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Unlocking Lossless Speedups in LLMs via Discrete Diffusion (5000 Tk/S)

Researchers from the Institute of Foundation Models, Cornell Tech, Cerebras Systems, and other institutions introduced a discrete diffusion method that achieves lossless speedups in large language models (LLMs), reaching 5,000 tokens per second. The approach enables faster inference without sacrificing output quality, marking a significant advance in LLM efficiency.

read1 min views1 publishedSep 4, 2026
Unlocking Lossless Speedups in LLMs via Discrete Diffusion (5000 Tk/S)
Image: source

via Discrete Diffusion

Subham Sekhar Sahoo†,1, Lingjie Chen†,1,2, Khiem Pham†,1,3, Jonathan Geuter†,1,4,

[Junlin Chen]1,5,
[Chaitanya Dwivedi](https://scholar.google.com/citations?user=ghpn6JkAAAAJ&hl=en)1,
[Varad Pimpalkhute](https://nightlessbaron.github.io)1,
[Yash Akhauri](https://akhauriyash.github.io)1,
[Alexander Moreno](https://www.linkedin.com/in/alexander-moreno-ab151542/)1,
[Mikhail Yurochkin](https://moonfolk.github.io)1,
[Zhenting Wang](https://zhentingwang.github.io)1,
[Mostafa Elhoushi](https://scholar.google.com/citations?user=y_cwSKAAAAAJ&hl=en)6,
[Nolan Dey](https://scholar.google.com/citations?user=JHUfMr0AAAAJ&hl=en)6,
[Shane Bergsma](https://sites.google.com/site/shaneabergsma/)6,
[Joel Hestness](https://scholar.google.com/citations?user=wkbvCf0AAAAJ&hl=en)6,
[Hongyi Wang](https://hwang595.github.io)1,5,
[John Thickstun](https://johnthickstun.com)3,
[Eric Xing](https://mbzuai.ac.ae/study/faculty/professor-eric-xing/)1,
[Zhengzhong Liu](https://hunterhector.github.io)1

1Institute of Foundation Models 2University of Illinois Urbana-Champaign 3Cornell Tech

4Harvard University 5Rutgers University 6Cerebras Systems

†Core contributors

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