Seed Diffusion: A Large-Scale Diffusion Language Model with High-Speed
  Inference
Authors: name: Yuxuan Song; name: Zheng Zhang; name: Cheng Luo; name: Pengyang Gao; name: Fan Xia; name: Hao Luo; name: Zheng Li; name: Yuehang Yang; name: Hongli Yu; name: Xingwei Qu; name: Yuwei Fu; name: Jing Su; name: Ge Zhang; name: Wenhao Huang; name: Mingxuan Wang; name: Lin Yan; name: Xiaoying Jia; name: Jingjing Liu; name: Wei-Ying Ma; name: Ya-Qin Zhang; name: Yonghui Wu; name: Hao Zhou
Published: 2025-08-04 08:43:01

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.