Status: ✅ CONFIRMED WORKING — No more “invalid resource handle” errors Wheel:llama_cpp_python-0.3.16-cp312-cp312-win_amd64.whl License: MIT (same as upstream llama-cpp-python)
Platform: Windows 10/11 x64 Python: 3.12 CUDA: 12.8 (optimized for Blackwell)
🚀 Performance (Verified on RTX 5090)
~64 tokens/sec on Mistral Small 24B (5-bit quant)
Full GPU offload (n_gpu_layers = -1) working as expected
~1.83× faster than RTX 3090 in the same setup (35 tok/s → 64 tok/s)
32 GB VRAM fully utilized (no kernel crashes)
Notes: numbers vary with quant, context, and params; these are representative.
🔧 Why This Works
The wheel forces cuBLAS instead of ggml’s custom CUDA kernels.
On RTX 5090 (Blackwell, sm_120), ggml’s custom kernels can trigger:
“CUDA error: invalid resource handle”.
cuBLAS is stable on 5090 and avoids those kernel issues.
Key CMake flags used:
-DGGML_CUDA=ON
-DGGML_CUDA_FORCE_CUBLAS=1 # Use cuBLAS instead of custom kernels
-DGGML_CUDA_NO_PINNED=1 # Avoid pinned memory issues with GDDR7
-DGGML_CUDA_F16=0 # Disable problematic FP16 code paths
-DCMAKE_CUDA_ARCHITECTURES=all-major # Ensure sm_120 is included
📋 Requirements
NVIDIA RTX 5090 (or other Blackwell GPU)
NVIDIA drivers 570.86.10+
CUDA Toolkit 12.8
Python 3.12
Windows 10/11 x64
Microsoft Visual C++ Redistributable 2015–2022
🛠️ Installation
Download the wheel: llama_cpp_python-0.3.16-cp312-cp312-win_amd64.whl
from llama_cpp import Llama
# Full GPU offload on 5090
llm = Llama(
model_path="your_model.gguf",
n_gpu_layers=-1, # full GPU
n_ctx=2048,
verbose=True
)
out = llm("Hello, how are you?", max_tokens=20)
print(out["choices"][0]["text"])
What to look for in stdout:
CUDA device assignment lines (e.g., using CUDA:0)
VRAM allocations without any “invalid resource handle” errors
🏗️ Build It Yourself (Advanced)
Prereqs: CUDA 12.8, Visual Studio Build Tools 2022 (with C++), Python 3.12
mkdir C:\wheels
cd C:\wheels
set FORCE_CMAKE=1
set CMAKE_BUILD_PARALLEL_LEVEL=15
set CMAKE_ARGS=-DGGML_CUDA=ON -DGGML_CUDA_FORCE_CUBLAS=1 -DGGML_CUDA_NO_PINNED=1 -DGGML_CUDA_F16=0 -DCMAKE_CUDA_ARCHITECTURES=all-major
pip wheel llama-cpp-python --no-cache-dir --wheel-dir C:\wheels --verbose
Build time: ~10 minutes on a modern CPU Wheel size: ~231 MB (larger due to cuBLAS inclusion)
🐛 Troubleshooting
“Invalid resource handle” errors
This wheel specifically fixes this. If you still see them, verify:
CUDA 12.8 is installed
Latest NVIDIA drivers are installed
No other CUDA apps are interfering
CPU fallback
If GPU isn’t detected, check nvidia-smi and ensure CUDA_VISIBLE_DEVICES isn’t set.
🙏 Credits
Built using the open-source llama-cpp-python project by abetlen and the llama.cpp project by ggml-org.
This wheel provides RTX 5090 compatibility by configuring cuBLAS fallback; it is not an official upstream release.
For issues with this specific wheel: open an issue here (this repo/thread).
For general llama-cpp-python issues: use the official repository.
Finally — RTX 5000 series owners can use their flagship GPU for local LLM inference without crashes! 🎉
