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 overflow handling in the nvlink management microcode inside the vbios firmware, fixed in a later firmware release. Validating against the symptom list in this entry is the fastest way to confirm the diagnosis.

Is this a hardware or a software issue?

Reliability 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 reliability 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 GB200 NVL72 racks 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 #gb200, #nvl72, #xid-150. 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 gb200 is upgraded. Pin compatible versions and rerun the prevention smoke test on every dependency change.

Is there a fingerprint I can monitor?

Yes, Xid 150 and Xid 154 appearing in the kernel log on R580 or later drivers 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.

GB200 NVL72 driver hang with Xid 150 and 154 after long uptime is a firmware bug, not a dead GPU

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 overflow handling in the nvlink management microcode inside the vbios firmware, fixed in a later firmware release 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.

On GB200 NVL72 racks running R580 or later drivers, NVLink task scheduling can hang the GPU driver after roughly sixty days of uptime and emit Xid 150 and Xid 154. The cause is an overflow in the NVLink management microcode in the vBIOS, so replacing the card does not fix it.

Quick answer

On GB200 NVL72 racks running R580 or later drivers, NVLink task scheduling can hang the GPU driver after roughly sixty days of uptime and emit Xid 150 and Xid 154.

Symptom: NVRM: Xid (PCI:0009:01:00): 150, GPU driver hang in NVLink task scheduling
Root cause: Overflow handling in the NVLink management microcode inside the vBIOS firmware, fixed in a later firmware release. The failure accumulates with uptime, which is why it presents as a sudden fleet-wide problem on a mature cluster.
Recommended fix: Check the vBIOS version against the DGX GB200 NVL72 release notes before treating the card as faulty, because the fix ships as firmware.
How Denpex helps: Denpex matches GB200 NVL72 driver hang with Xid 150 and 154 after long uptime is a firmware bug, not a dead GPU 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.

Reliability#gb200#nvl72#xid-150#xid-154#vbios#firmware

What this failure is

GB200 NVL72 driver hang with Xid 150 and 154 after long uptime is a firmware bug, not a dead GPU is a Reliability failure seen during ML training runs. On GB200 NVL72 racks running R580 or later drivers, NVLink task scheduling can hang the GPU driver after roughly sixty days of uptime and emit Xid 150 and Xid 154. The cause is an overflow in the NVLink management microcode in the vBIOS, so replacing the card does not fix it. Common tags: Gb200, Nvl72, Xid 150, Xid 154.

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Why it happens (the mechanism)

Overflow handling in the NVLink management microcode inside the vBIOS firmware, fixed in a later firmware release. The failure accumulates with uptime, which is why it presents as a sudden fleet-wide problem on a mature cluster. Taken together, these mechanisms explain why the failure is reproducible, why it tends to surface on specific workloads or scales, and why generic mitigation attempts often fall short without addressing the underlying cause.

What you'll observe

A long-running GB200 rack starts hanging the GPU driver with no hardware change
Cards are pulled and RMA-ed but replacements fail the same way after similar uptime
Failures cluster by uptime rather than by workload or by individual card

Common symptoms and what they mean

Symptom	Why it happens
Xid 150 and Xid 154 appearing in the kernel log on R580 or later drivers	Overflow handling in the NVLink management microcode inside the vBIOS firmware, fixed in a later firmware release
GPU driver hangs tied to NVLink task scheduling	The failure accumulates with uptime, which is why it presents as a sudden fleet-wide problem on a mature cluster
Onset after roughly sixty or more days of continuous uptime	Overflow handling in the NVLink management microcode inside the vBIOS firmware, fixed in a later firmware release

Which systems are affected

GB200 NVL72 racks
Systems on R580 and later GPU drivers
vBIOS releases predating the NVLink microcode overflow fix

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.

✓Correlate the timestamps of Xid 150 and 154 events against node uptime across the fleet
✓Confirm the driver is R580 or later and record the current vBIOS version
✓Confirm the same signature appears on more than one card before suspecting hardware

Searchable error signature

search key

NVRM: Xid (PCI:0009:01:00): 150, GPU driver hang in NVLink task scheduling
NVRM: Xid (PCI:0009:01:00): 154, GPU recovery action changed
uptime: 63 days,  4:12,  0 users,  load average: 41.88, 40.92, 40.11
NVRM: loading NVIDIA UNIX Open Kernel Module for x86_64  580.65.06

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

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Root cause

Overflow handling in the NVLink management microcode inside the vBIOS firmware, fixed in a later firmware release
The failure accumulates with uptime, which is why it presents as a sudden fleet-wide problem on a mature cluster

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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