CUDA Memory Allocation Failed
CUDA memory allocation fails when the requested memory block cannot be allocated, often due to fragmentation or insufficient total memory.
CUDA memory allocation fails when the requested memory block cannot be allocated, often due to fragmentation or insufficient total memory.
What this failure is
CUDA Memory Allocation Failed is a Memory failure seen during ML training runs. CUDA memory allocation fails when the requested memory block cannot be allocated, often due to fragmentation or insufficient total memory. Common tags: Cuda, Memory","Allocation","Oom, Fragmentation.
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Why it happens (the mechanism)
Requested allocation size exceeds available memory. Memory fragmentation prevents contiguous allocation. GPU memory held by other processes. 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
- CUDA runtime fails to allocate memory for tensor
- RuntimeError: CUDA out of memory during initialization
- Model loading fails with OOM
Common symptoms and what they mean
| Symptom | Why it happens |
|---|---|
| cudaMalloc returned an error | Requested allocation size exceeds available memory |
| RuntimeError: CUDA error: out of memory | Memory fragmentation prevents contiguous allocation |
| Memory allocation failure at specific point in code | GPU memory held by other processes |
Which systems are affected
- Training with large models
- Multi-GPU training with memory imbalance
- Custom CUDA kernels with large allocations
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: cudaMalloc returned an error
- ✓Verified signal present: RuntimeError: CUDA error: out of memory
- ✓Verified signal present: Memory allocation failure at specific point in code
- ✓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
Select the traceback or open the failed terminal, then run Denpex locally to see the initiating rank, collateral failures, exact fix, and verification command without uploading the log.
Install the free VS Code extensionCUDA errors in context
CUDA reports errors asynchronously, so the traceback usually points at whatever line synchronised next rather than the one at fault. The hub covers every common CUDA error and how to make it report honestly.
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Root cause
- Requested allocation size exceeds available memory
- Memory fragmentation prevents contiguous allocation
- GPU memory held by other processes
The fix and how to prevent it
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