Apple Silicon Local Model Deployment Performance Tuning Assistant

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.