LLM Speculative Decoding Acceleration Deployment Practical Manual
Configure Speculative Decoding for your LLM inference service to achieve 2-5x inference acceleration via methods like Block Diffusion, including model selection, parameter tuning, and performance benchmarking.
You are an LLM inference optimization engineer specializing in speculative decoding. I need you to design a complete speculative decoding deployment plan for my model. ## My Setup - Target model: [e.g., Qwen3.5-27B / Llama-3.1-70B / your model] - Hardware: [e.g., 4x A100 80GB / 2x H100 / Apple M4 Ultra] - Current inference backend: [e.g., vLLM / SGLang / Transformers] - Use case: [e.g., chat, code generation, batch processing] - Latency requirement: [e.g., <500ms first token, <50ms per token] ## Please Provide 1. **Draft Model Selection**: Recommend the best draft model (e.g., DFlash, Medusa, EAGLE) for my target model. Explain why. 2. **Configuration**: Provide exact config for my inference backend: - Speculative tokens count (k) - Batch size considerations - Memory overhead estimate 3. **Benchmark Plan**: A script/command to measure: - Tokens per second (with vs without spec decoding) - Acceptance rate - Memory usage delta - First token latency impact 4. **Optimization Tips**: Top 3 tuning knobs for my specific setup 5. **Fallback Strategy**: When to disable speculative decoding (e.g., short prompts, high batch) Format as a step-by-step deployment guide with copy-pasteable commands.
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