Qwen3-Coder-480B-A35B-Instruct Setup Guide

Complete automated installation guide for Qwen3-Coder-480B-A35B-Instruct model on Ubuntu systems.

📌 Important: For the verified working installation process, see INSTALLATION_GUIDE_480B.md. This guide provides the exact steps that successfully installed the 480B model with GGUF format.

🚀 Quick Start

# One-line installation
curl -fsSL https://raw.githubusercontent.com/twobitapps/480b-setup/main/install.sh | bash

📋 System Requirements

Minimum Hardware Requirements

• GPU: NVIDIA H100 80GB HBM3 (recommended) or A100 80GB
• RAM: 64GB+ system RAM
• Storage: 500GB+ free space (450GB for model + dependencies)
• CPU: 16+ cores recommended
• Network: High-speed internet for initial model download

Software Requirements

• OS: Ubuntu 20.04+ or 22.04 LTS (recommended)
• Python: 3.8-3.11 (3.10 recommended)
• CUDA: 12.1+ (will be installed automatically)
• Git: Latest version
• Git LFS: For large file handling

🔧 Installation Methods

Method 1: Automated Script (Recommended)

./install.sh

Method 2: Manual Step-by-Step

Follow the detailed instructions in MANUAL_INSTALL.md

Method 3: Docker Setup

docker-compose up -d

📁 Repository Structure

480b-setup/
├── README.md                 # This file
├── install.sh               # Main installation script
├── MANUAL_INSTALL.md        # Step-by-step manual guide
├── scripts/
│   ├── system_check.sh      # System requirements verification
│   ├── dependencies.sh      # Install system dependencies
│   ├── python_env.sh        # Python environment setup
│   ├── cuda_setup.sh        # CUDA installation
│   ├── model_download.sh    # Model download with resume
│   ├── test_installation.sh # Installation verification
│   └── benchmark.sh         # Performance testing
├── config/
│   ├── requirements.txt     # Python dependencies
│   ├── environment.yml      # Conda environment
│   └── model_config.json    # Model configuration
├── examples/
│   ├── basic_inference.py   # Simple inference example
│   ├── benchmark_test.py    # Performance benchmark
│   └── comparison_demo.py   # 480B vs 7B comparison
├── docker/
│   ├── Dockerfile           # Docker container setup
│   └── docker-compose.yml   # Docker Compose configuration
└── docs/
    ├── TROUBLESHOOTING.md   # Common issues and solutions
    ├── PERFORMANCE.md       # Performance tuning guide
    └── API_REFERENCE.md     # API usage documentation

⚡ Quick Verification

After installation, verify everything works:

# Run system verification
./scripts/test_installation.sh

# Run basic inference test
python examples/basic_inference.py

# Run performance benchmark
./scripts/benchmark.sh

🐛 Troubleshooting

If you encounter issues:

1. Check TROUBLESHOOTING.md
2. Run ./scripts/system_check.sh to verify requirements
3. Check logs in ~/qwen480b_env/logs/
4. Open an issue with detailed error logs

📊 Performance Expectations

On NVIDIA H100 80GB:

• Model Loading: ~2-3 minutes
• First Inference: ~10-15 seconds (cold start)
• Subsequent Inference: ~3-6 seconds
• Tokens per Second: 100-200 (depends on prompt complexity)
• Memory Usage: ~45-50GB VRAM

🔗 Related Projects

• Qwen Model Comparison Platform - Visual comparison demo
• Original Qwen Repository
• Hugging Face Model (Full)
• Hugging Face Model (GGUF)

📝 License

This setup guide is provided under MIT License. The Qwen model follows its own licensing terms.

🤝 Contributing

1. Fork the repository
2. Create a feature branch
3. Test your changes thoroughly
4. Submit a pull request

📞 Support

• Issues: GitHub Issues
• Discussions: GitHub Discussions
• Documentation: Wiki

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⚠️ Note: This is a large model requiring significant computational resources. Ensure your system meets the minimum requirements before installation.