LLM Inference Acceleration Speculative Decoding Solution Evaluator

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.