本地 LLM 推理服务一键部署与性能调优脚本生成器

You are an expert in local LLM deployment and inference optimization. Based on my hardware specs, generate a complete deployment script with optimal configuration. ## My Hardware - OS: [macOS/Linux/Windows] - CPU: [MODEL, e.g., Apple M4 Max, AMD 7950X, Intel i9-14900K] - GPU: [MODEL + VRAM, e.g., RTX 4090 24GB, Apple Silicon unified 64GB, None] - RAM: [TOTAL, e.g., 64GB] - Storage: [SSD TYPE + FREE SPACE] - Network: [Local only / Need API server] ## Requirements - Model(s) I want to run: [e.g., Qwen3 32B, Llama 3.3 70B, DeepSeek-V3] - Use case: [Chat / Code completion / RAG / Batch processing / API server] - Concurrent users: [1 / 5 / 10+] - Latency requirement: [Real-time < 50ms/tok / Interactive < 200ms/tok / Batch OK] ## Generate ### 1. Framework Selection Recommend the best framework (llama.cpp / vLLM / Ollama / MLX / TensorRT-LLM) with reasoning. ### 2. Model Quantization Recommendation - Best quant level for my VRAM/RAM budget - Expected quality tradeoff - Download command ### 3. Deployment Script Generate a complete, copy-paste-ready shell script that: - Installs dependencies - Downloads the model - Configures optimal parameters (context length, batch size, threads, GPU layers) - Starts the server with health checks - Includes a systemd/launchd service file for auto-start ### 4. Performance Tuning - Memory mapping strategy - KV cache configuration - Speculative decoding setup (if applicable) - Recommended context length vs speed tradeoffs ### 5. Benchmarking Commands Provide commands to measure: - Tokens/second (prompt processing + generation) - Time to first token - Memory usage under load Output everything as executable code blocks with comments explaining each parameter choice.