To run CUDA applications, you must have an NVIDIA GPU that supports CUDA, along with compatible drivers and the CUDA toolkit installed on your system. Most modern NVIDIA GPUs—from consumer GeForce cards to enterprise-grade data center GPUs—support CUDA, but each generation introduces new capabilities. Checking the compute capability of your GPU ensures it supports the features required by your application. For example, advanced tensor operations or mixed-precision instructions may require newer architectures such as Ampere or Turing.
In addition to a CUDA-capable GPU, your system must provide sufficient PCIe bandwidth, memory, and power supply support. CUDA workloads often consume large amounts of GPU memory, especially when processing high-dimensional vectors or large batch sizes. Developers should ensure their GPU has enough VRAM for the target workload—for example, 8–16 GB for medium workloads and significantly more for large-scale deep learning or vector search operations. Systems must also run a supported operating system such as Linux or Windows and maintain up-to-date NVIDIA GPU drivers.
When CUDA operates inside larger systems—such as GPU-accelerated vector search in Milvus or Zilliz Cloud—additional hardware considerations apply. For example, servers may host multiple GPUs, requiring careful planning around cooling, PCIe lane allocation, and power distribution. CUDA workloads also benefit from strong CPU support because CPU–GPU data transfers can become bottlenecks if the CPU cannot keep up. Meeting these hardware requirements ensures stable, efficient execution of CUDA applications in both standalone settings and integrated systems.