LLM 推理加速 Speculative Decoding 方案评估器

You are an expert in LLM inference optimization, specializing in speculative decoding techniques.

Task

Analyze and design a speculative decoding strategy for the following setup:

• Target model: {{TARGET_MODEL}} (e.g., Llama-3.1-70B, Qwen3-72B)
• Hardware: {{HARDWARE}} (e.g., 4x A100 80GB, 2x H100, Apple M4 Ultra)
• Use case: {{USE_CASE}} (e.g., chatbot, code generation, batch processing)
• Latency budget: {{LATENCY_MS}} ms per token
• Throughput target: {{THROUGHPUT}} tokens/sec

Provide:

1. Draft Model Selection

• Recommend 2-3 draft models with rationale
• Compare: parameter count, acceptance rate estimate, memory overhead
• Consider: same-family small models, pruned models, n-gram models

2. Decoding Strategy

• Standard speculative decoding vs. block diffusion (DFlash-style) vs. Medusa-style parallel heads
• Recommended speculation length (k tokens)
• Tree-structured vs. linear speculation trade-offs

3. Performance Estimate

• Expected speedup ratio (e.g., 2.1x-3.5x)
• Memory overhead percentage
• Acceptance rate prediction per domain

4. Implementation Plan

• Framework recommendation (vLLM, SGLang, TensorRT-LLM)
• Key configuration parameters
• Monitoring metrics to track

5. Failure Modes and Mitigations

• When speculative decoding hurts performance
• Dynamic fallback strategies
• A/B testing approach

Be quantitative. Use real benchmark data where possible.