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One free scan finds every outdated or missing driver and matches the right update for your exact hardware.Free scan · exact hardware matchTo get started with CUDA, you need a compatible NVIDIA GPU, an appropriate NVIDIA driver, and the CUDA Toolkit release that fits your operating system and hardware. Install them using NVIDIA’s current instructions, then build and run a sample to confirm the setup works. You can first check whether hardware you already own is compatible; buying a GPU is not a prerequisite for learning the basics.
What do I need to get started with CUDA?
CUDA is NVIDIA’s platform for computing on GPUs. A working development setup involves several pieces that do different jobs:
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- NVIDIA GPU: The hardware that performs GPU work. Each model has a compute capability, a hardware feature level written as a major/minor number such as X.Y.
- NVIDIA driver: Software installed on the host operating system that enables GPU use. NVIDIA’s CUDA Programming Guide says, “The NVIDIA Driver can be thought of as the operating system of the GPU.” This is an analogy for its foundational role, not a literal description.
- CUDA Toolkit: Development tools, headers, and libraries used to write, build, and analyze GPU software.
- CUDA runtime: A Toolkit library that provides APIs and language extensions for common operations, including allocating memory, moving data between CPUs and GPUs (or between GPUs), and launching kernels.
The driver and Toolkit are separate components; the runtime is part of the Toolkit. The right combination depends on the GPU architecture, driver, Toolkit release, application, and operating system—not simply on choosing the newest Toolkit.
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Does my GPU support CUDA?
Look up the exact GPU model in NVIDIA’s maintained compute-capability list, then check NVIDIA’s current compatibility guidance for the driver and Toolkit release you plan to use. Compute capability describes hardware features; a CUDA version identifies a software-platform or Toolkit release. They are related to compatibility, but they are not interchangeable version numbers.
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If you are shopping for a GPU, confirm the exact model’s compute capability and compatibility with your intended software and system before buying. The information here does not identify a suitable card for every budget or computer.
How do I install CUDA Toolkit?
Use the installation guide for your actual operating system and the CUDA release you intend to install. NVIDIA’s CUDA Toolkit Documentation hub links to its current Quick Start, OS-specific installation guides, programming documentation, samples, compiler information, libraries, and tools.
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- Identify your GPU and operating system. Check the exact GPU’s compute capability and NVIDIA’s compatibility guidance before selecting a release.
- Open the current Quick Start and OS-specific guide. Follow the instructions for your platform rather than copying commands from a guide for another operating system or release.
- Install the driver and Toolkit as instructed. In the CUDA 13.4 Quick Start Guide, NVIDIA states that the Linux driver is no longer bundled with the Toolkit and must be installed separately. This is specific to Linux and CUDA 13.4; consult the instructions for your own platform and release.
Installation options and exact commands can change between platforms and releases, so use NVIDIA’s live CUDA Quick Start Guide instead of treating a fixed command sequence as universal.
How do I know CUDA is working?
Do more than confirm that an installer finished: build and run an official sample. NVIDIA’s Quick Start workflow directs Linux users to install a writable copy of CUDA Samples, build a sample such as nbody, and run the executable from its directory so it can locate dependent resources. NVIDIA’s Windows guide also uses nbody as a verification route. Follow the current guide for the commands and prerequisites that apply to your setup.
If the sample does not build or run, check the GPU, operating system, driver, and Toolkit against the compatibility instructions for the release you installed. A successful sample run is a practical check that the environment can build and execute CUDA software; it is the right point to begin working through the CUDA Programming Guide and samples. The Best Practices Guide is more useful later, when you have working code to optimize.
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