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Yes, but “CUDA for Rust” currently covers two different jobs, and the difference determines which tool you need. Rust code running on the CPU can call CUDA’s APIs to select a GPU, manage memory, and launch work; the cudarc crate is a Rust binding for that layer. The newer development is writing the GPU kernel itself in Rust. NVIDIA announced two native kernel tracks on September 8, 2026: cuda-oxide, which uses a SIMT (single instruction, multiple threads) model, and cuTile Rust, which uses a tile-based model. Both are real, but they are at different maturity levels, and their requirements differ.
Two layers sit under the phrase “CUDA for Rust”
CUDA is NVIDIA’s GPU programming platform. The CUDA Programming Guide defines it as “a parallel computing platform and programming model developed by NVIDIA that enables dramatic increases in computing performance by harnessing the power of the GPU.” Because it is tied to NVIDIA hardware, every project in this guide needs an NVIDIA GPU, and none of them run on other vendors’ chips.
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For a Rust developer, the work splits into two layers:
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- Host code runs on the CPU and controls the GPU. It selects a device, allocates memory, transfers data, and launches kernels. Bindings such as
cudarcexpose CUDA’s APIs to Rust for this job. - Device code is the kernel, which runs on the GPU. Rust developers could already launch kernels from Rust, but they often had to write the kernel itself in another language. The newer projects target that gap by compiling Rust kernel code for the GPU.
Most questions about “CUDA in Rust” come down to which of these layers you need to write.
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Which project covers which need
| Project | Layer | Kernel model | Maturity, as described in its own material |
|---|---|---|---|
| cudarc | Host-side Rust bindings for CUDA APIs | Does not author kernels | Not stated |
| cuda-oxide | Device kernels written in Rust | SIMT, compiled to PTX through a custom backend | The project book labels v0.1.0 an “early-stage alpha” |
| cuTile Rust | Device kernels written in Rust | Tile-based, mapped through CUDA Tile IR | NVIDIA describes the larger effort as continuing to mature and says development continues into 2027 and beyond |
| Rust-CUDA | Rust-to-PTX compilation plus CUDA ecosystem libraries | SIMT-style kernels compiled to PTX | The project guide describes an effort to make Rust a tier-1 language for GPU computing; release status not stated |
| CubeCL | Cross-vendor GPU compute, per NVIDIA’s ecosystem appendix | Portability and DSL-oriented goals | Not CUDA-specific; evaluate separately |
SIMT means you write code for individual threads, and the hardware executes them in groups. Tile-based programming means you describe operations on blocks of data instead.
cuda-oxide: SIMT kernels in Rust
cuda-oxide compiles Rust kernel code to PTX through a custom backend. Its model is the one CUDA C++ programmers already use, so it suits teams whose existing kernels are thread-oriented and who want Rust’s language features in device code.
Maturity is the main caveat. The cuda-oxide book describes v0.1.0 as “an early-stage alpha” and warns that it may contain bugs, incomplete features, and API breakage. Expect to revise code as the project changes.
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cuTile Rust: tile-based kernels
cuTile Rust takes a different approach. Instead of writing per-thread logic, you express work as operations on tiles, which are blocks of data, and the tile model is mapped through CUDA Tile IR. This fits workloads that are naturally blocked array operations, such as matrix multiplication.
The most specific performance figures available come from the 2026 paper Fearless Concurrency on the GPU. For cuTile Rust on an NVIDIA B200, the authors report 7 TB/s for element-wise operations and 2 PFlop/s for GEMM, which they put at 96% of cuBLAS. These numbers come from that device and those workloads. They say nothing about other GPUs, other kernels, or other problem sizes, and they are not a guarantee for your code.
Earlier and complementary projects
Rust-CUDA
The Rust-CUDA project guide describes an effort to make Rust a tier-1 language for GPU computing with CUDA. That includes tooling to compile Rust to PTX and to use CUDA libraries from Rust. Its setup page notes that the LLVM 7.x requirement can make installation difficult and points to Docker images that include CUDA and LLVM, which is a practical way around local toolchain problems.
cudarc
cudarc provides Rust bindings to CUDA APIs. It is the right choice when the host program is the part you are writing in Rust and you need to drive the GPU from it. It does not let you write kernel code in Rust, so pair it with a kernel from one of the projects above or with kernels that already exist. The guide’s requirements table omits cudarc because the cited material does not give its compatibility baseline; check its own documentation.
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Requirements differ by project
Do not treat the following as one “Rust CUDA” minimum. Each column describes one project’s own setup requirements.
| Requirement | Rust-CUDA (project setup guide) | cuda-oxide SIMT track (NVIDIA announcement) | cuTile Rust (NVIDIA announcement) |
|---|---|---|---|
| Operating system | Not stated | Linux | Not stated |
| GPU | Compute Capability 5.0 (Maxwell) or later | Compute Capability 8.0 or later | Not stated |
| CUDA | CUDA 12.0 or newer | CUDA Toolkit 12.x or newer | Not stated |
| Driver | An appropriate NVIDIA driver | Not stated | Not stated |
| Compiler | LLVM 7.x | clang with libclang headers | Not stated |
| Rust toolchain | Not stated | A pinned nightly toolchain | Not stated |
“Not stated” means the project’s cited setup material does not give that requirement, so check its setup page before assuming it is unneeded. A GPU below Compute Capability 8.0 can meet Rust-CUDA’s stated baseline and still fall outside the cuda-oxide track.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Installing the CUDA Toolkit on Linux
NVIDIA’s installation guide documents three Linux routes for the CUDA Toolkit:
- Package manager installation
- Runfile installer
- Conda
The guide also covers pip wheels, which are oriented toward Python runtime use. Check the installation page before relying on them to build kernels.
Version numbers need care. NVIDIA’s CUDA Toolkit documentation landing page highlights CUDA 13.4, but the CUDA Programming Guide it links is Release 13.2. These two may not describe the same release, so take the version from the page you are reading and confirm supported distributions and drivers on NVIDIA’s installation page.
Choosing a path
- You only need Rust host code that calls CUDA. Use cudarc for the host side. Your kernels come from elsewhere.
- You want kernels written in Rust and your algorithm is thread-oriented. Evaluate cuda-oxide, but only if you run Linux, have a GPU at Compute Capability 8.0 or later, and can maintain a pinned nightly toolchain and absorb API changes in an alpha release.
- Your workload is blocked array math, such as GEMM-heavy code. Evaluate cuTile Rust, and benchmark it on your own shapes and hardware rather than relying on the paper’s numbers.
- You need Rust-to-PTX compilation with CUDA libraries and can run LLVM 7.x on older hardware. Consider Rust-CUDA, starting from its Docker images if the local LLVM setup fails.
- You need one kernel codebase across GPU vendors. Look at portability projects such as CubeCL. CUDA itself is not the path.
Project versions, toolchains, and GPU support change quickly. The most recent source behind this guide is NVIDIA’s September 8, 2026 announcement, so confirm the current state on each project’s own release notes and setup page before you adopt one. For production use, also check issue activity and supported features, and validate the chosen project against your own workload.
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