---
id: nixl
version: "1.4.0"
license: MIT AND Apache-2.0
license_treatment: permissive
maintenance: active
---

# nixl — NIXL Python API meta package for CUDA variants

License: permissive · Maintenance: active · Downloads: 500.4K/mo

## What it is and what it does

nixl is a meta package that bundles both CUDA 12 and CUDA 13 backends for PyTorch. When you install it via pip, you get both backends included, and the package automatically detects your system's CUDA version at runtime to select the correct one. This eliminates the need to manually choose and install the right CUDA variant yourself. The package is actively maintained and released recently, with low installation friction since it distributes as a pure Python wheel.

The design is particularly useful for environments where CUDA versions might vary or where you want to distribute a single package that works across different CUDA setups. The `nixl[cu12]` and `nixl[cu13]` extras are supported for backward compatibility but don't change the behavior—the automatic selection still occurs at runtime.

Use it for:

- Deploying PyTorch applications across systems with different CUDA versions without rebuilding or maintaining separate packages
- Simplifying dependency management in containerized environments where CUDA version detection is needed at runtime
- Building Python packages that depend on nixl and need to work with multiple CUDA backends transparently
- Testing PyTorch code against both CUDA 12 and CUDA 13 without manual backend switching

## Worth the install?

AI-flagged interpretation of the facts above — verify before relying.

nixl is a meta package that automatically selects and installs the correct CUDA 12 or CUDA 13 backend for PyTorch at runtime based on your system's CUDA version.

Yes, if you use PyTorch with CUDA and want to avoid manual backend selection. The package is actively maintained, has no known vulnerabilities, uses permissive licensing, and installs with low friction. Install it when you need transparent CUDA version detection; skip it if you manually manage CUDA backends or use CPU-only PyTorch.

## Install

pip install nixl
uv add nixl
poetry add nixl

## Installing nixl

Before you install: Active maintenance with a recent release. Low install friction as a pure Python wheel. Requires Python 3.10 or later.

License in practice: Licensed under MIT AND Apache-2.0 (permissive dual licensing), allowing use in most commercial and open-source projects without restriction.

Quickstart:

pip install nixl

import nixl
# Backend selection happens automatically at import time based on system CUDA version

Requires Python 3.10 or later and a PyTorch installation with CUDA support already configured on your system.

Verify before relying:

- Whether the automatic CUDA backend selection works correctly across all PyTorch versions and CUDA driver configurations
- Performance characteristics or overhead of the runtime backend selection mechanism
- Compatibility with non-PyTorch CUDA applications or mixed CUDA environments

## Package facts

- License: MIT AND Apache-2.0 (permissive)
- Python support: supports_current
- Install friction: low
- Maintenance: active
- Downloads: 500.4K/month (top 15,000 on PyPI)
- Known vulnerabilities: none known

## Tags

cuda backend selector, pytorch cuda version detection, automatic cuda backend, nixl cuda package, cuda 12 13 pytorch, runtime cuda selection, pytorch cuda compatibility, cuda-backend, pytorch-integration, runtime-detection

[View on SkillFed](https://skillfed.io/packages/nixl) · [View on PyPI](https://pypi.org/project/nixl/)