Pro Tools 8 System Requirements Windows

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This page contains the minimum system requirements for the Visual Studio 2022 family of products. For information on compatibility, see Visual Studio 2022 Platform Targeting and Compatibility. If you need help with improving performance, see Visual Studio performance tips and tricks.

Click a button to download the latest version of Visual Studio 2022. For instructions on installing and updating Visual Studio 2022, see Update Visual Studio 2022 to the most recent release. Also, see instructions on how to install offline.

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Visual Studio is available in English, Chinese (Simplified), Chinese (Traditional), Czech, French, German, Italian, Japanese, Korean, Polish, Portuguese (Brazil), Russian, Spanish, and Turkish. You can select the language of Visual Studio during installation. The Visual Studio Installer is available in the same fourteen languages, and will match the language of Windows, if available.

Note Box Tools v4 is only supported on the configurations listed below. Older operating systems or browsers may not function with Box Tools v4, and when you log in and try to use Box Edit or pass Device Trust, you may be prompted to update to a supported configuration.

No browser extensions are required for Box Tools v4 to work in the supported versions of Firefox and Chrome, as these browsers should be able to communicate with 127.0.0.1 over HTTP. Please see the network requirements above for more details.

The Windows SDK (10.0.26100) for Windows 11 provides the latest headers, libraries, metadata, and tools for building Windows applications. Use this SDK to build Universal Windows Platform (UWP) and Win32 applications for Windows 11, version 24H2 preview and previous Windows releases.

Windows App SDK

The Windows App SDK provides a unified set of APIs and tools that are decoupled from the OS and released to developers via NuGet packages. These APIs and tools can be used in a consistent way by any desktop app on Windows 11 and downlevel to Windows 10, version 1809.

Installation on Windows 8.1 and earlier operating systems requires an Update for Universal C Runtime in Windows. To install through Windows Update, make sure you install the latest recommended updates and patches from Microsoft Update before you install the Windows SDK.

Windows app samples are now available through GitHub. You can browse the code on GitHub, clone a personal copy of the repository from Git, or download a zipped archive of all the samples. We welcome feedback, so feel free to open an issue within the repository if you have a problem or question. These samples are designed to run on desktop, mobile, and future devices that support the Universal Windows Platform (UWP).

When you use new APIs, consider writing your app to be adaptive so that it runs correctly on the widest array of Windows devices. An adaptive app "lights up" with new features wherever the devices and Windows version supports them, but otherwise offers only the functionality available on the detected platform version. For implementation details, see the Version adaptive code article.

The Windows 10 WinRT API Pack lets you add the latest Windows Runtime APIs support to your .NET Framework 4.5+ and .NET Core 3.0+ libraries and apps. To access the Windows 10 WinRT API Pack, see the Microsoft.Windows.SDK.Contracts nuget package.

The printf family of functions now conforms with the IEEE 754 rounding rules when printing exactly representable floating-point numbers and will honor the rounding mode requested via calls to fesetround. Legacy behavior is available when linking with legacy_stdio_float_rounding.obj.

Windows App Certification Kit. Several new APIs were added to the Supported APIs list in the App Certification Kit and Windows Store. If there are APIs in the supported list that appear greyed out or disabled in Visual Studio, you can make a small change to your source file, to access them. For more details, see this known issue. Find more updates to tests.

Signing your apps. Device Guard signing is a Device Guard feature that is available in Microsoft Store for Business and Education, which allows enterprises to guarantee every app comes from a trusted source. See the documentation about Device Guard Signing.

Provide a small set of extensions to standard programming languages, like C, that enable a straightforward implementation of parallel algorithms. With CUDA C/C++, programmers can focus on the task of parallelization of the algorithms rather than spending time on their implementation.

Support heterogeneous computation where applications use both the CPU and GPU. Serial portions of applications are run on the CPU, and parallel portions are offloaded to the GPU. As such, CUDA can be incrementally applied to existing applications. The CPU and GPU are treated as separate devices that have their own memory spaces. This configuration also allows simultaneous computation on the CPU and GPU without contention for memory resources.

CUDA-capable GPUs have hundreds of cores that can collectively run thousands of computing threads. These cores have shared resources including a register file and a shared memory. The on-chip shared memory allows parallel tasks running on these cores to share data without sending it over the system memory bus.

32-bit compilation native and cross-compilation is removed from CUDA 12.0 and later Toolkit. Use the CUDA Toolkit from earlier releases for 32-bit compilation. CUDA Driver will continue to support running 32-bit application binaries on GeForce GPUs until Ada. Ada will be the last architecture with driver support for 32-bit applications. Hopper does not support 32-bit applications.

This document is intended for readers familiar with Microsoft Windows operating systems and the Microsoft Visual Studio environment. You do not need previous experience with CUDA or experience with parallel computation.

You can verify that you have a CUDA-capable GPU through the Display Adapters section in the Windows Device Manager. Here you will find the vendor name and model of your graphics card(s). If you have an NVIDIA card that is listed in -gpus, that GPU is CUDA-capable. The Release Notes for the CUDA Toolkit also contain a list of supported products.

Network Installer: A minimal installer which later downloads packages required for installation. Only the packages selected during the selection phase of the installer are downloaded. This installer is useful for users who want to minimize download time.

Full Installer: An installer which contains all the components of the CUDA Toolkit and does not require any further download. This installer is useful for systems which lack network access and for enterprise deployment.

The download can be verified by comparing the MD5 checksum posted at with that of the downloaded file. If either of the checksums differ, the downloaded file is corrupt and needs to be downloaded again.

The installer can be executed in silent mode by executing the package with the -s flag. Additional parameters can be passed which will install specific subpackages instead of all packages. See the table below for a list of all the subpackage names.

Sometimes it may be desirable to extract or inspect the installable files directly, such as in enterprise deployment, or to browse the files before installation. The full installation package can be extracted using a decompression tool which supports the LZMA compression method, such as 7-zip or WinZip.

Once extracted, the CUDA Toolkit files will be in the CUDAToolkit folder, and similarily for CUDA Visual Studio Integration. Within each directory is a .dll and .nvi file that can be ignored as they are not part of the installable files.

Some CUDA releases do not move to new versions of all installable components. When this is the case these components will be moved to the new label, and you may need to modify the install command to include both labels such as:

TCC is enabled by default on most recent NVIDIA Tesla GPUs. To check which driver mode is in use and/or to switch driver modes, use the nvidia-smi tool that is included with the NVIDIA Driver installation (see nvidia-smi -h for details).

Before continuing, it is important to verify that the CUDA toolkit can find and communicate correctly with the CUDA-capable hardware. To do this, you need to compile and run some of the included sample programs.

To verify a correct configuration of the hardware and software, it is highly recommended that you build and run the deviceQuery sample program. The sample can be built using the provided VS solution files in the deviceQuery folder.

The exact appearance and the output lines might be different on your system. The important outcomes are that a device was found, that the device(s) match what is installed in your system, and that the test passed.

Running the bandwidthTest program, located in the same directory as deviceQuery above, ensures that the system and the CUDA-capable device are able to communicate correctly. The output should resemble Figure 2.

The device name (second line) and the bandwidth numbers vary from system to system. The important items are the second line, which confirms a CUDA device was found, and the second-to-last line, which confirms that all necessary tests passed.

NVIDIA provides Python Wheels for installing CUDA through pip, primarily for using CUDA with Python. These packages are intended for runtime use and do not currently include developer tools (these can be installed separately).

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