LLM Inference Acceleration Speculative Decoding Solution Comparison Evaluator

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.