---
id: nvidia-cusparselt-cu12
version: "0.8.1"
license: NVIDIA Proprietary Software
license_treatment: unclear
maintenance: aging
---

# nvidia-cusparselt-cu12 — NVIDIA cuSPARSELt

License: unclear · Maintenance: aging · Downloads: 20.0M/mo

## What it is and what it does

nvidia-cusparselt-cu12 is a Python wrapper around NVIDIA's cuSPARSELt CUDA library, which accelerates sparse matrix-matrix multiplication on NVIDIA GPUs. It targets workloads where at least one operand is a structured sparse matrix with 50% sparsity ratio, enabling mixed-precision computation across formats like FP32, BF16, FP16, INT8, E4M3, and E5M2. The library leverages NVIDIA's Sparse MMA tensor cores for performance.

The package has no Python runtime dependencies and is distributed as platform-specific wheels for x86_64, aarch64, and Windows. It is intended for machine learning and high-performance computing applications that can exploit structured sparsity patterns on modern NVIDIA GPUs. Users access the library through NVIDIA's cuSPARSELt C/CUDA APIs exposed to Python, with flexibility in algorithm selection, epilogue operations, memory layout, and data types.

Use it for:

- Accelerate sparse matrix operations in machine learning inference on NVIDIA GPUs with structured sparsity support.
- Optimize large-scale linear algebra computations in scientific computing that benefit from 50% sparsity patterns.
- Implement high-performance search engine ranking or recommendation systems exploiting structured sparsity.
- Mixed-precision deep learning workloads requiring INT8, FP16, or BF16 sparse matrix multiplication.
- GPU-accelerated numerical simulations where sparse matrix-matrix products dominate runtime.

## Worth the install?

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

Provides NVIDIA's cuSPARSELt CUDA library for high-performance sparse matrix-matrix multiplication on NVIDIA GPUs with structured sparsity patterns.

Yes, if you have a compatible NVIDIA GPU and need sparse matrix-matrix multiplication acceleration. The package is popular and has no known vulnerabilities. However, the proprietary license requires verification against your project's licensing terms, and the aging maintenance status warrants checking NVIDIA's official support channels before adopting in production.

## Install

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

## Installing nvidia-cusparselt-cu12

Before you install: Medium install friction due to platform-specific wheel distribution (aarch64, x86_64, Windows). Package is aging (343 days since release) with no visible repository or commit history in the fact sheet, limiting transparency on active maintenance.

License in practice: Licensed under NVIDIA Proprietary Software with unclear treatment—not an open-source license. Users should verify compatibility with their project's licensing requirements and review NVIDIA's terms before deployment.

Quickstart:

pip install nvidia-cusparselt-cu12

import nvidia.cusparselt
# Use cuSPARSELt APIs for sparse matrix operations on CUDA-capable GPUs

Requires NVIDIA CUDA 12 and a compatible NVIDIA GPU with structured sparsity support; Windows, Linux x86_64, or Linux aarch64 platform.

Verify before relying:

- Specific CUDA compute capability (SM architecture) requirements beyond those listed in the description
- Whether Python version constraints exist despite requires_python being unspecified
- Active maintenance status and update cadence given aging signal and no visible repository
- Licensing restrictions on commercial or redistributed use of applications built with cuSPARSELt
- Download volume and actual adoption patterns beyond raw monthly download counts

## Package facts

- License: NVIDIA Proprietary Software (unclear)
- Python support: unspecified
- Install friction: medium
- Maintenance: aging
- Downloads: 20.0M/month (top 5,000 on PyPI)
- Known vulnerabilities: none known

## Tags

sparse matrix multiplication cuda, nvidia cusparselt gpu, structured sparsity cuda library, sparse gemm nvidia, cuda sparse tensor operations, high performance sparse matrix gpu, nvidia ampere sparsity, gpu-acceleration, sparse-linear-algebra, nvidia-cuda

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