今年 GTC 的开场极其明确！NVIDIA 官方将 Token 定义为 AI 时代的基本单位。 黄仁勋强调过去两年是 AI 史上最重要的转折期。 ChatGPT 点燃生成式 AI 浪 潮，推理模型赋予了 AI 反思与规划能力，而 Codex 与 Cusor 等 AI 助手的问世，则让 AI 首度具备了真正"做事"的能力。这三个节点构成了 AI 从"感知 到 生成 到 推理 到 执行"的演化轴线。 在开场中， 黄仁勋宣示了 NVIDIA 战略声明。全栈五层架构：战略能源、芯片、基础设施、模型、应用。 核心观点速览： 未来基建： NVIDIA 正从芯片公司转型为基础设施级计算公司，通过 Omni verse DSX 平台重塑全球 AI 工厂。 01. CUDA 20年造就绝对护城河 NVIDIA 专注 CUDA 已经整整 20 年。黄仁勋称其为一项真正的革命性发明。其单指令多线程（SIMT）架构让开发者只需编写标量代码，就能轻松生成多线程 应用，这比传统的 SIMD 架构要简单得多。最近，CUDA 又加入了 tile 技术，极大地方便了开发者对张量核心进行编程，而这些数学结构正是今天人工智能的 坚实基础。 如今， ...

Core Insights - The article discusses the key announcements made by NVIDIA during the GTC 2026 conference, focusing on advancements in AI technology, data center revenue projections, and the introduction of new products and platforms that enhance computational capabilities and AI applications. Group 1: AI Technology Advancements - cuDF and cuVS technologies are set to handle structured and unstructured data, with a significant increase in computational demand, projected to grow by 1 million times [2][3] - DLSS 5 merges 3D graphics with AI, enhancing the generation of structured data and achieving remarkable results in visual fidelity [2][3] - The introduction of the Rubin platform, which includes multiple chips and a supercomputer, is expected to increase computational power by 40 million times over the next decade [3] Group 2: Data Center Revenue Projections - NVIDIA anticipates data center orders to reach $1 trillion by 2027, up from the previously mentioned $500 billion [3][61][62] - The company derives 60% of its revenue from the top five cloud service providers, with the remaining 40% from regional and sovereign clouds [3] Group 3: New Product Launches - The Rubin+LPX system has entered mass production, featuring advanced cooling and design innovations [2][3] - The Groq LPU, now in production, is designed to enhance token processing capabilities significantly [3][5] - The introduction of the OpenClaw platform aims to revolutionize personal agent services, positioning every IT and SaaS company as an AI-as-a-Service provider [4][6] Group 4: Industry Collaborations - NVIDIA has formed partnerships with major companies like IBM and Dell to accelerate data processing for AI applications [22][24] - Collaborations with cloud service providers such as Google Cloud and AWS are enhancing the capabilities of platforms like BigQuery and SageMaker [25][29][32] Group 5: Market Trends and Future Outlook - The AI industry is experiencing explosive growth, with venture capital investments reaching $150 billion, indicating a strong demand for computational resources [52] - The shift towards generative AI is transforming traditional computing paradigms, with NVIDIA positioned to lead this change [54][55] - The company emphasizes the importance of vertical integration in AI applications across various industries, including automotive, healthcare, and finance [41][46][47]

一切都要为Agent让路。 来源｜ APPSO（ID：appsolution） 封面来源 ｜ IC photo 以下文章来源于APPSO ，作者发现明日产品的 APPSO . AI 第一新媒体，「超级个体」的灵感指南。 #AIGC #智能设备 #独特应用 #Generative AI 今年英伟达GTC主题演讲，应该是史上悬念最少的一届。 2022年说元宇宙，2023-2024年说生成式AI，2025年说物理AI。但今年不一样，即便台上英伟达创始人黄仁勋的演讲还没有开始，但台下所有人已经知道 答案了——Agent。 包括英伟达也悄悄在GTC园区里开设了「Build-a-Claw」互动专区，让与会者现场搭建自己的AI Agent。从芯片到模型，从英伟达版龙虾到数据中心，今 年主题演讲的潜台词只有一句话： 一切都要为Agent让路。 专为Agentic AI打造的 Vera Rubin正式发布 如果说Hopper架构开启了生成式AI（Generative AI）的时代，让机器学会了「说话」；那么Vera Rubin的使命，就是开启智能体（Agentic AI）时代，让 机器学会「干活」。 过去的AI像是一个极其聪明 ...

据介绍，cuDF与cuVS针对不同类型数据处理实现了效率大幅提升。其中，cuDF在处理结构化数据时， 能让数据引擎运行速度提升高达5倍；cuVS则有效加速非结构化数据的特征提取过程。实际应用中，社 交平台Snap部署cuDF后，每日数据处理成本锐减76%，仅用三小时就能完成10 PB数据的分析工作，直 接节省数百万美元成本，彰显了英伟达数据加速技术的商业价值与实用价值。 本次大会还重点展示了开源项目OpenClaw，并推出"Build-a-Claw"特色活动。参会者可借助DGX Spark 工作站或GeForce RTX笔记本，在保障个人隐私不泄露的前提下，快速部署"始终在线"的本地专属AI助 手，让AI技术的应用更贴近用户需求，也为开源生态发展提供了新实践。 在演讲中，黄仁勋回顾了CUDA架构20年的发展历程。他表示，经过20年的深耕，英伟达已在全球构建 起数亿台运行CUDA的GPU及计算系统，相关技术深度渗透至各大云端平台与计算机企业，服务覆盖全 球几乎所有行业，成为加速计算领域的重要支撑。黄仁勋还指出，英伟达的定位是算法平台公司，其打 造的cuDF、cuVS等数据加速库已被谷歌云、Azure、AWS等全 ...

今年英伟达 GTC 主题演讲，应该是史上悬念最少的一届。 2022 年说元宇宙，2023-2024 年说生成式 AI，2025 年说物理 AI。但今年不一样，即便台上英伟达创始人黄仁勋的演讲还没有开始，但台下所有人已经知 道答案了——Agent。 一切都要为 Agent 让路。 专为 Agentic AI 打造的 Vera Rubin 正式发布 如果说 Hopper 架构开启了生成式 AI（Generative AI）的时代，让机器学会了「说话」；那么 Vera Rubin 的使命，就是开启智能体（Agentic AI）时代， 让机器学会「干活」。 包括英伟达也悄悄在 GTC 园区里开设了「Build-a-Claw」互动专区，让与会者现场搭建自己的AI Agent。 从芯片到模型，从英伟达版龙虾到数据中心，今 年主题演讲的潜台词只有一句话： 过去的 AI 像是一个极其聪明的图书馆管理员，我们问一个问题，它慢条斯理地翻书，然后把答案整理出来。我们对这种速度是宽容的，因为我们自己打 字看书也慢。 但 Agent 完全不同。它不仅要用大模型思考，还要疯狂地调用工具——比如打开浏览器、控制云端的虚拟 PC、在无数个数 ...

在GTC 2026大会上，英伟达CEO黄仁勋将公司定位为"AI工厂"构建者，称"到2027年将看到至少1万亿美元的高确信度需 求"。他提出"Token工厂经济学"，强调每瓦性能是商业变现的核心。黄仁勋断言Agent（智能体）将终结传统SaaS模式，未 来"年薪+Token预算"将成为职场新标配。 华尔街见闻 开场致辞 主持人： 欢迎英伟达创始人兼首席执行官黄仁勋登台。 黄仁勋，创始人兼首席执行官： 欢迎来到GTC。这是一场技术大会，看到这么多人一大早就排队入场，非常高兴。 今天我们将围绕三大平台展开讨论：CUDA-X平台、系统平台，以及全新的AI工厂平台。当然，最重要的是生态系统。 首先，我要感谢我们的"赛前热身"主持人，他们表现出色：来自Conviction的Sarah Guo、红杉资本的Alfred Lin（英伟达的第 一位风险投资人），以及英伟达第一位重要机构投资人Gavin Baker。这三位对技术有着深刻的理解，在技术生态系统中拥有 广泛的影响力。此外，也感谢我亲自挑选的所有贵宾。 同时，我要感谢所有参会企业。英伟达作为平台公司，拥有技术、平台和丰富的生态系统。今天，涵盖百万亿美元产业的企 业几乎全 ...

作者｜冬梅 北京时间 2026 年 3 月 17 日凌晨两点半，当英伟达 CEO 黄仁勋穿着那件标志性的黑色皮衣踏上 SAP 中心的舞台时，台下近万名开发者心里清楚：这一次，老黄要讲的不是某个单一芯片，而是一 整套 AI"全家桶"。 这场演讲之前，市场早已躁动不安。黄仁勋曾在 2 月预告将发布一款"前所未见的芯片"，被普遍认为 是采用台积电 1.6nm 制程、引入光通信技术的下一代 Feynman 架构。 今天揭晓的 Feynman 架构、Vera Rubin 平台的量产进展，以及面向企业级自主代理的开源平台 NemoClaw，不过是这座基础设施落地所需的"钢筋水泥"。黄仁勋用两个小时向资本市场证明了一件 事：英伟达早已不是那个卖显卡的公司，现在的英伟达是一家为"数万亿美元 AI 基建时代" 搭建完整 技术栈的"总包工头"。 回顾 CUDA 20 年：安装基数引爆飞轮，GPU 算力成本持续下降 演讲刚开始，黄仁勋用近十分钟篇幅，回溯了 CUDA 架构诞生 20 年的演进历程。他将这套软件生 态定义为英伟达一切业务的"中心"，并直言：真正难以复制的壁垒，是底层的安装基数。 "二十年来，我们一直致力于这种革命性架 ...

Core Insights - The event focuses on three major platforms: CUDA-X, system platform, and the new AI factory platform, emphasizing the importance of the ecosystem [1] - NVIDIA celebrates the 20th anniversary of CUDA, highlighting its revolutionary architecture and extensive integration into mainstream ecosystems [2] - The company has built a vast installation base of CUDA GPUs and computing systems over 20 years, which accelerates growth through a flywheel effect [3][4] - NVIDIA's libraries and tools are crucial assets for activating computing platforms and solving real-world problems, with significant updates announced at the event [10] Group 1: AI and Computing Evolution - The rise of generative AI and the launch of ChatGPT have fundamentally changed computing architecture and logic [13] - The demand for inference has skyrocketed, with computational needs increasing by approximately 1 million times [14] - NVIDIA's infrastructure is positioned to support the growing demand for AI across various fields, with a projected demand reaching $1 trillion by 2027 [15] Group 2: AI Factory and Token Production - Data centers are evolving from traditional storage to AI factories focused on token production, with the Vera Rubin system expected to enhance revenue by about 5 times [40] - NVIDIA's architecture allows for the lowest token cost globally, making it a competitive choice for data center installations [18] Group 3: OpenClaw and Agentic Systems - The introduction of OpenClaw represents a significant shift in enterprise IT, necessitating every company to develop an Agent strategy [30] - The NemoClaw reference design provides a secure framework for implementing Agentic systems in enterprises [31] Group 4: Physical AI and Robotics - The era of physical AI is emerging, with partnerships in autonomous driving, industrial robotics, and humanoid robots [35] - NVIDIA's collaborations with major automotive companies aim to integrate AI into RoboTaxi platforms, enhancing the future of transportation [36]

NVIDIA Conference Summary Company Overview - **Company**: NVIDIA (NasdaqGS: NVDA) - **Event**: 2026 Conference held on March 16, 2026 - **Speaker**: Jensen Huang, Founder and CEO Key Points Industry and Technology Platforms - NVIDIA operates as a platform company with three main platforms: CUDA-X, systems, and a new platform called AI Factories [2][3] - The conference highlighted the importance of ecosystems in technology, with 450 companies sponsoring the event and a focus on artificial intelligence (AI) across various layers [3][4] CUDA and Install Base - CUDA has been a foundational technology for NVIDIA for 20 years, enabling a vast install base of hundreds of millions of GPUs and computing systems globally [5][6] - The install base attracts developers, leading to breakthroughs in algorithms and new markets, creating a flywheel effect that accelerates growth [5][6] Accelerated Computing - NVIDIA emphasizes the shift towards accelerated computing for AI, which allows for significant improvements in speed and cost efficiency [18][19] - The company has partnered with major cloud service providers like Google Cloud and AWS to enhance data processing capabilities [18][24] AI and Data Processing - NVIDIA's libraries, such as cuDF for structured data and cuVS for unstructured data, are crucial for accelerating AI applications [15][16] - The collaboration with IBM to accelerate watsonx.data demonstrates the integration of NVIDIA's technology into existing data processing systems [17] Market Demand and Growth - The demand for NVIDIA's GPUs has surged, with a reported increase in computing demand by 10,000 times over the last two years [49][50] - NVIDIA anticipates a revenue potential of at least $1 trillion through 2027, driven by the growing need for AI infrastructure [51][54] Inference and Performance - NVIDIA has focused on optimizing inference capabilities, which are critical for AI applications, leading to a significant increase in performance metrics [58][61] - The introduction of NVLink 72 and NVFP4 has resulted in a 35 to 50 times improvement in performance per watt, making NVIDIA's offerings highly competitive [61][62] Vertical Integration and Ecosystem - NVIDIA positions itself as a vertically integrated but horizontally open company, allowing for extensive collaboration with various platforms and industries [28][29] - The company is involved in multiple sectors, including healthcare, financial services, and telecommunications, showcasing its broad impact across industries [30][32][34] Future Outlook - The conference underscored the transformative potential of AI and accelerated computing, with NVIDIA at the forefront of this technological shift [42][43] - The emergence of AI-native companies and the substantial venture capital investment in AI startups signal a new era of innovation and growth in the tech industry [40][41] Additional Important Insights - The integration of generative AI with traditional computing paradigms is reshaping how applications are developed and deployed [44][45] - NVIDIA's commitment to continuous software updates and support for its GPUs enhances the longevity and utility of its products [7][8] - The company is actively working on confidential computing to ensure data security in AI applications, which is becoming increasingly important in cloud environments [25][26] This summary encapsulates the key themes and insights from the NVIDIA conference, highlighting the company's strategic direction, technological advancements, and market positioning in the rapidly evolving AI landscape.