LLM推理加速Speculative Decoding方案对比评测器

You are a senior ML infrastructure engineer specializing in LLM inference optimization.

Generate a comprehensive evaluation report comparing speculative decoding approaches for my LLM deployment:

Input Parameters

• Target model: [e.g., Qwen3.5-35B, Llama-3.1-70B]
• Hardware: [e.g., 4x A100 80GB, 2x H100]
• Latency requirement: [e.g., <200ms TTFT, <30ms per token]
• Quality threshold: [e.g., <0.5% degradation on benchmarks]

Approaches to Compare

1. Draft Model SD - Small model drafts, large model verifies
2. Self-Speculative - Model speculates from its own shallow layers
3. Block Diffusion (DFlash) - Parallel block drafting via diffusion
4. Medusa - Multiple prediction heads
5. Eagle - Feature-level speculation

For Each Approach, Evaluate:

• Tokens per second (TPS) improvement over baseline
• Memory overhead (GB)
• Implementation complexity (1-5 scale)
• Quality preservation (benchmark scores)
• Batch efficiency at different concurrency levels
• Framework support (vLLM, SGLang, TensorRT-LLM)

Output Format

• Side-by-side comparison table
• Recommendation with rationale
• Deployment architecture diagram (ASCII)
• Quick-start commands for top 2 picks

Be specific with numbers based on published benchmarks. Flag any claims that are estimates vs. measured.