ATTENTION! Hello, my name is Spunch Bob and this report I wrote with AI help. I ran into a problem when starting a node QwenVL(GGUF) and here I'll tell you how to solve it. I'm NOT professional programmer and my solve was did with AI helping, so read carefully my report and think is it good for you or not
p.s. my english not so nice, thank you for you patience

here is it:

Technical Report: Resolving OSError [WinError -1073741795] When Loading Qwen-VL GGUF in ComfyUI

Introduction

This report documents the diagnostic process and solution for the error OSError: [WinError -1073741795] Windows Error 0xc000001d that occurred when attempting to load a Qwen-VL multimodal model in GGUF format through the AILab_QwenVL_GGUF node in ComfyUI.

User Profile: Beginner ComfyUI user, collaborating with an AI assistant to troubleshoot and resolve the issue.

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1. Problem Description

Symptoms

• The AILab_QwenVL_GGUF node threw an error during execution:

  OSError: [WinError -1073741795] Windows Error 0xc000001d
  

• The error occurred during model initialization in llama_cpp.Llama()
• Model used: Qwen3VL-4B-Instruct-F16.gguf

System Specifications

• CPU: AMD Ryzen 7 5800H (AVX2 supported)
• GPU: NVIDIA GeForce RTX 3060 Laptop GPU (6GB VRAM)
• OS: Windows 10/11
• ComfyUI: version 0.29.2
• Python: 3.13.12
• PyTorch: 2.10.0+cu130 (CUDA 13.0)
• llama-cpp-python: 0.3.34 (CPU version initially)

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2. Diagnostic Process

Step 1: Verify Model File Location

Command executed:

dir "C:\...\models\LLM\Qwen3VL-4B-Instruct-F16.gguf"

Result: File not found. The model was stored in a different directory.

Finding: The node was searching for the model in ComfyUI-Shared\models\LLM\, but the actual file was located in ComfyUI-Installs\ComfyUI\ComfyUI\models\LLM\GGUF\Qwen\Qwen3-VL-4B-Instruct-GGUF\.

Step 2: Create Symbolic Link

Action: Created a symbolic link to make the model accessible at the path expected by the node:

mklink "C:\...\ComfyUI-Shared\models\LLM\Qwen3VL-4B-Instruct-F16.gguf" "C:\...\ComfyUI-Installs\...\models\LLM\GGUF\Qwen\Qwen3-VL-4B-Instruct-GGUF\Qwen3VL-4B-Instruct-F16.gguf"

Result: The file became visible at the expected path, but the error persisted.

Step 3: Verify llama_cpp Library Loading

Command executed:

python -c "import llama_cpp; print('OK')"

Result: Error: Could not find module 'llama.dll'

Finding: llama_cpp could not load the main library.

Step 4: Check llama.dll Existence

Command executed:

dir "C:\...\.venv\Lib\site-packages\llama_cpp\lib\llama.dll"

Result: File exists (6.4 MB).

Step 5: Test Dependency Loading

Command executed:

python -c "import ctypes; ctypes.CDLL('ggml-cpu.dll'); print('CPU loaded')"

Result: CPU loaded — CPU component works.

python -c "import ctypes; ctypes.CDLL('ggml-cuda.dll'); print('CUDA loaded')"

Result: Error — CUDA component failed to load.

Finding: The issue is specifically with the CUDA portion of the library.

Step 6: Identify Missing Dependencies

Checked and found missing:

• cudart64_12.dll — missing from llama_cpp\lib, copied from CUDA\v12.8\bin\
• cufft64_12.dll — missing, copied from torch\lib\
• cublas64_12.dll — missing, copied from CUDA\v12.8\bin\
• cublasLt64_12.dll — missing, copied from CUDA\v12.8\bin\

Result: Even after copying all dependencies, ggml-cuda.dll still failed to load.

Step 7: Determine llama-cpp-python Version

Command executed:

python -m pip show llama-cpp-python

Result: Version 0.3.34 installed (CPU version, without CUDA support).

Step 8: Verify CUDA Compatibility

Command executed:

python -c "import torch; print(torch.version.cuda)"

Result: PyTorch is using CUDA 13.0.

Finding: The installed CPU version of llama-cpp-python cannot utilize the GPU. A CUDA-enabled version is required.

Step 9: Reinstall llama-cpp-python with CUDA Support

Action: Removed the old version and installed the CUDA-enabled version using the cu120 index:

python -m pip uninstall llama-cpp-python -y
python -m pip install llama-cpp-python --force-reinstall --no-cache-dir --extra-index-url https://abetlen.github.io/llama-cpp-python/whl/cu120

Result: After installation, import llama_cpp executed without errors.

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3. Root Cause Analysis

Primary Cause: The system had the CPU version of llama-cpp-python installed, which does not include the ggml-cuda.dll file and cannot utilize GPU acceleration.

Secondary Causes:

1. The model file was located in a directory different from where the node was searching.
2. Required CUDA system libraries were missing from the llama_cpp\lib folder:
   • cudart64_12.dll
   • cufft64_12.dll
   • cublas64_12.dll
   • cublasLt64_12.dll

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4. Solution Steps

Step 1: Place the Model in the Correct Location

Ensure the .gguf model file is located in the directory expected by the node, or create a symbolic link to it.

Step 2: Install the CUDA Version of llama-cpp-python

Check which version is currently installed:

python -c "import llama_cpp; print(llama_cpp.__version__)"

If it is the CPU version, reinstall with CUDA support:

python -m pip uninstall llama-cpp-python -y
python -m pip install llama-cpp-python --force-reinstall --no-cache-dir --extra-index-url https://abetlen.github.io/llama-cpp-python/whl/cu120

Important: The CUDA version in the index (e.g., cu120 for CUDA 12.x) should match or be compatible with your installed CUDA version.

Step 3: Verify CUDA Libraries Are Present

Ensure the following files exist in ...\llama_cpp\lib\:

• ggml-cuda.dll
• cudart64_12.dll (or your specific version)
• cublas64_12.dll
• cufft64_12.dll
• cublasLt64_12.dll

If any are missing, copy them from:

• C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v12.x\bin\
• Or from ...\.venv\Lib\site-packages\torch\lib\

Step 4: Test Library Loading

python -c "import llama_cpp; print('OK')"

If no errors appear, the library is working correctly.

Step 5: Test Model Loading with GPU

python -c "from llama_cpp import Llama; llm = Llama(model_path='path_to_model.gguf', n_gpu_layers=-1, verbose=True)"

If the model loads successfully, the issue is resolved.

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5. Important Notes

1. Check your llama-cpp-python version carefully. The CPU version cannot utilize the GPU, even if all CUDA libraries are present.

2. CUDA Compatibility: PyTorch may use CUDA 13.0 while llama-cpp-python may use CUDA 12.x. They are generally compatible, but version mismatches can cause issues.

3. System Dependencies: Ensure Microsoft Visual C++ Redistributable 2022 is installed on your system.

4. Fallback to CPU: If you are unsure, you can set n_gpu_layers=0 in the node configuration to use only the CPU.

5. Symbolic Links: The mklink command requires administrator privileges.

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6. Conclusion

The problem was resolved by:

1. Properly locating the model file in the expected directory
2. Installing the CUDA-enabled version of llama-cpp-python
3. Adding the missing CUDA runtime libraries to the llama_cpp\lib folder

This solution is applicable to ComfyUI users encountering the 0xc000001d error when loading GGUF models, particularly those with NVIDIA GPUs and CUDA-enabled PyTorch installations.

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This report was prepared based on a real troubleshooting experience. All commands have been tested and can be used provided they are carefully reviewed and adapted to your specific system configuration.