Which cuda toolkit to use. Jul 30, 2020 · I imagine it is probably possible to get a conda-installed pytorch to use a non-conda-installed CUDA toolkit. 0 Release Notes. The CUDA Toolkit supports a wide range of A: Yes, you can uninstall CUDA even if you have CUDA-enabled applications installed. Make sure the method you use to install cuda toolkit. cuda(): Returns CUDA version of the currently installed packages; torch. In this guide I will be using a Paperspace GPU instance with Ubuntu 22. Sep 12, 2023 · The essentials of NVIDIA’s CUDA Toolkit and its importance for GPU-accelerated tasks. x, and threadIdx. Note that any given CUDA toolkit has specific Linux distros (including version number) that are supported. 8, but would fail to run the binary with CUDA 12. CUDA Programming Model . For convenience, threadIdx is a 3-component vector, so that threads can be identified using a one-dimensional, two-dimensional, or three-dimensional thread index, forming a one-dimensional, two-dimensional, or three-dimensional block of threads, called a thread block. And when you try and use CUDA 10. x, then you will be using the command pip3. 1. MacOS Tools. > 10. About. Just select the driver, apply, then use a matching toolkit. first open the jupyter notebbok server: jupyter notebook. 1. Applications that use the runtime API also require the runtime library ("cudart. e. dll" under Windows), which is included in the CUDA Toolkit. For example $> nvcc hello. Jul 31, 2024 · CUDA Compatibility describes the use of new CUDA toolkit components on systems with older base installations. If you need to use CUDA-enabled applications, you can reinstall CUDA after uninstalling it. Oct 3, 2022 · To perform a basic install of all CUDA Toolkit components using Conda, run the following command: conda install cuda -c nvidia. May 26, 2024 · On Linux, you can debug CUDA kernels using cuda-gdb. 04 nvidia-smi There are many CUDA code samples included as part of the CUDA Toolkit to help you get started on the path of writing software with CUDA C/C++ The code samples covers a wide range of applications and techniques, including: Jan 25, 2017 · CUDA provides gridDim. 8 installed in my local machine, but Pytorch can't recognize my GPU. The user manual for NVIDIA profiling tools for optimizing performance of CUDA applications. Older CUDA toolkits are available for download here. It has components that support Aug 4, 2020 · If you use the $(CUDA_PATH) environment variable to target a version of the CUDA Toolkit for building, and you perform an installation or uninstallation of any version of the CUDA Toolkit, you should validate that the $(CUDA_PATH) environment variable points to the correct installation directory of the CUDA Toolkit for your purposes. Open Jupyter Notebooks . During the build process, environment variable CUDA_HOME or CUDA_PATH are used to find the location of CUDA headers. With CUDA, developers can dramatically speed up computing applications by harnessing the power of GPUs. Jan 12, 2024 · End User License Agreement. Uninstallation. CUDA (or Compute Unified Device Architecture) is a parallel computing platform and application programming interface (API) that allows software to use certain types of graphics processing units (GPUs) for general purpose processing, an approach called general-purpose computing on GPUs (GPGPU). Set cuda-gdb as a custom debugger. 3 (November 2021), Versioned Online Documentation Apr 3, 2020 · CUDA Version: ##. Aug 29, 2024 · CUDA C++ Best Practices Guide. Linux. 0. Ada will be the last architecture with driver support for 32-bit applications. Jul 1, 2024 · To use these features, you can download and install Windows 11 or Windows 10, version 21H2. It explores key features for CUDA profiling, debugging, and optimizing. 0 for Windows, Linux, and Mac OSX operating systems. Preface . This Best Practices Guide is a manual to help developers obtain the best performance from NVIDIA ® CUDA ® GPUs. In particular, if your headers are located in path /usr/local/cuda/include, then you Jan 23, 2017 · Don't forget that CUDA cannot benefit every program/algorithm: the CPU is good in performing complex/different operations in relatively small numbers (i. Tip: If you want to use just the command pip, instead of pip3, you can symlink pip to the pip3 binary. The list of CUDA features by release. CUDA is a parallel computing platform and programming model developed by NVIDIA for general computing on graphical processing units (GPUs). Jan 29, 2024 · Prerequisites. xls and is located in the tools subdirectory of the CUDA Toolkit installation. exe; There is important driver version and the CUDA version. Applications Using CUDA Toolkit 9. txt, or version. 2 update 1 or earlier runs with cuBLASLt from CUDA Toolkit 12. For GCC and Clang, the preceding table indicates the minimum version and the latest version supported. It is a subset, to provide the needed components for other packages installed by conda such as pytorch . Note: The CUDA Version displayed in this table does not indicate that the CUDA toolkit or runtime are actually installed on your system. : Tensorflow-gpu == 1. Please refer to the official docs, and to Rohit's answer. 2, it is why nothing works. In computing, CUDA (originally Compute Unified Device Architecture) is a proprietary [1] parallel computing platform and application programming interface (API) that allows software to use certain types of graphics processing units (GPUs) for accelerated general-purpose processing, an approach called general-purpose computing on GPUs (). Applications Built Using CUDA Toolkit 11. 4 (February 2022), Versioned Online Documentation CUDA Toolkit 11. To install CUDA Toolkit and cuDNN with Conda, follow these steps: 1. To address this issue, it is recommended to ensure that you are using a TensorFlow version that is compatible with your Python version and supports GPU functionality. json). x, older CUDA GPUs of compute capability 2. Because I have some custom jupyter image, and I want to base from that. The minor version of the CUDA Toolkit. If so why is it same in all the enviroments [sic]? Because it is a property of the driver. NVIDIA driver compatible with the CUDA Toolkit version. Additionally, verifying the CUDA version compatibility with the selected TensorFlow version is crucial for leveraging GPU acceleration effectively. Training. 6 by mistake. Nov 6, 2019 · I have a confusion whether in 2021 we still need to have CUDA toolkit installed in system before we install pytorch gpu version. Aug 29, 2024 · CUDA Quick Start Guide. Mar 11, 2020 · cmake mentioned CUDA_TOOLKIT_ROOT_DIR as cmake variable, not environment one. I don't know how to do it, and in my experience, when using conda packages that depend on CUDA, its much easier just to provide a conda-installed CUDA toolkit, and let it use that, rather than anything else. Select the GPU and OS version from the drop-down menus. Install the GPU driver. x, which contains the index of the current thread block in the grid. This tutorial provides step-by-step instructions on how to verify the installation of CUDA on your system using command-line tools. One has to understand that there's a difference between: - NVIDIA CUDA library - NVIDIA RTC library The NVIDIA CUDA library comes with the CUDA SDK, but also with the NVIDIA Driver. nvidia. In the example above the graphics driver supports CUDA 10. cuda to check the actual CUDA version PyTorch is using. However, if you do so, those applications will no longer be able to use CUDA. In the future, when more CUDA Toolkit libraries are supported, CuPy will have a lighter maintenance overhead and have fewer wheels to release. Use CUDA within WSL and CUDA containers to get started quickly. For convenience, NVIDIA includes a compatible CUDA driver with the toolkit. That's why it does not work when you put it into . sudo apt-get autoremove --purge cuda Jul 31, 2024 · CUDA Compatibility describes the use of new CUDA toolkit components on systems with older base installations. 6 for Linux and Windows operating systems. Installing NVIDIA Graphic Drivers Install up-to-date NVIDIA graphics drivers on your Windows system. x . Dec 30, 2019 · All you need to install yourself is the latest nvidia-driver (so that it works with the latest CUDA level and all older CUDA levels you use. Deployment and execution of CUDA applications on x86_32 is still supported, but is limited to use with GeForce GPUs. It provides tools for compiling Rust to extremely fast PTX code as well as libraries for using existing CUDA libraries with it. cmake it clearly says that: Feb 20, 2024 · Finally, ensure that you can use CUDA. Download and install the NVIDIA CUDA enabled driver for WSL to use with your existing CUDA ML workflows. Description. CUDAToolkit_VERSION_MAJOR. Step 5: Using the CUDA Kernel in Jupyter Notebooks. In addition, you should check that your operating system is supported. Profiling Overview. Starting with CUDA 9. This script ensures the clean removal of the CUDA toolkit from your system. 0 or Earlier) or both. Python 3. I have tried to run the following script to chec Feb 1, 2011 · When an application compiled with cuBLASLt from CUDA Toolkit 12. Learn how to set up a CUDA environment on Microsoft Windows WSL2 after installing the CUDA Toolkit on Windows. 3, matrix multiply descriptors initialized using cublasLtMatmulDescInit() sometimes did not respect attribute changes using cublasLtMatmulDescSetAttribute(). Aug 29, 2024 · Option 1: Installation of Linux x86 CUDA Toolkit using WSL-Ubuntu Package - Recommended. 0 Is the cuda version shown above is same as cuda toolkit version? It has nothing to do with CUDA toolkit versions. Minimal first-steps instructions to get CUDA running on a standard system. The programming guide to using the CUDA Toolkit to obtain the best performance from NVIDIA GPUs. Make sure to download the correct version of CUDA toolkit that is Jul 31, 2018 · I had installed CUDA 10. Users will benefit from a faster CUDA runtime! Sep 16, 2022 · CUDA Toolkit. Resources. 5. These packages have dependencies on the NVIDIA driver and the package manager will attempt to install the NVIDIA Linux driver which may result in issues. If you look into FindCUDA. Figure 1 illustrates the the approach to indexing into an array (one-dimensional) in CUDA using blockDim. 5 should work. Aug 19, 2024 · Replace X. I used different options for downloading, the last one: conda install pytorch torchvision torchaudio pytorch-cuda=11. 1 as well as all compatible CUDA versions before 10. Jun 6, 2019 · The cudatoolkit installed using conda install is not the same as the CUDA toolkit packaged up by NVIDIA. CUDA Documentation/Release Notes; MacOS Tools; Training; Sample Code; Forums; Archive of Previous CUDA Releases; FAQ; Open Source Packages; Submit a Bug; Tarball and Zi Feb 9, 2021 · If you use the $(CUDA_PATH) environment variable to target a version of the CUDA Toolkit for building, and you perform an installation or uninstallation of any version of the CUDA Toolkit, you should validate that the $(CUDA_PATH) environment variable points to the correct installation directory of the CUDA Toolkit for your purposes. If you do not have supported hardware, you will not be able to fully use CUDALink. cuda. Y with the version number of the CUDA toolkit you have installed. cu. If you are on a Linux distribution that may use an older version of GCC toolchain as default than what is listed above, it is recommended to upgrade to a newer toolchain CUDA 11. Jun 2, 2023 · Once installed, we can use the torch. Aug 20, 2022 · I have created a python virtual environment in the current working directory. CUDA Download CUDA Toolkit 10. It covers methods for checking CUDA on Linux, Windows, and macOS platforms, ensuring you can confirm the presence and version of CUDA and the associated NVIDIA drivers. ) A boolean specifying whether or not the CUDA Toolkit was found. Use the CUDA Toolkit from earlier releases for 32-bit compilation. 04, you need the following: A supported NVIDIA GPU with a minimum compute capability of 3. Aug 29, 2024 · This spreadsheet, shown in Figure 15, is called CUDA_Occupancy_Calculator. To create 32-bit CUDA applications, use the cross-development capabilities of the CUDA Toolkit on x86_64. CUDA on WSL2 can be used to run existing GPU-a Nov 19, 2014 · Briefly, CUDA apps fall into 2 categories. com/cuda-downloads) Supported Microsoft Windows ® operating systems: Microsoft Windows 11 21H2. < 10 threads/processes) while the full power of the GPU is unleashed when it can do simple/the same operations on massive numbers of threads/data points (i. CUDA Documentation/Release Notes; MacOS Tools; Training; Archive of Previous CUDA Releases; FAQ; Open Source Packages The version of CUDA Toolkit headers must match the major. Use this command to run the cuda-uninstall script that comes with the runfile installation of the CUDA toolkit. . Oct 20, 2021 · While Option 2 will allow your project to automatically use any new CUDA Toolkit version you may install in the future, selecting the toolkit version explicitly as in Option 1 is often better in practice, because if there are new CUDA configuration options added to the build customization rules accompanying the newer toolkit, you would not see For those GPUs, CUDA 6. Mar 10, 2023 · To use CUDA, you need a compatible NVIDIA GPU and the CUDA Toolkit, which includes the CUDA runtime libraries, development tools, and other resources. The CUDA Toolkit provides everything developers need to get started building GPU accelerated applications - including compiler toolchains, Optimized libraries, and a suite of developer tools. Next, we need to make the . Open a Jupyter Notebook in VS Code and execute the following code: import torch torch. CUDA Python simplifies the CuPy build and allows for a faster and smaller memory footprint when importing the CuPy Python module. The easiest way to install CUDA Toolkit and cuDNN is to use Conda, a package manager for Python. CUDAToolkit_VERSION. Operating System. \nvidia-smi. This package introduces a new CUDA compatibility package on Linux cuda-compat-<toolkit-version>, available on enterprise Tesla systems. This doesn’t apply to every GPU and every CUDA version, and may no longer be valid months or years into the future. By downloading and using the software, you agree to fully comply with the terms and conditions of the CUDA EULA. Compiling CUDA programs. CUDA Features Archive. Jul 4, 2016 · Figure 1: Downloading the CUDA Toolkit from NVIDIA’s official website. run files. EULA. 14. 0 . Best practices for maintaining and updating your CUDA-enabled Docker environment. If you installed Python via Homebrew or the Python website, pip was installed with it. Mar 16, 2012 · As Jared mentions in a comment, from the command line: nvcc --version (or /usr/local/cuda/bin/nvcc --version) gives the CUDA compiler version (which matches the toolkit version). Right at the moment, GTX 1650 is a very new GPU, and so any driver that works with GTX 1650 will work with any currently available CUDA toolkit version. The CUDA Toolkit includes libraries, debugging and optimization tools, a compiler, documentation, and a runtime library to deploy your applications. is_available(): Returns True if CUDA is supported by your system, else False; torch. docker run -it --gpus all nvidia/cuda:11. 0 are compatible with the NVIDIA Ampere GPU architecture as long as they are built to include kernels in native cubin (compute capability 8. Aug 7, 2014 · My goal was to make a CUDA enabled docker image without using nvidia/cuda as base image. This wasn’t the case before and you would still only need to install the NVIDIA driver to run GPU workloads using the PyTorch binaries with the appropriately specified cudatoolkit version. 06) with CUDA 11. If you use the repo, you don't have to worry about blacklisting nouveau, or stopping lightdm, or any of that. bashrc. CUDA provides gridDim. Driver API apps and those that use the runtime API. These dependencies are listed below. 4. 2. Introduction . Sep 27, 2018 · The tight coupling of the CUDA runtime with the NVIDIA display driver requires customers to update the NVIDIA driver in order to use the latest CUDA software, such as compiler, libraries, and tools. Aug 1, 2024 · For the latest compatibility software versions of the OS, CUDA, the CUDA driver, and the NVIDIA hardware, refer to the cuDNN Support Matrix. 0 or later toolkit. Meta-package containing all toolkit packages for CUDA development Aug 29, 2024 · NVIDIA CUDA Compiler Driver NVCC. If you installed Python 3. I have all the drivers (522. Select Linux or Windows operating system and download CUDA Toolkit 11. Aug 29, 2024 · To use CUDA on your system, you will need the following installed: A CUDA-capable GPU. Overview 1. 0 (October 2021), Versioned Online Documentation CUDA Toolkit 11. This answer is for whom use deb files to install cuda. Select Target Platform. Aug 29, 2024 · 1. Use this guide to install CUDA. 04 LTS operating system. The CUDA Toolkit includes the drivers Mar 18, 2019 · CUDA. current_device(): Returns ID of Download CUDA Toolkit 11. Jul 29, 2020 · And since conda cannot use the "CUDA Toolkit", see How to run pytorch with NVIDIA "cuda toolkit" version instead of the official conda "cudatoolkit" version?, using "CUDA Toolkit" is not recommended either, which should mean the same for Tensorflow - and it does, see the last bullet point. For, or by distributing parallel work explicitly as you would in CUDA, you can benefit from the compute horsepower of accelerators without learning all the details of their internal architecture. For with a lambda. CUDA Driver will continue to support running 32-bit application binaries on GeForce GPUs until Ada. This guide covers the basic instructions needed to install CUDA and verify that a CUDA application can run on each supported platform. 0-base-ubuntu20. Use the -G compiler option to add CUDA debug symbols: add_compile_options(-G). You can use following configurations (This worked for me - as of 9/10). Thread Hierarchy . Not all distros are supported on every CUDA toolkit version. cu -o hello You might see following warning when compiling a CUDA program using above command Feb 14, 2023 · Installing CUDA using PyTorch in Conda for Windows can be a bit challenging, but with the right steps, it can be done easily. For more info about which driver to install, see: Getting Started with CUDA on WSL 2; CUDA on Windows Subsystem for Linux 32-bit compilation native and cross-compilation is removed from CUDA 12. Install CUDA Toolkit via APT commands pip. NVIDIA provides a CUDA compiler called nvcc in the CUDA toolkit to compile CUDA code, typically stored in a file with extension . This document describes NVIDIA profiling tools that enable you to understand and optimize the performance of your CUDA, OpenACC or OpenMP applications. The first step in enabling GPU support for llama-cpp-python is to download and install the NVIDIA CUDA Toolkit. 7 -c pytorch -c nvidia Aug 29, 2024 · CUDA Quick Start Guide. 3. Your current driver should allow you to run the PyTorch binary with CUDA 11. The CUDA Toolkit targets a class of applications whose control part runs as a process on a general purpose computing device, and which use one or more NVIDIA GPUs as coprocessors for accelerating single program, multiple data (SPMD) parallel jobs. To uninstall the CUDA Toolkit using Conda, run the following command: conda remove cuda Nov 2, 2022 · I'm trying to use my GPU as compute engine with Pytorch. Q: How do I reinstall CUDA if I uninstall it? A: To reinstall CUDA, follow these steps: 1. 2 (February 2022), Versioned Online Documentation CUDA Toolkit 11. CUDA Toolkit 11. x, gridDim. It is the maximum CUDA version that the active driver in your system supports. minor of CUDA Python. cuda interface to interact with CUDA using Pytorch. CUDAToolkit_VERSION_PATCH Click on the green buttons that describe your target platform. Microsoft Windows 11 22H2-SV2 CUDA Developer Tools is a series of tutorial videos designed to get you started using NVIDIA Nsight™ tools for CUDA development. run Followed by extracting the individual installation scripts into an installers directory: Dec 15, 2021 · The output should match what you saw when using nvidia-smi on your host. Oct 11, 2023 · No, you don’t need to download a full CUDA toolkit and would only need to install a compatible NVIDIA driver, since PyTorch binaries ship with their own CUDA dependencies. Then just download and install the toolkit and skip the driver installation. If a sample has a third-party dependency that is available on the system, but is not installed, the sample will waive itself at build time. 18_linux. Dec 12, 2022 · New nvJitLink library in the CUDA Toolkit for JIT LTO; Library optimizations and performance improvements; Updates to Nsight Compute and Nsight Systems Developer Tools; Updated support for the latest Linux versions; For more information, see CUDA Toolkit 12. version. is_available () Oct 3, 2022 · If you use the $(CUDA_PATH) environment variable to target a version of the CUDA Toolkit for building, and you perform an installation or uninstallation of any version of the CUDA Toolkit, you should validate that the $(CUDA_PATH) environment variable points to the correct installation directory of the CUDA Toolkit for your purposes. 2. 0) or PTX form or both. Go to Settings | Build, Execution, Deployment | Toolchains and provide the path in the Debugger field of the current toolchain. Go to: NVIDIA drivers. 1 and CUDNN 7. CUDA Documentation/Release Notes. The CUDA WSL-Ubuntu local installer does not contain the NVIDIA Linux GPU driver, so by following the steps on the CUDA download page for WSL-Ubuntu, you will be able to get just the CUDA toolkit installed on WSL. 1 (November 2021), Versioned Online Documentation CUDA Toolkit 11. Troubleshooting common issues and ensuring optimal GPU performance. The documentation for nvcc, the CUDA compiler driver. run file executable: $ chmod +x cuda_7. Here is a simple example using Parallel. 0 and later Toolkit. It is permissible to distribute this library with your application under the terms of the End User License Agreement included with the CUDA Toolkit. Runtime API apps, arguably more common, do not require linking against libcuda. CUDA applications built using CUDA Toolkit 11. This just Sep 14, 2022 · To correctly select the CUDA toolkit vesion you need:. Aug 29, 2024 · Release Notes. Mar 9, 2021 · When installing CUDA using the package manager, do not use the cuda, cuda-11-0, or cuda-drivers meta-packages under WSL 2. We’ll use the following functions: Syntax: torch. Jun 3, 2021 · If you want to use some CUDA feature that is not available across all cuda versions, then check the value of the macro CUDA_ARCH in your code and write different code for different CUDA architecture, and change the "Code Generation" settings under CUDA C/C++ --> Device, to the correct one, e. Jul 27, 2024 · Once installed, use torch. Jul 7, 2021 · The CUDA SDK is not installed correctly. x are also not supported. Introduction 1. Sep 2, 2019 · (*) (Note for future readers: this doesn’t necessarily apply to you. "compute_30,sm_30". ) This has many advantages over the pip install tensorflow-gpu method: Aug 29, 2024 · With the CUDA Toolkit, you can develop, optimize, and deploy your applications on GPU-accelerated embedded systems, desktop workstations, enterprise data centers, cloud-based platforms and HPC supercomputers. cuda. Steps to integrate the CUDA Toolkit into a Docker container seamlessly. I have no idea if this works for . The exact version of the CUDA Toolkit found (as reported by nvcc--version, version. The CUDA Toolkit End User License Agreement applies to the NVIDIA CUDA Toolkit, the NVIDIA CUDA Samples, the NVIDIA Display Driver, NVIDIA Nsight tools (Visual Studio Edition), and the associated documentation on CUDA APIs, programming model and development tools. The CUDA version could be different depending on the toolkit versions on your host and in your selected container image. The Rust CUDA Project is a project aimed at making Rust a tier-1 language for extremely fast GPU computing using the CUDA Toolkit. The Release Notes for the CUDA Toolkit. Compiling a CUDA program is similar to C program. Using the CUDA Occupancy Calculator to project GPU multiprocessor occupancy In addition to the calculator spreadsheet, occupancy can be determined using the NVIDIA Nsight Compute Profiler. # is the latest version of CUDA supported by your graphics driver. 2 update 2 or CUDA Toolkit 12. x are compatible with Turing as long as they are built to include kernels in either Volta-native cubin format (see Compatibility between Volta and Turing) or PTX format (see Applications Using CUDA Toolkit 8. May 1, 2020 · If you want to actually compile and build CUDA code, you need to install a separate CUDA toolkit which contains all the the development components which conda deliberately omits from their distribution. Nov 13, 2023 · python -m ipykernel install --user --name=cuda --display-name "cuda-gpt" Here, --name specifies the virtual environment name, and --display-name sets the name you want to display in Jupyter Notebooks. Using parallelization patterns such as Parallel. NVIDIA Software License Agreement and CUDA Supplement to Software License Agreement. It has cuda-python installed along with tensorflow and other packages. To install the CUDA Toolkit on Ubuntu 22. If you don’t have a CUDA-capable GPU, you can access one of the thousands of GPUs available from cloud service providers, including Amazon AWS, Microsoft Azure, and IBM SoftLayer. Aug 29, 2024 · Profiler User’s Guide. g. The repo is kept up to date, but make sure your driver version matches the CUDA toolkit you're using. Check the driver version For Windows in C:\Program Files\NVIDIA Corporation\NVSMI run . CUDAToolkit_VERSION_MINOR. Conda can be used to install both CUDA Toolkit and cuDNN from the Anaconda repository. Jul 1, 2024 · Release Notes. Why CUDA Compatibility The NVIDIA® CUDA® Toolkit enables developers to build NVIDIA GPU accelerated compute applications for desktop computers, enterprise, and data centers to hyperscalers. x. Open a terminal window. Native development using the CUDA Toolkit on x86_32 is unsupported. Some CUDA Samples rely on third-party applications and/or libraries, or features provided by the CUDA Toolkit and Driver, to either build or execute. Windows. To run CUDA Python, you’ll need the CUDA Toolkit installed on a system with CUDA-capable GPUs. x, which contains the number of blocks in the grid, and blockIdx. 0 is available to download. CUDA applications built using CUDA Toolkit 9. Verifying Compatibility: Before running your code, use nvcc --version and nvidia-smi (or similar commands depending on your OS) to confirm your GPU driver and CUDA toolkit versions are compatible with the PyTorch installation. Click on the green buttons that describe your target platform. Here, each of the N threads that execute VecAdd() performs one pair-wise addition. Dec 31, 2023 · Step 1: Download & Install the CUDA Toolkit. Prerequisite: The host machine had nvidia driver, CUDA toolkit, and nvidia-container-toolkit already installed. NVIDIA CUDA Toolkit (available at https://developer. A supported version of Linux with a gcc compiler and toolchain. NVIDIA CUDA Toolkit and compatible CUDA driver is required for CUDALink to work. 000). CUDA Toolkit 12. 1 Update 1 as it’s too old. Note that minor version compatibility will still be maintained. Only supported platforms will be shown. The major version of the CUDA Toolkit. wzd friu fohb eosnxo eoeb nxtaw gskb ygfiq bhwkr ttnrlz