How to upgrade your GPU

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Follow along the instructions on properly removing the previous drivers using the DDU utility. Click on the links below to download and install drivers for your new NVIDIA or AMD card. Once you install the latest drivers for your new graphics card, it is game time. Before installing your new, beefy graphics card, you will want to make sure that you have removed any older drivers for the card you are replacing. If you bought a new card from a different manufacturer (AMD or NVIDIA) from one you already have, you will want to completely uninstall all drivers from the whole PC prior to installing your new graphics card.

Like everything else within your PC, you have to take care in removing the old graphics card and installing a new one. If you are installing a new GPU, and you do not yet have a dedicated card, then you can skip this section. Before you can use your GPU for intensive tasks such as gaming, you will want to install new drivers to ensure that Windows and your software are effectively communicating with your card.

Once the GPU drivers are removed from windows, it is time to turn the PC on and start working. Following the tips from the above links, there is a handy tool to help you fully remove GPU drivers on Windows called Display Driver Uninstaller GPU drivers on Windows. So, you are best off installing Display Driver Uninstaller and running it in Safe Mode. It is free, easy-to-use software that makes this process a lot easier.

Cuda 11.0 seems to require a newer version of NVIDIA drivers, so we are going to be installing that manually. The CUDA Runfile also includes the version of the NVIDIA graphics driver, but I like to split installing one or the other, since the version provided with CUDA is not necessarily the latest 440.100 NVIDIA driver release. For example, if you have an earlier version of Tensorflow which works better with the earlier version of the CUDA toolset, but the GPU that you want to use requires the newer NVIDIA drivers, you may wish to install the earlier version of the CUDA toolset together with the newer NVIDIA drivers.

Docker is the easiest way to create GPU support for TensorFlow, as your host machine just needs NVIDIA(r) drivers (you do not need to install the NVIDIA(r) cuda(r) toolkit). The best reason for using Pythons Anaconda in the context of installing TensorFlow with GPU acceleration is that doing this you do not need to perform the CUDA installation in your system.

There are three supported variants of the tensorflow package in the Anaconda distribution, one of them being a quadro GPU variant. The Anaconda distribution requires the user to install the latest NVIDIA drivers, meeting the requirements for versions listed in the table below. The Quadro Driver is designed for backwards compatibility with older versions of the Cuda, so a system running NVIDIA Driver version 384.81 may support Cuda 9.0 packages and older. As a result, if the user is not using the latest NVIDIA drivers, he or she might have to manually choose a specific version of the CUDA, selecting a conda version of the CudaToolkit package in his or her environment.
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