Memory Fragmentation
Memory fragmentation causes OOM even when total free memory is sufficient, because no contiguous block is available for the allocation.
Memory fragmentation causes OOM even when total free memory is sufficient, because no contiguous block is available for the allocation.
What this failure is
Memory Fragmentation is a Memory failure seen during ML training runs. Memory fragmentation causes OOM even when total free memory is sufficient, because no contiguous block is available for the allocation. Common tags: Cuda, Memory","Fragmentation","Pytorch","Oom, Allocator.
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Why it happens (the mechanism)
Variable-size allocations create non-contiguous free blocks. PyTorch's caching allocator doesn't merge free blocks. Long-running training accumulates fragmentation over thousands of steps. 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 with seemingly sufficient free memory
- Intermittent OOM that sometimes works on retry
- OOM after many training steps but not at start
Common symptoms and what they mean
| Symptom | Why it happens |
|---|---|
| torch.cuda.memory_summary() shows fragmented free blocks | Variable-size allocations create non-contiguous free blocks |
| OOM for allocation size that fits in total free memory | PyTorch's caching allocator doesn't merge free blocks |
| Different OOM behavior across runs with same configuration | Long-running training accumulates fragmentation over thousands of steps |
Which systems are affected
- Long-running training with varying tensor sizes
- Training with dynamic batch sizes
- Models with attention layers of varying sequence length
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_summary() shows fragmented free blocks
- ✓Verified signal present: OOM for allocation size that fits in total free memory
- ✓Verified signal present: Different OOM behavior across runs with same configuration
- ✓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-size allocations create non-contiguous free blocks
- PyTorch's caching allocator doesn't merge free blocks
- Long-running training accumulates fragmentation over thousands of steps
The fix and how to prevent it
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