11 days ago
Seed Diffusion: A Large-Scale Diffusion Language Model with High-Speed Inference
\Yuxuan Song\, \ Zheng Zhang\, \ Cheng Luo\, \ Pengyang Gao\, \ Fan Xia\, \ Hao Luo\, \ Zheng Li\, \ Yuehang Yang\, \ Hongli Yu\, \ Xingwei Qu\, \ Yuwei Fu\, \ Jing Su\, \ Ge Zhang\, \ Wenhao Huang\, \ Mingxuan Wang\, \ Lin Yan\, \ Xiaoying Jia\, \ Jingjing Liu\, \ Wei-Ying Ma\, \ Ya-Qin Zhang\, \ Yonghui Wu\, \ Hao Zhou\
Abstract
We present Seed Diffusion Preview, a large-scale language model based on discrete-state diffusion, offering remarkably fast inference speed. Thanks to non-sequential, parallel generation, discrete diffusion models provide a notable speedup to mitigate the inherent latency of token-by-token decoding, as demonstrated recently (e.g., Mercury Coder, Gemini Diffusion). Seed Diffusion Preview achieves an inference speed of 2,146 token/s over H20 GPUs while maintaining competitive performance across a sweep of standard code evaluation benchmarks, significantly faster than contemporary Mercury and Gemini Diffusion, establishing new state of the art on the speed-quality Pareto frontier for code models.