boneylizardwizard/llama_cpp_python-0.3.16-cp312-cp312-win_amd64

RTX 5000 Series–Ready llama-cpp-python Wheel (Python 3.12, Windows)

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: -DGGMLCUDA=ON -DGGMLCUDAFORCECUBLAS=1 # Use cuBLAS instead of custom kernels -DGGMLCUDANOPINNED=1 # Avoid pinned memory issues with GDDR7 -DGGMLCUDAF16=0 # Disable problematic FP16 code paths -DCMAKECUDAARCHITECTURES=all-major # Ensure sm120 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

1) Download the wheel: llama_cpp_python-0.3.16-cp312-cp312-win_amd64.whl

2) Install: pip install llamacpppython-0.3.16-cp312-cp312-win_amd64.whl

✅ Quick Verification

from llama_cpp import Llama

# Full GPU offload on 5090 llm = Llama( modelpath="yourmodel.gguf", ngpulayers=-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 FORCECMAKE=1 set CMAKEBUILDPARALLELLEVEL=15 set CMAKEARGS=-DGGMLCUDA=ON -DGGMLCUDAFORCECUBLAS=1 -DGGMLCUDANOPINNED=1 -DGGMLCUDAF16=0 -DCMAKECUDAARCHITECTURES=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! 🎉