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一 |     7月20日,为期4天的2026世界人工智能大会(WAIC)正式落幕,从AI算力、具身智能到大模型应用,各类前沿技术在这场行业盛会上集体亮相。一众展品当中,曾经冷门的算力基础设施超节点,被众多厂商搬到了聚光灯下,风头甚至不输消费电子产品。    

An illustration of the “5A Framework” for enterprise intelligent transformation unveiled at the 2026 Digital Intelligent Economy Forum in Langfang, North China’s Hebei Province, on August 12, 2026. Photo: people.cn
    An illustration of the “5A Framework” for enterprise intelligent transformation unveiled at the 2026 Digital Intelligent Economy Forum in Langfang, North China’s Hebei Province, on August 12, 2026. Photo: people.cn     
    

The 2026 Digital Intelligent Economy Forum was held in Langfang, North China’s Hebei Province, on Wednesday. At the forum, organizer People's Daily Cultural Technology Corporation unveiled a “5A Framework” for enterprise intelligent transformation, aimed at helping businesses comprehensively innovate their business models, organizational structures and service systems in the artificial intelligence (AI) era.
A To A: Exploring new models for agent collaboration
AI is pushing the internet into a new stage characterized by the widespread participation of AI agents. In the future, China could develop an ecosystem comprising tens of billions of agent applications, while the global “digital population” could reach hundreds of billions. Following To B (business-oriented) and To C (consumer-oriented) models, enterprises and institutions should actively explore the emerging To A (agent-oriented) model. To enable better interaction and coordination among agents, unified data, technology, interface and service standards need to be established, while new business systems and service models should be cultivated.
AI-friendly: Building machine-readable data infrastructure
Today’s internet user interfaces are primarily designed for human browsing. The first step, therefore, should be to build an “AI-friendly” data system that is better suited to being read, understood and accessed by AI. By structuring content resources and turning them into organized knowledge, while strengthening ontology systems and knowledge graphs, such a system can improve AI’s ability to understand and apply information. On this basis, integrated hardware and software infrastructure combining computing architecture with data systems can be developed for AI applications.
AI-native teams: Reshaping organizations and workflows
AI is profoundly reshaping organizational structures and ways of working. AI-native teams should redefine job responsibilities, organizational structures and business processes around the A-To-A model and AI-friendly services, enabling “everyone to have multiple AI agents.” Professional capabilities and business experience can be encapsulated into “digital employees,” promoting autonomous learning by agents, multi-agent collaboration and the evolution of collective intelligence, thereby multiplying per-capita productivity.
AI for good: Putting values first, serving social development
AI also requires values-based guidance. Guided by the principle of “being people-centered and using AI for good,” efforts should follow the approach of “governing AI with AI and guiding intelligence with wisdom,” ensuring that AI always serves social development, the public interest and people’s needs. The “Mainstream Values Corpus,” whose development was launched by the People’s Daily in May 2023, has already begun to take shape and has been validated through numerous large-model applications.
Inclusive AI: Protecting cultural diversity, advancing global sustainable development
AI should not become merely “a game for rich countries and rich people,” but should benefit all countries. Efforts should be made to actively participate in global initiatives for inclusive AI, bridge the digital and intelligent divide, and enable more organizations and individuals to share the benefits of development on an equal footing, so that AI can truly become a powerful positive force for improving the well-being of all humanity and advancing human civilization.
This was translated and compiled by the Global Times, based on an article originally published on people.cn on August 12, 2026.

。         活动现场,众多厂商展示出了一排排黑色机柜,这套系统机柜造价动辄上亿元,运输困难,展出机会十分有限。不少展台前都挤满了观众,有人举起手机拍摄,有人围着讲解员追问参数,还有参观团在人群外排队等待。         所谓“超节点”,通俗来讲,就是通过高速互联把多张AI芯片或加速卡,组织成一个更大的计算单元,让它们能像一台“大计算机”一样高效协同工作。         随着单块芯片的性能提升越来越难,厂商们想要继续把整体算力往上推,就只能想办法让芯片之间更紧密地协作。因此,在今年的WAIC上,各大公司集体冲进了超节点赛道。         其中,华为Atlas 950是本届WAIC最受关注的算力展品之一。华为披露的信息显示,Atlas 950 SuperPoD基于灵衢互联协议和超节点架构,将1024张昇腾卡组织成一个计算单元,提供256TB全局统一内存编址空间,面向万亿参数大模型训练和高并发推理。         在中科曙光展区,曙光8000“登峰”同样吸引了大量观众。它并非一个单体超节点,而是一个全国产十万卡AI超集群,覆盖科学计算、大模型训练、AI推理和工业仿真等不同任务。         对比来看,两套厂商系统都展示了国产算力向外扩张的不同层级:从造出一颗芯片,到把上千张卡连成一台大计算机,再到把大量节点组织成可以持续运行的算力集群。         此外,阿里、百度等头部厂商也没闲着,携自研超节点产品登场。阿里真武M890x磐久AL128超节点、百度天池512超节点同台竞技,展现出在中国AI基础设施下的不同路径。

二 |          国产GPU厂商也在加速涌入这一市场。如沐曦股份首发“曦景”S系列超节点产品,构建全栈国产算力产品矩阵;燧原科技发布多款高性能超节点方案,满足万卡级以上大规模集群组网需求等。

三 |          可以说,到了今年,超节点几乎成了整个国产算力行业的集体共识,一些机构甚至将2026年称为“国产超节点方案量产元年”。华泰证券测算称,2028年国产超节点市场空间有望达到3414亿元,2026年至2028年复合年均增长率高达194%。         超节点之所以受到追捧的一个重要背景在于,大模型正在从“参数规模竞赛”走向“交付能力竞赛”。一方面,各家模型的规模持续扩大,参数动辄达到万亿级,对算力集群的训练效率、通信能力和稳定性都提出了更高要求;另一方面,随着大模型加速进入产业应用,模型不再只是生成“看起来不错”的内容,而是要能够围绕复杂目标,直接产出可用、可交付的结果。         这类需求在今年WAIC上已经有明显体现。商汤在活动现场发布了其多模态基座模型SenseNova U1 Pro,按照商汤的说法,新模型要解决的核心问题,是让模型能够围绕一个复杂目标,直接生成可用于实际场景的交付产品。         商汤科技首席科学家林达华对《财经天下》表示,“以前的生图软件通常采用‘平均分’式的评价逻辑,一张图里有100个字,即便错了1个字,也可能被视为99分。但对于交付级设计模型而言,只要错一个字,或者出现一个明显瑕疵,本质上就是不合格的”。         正因为如此,交付级AI对底层算力的要求,已经不再只是“跑得快”这么简单。过去,市场更关注单颗芯片的算力和制程。

四 | 但在大模型时代,更关键的是上百张、上千张卡能否高效协同、稳定运行。         未来,超节点不只是厂商展示技术实力的产品,也可能成为国产AI基础设施竞争的重要入口。在众多厂商中,谁能更好地打通芯片、互联、软件和应用,谁就更可能在下一个阶段占据更多主动。         (文 | 豆蔻,图片来源 | 视觉中国,本内容转载自财经天下WEEKLY)。

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