Step-3 is Large yet Affordable: Model-system Co-design for
  Cost-effective Decoding
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
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Bin Wang
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Bojun Wang
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Changyi Wan
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Guanzhe Huang
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Hanpeng Hu
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Haonan Jia
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Hao Nie
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Mingliang Li
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Nuo Chen
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Siyu Chen
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Song Yuan
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Wuxun Xie
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Xiaoniu Song
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Xing Chen
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Xingping Yang
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Xuelin Zhang
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Yanbo Yu
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Yaoyu Wang
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Yibo Zhu
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Yimin Jiang
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Yu Zhou
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Yuanwei Lu
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Houyi Li
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Jingcheng Hu
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Ka Man Lo
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Ailin Huang
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Binxing Jiao
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Bo Li
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Boyu Chen
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Changxin Miao
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Chang Lou
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Chen Hu
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Chen Xu
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Chenfeng Yu
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Chengyuan Yao
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Daokuan Lv
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Dapeng Shi
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Deshan Sun
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Ding Huang
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Dingyuan Hu
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Dongqing Pang
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Enle Liu
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Fajie Zhang
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Fanqi Wan
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Gulin Yan
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Han Zhang
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Han Zhou
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Hanshan Zhang
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Hao Wu
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Haocheng Zhang
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Haolong Yan
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Heng Wang
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Heng Wang
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Hongyuan Wang
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Huiyong Guo
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Jia Wang
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Jiahao Gong
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Jian Zhou
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Jiayu Liu
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Jie Cheng
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Jie Yang
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Jieyi Hou
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Jinguang Zhang
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Jinlan Cao
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Jisheng Yin
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Junfeng Liu
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Junhao Huang
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Junzhe Lin
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Kaijun Tan
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Kaixiang Li
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Kang An
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Kangheng Lin
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Kenkun Liu
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Lei Yang
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Liang Zhao
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Liangyu Chen
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Lieyu Shi
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Liguo Tan
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Lin Lin
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Lin Zhang
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Lina Chen
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Liwen Huang
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Liying Shi
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Longlong Gu
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Mei Chen
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Mengqiang Ren
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Ming Li
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Mingzhe Chen
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Na Wang
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Nan Wu
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Qi Han
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Qian Zhao
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Qiang Zhang
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Qianni Liu
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Qiaohui Chen
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Qiling Wu
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Qinglin He
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Qinyuan Tan
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Qiufeng Wang
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Qiuping Wu
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Qiuyan Liang
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Quan Sun
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Rui Li
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Ruihang Miao
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Ruosi Wan
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Ruyan Guo
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Shangwu Zhong
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Shaoliang Pang
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Shijie Shang
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Shilei Jiang
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Shiliang Yang
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Shiming Hao
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Shuli Gao
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Siming Huang
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Siqi Liu
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Tiancheng Cao
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Tianhao Cheng
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Tianhao Peng
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Wang You
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Wei Ji
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Wen Sun
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Wenjin Deng
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Wenqing He
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Wenzhen Zheng
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Xi Chen
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Xiangwen Kong
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Xianzhen Luo
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Xiaobo Yang
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Xin Han
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Xin Wu
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Yanan Wei
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Yang Li
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Yangguang Li
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Yangshijie Xu
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Yanming Xu
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Yaqiang Shi
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Yeqing Shen
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Yi Yang
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Yifei Yang
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Yifeng Gong
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Yihan Chen
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Yijing Yang
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Yuanhao Ding
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Yuantao Fan
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Yuchu Luo
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Yue Peng
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Yufan Lu
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Yuhang Deng
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Yuhe Yin
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Yujie Liu
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Yukun Chen
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Yuling Zhao
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Yun Mou
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Yunlong Li
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Yunzhou Ju
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Yuyang Chen
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Zhe Xie
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Zheng Ge
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Zhenyi Lu
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Zhewei Huang
claimed_verified
2025-07-31T21:00:24.735Z
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Zhewei Huang
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Zhichao Chang
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Zhiguo Huang
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Zhirui Wang
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Zidong Yang
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Zili Wang
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Ziqi Wang
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Zixin Zhang
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Binxing Jiao
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Daxin Jiang
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Heung-Yeung Shum
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Xiangyu Zhang
Step-3 is Large yet Affordable: Model-system Co-design for
  Cost-effective Decoding
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.
Step-3, a 321B-parameter VLM, optimizes decoding costs through Multi-Matrix Factorization Attention and Attention-FFN Disaggregation, achieving high efficiency and throughput compared to other models.
Multi-Matrix Factorization Attention Attention-FFN Disaggregation VLM KV cache decoding costs DeepSeek-V3 Qwen3 MoE hardware-aware model-system co-design attention arithmetic intensity MoE sparsity decoding throughput TPOT SLA Hopper GPUs