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GPU Temperature Throttling

GPU temperature throttling reduces clock speed and performance when GPUs overheat, sometimes causing training failures.

Quick answer

GPU temperature throttling reduces clock speed and performance when GPUs overheat, sometimes causing training failures.

Infrastructure#temperature#thermal#throttling#cooling#gpu#infrastructure

What this failure is

GPU Temperature Throttling is a Infrastructure failure seen during ML training runs. GPU temperature throttling reduces clock speed and performance when GPUs overheat, sometimes causing training failures. Common tags: Temperature, Thermal, Throttling, Cooling.

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Why it happens (the mechanism)

Insufficient cooling in server room. GPU fans not working at full speed. Thermal paste degraded. Blocked airflow due to dust. Ambient temperature too high. 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

  • Training is slower than expected
  • nvidia-smi shows lower clock than base
  • GPU performance degrades over time

Common symptoms and what they mean

SymptomWhy it happens
GPU temperature exceeds 85CInsufficient cooling in server room
GPU clocks are throttledGPU fans not working at full speed
XID 62 thermal slowdown messagesThermal paste degraded

Which systems are affected

  • Long-running training in warm environments
  • Datacenter with insufficient cooling
  • GPU server with blocked airflow

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: GPU temperature exceeds 85C
  • ✓Verified signal present: GPU clocks are throttled
  • ✓Verified signal present: XID 62 thermal slowdown messages
  • ✓A targeted fix from the "How to fix it" section eliminates or substantially reduces the symptom within one validation pass.

The fix and the prevention pattern

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Related failures to investigate next

Lambda Bgp Route Withdrawal Region Outage often co-occurs with this failure because both share underlying assumptions about infrastructure; if you fix this entry but still see drift, validate the related primary cause next. lambda bgp route withdrawal region outage →
Kubernetes Gpu Pod Pending often co-occurs with this failure because both share underlying assumptions about infrastructure; if you fix this entry but still see drift, validate the related neighboring cause next. kubernetes gpu pod pending →
Slurm Node Oom often co-occurs with this failure because both share underlying assumptions about infrastructure; if you fix this entry but still see drift, validate the related neighboring cause next. slurm node oom →

Root cause

  • Insufficient cooling in server room
  • GPU fans not working at full speed
  • Thermal paste degraded
  • Blocked airflow due to dust
  • Ambient temperature too high

The fix and how to prevent it

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References

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