nvidia-nvshmem-cu13

NVSHMEM creates a global address space that provides efficient and scalable communication for NVIDIA GPU clusters.

nvidia-nvshmem-cu13 License unclear Active v3.7.2 released 2026-07-17

Install

nvidia-nvshmem-cu13 on PyPI

pip

pip install nvidia-nvshmem-cu13

uv

uv add nvidia-nvshmem-cu13

poetry

poetry add nvidia-nvshmem-cu13

Package facts

License	not declared (unclear)
Python support	supports the current Python release (>=3)
Install friction	medium — platform-specific wheel
Runtime dependencies	1 — nvidia-cuda-cccl
Maintenance	actively maintained — 27 days since the last release
First released	2025-04-09
Popularity	one of the top 1,000 most-downloaded packages on PyPI (30-day window, as of 2026-08-13)
Known vulnerabilities	none known (OSV.dev, checked 2026-08-13)

Evidence: nvidia_nvshmem_cu13-3.7.2-py3-none-manylinux2014_aarch64.manylinux_2_17_aarch64.whl; nvidia_nvshmem_cu13-3.7.2-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl

Keywords: cuda, nvidia, runtime, machine learning, deep learning

Development Status :: 4 - BetaIntended Audience :: DevelopersIntended Audience :: EducationIntended Audience :: Science/ResearchNatural 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

About nvidia-nvshmem-cu13

from the package's own PyPI description — quoted content, verbatim

NVSHMEM is a parallel programming interface based on OpenSHMEM that provides efficient and scalable communication for NVIDIA GPU clusters. NVSHMEM creates a global address space for data that spans the memory of multiple GPUs and can be accessed with fine-grained GPU-initiated operations, CPU-initiated operations, and operations on CUDA streams.

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AI interpretation — verify before relying

AI-generated interpretation of the package facts above; every digit, version, license, or vulnerability id it cites is grounded in the facts already shown on this page

NVSHMEM provides a parallel programming interface for NVIDIA GPU clusters, creating a global address space across multiple GPUs' memory that supports fine-grained GPU-initiated and CPU-initiated communication operations.

Medium install friction due to platform-specific wheels (x86_64 and aarch64 Linux only) and a required runtime dependency on nvidia-cuda-cccl. Recently released (27 days old) with active maintenance status.

License terms are unclear—no SPDX identifier or raw license text is available in the package metadata. Verify licensing directly with NVIDIA before production use.

Usage

pip install nvidia-nvshmem-cu13
# Requires nvidia-cuda-cccl runtime dependency and NVIDIA GPU cluster setup

Requires NVIDIA CUDA 13 toolkit, nvidia-cuda-cccl runtime dependency, and Linux (x86_64 or aarch64) with compatible GPUs; Python ≥3.

Verdict: NVSHMEM is a recently released (27 days old), actively maintained GPU cluster communication library in Beta status, positioned for HPC and ML workloads. Medium install friction and unclear licensing are the primary concerns; no known vulnerabilities. Suitable for developers working with multi-GPU NVIDIA systems who can verify the license terms independently.

Needs verification

Exact license terms and any redistribution restrictions for NVSHMEM
Whether nvidia-cuda-cccl is automatically available or requires separate CUDA toolkit installation
Performance characteristics and scalability limits for the cluster sizes this version targets

nvidia gpu cluster communicationnvshmem parallel programminggpu global address spacecuda multi-gpu synchronizationopenshmem nvidia implementationgpu collective operationsdistributed gpu memory access