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.