In the computer which I use equipped with two GTX 680 cards I use housing Master HAF XM Cooler (see link below), which provides a very good cooling of all components. It is therefore necessary to choose housing that will provide good cooling. PC housing - When performing calculations using graphics cards is growing rapidly the temperature.In the computer which I use I have 1000W Power Supply (2X GTX graphic card - see picture below). In the specification of each card you can find such information. Test that the installed software runs correctly and communicates with the hardware. power supply - The power supply should have sufficient power to easily handle the PC and installed graphics card. The setup of CUDA development tools on a system running the appropriate version of Windows consists of a few simple steps: Verify the system has a CUDA-capable GPU.If you want to add more cards you have on board an adequate number of PCI slots. motherboard - you mast remember that in better graphic card Supplementary Power Connectors need 2x 6-pins and the Bus Support - PCI Express 3.0.You do not need to install the CUDA SDK from NVIDIA. Another important thing is to have a proper: To enable CUDA GPU support for Numba, install the latest graphics drivers from NVIDIA for your. Using one of these methods, you will be able to see the CUDA version regardless the software you are using, such as PyTorch, TensorFlow, conda (Miniconda/Anaconda) or inside docker. The simplest solution is to use geforce card (the same that we use in games) or a more efficient Tesla cards (see links below). Here you will learn how to check NVIDIA CUDA version in 3 ways: nvcc from CUDA toolkit, nvidia-smi from NVIDIA driver, and simply checking a file. The first very important thing is to have your graphics card (best from nVidia).
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