nvidia-nvshmem-cu13
NVSHMEM creates a global address space that provides efficient and scalable communication for NVIDIA GPU clusters.
Decision gist · record as of 2026-08-14
Yes, if you are developing multi-GPU applications on Linux clusters and need efficient GPU-to-GPU communication. The package is actively maintained, has no known vulnerabilities, and targets a legitimate use case. However, the unclear license status and lack of public documentation are concerns—verify licensing terms and review NVIDIA's CUDA Zone documentation before committing to production use.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires NVIDIA CUDA Compute Capability 1.3+ GPUs, Linux (x86_64 or aarch64), and the nvidia-cuda-cccl runtime dependency installed.
- Medium install friction due to platform-specific wheels (x86_64 and aarch64 Linux only) and a dependency on nvidia-cuda-cccl.
- The package is actively maintained with a recent release.
License · maintenance · safety
(unclear) — License status is unclear—no SPDX identifier or raw license text is available in the package metadata, so you cannot determine licensing obligations before installation.
last release 2026-07-17 (28 days)
0 known vulnerabilities (OSV.dev, 2026-08-14) · 42,207,426 downloads/mo, #658 on PyPI
Alternatives
Verify before relying
pip install nvidia-nvshmem-cu13
import nvshmem
# Initialize NVSHMEM for multi-GPU communication- Specific NVSHMEM API surface and whether it requires direct C/C++ interop or provides a pure Python interface.
- Whether the package includes documentation or examples beyond the NVIDIA CUDA Zone homepage.
- Compatibility with specific CUDA versions beyond the cu13 variant designation.
- Performance characteristics and typical use patterns in production GPU clusters.
What it is and what it does
NVSHMEM is an NVIDIA library that implements a parallel programming model for GPU clusters, extending OpenSHMEM semantics to NVIDIA GPUs. It creates a unified global address space spanning memory across multiple GPUs, allowing both GPU kernels and CPU code to perform fine-grained remote memory operations on other GPUs in the cluster without explicit data movement. This is particularly useful for distributed machine learning and scientific computing workloads that need efficient inter-GPU communication at scale.
The package is distributed as a Python wrapper around the underlying NVSHMEM runtime and requires the nvidia-cuda-cccl dependency. It targets developers working with multi-GPU systems on Linux (x86_64 or aarch64 architectures) and supports Python 3.5 through 3.11. Installation has medium friction due to platform-specific wheels and CUDA runtime requirements.
Use it for
- Distributed deep learning training across multiple GPUs in a cluster with low-latency inter-GPU communication.
- Large-scale scientific simulations requiring efficient collective operations and remote memory access on GPU clusters.
- Building custom communication patterns for GPU-accelerated applications that need fine-grained control over multi-GPU data movement.
- Implementing collective algorithms (reductions, broadcasts, scatters) optimized for GPU memory hierarchies in parallel applications.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you are developing multi-GPU applications on Linux clusters and need efficient GPU-to-GPU communication.
The package is actively maintained, has no known vulnerabilities, and targets a legitimate use case. However, the unclear license status and lack of public documentation are concerns—verify licensing terms and review NVIDIA's CUDA Zone documentation before committing to production use.
Install
nvidia-nvshmem-cu13 on PyPI
Before you install
Medium install friction due to platform-specific wheels (x86_64 and aarch64 Linux only) and a dependency on nvidia-cuda-cccl. The package is actively maintained with a recent release.
Requires NVIDIA CUDA Compute Capability 1.3+ GPUs, Linux (x86_64 or aarch64), and the nvidia-cuda-cccl runtime dependency installed.
License in practice
License status is unclear—no SPDX identifier or raw license text is available in the package metadata, so you cannot determine licensing obligations before installation.
Quickstart
pip install nvidia-nvshmem-cu13
import nvshmem
# Initialize NVSHMEM for multi-GPU communicationVerify before relying
- Specific NVSHMEM API surface and whether it requires direct C/C++ interop or provides a pure Python interface.
- Whether the package includes documentation or examples beyond the NVIDIA CUDA Zone homepage.
- Compatibility with specific CUDA versions beyond the cu13 variant designation.
- Performance characteristics and typical use patterns in production GPU clusters.
Package facts
| License | Not declared unclear |
| Python support | Supports the current Python release >=3 |
| Install friction | Medium. Platform-specific wheel |
| Runtime dependencies | 1 packagenvidia-cuda-cccl |
| Maintenance | Actively maintained 28 days since the last release |
| First released | |
| Downloads | 42,207,426 / month, #658 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/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 |
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
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See also nvidia-nvshmem-cu12 · nvshmem4py-cu13 · cosmos-xenna · nvidia-nccl-cu13 · nvidia-nccl-cu12 · impi-rt · mooncake-transfer-engine-cuda13 · nvidia-nccl-cu11 · nvidia-cuda-cccl-cu12 · nvgpu