NVIDIA has announced CUDA Rust, a push to make Rust a first-class language for writing GPU kernels. Rust code could already launch CUDA kernels, but the kernel body usually had to be written elsewhere. CUDA Rust closes that gap with two NVlabs open-source projects: cuda-oxide for the SIMT model and cutile-rs for the newer Tile model. Both compile Rust kernels natively and use Rust’s ownership rules to reject aliasing bugs at compile time.
Is it deployable? Partially. cutile-rs is published on crates.io, runs on stable Rust 1.89+, and is already used in Hugging Face’s Grout inference engine and in mistral.rs. cuda-oxide is early alpha. The both projects are in alpha phase and not confirmed for production.
Why Rust for the GPU Kernel
The systems layer of AI, from inference engines to drivers and agent runtimes, is increasingly written in Rust. NVIDIA’s Nova Linux driver is in Rust, NVIDIA Dynamo has a Rust core, and NVTX has Rust bindings. The GPU kernel was the exception.
The two tracks mirror the two programming models CUDA already offers. SIMT is the model used in CUDA C++ and numba-cuda: you describe what one thread does and launch thousands of them. Tile is the newer model, also available in C++ and Python: you describe what one tile of data does, and the Tile IR compiler handles thread mapping and memory layout. NVIDIA recommends Tile first, with SIMT for explicit thread and memory control. Planned inter-language interop means choosing Rust will not lock developers out of C++ or Python.
