nvidia-nccl-cu11
NVIDIA Collective Communication Library (NCCL) Runtime
Decision gist · record as of 2026-08-14
Yes, but only if you are locked to CUDA 11 and require NCCL for multi-GPU work. The package is dormant and will not receive updates, so it is suitable only for stable, legacy deployments. If you are starting a new project, prefer a current CUDA version and its corresponding NCCL package instead. Verify NVIDIA's licensing terms before production use.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires NVIDIA CUDA 11 and compatible GPU hardware; x86_64 Linux only (manylinux2014 wheel).
- Not a standalone Python library—used as a runtime dependency.
- Medium install friction due to platform-specific wheel (manylinux2014 x86_64).
License · maintenance · safety
NVIDIA Proprietary Software (unclear) — Licensed under NVIDIA Proprietary Software with unclear treatment. Review NVIDIA's licensing terms before deploying in production or redistributing.
last release 2024-04-03 (863 days)
0 known vulnerabilities (OSV.dev, 2026-08-14) · 1,472,815 downloads/mo, #3,865 on PyPI
Alternatives
Verify before relying
pip install nvidia-nccl-cu11==2.21.5
# Typically imported indirectly through frameworks that depend on this runtime- Whether this package is still the recommended way to install NCCL for CUDA 11, or if newer CUDA versions have superseded it.
- Compatibility with specific framework versions that depend on this NCCL runtime.
- How to invoke NCCL primitives directly from Python code using this package.
What it is and what it does
This package bundles NVIDIA's Collective Communication Library (NCCL) runtime for CUDA 11, implementing collective communication routines like all-reduce, all-gather, reduce, broadcast, and reduce-scatter. It has been optimized to achieve high bandwidth on any platform using PCIe, NVLink, NVswitch, as well as networking using InfiniBand Verbs or TCP/IP sockets. The package is not a standalone library you call directly from Python; instead, it serves as a runtime dependency for frameworks that need to coordinate computation across multiple GPUs.
Because this is a platform-specific wheel (manylinux2014 x86_64 only) and the package has been dormant for 863 days with no recent maintenance, it is primarily useful for legacy projects already pinned to CUDA 11. New projects should evaluate whether a current CUDA version and its corresponding NCCL package better suit their needs.
Use it for
- Provide the NCCL runtime for distributed deep learning frameworks on CUDA 11 systems.
- Support legacy production deployments that are locked to CUDA 11 and need the matching NCCL library.
- Enable multi-GPU collective communication on systems using PCIe, NVLink, NVswitch, or InfiniBand.
- Facilitate research or development on multi-GPU systems where collective communication is critical.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, but only if you are locked to CUDA 11 and require NCCL for multi-GPU work.
The package is dormant and will not receive updates, so it is suitable only for stable, legacy deployments. If you are starting a new project, prefer a current CUDA version and its corresponding NCCL package instead. Verify NVIDIA's licensing terms before production use.
Install
nvidia-nccl-cu11 on PyPI
Before you install
Medium install friction due to platform-specific wheel (manylinux2014 x86_64). Package is dormant—last release was 863 days ago—so expect no active maintenance or bug fixes going forward.
Requires NVIDIA CUDA 11 and compatible GPU hardware; x86_64 Linux only (manylinux2014 wheel). Not a standalone Python library—used as a runtime dependency.
License in practice
Licensed under NVIDIA Proprietary Software with unclear treatment. Review NVIDIA's licensing terms before deploying in production or redistributing.
Quickstart
pip install nvidia-nccl-cu11==2.21.5
# Typically imported indirectly through frameworks that depend on this runtimeVerify before relying
- Whether this package is still the recommended way to install NCCL for CUDA 11, or if newer CUDA versions have superseded it.
- Compatibility with specific framework versions that depend on this NCCL runtime.
- How to invoke NCCL primitives directly from Python code using this package.
Package facts
| License | NVIDIA Proprietary Software unclear |
| Python support | Supports the current Python release >=3 |
| Install friction | Medium. Platform-specific wheel |
| Runtime dependencies | None |
| Maintenance | Dormant 863 days since the last release |
| First released | |
| Downloads | 1,472,815 / month, #3,865 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 4 - BetaIntended Audience :: DevelopersIntended Audience :: EducationIntended Audience :: Science/ResearchLicense :: Other/Proprietary LicenseNatural Language :: EnglishOperating System :: Microsoft :: WindowsOperating System :: POSIX :: LinuxProgramming Language :: Python :: 3Programming Language :: Python :: 3 :: OnlyProgramming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.5Programming Language :: Python :: 3.6Programming Language :: Python :: 3.7Programming Language :: Python :: 3.8Programming Language :: Python :: 3.9Topic :: Scientific/EngineeringTopic :: Scientific/Engineering :: Artificial IntelligenceTopic :: Scientific/Engineering :: MathematicsTopic :: Software DevelopmentTopic :: Software Development :: Libraries |
Evidence: nvidia_nccl_cu11-2.21.5-py3-none-manylinux2014_x86_64.whl
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See also nvidia-nccl-cu12 · nvidia-nccl-cu13 · nccl4py · nvidia-cuda-runtime-cu11 · nvidia-cudnn-cu11 · nvidia-cusolver-cu11 · nvidia-curand-cu11 · nvidia-cuda-cccl-cu12 · nvidia-nvshmem-cu12 · nvidia-nvshmem-cu13