Cuda versions are different with nvcc --version and nvidia-smi

C:\Users\Kerem>nvidia-smi
Thu Feb 18 14:27:33 2021
±----------------------------------------------------------------------------+
| NVIDIA-SMI 442.23 Driver Version: 442.23 CUDA Version: 10.2 |
|-------------------------------±---------------------±---------------------+
| GPU Name TCC/WDDM | Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |
|===============================+======================+======================|
| 0 GeForce GTX 165… WDDM | 00000000:01:00.0 Off | N/A |
| N/A 36C P8 5W / N/A | 134MiB / 4096MiB | 0% Default |
±------------------------------±---------------------±---------------------+

±----------------------------------------------------------------------------+
| Processes: GPU Memory |
| GPU PID Type Process name Usage |
|=============================================================================|
| No running processes found |
±----------------------------------------------------------------------------+
WARNING: infoROM is corrupted at gpu 0000:01:00.0

(gputens) C:\Users\Kerem>nvcc --version
nvcc: NVIDIA ® Cuda compiler driver
Copyright © 2005-2020 NVIDIA Corporation
Built on Wed_Jul_22_19:09:35_Pacific_Daylight_Time_2020
Cuda compilation tools, release 11.0, V11.0.221
Build cuda_11.0_bu.relgpu_drvr445TC445_37.28845127_0

I downloaded CUDA 11.0 and cudNN 8.0 for tensorflow-gpu. My gpu is Geforce GTX 1650 TI.
And when I run this command on jupyter

from tensorflow.python.client import device_lib
print(device_lib.list_local_devices())

I get this error:
RuntimeError: cudaGetDevice() failed. Status: CUDA driver version is insufficient for CUDA runtime version

You are using driver 442.23 with Cuda 11, which requires a driver >=451.22 on Windows: Release Notes :: CUDA Toolkit Documentation

Hey, sorry for late reaction. And thank you for your answer. I did everything even tried 10.1 cuda and cuddn , tensorflow, python versions for that created new env etc. But as your answer I saw that the problem is probably about my GTX 1650 Ti. Reason that I think this is because I have tried and listed every graphic driver available in nvidia finder. But my GTX 1650 Ti only support driver versions that supports Cuda 11.1 and higher. And I think tensorflow 2.4.1 still can’t support CUDA 11.2 so I am waiting for tensorflow team to give support to CUDA 11.2 basically…