Mature CNN/RNN and small LLMs deployed in industrial settings
LatePost reports that as frontier models dominate headlines, mature and cheaper CNN/RNN models and small LLMs are being deployed in real-world industrial settings such as logistics sorting centers and manufacturing lines. Examples include J&T Express using AI servers to monitor conveyor faults, an Apple supplier using vision models for hinge inspection, and judicial departments using a 27B Qwen model for archival processing. The article notes that these deployments often use GPUs from Denglin, which sold over 10,000 units in the first four months of the year.
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晚点 LatePost龚方毅
几万亿参数的前沿大模型不断在新闻里改变着世界，让全球股市在一天内涨跌几千亿美元。但公司用什么技术，通常取决于它的预算。离开雇佣大批高薪程序员的科技行业，最先进的大模型更多是谈资。 一项前沿技术真正进入各行各业、改变每个人的生活，往往要等到它不再性感，成熟到 “无聊”。最近进入这个阶段的，是 Transformer 之前的明星——CNN、RNN 架构的模型。 十年前，科技媒体最关注的创新科技公司是做这些模型的 “AI 四小龙”。英伟达来中国办活动，黄仁勋将海康列为跟阿里云一样重要的 AI 合作伙伴。 今天，这些公司不再被人讨论，而曾经的前沿技术也足够便宜，进入了生产线。 双十一前夕，多个极兔转运中心装上了新的 AI 服务器。每台服务器盯着一个转运中心上千个摄像头的实时画面，每秒刷新 8 次。包裹卡住、分拣员用力扔快递，服务器一分钟内就会发出通知，派人处理。 在苹果一家中国供应商的生产线上，一百多个激光 3D 打印的钛合金零件组装成折叠屏铰链。每个零件和装好的铰链，最后都要放上支架检查。产线上没有纪录片里那种扭着脖子看放大镜的工人。干活的是一排十几个摄像头，每秒拍 10 张，边拍边调整位置。照片全部在流水线里一台类似 PC 的 AI 服务器上检测。人只负责把合格的零件搬走。 一些司法部门下班后，办公楼里的小型服务器还在处理历史档案。这些数据不能、也不需要上传到对外服务的大模型，楼内服务器上的 27B Qwen 就能把它们整理成数据库。第二天上班，工作人员像用豆包一样提问，就能准确查到相关历史案件怎么处理，附上出处。 这些场景不需要最大的模型。十年前流行的视觉模型，加上规模较小的大语言模型——参数规模通常只有 Kimi K3 几分之一甚至几十分之一，已经能完成不少工作。 这些场景里的 GPU 都由登临提供，一块卖几千元到几万元。今年前 4 个月，登临卖了 10000 多块。在价格敏感的行业里，它一单一单替掉了英伟达的推理芯片，甚至游戏显卡。 从顺丰和极兔，到工厂流水线，AI 已经是一笔算得过来的账 快递业今天更像自动化工厂。占地 30 万平米的极兔大型转运中心，传送带总长 40 公里。货车运来的快件倒上分拣机，扫码后进入不同的传送带，再装车发往全国。 这里的传送带，平均一天出 1000 多起故障。包裹被护栏挂住，就可能挡住后面的包裹，堵住一整条传送带；有时包装被撕裂卷进机
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