Qwen3-Coder RunPod Ollama API

Deploy Qwen3-Coder (30B/480B) on RunPod GPU with Ollama and LiteLLM proxy. Complete setup with secure OpenAI-compatible API endpoint, authentication, persistent storage, automated backups, and VS Code integration.

🚀 Features

• Secure OpenAI-compatible API endpoint
• API key authentication
• Persistent storage configuration
• Automated backup system
• VS Code integration ready
• Multi-model support (30B/480B)
• Production-ready deployment

🛠️ Requirements

• RunPod account with GPU instance
• Docker (optional but recommended)
• VS Code with Cline extension

📁 Project Structure

runpod/
├── README.md
├── VSCODE_INTEGRATION.md
├── deployment/
│   ├── start_ollama.sh
│   ├── backup_config.sh
│   └── litellm-config.yaml
├── scripts/
│   ├── model_management.sh
│   └── health_check.sh
├── configs/
│   ├── .env.example
│   └── docker-compose.yml
└── docs/
    ├── TROUBLESHOOTING.md
    └── PERFORMANCE_TUNING.md

📋 Setup Instructions

1. Create RunPod instance with PyTorch template
2. Install Ollama and required dependencies
3. Pull Qwen3-Coder model
4. Configure LiteLLM proxy with authentication
5. Start services with deployment scripts
6. Configure VS Code integration

🎯 Usage

• API Endpoint: https://your-runpod-endpoint.runpod.io/v1
• Model Names: qwen3-coder, gpt-3.5-turbo, gpt-4
• Authentication: Bearer token required