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By Tasnia Salinas, on January 31, 2020, in Mac Uninstaller

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Summary: In this article, we help you to learn How To Completely Uninstall Nvidia CUDA driver on Mac by using our best Nvidia CUDA Uninstaller software - Omni Remover. Make sure you have downloaded the latest version here before continuing.

What Is Nvidia CUDA Software? CUDA is a parallel computing platform and application programming interface (API) model created by Nvidia. Nvidia CUDA allows software developers and software engineers to use a CUDA-enabled graphics processing unit (GPU) for general purpose processing. The CUDA platform is a software layer that gives direct access to the GPU's virtual instruction set and parallel computational elements, for the execution of compute kernels (How To Uninstall VMware Fusion 11 on Mac).

Why uninstalling Nvidia CUDA Music Making Software on Mac? Users using Macs with NVIDIA graphics cards recently are reporting that after upgrading from macOS 10.13 to 10.14 (Mojave) they are experiencing rendering regressions and slow performance. Apple has officially announced the incompatibility with NVIDIA CUDA in macOS 10.14 (Mojave) so if you have upgraded your macOS, then it's suggested to remove NVIDIA CUDA driver before it gets fully supported (How To Uninstall VirtualBox on Mac).

Table of Content:

First up, Nvidia CUDA is a driver instead of dependent application program on macOS. To remove Nvidia CUDA on Mac, you cannot just drag it to Trash since it will not appear in Applications foler. Follow steps below to learn how to easily remove Nvidia CUDA driver on Mac manually (How To Uninstall Google Play Music on Mac OS).

• Step 1: Quit Nvidia CUDA > Open System Preferences > CUDA > Nvidia CUDA > Right click on it and select Remove 'CUDA' Preference Pane:
• Step 2: Move forward to remove Nvidia CUDA related leftover junk. Open Finder > Go > Go To Folder > Enter ~/Library/Application/Support/Nvidia CUDA/
• Step 3: Locate all the orphaned Nvidia CUDA files by digging into each folder. Most of them are stored in: Application Support | Caches | Containers | Frameworks | Logs | Preferences | Saved | Application State | WebKit

~/Library/Application Support/

~/Library/Caches/

~/Library/LaunchAgents/

~/Library/LaunchDaemons/

• Step 4: Delete the files associated with Nvidia CUDA to Trash > Empty Trash and restart your Mac to finish the removal of Nvidia CUDA Driver on your Mac.

If you feel bothered to visit macOS file system, get a try with Omni Remover - an expert Nvidia CUDA Uninstaller software that can help uninstall Nvidia CUDA and other stubborn applications on your Mac. Here are the detailed steps about how to uninstall Nvidia CUDA Client with Omni Remover. Free Download

1. Step 1:

   Click here to download Omni Remover > Install and Open Omni Remover on your Mac.

2. Step 2:

   In Omni Remover > App Uninstall > Choose Nvidia CUDA Driver Client icon > Clean Uninstall.

3. Step 3:

   Then Omni Remover will start scanning all Nvidia CUDA Driver data/files > After it finished scanning, click Clean Uninstall to start removing Nvidia CUDA Driver from your Mac.

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A more recent release is available see the CUDA Toolkit and GPU Computing SDK home page

For older releases, see theCUDA Toolkit Release Archive

Release Highlights

• Support for the new Fermi architecture, with:
  • Native 64-bit GPU support
  • Multiple Copy Engine support
  • ECC reporting
  • Concurrent Kernel Execution
  • Fermi HW debugging support in cuda-gdb
  • Fermi HW profiling support for CUDA C and OpenCL in Visual Profiler
• C++ Class Inheritance and Template Inheritance support for increased programmer productivity
• A new unified interoperability API for Direct3D and OpenGL, with support for:
  • OpenGL texture interop
  • Direct3D 11 interop support
• CUDA Driver / Runtime Buffer Interoperability, which allows applications using the CUDA Driver API to also use libraries implemented using the CUDA C Runtime such as CUFFT and CUBLAS.
• CUBLAS now supports all BLAS1, 2, and 3 routines including those for single and double precision complex numbers
• Up to 100x performance improvement while debugging applications with cuda-gdb
• cuda-gdb hardware debugging support for applications that use the CUDA Driver API
• cuda-gdb support for JIT-compiled kernels
• New CUDA Memory Checker reports misalignment and out of bounds errors, available as a stand-alone utility and debugging mode within cuda-gdb
• CUDA Toolkit libraries are now versioned, enabling applications to require a specific version, support multiple versions explicitly, etc.
• CUDA C/C++ kernels are now compiled to standard ELF format
• Support for device emulation mode has been packaged in a separate version of the CUDA C Runtime (CUDART), and is deprecated in this release. Now that more sophisticated hardware debugging tools are available and more are on the way, NVIDIA will be focusing on supporting these tools instead of the legacy device emulation functionality.
  • On Windows, use the new Parallel Nsight development environment for Visual Studio, with integrated GPU debugging and profiling tools (was code-named 'Nexus'). Please seewww.nvidia.com/nsightfor details.
  • On Linux, use cuda-gdb and cuda-memcheck, and check out the solutions from Allinea and TotalView that will be available soon.
• Support for all the OpenCL features in the latest R195 production driver package:
  • Double Precision
  • Graphics Interoperability with OpenCL, Direc3D9, Direct3D10, and Direct3D11 for high performance visualization
  • Query for Compute Capability, so you can target optimizations for GPU architectures (cl_nv_device_attribute_query)
  • Ability to control compiler optimization settings via support for pragma unroll in OpenCL kernels and an extension that allows programmers to set compiler flags. (cl_nv_compiler_options)
  • OpenCL Images support, for better/faster image filtering
  • 32-bit global and local atomics for fast, convenient data manipulation
  • Byte Addressable Stores, for faster video/image processing and compression algorithms
  • Support for the latest OpenCL spec revision 1.0.48 and latest official Khronos OpenCL headers as of 2010-02-17

Note: The developer driver packages below provide baseline support for the widest number of NVIDIA products in the smallest number of installers. More recent production driver packages for developers and end users may be available atwww.nvidia.com/drivers.

For additional tools and solutions for Windows, Linux and MAC OS , such as CUDA Fortran, CULA, CUDA-dgb , please visit our Tools and Ecosystem Page

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