Meta and Stanford Researchers Propose Fast Byte Latent Transformer That Reduces Inference Memory Bandwidth by Over 50% Without Tokenization

Researchers from Meta, Stanford, and the University of Washington proposed three acceleration methods for Byte Latent Transformers (BLT).

The lead approach uses a diffusion-based decoder that predicts multiple bytes in a single forward pass.

This cuts inference memory bandwidth by more than 50% versus byte-by-byte decoding.

The goal is to make byte-level language models more practical to serve by lowering latency without tokenization.