We are running on Windows 10, TensorFlow 2.7 (C API) to run the inference for our detection deep learning model based SSD.
The CUDA installed is 11.2.2 with CUDNN 8.1.1.33, driver 30.0.14.7247.
The typical performance of our model is 4.5 ms.
We recently upgraded the CUDA to 11.8, CUDNN 8.5.0.96, driver 31.0.15.3742
(472.12-quadro-rtx-desktop-notebook-win10-win11-64bit-international-dch-whql.exe)
to support installing onnxruntime version 1.16.3 for other project (it needs CUDA 11.8)
We noticed a degradation in time inference of ~0.5 ms.
After our investigation we found that the reason was the new CUDNN.
Thus we reinstalled back the CUDNN version 8.1.1.33 and degradation was eliminated.
We tested other CUDNN versions but this was our best combination for time performance.
At our tests we did not faced issues running: CUDA 11.8 with CUDNN 8.1.1.33 and driver 31.0.15.3742
Our question is CUDA 11.8 is formally compatible to run with CUDNN 8.1.1.33 ? does we take here any risks?