Speculative decoding has emerged as a promising approach to accelerate large language model (LLM) inference, yet existing methods face a tradeoff: parallel designs achieve higher speed but lose accuracy, while serial designs gain accuracy at the cost of efficiency. In our recent paper Gumiho: A Hybrid Architecture to Prioritize Early Tokens in Speculative Decoding, we introduce a new paradigm that addresses this bottleneck by prioritizing accuracy on the earliest draft tokens, which matters m...
