CUDA 7.0 Downloads

Please Note: There is a recommended patch for CUDA 7.0 which resolves an issue in the cuFFT library that can lead to incorrect results for certain inputs sizes less than or equal to 1920 in any dimension when cufftSetStream() is passed a non-blocking stream (e.g., one created using the cudaStreamNonBlocking flag of the CUDA Runtime API or the CU_STREAM_NON_BLOCKING flag of the CUDA Driver API).

Q: Where is the notebook installer?
A: Previous releases of the CUDA Toolkit had separate installation packages for notebook and desktop systems. Beginning with CUDA 7.0, these packages have been merged into a single package that is capable of installing on all supported platforms.

Q: What is the difference between the Network Installer and the Local Installer?
A: The Local Installer has all of the components embedded into it (toolkit, driver, samples). This makes the installer very large, but once downloaded, it can be installed without an internet connection. The Network Installer is a small executable that will only download the necessary components dynamically during the installation so an internet connection is required.

Q: Where do I get the GPU Deployment Kit (GDK) for Windows?
A: The installers give you an option to install the GDK. If you only want to install the GDK, then you should use the network installer, for efficiency.

Q: Where can I find old versions of the CUDA Toolkit?
A: Older versions of the toolkit can be found on the Legacy CUDA Toolkits page.

Q: Is cuDNN included as part of the CUDA Toolkit?
A: cuDNN is our library for Deep Learning frameworks, and can be downloaded separately from the cuDNN home page.

* Includes POWER8 cross-compilation tools.

Q: Where can I find the CUDA 7 Toolkit for my Jetson TK1?
A: Jetson TK1 is not supported by the CUDA 7 Toolkit. Please download the CUDA 6.5 Toolkit for Jetson TK1 instead.

Q: What is the difference between the Network Installer and the Local Installer?
A: The Local Installer has all of the components embedded into it (toolkit, driver, samples). This makes the installer very large, but once downloaded, it can be installed without an internal internet connection. The Network Installer is a small executable that will only download the necessary components dynamically during the installation so an internet connection is required to use this installer.

Q: Is cuDNN included as part of the CUDA Toolkit?
A: cuDNN is our library for Deep Learning frameworks, and can be downloaded separately from the cuDNN home page.

Q: What is the difference between the Network Installer and the Local Installer?
A: The Local Installer has all of the components embedded into it (toolkit, driver, samples). This makes the installer very large, but once downloaded, it can be installed without an internal internet connection. The Network Installer is a small executable that will only download the necessary components dynamically during the installation so an internet connection is required to use this installer.

Q: Is cuSOLVER available for the POWER8 architecture?
A: The initial release of the CUDA 7.0 toolkit omitted the cuSOLVER library from the installer. On May 29, 2015, new CUDA 7.0 installers were posted for the POWER8 architecture that included the cuSOLVER library. If you downloaded the CUDA 7.0 toolkit for POWER8 on or earlier than this date, and you need to use cuSOLVER, you will need to download the latest installer and re-install.

Q: What is the difference between the Network Installer and the Local Installer?
A: The Local Installer has all of the components embedded into it (toolkit, driver, samples). This makes the installer very large, but once downloaded, it can be installed without an internal connection. The Network Installer is a small executable that will only download the necessary components dynamically during the installation so an internet connection is required to use this installer.

Q: Is cuDNN included as part of the CUDA Toolkit?
A: cuDNN is our library for Deep Learning frameworks, and can be downloaded separately from the cuDNN home page.

Q: What do I do if the Network Installer fails to run with the error message «The package is damaged and can’t be opened. You should eject the disk image»?
A: Check that your security preferences are set to allow apps downloaded from anywhere to run. This setting can be found under: System Preferences > Security & Privacy > General

CUDA Toolkit 3.1 Downloads

CUDA Toolkit 3.1

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

• 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 at www.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

Windows XP, Windows VISTA, Windows 7

• C/C++ compiler
• CUDA Visual Profiler
• OpenCL Visual Profiler
• GPU-accelerated BLAS library
• GPU-accelerated FFT library
• Additional tools and documentation

*New* Updated versions of the CUDA C Programming Guide (Version 3.1.1) and the Fermi Tuning Guide (Version 1.2) are available via the links to the right.

Linux

• C/C++ compiler
• cuda-gdb debugger
• CUDA Visual Profiler
• OpenCL Visual Profiler
• GPU-accelerated BLAS library
• GPU-accelerated FFT library
• Additional tools and documentation

*New* Updated versions of the CUDA C Programming Guide (Version 3.1.1) and the Fermi Tuning Guide (Version 1.2) are available via the links to the right.

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