CUDA Graph Memory Trap
CUDA Graphs capture memory allocations that are hard to release, causing memory leaks across graph replays.
CUDA Graphs capture memory allocations that are hard to release, causing memory leaks across graph replays.
What this failure is
CUDA Graph Memory Trap is a Memory failure seen during ML training runs. CUDA Graphs capture memory allocations that are hard to release, causing memory leaks across graph replays. Common tags: Cuda Graph, Memory Pool, Capture, Memory.
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Why it happens (the mechanism)
CUDA graph holds memory pool for replay. Graph captures include all intermediate tensors. Multiple graphs share memory pool. Graph deletion doesn't immediately release memory. 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
- Memory grows with each CUDA graph replay
- CUDA graph capture fails with OOM
- Memory not released after deleting graph
Common symptoms and what they mean
| Symptom | Why it happens |
|---|---|
| Reserved memory grows during graph capture | CUDA graph holds memory pool for replay |
| CUDA graph holds memory that prevents reuse | Graph captures include all intermediate tensors |
| Memory snapshot shows graph allocations persist | Multiple graphs share memory pool |
Which systems are affected
- Training with CUDA graphs for performance
- Inference optimization with CUDA graphs
- torch.compile with cudagraphs
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: Reserved memory grows during graph capture
- ✓Verified signal present: CUDA graph holds memory that prevents reuse
- ✓Verified signal present: Memory snapshot shows graph allocations persist
- ✓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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Diagnose this failure in VS Code
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Root cause
- CUDA graph holds memory pool for replay
- Graph captures include all intermediate tensors
- Multiple graphs share memory pool
- Graph deletion doesn't immediately release memory
The fix and how to prevent it
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