GPU Utilization Low
Low GPU utilization indicates training is bottlenecked by data loading, CPU work, or communication.
Low GPU utilization indicates training is bottlenecked by data loading, CPU work, or communication.
What this failure is
GPU Utilization Low is a Performance failure seen during ML training runs. Low GPU utilization indicates training is bottlenecked by data loading, CPU work, or communication. Common tags: Gpu Utilization, Bottleneck, Data Loading, Performance.
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Why it happens (the mechanism)
DataLoader is single-threaded (num_workers=0). Augmentation done on CPU. Data fetching from slow storage. Communication bottleneck. CPU-GPU transfer overhead. 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
- GPU utilization is low (10-30%)
- Training is slower than expected
- GPUs are starving for data
Common symptoms and what they mean
| Symptom | Why it happens |
|---|---|
| nvidia-smi shows low % | DataLoader is single-threaded (num_workers=0) |
| Training time is mostly data loading | Augmentation done on CPU |
| GPU power is low | Data fetching from slow storage |
Which systems are affected
- Data loading is the bottleneck
- Small batch sizes
- CPU preprocessing too slow
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 low %
- ✓Verified signal present: Training time is mostly data loading
- ✓Verified signal present: GPU power is low
- ✓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
- DataLoader is single-threaded (num_workers=0)
- Augmentation done on CPU
- Data fetching from slow storage
- Communication bottleneck
- CPU-GPU transfer overhead
The fix and how to prevent it
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