Multi-token Residual Prediction

Multi-token Residual Prediction is a technique for improving language model inference efficiency by predicting multiple tokens simultaneously rather than one at a time, reducing latency and computational overhead. This approach leverages residual connections and prediction methods to forecast several future tokens in parallel, enabling faster text generation while maintaining model quality. The method represents an advancement in optimizing the inference speed of large language models, particularly relevant for real-time applications.

Source: Modal Blog | 2026-07-01