PyTorch CUDA Mismatch
PyTorch CUDA version mismatch with installed CUDA toolkit/driver causes import errors or runtime failures.
PyTorch CUDA version mismatch with installed CUDA toolkit/driver causes import errors or runtime failures.
What this failure is
PyTorch CUDA Mismatch is a Environment failure seen during ML training runs. PyTorch CUDA version mismatch with installed CUDA toolkit/driver causes import errors or runtime failures. Common tags: Pytorch, Cuda, Driver, Version Mismatch.
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Why it happens (the mechanism)
PyTorch built for CUDA 11.8 but driver supports CUDA 12.0+. PyTorch built for CUDA 12.x but only CUDA 11.x driver. PyTorch CPU-only install in GPU container. CUDA toolkit version different from PyTorch bundled CUDA. 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
- PyTorch can't detect CUDA
- RuntimeError: CUDA not available
- Torch was not built with CUDA enabled
Common symptoms and what they mean
| Symptom | Why it happens |
|---|---|
| torch.cuda.is_available() returns False | PyTorch built for CUDA 11.8 but driver supports CUDA 12.0+ |
| CUDA driver version is insufficient for CUDA runtime version | PyTorch built for CUDA 12.x but only CUDA 11.x driver |
| libcudart.so not found | PyTorch CPU-only install in GPU container |
Which systems are affected
- Fresh PyTorch install
- Container with mismatched CUDA
- GPU driver update
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.is_available() returns False
- ✓Verified signal present: CUDA driver version is insufficient for CUDA runtime version
- ✓Verified signal present: libcudart.so not found
- ✓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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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.
Compare every cuda error side by sideRelated failures to investigate next
Root cause
- PyTorch built for CUDA 11.8 but driver supports CUDA 12.0+
- PyTorch built for CUDA 12.x but only CUDA 11.x driver
- PyTorch CPU-only install in GPU container
- CUDA toolkit version different from PyTorch bundled CUDA
The fix and how to prevent it
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References
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