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bugstatus: waiting-for-feedbackMaintainer is blocker and waiting for user feedbackMaintainer is blocker and waiting for user feedback
Description
Version
1.0.1
Version
13.1
Which installation method(s) does this occur on?
Pip
Describe the bug.
I copied the example code from the cuTile quickstart and tried running it.
https://docs.nvidia.com/cuda/cutile-python/quickstart.html
Project Setup:
uv init .
uv add cuda-tile
uv add numpy
uv add pytest
uv add cup-cuda13x
# Copy the quickstart sample code into main.py
uv run main.py
This results in the following error:
Traceback (most recent call last):
File "C:\git\cutile-test\.venv\Lib\site-packages\cupy\cuda\compiler.py", line 731, in compile
nvrtc.compileProgram(self.ptr, options)
File "cupy_backends\\cuda\\libs\\nvrtc.pyx", line 125, in cupy_backends.cuda.libs.nvrtc.compileProgram
File "cupy_backends\\cuda\\libs\\nvrtc.pyx", line 138, in cupy_backends.cuda.libs.nvrtc.compileProgram
File "cupy_backends\\cuda\\libs\\nvrtc.pyx", line 53, in cupy_backends.cuda.libs.nvrtc.check_status
cupy_backends.cuda.libs.nvrtc.NVRTCError: NVRTC_ERROR_BUILTIN_OPERATION_FAILURE (7)
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "C:\git\cutile-test\main.py", line 60, in <module>
test()
File "C:\git\cutile-test\main.py", line 37, in test
a = cp.random.uniform(-1, 1, vector_size)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\git\cutile-test\.venv\Lib\site-packages\cupy\random\_distributions.py", line 843, in uniform
return rs.uniform(low, high, size=size, dtype=dtype)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\git\cutile-test\.venv\Lib\site-packages\cupy\random\_generator.py", line 984, in uniform
rand = self.random_sample(size=size, dtype=dtype)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\git\cutile-test\.venv\Lib\site-packages\cupy\random\_generator.py", line 624, in random_sample
RandomState._mod1_kernel(out)
File "cupy\\_core\\_kernel.pyx", line 920, in cupy._core._kernel.ElementwiseKernel.__call__
File "cupy\\_core\\_kernel.pyx", line 945, in cupy._core._kernel.ElementwiseKernel._get_elementwise_kernel
File "cupy\\_util.pyx", line 67, in cupy._util.memoize.decorator.ret
File "cupy\\_core\\_kernel.pyx", line 728, in cupy._core._kernel._get_elementwise_kernel
File "cupy\\_core\\_kernel.pyx", line 82, in cupy._core._kernel._get_simple_elementwise_kernel_from_code
File "cupy\\_core\\core.pyx", line 2377, in cupy._core.core.compile_with_cache
File "C:\git\cutile-test\.venv\Lib\site-packages\cupy\cuda\compiler.py", line 536, in _compile_module_with_cache
return _compile_with_cache_cuda(
^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\git\cutile-test\.venv\Lib\site-packages\cupy\cuda\compiler.py", line 580, in _compile_with_cache_cuda
base = _preprocess('', options, arch, backend)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\git\cutile-test\.venv\Lib\site-packages\cupy\cuda\compiler.py", line 473, in _preprocess
result, _ = prog.compile(options)
^^^^^^^^^^^^^^^^^^^^^
File "C:\git\cutile-test\.venv\Lib\site-packages\cupy\cuda\compiler.py", line 750, in compile
raise CompileException(log, self.src, self.name, options,
cupy.cuda.compiler.CompileException: nvrtc: error: failed to open nvrtc-builtins64_131.dll.
Make sure that nvrtc-builtins64_131.dll is installed correctly.
Minimum reproducible example
Provided aboveRelevant log output
Provided aboveFull env printout
Doesn't work on Windows 11. I don't have Bash. Bash refers to Windows Subsystem for Linux (WSL), not Bash itself running on Windows.- Windows 11 Pro
- PNY NVIDIA GeForce RTX 5070 Ti
- NVIDIA Driver 591.74
- NVIDIA CUDA Toolkit 13.1 installed
Output of nvidia-smi:
+-----------------------------------------------------------------------------------------+
| NVIDIA-SMI 591.74 Driver Version: 591.74 CUDA Version: 13.1 |
+-----------------------------------------+------------------------+----------------------+
| GPU Name Driver-Model | Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap | Memory-Usage | GPU-Util Compute M. |
| | | MIG M. |
|=========================================+========================+======================|
| 0 NVIDIA GeForce RTX 5070 Ti WDDM | 00000000:01:00.0 On | N/A |
| 0% 50C P0 36W / 300W | 2989MiB / 16303MiB | 1% Default |
| | | N/A |
+-----------------------------------------+------------------------+----------------------+
Other/Misc.
No response
Contributing Guidelines
- I agree to follow cuTile Python's contributing guidelines
- I have searched the open bugs and have found no duplicates for this bug report
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bugstatus: waiting-for-feedbackMaintainer is blocker and waiting for user feedbackMaintainer is blocker and waiting for user feedback
Activity
haijieg commentedon Jan 19, 2026
@pcgeek86 this is a cupy setup issue. Please refer to
cupyinstallation guide. https://docs.cupy.dev/en/stable/install.htmlZhangZhiPku commentedon Jan 19, 2026
I encountered a similar issue on a B200 GPU, and I resolved it by reinstalling CUDA Toolkit 13.1.
https://developer.nvidia.com/cuda-downloads
leofang commentedon Jan 20, 2026
NVRTC is the bare minimum for CuPy to run. In CuPy v13.x and earlier, CuPy assumes that a system CUDA Toolkit (CTK) is installed. However, NVRTC and other CTK components can be installed via pip/conda to the user space as well, and to use them today we have a few options:
pip install "cuda-toolkit[nvrtc]==13.1.*" cuda-pathfinderafter installing cuTile Python and CuPy, and then callfrom cuda import pathfinder; pathfinder.load_nvidia_dynamic_lib("nvrtc")after importing CuPy. This will force the OS to load NVRTC from a non-system installation location (aka pip and conda) for CuPy to make use of it.leofang commentedon Jan 20, 2026
Sorry, was juggling between meetings and did not give the full picture here. CuPy has been fully working with conda CTK for many years, so here is the Option 3 (if you use conda/pixi to manage your Python env):
conda install -c conda-forge cupy cuda-version=13.1and then pip install cuTile Python to the same env.pcgeek86 commentedon Jan 23, 2026
Conda seems really bloated. There isn't a way to just use the
uvpackage manager? It's used on lots of different Python projects, and does not seem as bloated as Conda. I would much prefer not to install Conda.pcgeek86 commentedon Jan 23, 2026
I just restarted my development workstation, and it seems like the CUDA Toolkit installation is working fine now. I was able to run the cuTile example code.
Here's the values of the CUDA environment variables:
And the actual program running:
I am still not using Conda at all. I simply installed the dependencies with the
uvpackage manager. Here's the correspondingpyproject.tomlfile, just for reference:pcgeek86 commentedon Jan 23, 2026
Since the
print_env.shscript doesn't work for reporting environment details on Windows 11, I added a pull request with a PowerShell script, that writes out info to a text file.#64
leofang commentedon Feb 9, 2026
Conda is by-design a self-contained, cross-platform, user-space package manager. It is meant to be used in isolation from most of the system package management, meaning what's already installed on the system is simply ignored, and for that there are extra packages that need to be installed as required dependency for consistency and safe usage. This is a feature not a bug 🙂
FWIW, all NVIDIA CUDA conda packages on the conda-forge channel are officially maintained by NVIDIA.