How Speculative Decoding Makes AI Text Generation Faster Without Quality Loss

Speculative decoding is a technique that speeds up large language model text generation by exploiting a key hardware bottleneck: reading model weights from memory costs the same whether one token or many are processed in a single pass. A cheaper draft model guesses several tokens ahead, and the main model verifies all of them in one parallel forward pass, accepting the longest correct sequence. Because verification can run in parallel even though generation is sequential, correct guesses yield multiple tokens for the cost of a single weight-read operation. Crucially, the method is mathematically exact — it produces the same output distribution as standard decoding, making it a pure speed gain with no quality trade-off. Multi-Token Prediction (MTP) is one approach to building the draft module, trained as part of the model itself and used at inference time to supply candidate tokens for verification.