Why Nvidia Is Making Python a First‑Class Citizen in CUDA

Nvidia Puts Python at the Core of CUDA

In 2024 Nvidia announced at GTC that the CUDA toolkit will provide native Python support, aiming to make Python a first‑class language for GPU parallel programming.

For years CUDA lacked native Python bindings; the new integration lets developers write algorithms directly in Python that run on Nvidia GPUs.

2025 is billed as the “CUDA Python year”, with internal consensus at Nvidia and a focus on expanding the Python ecosystem.

Developers can now use Python without mastering C/C++, thanks to projects like PyTorch, OpenAI Triton, and the upcoming Python‑based Cutlass interface, which expose high‑performance GPU kernels through Python.

CUDA architect Stephen Jones highlighted the explosive growth of Python users, predicting tens of millions of developers, and emphasized that improvements at the lower layers will propagate through the Python ecosystem.

New tools such as CuTile and a Python version of Cutlass allow high‑level array operations to be mapped efficiently to GPU tiles, preserving performance while offering a more Pythonic programming model.

The layered approach—from low‑level CUDA C++ to intermediate Python interfaces like Triton and Cutlass, up to high‑level frameworks like PyTorch—democratizes GPU programming, enabling AI developers and startups to achieve performance without dedicated CUDA engineers.

Nvidia also plans to support other languages such as Rust and Julia, further broadening the ecosystem.