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| Main Authors: | , , , , , , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2508.02193 |
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| _version_ | 1866908477846192128 |
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| author | Song, Yuxuan Zhang, Zheng Luo, Cheng Gao, Pengyang Xia, Fan Luo, Hao Li, Zheng Yang, Yuehang Yu, Hongli Qu, Xingwei Fu, Yuwei Su, Jing Zhang, Ge Huang, Wenhao Wang, Mingxuan Yan, Lin Jia, Xiaoying Liu, Jingjing Ma, Wei-Ying Zhang, Ya-Qin Wu, Yonghui Zhou, Hao |
| author_facet | Song, Yuxuan Zhang, Zheng Luo, Cheng Gao, Pengyang Xia, Fan Luo, Hao Li, Zheng Yang, Yuehang Yu, Hongli Qu, Xingwei Fu, Yuwei Su, Jing Zhang, Ge Huang, Wenhao Wang, Mingxuan Yan, Lin Jia, Xiaoying Liu, Jingjing Ma, Wei-Ying Zhang, Ya-Qin Wu, Yonghui Zhou, Hao |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_02193 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Seed Diffusion: A Large-Scale Diffusion Language Model with High-Speed Inference Song, Yuxuan Zhang, Zheng Luo, Cheng Gao, Pengyang Xia, Fan Luo, Hao Li, Zheng Yang, Yuehang Yu, Hongli Qu, Xingwei Fu, Yuwei Su, Jing Zhang, Ge Huang, Wenhao Wang, Mingxuan Yan, Lin Jia, Xiaoying Liu, Jingjing Ma, Wei-Ying Zhang, Ya-Qin Wu, Yonghui Zhou, Hao Computation and Language Machine Learning 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. |
| title | Seed Diffusion: A Large-Scale Diffusion Language Model with High-Speed Inference |
| topic | Computation and Language Machine Learning |
| url | https://arxiv.org/abs/2508.02193 |