nixl-cu13
NIXL Python API
Decision gist · record as of 2026-08-14
Yes, if you are building or deploying distributed AI inference on Linux with PyTorch and need to optimize inter-GPU or GPU-storage communication. The permissive dual license, active maintenance, and prebuilt wheels for modern Python versions lower friction. No if you are on macOS, Windows, or not using inference frameworks that benefit from explicit communication acceleration.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Linux-only; macOS and Windows not supported.
- Requires CUDA 13 runtime and PyTorch with matching CUDA version for automatic backend selection.
- Medium install friction: prebuilt wheels target Linux only (manylinux_2_28, aarch64 and x86_64) for Python 3.10–3.14.
License · maintenance · safety
MIT AND Apache-2.0 (permissive) — Dual-licensed under MIT and Apache-2.0 (permissive). Both licenses allow commercial and private use with minimal restrictions, making the package safe for most deployment scenarios.
last release 2026-08-14 (0 days)
0 known vulnerabilities (OSV.dev, 2026-08-14) · 548,871 downloads/mo, #6,058 on PyPI
Alternatives
Verify before relying
pip install nixl-cu13
import nixl
# Backend selected automatically based on CUDA version from PyTorch
# See docs/python_api.md for detailed usage examples- Specific performance gains or benchmarks for typical inference workloads compared to direct PyTorch communication
- Compatibility matrix with specific PyTorch versions and CUDA 13 minor releases
- Whether the package works with PyTorch built against CUDA 12 despite being named cu13
What it is and what it does
NIXL is a Python library that wraps NVIDIA's Inference Xfer Library, a C++ communication acceleration layer designed for distributed AI inference. It abstracts over different memory types (CPU, GPU) and storage backends (file, block, object store) through a plugin system, allowing inference frameworks like NVIDIA Dynamo to optimize data movement without reimplementing transport logic.
The package ships as a prebuilt wheel for Linux (Python 3.10–3.14, aarch64 and x86_64) with both CUDA 12 and CUDA 13 backends included; the correct backend is selected automatically at runtime based on the CUDA version reported by PyTorch. It depends on torch and numpy. Development is active, and the library is permissively licensed under MIT and Apache-2.0.
Use it for
- Accelerate tensor transfers between GPUs in multi-GPU inference deployments running NVIDIA Dynamo
- Abstract GPU memory and storage operations to simplify distributed inference framework implementation
- Benchmark and profile point-to-point communication performance in inference workloads using NIXLBench
- Integrate custom storage backends (file, block, object store) into inference pipelines via NIXL's plugin architecture
- Enable metadata coordination across distributed inference nodes using ETCD integration
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you are building or deploying distributed AI inference on Linux with PyTorch and need to optimize inter-GPU or GPU-storage communication.
The permissive dual license, active maintenance, and prebuilt wheels for modern Python versions lower friction. No if you are on macOS, Windows, or not using inference frameworks that benefit from explicit communication acceleration.
Install
nixl-cu13 on PyPI
Before you install
Medium install friction: prebuilt wheels target Linux only (manylinux_2_28, aarch64 and x86_64) for Python 3.10–3.14. Requires torch and numpy at runtime. Package is actively maintained with recent releases.
Linux-only; macOS and Windows not supported. Requires CUDA 13 runtime and PyTorch with matching CUDA version for automatic backend selection.
License in practice
Dual-licensed under MIT and Apache-2.0 (permissive). Both licenses allow commercial and private use with minimal restrictions, making the package safe for most deployment scenarios.
Quickstart
pip install nixl-cu13
import nixl
# Backend selected automatically based on CUDA version from PyTorch
# See docs/python_api.md for detailed usage examplesVerify before relying
- Specific performance gains or benchmarks for typical inference workloads compared to direct PyTorch communication
- Compatibility matrix with specific PyTorch versions and CUDA 13 minor releases
- Whether the package works with PyTorch built against CUDA 12 despite being named cu13
Package facts
| License | MIT AND Apache-2.0 permissive |
| Python support | Supports the current Python release >=3.10 |
| Install friction | Medium. Platform-specific wheel |
| Runtime dependencies | 2 packagestorchnumpy |
| Maintenance | Actively maintained 0 days since the last release |
| First released | |
| Downloads | 548,871 / month, #6,058 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
Evidence: nixl_cu13-1.4.0-cp310-cp310-manylinux_2_28_aarch64.whl; nixl_cu13-1.4.0-cp310-cp310-manylinux_2_28_x86_64.whl; nixl_cu13-1.4.0-cp311-cp311-manylinux_2_28_aarch64.whl; nixl_cu13-1.4.0-cp311-cp311-manylinux_2_28_x86_64.whl; nixl_cu13-1.4.0-cp312-cp312-manylinux_2_28_aarch64.whl; nixl_cu13-1.4.0-cp312-cp312-manylinux_2_28_x86_64.whl; nixl_cu13-1.4.0-cp313-cp313-manylinux_2_28_aarch64.whl; nixl_cu13-1.4.0-cp313-cp313-manylinux_2_28_x86_64.whl; nixl_cu13-1.4.0-cp314-cp314-manylinux_2_28_aarch64.whl; nixl_cu13-1.4.0-cp314-cp314-manylinux_2_28_x86_64.whl
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See also nixl · nixl-cu12 · libucxx-cu12 · ucxx-cu12 · nccl4py · nvidia-nccl-cu13 · nvidia-cufile-cu12 · nvidia-cublas · sgl-kernel · flash-attn