NVIDIA has just announced native support for Python in its CUDA platform, marking a major shift in how developers can access GPU acceleration. For the first time, Python developers can write CUDA programs without needing to rely on C or C++ bindings. This native integration drastically lowers the barrier for using GPUs in scientific computing, AI, and data-heavy Python applications.
The new cuda-python package gives direct access to CUDA’s driver and runtime APIs, letting users launch kernels, manage memory, and control streams entirely from Python. It also includes support for just-in-time (JIT) compilation, which means you can write dynamic GPU code directly in Python, compile it on the fly, and run it immediately.
A key innovation is the new CuTile programming model, which brings a tile-based structure to CUDA operations. CuTile is designed to feel natural to Python users familiar with NumPy and CuPy, and it allows for efficient manipulation of large data arrays without needing to manage threads manually.
This move brings CUDA closer to Python’s ecosystem and could reshape how GPU computing is taught, deployed, and scaled across AI and HPC workloads.
Read the full announcement here:
https://thenewstack.io/nvidia-finally-adds-native-python-support-to-cuda/
Official documentation from NVIDIA:
https://developer.nvidia.com/cuda-python