Seed Diffusion: A Large-Scale Diffusion Language Model with High-Speed
  Inference
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.
6892c77d8da45ffb0a2b2471
Yuxuan Song
claimed_verified
2025-08-06T19:16:45.791Z
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/avatars/1103f68b978238d5bce604294d467e00.svg
Yuxuan Song
user
yxsong
6892c77d8da45ffb0a2b2472
Zheng Zhang
6892c77d8da45ffb0a2b2473
Cheng Luo
claimed_verified
2025-08-06T19:16:41.735Z
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Luo Cheng
user
Hmdlc
6892c77d8da45ffb0a2b2474
Pengyang Gao
6892c77d8da45ffb0a2b2475
Fan Xia
6892c77d8da45ffb0a2b2476
Hao Luo
6892c77d8da45ffb0a2b2477
Zheng Li
6892c77d8da45ffb0a2b2478
Yuehang Yang
6892c77d8da45ffb0a2b2479
Hongli Yu
6892c77d8da45ffb0a2b247a
Xingwei Qu
6892c77d8da45ffb0a2b247b
Yuwei Fu
6892c77d8da45ffb0a2b247c
Jing Su
6892c77d8da45ffb0a2b247d
Ge Zhang
claimed_verified
2025-08-13T07:28:18.829Z
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Ge Zhang
user
zhangysk
6892c77d8da45ffb0a2b247e
Wenhao Huang
6892c77d8da45ffb0a2b247f
Mingxuan Wang
6892c77d8da45ffb0a2b2480
Lin Yan
6892c77d8da45ffb0a2b2481
Xiaoying Jia
6892c77d8da45ffb0a2b2482
Jingjing Liu
6892c77d8da45ffb0a2b2483
Wei-Ying Ma
6892c77d8da45ffb0a2b2484
Ya-Qin Zhang
6892c77d8da45ffb0a2b2485
Yonghui Wu
6892c77d8da45ffb0a2b2486
Hao Zhou
Seed Diffusion: A Large-Scale Diffusion Language Model with High-Speed
  Inference
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.
Seed Diffusion Preview, a discrete-state diffusion language model, achieves fast inference speeds through parallel generation, outperforming Mercury and Gemini Diffusion in speed and quality.
discrete-state diffusion non-sequential parallel generation token-by-token decoding Seed Diffusion Preview H20 GPUs code evaluation benchmarks speed-quality Pareto frontier