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 NVIDIA Driver 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 #InfoROM Failure. 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 InfoROM Failure is upgraded. Pin compatible versions and rerun the prevention smoke test on every dependency change.

Is there a fingerprint I can monitor?

Yes. PyTorch cannot initialize the GPU or crashes on startup. 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 64 InfoROM Page Retirement Failure

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 gpu detected a bad hbm memory page and attempted to record it in the inforom (eeprom) for permanent retirement. however, the inforom is either full or physically degraded, preventing the page remap table from being updated. the driver halts initialization to prevent data corruption.. 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 gpu detected a bad hbm memory page and attempted to record it in the inforom (eeprom) for permanent retirement. however, the inforom is either full or physically degraded, preventing the page remap table from being updated. the driver halts initialization to prevent data corruption. 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 GPU detected a bad HBM memory page and attempted to record it in the InfoROM (EEPROM) for permanent retirement. However, the InfoROM is either full or physically degraded, preventing the page remap table from being updated. The driver halts initialization to prevent data corruption.

Quick answer

The GPU detected a bad HBM memory page and attempted to record it in the InfoROM (EEPROM) for permanent retirement.

Symptom: CUDA error: all CUDA-capable devices are busy or unavailable
Root cause: The GPU detected a bad HBM memory page and attempted to record it in the InfoROM (EEPROM) for permanent retirement. However, the InfoROM is either full or physically degraded, preventing the page remap table from being updated. The driver halts initialization to prevent data corruption.
Recommended fix: Hardware replacement (RMA) N/A If the InfoROM cannot accept new bad page records, the GPU's self-healing is permanently broken. It must be returned.
How Denpex helps: Denpex matches Xid 64 InfoROM Page Retirement Failure 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#InfoROM Failure

What this failure is

Xid 64 InfoROM Page Retirement Failure is a Hardware failure seen during ML training runs. The GPU detected a bad HBM memory page and attempted to record it in the InfoROM (EEPROM) for permanent retirement. However, the InfoROM is either full or physically degraded, preventing the page remap table from being updated. The driver halts initialization to prevent data corruption. Common tags: InfoROM Failure.

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Why it happens (the mechanism)

The symptom ('devices are busy or unavailable') looks like a software lock, zombie process, or a bad driver installation. Users often reinstall drivers or kill processes in vain.

What you'll observe

NVRM: Xid (PCI:0000:8a:00): 64, Page Retirement Failure
CUDA error: all CUDA-capable devices are busy or unavailable

Common symptoms and what they mean

Symptom	Why it happens
PyTorch cannot initialize the GPU or crashes on startup.	The GPU detected a bad HBM memory page and attempted to record it in the InfoROM (EEPROM) for permanent retirement. However, the InfoROM is either full or physically degraded, preventing the page remap table from being updated. The driver halts initialization to prevent data corruption.
The GPU disappears from nvidia-smi or reports an ERR state.	The GPU detected a bad HBM memory page and attempted to record it in the InfoROM (EEPROM) for permanent retirement. However, the InfoROM is either full or physically degraded, preventing the page remap table from being updated. The driver halts initialization to prevent data corruption.
System reboots temporarily resolve the issue, but it returns shortly after.	The GPU detected a bad HBM memory page and attempted to record it in the InfoROM (EEPROM) for permanent retirement. However, the InfoROM is either full or physically degraded, preventing the page remap table from being updated. The driver halts initialization to prevent data corruption.

Which systems are affected

NVIDIA Driver
CUDA

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 dmesg for Xid 64: `dmesg -T | grep Xid.*64`
✓Attempt to flush the InfoROM (often unsupported on enterprise cards, but a diagnostic step).

Searchable error signature

search key

CUDA error: all CUDA-capable devices are busy or unavailable
The GPU disappears from nvidia-smi or reports an ERR state.

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 64 in context

Xid 64 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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Root cause

The GPU detected a bad HBM memory page and attempted to record it in the InfoROM (EEPROM) for permanent retirement. However, the InfoROM is either full or physically degraded, preventing the page remap table from being updated. The driver halts initialization to prevent data corruption.

The fix and how to prevent it

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References

↗NVIDIA Xid 64 Documentation

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