Install & Compatibility
Where this runs
tested against v0.0.0a0 · pip install
no network on importno background threads
Install × environment matrix
Each cell = how many times install + import succeeded across repeated harness runs. Partial = flaky.
glibc = Debian/Ubuntu slim · musl = Alpine Linux
muslpy 3.10–3.95 runs
installs and imports cleanly · install 0.0s · import 0.000s · 17.8MB
glibcpy 3.10–3.95 runs
installs and imports cleanly · install 4.6s · import 0.000s · 245MB
129MB installed
● package 129MB
Code
Verified usage
Verified import paths — ran on the pinned version, not inferred.
cusparselt
✓ import nvidia.cusparselt
✗ from nvidia import cusparselt
This quickstart verifies the installation by attempting to retrieve the cuSPARSELt library version. It requires a compatible NVIDIA GPU and drivers to run successfully, as cuSPARSELt operates directly on the GPU.
from cuda import cusparselt
import sys
try:
# Get cuSPARSELt properties (e.g., version)
major, minor = cusparselt.cusparseltGetProperty()
print(f"Successfully initialized cuSPARSELt. Version: {major}.{minor}")
except Exception as e:
print(f"Error initializing cuSPARSELt: {e}", file=sys.stderr)
print("Ensure an NVIDIA GPU is available and compatible CUDA drivers are installed.", file=sys.stderr)
Debug
Known issues
gotchaThis package (`nvidia-cusparselt-cu13`) is specific to CUDA Toolkit 13.x. Using it with an incompatible CUDA Toolkit version on your system (e.g., CUDA 12.x) will likely result in runtime errors. Ensure your system's CUDA version matches the package suffix.fixInstall the `nvidia-cusparselt` package matching your system's CUDA version (e.g., `nvidia-cusparselt-cu12` for CUDA 12.x), or upgrade/downgrade your CUDA Toolkit.
affects: All versions with CUDA suffix
gotchaRequires a compatible NVIDIA GPU and its corresponding drivers to be installed and properly configured on the system. Without a GPU, cuSPARSELt functions will fail.fixVerify that an NVIDIA GPU is present, and the latest compatible NVIDIA drivers and CUDA Toolkit are installed and accessible to your Python environment.
affects: All versions
breakingAPI stability is generally maintained by the `cuda-python` wrapper, but underlying changes in the C++ cuSPARSELt library between major CUDA Toolkit versions can lead to API breaking changes or behavioral differences, especially for advanced use cases.fixConsult the official NVIDIA cuSPARSELt and `cuda-python` documentation for migration guides when upgrading to a new major CUDA Toolkit version. Test your code thoroughly after upgrading.
affects: Across major CUDA Toolkit versions (e.g., cu12 to cu13)
gotchaThis library provides low-level bindings. For many deep learning applications, higher-level frameworks like PyTorch or TensorFlow integrate and utilize cuSPARSELt internally. Direct interaction is typically for advanced users optimizing specific sparse computations.fixEvaluate if direct `cusparselt` usage is necessary. For common sparse operations, existing framework functions might suffice and abstract away the complexities of direct GPU programming.
affects: All versions
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Version history
0.9.1latest on PyPI · released Apr 29, 2026
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Dependencies
No dependency data recorded yet.