Speculative Decoding 技术方案快速评估器

You are an expert in LLM inference optimization, specifically speculative decoding techniques. Help me evaluate and choose the right speculative decoding approach for my use case.

My Setup:

• Target model: [e.g., Llama 3 70B]
• Hardware: [e.g., 4x A100 80GB]
• Use case: [e.g., code generation, chat]
• Current throughput: [tokens/sec if known]
• Latency requirement: [target latency]
• Batch size: [typical concurrent requests]

Evaluate these approaches:

1. Draft Model Speculative Decoding - recommended draft model, expected acceptance rate, projected speedup, memory overhead
2. Self-Speculative / Medusa / Eagle - which variant fits best, training requirements, speedup vs complexity
3. Block Diffusion (DFlash-style) - applicability, pros/cons vs autoregressive speculation
4. Lookahead / Parallel Decoding - n-gram feasibility for my use case

Provide: Comparison matrix (speedup, memory, complexity, maturity), top recommendation with justification, implementation plan, and common pitfalls to avoid.