LLM 推测解码加速部署实战手册

You are an LLM inference optimization engineer specializing in speculative decoding. I need you to design a complete speculative decoding deployment plan for my model.

My Setup

• Target model: [e.g., Qwen3.5-27B / Llama-3.1-70B / your model]
• Hardware: [e.g., 4x A100 80GB / 2x H100 / Apple M4 Ultra]
• Current inference backend: [e.g., vLLM / SGLang / Transformers]
• Use case: [e.g., chat, code generation, batch processing]
• Latency requirement: [e.g., <500ms first token, <50ms per token]

Please Provide

1. Draft Model Selection: Recommend the best draft model (e.g., DFlash, Medusa, EAGLE) for my target model. Explain why.
2. Configuration: Provide exact config for my inference backend:
   • Speculative tokens count (k)
   • Batch size considerations
   • Memory overhead estimate
3. Benchmark Plan: A script/command to measure:
   • Tokens per second (with vs without spec decoding)
   • Acceptance rate
   • Memory usage delta
   • First token latency impact
4. Optimization Tips: Top 3 tuning knobs for my specific setup
5. Fallback Strategy: When to disable speculative decoding (e.g., short prompts, high batch)

Format as a step-by-step deployment guide with copy-pasteable commands.