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 failed chassis fan / blocked inlet on one gpu position. Validating against the symptom list in this entry is the fastest way to confirm the diagnosis.

Is this a hardware or a software issue?

Performance 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 performance 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 Dense DGX/HGX chassis with chassis-fan failures 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 #straggler, #thermal, #hw-slowdown. 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 straggler is upgraded. Pin compatible versions and rerun the prevention smoke test on every dependency change.

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 failed chassis fan / blocked inlet on one gpu position 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.

Is there a fingerprint I can monitor?

Yes. Step_time p50 healthy, p99 = one rank at 3x 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.

Persistent Straggler Rank from Single-GPU Thermal Throttling

One rank consistently 2-4x slower drags every synchronous collective. When the cause is a failed fan/blocked airflow on exactly one GPU, the fleet-wide symptom (slow allreduce everywhere) hides a single-node hardware root cause.

Quick answer

One rank consistently 2-4x slower drags every synchronous collective.

Symptom: nvidia-smi on that GPU: HW Thermal Slowdown Active, temperature 15-30C above fleet
Root cause: failed chassis fan / blocked inlet on one GPU position. degraded thermal paste or cold-plate contact on one SXM site. BMC fan-policy misconfiguration on one node.
Recommended fix: Confirm: nvidia-smi -q -d PERFORMANCE,TEMPERATURE on the flagged rank
How Denpex helps: Denpex investigates Persistent Straggler Rank from Single-GPU Thermal 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.

Performance#straggler#thermal#hw-slowdown#slow-rank#fan-failure

What this failure is

Persistent Straggler Rank from Single-GPU Thermal Throttling is a Performance failure seen during ML training runs. One rank consistently 2-4x slower drags every synchronous collective. When the cause is a failed fan/blocked airflow on exactly one GPU, the fleet-wide symptom (slow allreduce everywhere) hides a single-node hardware root cause. Common tags: Straggler, Thermal, Hw Slowdown, Slow Rank.

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Why it happens (the mechanism)

Failed chassis fan / blocked inlet on one GPU position. Degraded thermal paste or cold-plate contact on one SXM site. BMC fan-policy misconfiguration on one node. 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

collective time dominated by one rank (straggler detector flags the same rank every window)
nvidia-smi on that GPU: HW Thermal Slowdown Active, temperature 15-30C above fleet

Common symptoms and what they mean

Symptom	Why it happens
step_time p50 healthy, p99 = one rank at 3x	failed chassis fan / blocked inlet on one GPU position
clocks on the hot GPU pinned far below application clocks	degraded thermal paste or cold-plate contact on one SXM site
problem follows the node, not the rank id	BMC fan-policy misconfiguration on one node

Which systems are affected

dense DGX/HGX chassis with chassis-fan failures
air-cooled retrofits
any synchronous data-parallel training

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.

✓Reproduce the failure from a clean checkpoint/seed: the symptom must appear without warm-up state from a previous run.
✓Verified signal present: step_time p50 healthy, p99 = one rank at 3x
✓Verified signal present: clocks on the hot GPU pinned far below application clocks
✓Verified signal present: problem follows the node, not the rank id
✓A targeted fix from the "How to fix it" section eliminates or substantially reduces the symptom within one validation pass.

Root cause

failed chassis fan / blocked inlet on one GPU position
degraded thermal paste or cold-plate contact on one SXM site
BMC fan-policy misconfiguration on one node

The fix and how to prevent it

Searchable error signature

search key

nvidia-smi on that GPU: HW Thermal Slowdown Active, temperature 15-30C above fleet

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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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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