---
id: nvidia-nvshmem-cu12
version: "3.7.2"
license: unclear
license_treatment: unclear
maintenance: active
---

# nvidia-nvshmem-cu12 — NVSHMEM creates a global address space that provides efficient and scalable communication for NVIDIA GPU clusters.

License: unclear · Maintenance: active · Downloads: 7.6M/mo

## What it is and what it does

NVSHMEM is NVIDIA's implementation of the OpenSHMEM parallel programming standard, adapted for GPU clusters. It creates a unified memory address space spanning multiple GPUs, allowing kernels and CPU code to read and write remote GPU memory with fine-grained control. The package is built on top of CUDA 12 and is intended for researchers and engineers building distributed machine learning and scientific computing workloads that need efficient inter-GPU communication.

The package is in Beta status and actively maintained. It targets modern Python versions (3.5 through 3.11) and runs on Linux (both aarch64 and x86_64 architectures). Installation requires the nvidia-cuda-cccl-cu12 runtime dependency, and the platform-specific wheels mean you must match your system architecture exactly.

Use it for:

- Implement collective operations (allreduce, broadcast) across GPUs in a cluster without explicit message passing.
- Build distributed training loops where multiple GPUs access shared parameter buffers via global memory semantics.
- Develop scientific simulations that require fine-grained synchronization and data movement between GPU memories.
- Optimize communication in multi-GPU inference pipelines by leveraging GPU-initiated remote memory access.

## Worth the install?

AI-flagged interpretation of the facts above — verify before relying.

NVSHMEM provides a global address space for GPU cluster communication, enabling fine-grained GPU and CPU-initiated operations across multiple GPU memories using OpenSHMEM-based primitives.

Yes, if you are building multi-GPU CUDA applications on Linux and need efficient inter-GPU communication. The package is actively maintained, has no known vulnerabilities, and targets a real use case in distributed GPU computing. However, verify the unclear license terms and confirm Windows support is not actually available despite classifier claims. Medium install friction is acceptable for a specialized GPU library.

## Install

pip install nvidia-nvshmem-cu12
uv add nvidia-nvshmem-cu12
poetry add nvidia-nvshmem-cu12

## Installing nvidia-nvshmem-cu12

Before you install: Medium install friction due to platform-specific wheels (aarch64 and x86_64 Linux only). Active maintenance with a recent release, though no public repository or commit history is available to verify ongoing development velocity.

License in practice: License terms are unclear—no SPDX identifier or raw license text is published. Verify licensing compatibility with your project before production use.

Quickstart:

pip install nvidia-nvshmem-cu12
import nvshmem
# Initialize NVSHMEM for multi-GPU communication
nvshmem.init()

Requires NVIDIA CUDA 12.x runtime, NVIDIA GPUs, and Linux (aarch64 or x86_64); not available on Windows or macOS despite classifier claims.

Verify before relying:

- Actual Windows support status—classifiers list Windows but wheels are Linux-only.
- Whether public documentation exists beyond the NVIDIA CUDA Zone homepage.
- Specific CUDA 12 minor version requirements and GPU compute capability minimums.
- Whether the package works standalone or requires additional NVIDIA libraries beyond nvidia-cuda-cccl-cu12.

## Package facts

- License: not declared (unclear)
- Python support: supports_current
- Install friction: medium
- Maintenance: active
- Downloads: 7.6M/month (top 5,000 on PyPI)
- Known vulnerabilities: none known

## Tags

gpu cluster communication, nvidia nvshmem, multi-gpu shared memory, openshmem gpu, cuda inter-gpu communication, distributed gpu memory, gpu collective operations, gpu-computing, distributed-systems, cuda

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