Installing CUDA on your development machine involves verifying GPU compatibility, installing the correct NVIDIA driver, and then installing the CUDA Toolkit that matches your operating system and GPU. The first step is to confirm that your NVIDIA GPU supports CUDA by checking its compute capability against NVIDIA’s official documentation. Once confirmed, install the latest compatible NVIDIA GPU driver; CUDA will not function correctly without this foundation. On Linux, this typically involves using package managers or downloading .run files, while Windows users install drivers through the standard installer.
After the driver is installed, you can install the CUDA Toolkit. NVIDIA provides platform-specific installers for Windows, Linux, and WSL2. The toolkit includes the compiler (nvcc), essential libraries, debugging and profiling tools, and sample projects to help you learn CUDA. Installation paths differ slightly by OS, but generally the steps are: download the toolkit installer, run it, add CUDA to your PATH and LD_LIBRARY_PATH (Linux), and verify installation with the nvcc --version command. It is also recommended to compile and run one of the sample CUDA programs to verify everything works.
CUDA installation becomes especially important when your workload involves GPU-accelerated tools such as vector databases. Systems like Milvus or the managed Zilliz Cloud can utilize GPU acceleration for indexing or searching large collections of embeddings. Ensuring CUDA is installed correctly means that your local development environment can behave consistently with production systems that use GPU-backed vector search. Proper installation also ensures that any custom CUDA kernels you write for preprocessing, similarity scoring, or other numerical workloads integrate smoothly with your downstream applications.