nccl4py
NCCL4Py: Python bindings for NCCL
Decision gist · record as of 2026-08-14
Yes, if you are building distributed GPU applications on Linux clusters and need low-latency collective communication. The active maintenance, permissive Apache-2.0 license, and strong repository signals indicate a well-supported project. Medium install friction (CUDA runtime dependency, platform-specific wheels) is typical and expected for GPU libraries. No known security vulnerabilities. Not suitable for Windows or macOS, or for single-GPU workloads.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires NVIDIA CUDA 12.x or 13.x installed and NVIDIA GPU hardware; Linux (x86_64 or aarch64) only; Python 3.10 or later.
- Medium install friction due to CUDA runtime dependencies (cuda-core, cuda-pathfinder) and platform-specific wheels (x86_64 and aarch64 Linux only).
- Active maintenance with a recent release (3 days old) and strong repository signals (4996 stars, last commit 2026-08-14).
License · maintenance · safety
Apache-2.0 (permissive) — Apache-2.0 permissive license allows commercial and private use with minimal restrictions, making it suitable for production deployments in research and industry settings.
last release 2026-08-11 (3 days) · last repo commit 2026-08-14 · 4,996 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 323,537 downloads/mo, #7,605 on PyPI
Alternatives
Verify before relying
pip install nccl4py[cu12]
import nccl4py
import numpy as np
# Initialize NCCL communicator for multi-GPU operations
comm = nccl4py.NcclComm()
data = np.array([1.0, 2.0, 3.0])
comm.AllReduce(data)- Whether nccl4py requires explicit NCCL library installation or bundles it with the wheel.
- Performance characteristics compared to direct NCCL C API or other Python distributed frameworks.
- Supported collective operations beyond AllReduce and their API signatures.
What it is and what it does
nccl4py is a Python wrapper around NVIDIA's Collective Communications Library (NCCL), designed to bring GPU-accelerated communication to Python applications running on multi-GPU and multi-node clusters. It abstracts NCCL's C API into a Pythonic interface, allowing distributed computing frameworks and custom applications to coordinate GPU computations across multiple nodes without dropping to C code.
The package targets researchers and engineers building distributed machine learning systems, high-performance computing applications, and other workloads that need efficient all-reduce, broadcast, and other collective operations across GPUs. It depends on numpy for array handling, packaging for version management, and CUDA runtime libraries (cuda-core and cuda-pathfinder) to interface with GPU hardware. Installation requires CUDA 12.x or 13.x and is limited to Linux on x86_64 or aarch64 architectures.
Use it for
- Coordinate gradient synchronization across multiple GPUs during distributed deep learning training.
- Implement custom all-reduce and broadcast operations in multi-node HPC applications.
- Build distributed data-parallel inference pipelines that need efficient GPU-to-GPU communication.
- Integrate NCCL communication into Python-based simulation or numerical computing frameworks.
- Prototype distributed algorithms that require low-latency collective operations on GPU clusters.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you are building distributed GPU applications on Linux clusters and need low-latency collective communication.
The active maintenance, permissive Apache-2.0 license, and strong repository signals indicate a well-supported project. Medium install friction (CUDA runtime dependency, platform-specific wheels) is typical and expected for GPU libraries. No known security vulnerabilities. Not suitable for Windows or macOS, or for single-GPU workloads.
Install
nccl4py on PyPI
Before you install
Medium install friction due to CUDA runtime dependencies (cuda-core, cuda-pathfinder) and platform-specific wheels (x86_64 and aarch64 Linux only). Active maintenance with a recent release (3 days old) and strong repository signals (4996 stars, last commit 2026-08-14).
Requires NVIDIA CUDA 12.x or 13.x installed and NVIDIA GPU hardware; Linux (x86_64 or aarch64) only; Python 3.10 or later.
License in practice
Apache-2.0 permissive license allows commercial and private use with minimal restrictions, making it suitable for production deployments in research and industry settings.
Quickstart
pip install nccl4py[cu12]
import nccl4py
import numpy as np
# Initialize NCCL communicator for multi-GPU operations
comm = nccl4py.NcclComm()
data = np.array([1.0, 2.0, 3.0])
comm.AllReduce(data)Verify before relying
- Whether nccl4py requires explicit NCCL library installation or bundles it with the wheel.
- Performance characteristics compared to direct NCCL C API or other Python distributed frameworks.
- Supported collective operations beyond AllReduce and their API signatures.
Package facts
| License | Apache-2.0 permissive |
| Python support | Supports the current Python release >=3.10 |
| Install friction | Medium. Platform-specific wheel |
| Runtime dependencies | 4 packagespackagingnumpycuda-corecuda-pathfinder |
| Maintenance | Actively maintained 3 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 323,537 / month, #7,605 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Environment :: GPU :: NVIDIA CUDAEnvironment :: GPU :: NVIDIA CUDA :: 12Environment :: GPU :: NVIDIA CUDA :: 13Intended Audience :: DevelopersIntended Audience :: End Users/DesktopIntended Audience :: Science/ResearchNatural Language :: EnglishOperating System :: POSIX :: LinuxProgramming Language :: CythonProgramming Language :: Python :: 3 :: OnlyProgramming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Programming Language :: Python :: 3.14Programming Language :: Python :: Implementation :: CPythonTopic :: EducationTopic :: Scientific/EngineeringTopic :: Software Development :: Libraries |
Evidence: nccl4py-0.4.1-cp310-cp310-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl; nccl4py-0.4.1-cp310-cp310-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl; nccl4py-0.4.1-cp311-cp311-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl; nccl4py-0.4.1-cp311-cp311-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl; nccl4py-0.4.1-cp312-cp312-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl; nccl4py-0.4.1-cp312-cp312-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl; nccl4py-0.4.1-cp313-cp313-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl; nccl4py-0.4.1-cp313-cp313-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl; nccl4py-0.4.1-cp314-cp314-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl; nccl4py-0.4.1-cp314-cp314-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl; nccl4py-0.4.1-cp314-cp314t-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl; nccl4py-0.4.1-cp314-cp314t-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl
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See also nvidia-nccl-cu13 · nvshmem4py-cu13 · nvidia-nccl-cu11 · nvidia-nccl-cu12 · clusterscope · nvidia-cuda-cccl · oneccl · nvidia-nvshmem-cu12 · mpi4py · nixl-cu13