the memory controller and destroys memory bandwidth.
Recommended fix: Quarantine the node and request an RMA scontrol update NodeName=<node> State=DRAIN Reason="Xid 63 storm" A storm of Xid 63 events guarantees that the memory module is failing physically. It cannot be fixed via software.
How Denpex helps: Denpex investigates Xid 63 Page Retirement Storm causing severe throttling 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#ECC Page Retirement

What this failure is

Xid 63 Page Retirement Storm causing severe throttling is a Hardware failure seen during ML training runs. The HBM is rapidly degrading, triggering single-bit ECC errors at an extremely high frequency. The GPU's self-healing mechanism dynamically retires and remaps these bad memory pages (Xid 63). Doing this hundreds of times per second stalls the memory controller and destroys memory bandwidth. Common tags: ECC Page Retirement.

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Why it happens (the mechanism)

Because the PyTorch job doesn't explicitly crash (ECC corrects the errors), the symptom just looks like a massive performance regression or a network bottleneck (NCCL timeouts). Engineers often waste time debugging network topologies or model code.

What you'll observe

NVRM: Xid (PCI:0000:4a:00): 63, Row Remapper: New bad page
NCCL WARN Timeout waiting for data

Common symptoms and what they mean

Symptom	Why it happens
System logs (dmesg) are flooded with hundreds of Xid 63 messages.	The HBM is rapidly degrading, triggering single-bit ECC errors at an extremely high frequency. The GPU's self-healing mechanism dynamically retires and remaps these bad memory pages (Xid 63). Doing this hundreds of times per second stalls the memory controller and destroys memory bandwidth.
PyTorch training throughput drops significantly (e.g., iterations per second falls by 50%+).	The HBM is rapidly degrading, triggering single-bit ECC errors at an extremely high frequency. The GPU's self-healing mechanism dynamically retires and remaps these bad memory pages (Xid 63). Doing this hundreds of times per second stalls the memory controller and destroys memory bandwidth.
Other nodes in the distributed training cluster timeout waiting for the affected node in NCCL operations.	The HBM is rapidly degrading, triggering single-bit ECC errors at an extremely high frequency. The GPU's self-healing mechanism dynamically retires and remaps these bad memory pages (Xid 63). Doing this hundreds of times per second stalls the memory controller and destroys memory bandwidth.

Which systems are affected

CUDA
NVIDIA Drivers
PyTorch Distributed

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.

✓Monitor dmesg for high frequencies of Xid 63: `dmesg -T | grep -c "Xid.*63"`
✓Run `nvidia-smi -q -d ECC` and look for rapidly increasing 'SRAM/DRAM Correctable' error counts.
✓Correlate GPU utilization drops with the timestamp of the Xid 63 storms.

Root cause

The HBM is rapidly degrading, triggering single-bit ECC errors at an extremely high frequency. The GPU's self-healing mechanism dynamically retires and remaps these bad memory pages (Xid 63). Doing this hundreds of times per second stalls the memory controller and destroys memory bandwidth.

The fix and how to prevent it

Searchable error signature

search key

System logs (dmesg) are flooded with hundreds of Xid 63 messages.
NCCL WARN Timeout waiting for data

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 63 in context

Xid 63 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

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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References

↗NVIDIA Xid 63 Documentation

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