For software compatability, please see the section on version support information for details.

Our supported platforms are:

  • Linux x86_64

  • Linux ppcle64 (POWER8, POWER9)

  • Windows 10 and later (64-bit)

  • OS X 10.9 and later (64-bit and unofficial support on M1/Arm64)

  • *BSD (unofficial support only)

  • NVIDIA GPUs of compute capability 5.0 and later

    • Compute capabilities 3.5 and 3.7 are supported, but deprecated.

  • ARMv8 (64-bit little-endian, such as the NVIDIA Jetson)

Automatic parallelization with @jit is only available on 64-bit platforms.

Installing using conda on x86/x86_64/POWER Platforms

The easiest way to install Numba and get updates is by using conda, a cross-platform package manager and software distribution maintained by Anaconda, Inc. You can either use Anaconda to get the full stack in one download, or Miniconda which will install the minimum packages required for a conda environment.

Once you have conda installed, just type:

$ conda install numba


$ conda update numba

Note that Numba, like Anaconda, only supports PPC in 64-bit little-endian mode.

To enable CUDA GPU support for Numba, install the latest graphics drivers from NVIDIA for your platform. (Note that the open source Nouveau drivers shipped by default with many Linux distributions do not support CUDA.) Then install the cudatoolkit package:

$ conda install cudatoolkit

You do not need to install the CUDA SDK from NVIDIA.

Installing using pip on x86/x86_64 Platforms

Binary wheels for Windows, Mac, and Linux are also available from PyPI. You can install Numba using pip:

$ pip install numba

This will download all of the needed dependencies as well. You do not need to have LLVM installed to use Numba (in fact, Numba will ignore all LLVM versions installed on the system) as the required components are bundled into the llvmlite wheel.

To use CUDA with Numba installed by pip, you need to install the CUDA SDK from NVIDIA. Please refer to Setting CUDA Installation Path for details. Numba can also detect CUDA libraries installed system-wide on Linux.

Installing on Linux ARMv8 (AArch64) Platforms

We build and test conda packages on the NVIDIA Jetson TX2, but they are likely to work for other AArch64 platforms. (Note that while the CPUs in the Raspberry Pi 3, 4, and Zero 2 W are 64-bit, Raspberry Pi OS may be running in 32-bit mode depending on the OS image in use).

Conda-forge support for AArch64 is still quite experimental and packages are limited, but it does work enough for Numba to build and pass tests. To set up the environment:

  • Install miniforge. This will create a minimal conda environment.

  • Then you can install Numba from the numba channel:

    $ conda install -c numba numba

On CUDA-enabled systems, like the Jetson, the CUDA toolkit should be automatically detected in the environment.

Installing from source

Installing Numba from source is fairly straightforward (similar to other Python packages), but installing llvmlite can be quite challenging due to the need for a special LLVM build. If you are building from source for the purposes of Numba development, see Build environment for details on how to create a Numba development environment with conda.

If you are building Numba from source for other reasons, first follow the llvmlite installation guide. Once that is completed, you can download the latest Numba source code from Github:

$ git clone

Source archives of the latest release can also be found on PyPI. In addition to llvmlite, you will also need:

  • A C compiler compatible with your Python installation. If you are using Anaconda, you can use the following conda packages:

    • Linux x86_64: gcc_linux-64 and gxx_linux-64

    • Linux POWER: gcc_linux-ppc64le and gxx_linux-ppc64le

    • Linux ARM: no conda packages, use the system compiler

    • Mac OSX: clang_osx-64 and clangxx_osx-64 or the system compiler at /usr/bin/clang (Mojave onwards)

    • Mac OSX (M1): clang_osx-arm64 and clangxx_osx-arm64

    • Windows: a version of Visual Studio appropriate for the Python version in use

  • NumPy

Then you can build and install Numba from the top level of the source tree:

$ python install

If you wish to run the test suite, see the instructions in the developer documentation.

Build time environment variables and configuration of optional components

Below are environment variables that are applicable to altering how Numba would otherwise build by default along with information on configuration options.

NUMBA_DISABLE_OPENMP (default: not set)

To disable compilation of the OpenMP threading backend set this environment variable to a non-empty string when building. If not set (default):

  • For Linux and Windows it is necessary to provide OpenMP C headers and runtime libraries compatible with the compiler tool chain mentioned above, and for these to be accessible to the compiler via standard flags.

  • For OSX the conda package llvm-openmp provides suitable C headers and libraries. If the compilation requirements are not met the OpenMP threading backend will not be compiled.

NUMBA_DISABLE_TBB (default: not set)

To disable the compilation of the TBB threading backend set this environment variable to a non-empty string when building. If not set (default) the TBB C headers and libraries must be available at compile time. If building with conda build this requirement can be met by installing the tbb-devel package. If not building with conda build the requirement can be met via a system installation of TBB or through the use of the TBBROOT environment variable to provide the location of the TBB installation. For more information about setting TBBROOT see the Intel documentation.

Dependency List

Numba has numerous required and optional dependencies which additionally may vary with target operating system and hardware. The following lists them all (as of July 2020).

  • Required build time:

    • setuptools

    • numpy

    • llvmlite

    • Compiler toolchain mentioned above

  • Required run time:

    • numpy

    • llvmlite

  • Optional build time:

    See Build time environment variables and configuration of optional components for more details about additional options for the configuration and specification of these optional components.

    • llvm-openmp (OSX) - provides headers for compiling OpenMP support into Numba’s threading backend

    • tbb-devel - provides TBB headers/libraries for compiling TBB support into Numba’s threading backend (version >= 2021.6 required).

    • importlib_metadata (for Python versions < 3.9)

  • Optional runtime are:

    • scipy - provides cython bindings used in Numba’s np.linalg.* support

    • tbb - provides the TBB runtime libraries used by Numba’s TBB threading backend (version >= 2021 required).

    • jinja2 - for “pretty” type annotation output (HTML) via the numba CLI

    • cffi - permits use of CFFI bindings in Numba compiled functions

    • llvm-openmp - (OSX) provides OpenMP library support for Numba’s OpenMP threading backend.

    • intel-openmp - (OSX) provides an alternative OpenMP library for use with Numba’s OpenMP threading backend.

    • ipython - if in use, caching will use IPython’s cache directories/caching still works

    • pyyaml - permits the use of a .numba_config.yaml file for storing per project configuration options

    • colorama - makes error message highlighting work

    • intel-cmplr-lib-rt - allows Numba to use Intel SVML for extra performance

    • pygments - for “pretty” type annotation

    • gdb as an executable on the $PATH - if you would like to use the gdb support

    • setuptools - permits the use of pycc for Ahead-of-Time (AOT) compilation

    • Compiler toolchain mentioned above, if you would like to use pycc for Ahead-of-Time (AOT) compilation

    • r2pipe - required for assembly CFG inspection.

    • radare2 as an executable on the $PATH - required for assembly CFG inspection. See here for information on obtaining and installing.

    • graphviz - for some CFG inspection functionality.

    • typeguard - used by for runtime type-checking.

    • cuda-python - The NVIDIA CUDA Python bindings. See CUDA Bindings. Numba requires Version 11.6 or greater.

    • cubinlinker and ptxcompiler to support CUDA Minor Version Compatiblity.

  • To build the documentation:

    • sphinx

    • pygments

    • sphinx_rtd_theme

    • numpydoc

    • make as an executable on the $PATH

Version support information

This is the canonical reference for information concerning which versions of Numba’s dependencies were tested and known to work against a given version of Numba. Other versions of the dependencies (especially NumPy) may work reasonably well but were not tested. The use of x in a version number indicates all patch levels supported. The use of ? as a version is due to missing information.


Release date








3.8.x <= version < 3.12

1.21 <= version < 1.26



2021.6 <= version



3.8.x <= version < 3.12

1.21 <= version < 1.25



2021.6 <= version



3.8.x <= version < 3.12

1.21 <= version < 1.25



2021.6 <= version



3.7.x <= version < 3.11

1.18 <= version < 1.24






3.7.x <= version < 3.11

1.18 <= version < 1.24






3.7.x <= version < 3.11

1.18 <= version < 1.24








3.7.x <= version < 3.11

1.18 <= version < 1.23






3.7.x <= version < 3.11

1.18 <= version < 1.23






3.7.x <= version < 3.11

1.18 <= version < 1.22






3.6.x <= version < 3.10

1.17 <= version < 1.21






3.6.x <= version < 3.10

1.15 <= version < 1.21



2019.5 <= version < 2021.4



3.6.x <= version < 3.9

1.15 <= version < 1.20


10.x (9.x for aarch64)

2019.5 <= version < 2020.3



3.6.x <= version < 3.9

1.15 <= version < 1.19


10.x (9.x for aarch64)

2019.5 <= version < 2020.0



3.6.x <= version < 3.9

1.15 <= version < 1.19



2019.5 <= version < 2020.0



3.6.x <= version < 3.9

1.15 <= version < 1.18

0.31.x <= version < 0.33.x


2019.5 <= version < 2020.0



3.6.x <= version < 3.9

1.15 <= version < 1.18


8.x (7.x for ppc64le)

2018.0.5 <= version < ?



3.5.x <= version < 3.9; version == 2.7.x

1.15 <= version < 1.18


8.x (7.x for ppc64le)

2018.0.5 <= version < ?

Checking your installation

You should be able to import Numba from the Python prompt:

$ python
Python 3.10.2 | packaged by conda-forge | (main, Jan 14 2022, 08:02:09) [GCC 9.4.0] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> import numba
>>> numba.__version__

You can also try executing the numba --sysinfo (or numba -s for short) command to report information about your system capabilities. See Command line interface for further information.

$ numba -s
System info:
__Time Stamp__
Report started (local time)                   : 2022-01-18 10:35:08.981319

__Hardware Information__
Machine                                       : x86_64
CPU Name                                      : skylake-avx512
CPU Count                                     : 12
CPU Features                                  :
64bit adx aes avx avx2 avx512bw avx512cd avx512dq avx512f avx512vl bmi bmi2
clflushopt clwb cmov cx16 cx8 f16c fma fsgsbase fxsr invpcid lzcnt mmx
movbe pclmul pku popcnt prfchw rdrnd rdseed rtm sahf sse sse2 sse3 sse4.1
sse4.2 ssse3 xsave xsavec xsaveopt xsaves

__OS Information__
Platform Name                                 : Linux-5.4.0-94-generic-x86_64-with-glibc2.31
Platform Release                              : 5.4.0-94-generic
OS Name                                       : Linux
OS Version                                    : #106-Ubuntu SMP Thu Jan 6 23:58:14 UTC 2022

__Python Information__
Python Compiler                               : GCC 9.4.0
Python Implementation                         : CPython
Python Version                                : 3.10.2
Python Locale                                 : en_GB.UTF-8

__LLVM information__
LLVM Version                                  : 11.1.0

__CUDA Information__
Found 1 CUDA devices
id 0      b'Quadro RTX 8000'                              [SUPPORTED]
                      Compute Capability: 7.5
                           PCI Device ID: 0
                              PCI Bus ID: 21
                                    UUID: GPU-e6489c45-5b68-3b03-bab7-0e7c8e809643
                                Watchdog: Enabled
             FP32/FP64 Performance Ratio: 32

(output truncated due to length)