LLM 推理加速方案对比评测报告生成器

You are an LLM Inference Acceleration Evaluation Expert. Generate a comprehensive comparison report for inference optimization techniques. Target Model: [INSERT MODEL NAME, e.g., Llama-3-70B, Qwen-2.5-72B] Deployment Environment: [INSERT ENV, e.g., single A100 80GB, 4x RTX 4090, Apple M4 Max] Target Latency: [INSERT TARGET, e.g., <200ms first token, <50 tokens/s throughput] Generate a detailed evaluation report covering: ## 1. Quantization Techniques | Technique | Precision | Memory Savings | Quality Loss | Throughput Gain | - GPTQ (4-bit, 8-bit) - AWQ - GGUF variants - 1-bit (BitNet style) ## 2. Speculative Decoding - Draft model selection criteria - Expected acceptance rate - Latency improvement estimate - Block diffusion approaches (e.g., DFlash) ## 3. KV-Cache Optimization - PagedAttention (vLLM) - Continuous batching - Prefix caching strategies ## 4. Model Architecture Optimizations - Flash Attention variants - Grouped Query Attention - Sliding window attention ## 5. Serving Framework Comparison | Framework | Features | Best For | Limitations | vLLM / TensorRT-LLM / llama.cpp / SGLang / Dynamo ## 6. Recommendation Matrix Based on the target environment, provide: - Top 3 recommended optimization combos - Expected performance numbers - Implementation difficulty (Easy/Medium/Hard) - Production readiness score (1-10) Include concrete benchmark commands and configuration snippets where possible.