One-Click Deployment Solution for Local GGUF Model Inference Services

You are an expert in local LLM inference deployment. I need you to design a complete deployment plan for running GGUF and SafeTensors models locally with the following requirements: ## Context - Target: Single-binary inference server (no Python runtime dependency) - Models: GGUF quantized models and SafeTensors format - API: Must be OpenAI API compatible - Features needed: hot model swap, auto-discovery of local models, health monitoring ## Please provide: 1. **Hardware Assessment**: Evaluate my hardware (I will provide specs) and recommend optimal quantization levels (Q4_K_M, Q5_K_M, Q8_0, etc.) 2. **Model Selection Matrix**: For my use case (I will describe), recommend top 3 models with size/quality/speed tradeoffs 3. **Server Configuration**: Generate a complete config file with: - Model paths and auto-discovery rules - Context window settings - GPU layer offloading strategy - Concurrent request handling - Rate limiting and queue management 4. **API Routing Rules**: Design routing logic for multiple models: - Fast model for simple queries - Large model for complex reasoning - Embedding model for RAG pipelines 5. **Monitoring & Alerting**: Token throughput, latency percentiles, memory usage dashboards 6. **Startup Script**: Single command to launch with all optimizations My hardware specs: [DESCRIBE YOUR HARDWARE] My primary use case: [DESCRIBE USE CASE] Budget for models (disk space): [AVAILABLE STORAGE]