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 multi-bit memory error occurred in the high bandwidth memory (hbm) that the hardware ecc engine cannot correct. to prevent silent data corruption, the gpu driver marks this as a 'sticky' hardware error and halts all compute operations on the device.. Validating against the symptom list in this entry is the fastest way to confirm the diagnosis.

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

Is there a fingerprint I can monitor?

Yes. PyTorch training job crashes immediately and unexpectedly. 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 in HBM

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 multi-bit memory error occurred in the high bandwidth memory (hbm) that the hardware ecc engine cannot correct. to prevent silent data corruption, the gpu driver marks this as a 'sticky' hardware error and halts all compute operations on the device. 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 multi-bit memory error occurred in the High Bandwidth Memory (HBM) that the hardware ECC engine cannot correct. To prevent silent data corruption, the GPU driver marks this as a 'sticky' hardware error and halts all compute operations on the device.

Quick answer

A multi-bit memory error occurred in the High Bandwidth Memory (HBM) that the hardware ECC engine cannot correct.

Symptom: RuntimeError: CUDA error: uncorrectable ECC error encountered
Root cause: A multi-bit memory error occurred in the High Bandwidth Memory (HBM) that the hardware ECC engine cannot correct. To prevent silent data corruption, the GPU driver marks this as a 'sticky' hardware error and halts all compute operations on the device.
Recommended fix: Reset the GPU to clear the sticky error state sudo nvidia-smi -i <GPU_UUID> -r Clearing the GPU resets its internal state, allowing it to be used again if the error was a transient single-event upset.
How Denpex helps: Denpex investigates Xid 48 Double-Bit ECC Error in HBM 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#Uncorrectable ECC Error

What this failure is

Xid 48 Double-Bit ECC Error in HBM is a Hardware failure seen during ML training runs. A multi-bit memory error occurred in the High Bandwidth Memory (HBM) that the hardware ECC engine cannot correct. To prevent silent data corruption, the GPU driver marks this as a 'sticky' hardware error and halts all compute operations on the device. Common tags: Uncorrectable ECC Error.

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Why it happens (the mechanism)

Users often mistake this for a software bug, an Out-Of-Memory (OOM) error, or an illegal memory access caused by their PyTorch code. In reality, it is a physical hardware fault (e.g., caused by cosmic rays or a failing memory module).

What you'll observe

RuntimeError: CUDA error: uncorrectable ECC error encountered
NVRM: Xid (PCI:0000:ca:00): 48, Uncorrectable double bit error

Common symptoms and what they mean

Symptom	Why it happens
PyTorch training job crashes immediately and unexpectedly.	A multi-bit memory error occurred in the High Bandwidth Memory (HBM) that the hardware ECC engine cannot correct. To prevent silent data corruption, the GPU driver marks this as a 'sticky' hardware error and halts all compute operations on the device.
The GPU becomes completely inaccessible to new processes.	A multi-bit memory error occurred in the High Bandwidth Memory (HBM) that the hardware ECC engine cannot correct. To prevent silent data corruption, the GPU driver marks this as a 'sticky' hardware error and halts all compute operations on the device.
nvidia-smi shows the GPU in an error state or processes fail to attach to it.	A multi-bit memory error occurred in the High Bandwidth Memory (HBM) that the hardware ECC engine cannot correct. To prevent silent data corruption, the GPU driver marks this as a 'sticky' hardware error and halts all compute operations on the device.

Which systems are affected

PyTorch
CUDA
NVIDIA A100
NVIDIA H100

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.

✓Run `dmesg -T | grep Xid` to check for Xid 48 errors.
✓Run `nvidia-smi -q -d ECC` to inspect the DRAM Uncorrectable error count.
✓Check if the error is isolated or recurs frequently on the same GPU UUID.

Root cause

A multi-bit memory error occurred in the High Bandwidth Memory (HBM) that the hardware ECC engine cannot correct. To prevent silent data corruption, the GPU driver marks this as a 'sticky' hardware error and halts all compute operations on the device.

The fix and how to prevent it

Searchable error signature

search key

RuntimeError: CUDA error: uncorrectable ECC error encountered
nvidia-smi shows the GPU in an error state or processes fail to attach to it.

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

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 48 Documentation

Don't just read the fix, diagnose your run

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