Co-Designing AI Models Using Speculative Decoding for Faster LLM Inference

Speculative decoding speeds up large language model (LLM) inference by using a smaller draft model to predict multiple tokens at once, which are then verified in parallel by the main model. This reduces decoding iterations and improves throughput without sacrificing output accuracy. The article provides guidelines for optimizing draft length and mechanism to balance speed and interactivity.