CUDA Caching Allocator Fragmentation
CUDA caching allocator fragmentation causes OOM despite enough total free memory, due to non-contiguous blocks.
CUDA caching allocator fragmentation causes OOM despite enough total free memory, due to non-contiguous blocks.
What this failure is
CUDA Caching Allocator Fragmentation is a Memory failure seen during ML training runs. CUDA caching allocator fragmentation causes OOM despite enough total free memory, due to non-contiguous blocks. Common tags: Cuda Allocator, Fragmentation, Memory, Memory Pool.
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Why it happens (the mechanism)
Variable tensor sizes cause fragmentation. Allocs and frees of different sizes. Long-running training accumulates fragmentation. Memory pool cannot coalesce small blocks. 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
- OOM despite enough free memory
- Reserved memory is high but allocated is low
- Memory fragmentation visible in snapshot
Common symptoms and what they mean
| Symptom | Why it happens |
|---|---|
| torch.cuda.memory_reserved() >> memory_allocated() | Variable tensor sizes cause fragmentation |
| Empty cache doesn't help | Allocs and frees of different sizes |
| Snapshot shows many small free blocks | Long-running training accumulates fragmentation |
Which systems are affected
- Variable-shape training
- Long-running training
- Models with dynamic shapes
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: torch.cuda.memory_reserved() >> memory_allocated()
- ✓Verified signal present: Empty cache doesn't help
- ✓Verified signal present: Snapshot shows many small free blocks
- ✓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
- Variable tensor sizes cause fragmentation
- Allocs and frees of different sizes
- Long-running training accumulates fragmentation
- Memory pool cannot coalesce small blocks
The fix and how to prevent it
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