Setting Up Text Generation WebUI (No AVX Required)
Text Generation WebUI is a great alternative to LM Studio that offers non-AVX builds, making it compatible with older CPUs. There are several installation options available:
Option-1 One-Click Installer (Recommended)
Visit the official GitHub repository
Download the installer that specifies “Non-AVX” support:
- For Windows:
oobabooga-windows-noavx.zip - For Linux:
oobabooga-linux-noavx.zip
- For Windows:
Extract the zip file and run:
- Windows:
start_windows.bat - Linux:
start_linux.sh
- Windows:
Option-2 Manual Installation
If you prefer manual installation:
1# Clone the repository
2git clone https://github.com/oobabooga/text-generation-webui
3cd text-generation-webui
4
5# Create and activate virtual environment
6python -m venv venv
7source venv/bin/activate # On Windows: venv\Scripts\activate
8
9# Install with the noavx flag
10pip install -r requirements.txt --extra-index-url https://download.pytorch.org/whl/cpu/torch_stable.html --prefer-binary --no-cache-dir
11
12# Start the UI
13python server.py --listen --no-download-loader
Running Gemma 2 2b
- Launch the web interface (it should open at http://127.0.0.1:7860)
- Go to the “Model” tab
- Select “Download model or LoRA”
- In the “Hugging Face Hub model” field, enter:
google/gemma-2-2b-it - Click “Download”
- After downloading, select the model from the dropdown and click “Load”
Configuration Options
For better performance on CPUs without AVX2:
In the “Parameters” tab:
- Set Context Length to a lower value (like 2048)
- Enable “8-bit” or “4-bit” quantization
- Use “cpu” as the Inference Device
In the “Session” tab:
- Set “Instructions template” to match Gemma 2
Alternative Models
If Gemma 2 2b is still too demanding, try these smaller models that work better on older CPUs:
- TinyLlama (1.1B parameters)
- Phi-2 (2.7B parameters)
- Mistral 7B in 4-bit quantization
Conclusion
In this guide, we covered the installation and configuration of Text Generation WebUI without requiring AVX support. By following the outlined steps, you should be able to set up the web interface and run various models on older CPUs. Remember to explore alternative models if you encounter performance issues with larger ones. Happy experimenting!












