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For the latest releases see the CUDA Toolkit and GPU Computing SDK home page

CUDA Toolkit 3.1 Download Quick Links Windows Linux MacOS For the latest releases see the CUDA Toolkit and GPU Computing SDK home page For older releases, see the CUDA Toolkit Release Archive Release Highlights GPUDirect(tm) gives 3rd party devices direct access to CUDA Memory Support for 16-way concurrency allows up to 16 different kernels to run at the same time on Fermi. CUDA Application Support: - In order to run macOS Applications that leverage the CUDA architecture of certain NVIDIA graphics cards, users will need to download and install the NVIDIA CUDA 10.1 Driver 418.163 for macOS. Installation Note.

For older releases, see theCUDA Toolkit Release Archive

Release Highlights

• GPUDirect(tm) gives 3rd party devices direct access to CUDA Memory
• Support for 16-way concurrency allows up to 16 different kernels to run at the same time on Fermi architecture GPUs
• Runtime / Driver interoperability enables applications to mix-n-match use of the CUDA Driver API with CUDA C Runtim and math libraries via buffer sharing and context migration
• New language features added to CUDA C / C++ include:
  • Support for printf() in device code
  • Support for function pointers and recursion make it easier to port many existing algorithms to Fermi GPUs
• Unified Visual Profiler now supports both CUDA C/C++ and OpenCL, and now includes support for CUDA Driver API tracing
• Math Libraries Performance Improvements, including:
  • Improved performance of selected transcendental functions from the log, pow, erf, and gamma families
  • Significant improvements in double-precision FFT performance on Fermi-architecture GPUs for 2^n transform sizes
  • Streaming API now supported in CUBLAS for overlapping copy and compute operations
  • CUFFT Real-to-complex (R2C) and complex-to-real (C2R) optimizations for 2^n data sizes
  • Improved performance for GEMV and SYMV subroutines in CUBLAS
  • Optimized double-precision implementations of divide and reciprocal routines for the Fermi architecture
• New and updated SDK code samples demonstrating how to use:
  • Function pointers in CUDA C/C++ kernels
  • OpenCL / Direct3D buffer sharing
  • Hidden Markov Model in OpenCL
  • Microsoft Excel GPGPU example showing how to run an Excel function on the GPU

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

Download Quick Links [ Windows ] [ Linux ] [ MacOS ]

Windows XP, Windows VISTA, Windows 7

Linux

MacOS

Download Quick Links [ Windows ] [ Linux ] [ MacOS ]

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

For older releases, see theCUDA Toolkit Release Archive

Nvidia Cuda Download

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

Download Quick Links [ Windows ] [ Linux ] [ MacOS ]

Windows XP, Windows VISTA, Windows 7

Linux

Nvidia Cuda Driver Download Mac Installer

MacOS

Nvidia Cuda Driver Download Windows 10