CUDA Driver / Runtime Mismatch
CUDA driver and runtime version mismatches prevent PyTorch from initializing CUDA.
CUDA driver and runtime version mismatches prevent PyTorch from initializing CUDA.
What this failure is
CUDA Driver / Runtime Mismatch is a Environment failure seen during ML training runs. CUDA driver and runtime version mismatches prevent PyTorch from initializing CUDA. Common tags: Cuda Driver Mismatch, Environment, Nvidia, Compatibility.
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Why it happens (the mechanism)
PyTorch compiled against CUDA 12.1 but driver only supports up to CUDA 11.8. 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 fails with insufficient CUDA driver
- nvidia-smi works but torch.cuda.is_available() returns False
Common symptoms and what they mean
| Symptom | Why it happens |
|---|---|
| CUDA driver version is insufficient for CUDA runtime version | PyTorch compiled against CUDA 12.1 but driver only supports up to CUDA 11.8 |
| torch.cuda.is_available() returns False with nvidia-smi showing GPUs | PyTorch compiled against CUDA 12.1 but driver only supports up to CUDA 11.8 |
| ImportError: libcudart.so.12 cannot open | PyTorch compiled against CUDA 12.1 but driver only supports up to CUDA 11.8 |
Which systems are affected
- PyTorch with CUDA support
- Multi-node with different driver versions
- Container CUDA toolkit differs from host driver
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: CUDA driver version is insufficient for CUDA runtime version
- ✓Verified signal present: torch.cuda.is_available() returns False with nvidia-smi showing GPUs
- ✓Verified signal present: ImportError: libcudart.so.12 cannot open
- ✓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
- PyTorch compiled against CUDA 12.1 but driver only supports up to CUDA 11.8
The fix and how to prevent it
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