DeepSeek-V4.1-Flash: Pushing the Limits of KV Cache Compression

📄 arXiv: 2609.19969v1 📥 PDF

作者: DeepSeek-AI, :, Anyi Xu, B. Li, Bangcai Lin, Bing Xue, BingCheng Xian, Bingzheng Xu, Bochao Wu, Bowei Zhang, Boyi Deng, C. C. Yu, Chao Jin, Chaofan Lin, Chen Dong, Chenbing Wang, Chenfan Feng, Chengda Lu, Chenggang Zhao, Chengqi Deng, Chengyuan Zhang, Chenhao Xu, Chenqi Zhao, Chenze Shao, Chuhao Wang, Chuqi Zhang, Damai Dai, Dejian Yang, Deli Chen, Di Huang, Di Wu, Donghao Li, Erhang Li, Eric Fu, F. Zhou, Fangwei Zhou, Fangyun Lin, Fangzhou Yuan, Feiyu Xia, Fucong Dai, Guangbo Hao, Guanglin Li, Guanting Chen, Guoai Cao, Guofan Fan, Guolai Meng, Guowei Li, Haichuan Zhang, Haiyang Ma, Haiyang Shen, Han Li, Han Yu, Han Zhang, Hangyuan Deng, Hanwei Xu, Hanxiang Xu, Hanxun Zhong, Hao Guo, Hao Jiang, Hao Li, Hao Qin, Haodong Wen, Haofen Liang, Haofeng Huang, Haohua Liu, Haoling Zhang, Haoming Luo, Haoran Yang, Haotian Xu, Haotian Yuan, Haoting Huang, Haowen Luo, Haoyang Cai, Haoyu Chen, Haozhe Ji, Hengran Zhang, Hengrui Wang, Hengxu Wu, Honghui Ding, Hongxuan Tang, Huadong Wang, Huanqi Cao, Huazuo Gao, Hui Qu, Hui Zeng, J. Yang, J. H. Jin, J. H. Zhang, J. X. Zou, Jia Yu, Jiahui Zhou, Jiajun Chen, Jialiang Huang, Jialin Zhao, Jiamin Tang, Jian Zhou, Jianan Tong, Jianwen Li, Jiaqi Zhu, Jiarui Wang, Jiasheng Ye, Jiashi Li, Jiaxin Xu, Jiaying Ding, Jibai Lu, Jiewen Hu, Jin Yan, Jincheng Zhai, Jingchang Chen, Jingcheng Hu, Jingli Zhou, Jingsheng Xu, Jingting Xiang, Jingyan Yun, Jingyang Yuan, Jingyuan Cheng, Jinhua Zhu, Jinpeng Wang, Jinyi Chen, Jinyi Hu, Jiping Yu, Jueliang Guo, Junbo Pei, Junbo Sun, Junguang Jiang, Junjie Qiu, Junkang Zhou, Junqi Liu, Junren Li, Junxian Li, Junxiao Song, Junyi Guo, Kai Dong, Kaifeng Chen, Kaige Gao, Kang Guan, Kangdong Yuan, Ke Hong, Ke Xu, Kefan Zhao, Kexin Ji, Kexin Zhang, Kexing Zhou, Kuai Yu, Lan Zhang, Lean Wang, Lecong Zhang, Lei Wang, Letian Gao, Liang Zhao, Liansheng Xu, Lihua Guo, Lingxiao Luo, Lingyue Fu, Litao Deng, Litong Wang, Liyue Zhang, Longhao Chen, Lu Chen, Luotian Huang, Luyao Ma, Luyao Wang, M. S. Di, Max Mei, Menghao Ye, Miao Cui, Mingchuan Zhang, Minghua Zhang, Minghui Tang, Mingjing Zhang, Mingqi Wei, Mingshu Chen, Mingxing Liu, Mingxu Zhou, Mingyu Xu, Mingyu Yang, Mingze Wang, Muyang Chen, Ni Shentu, Ning Wang, Niufang Ning, Panpan Huang, Peixin Cong, Peiyi Wang, Peiyuan Xin, Pengfei Ren, Pengfei Yan, Pengle Zhang, Qi Kang, Qi Tang, Qiancheng Wang, Qiang Li, Qihao Zhu, Qingyang Li, Qinyu Chen, Qiushi Du, Qizhou Guo, Rongxian Xu, Rui Ding, Rui Hu, Rui Tian, Rui Yu, Ruidong Zhu, Ruifan Xu, Ruihan Yang, Ruihang Xia, Ruijie Lu, Ruilin Geng, Ruipeng Hong, Ruiqi Ge, Ruisong Zhang, Ruize Sun, Ruizhe Pan, Runji Wang, Runqian Chen, Runxin Xu, Ruohong Tian, Ruomeng Shen, Ruoyu Zhang, Ryan X., S. H. Liu, Shanghao Lu, Shangyan Zhou, Shanhuang Chen, Shaofei Cai, Shaoheng Nie, Shaoyuan Chen, Shengding Hu, Shengkai Lin, Shengwen Ran, Shengyu Liu, Shengyuan Jia, Shi Bai, Shi Feng, Shicheng Xu, Shichun Liu, Shiqiang Hu, Shirong Ma, Shiyu Wang, Shiyuan Feng, Shufan Gong, Shuhan Lin, Shuiping Yu, Shunfeng Zhou, Shuo Yang, Shuomeng Wang, Shuting Guo, Shuting Pan, Shuying Yu, Sinuo Cao, Siyi Lin, Sizhe Chen, Songyang Chen, Songyang Zhou, Tao Ni, Tao Yun, Tian Jin, Tian Pei, Tian Ye, Tianle Lin, Tianran Ji, Tianyi Cui, Tianyuan Yue, Tingting Yu, Tongrui Xiong, Wangding Zeng, Wei Liu, Wei Zhang, Weibin Xu, Weihao Zeng, Weilin Zhao, Wen Liu, Wenfeng Liang, Wenjie Pang, Wenjing Luo, Wenjing Yao, Wenjun Gao, Wenkai Shao, Wenkai Yang, Wenli Zhang, Wenlu Wang, Wenlve Huang, Wenqian Yan, Wentao Zhang, Xi Gao, Xiang He, Xiang Li, Xiangli Li, Xiangwen Wang, Xiangying Zhang, Xiankui Wei, Xiao Bi, Xiaodong Liu, Xiaohan Wang, Xiaojian Qu, Xiaokang Chen, Xiaokang Zhang, Xiaotao Nie, Xiaoyao Zou, Xiaoyuan Li, Xicheng Guo, Xieting Chu, Xin Cheng, Xin Liu, Xin Xie, Xinbo Xu, Xingchao Liu, Xingchen Liu, Xingkai Yu, Xingyou Li, Xintong Yao, Xinyang Chen, Xinyong Jiang, Xinyu Yang, Xinyu Yang, Xu Chen, Xuanyu Wang, Xubei Zhong, Xuecheng Su, Xuejie Liu, Xuheng Lin, Xujie Fan, Xuncheng Zhao, Xuwei Fu, Y. C. Yan, Y. H. Jiang, Y. T. Wu, Y. W. M., Y. Z. Wang, Yafei Gao, Yang Yang, Yang Zhang, Yanru Ma, Yanwen Huang, Yao Li, Yao Li, Yao Meng, Yao Zhao, Yaofeng Sun, Yaohui Wang, Yaoyang Ye, Yehang Yin, Yexinrui Wu, Yi Qian, Yi Tao, Yi Yu, Yichao Zhang, Yichen Jiang, Yicheng Wang, Yifan Ding, Yifan Shi, Yifeng Peng, Yifeng Zhai, Yijia Wu, Yiliang Xiong, Yilun Wang, Ying He, Ying Zhou, Yingjia Luo, Yinmin Zhong, Yiping Wang, Yisong Wang, Yixiang Zhang, Yixiao Chen, Yixuan Tan, Yixuan Wei, Yiyang Ma, Yiyao Yang, Yiyuan Liu, Yizai Cai, Yizhen Wei, Yizhi Wang, Yonglun Yang, Yongqi Zhuo, Yongqiang Guo, Yongtong Wu, Yu Wu, Yu Zhang, Yuan Bian, Yuan Cheng, Yuan Ou, Yuan Sun, Yuanfan Xu, Yuanhang Sun, Yuanhao Li, Yuchen Liu, Yuchen Yao, Yudong Han, Yuduan Wang, Yuhan Wu, Yuhao Meng, Yuheng Zou, YuKun Li, Yunchuan Wang, Yunfan Xiao, Yunfan Xiong, Yupeng Chen, Yuqian Cao, Yuqian Wang, Yuqing Chen, Yushun Zhang, Yutong Lin, Yuwei Xiao, Yuxian Gu, Yuxiang Chen, Yuxiang Huang, Yuxiang Luo, Yuxiang You, Yuxin Chen, Yuxin Xiang, Yuxuan Liu, Yuxuan Zhou, Yuyang Zhou, Yuzhe Guo, Yuzhen Huang, Yuzhuo Bai, Z. Y. Z., Zanlin Ni, Zehao Wang, Zehua Zhao, Zehui Ren, Zejun Zhao, Zhangli Sha, Zhanying Wang, Zhaochen Zhang, Zhaoshuai Du, Zhe Fu, Zhean Xu, Zhenda Xie, Zheng Liu, Zhengyan Zhang, Zhenhua Dong, Zhewen Hao, Zhibang Wang, Zhibin Gou, Zhicheng Ma, Zhihao Li, Zhihong Shao, Zhihuan Huang, Zhijie Li, Zhirui Lu, Zhixian Huang, Zhixuan Chen, Zhixuan Chen, Zhixuan Pan, Zhiyu Wu, Zhizhou Ren, Zhu He, Zhuoshu Li, Zhuping Zhang, Zian Xu, Zihao Wang, Zihui Gu, Zijia Zhu, Zili Zhang, Zilin Li, Zilong Hou, Zilong Lyu, Ziqiao Wang, Ziwei Xie, Ziya Zhang, Ziyi Gao, Zizheng Pan, Zonglin Li, Zongqing Yao, Zui Chen, Zuofan Wu, Chenchen Ling, Chengyu Hou, Chong Chen, D. Li, Di Qi, Dongjie Ji, Fang Wei, Fanyi Xia, Fei Xie, Feiyi Tan, Hailong Guo, Haiyan Zhai, Hui Zhou, Huihui Tan, Huijie Li, Jia Luo, Jia Song, Jialu Cai, Jian Liang, Jiangting Zhou, Jiaqi Gao, Jiayi Shao, Jie Chen, Jieyu Yang, Jin Chen, Jingde Zhang, Jingzi Zhou, Jinqian Wang, Jinyang Liu, JinZhao Sun, Junhua Ling, Junmin Zheng, Kaicheng Yang, Ke Xu, Le Su, Leyi Xia, Liangfeng Ding, Lin Zhuo, Linwang Ma, Linyan Zhu, Liyu Cai, Luqi Yao, M. K. Zhang, Meng Li, Miao Lin, Miaojun Wang, Min Zhang, Mingming Li, Mingming Wang, Mingze Yin, Minmin Han, Nan Cao, Ning Wang, Ningxin Ma, Panpan Wang, Peihan Lin, Peng Sun, Peng Zhang, Qian Ying, Qiang Xiang, Qiao Wang, Qingmiao Mao, Qiwei Jiang, Rongli Jin, Ruyi Chen, Sha Tao, Shangmian Sun, Shaoqing Wu, Shichao Zou, Si Lei, Tianyang Zhang, Tianyu Sun, Tingting Yin, W. L. Xiao, Wei An, Wei Li, Wei Wang, Weiwei Lin, Wenqing Hou, X. Lin, Xiangfei Meng, Xianzhu Huang, Xiao Peng, Xiaoqian Li, Xiaoting Zhang, Xiaowen Sun, Xiaoxiang Wang, Xiaoyu Ye, Xinrou Zhang, Xinyu Zhang, Xue Cao, Xueyin Chen, Yanan Zhou, Yanhong Xu, Yao Xia, Yao Xu, Yi Shao, Yihong Zhang, Yiling Ma, Ying Tang, Yining Lou, Yiru Chen, Yishi Piao, Yixuan Chen, Yong Xiong, Yuchen Xuan, Yuehan Yang, Yuer Xu, Yukun Zha, Yunxian Ma, Yuping Lin, Yuting Yan, Yutong Xie, Yuwen Sheng, Yuxuan Zhu, Zekai Zhang, Zhe Ju, Zhenzhen Lin, Zheren Gao, Zheyang Sun, Zhigang Yan, Zhongyu Wu, Zi Wang, Zihua Qu, Ziling Yan, Ziyi Wan

分类: cs.CL

发布日期: 2026-09-17

🔗 代码/项目: HUGGINGFACE

💡 一句话要点

提出DeepSeek-V4.1-Flash以解决KV缓存压缩问题

🎯 匹配领域: 支柱九：具身大模型 (Embodied Foundation Models)

关键词: 长上下文计算 KV缓存压缩 多模态模型 专家混合模型 计算效率

📋 核心要点

  1. 现有方法在长上下文计算中仍面临高昂的预填充成本和KV缓存容量限制，影响了部署效率。
  2. DeepSeek-V4.1-Flash通过引入多模态Mixture-of-Experts模型和优化的KV缓存机制，显著降低计算和存储需求。
  3. 实验表明，该模型在KV缓存占用和性能上均优于基线，提供了更高的效率和更好的性能表现。

📝 摘要（中文）

随着长时间代理的广泛应用，模型工作负载变得越来越依赖输入。尽管之前的研究显著降低了长上下文计算的成本，但预填充仍然计算开销大，且大型KV缓存继续对HBM和SSD容量及数据传输带宽造成压力。为了解决这一挑战，我们提出了DeepSeek-V4.1-Flash，这是一种具有552B主干参数的多模态专家混合模型，支持高达一百万个标记的上下文。该模型通过Causal Encoder-Decoder架构在解码时激活16B参数，而在预填充时仅激活8B参数，显著提高了代理工作负载的成本效率。通过结合压缩稀疏注意力2（CSA2）中的跨层KV缓存重用和FP4 KV缓存，DeepSeek-V4.1-Flash将其全局KV缓存占用减少到每个标记890字节，约为DeepSeek-V4-Flash的1/4。

🔬 方法详解

问题定义：论文旨在解决长上下文计算中KV缓存的高成本和存储需求问题。现有方法在处理大规模输入时，预填充和KV缓存的计算开销依然很大，限制了模型的实际应用。

核心思路：DeepSeek-V4.1-Flash通过引入多模态Mixture-of-Experts架构和优化的KV缓存机制，减少了计算和存储的需求。模型在解码时激活更多参数，而在预填充时则减少激活，从而提高了效率。

技术框架：该模型采用Causal Encoder-Decoder架构，支持高达一百万个标记的上下文。通过Compressed Sparse Attention 2（CSA2）实现跨层KV缓存重用，并结合FP4 KV缓存，优化了缓存的使用。

关键创新：DeepSeek-V4.1-Flash的主要创新在于其KV缓存的压缩机制，将全局KV缓存占用减少到每个标记890字节，显著低于DeepSeek-V4-Flash的占用。

关键设计：模型在参数设置上进行了优化，解码时激活16B参数，预填充时仅激活8B参数。此外，通过SWA Bounded Replay优化持久KV缓存的占用，进一步提升了模型的性能和效率。

🖼️ 关键图片

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📊 实验亮点

实验结果显示，DeepSeek-V4.1-Flash在KV缓存占用方面减少至每个标记890字节，约为DeepSeek-V4-Flash的1/4。同时，该模型在性能上显著优于基线，展示了更高的计算效率和更好的任务表现。

🎯 应用场景

DeepSeek-V4.1-Flash在多模态任务中具有广泛的应用潜力，尤其是在需要处理长上下文的智能代理系统中。其高效的KV缓存机制和优化的计算架构使其能够在资源有限的环境中运行，降低了部署成本，提升了实际应用的可行性。

📄 摘要（原文）

The widespread adoption of long-horizon agents has made model workloads increasingly input-heavy. Although prior work has substantially reduced the cost of long-context computation, prefill remains computationally expensive, and large KV caches continue to strain HBM and SSD capacity and data-transfer bandwidth. Together, these compute, storage, and bandwidth demands constitute the primary bottleneck to further lowering deployment costs. To address this challenge, we introduce DeepSeek-V4.1-Flash, a multimodal Mixture-of-Experts (MoE) model with 552B backbone parameters and support for contexts of up to one million tokens. With its Causal Encoder-Decoder (CED) architecture, the model activates 16B parameters per token during decode but only 8B parameters during prefill, substantially improving cost efficiency for agentic workloads. To push the limits of KV cache compression, DeepSeek-V4.1-Flash combines cross-layer KV cache reuse in Compressed Sparse Attention 2 (CSA2) with FP4 KV caching. These designs reduce its global KV cache footprint (always in HBM) to 890 bytes per token, roughly 1/4 of the corresponding footprint of DeepSeek-V4-Flash. Further, through a dedicated deployment optimization known as SWA Bounded Replay, DeepSeek-V4.1-Flash reduces its persistent KV cache footprint (always on SSD or in host memory) to roughly 1/8 of that of DeepSeek-V4-Flash. Despite its much smaller KV cache footprint, the model delivers substantially better performance than the baseline. In addition, we streamline the DeepSeek-V4 architecture and introduce several efficient architectural extensions. We pretrain DeepSeek-V4.1-Flash on a multimodal corpus comprising 45T tokens and conduct comprehensive post-training, yielding strong performance across diverse text-based and multimodal agentic scenarios. Model checkpoints are available at https://huggingface.co/deepseek-ai/DeepSeek-V4.1-Flash.