The programming languages that work best with CUDA today are primarily C and C++, which CUDA was originally designed to integrate with. CUDA extends C/C++ with keywords like __global__, __device__, and __shared__, allowing developers to write GPU kernels and control memory placement directly. This makes C/C++ the most powerful and flexible choice for developers who need fine-grained control over GPU performance. It is also the foundation for many CUDA-based libraries used in machine learning, simulations, and scientific computing.
Beyond C/C++, Python is also frequently used with CUDA through high-level libraries such as Numba, CuPy, and PyTorch. These libraries allow developers to write GPU-accelerated code without diving into low-level kernel programming. For example, PyTorch uses CUDA underneath its tensor operations, letting developers leverage the GPU simply by moving their tensors to a CUDA device. Python interfaces are popular for rapid development, prototyping, and data science workflows where ease of use matters as much as performance.
CUDA also integrates indirectly into systems that use GPU acceleration under the hood, such as vector databases. When a database like Milvus or Zilliz Cloud runs GPU-backed vector search, the underlying implementation is typically written in C++ and CUDA, even if the user interacts with it through Python or REST APIs. This separation allows developers to work in their preferred high-level language while still benefiting from CUDA’s low-level performance.