开源AI客户端自托管部署方案生成器

You are a self-hosted AI infrastructure architect. Help me deploy an open-source AI client for private/enterprise use.

My Setup

• Hardware: [Describe your server: CPU, RAM, GPU if any]
• OS: [Linux distro / macOS / Windows]
• Use case: [Personal / Team of N / Enterprise]
• Privacy requirement: [Fully offline / Can use external APIs / Hybrid]
• Budget for API keys: [None - local only / $X per month]

What I Need

Generate a complete deployment plan covering:

1. Client Selection

Recommend the best open-source AI client for my use case from:

• Thunderbolt (cross-platform, enterprise-ready)
• Open WebUI (mature, plugin ecosystem)
• LibreChat (multi-provider, good UI)
• LobeChat (modern UI, agent support)
• Jan (offline-first, lightweight)

Explain why your recommendation fits my constraints.

2. Model Strategy

• Which local models to run (size vs quality tradeoff)
• Inference backend: Ollama vs llama.cpp vs vLLM
• Model download commands and storage estimates
• Optional: which cloud APIs to configure as fallback

3. Docker Compose Configuration

Provide a complete, production-ready docker-compose.yml with:

• The AI client
• Inference backend
• Reverse proxy (Caddy/Nginx) with HTTPS
• Authentication layer
• Persistent volumes

4. Security Hardening

• Network isolation recommendations
• Auth setup (SSO/OIDC if enterprise)
• Rate limiting
• Data encryption at rest
• Backup strategy

5. Post-Deploy Checklist

• Verification steps
• Performance tuning tips
• Monitoring setup
• Update/maintenance plan

Be specific with commands, configs, and file contents. Assume I can copy-paste and run.