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 GPU Hardware 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. Training crashes randomly with NCCL timeouts or corrupted output tensors. 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 48: Double Bit ECC Error from VRAM Degradation

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 physical vram (hbm or gddr) on the gpu has degraded over time due to thermal stress or manufacturing defects. a double-bit error is mathematically uncorrectable by the gpu ecc engine, meaning data corruption has physically occurred in the memory cell. the driver kills the context to prevent silent 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 physical vram (hbm or gddr) on the gpu has degraded over time due to thermal stress or manufacturing defects. a double-bit error is mathematically uncorrectable by the gpu ecc engine, meaning data corruption has physically occurred in the memory cell. the driver kills the context to prevent silent 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 physical VRAM (HBM or GDDR) on the GPU has degraded over time due to thermal stress or manufacturing defects. A double-bit error is mathematically uncorrectable by the GPU ECC engine, meaning data corruption has physically occurred in the memory cell. The driver kills the context to prevent silent data corruption.

Quick answer

The physical VRAM (HBM or GDDR) on the GPU has degraded over time due to thermal stress or manufacturing defects.

Symptom: dmesg logs flooded with Xid 48 Double Bit ECC errors.
Root cause: The physical VRAM (HBM or GDDR) on the GPU has degraded over time due to thermal stress or manufacturing defects. A double-bit error is mathematically uncorrectable by the GPU ECC engine, meaning data corruption has physically occurred in the memory cell. The driver kills the context to prevent silent data corruption.
Recommended fix: Decommission and RMA the GPU kubectl cordon <node_name> && kubectl drain <node_name> Once uncorrectable DBEs occur repeatedly, the memory is physically failing and software cannot fix it. The card must be removed from the cluster.
How Denpex helps: Denpex matches Xid 48: Double Bit ECC Error from VRAM Degradation 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

Xid 48: Double Bit ECC Error from VRAM Degradation is a Hardware failure seen during ML training runs. The physical VRAM (HBM or GDDR) on the GPU has degraded over time due to thermal stress or manufacturing defects. A double-bit error is mathematically uncorrectable by the GPU ECC engine, meaning data corruption has physically occurred in the memory cell. The driver kills the context to prevent silent data corruption. Common tags: Xid Error.

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Why it happens (the mechanism)

It is a pure hardware failure, but because it often causes PyTorch to crash with a generic NCCL timeout (due to a participating GPU dying), distributed training pipelines just show network timeout errors on the surface.

What you'll observe

NVRM: Xid (PCI:0000:03:00): 48, Double Bit ECC Error
Uncorrectable ECC error encountered

Common symptoms and what they mean

Symptom	Why it happens
Training crashes randomly with NCCL timeouts or corrupted output tensors.	The physical VRAM (HBM or GDDR) on the GPU has degraded over time due to thermal stress or manufacturing defects. A double-bit error is mathematically uncorrectable by the GPU ECC engine, meaning data corruption has physically occurred in the memory cell. The driver kills the context to prevent silent data corruption.
dmesg logs flooded with Xid 48 Double Bit ECC errors.	The physical VRAM (HBM or GDDR) on the GPU has degraded over time due to thermal stress or manufacturing defects. A double-bit error is mathematically uncorrectable by the GPU ECC engine, meaning data corruption has physically occurred in the memory cell. The driver kills the context to prevent silent data corruption.
Sometimes accompanied by Xid 64 (Failed page retirement).	The physical VRAM (HBM or GDDR) on the GPU has degraded over time due to thermal stress or manufacturing defects. A double-bit error is mathematically uncorrectable by the GPU ECC engine, meaning data corruption has physically occurred in the memory cell. The driver kills the context to prevent silent data corruption.

Which systems are affected

GPU Hardware
VRAM

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 ECC error counters: nvidia-smi -q -d ECC
✓Check retired memory pages: nvidia-smi -q -d RETIRED_PAGES. A count approaching 64 strongly indicates a dead card.
✓Look for Xid 48 and Xid 64 in /var/log/syslog or dmesg.

Searchable error signature

search key

dmesg logs flooded with Xid 48 Double Bit ECC errors.
Sometimes accompanied by Xid 64 (Failed page retirement).
NVRM: Xid (PCI:0000:03:00): 48, Double Bit ECC 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 48 in context

Xid 48 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

The physical VRAM (HBM or GDDR) on the GPU has degraded over time due to thermal stress or manufacturing defects. A double-bit error is mathematically uncorrectable by the GPU ECC engine, meaning data corruption has physically occurred in the memory cell. The driver kills the context to prevent silent data corruption.

The fix and how to prevent it

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References

Don't just read the fix, diagnose your run

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Related Hardware errors

GPU Memory Clock Throttle

Hardware · medium

NVLink Error

Hardware · high

GPU Thermal Throttling

Hardware · high

GPU Power Cap Reached

Hardware · medium