Download nvidia cuda toolkit 12 2 0 for windows 10

Powerful and reliable programming model and computing toolkit Home Developer Tools NVIDIA CUDA Toolkit 12.8.0 (for Windows 11) Old Versions Browse by CompanyAdobe, Apowersoft, Ashampoo, Autodesk, Avast, Corel, Cyberlink, Google, iMyFone, iTop, Movavi, PassFab, Passper, Stardock, Tenorshare, Wargaming, Wondershare Sponsored January, 24th 2025 - 3.2 GB - Freeware Review Change Log Old Versions NVIDIA CUDA Toolkit 12.8.0 (for Windows 11) Date released: 24 Jan 2025 (one month ago) NVIDIA CUDA Toolkit 12.8.0 (for Windows 10) Date released: 24 Jan 2025 (one month ago) NVIDIA CUDA Toolkit 12.6.0 (for Windows 11) Date released: 02 Aug 2024 (8 months ago) NVIDIA CUDA Toolkit 12.5.0 (for Windows 11) Date released: 22 May 2024 (10 months ago) NVIDIA CUDA Toolkit 12.4.0 (for Windows 11) Date released: 06 Mar 2024 (one year ago) NVIDIA CUDA Toolkit 12.3.0 (for Windows 11) Date released: 20 Oct 2023 (one year ago) NVIDIA CUDA Toolkit 12.2.0 (for Windows 11) Date released: 29 Jun 2023 (one year ago) NVIDIA CUDA Toolkit 12.0.1 (for Windows 11) Date released: 01 Mar 2023 (2 years ago) NVIDIA CUDA Toolkit 12.0.0 (for Windows 11) Date released: 09 Dec 2022 (2 years ago) NVIDIA CUDA Toolkit 11.8.0 (for Windows 11) Date released: 04 Oct 2022 (2 years ago) NVIDIA CUDA Toolkit 11.7.0 (for Windows 11) Date released: 12 May 2022 (3 years ago) NVIDIA CUDA Toolkit 11.6.1 (for Windows 11) Date released: 11 Mar 2022 (3 years ago) NVIDIA CUDA Toolkit 11.6.0 (for Windows 11) Date released: 13 Jan 2022 (3 years ago) NVIDIA CUDA Toolkit 12.2.0 (for Windows 10) Date released: 29 Jun 2023 (one year ago) NVIDIA CUDA Toolkit 12.0.1 (for Windows 10) Date released: 01 Mar 2023 (2 years ago) NVIDIA CUDA Toolkit 12.0.0 (for Windows 10) Date released: 09 Dec 2022 (2 years ago) NVIDIA CUDA Toolkit 11.8.0 (for Windows 10)

]cuda-runtime-11-7 11.7.1-1 [NVIDIA CUDA/ ]cuda-samples-10-0 10.0.130-1 [NVIDIA CUDA/ ]cuda-samples-10-1 10.1.243-1 [NVIDIA CUDA/ ]cuda-samples-10-2 10.2.89-1 [NVIDIA CUDA/ ]cuda-samples-11-0 11.0.221-1 [NVIDIA CUDA/ ]cuda-samples-11-1 11.1.105-1 [NVIDIA CUDA/ ]cuda-samples-11-2 11.2.152-1 [NVIDIA CUDA/ ]cuda-samples-11-3 11.3.58-1 [NVIDIA CUDA/ ]cuda-samples-11-4 11.4.120-1 [NVIDIA CUDA/ ]cuda-samples-11-5 11.5.56-1 [NVIDIA CUDA/ ]cuda-samples-11-6 11.6.101-1 [NVIDIA CUDA/ ]cuda-sanitizer-11-0 11.0.221-1 [NVIDIA CUDA/ ]cuda-sanitizer-11-1 11.1.105-1 [NVIDIA CUDA/ ]cuda-sanitizer-11-2 11.2.152-1 [NVIDIA CUDA/ ]cuda-sanitizer-11-3 11.3.111-1 [NVIDIA CUDA/ ]cuda-sanitizer-11-4 11.4.120-1 [NVIDIA CUDA/ ]cuda-sanitizer-11-5 11.5.114-1 [NVIDIA CUDA/ ]cuda-sanitizer-11-6 11.6.124-1 [NVIDIA CUDA/ ]cuda-sanitizer-11-7 11.7.91-1 [NVIDIA CUDA/ ]cuda-sanitizer-api-10-1 10.1.243-1 [NVIDIA CUDA/ ]cuda-sanitizer-api-10-2 10.2.89-1 [NVIDIA CUDA/ ]cuda-thrust-11-3 11.3.109-1 [NVIDIA CUDA/ ]cuda-toolkit-10-0 10.0.130-1 [NVIDIA CUDA/ ]cuda-toolkit-10-1 10.1.243-1 [NVIDIA CUDA/ ]cuda-toolkit-10-2 10.2.89-1 [NVIDIA CUDA/ ]cuda-toolkit-11-0 11.0.3-1 [NVIDIA CUDA/ ]cuda-toolkit-11-1 11.1.1-1 [NVIDIA CUDA/ ]cuda-toolkit-11-2 11.2.2-1 [NVIDIA CUDA/ ]cuda-toolkit-11-3 11.3.1-1 [NVIDIA CUDA/ ]cuda-toolkit-11-3-config-common 11.3.109-1 [NVIDIA CUDA/ ]cuda-toolkit-11-4 11.4.4-1 [NVIDIA CUDA/ ]cuda-toolkit-11-4-config-common 11.4.148-1 [NVIDIA CUDA/ ]cuda-toolkit-11-5 11.5.2-1 [NVIDIA CUDA/ ]cuda-toolkit-11-5-config-common 11.5.117-1 [NVIDIA CUDA/ ]cuda-toolkit-11-6 11.6.2-1 [NVIDIA CUDA/ ]cuda-toolkit-11-6-config-common 11.6.55-1 [NVIDIA CUDA/ ]cuda-toolkit-11-7 11.7.1-1 [NVIDIA CUDA/ ]cuda-toolkit-11-7-config-common 11.7.99-1 [NVIDIA CUDA/ ]cuda-toolkit-11-config-common 11.7.99-1 [NVIDIA CUDA/ ]cuda-toolkit-config-common 11.7.99-1 [NVIDIA CUDA/ ]cuda-tools-10-0 10.0.130-1 [NVIDIA CUDA/ ]cuda-tools-10-1 10.1.243-1 [NVIDIA CUDA/ ]cuda-tools-10-2 10.2.89-1 [NVIDIA CUDA/ ]cuda-tools-11-0 11.0.3-1 [NVIDIA CUDA/ ]cuda-tools-11-1 11.1.1-1 [NVIDIA CUDA/ ]cuda-tools-11-2 11.2.2-1 [NVIDIA CUDA/ ]cuda-tools-11-3 11.3.1-1 [NVIDIA CUDA/ ]cuda-tools-11-4 11.4.4-1 [NVIDIA CUDA/ ]cuda-tools-11-5 11.5.2-1 [NVIDIA CUDA/ ]cuda-tools-11-6 11.6.2-1 [NVIDIA CUDA/ ]cuda-tools-11-7 11.7.1-1 [NVIDIA CUDA/ ]cuda-visual-tools-10-0 10.0.130-1 [NVIDIA CUDA/ ]cuda-visual-tools-10-1 10.1.243-1 [NVIDIA CUDA/ ]cuda-visual-tools-10-2 10.2.89-1 [NVIDIA CUDA/ ]cuda-visual-tools-11-0 11.0.3-1 [NVIDIA CUDA/ ]cuda-visual-tools-11-1 11.1.1-1 [NVIDIA CUDA/ ]cuda-visual-tools-11-2 11.2.2-1 [NVIDIA CUDA/ ]cuda-visual-tools-11-3 11.3.1-1 [NVIDIA CUDA/ ]cuda-visual-tools-11-4 11.4.4-1 [NVIDIA CUDA/ ]cuda-visual-tools-11-5 11.5.2-1 [NVIDIA CUDA/ ]cuda-visual-tools-11-6 11.6.2-1 [NVIDIA CUDA/ ]cuda-visual-tools-11-7 11.7.1-1 [NVIDIA CUDA/ ]graphsurgeon-tf 8.4.3-1+cuda11.6 [NVIDIA CUDA/ ]libcuda1-340 340.108-0ubuntu8 [Ubuntu/jammy multiverse]libcuda1-384 418.226.00-0ubuntu1 [NVIDIA CUDA/ ]libcudart11.0 11.5.117~11.5.1-1ubuntu1 [Ubuntu/jammy multiverse]libcudnn8 8.5.0.96-1+cuda11.7 [NVIDIA CUDA/ ]libcudnn8-dev 8.5.0.96-1+cuda11.7 [NVIDIA CUDA/ ]libnccl-dev 2.14.3-1+cuda11.7 [NVIDIA CUDA/ ]libnccl2 2.14.3-1+cuda11.7 [NVIDIA CUDA/ ]libnvinfer-bin 8.4.3-1+cuda11.6 [NVIDIA CUDA/ ]libnvinfer-dev 8.4.3-1+cuda11.6 [NVIDIA CUDA/ ]libnvinfer-plugin-dev 8.4.3-1+cuda11.6 [NVIDIA CUDA/ ]libnvinfer-plugin8 8.4.3-1+cuda11.6 [NVIDIA CUDA/ ]libnvinfer-samples 8.4.3-1+cuda11.6 [NVIDIA CUDA/ ]libnvinfer8 8.4.3-1+cuda11.6 [NVIDIA CUDA/ ]libnvonnxparsers-dev 8.4.3-1+cuda11.6 [NVIDIA CUDA/ ]libnvonnxparsers8 8.4.3-1+cuda11.6 [NVIDIA CUDA/ ]libnvparsers-dev 8.4.3-1+cuda11.6 [NVIDIA CUDA/ ]libnvparsers8 8.4.3-1+cuda11.6 [NVIDIA CUDA/ ]nvidia-cuda-dev 11.5.1-1ubuntu1 [Ubuntu/jammy multiverse]nvidia-cuda-gdb 11.5.114~11.5.1-1ubuntu1 [Ubuntu/jammy multiverse]nvidia-cuda-toolkit 11.5.1-1ubuntu1 [Ubuntu/jammy multiverse]nvidia-cuda-toolkit-doc 11.5.1-1ubuntu1 [Ubuntu/jammy multiverse]nvidia-cuda-toolkit-gcc 11.5.1-1ubuntu1 [Ubuntu/jammy multiverse]nvidia-cudnn 8.2.4.15~cuda11.4 [Ubuntu/jammy multiverse]onnx-graphsurgeon 8.4.3-1+cuda11.6 [NVIDIA CUDA/ ]python-pycuda-doc 2021.1~dfsg-2build2 [Ubuntu/jammy multiverse]python3-libnvinfer 8.4.3-1+cuda11.6 [NVIDIA CUDA/ ]python3-libnvinfer-dev 8.4.3-1+cuda11.6 [NVIDIA CUDA/ ]python3-pycuda 2021.1~dfsg-2build2 [Ubuntu/jammy multiverse]python3-pyhst2-cuda 2020c-5build1 [Ubuntu/jammy multiverse]relion-cuda 3.1.0-2 [Ubuntu/jammy multiverse]relion-gui-cuda 3.1.0-2 [Ubuntu/jammy multiverse]tensorrt 8.4.3.1-1+cuda11.6 [NVIDIA CUDA/ ]tensorrt-dev 8.4.3.1-1+cuda11.6 [NVIDIA CUDA/ ]tensorrt-libs 8.4.3.1-1+cuda11.6 [NVIDIA CUDA/ ]uff-converter-tf 8.4.3-1+cuda11.6 [NVIDIA CUDA/ ]Seems versions match up, 11.7Would love some help! Did you got any help , I am facing the same issue .

C:\ drive unless made visible by you through folder options and show hidden files/folders (you can also see the folder in a command console). That is an important note because the CUDA SDK downloads all sample programs in that folder. Cuda 8 also install the GeForce driver version 369.30, which is not the latest version!The latest version is 375.95, so to download that driver, you need to get it from Note that if you are happy with the resolution of your computer, you may want to download the driver package, but not upgrade your current display driver (I upgraded mine, which demoted the factory resolution on my ASUS ROG, bad for gaming, but good for readability and Machine Learning).Now, let's do some testing: Open C:\ProgramData\NVIDIA Corporation\CUDA Samples\v8.0\0_Simple\matrixMul_vs2015.sln in Visual Studio 2015. Compile in debug mode, go to a command line at C:\ProgramData\NVIDIA Corporation\CUDA Samples\v8.0\bin\win64\Debug and run matrixMul.exeYou should pass the test.Now, note that cuDNN has specific installation instructions per platform. For Windows, it says you need to add the cuDNN install path to your PATH envionment variable, and various other mods to your Visual Studio projects for Include and Library folders. Make a note of these ( Since we're focusing on Theano, it is simpler to actually take the cuDNN binaries and copy them over to the CUDA SDK folders: Copy cudnn64_5.dll to C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v8.0\bin Copy cudnn.h to C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v8.0\include Copy cudnn.lib to C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v8.0\lib\x64Next, we need to install the Windows 10 SDK from There must be a reason why that download does not ship with Windows by default nor installs with Visual Studio. Maybe someone can tell me.Next we install the Microsoft Visual C++ Compiler for Python 2.7. Yup, 2.7, even though we are going to use Python 3.4 on Theano. That is because they are used in different layers in the Theano-to-GPU toolchain. Download from now now we are finally ready to modify the Nvidia CUDA profile at C:\Program Files\NVIDIA GPU Computing Toolkit\v8.0\bin\nvcc.profile. This is the new content, specialized for Windows 10, CUDA 8, and Visual Studio 2015:TOP = $(_HERE_)/..NVVMIR_LIBRARY_DIR = $(TOP)/nvvm/libdevicePATH += $(TOP)/open64/bin;$(TOP)/nvvm/bin;$(_HERE_);$(TOP)/lib;INCLUDES += "-I$(TOP)/include" "-I$(TOP)/include/cudart" "-IC:/Program Files (x86)/Microsoft Visual Studio 12.0/VC/include" "-IC:\Program Files (x86)\Microsoft SDKs\Windows\v7.1A\Include" $(_SPACE_)LIBRARIES =+ $(_SPACE_) "/LIBPATH:$(TOP)/lib/$(_WIN_PLATFORM_)" "/LIBPATH:C:/Program Files/NVIDIA GPU Computing Toolkit/CUDA/v8.0/lib/x64" "/LIBPATH:C:/Program Files (x86)/Common Files/Microsoft/Visual C++ for Python/9.0/VC/lib/amd64" "/LIBPATH:C:\Program Files (x86)\Microsoft SDKs\Windows\v7.1A\Lib\x64"CUDAFE_FLAGS +=PTXAS_FLAGS += And with that, we should be done with Visual Studio, CUDA, cuDNN, and GPU setup (we should, but we'll find out soon enough not..). Onto Theano for now.Setting up TheanoTheano is one of the great Machine Learning frameworks, together with Facebooks' Torch, Google's TensorFlow, U Berkeley's Caffe, and Microsoft's CNTK. Keras is an awesome deep learning framework, too, but it's more of a wrapper over Theano, simplifying Theano neural network programming for us. Theano is brought to us by Yoshua Bengio and his ML group at Universite de Montreal ( Why Canada? Because their equivalent of our National Science Foundation was more forward thinking than our NSF as it extended

October 30, 2010, 9:48am 1 Is it really in the spirit of openness to make CUDA available to all but demand a complex and multiple-week registration process for openCL? derek_c October 30, 2010, 9:48am 2 Is it really in the spirit of openness to make CUDA available to all but demand a complex and multiple-week registration process for openCL? OpenCL is an interface + an implementation. For the interface, there is no code to “open” really…On the other hand, CUDA’s compiler is Open64 which is already an open-source project…So… I really cannot understand your question, sorry. OpenCL is an interface + an implementation. For the interface, there is no code to “open” really…On the other hand, CUDA’s compiler is Open64 which is already an open-source project…So… I really cannot understand your question, sorry. derek_c November 10, 2010, 12:19am 5 The Nvidia implementation of OpenCL is only available to registered developers, and the registration process is horrible! There are dozens of companies with developer programmes that require minimal registration and grant immediate access to materials. They don’t say on the forms that approval might take weeks… derek_c November 10, 2010, 12:19am 6 The Nvidia implementation of OpenCL is only available to registered developers, and the registration process is horrible! There are dozens of companies with developer programmes that require minimal registration and grant immediate access to materials. They don’t say on the forms that approval might take weeks… What are you asking for exactly? If it’s just access to the SDK you want, Nvidia’s OpenCL 1.0 implementation is included in the CUDA toolkit, although I have to admit it wasn’t obvious where to find it going from their OpenCL page alone. All the references on the pages linked from there claim the SDK is available in the OpenCL toolkit, which doesn’t actually exist as far as I can tell. What are you asking for exactly? If it’s just access to the SDK you want, Nvidia’s OpenCL 1.0 implementation is included in the CUDA toolkit, although I have to admit it wasn’t obvious where to find it going from their OpenCL page alone. All the references on the pages linked from there claim the SDK is available in the OpenCL toolkit, which doesn’t actually exist as far as I can tell. avidday November 10, 2010, 4:50am 9 Err, no it isn’t. The current CUDA toolkit contains the complete OpenCL “toolchain” (such as it is, OpenCL is JIT compiled), code exmaples, and the OpenCL runtime ships with all modern driver releases on both Windows and Linux. Everything you need is available for download here without any form of registration or access limitations. avidday November 10, 2010, 4:50am 10 Err, no it isn’t. The

Skip to content Navigation Menu GitHub Copilot Write better code with AI Security Find and fix vulnerabilities Actions Automate any workflow Codespaces Instant dev environments Issues Plan and track work Code Review Manage code changes Discussions Collaborate outside of code Code Search Find more, search less Explore Learning Pathways Events & Webinars Ebooks & Whitepapers Customer Stories Partners Executive Insights GitHub Sponsors Fund open source developers The ReadME Project GitHub community articles Enterprise platform AI-powered developer platform Pricing Provide feedback Saved searches Use saved searches to filter your results more quickly //voltron/issues_fragments/issue_layout;ref_cta:Sign up;ref_loc:header logged out"}"> Sign up Notifications You must be signed in to change notification settings Fork 4k Star 38.7k DescriptionDescriptionI am trying to use whisper-cli.exe with CUDA support on Windows to leverage my NVIDIA GeForce RTX 2050 GPU, but the application is not utilizing CUDA (showing CUBLAS = 0 in system_info). Despite following the instructions in the README and making the suggested changes, the program continues to run on the CPU instead of the GPU.Steps to ReproduceInstalled CUDA Toolkit 12.8 from and NVIDIA driver version 571.96 (confirmed via nvidia-smi).Cloned the whisper.cpp repository (version 1.7.4) and navigated to the directory:cd C:\Users\carwy\Downloads\Compressed\whisper.cpp-1.7.4\whisper.cpp-1.7.4Built the project with CUDA support:cmake -B build -DGGML_CUDA=1 -DCMAKE_BUILD_TYPE=Release -DCMAKE_CUDA_ARCHITECTURES=native -DCUDAToolkit_ROOT="C:/Program Files/NVIDIA GPU Computing Toolkit/CUDA/v12.8"cmake --build build --config Release --verboseEnsured all necessary DLLs (ggml-cuda.dll, cudart64_12.dll, cublas64_12.dll) are in build\bin\Release.Modified src/CMakeLists.txt to include:if (GGML_CUDA)target_link_libraries(whisper PRIVATE ggml-cuda)endif()Ran the following command:.\whisper-cli.exe -m "C:\Users\carwy\Desktop\SubtitleApp\SubtitleApp Backupw\SubtitleApp\CoreAssets\Window\ggml-large-v3-turbo-q8_0.bin" -f "C:\Users\carwy\Downloads\Thoi gian troi qua that nhanh.wav"Expected Behaviorsystem_info in the output should show CUBLAS = 1, indicating CUDA is being used.GPU usage should increase (visible in nvidia-smi), and processing times (whisper_print_timings) should be significantly faster on the NVIDIA GeForce RTX 2050.Actual Behaviorsystem_info shows CUBLAS = 0:system_info: n_threads = 4 / 12 | AVX = 1 | AVX2 = 1 | AVX512 = 0 | FMA = 1 | NEON = 0 | ARM_FMA = 0 | F16C = 1 | FP16_VA = 0 | WASM_SIMD = 0 | SSE3 = 1 | SSSE3 = 1 | VSX = 0 | COREML = 0 | OPENVINO = 0 |nvidia-smi shows 0% GPU utilization and 0MiB memory usage during