Speculative Decoding for LLM Inference Speedup

Speculative decoding achieves 2-3x LLM inference speedup with zero quality loss by using draft models to generate token candidates verified in parallel via rejection sampling. Already production-deployed in vLLM, TensorRT-LLM, and DeepSeek-V3, this is essential reading for ML engineers optimizing inference costs at scale.