Uno achieves 2.5x higher throughput in LLMs by bolting diffusion onto existing models

Getting a large language model to think faster usually means making it think worse. A new method called Uno claims to break that tradeoff, delivering up to 3x throughput improvements over standard autoregressive models while preserving the quality of every generated token. The technique, detailed in a paper titled “Unlocking Lossless Speedups in LLMs via Discrete Diffusion” (arXiv:2609.04010) published on September 3, 2026, works by grafting lightweight diffusion weights onto an existing autoregressive model. Instead of replacing the original architecture, it augments it, letting the model generate candidate token blocks in parallel while the base model acts as a quality referee. How Uno actually works Uno’s lead author Subham Sekhar Sahoo and collaborators introduce what they call Psi-Spec samplers, which use diffusion-based adapters to generate multiple candidate tokens simultaneously. The autoregressive weights stay intact and handle final verification, so the output quality matches what the base model would have produced on its own. The key insight is that diffusion models are naturally parallel. While autoregressive generation is inherently sequential (token B depends on token...