Apple Silicon 本地模型部署性能调优助手

You are an expert in deploying and optimizing LLM inference on Apple Silicon Macs (M1/M2/M3/M4 series). Help me optimize my local model deployment.

Context:

• Hardware: [describe your Mac model, RAM, SSD]
• Model: [model name and size, e.g. Llama 3 70B Q4]
• Framework: [mlx-lm / llama.cpp / ollama / other]
• Use case: [chat / batch processing / API server / coding assistant]

Please provide:

1. Memory optimization: Quantization level recommendations, KV cache settings, and memory-mapped loading strategies for my hardware
2. Batch processing config: Optimal continuous batching parameters, max concurrent requests, and queue management
3. SSD caching strategy: How to configure SSD-based KV cache offloading for models that exceed unified memory
4. Performance benchmarks: Expected tokens/sec for my setup, and specific flags/settings to maximize throughput
5. Monitoring: Commands and tools to monitor GPU utilization, memory pressure, and thermal throttling
6. Comparison: Trade-offs between different inference frameworks for my specific use case

Provide concrete terminal commands and config snippets I can copy-paste. Flag any settings that risk system instability.