Finally my new laptop that sports the new GeForce Nvidia RTX 2060 arrived. It is time to check out the muscle of this little beast with the toolset I’m familiar with.

On the hardware, the laptop is a i7-8750 and 16G RAM with a Turing architecture based GeForce RTX 2060.

The laptop came with full drivers installed. Nevertheless I downloaded the latest drivers and CUDA for the most up-to-date experience. The software include Nvidia GeForce drivers, Visual Studio Express 2017, CUDA Toolkit, and TensorFlow.

Be careful when trying all these bleeding edge technologies, not only because TensorFlow 2.0 is currently in Alpha, compatibility issues may haunt like with previous 1.x TensorFlow on CUDA 10.1. I have to fallback to 10.0 to have TF happy with it (although one can always choose the compile from source approach).

And here are my favorite nbody and Mandelbrot simulation, and also the Black Scholes sample in CUDA. The diagnostic tool in VS gives a nice real time profiling interface with graphs.

Finally for this test drive – TensorFlow with GPU. The installation is smooth until I tried to verify TF on GPU. After several failed attempts I realized it could be that CUDA 10.1 may not be compatible with the TF version installed. There are couples of suggested solutions out there, including downgrading to CUDA 9, but since my GPU is the Turing series this is not an option. Actually TF supports CUDA 10 since v.13. So I finally decided to fall back CUDA to 10.0 from 10.1 and it worked!