---
metadata:
  - name: generator
    content: Diplodoc Platform v5.54.5
alternate:
  - https://catboost.ai/docs/en/installation/cli-installation-binaries.md
  - href: en/installation/cli-installation-binaries.md
    type: text/markdown
    title: Markdown version
  - href: ../llms.txt
    type: text/markdown
    title: llms.txt
---
> **Documentation Index:** Fetch the complete configuration index at https://catboost.ai/docs/en/llms.txt

# Download

<!-- source: en/_includes/work_src/reusage-cli/releases-page.md -->
An up-to-date list of available CatBoost releases and the corresponding binaries for different operating systems is available in the **Download** section of the [releases](https://github.com/catboost/catboost/releases) page on GitHub.

|Operating system|CPU architectures|GPU support using [CUDA](https://developer.nvidia.com/cuda-zone)|
|--------|-----------------|------------|
| macOS (versions currently supported by Apple) | x86_64 and arm64 |no|
| Linux (compatible with [manylinux2014 platform tag](https://peps.python.org/pep-0599/) ) | x86_64 and aarch64 |yes|
| Windows (versions 10, 11, Server 2022, Server 2025) | x86_64 |yes|

{% note info %}

Release binaries for x86_64 CPU architectures are built with SIMD extensions SSE2, SSE3, SSSE3, SSE4 enabled. If you need to run CatBoost on older CPUs that do not support these instruction sets [build CatBoost artifacts yourself](https://catboost.ai/docs/en/concepts/build-from-source.md)

{% endnote %}
<!-- endsource: en/_includes/work_src/reusage-cli/releases-page.md -->

{% note info %}

<!-- source: en/_includes/work_src/reusage-installation/cli__gpu-support-from-the-box__p.md -->
The built versions of CatBoost have GPU support out-of-the-box.
<!-- endsource: en/_includes/work_src/reusage-installation/cli__gpu-support-from-the-box__p.md -->

<!-- source: en/_includes/work_src/reusage-code-examples/compute-capability-requirements.md -->
As of CatBoost 1.2.10, devices with CUDA compute capability >= 3.5 are supported in released packages.

All necessary CUDA libraries are statically linked in the released Linux and Windows binaries, the only installation necessary is the appropriate version of the CUDA driver.
<!-- endsource: en/_includes/work_src/reusage-code-examples/compute-capability-requirements.md -->

<!-- source: en/_includes/work_src/reusage-code-examples/nvidia-driver-reqs.md -->
Training or inference on CUDA-enabled GPUs requires NVIDIA Driver of version 450.80.02 or higher.
<!-- endsource: en/_includes/work_src/reusage-code-examples/nvidia-driver-reqs.md -->

{% endnote %}