GPU Power Cap Reached
GPU power cap limits GPU power consumption, reducing performance for power-constrained deployments.
GPU power cap limits GPU power consumption, reducing performance for power-constrained deployments.
What this failure is
GPU Power Cap Reached is a Hardware failure seen during ML training runs. GPU power cap limits GPU power consumption, reducing performance for power-constrained deployments. Common tags: Power, Cap, Limit, Gpu.
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Why it happens (the mechanism)
Cloud provider caps GPU power. Power infrastructure limit. Energy efficiency requirements. 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
- GPU clock speeds vary with workload
- Performance inconsistent across jobs
Common symptoms and what they mean
| Symptom | Why it happens |
|---|---|
| nvidia-smi shows power limit reached | Cloud provider caps GPU power |
| Clock speeds vary with workload | Power infrastructure limit |
| Power consumption capped at configured limit | Energy efficiency requirements |
Which systems are affected
- Cloud GPU instances with power caps
- Power-constrained data centers
- Shared power infrastructure
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: nvidia-smi shows power limit reached
- ✓Verified signal present: Clock speeds vary with workload
- ✓Verified signal present: Power consumption capped at configured limit
- ✓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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Root cause
- Cloud provider caps GPU power
- Power infrastructure limit
- Energy efficiency requirements
The fix and how to prevent it
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