# InternalError: CUDA driver version is insufficient for CUDA runtime version

- **ID:** `tensorflow/cuda-driver-version-mismatch`
- **Domain:** tensorflow
- **Category:** install_error
- **Error Code:** `CUD`
- **Verification:** ai_generated
- **Fix Rate:** 92%

## Root Cause

The installed NVIDIA driver is older than the version required by the CUDA toolkit that TensorFlow was compiled against.

## Version Compatibility

| Version | Status | Introduced | Deprecated |
|---------|--------|------------|------------|
| tensorflow 2.15 | active | — | — |
| tensorflow 2.16 | active | — | — |
| tensorflow 2.17 | active | — | — |

## Workarounds

1. **Update the NVIDIA driver to the required version. Check current driver with `nvidia-smi` and download the latest driver from https://www.nvidia.com/Download/index.aspx. For Ubuntu: `sudo apt update && sudo apt install nvidia-driver-545`.** (92% success)
   ```
   Update the NVIDIA driver to the required version. Check current driver with `nvidia-smi` and download the latest driver from https://www.nvidia.com/Download/index.aspx. For Ubuntu: `sudo apt update && sudo apt install nvidia-driver-545`.
   ```
2. **Downgrade TensorFlow to a version compatible with the current driver. For example, use `pip install tensorflow==2.12.0` if the driver is older.** (70% success)
   ```
   Downgrade TensorFlow to a version compatible with the current driver. For example, use `pip install tensorflow==2.12.0` if the driver is older.
   ```

## Dead Ends

- **** — Reinstalling TensorFlow alone does not update the NVIDIA driver; the driver is a system-level component. (95% fail)
- **** — Installing a newer CUDA toolkit via conda often fails because it does not update the kernel-mode driver. (80% fail)