---
id: nvidia-cusparselt-cu13
version: "0.9.1"
license: NVIDIA Proprietary Software
license_treatment: unclear
maintenance: active
---

# nvidia-cusparselt-cu13 — NVIDIA cuSPARSELt

License: unclear · Maintenance: active · Popularity: top 1,000 on PyPI

## Install

pip install nvidia-cusparselt-cu13
uv add nvidia-cusparselt-cu13
poetry add nvidia-cusparselt-cu13

## Description

###################################################################################
cuSPARSELt: A High-Performance CUDA Library for Sparse Matrix-Matrix Multiplication
###################################################################################

**NVIDIA cuSPARSELt** is a high-performance CUDA library dedicated to general matrix-matrix operations in which at least one operand is a structured sparse matrix with 50\% sparsity ratio:

.. math::

   D = Activation(\alpha op(A) \cdot op(B) + \beta op(C) + bias)

where :math:`op(A)/op(B)` refers to in-place operations such as transpose/non-transpose, and :math:`alpha, beta` are scalars or vectors.

The *cuSPARSELt APIs* allow flexibility in the algorithm/operation selection, epilogue, and matrix characteristics, including memory layout, alignment, and data types.

**Download:** `developer.nvidia.com/cusparselt/downloads <https://developer.nvidia.com/cusparselt/downloads>`_

**Provide Feedback:** `Math-Libs-Feedback@nvidia.com <mailto:Math-Libs-Feedback@nvidia.com?subject=cuSPARSELt-Feedback>`_

**Examples**:
`cuSPARSELt Example 1 <https://github.com/NVIDIA/CUDALibrarySamples/tree/main/cuSPARSELt/matmul>`_,
`cuSPARSELt Example...

## AI interpretation — verify before relying

NVIDIA cuSPARSELt is a high-performance CUDA library for sparse matrix-matrix multiplication, optimized for structured sparsity with 50% sparsity ratio and mixed-precision computation support.

Verdict: nvidia-cusparselt-cu13 is a specialized, actively maintained CUDA library for high-performance sparse matrix operations on NVIDIA GPUs. Install friction is moderate due to platform-specific wheels and hardware requirements. The proprietary license treatment is unclear and warrants review before adoption. No known security vulnerabilities.

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