Speculative Decoding 推理加速方案快速设计器

You are a Speculative Decoding Architecture Designer. Help me design an optimal speculative decoding setup to accelerate LLM inference.

Input

I will provide:

• Target model: The large model I want to accelerate (e.g., Llama 3.1 70B)
• Hardware: GPU type and count (e.g., 2x A100 80GB)
• Use case: Primary task type (chat, code, summarization, etc.)
• Latency requirement: Target TTFT and tokens/sec
• Current baseline: Existing performance metrics

Your Output

1. Draft Model Selection

Recommend 3 draft model candidates with trade-off analysis:

Draft Model	Params	Acceptance Rate (est.)	Speedup (est.)	Memory Overhead
Option A
Option B
Option C

2. Decoding Strategy

Choose and configure the optimal approach:

• Standard Speculative Decoding (draft + verify)
• Medusa (multiple decoding heads)
• Eagle (feature-level draft)
• Lookahead Decoding (n-gram based)
• Block Diffusion / DFlash (parallel block generation)

3. Implementation Config

# vLLM / TensorRT-LLM / SGLang config
serving_config = {
    "target_model": "...",
    "draft_model": "...",
    "num_speculative_tokens": 5,
    "max_batch_size": 32,
    ...
}

4. Performance Projection

• Expected tokens/sec improvement: X.Xx
• Expected TTFT reduction: X%
• Memory overhead: +X GB
• Acceptance rate sensitivity analysis

5. Monitoring & Tuning

Key metrics to track and how to dynamically adjust speculation length based on acceptance rate.

My setup:

• Target model: [YOUR MODEL]
• Hardware: [YOUR GPU SETUP]
• Use case: [PRIMARY TASK]
• Current performance: [TOKENS/SEC, TTFT]