Apple’s LLM Technology Boosts Prediction Speed. What is “multi-token prediction” (MTP) framework?

Apple’s innovation in large language models centers on a “multi-token prediction” (MTP) framework, which enables models to predict multiple tokens simultaneously rather than generating text one token at a time as in traditional autoregressive models. This approach improves inference speed significantly, with reported speedups of 2–3× on general tasks and up to 5× in more predictable domains like coding and math, while maintaining output quality.

The core of Apple’s MTP framework involves inserting special “mask” tokens into the input prompts. These placeholders allow the model to speculate on several upcoming tokens at once. Each predicted token sequence is then immediately verified against what standard sequential decoding would produce, reverting to single-token prediction if needed to ensure accuracy. This leads to faster text generation without degrading quality, thanks to techniques such as a “gated LoRA adaptation” that balances speculation and verification.

In training, Apple’s method augments input sequences by appending multiple mask tokens corresponding to future tokens to be predicted. The model learns to output these future tokens jointly while preserving its ability to predict the next token normally. This involves a carefully designed attention mechanism that supports parallel prediction while maintaining autoregressive properties. The training process parallelizes what would otherwise be sequential queries, improving training efficiency and improving the model’s ability to “think ahead” beyond the immediate next token.

This innovation addresses the inherent bottleneck in traditional autoregressive models, which generate text sequentially, limiting speed and efficiency. By enabling multi-token simultaneous prediction, Apple’s research unlocks latent multi-token knowledge implicitly present in autoregressive models, essentially teaching them to anticipate multiple future words at once, much like human language planning.

Overall, Apple’s multi-token prediction framework represents a significant advancement in AI language model inference, promising faster, more efficient generation without sacrificing accuracy—key for real-world applications like chatbots and coding assistants.