刚刚,全球首个超高帧世界模型诞生,英伟达含量0,狂飙50帧

marsbitPublished on 2026-07-08Last updated on 2026-07-08

Abstract

中国团队魔芯科技联合浙江大学发布全球首个Flash World Model——MoWorld,首次在国产NPU平台上实现超过50FPS的实时推理速度,将世界模型带入实时交互时代。该模型全栈基于国产算力构建,从训练到部署完成闭环,推理成本较同规模GPU方案降低70%。MoWorld具备2000帧长时训练与推理能力,支持6自由度相机控制和多场景高清漫游,可应用于游戏娱乐、具身智能、影视创作及数字孪生等领域。此举证明了国产算力支撑世界模型产业化的可行性,为降低部署门槛、推动技术落地开辟了新路径。

世界模型终于跑进实时时代。当行业还在为5FPS、10FPS挣扎时,一支中国团队直接把实时交互世界模型推上50FPS。更重要的是,他们没有使用英伟达GPU。

专注于4D世界模型研发和产业化的魔芯科技联合浙江大学潘云鹤院士团队发布MoWorld——全球首个Flash World Model,也是首个全栈基于国产NPU构建的实时交互世界模型。

从训练、蒸馏到部署,全流程跑通国产算力闭环;推理成本比同规模GPU方案降低70%。

世界模型的产业化拐点,可能比所有人预想得来得更早。

卡住整个行业的实时难题

被中国团队捅破了

如果你体验过今天的主流世界模型,大概率会有一种共同感受:

  • 能看,但不能玩。
  • 机器人需要实时决策。
  • 游戏需要实时反馈。
  • 数字世界需要实时推演。

过去一些研究表明,帧率低于30FPS,一切沉浸感都会被打碎。

这也是为什么过去很长时间里,世界模型始终停留在实验室,而难以真正进入产业场景。

实时性,曾是横亘在世界模型商业化之路上的最后一道天堑。

如今,这道天堑被一支中国团队一举攻克。

50FPS!

世界模型第一次真正跑进实时时代

技术报告已经发布,近期将开源权重和代码,并基于国产 NPU 超节点向公众提供服务。

技术报告:https://moxin-tech.github.io/moworld/

魔芯科技首次提出 Flash World Model 概念,MoWorld是行业率先实现>50FPS推理的「Flash World Model」,也是首个全栈基于国产NPU构建的低成本实时交互世界模型。

MoWorld首次在全栈国产算力平台上实现了从训练、蒸馏到实时推理部署的完整闭环,同时典型推理配置下的成本比同规模GPU方案降低70%

当所有人都在堆GPU时

他们选择了另外一条难而正确的路

MoWorld从一开始,就没有围绕GPU构建,而是选择了一条更难、也更少有人尝试的路线——全栈国产NPU。

这意味着,它不是简单地把模型迁移到国产芯片上进行推理,而是从数据、训练、蒸馏到推理部署,每一个环节都围绕国产NPU重新设计。

首先,是数据。

相比视频生成模型,世界模型不仅需要视频和文本,更需要相机轨迹等信息。互联网视频远无法满足训练需求。

为此,MoWorld建立了一套完整的数据生产与治理体系,并将此升级为服务于世界模型的数据引擎。

基于魔芯团队多年3D/4D建模的技术积累,MoWorld背后的数据管线是全自采、全3D标注的管线,不仅标注了相机,还标注了物体的几何尺寸和空间结构,这些信息构建了MoWorld坚实数据基座。

而更加困难的挑战,则发生在训练和推理阶段。

针对国产NPU的硬件特点,MoWorld重新设计了训练系统,引入超密集注意力并行、长序列Token并行等技术,大幅缓解超长视频训练带来的显存压力,让世界模型首次具备2000帧长时训练与推理能力。

而到了推理阶段,团队继续针对国产NPU进行了流水线执行、层级化序列并行、动态混合精度量化等一系列系统级优化。

最终,一个14B参数的MoE世界模型,在华为Ascend 910C CloudMatrix384 NPU平台上实现了极限超过50FPS的实时推理速度

更重要的是,在典型的推理配置下,推理成本比同规模GPU方案降低70%。

对于整个行业而言,这不仅是一条新的工程路线,更是一种新的产业化思路。

世界模型最贵的一道门,被推开了

长期以来,世界模型的发展始终存在一个矛盾。

模型越来越强。

成本也越来越高。

过去,部署一个世界模型,意味着高昂的GPU投入、复杂的集群维护,以及难以复制的部署成本。

而现在,同样的世界模型能力,可以运行在更具成本优势的国产算力平台上。

这不仅改变了模型运行的方式,也改变了世界模型走向产业化的路径。

对于企业而言,这意味着更低的部署门槛、更快的应用验证,以及更容易实现规模化复制。

对于整个行业而言,这意味着世界模型开始从「能做」迈向「能用」,再迈向「用得起」。

真正推动一项技术改变产业的,从来不是实验室里的最高纪录。

而是它第一次,让更多人能够真正用起来。

世界模型走出实验室

哪些行业将迎来变革

过去几年,世界模型一直被认为是未来技术。

但未来,终究要回到现实。

当实时交互成为可能,当部署成本开始下降,世界模型真正的价值,才刚刚开始释放。

率先发生变化的,将是那些最依赖真实世界理解与实时反馈的行业。

游戏与互动娱乐:实时交互,自由探索

MoWorld支持完整的6自由度相机控制,用户通过W/A/S/D和鼠标即可实现影视级和游戏级的沉浸式体验漫游。

场景真实、高清,支持1080P及以上分辨率。无论是自然风光、二次元还是游戏动漫,均全面支持。

具身智能与自动驾驶:虚拟训练,真实验证

世界模型已成为连接生成式AI与具身智能的关键桥梁。

MoWorld可为机器人、自动驾驶系统提供低成本、高保真的「数字演练场」,是行业兼具仿真价值和经济价值的最具潜力的世界模拟器,能够为所有的智驾团队提供大量且高精度的环境,让AI在虚拟世界中学习如何与现实物理环境交互。

影视创作:导演运镜,实时预演

传统影视分镜需要漫长的渲染周期。

MoWorld让创作者可以在生成的虚拟世界中自由调整视角、实时预览画面效果、精准编辑镜头画面,镜头控制丝滑,支持超越想象力的导演级运镜。

数字孪生与三维重建:空间重建,精准还原

MoWorld生成的视频具有超越行业的几何一致性,可直接用于室内场景的三维重建——精度高、结构稳定、空间一致性好是MoWorld区别于同行的显著效果。

这为数字孪生、建筑可视化、虚拟展厅、沉浸式游戏等场景提供了兼具高精度和性价比的解决方案。

MoWorld代表的世界模型

此刻正站在物理AI的DeepSeek时刻

过去几年,AI完成了从文本生成、图像生成到视频生成的连续跨越,每一次技术跃迁,都催生了一批新的行业领导者。

而当AI开始迈向实时交互世界模型,一个新的窗口正在打开。

相比已经逐渐形成竞争格局的大语言模型和视频生成模型,世界模型仍处于产业早期,全球都在探索工程化落地路径,行业标准远未形成。

这意味着,国产世界模型迎来了少有的「同一起跑线」机会——不仅有机会参与竞争,更有机会参与定义下一代空间智能的技术标准。

MoWorld的意义,并不仅仅在于实现了超过50FPS的实时交互,也不仅仅在于将推理成本比同规模GPU方案降低70%。

更重要的是,它证明了一个此前行业一直在探索、却始终没有验证的问题:

全栈国产算力,同样能够支撑世界模型走向实时交互与产业部署。

这意味着,世界模型的竞争,正在从「谁拥有更大的模型」,转向「谁能够真正进入真实世界」。

MoWorld 背后的魔芯科技凭借其独特的模型能力和突出的产业化进展,于近期获得国家级战略储备资本、中东知名美元机构、头部市场化基金以及十余家产业资本过亿美元融资;在此之前,魔芯科技已获得华为旗下哈勃投资、联想控股旗下基金投资。

真正属于世界模型的时代,从Flash World Model开始,将快步向前。

本文来自微信公众号“新智元”,作者:ASI启示录

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Related Questions

Q全球首个超高帧世界模型MoWorld实现了怎样的推理速度?

AMoWorld在华为Ascend 910C CloudMatrix384 NPU平台上实现了超过50FPS的实时推理速度。

QMoWorld模型在哪个关键技术环节实现了全栈国产化?

AMoWorld从数据、训练、蒸馏到推理部署,实现了全栈基于国产NPU构建的完整闭环。

Q相比同规模的GPU方案,MoWorld的推理成本降低了多少?

A在典型的推理配置下,MoWorld的推理成本比同规模GPU方案降低了70%。

Q根据文章,MoWorld模型主要将应用于哪些行业?

A文章指出,MoWorld将主要应用于游戏与互动娱乐、具身智能与自动驾驶、影视创作、数字孪生与三维重建等行业。

Q魔芯科技在MoWorld的研发中,针对国产NPU引入了哪些关键技术以优化训练?

A针对国产NPU的硬件特点,MoWorld重新设计了训练系统,引入了超密集注意力并行、长序列Token并行等技术,以缓解显存压力,使模型具备2000帧长时训练与推理能力。

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This dual approach empowers it to build a rich repository of domain-specific knowledge, enhancing its performance in task execution. Planning Over Long Task Horizons: Agent S employs experience-augmented hierarchical planning, a strategic approach that facilitates efficient breakdown and execution of intricate tasks. This feature significantly enhances its ability to manage multiple subtasks efficiently and effectively. Handling Dynamic, Non-Uniform Interfaces: The project introduces the Agent-Computer Interface (ACI), an innovative solution that enhances the interaction between agents and users. Utilizing Multimodal Large Language Models (MLLMs), Agent S can navigate and manipulate diverse graphical user interfaces seamlessly. Through these pioneering features, Agent S provides a robust framework that addresses the complexities involved in automating human interaction with machines, setting the stage for myriad applications in AI and beyond. Who is the Creator of Agent S? While the concept of Agent S is fundamentally innovative, specific information about its creator remains elusive. The creator is currently unknown, which highlights either the nascent stage of the project or the strategic choice to keep founding members under wraps. Regardless of anonymity, the focus remains on the framework's capabilities and potential. Who are the Investors of Agent S? As Agent S is relatively new in the cryptographic ecosystem, detailed information regarding its investors and financial backers is not explicitly documented. The lack of publicly available insights into the investment foundations or organisations supporting the project raises questions about its funding structure and development roadmap. Understanding the backing is crucial for gauging the project's sustainability and potential market impact. How Does Agent S Work? At the core of Agent S lies cutting-edge technology that enables it to function effectively in diverse settings. Its operational model is built around several key features: Human-like Computer Interaction: The framework offers advanced AI planning, striving to make interactions with computers more intuitive. By mimicking human behaviour in tasks execution, it promises to elevate user experiences. Narrative Memory: Employed to leverage high-level experiences, Agent S utilises narrative memory to keep track of task histories, thereby enhancing its decision-making processes. Episodic Memory: This feature provides users with step-by-step guidance, allowing the framework to offer contextual support as tasks unfold. Support for OpenACI: With the ability to run locally, Agent S allows users to maintain control over their interactions and workflows, aligning with the decentralised ethos of Web3. Easy Integration with External APIs: Its versatility and compatibility with various AI platforms ensure that Agent S can fit seamlessly into existing technological ecosystems, making it an appealing choice for developers and organisations. These functionalities collectively contribute to Agent S's unique position within the crypto space, as it automates complex, multi-step tasks with minimal human intervention. As the project evolves, its potential applications in Web3 could redefine how digital interactions unfold. Timeline of Agent S The development and milestones of Agent S can be encapsulated in a timeline that highlights its significant events: September 27, 2024: The concept of Agent S was launched in a comprehensive research paper titled “An Open Agentic Framework that Uses Computers Like a Human,” showcasing the groundwork for the project. October 10, 2024: The research paper was made publicly available on arXiv, offering an in-depth exploration of the framework and its performance evaluation based on the OSWorld benchmark. October 12, 2024: A video presentation was released, providing a visual insight into the capabilities and features of Agent S, further engaging potential users and investors. These markers in the timeline not only illustrate the progress of Agent S but also indicate its commitment to transparency and community engagement. Key Points About Agent S As the Agent S framework continues to evolve, several key attributes stand out, underscoring its innovative nature and potential: Innovative Framework: Designed to provide an intuitive use of computers akin to human interaction, Agent S brings a novel approach to task automation. Autonomous Interaction: The ability to interact autonomously with computers through GUI signifies a leap towards more intelligent and efficient computing solutions. Complex Task Automation: With its robust methodology, it can automate complex, multi-step tasks, making processes faster and less error-prone. Continuous Improvement: The learning mechanisms enable Agent S to improve from past experiences, continually enhancing its performance and efficacy. Versatility: Its adaptability across different operating environments like OSWorld and WindowsAgentArena ensures that it can serve a broad range of applications. As Agent S positions itself in the Web3 and crypto landscape, its potential to enhance interaction capabilities and automate processes signifies a significant advancement in AI technologies. Through its innovative framework, Agent S exemplifies the future of digital interactions, promising a more seamless and efficient experience for users across various industries. Conclusion Agent S represents a bold leap forward in the marriage of AI and Web3, with the capacity to redefine how we interact with technology. While still in its early stages, the possibilities for its application are vast and compelling. Through its comprehensive framework addressing critical challenges, Agent S aims to bring autonomous interactions to the forefront of the digital experience. As we move deeper into the realms of cryptocurrency and decentralisation, projects like Agent S will undoubtedly play a crucial role in shaping the future of technology and human-computer collaboration.

768 Total ViewsPublished 2025.01.14Updated 2025.01.14

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