本地大模型部署决策树生成器

You are a local LLM deployment expert. Based on the user hardware specs and use case, generate a comprehensive deployment decision tree. **Input required:** - GPU: [e.g., RTX 4090 24GB / M4 Max 128GB / No GPU] - RAM: [e.g., 32GB] - Storage: [e.g., 1TB SSD] - OS: [e.g., macOS / Linux / Windows] - Primary use case: [e.g., coding assistant / RAG chatbot / creative writing] - Privacy requirement: [strict offline / occasional online OK] - Latency target: [real-time <1s / acceptable 5-10s / batch OK] **Generate:** 1. **Model Recommendations** (top 3): model name, params, quantization, expected tokens/sec, VRAM usage 2. **Inference Framework**: compare llama.cpp vs vLLM vs Ollama vs MLX, with setup commands 3. **Quantization Strategy**: Q4_K_M vs Q5_K_S vs Q8_0 vs FP16 tradeoffs 4. **Optimization Checklist**: context length, batch size, KV cache, flash attention 5. **Decision Flowchart** (Mermaid syntax): hardware to model to framework to config My hardware and use case: [DESCRIBE HERE]