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arXiv · 2507.19427

Step-3 is Large yet Affordable: Model-system Co-design for Cost-effective Decoding

StepFun·:·Bin Wang·Bojun Wang·Changyi Wan·Guanzhe Huang·Hanpeng Hu·Haonan Jia·Hao Nie·Mingliang Li·Nuo Chen·Siyu Chen·Song Yuan·Wuxun Xie·Xiaoniu Song·Xing Chen·Xingping Yang·Xuelin Zhang·Yanbo Yu·Yaoyu Wang·Yibo Zhu·Yimin Jiang·Yu Zhou·Yuanwei Lu·Houyi Li·Jingcheng Hu·Ka Man Lo·Ailin Huang·Binxing Jiao·Bo Li·Boyu Chen·Changxin Miao·Chang Lou·Chen Hu·Chen Xu·Chenfeng Yu·Chengyuan Yao·Daokuan Lv·Dapeng Shi·Deshan Sun·Ding Huang·Dingyuan Hu·Dongqing Pang·Enle Liu·Fajie Zhang·Fanqi Wan·Gulin Yan·Han Zhang·Han Zhou·Hanghao Wu·Hangyu Guo·Hanqi Chen·Hanshan Zhang·Hao Wu·Haocheng Zhang·Haolong Yan·Haoran Lv·Haoran Wei·Hebin Zhou·Heng Wang·Hongxin Li·Hongyu Zhou·Hongyuan Wang·Huiyong Guo·Jia Wang·Jiahao Gong·Jialing Xie·Jian Zhou·Jianjian Sun·Jiaoren Wu·Jiaran Zhang·Jiayu Liu·Jie Cheng·Jie Luo·Jie Yan·Jie Yang·Jieyi Hou·Jinguang Zhang·Jinlan Cao·Jisheng Yin·Junfeng Liu·Junhao Huang·Junzhe Lin·Kaijun Tan·Kaixiang Li·Kang An·Kangheng Lin·Kenkun Liu·Lei Yang·Liang Zhao·Liangyu Chen·Lieyu Shi·Liguo Tan·Lin Lin·Lin Zhang·Lina Chen·Liwen Huang·Liying Shi·Longlong Gu·Mei Chen
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Abstract

Large language models (LLMs) face low hardware efficiency during decoding, especially for long-context reasoning tasks. This paper introduces Step-3, a 321B-parameter VLM with hardware-aware model-system co-design optimized for minimizing decoding costs. Step-3 innovates in two key dimensions: (1) A novel Multi-Matrix Factorization Attention (MFA) mechanism that significantly reduces both KV cache size and computation while maintaining high attention expressiveness, and (2) Attention-FFN Disaggregation (AFD), a distributed inference system that decouples attention and Feed-Forward Network (FFN) layers into specialized subsystems. This co-design achieves unprecedented cost efficiency: Step-3 significantly reduces theoretical decoding costs compared with models like DeepSeek-V3 and Qwen3 MoE 235B, with the gains widening at longer context. Step-3 achieves low cost while activating 38B parameters per token (more than DeepSeek-V3 and Qwen3 MoE 235B), demonstrating that hardware-aligned attention arithmetic intensity, MoE sparsity, and AFD are critical to cost-effectiveness. We perform a head-to-head comparison with DeepSeek-V3 in its favorable scenarios. Our implementation on Hopper GPUs achieves a decoding throughput of up to 4,039 tokens per second per GPU under 50ms TPOT SLA (4K context, FP8, no MTP). It is higher than DeepSeek-V3's 2,324 in the same setup and sets a new Pareto frontier for LLM decoding.

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StepFun, :, Bin Wang, Bojun Wang, Changyi Wan, Guanzhe Huang, Hanpeng Hu, Haonan Jia, Hao Nie, Mingliang Li, Nuo Chen, Siyu Chen, Song Yuan, Wuxun Xie, Xiaoniu Song, Xing Chen, Xingping Yang, Xuelin Zhang, Yanbo Yu, Yaoyu Wang, Yibo Zhu, Yimin Jiang, Yu Zhou, Yuanwei Lu, Houyi Li, Jingcheng Hu, Ka Man Lo, Ailin Huang, Binxing Jiao, Bo Li, Boyu Chen, Changxin Miao, Chang Lou, Chen Hu, Chen Xu, Chenfeng Yu, Chengyuan Yao, Daokuan Lv, Dapeng Shi, Deshan Sun, Ding Huang, Dingyuan Hu, Dongqing Pang, Enle Liu, Fajie Zhang, Fanqi Wan, Gulin Yan, Han Zhang, Han Zhou, Hanghao Wu, Hangyu Guo, Hanqi Chen, Hanshan Zhang, Hao Wu, Haocheng Zhang, Haolong Yan, Haoran Lv, Haoran Wei, Hebin Zhou, Heng Wang, Hongxin Li, Hongyu Zhou, Hongyuan Wang, Huiyong Guo, Jia Wang, Jiahao Gong, Jialing Xie, Jian Zhou, Jianjian Sun, Jiaoren Wu, Jiaran Zhang, Jiayu Liu, Jie Cheng, Jie Luo, Jie Yan, Jie Yang, Jieyi Hou, Jinguang Zhang, Jinlan Cao, Jisheng Yin, Junfeng Liu, Junhao Huang, Junzhe Lin, Kaijun Tan, Kaixiang Li, Kang An, Kangheng Lin, Kenkun Liu, Lei Yang, Liang Zhao, Liangyu Chen, Lieyu Shi, Liguo Tan, Lin Lin, Lin Zhang, Lina Chen, Liwen Huang, Liying Shi, Longlong Gu, Mei Chen. 2025-07-25. Step-3 is Large yet Affordable: Model-system Co-design for Cost-effective Decoding. https://arxiv.org/abs/2507.19427

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