端侧AI应用性能优化检查清单

You are an edge AI deployment specialist. Generate a comprehensive performance optimization checklist for deploying AI models on edge devices. ## Target Device Profile - Device type: [e.g., smartphone, Raspberry Pi, embedded board, browser] - Hardware specs: [e.g., 8GB RAM, Snapdragon 8 Gen 3, Apple M-series, WebGPU] - Model type: [e.g., LLM, vision model, speech recognition] - Model size: [e.g., 3B parameters, 500MB] - Latency requirement: [e.g., <100ms first token, real-time inference] ## Generate Optimization Checklist: ### Phase 1: Model Compression - [ ] Quantization strategy (INT8/INT4/GPTQ/AWQ/GGUF) - [ ] Knowledge distillation from larger teacher model - [ ] Pruning (structured vs unstructured) - [ ] Vocabulary reduction for target use case - [ ] LoRA/QLoRA fine-tuning for task-specific optimization ### Phase 2: Inference Engine Selection Compare and recommend from: LiteRT-LM, llama.cpp, MLC-LLM, ONNX Runtime, TensorRT, Core ML - Benchmark template for each engine - Platform compatibility matrix ### Phase 3: Runtime Optimization - [ ] KV-cache management and memory pooling - [ ] Speculative decoding configuration - [ ] Batch scheduling for concurrent requests - [ ] Context window sliding strategy - [ ] Prefill/decode phase optimization ### Phase 4: System-Level Tuning - [ ] Thermal throttling mitigation - [ ] Power consumption profiling - [ ] Memory mapping and swap configuration - [ ] GPU/NPU scheduling priorities ### Phase 5: Measurement & Validation - Benchmark script template (tokens/sec, TTFT, memory peak) - Quality regression test suite - A/B comparison framework For each item, provide: 1. Why it matters 2. How to implement (concrete commands/code) 3. Expected improvement range 4. Trade-offs to consider