Numba supports CUDA GPU programming by directly compiling a restricted subset of Python code into CUDA kernels and device functions following the CUDA execution model. Kernels written in Numba appear to have direct access to NumPy arrays. NumPy arrays are transferred between the CPU and the GPU automatically.


Several important terms in the topic of CUDA programming are listed here:

  • host: the CPU

  • device: the GPU

  • host memory: the system main memory

  • device memory: onboard memory on a GPU card

  • kernels: a GPU function launched by the host and executed on the device

  • device function: a GPU function executed on the device which can only be called from the device (i.e. from a kernel or another device function)

Programming model

Most CUDA programming facilities exposed by Numba map directly to the CUDA C language offered by NVidia. Therefore, it is recommended you read the official CUDA C programming guide.


Supported GPUs

Numba supports CUDA-enabled GPUs with Compute Capability 3.5 or greater. Support for devices with Compute Capability less than 5.0 is deprecated, and will be removed in a future Numba release.

Devices with Compute Capability 5.0 or greater include (but are not limited to):

  • Embedded platforms: NVIDIA Jetson Nano, Jetson Orin Nano, TX1, TX2, Xavier NX, AGX Xavier, AGX Orin.

  • Desktop / Server GPUs: All GPUs with Maxwell microarchitecture or later. E.g. GTX 9 / 10 / 16 series, RTX 20 / 30 / 40 series, Quadro / Tesla M / P / V / RTX series, RTX A series, RTX Ada / SFF, A / L series, H100.

  • Laptop GPUs: All GPUs with Maxwell microarchitecture or later. E.g. MX series, Quadro M / P / T series (mobile), RTX 20 / 30 series (mobile), RTX A series (mobile).


Numba aims to support CUDA Toolkit versions released within the last 3 years. Presently 11.2 is the minimum required toolkit version. An NVIDIA driver sufficient for the toolkit version is also required (see also CUDA Minor Version Compatibility).

Conda users can install the CUDA Toolkit into a conda environment.

For CUDA 12, cuda-nvcc and cuda-nvrtc are required:

$ conda install -c conda-forge cuda-nvcc cuda-nvrtc "cuda-version>=12.0"

For CUDA 11, cudatoolkit is required:

$ conda install -c conda-forge cudatoolkit "cuda-version>=11.2,<12.0"

If you are not using Conda or if you want to use a different version of CUDA toolkit, the following describes how Numba searches for a CUDA toolkit installation.

CUDA Bindings

Numba supports interacting with the CUDA Driver API via the NVIDIA CUDA Python bindings and its own ctypes-based bindings. Functionality is equivalent between the two bindings. The ctypes-based bindings are presently the default, but the NVIDIA bindings will be used by default (if they are available in the environment) in a future Numba release.

You can install the NVIDIA bindings with:

$ conda install -c conda-forge cuda-python

if you are using Conda, or:

$ pip install cuda-python

if you are using pip.

The use of the NVIDIA bindings is enabled by setting the environment variable NUMBA_CUDA_USE_NVIDIA_BINDING to "1".

Setting CUDA Installation Path

Numba searches for a CUDA toolkit installation in the following order:

  1. Conda installed CUDA Toolkit packages

  2. Environment variable CUDA_HOME, which points to the directory of the installed CUDA toolkit (i.e. /home/user/cuda-12)

  3. System-wide installation at exactly /usr/local/cuda on Linux platforms. Versioned installation paths (i.e. /usr/local/cuda-12.0) are intentionally ignored. Users can use CUDA_HOME to select specific versions.

In addition to the CUDA toolkit libraries, which can be installed by conda into an environment or installed system-wide by the CUDA SDK installer, the CUDA target in Numba also requires an up-to-date NVIDIA graphics driver. Updated graphics drivers are also installed by the CUDA SDK installer, so there is no need to do both. If the libcuda library is in a non-standard location, users can set environment variable NUMBA_CUDA_DRIVER to the file path (not the directory path) of the shared library file.

Missing CUDA Features

Numba does not implement all features of CUDA, yet. Some missing features are listed below:

  • dynamic parallelism

  • texture memory