NVIDIA 2027 财年第二季度：创纪录营收、供应受限与 AWS 扩展合作

下午好。我是 Tiffany，今天由我担任本次电话会议的操作员。现在，欢迎大家参加 NVIDIA 第二季度财报电话会议。[操作员说明] 谢谢。Toshiya Hari，您可以开始会议了。

Good afternoon. My name is Tiffany, and I will be your conference operator today. At this time, I would like to welcome everyone to NVIDIA's Second Quarter Earnings Call. [Operator Instructions] Thank you. Toshiya Hari, you may begin your conference.

谢谢。下午好，欢迎参加 NVIDIA 2027 财年第二季度财报电话会议。今天与我一同出席的有 NVIDIA 总裁兼首席执行官黄仁勋，以及执行副总裁兼首席财务官 Colette Kress。本次电话会议正在 NVIDIA 投资者关系网站上进行网络直播。重播将持续到讨论 2027 财年第三季度财务业绩的电话会议之前。今天电话会议的内容是 NVIDIA 的财产，未经我们事先书面同意，不得复制或转录。在本次电话会议中，我们可能会基于当前预期做出前瞻性陈述。这些陈述受到许多重大风险和不确定性的影响，我们的实际结果可能会有重大差异。有关可能影响我们未来财务业绩和业务的因素的讨论，请参阅今天的财报发布、我们最新的 10-K 和 10-Q 表格，以及我们可能向美国证券交易委员会提交的 8-K 表格报告中的披露。我们所有陈述均基于今天（2026 年 8 月 26 日）我们可获得的信息做出。除非法律要求，我们不承担更新任何此类陈述的义务。在本次电话会议中，我们将讨论非 GAAP 财务指标。您可以在我们网站上发布的 CFO 评论中找到这些非 GAAP 财务指标与 GAAP 财务指标的对账。接下来，请 Colette 发言。

Thank you. Good afternoon and welcome to NVIDIA's conference call for the 2nd quarter of fiscal 2027. With me today from NVIDIA are Jensen Huang, President and Chief Executive Officer, and Colette Kress, Executive Vice President and Chief Financial Officer. Our call is being webcast live on NVIDIA's investor relations website. The webcast will be available for replay until the conference call to discuss our financial results for the 3rd quarter of fiscal 2027. The content of today's call is NVIDIA's property. It can't be reproduced or transcribed without our prior written consent. During this call, we may make forward-looking statements based on current expectations. These are subject to a number of significant risks and uncertainties, and our actual results may differ materially. For a discussion of factors that could affect our future financial results and business, please refer to the disclosure in today's earnings release, our most recent Forms 10-K and 10-Q, and the reports that we may file on Form 8-K with the Securities and Exchange Commission. All our statements are made as of today, August 26th, 2026, based on information currently available to us. Except as required by law, we assume no obligation to update any such statements. During this call, we will discuss non-GAAP financial measures. You can find a reconciliation of these non-GAAP financial measures to GAAP financial measures in our CFO commentary, which is posted on our website. With that, let me turn the call over to Colette.

谢谢，Toshiya。我们再次交出了出色的季度业绩，营收、营业利润和每股收益均创下新高。总营收达 960 亿美元，同比增长超过一倍，增长势头连续第四个季度加快。AI 需求的激增正推动全球基础设施建设，并得到来自超大规模云厂商、AI 实验室、AI 原生企业、企业客户和主权客户等日益多元化的增长机会的支持。我们预计 2028 财年营收将增长约 70%。这是一个受供应约束的展望。

Thanks, Toshiya. We delivered another outstanding quarter with record revenue, operating income, and EPS. Total revenue of $96 billion more than doubled year over year as growth accelerated for the 4th consecutive quarter. The surge in AI demand is driving a global infrastructure buildout supported by an expanding and diverse set of growth opportunities spanning hyperscalers, AI labs, AI natives, enterprises, and sovereign customers. We expect to grow revenue by approximately 70% in fiscal 2028. This is a supply-constrained outlook.

第二季度数据中心营收环比增长 18%，达到 890 亿美元，其中超大规模和 ACI&E（包括我们的 NeoCloud 工业和企业客户）两个子板块均贡献强劲。超大规模营收为 490 亿美元，环比增长 13%，主要受 Blackwell 持续强劲的推动。随着新的 GPU 算力上线，更多的算力带来更多的营收，我们的超大规模客户本季度财务业绩强劲，营收增长加快，利润率扩大。云行业积压订单现已超过 2 万亿美元，前五大超大规模云厂商的资本支出预计将在 2026 年达到近 8000 亿美元，2027 年达到 1.3 万亿美元。

Q2 data center revenue increased 18% quarter over quarter to $89 billion, with strong contributions from both subsegments, hyperscale and ACI&E, which includes our NeoCloud industrial and enterprise customers. Hyperscale revenue of $49 billion grew 13% sequentially, driven by sustained strength in Blackwell. Reinforcing that more compute drives more revenue as new GPU capacity comes online, our hyperscale customers delivered strong financial results in the quarter with accelerating revenue growth and expanding margins. With cloud industry backlog now greater than $2 trillion, CapEx by the top 5 hyperscalers is expected to reach nearly $800 billion in 2026 and $1.3 trillion in 2027.

今天，我们很高兴地宣布扩大与 AWS 的合作，在其庞大的 NVIDIA 算力安装基础上进一步扩展。AWS 将从本季度开始到 2029 财年第二季度部署额外的 200 万个 GPU，以及 Vera CPU，其中部分与 Rubin 集成，部分独立使用。AWS 将在 Amazon Bedrock 和 SageMaker 上提供 NVIDIA Nemotron 系列开放模型。亚马逊还将采用我们的完整物理 AI 技术栈，包括 Omniverse、Cosmos、Isaac 和 Jetson，为其仓库机器人车队提供动力。

Today we are delighted to announce an expansion of our partnership with AWS, building on its already vast installed base of NVIDIA compute. AWS is deploying an additional 2 million GPUs starting this quarter through the second quarter of fiscal ’29, along with Vera CPUs, some integrated with Rubin, others standalone. AWS will serve NVIDIA Nemotron family of open models on Amazon Bedrock and SageMaker. Amazon will also adopt our full physical AI stack, Omniverse, Cosmos, Isaac, and Jetson, to power its fleet of warehouse robots.

ACIE 营收为 400 亿美元，环比增长 25%，同比增长 138%。增长主要得益于 NeoCloud 产能扩充，以满足企业、AI 初创公司和主权客户日益增长的需求，以及超大规模云厂商购买产能以补充自身建设。借助 NVIDIA DSX 参考设计，我们的 NeoCloud 合作伙伴正以更快的速度和更低的 token 成本上线产能。预计到今年年底，他们的总装机容量将达到 8 吉瓦，而 2025 年底约为 3 吉瓦。令人难以置信的是，即使在我们这样的规模下，需求仍在加速。客户的预测表明，我们的增长明年将翻倍。然而，正如我之前提到的，由于供应受限，我们预计增长约 70%。NVIDIA 算力在我们服务的每个云中都得到充分利用。它为我们的超大规模、NeoCloud 和 AI 实验室合作伙伴创造的经济价值持续上升。

ACIE revenue of $40 billion increased 25% sequentially and 138% year over year. Growth was driven by NeoCloud capacity additions to meet the rising demand from enterprises, AI startups, and sovereigns, as well as hyperscalers purchasing capacity to supplement their own buildouts. Using NVIDIA DSX reference designs, our NeoCloud partners are bringing capacity online faster and at lower token cost. They are expected to exit the year with 8 gigawatts in total installed capacity, up from approximately 3 gigawatts at the end of 2025. Incredibly, we are seeing demand acceleration even at our scale. Customers’ forecasts point to our growth doubling next year. However, as I mentioned earlier, we expect to grow approximately 70% as we are supply constrained. NVIDIA compute is fully utilized across every cloud we serve. The economic value it generates for our hyperscale NeoCloud, and AI lab partners keeps rising.

除了构建最好的 AI 计算技术和最强大的供应链之外，NVIDIA 还拥有三大独特能力，它们是推动我们增长的动力引擎。首先，NVIDIA 的架构运行所有模型，随着闭源和开源模型的采用增长，我们的份额也在扩大。闭源和开源模型的采用都在飞速增长。NVIDIA 运行领先的闭源模型——OpenAI、Anthropic、Groq、Meta Gemini——以及领先的开源模型——TML、Mistral、Quinn、Kime、GLM、DeepSeek、Minimax 和 Nemotron。我们擅长小模型和巨型模型。无论是大模型还是视频模型，自回归还是扩散模型，在云端还是边缘，NVIDIA 都表现出色。NVIDIA 擅长训练、推理和智能体式工作负载。一个平台可灵活适配所有模型和工作负载，并贯穿 AI 的整个生命周期。性能、灵活性和持久性的结合，使 NVIDIA 成为高效且可融资的计算基础设施。

Besides building the best AI computing technologies and the most capable supply chain, NVIDIA has 3 unique capabilities that are engines powering our growth. First, NVIDIA’s architecture runs every model, and we’re growing share as closed and open model adoption grow. Closed and open models alike, adoption is skyrocketing. NVIDIA runs the leading closed models — OpenAI, Anthropic, Groq, Meta Gemini — and the leading open models — TML, Mistral, Quinn, Kime, GLM, DeepSeek, Minimax, and Nemotron. We’re great at small models and giant ones. Large or video, auto, regressive, or diffusion in the cloud or in the edge. NVIDIA is great at training, great at inference, great at agentic workloads. One platform fungible for every model and workload, durable for the entire lifecycle of AI. That combination of performance, fungibility, and durability is what makes NVIDIA the productive and financeable compute infrastructure.

我们的第二个独特能力是我们的全栈 AI 工厂平台，它正在扩大我们在数据中心 TAM 中的份额。自 Hopper 以来，我们的每吉瓦营收机会从约 180 亿美元增长到 Blackwell 的 250 亿美元，再到 Vera Rubin 的 400 亿美元，后者现在涵盖 Vera CPU、Rubin GPU、NVLink、InfiniBand 或以太网，以及本周早些时候发布的 Groq LPU。我们在 GPU、CPU、NVLink 纵向扩展网络、横向扩展网络系统、算法和软件方面的极限协同设计能力，使我们能够每一代都实现 X 倍的性能提升。Vera Rubin 就是例证，与 Grace Blackwell Ultra 相比，每兆瓦吞吐量提高 30 倍，token 成本降低 35 倍。我们已于本月早些时候开始 Vera Rubin 的生产出货。我们已经收到了所有主要超大规模云厂商、AI 云和系统 OEM 的采购订单，预计 Vera Rubin 将成为 NVIDIA 历史上产品爬坡最快的产品。

Our second unique capability is our full-stack AI factory platform that is expanding our share of the data center TAM. Since Hopper, our revenue opportunity has grown from roughly $18 billion per gigawatt to $25 billion with Blackwell. To $40 billion with Vera Rubin, which now spans Vera CPU, Rubin GPU, NVLink, InfiniBand or Ethernet, and Groq LPU announced earlier this week. Our ability to extreme code design across GPU, CPU, NVLink scale-up networking, scale-out networking systems, algorithms, and software enables us to deliver X-factor performance gain every generation. Vera Rubin exemplifies this, delivering 30x higher throughput per megawatt and 35x lower token cost relative to Grace Blackwell Ultra. We commenced production shipments of Vera Rubin earlier this month. Having already received purchase orders from every major hyperscaler, AI cloud, and system OEM, we expect Vera Rubin to mark the fastest product ramp in NVIDIA’s history.

我们的网络业务又创下了创纪录的季度，营收环比增长 18%。Spectrum-X 以太网同比增长 2.6 倍，已经帮助我们成为全球最大、增长最快的网络公司。智能体式 AI 的日益普及正在推动数据中心 CPU 需求的加速增长。我们的 Grace CPU 于 2021 年推出，取得了巨大成功，过去 12 个月的营收超过 50 亿美元。今天，我们的下一代 Vero CPU 已全面投产。作为独立产品，Vero 进一步扩大了我们的 TAM。Vero 在 SPEC 基准测试中完成 GenIC 任务的速度快 1.8 倍，每瓦带宽是其他数据中心 CPU 的 5 倍。我们预计 Vera 将被所有主要超大规模云厂商、NeoCloud、AI 实验室和系统 OEM 采用，目前已经向我们的主要合作伙伴发货，包括 OCI、SpaceX AI，以及从本季度开始的 AWS。我们继续看到服务器 CPU 总需求约为 200 亿美元。基于客户需求和供应前景的改善，我们的初步预期是 2028 财年 CPU 营收将增长一倍以上，使我们成为全球领先的服务器 CPU 供应商之一。

Our networking business had another record quarter with revenue growing 18% on a sequential basis. Spectrum-X Ethernet, which grew 2.6x on a year-over-year basis, is already helping us become the largest and fastest-growing network company in the world. Rising adoption of agentic AI is driving an acceleration in demand for data center CPUs. Our Grace CPU, introduced in 2021, has been a great success, with revenue on a trailing 12-month basis exceeding $5 billion. Today we are in full production of our next-generation Vero CPU. As a standalone product, Vero expands our TAM even further. Vero completes a GenIC task 1.8x faster on the SPEC benchmark and provides 5 times the bandwidth per watt than any other data center CPU. We expect Vera to be deployed by every major hyperscaler, neo-cloud, AI lab, and system OEM, with shipments already underway to our lead partners, including OCI, SpaceX AI, and starting this quarter, AWS. We continue to see demand for approximately $20 billion in total server CPUs And based on our customer demand and improving supply outlook, our preliminary expectation is for CPU revenue to more than double in fiscal ’28, positioning us as one of the world’s leading server CPU suppliers.

自去年宣布与 Groq 合作以来，我们一直致力于将 NVIDIA 的高吞吐量与 Groq 的高交互性架构相结合。在本周早些时候的 Hot Chips 大会上，我们宣布我们的首个机架级 LPU 系统 Groq 3 LPX 已全面投产，并已创下纪录，在我们的人工分析基准测试中，每秒 token 数比次优方案高出近 4 倍。我们预计将在本季度晚些时候向早期采用者批量出货 Grok 3 LPX。Nebius 将是第一个。今天，我们不仅仅是在销售最好的芯片，我们还在销售全栈 AI 工厂平台，为客户提供卓越的经济性，并占据数据中心 TAM 的更大份额。

Since the announcement of our Groq partnership last year, we’ve been working to unite NVIDIA’s high-throughput and Groq’s high-interactivity architectures. At Hot Chips earlier this week, we announced that Groq 3 LPX, our first rack-scale LPU system, is in full production and already setting records, demonstrating nearly 4x the number of tokens per second against the next best alternative on our artificial analysis benchmark. We expect to ship Grok 3 LPX in volume later this quarter to early adopters. Nebius will be the first. Today, we’re not just selling the best chips, we’re selling a full-stack AI factory platform offering superior economics for customers and capturing a bigger share of the data center TAM.

我们的第三个独特能力是全栈 AI 工厂与丰富的 CUDA 生态系统的结合，使我们能够将 AI 扩展到单一芯片永远无法触及的市场。在超大规模云厂商之外，还有一个渴望采用 AI 的庞大市场，这些客户对设计自己的定制芯片毫无兴趣。

Our 3rd unique capability is the combination of our full-stack AI factory and rich CUDA ecosystem, allowing us to extend AI into markets a single chip alone can never reach. Beyond the hyperscalers lies a massive market anxious to adopt AI, customers with no interest in designing their own custom silicon.

NVIDIA 经过充分验证的全栈平台，独特地适合帮助主权国家、新型云和企业构建其 AI 基础设施，使其全面投入运营，通过 CUDA 软件持续优化，并将其与我们庞大开发者生态系统的承购需求连接起来。超大规模云厂商仍将是主要增长驱动力，但非超大规模的增长——我们的 AI C&E 部门，涵盖主权区域新型云、企业边缘和隔离数据中心——将约占我们数据中心业务的一半。我们以 AI 原生的初创企业生态系统，主要基于 NVIDIA 计算平台开发和运行，正在快速 Scaling（规模扩张）。全球 AI 风险投资，其中约 70% 用于算力，在 2026 年上半年超过 4000 亿美元，超过了 2025 年全年的 2650 亿美元。包括 Cursor（由 SpaceX 拥有）、Figma 和 Together AI 在内的近 20 家公司，年化运行率收入现已超过 10 亿美元，高于去年第四季度的 13 家，其中垂直企业软件增长最快。

NVIDIA’s fully proven full-stack platform is uniquely suited to help sovereigns, neo-clouds, and enterprises build their AI infrastructure, bring it to full operation, continuously optimize it through CUDA software, and connect it to offtake demand from our vast developer ecosystem. Hyperscalers will remain a major growth driver, but non-hyperscaler growth — our AI C&E segment spanning sovereign regional neo-clouds, enterprise edge, and air-gapped data centers will represent roughly half of our data center business. Our AI-native startup ecosystem, developed and running primarily on the NVIDIA compute platform, is scaling at a rapid pace. Global VC funding in AI, roughly 70% of which is spent on compute, exceeded $400 billion in the first half of 2026. Surpassing the $265 billion raised in all of 2025. Nearly 20 companies, including Cursor, owned by SpaceX, Figma, and Together AI, now exceed $1 billion in annualized run rate revenue, up from 13 companies in Q4 of last year, with vertical enterprise software logging the fastest growth.

在企业领域，过去 12 个月，汽车垂直行业的本地部署收入达到 80 亿美元，而金融服务、制造业和医疗保健合计贡献了 70 亿美元的收入。Hudson River Trading 和 Jane Street 正在利用 NVIDIA 驱动的 AI 工厂加速量化交易。三星电子正在使用 NVIDIA Kulitho 在计算光刻方面实现高达 20 倍的性能提升，而百时美施贵宝正在投资 Vera Rubin AI 工厂，这是继罗氏和礼来之后快速跟进的建设，因为药物研发时间线从数年压缩到数月。

In enterprise, on a trailing 12-month basis, on-prem revenue in the automotive vertical reached $8 billion, while financial services, manufacturing, and healthcare combined contributed $7 billion in revenue. Hudson River Trading and Jane Street are leveraging NVIDIA’s powered AI factories to accelerate quantitative trading. Samsung Electronics is using NVIDIA Kulitho to achieve up to 20x greater performance in computational lithography, while Bristol Myers Squibb is investing in Vera Rubin AI factory, a fast follow to the Roche and Lilly buildouts as drug R&D timelines compress from years to months.

在主权 AI 领域，我们主要通过区域新型云开展的业务在第二季度环比增长 35%，同比增长超过两倍。一个国家或地区可以直接将土地和电力分配给区域云合作伙伴，而绝不会这样对待外国超大规模云厂商。我们自己不拥有云。我们是每个主权国家和新型云的中立合作伙伴。由于 NVIDIA 计算具有生产力、可互换性、可租赁性和耐用性，全球区域云的兴趣正在激增。我们帮助 CoreWeave、Nebius 和 Nscale 建立了整个基础设施业务，新型云正在各地涌现。亚美尼亚的 Firebird、非洲的 Kasava Technologies、台湾的 GMI Cloud、印度的 Yotta 和 Nasa、澳大利亚的 Firmus、马来西亚的 YTL AI Cloud，都将当地的土地、电力和运营专业知识与我们的平台相结合。上个月，我们宣布与日本国家 AI 公司 Noatra 合作，建设 NVIDIA DSX AI 工厂，该工厂将创建开放模型，为 AI 智能体、数字孪生、机器人技术和物理 AI 应用提供动力。韩国的 LG 和现代汽车集团正在与 NVIDIA 合作构建和扩展 AI。在欧洲，创纪录的 35 台新的 NVIDIA 驱动的 AI 超级计算机亮相，以推动工业和科学突破。

In Sovereign AI, our business primarily through the regional NeoClouds grew 35% sequentially and more than tripled year over year in Q2. A country or region can allocate land and power directly to a regional cloud partner in ways it never would to a foreign hyperscaler. We don’t own a cloud ourselves. We are a neutral partner to every sovereign and neo-cloud. And because NVIDIA compute is productive, fungible, rentable, and durable, regional cloud interest is surging around the world. We helped CoreWeave, Nebius, and Nscale build entire infrastructure businesses, and NeoClouds are emerging everywhere. Firebird in Armenia, Kasava Technologies across Africa, GMI Cloud in Taiwan, Yotta and Nasa in India, Firmus in Australia, YTL AI Cloud in Malaysia, pairing local land, power, and operating expertise with our platform. Last month, we announced a partnership with Noatra, Japan’s national AI company, to build an NVIDIA DSX AI factory that will create open models to power AI agents, digital twins, robotics, and physical AI applications. South Korea’s LG and Hyundai Motor Group are partnering with NVIDIA to build and scale AI. And in Europe, a record 35 new NVIDIA-powered AI supercomputers were unveiled to advance industry and scientific breakthroughs.

新型云正看到来自许多不同承购方的强劲需求管道。我们没有将其全部容量分配给单一的长期承购保证（贷款人通常要求这种保证来独立资助数据中心），而是引入了收入分成结构。NVIDIA 对该设施部分容量提供照付不议承诺，即最低收入保证，使贷款人有信心承销该项目，作为交换，我们分享该新型云在该底线之上获得的部分收入。独立资本仍然根据每笔交易自身的优点进行承销。我们不是在做贷款。在这种模式下，我们获得两次报酬——一次是硬件销售，另一次是通过租金收入分成，这是一种高度经常性的流，叠加在一次性设备购买之上。随着时间的推移，这种模式可以扩大我们的可寻址市场，并在我们的核心平台收入之外创造经常性的、与使用相关的收入流，有可能在中长期内带来数十亿美元的收入。

NeoClouds are seeing strong demand pipelines from many diverse off-takers. Rather than allocating their entire capacity to a single long-term offtake guarantee that lenders typically require to finance a data center independently, we have introduced a revenue-sharing structure. NVIDIA provides a take-or-pay commitment on a portion of the facility’s capacity, a minimum revenue guarantee that gives lenders the confidence to underwrite the project, and, in exchange, we share in a portion of the NeoCloud’s revenue earned above that floor. Independent Capital still underwrites every deal on its own merits. We’re not making loans. In this model, we get paid twice — once on the hardware sale and again through the share of rental revenue, a highly recurring stream layered on top of a one-time equipment purchase. Over time, this model can expand our addressable market and create a recurring usage-linked revenue stream alongside our core platform revenue, with the potential to drive billions in revenue over the medium to long term.

总之，NVIDIA 的三大独特能力——一个运行所有模型的平台、一个捕获更多数据中心 TAM 的全栈 AI 工厂平台，以及一个将 AI 扩展到任何单一芯片都无法单独触及的市场的 CUDA 生态系统——相互增强，是我们增长的引擎。让我向您介绍我们在前沿 AI 实验室方面的进展。前沿 AI 实验室对训练和推理算力有着非凡的需求，但它们的增长速度超过了其资产负债表和信用状况所能支持的范围。它们有快速增长的客户需求，但仍缺乏独立获得 AI 工厂基础设施所需的数十年基础设施合同和投资级融资能力。换句话说，它们的增长不受技术或客户需求的限制。它受算力限制。对于这些公司来说，更多的算力意味着更多的智能。更多的用户和更多的收入。需要 NVIDIA 来帮助推动这个飞轮。

Together, NVIDIA’s 3 unique capabilities — a platform that runs every model, a full-stack AI factory platform capturing more of the data center TAM, and a CUDA ecosystem that extends AI into markets no single chip could reach alone reinforce one another and are the engines of our growth. Let me update you on our progress with our Frontier AI Labs. The Frontier AI Labs have extraordinary demand for training and inference compute, but they are growing faster than what their balance sheets and credit profiles can support. They have rapidly growing customer demand, yet still lack the decades-long infrastructure contracts and investment-grade financing capacity needed to secure the AI factory infrastructure independently. In other words, their growth isn’t limited by their technology or customer demand. It’s limited by compute. For these companies, more compute means more intelligence. More users, and more revenue. NVIDIA is needed to help power this flywheel.

首先，我们已向前沿 AI 实验室投资近 500 亿美元。这是一项有意义的承诺，但仅占我们同期预期自由现金流的一小部分。此外，为了支持前沿实验室的基础设施建设，我们最近宣布与世界领先的 6 家基础设施资本提供商——Apollo、BlackRock、Blackstone、Brookfield、Goldman Sachs 和 KKR——建立合作伙伴关系，建立融资平台，将筹集超过 5000 亿美元的第三方资本。凭借这些合作伙伴关系，基于我们独特的可互换和耐用的计算平台，AI 实验室将能够以相对有吸引力的利率，由长期机构资本资助建设和评估 AI 基础设施。

First, we’ve invested nearly $50 billion in the Frontier AI Labs. This was a meaningful commitment, but it represented a small fraction of our expected free cash flow over the same period. Further, to support the Frontier Labs infrastructure buildouts, we recently announced partnerships with 6 of the world’s leading infrastructure capital providers — Apollo, BlackRock, Blackstone, Brookfield, Goldman Sachs, and KKR — to establish financing platforms that will raise over $500 billion of third-party capital. With these partnerships building on our unique fungible and durable computing platform, the AI Labs will be able to build and assess AI infrastructure funded by long-term institutional capital at relatively attractive rates.

上周，我们宣布通过与 SoftBank Energy 的合作伙伴关系，获得了在其 Portsmouth 园区独家托管 NVIDIA 计算的土地、电力和外壳容量。初始部署预计支持 4.25 吉瓦的 AI 工厂容量，将由 OpenAI 使用。在 Port Spike 部署的每一代 NVIDIA AI 工厂系统可能代表约 150 万块 NVIDIA GPU，在 20 年内，该站点可以支持多个升级周期。这里的基本经济要点是：LPS 承诺确保了一个长期存在的 AI 工厂站点，而数据中心内的 NVIDIA 计算可以反复升级。这个项目加深了我们与 OpenAI 的长期合作伙伴关系。OpenAI 已承诺到 2030 年大幅部署 NVIDIA AI 基础设施。OpenAI 现有和计划中的承诺代表约 12 吉瓦的 NVIDIA 计算。对于另一个前沿 AI 实验室，我们将为近 2 吉瓦的计算提供选择性信用增强。这补充了他们独立获得的、无需 NVIDIA 信用支持的大量 NVIDIA 计算容量。我们认识到这种支持的规模，我们知道有些人会称之为循环融资。我们对此有不同的看法。我们正在经历一次重大的计算平台转变。

Last week, we announced that we secured land power shell capacity through our partnership with SoftBank Energy to exclusively host NVIDIA compute at their Portsmouth campus. The initial deployment expected to support 4.25 gigawatts of AI factory capacity will be utilized by OpenAI. Each generation of NVIDIA AI factory systems deployed at Port Spike could represent approximately 1.5 million NVIDIA GPUs, and over 20 years, the site could support multiple upgrade cycles. Here’s the essential economic point: the LPS commitment secures a long-lived AI factory site while the NVIDIA compute within the data center can be upgraded repeatedly. This project deepens our longstanding partnership with OpenAI. OpenAI has committed to substantially deployments of NVIDIA AI infrastructure through 2030. OpenAI’s existing and planned commitments represent approximately 12 gigawatts of NVIDIA compute. For another frontier AI lab, we will provide selective credit enhancement for nearly 2 gigawatts of compute. This complements the substantial NVIDIA compute capacity they’ve secured independently without NVIDIA’s credit support. We recognize the scale of this support, and we know some will call this circular financing. We see it differently. We’re going through a major computing platform shift.

这是人类历史上最重要技术之一的诞生。这些是百年一遇的公司。他们的技术领导力已被证明，客户吸引力和使用量正在飙升。我们预计它们将成为历史上最大的科技公司。我们相信，这些投资，以需求强度、为我们创造的业务、他们在 NVIDIA 平台上构建的生态系统以及我们投入资本的股权回报来衡量，将是极好的。我们的风险有限。NVIDIA 计算平台是可互换且持久的，可以重新部署以支持其他客户。作为背景，我们预计 AI 实验室的需求（我们计划利用资产负债表来支持）将贡献明年约四分之一的业务。这仍然是我们运出的算力将由投资级客户或由投资级客户支持的客户消费。在第二季度，根据美国政府许可证，我们向中国客户运送的 Hopper 200 产品占数据中心总营收不到 1%。当前的 Hopper 出货对整体毛利率有稀释作用，鉴于持续的地缘政治不确定性，我们的前瞻展望中不包括中国数据中心算力营收。接下来看损益表的其余部分，GAAP 和非 GAAP 毛利率均为 75%，与上季度基本持平，因为产品组合类似。GAAP 和非 GAAP 运营费用环比增长 10% 和 11%，主要由于高算力基础设施成本以及薪酬和福利成本。我们的非 GAAP 有效税率为 16%，较去年同期有所上升，主要由于营收增加。在资产负债表上，库存增至 320 亿美元，因为我们为 Vera Rubin 的发布做准备。销售天数增加至 60 天，反映了某些投资级客户大额采购的延长付款条件，这些采购将在多个季度内发货。在第二季度，我们向股东返还了创纪录的 260 亿美元，其中 200 亿美元通过股票回购，60 亿美元通过每股 0.25 美元的季度股息。相对于我们计划返还 50% 或更多的自由现金流，年初至今我们已返还 60%。展望未来，我们打算增加并返还扣除战略用途后的超额自由现金流。现在让我转向第三季度的展望。总营收预计为 1080 亿美元，上下浮动 2%。我们预计环比增长主要由 ACI 和 E 数据中心驱动，而超大规模数据中心增长预计将在第四季度和 2028 财年重新加速，随着 Vera Rubin 的供应逐步增加。我们预计 Vera Rubin 在第三季度占数据中心营收的约 20%。展望未来，我们初步预计 2028 财年营收同比增长约 70%。尽管我们将努力缩小供需缺口，但我们预计供应瓶颈至少将持续到 2028 财年底。你们中的许多人担心我们的毛利率，因为组件成本大幅上升。如你们所知，我们正经历内存的极端定价状况。价格上涨幅度超出了我们先前的预期，并且明年还会更高。因此，我们今天重新设定预期。对于第三季度，我们预计 GAAP 和非 GAAP 毛利率为 74%，上下浮动 50 个基点。我们预计毛利率将在第四季度触底，在 71% 至 72% 的范围内，然后在 2028 财年稳定在 72% 至 73%，因为执行的价格上涨将在第一季度生效。我们希望直接说明这一点，而不是让它作为一个悬而未决的问题。今天的内存稀缺在很大程度上是由 AI 建设本身驱动的，与单纯提高我们成本而没有抵消收益的组件不同，内存供应紧张是推动我们自身增长的同一需求激增的症状。我们与所有 3 家主要内存供应商有着长期深入的关系，我们正在与他们密切合作，以进一步增加我们路线图所需的产能。GAAP 和非 GAAP 运营费用预计分别约为 92 亿美元和 90 亿美元。全年来看，我们现在预计运营费用将增长至低 50% 区间，这得益于产品组合的扩大以及 AI 工具使用量的进一步增加，这已经并将继续提高工程生产力。对于整个 2027 财年，我们继续预计 GAAP 和非 GAAP 税率在 16% 至 18% 之间，不包括任何离散项目和税收环境的重大变化。至此，我们将进入问答环节。接线员，请开始提问。

The creation of one of the most important technologies in human history. And these are once-in-a-generation companies. Their technology leadership is proven, and their customer traction and usage are skyrocketing. We expect them to become the largest technology companies in history. We believe these investments, measured against the strength of their demand, the business they create for us, the ecosystem they build on NVIDIA’s platform, and the equity returns on our invested capital, will be excellent. And our risk is limited. The NVIDIA compute platform is fungible and durable and can be redeployed to support other customers. For context, we expect demand from the AI labs for which we expect to leverage our balance sheet to contribute toward roughly a quarter of our business next year. This remains compute we ship will be consumed by investment-grade customers or those that are backed by one. In Q2, we ship less than 1% of our total data center revenue in Hopper 200 products to customers based in China in accordance with the U.S. Government licenses. Current Hopper shipments are dilutive to corporate gross margins, and given ongoing geopolitical uncertainty, there is no China data center compute revenue in our forward outlook. Moving to the rest of the P&L, GAAP and non-GAAP gross margins were both 75%, largely unchanged from last quarter due to a similar product mix. GAAP and non-GAAP operating expenses were up 10% and 11% sequentially, primarily due to high compute infrastructure costs and compensation and benefits costs. Our non-GAAP effective tax rate of 16% increased from a year ago, primarily due to higher revenue. On our balance sheet, inventory increased to $32 billion as we prepared for the Vera Rubin launch. Days of sales outstanding increased to 60 days, reflecting extended payment terms for large purchases by certain investment-grade customers to be shipped over multiple quarters. In Q2, we returned a record $26 billion to shareholders, $20 billion through share repurchases and $6 billion through our quarterly dividend of $0.25 per share. Relative to our plan to return 50% or more of free cash flow, we have returned 60% on a year-to-date basis. And going forward, we intend to increase and return excess free cash flow net of strategic uses. Let me turn to the outlook for the 3rd quarter. Total revenue is expected to be $108 billion. Plus and minus 2%. We expect sequential growth to be driven primarily by ACI and E with data center, while growth in Hyperscale is expected to re-accelerate in Q4 and into fiscal year ’28 as supply of Vera Rubin grows over time. We see Vera Rubin accounting for about 20% of data center revenue in Q3. Looking ahead, our preliminary expectation is for fiscal year ’28 revenue to grow approximately 70% year over year. Although we will work to close the supply-demand gap, we expect supply to remain a bottleneck at least through the end of fiscal year ’28. Many of you have expressed concerns regarding our gross margins as component costs have risen significantly. As you are already aware, we are experiencing extreme pricing conditions in memory. The magnitude of the price increase has exceeded our prior expectations and are headed even higher into next year. As a result, we are resetting expectations today. For Q3, we expect GAAP and non-GAAP gross margins to be 74% plus or minus 50 basis points. We expect margins to bottom in Q4 in the 71 to 72% range before settling at 72 to 73% in fiscal year ’28 as executed price increases take effect in Q1. We want to be direct about this rather than let it linger as an open question. Memory scarcity today is being driven in large part by the AI buildout itself, and unlike a component that simply raises our costs with no offset benefit, tighter memory supply is a symptom of the same demand surge that’s driving our own growth. We have longstanding deep relationships with all 3 major memory suppliers, and we’re working closely with them to further increase the capacity our roadmap requires. GAAP and non-GAAP operating expenses are expected to be approximately $9.2 billion and $9.0 billion, respectively. For the full year, we now expect OpEx to grow in the low 50s, driven by a broadening of our product portfolio and a further increase in the usage of AI tools, which is already and will continue to enhance engineering productivity. For the full fiscal year ’27, we continue to expect GAAP and non-GAAP tax rates to be between 16% and 18%, excluding any discrete items and material changes to our tax environment. With that, we will now transition to Q&A. Operator, please poll for questions.

[接线员指示] 您的第一个问题来自摩根士丹利的 Joseph Moore。您的线路已接通。

[Operator Instructions] Your first question comes from the line of Joseph Moore with Morgan Stanley. Your line is open.

好的，谢谢。嗯，我想知道您能否就 70% 的增长提供更多细节？是什么给了你们信心来给出全年指引，而你们之前并没有这样做？然后，这个增长与 100% 的需求增长之间的差距是什么？您知道，是什么关键约束导致了这些数字之间的差异？随着时间的推移，你们能否缩小这些差距？

Great, thank you. Um, I wonder if you could give us color on the 70%? And what gives you the confidence to guide a full year out if you haven’t been doing that? And then what’s the gap between that amount of growth and the 100% demand growth? You know, what is the kind of key constraint that separates those numbers? And could you close those gaps over time?

是的，谢谢，Joe。如您所知，AI 已经变得实用了。而到处被采用的 AI 智能体使用了大量的算力。首先，大语言模型比以往任何时候都更大，因为它们比以往任何时候都更聪明，而且这些智能体会进行推理和规划，多次使用工具。一个智能体所需的算力与一个人使用它相比，可能是 15 到 100 倍，具体取决于您要解决的问题类型。所以所需的算力是极其巨大的。这是几乎所有人都能看到的一个因素。人们看不到的我们增长的部分，是因为我们几乎是独一无二的，这源于我们交付产品的方式。我的意思是，我们是世界上唯一一家创建、构建并提供整个 AI 工厂平台（一个全栈系统）的公司。而且，您知道，客户仍然可以混搭。然而，大多数公司没有这样做的技能或意愿。所以，我们经历增长的市场中还有整整一部分。有主权 AI、区域 AI、新云、AI 初创公司、企业，这些约占我们业务的一半，而且每年增长 100%。随着时间的推移，这部分世界计算量可能会比我们目前在云中经历的还要大。所以我认为我们看到的需求是由所有这些因素驱动的。同样，您不能再仅仅采购技术并搭建这些基础设施。您必须去确保土地、电力和外壳，这通常需要两三年时间。所有其他必要的供应链，以协调建设、电力、冷却，以及所有必要的劳动力。AI 基础设施在美国和世界各地创造了如此多的就业机会。这需要更多的规划。所以我们现在正在更下游的管道中确保基础设施，就像很久以前人们问我为什么我们是一家芯片公司却与内存供应商合作一样。今天人们明白了，我们在如此上游的供应链上工作，这确实很天才。我们与下游的发电机组公司合作。我们与世界各地的土地、电力和页岩公司合作。这有助于准备所有这些将要构建的计算，最终将部署到我们的生态系统和客户中。所以我们现在在上游和下游都有了更大的可见性。事实是，我们从未预测过或指导过一年后的情况。而且，尽管我们的需求远大于 70%，但我们的供应使我们能够自信地交付 70%。我们将继续与供应链合作，以提高这一比例。但我们想做的是与所有人保持一致，从我们的客户、股东到供应链。每个人都看到相同的视图。这之所以重要，是因为您知道，每个人都投入了大量资源。所以我们想确保每个人都有相同的信息，而且明年将是巨大的一年，将会非常非凡。

Yeah, thanks, Joe. As you probably are aware, AI has become useful. And the AI agents that are being adopted everywhere use an enormous amount of compute. First of all, the large language models are larger than ever because they’re smarter than ever, and these agents go through reasoning and planning, multiple turns of tool use. The amount of compute necessary for an agent versus a human using it is probably 15 to 100 times depending on the type of problem you’re trying to solve. And so the amount of compute necessary is just extraordinary. That’s a factor that almost everybody sees. The part that people don’t see about our growth, because we’re practically singular because of the nature of how we deliver products. I mean, we’re the only company in the world that creates and builds, offers an entire AI factory platform, a full-stack system. And, you know, customers can still mix and match. However, most companies just don’t have the skills to do that or desire to do that. And so there’s an entire part of the market that we experience growth. There’s sovereign AI, there’s regional AIs, there are neo-clouds, there are AI startups, there are enterprises where we’re seeing, which represents about half of our business, And that’s growing 100% a year. That part of the world’s computing is likely to be larger over time than even what we’re currently experiencing in the cloud. And so I think the demand that we see is driven by all of those factors. It is also the case that you can no longer procure technology per se and stand up these infrastructure. You’ve got to go secure the land, power, and shell, which you know, oftentimes is a couple, 2, 3 years out. All of the rest of the supply chain necessary to align the construction, the power, the cooling, you know, all of the labor that’s necessary. AI infrastructure is creating so many jobs all over the United States and all around the world. It just takes a lot more planning. And so we’re involved in securing infrastructure now further down the pipeline, you know, just as a long time ago people asked me why is it that we’re working with memory suppliers when we’re a chip company. And today people understand it’s, it’s really quite genius that we were working on our supply chain so far upstream. We work with power generator companies downstream. We work with, um, work with land power and shale companies all around the world. And that helps prepare all of this computing that’s going to be built that will ultimately deploy for our ecosystem and our customers. And so we just have a lot greater visibility now upstream and downstream. It is the case that we’ve never forecasted or never guided to a year in advance. And, and even though our demand is much greater than 70%, our supply allows us to confidently deliver 70%. And we’re going to continue to work with our supply chain to increase on, on that. But what we wanted to do is to be consistent with everybody, from our customers, our shareholders, our supply chain. Everybody sees the same view. And the reason why that’s important is because, you know, everybody’s putting a lot of resources at play. And so we wanted to make sure that everybody has the same set of information and We’ve got a huge year coming up next year, and it’s going to be pretty extraordinary.

下一个问题来自 Cantor Fitzgerald 的 CJ Muse。您的线路已接通。

Your next question comes from the line of CJ Muse with Cantor Fitzgerald. Your line is open.

是的，下午好。感谢您回答问题。投资者非常关注您的推理市场份额。您能谈谈您看到的智能体式 AI 工作负载的演变吗？以及您如何看待您的份额随着时间的推移而演变，特别是当您考虑到每个新的全栈代际带来的 TAM 价值增长、您对 ACIE 更大增长的预期，以及包括 Groq 3 LPX 在内的情况。我们很想听听您的想法。

Yeah, good afternoon. Thank you for taking the question. There’s tremendous investor focus on your inference market share. Can you speak to the evolving workloads you’re seeing with agentic AI? And how you see your share evolving here over time, particularly when you reflect on the growing value of the TAM you’re seeing with each new full-stack generation, your expectation for greater growth from ACIE, and then also including Groq 3 LPX. We’d love to hear your thoughts.

是的，谢谢 CJ。AI 生命周期比过去复杂得多，而且它比以往任何时候都更大地发挥 NVIDIA 架构的优势。所以你可以把它看作 4 个阶段。你知道，第一阶段是准备你需要的所有数据。有些是合成数据，有些是真实数据，有些是人工劳动、人工标注和生成的，预训练模型。然后是后训练，第三阶段，然后是智能体式推理。而智能体式推理极其复杂。所以每一个阶段都很复杂。NVIDIA 架构真正了不起的地方在于，我们用 NVLink 72 创造了它，当我们首次创造世界上第一个机架级架构时，这震惊了世界。这并不容易，构建第一代非常具有挑战性。我们现在已经是第三代 NVLink 72 机架级系统。我们必须重新发明整个供应链，重新发明系统，重新发明技术，重新分发我们的软件，重构我们的软件。每一个方面都很困难。但它让我们能够创建一个可互换的系统，使我们能够从数据创建、数据准备、预训练、后训练过渡到智能体式推理。对客户的好处是不可思议的。原因在于，你刚刚花了，你知道，我们刚刚提到每吉瓦技术和 NVIDIA 在 Hopper 时期的收入敞口，包括 Hopper 加 InfiniBand，现在是 Vera Rubin 和 CPU 以及 3 种不同类型的网络，因为需要这么多类型的网络来覆盖全球数据中心，更不用说规模内安全网络和跨园区规模网络。所以你可以说有 5 种不同类型的网络系统。然后当然还有 Groq。所有这些都将我们每吉瓦的收入贡献或收入机会提高到 400 亿美元。所以每个吉瓦的数据中心从大约 5 年前的 300 亿美元增加到今天的 600 亿美元。当然，生产力是巨大的。相比之下，性能也是惊人的。但你谈论的是 600 亿美元的投资。如果你能在 AI 生命周期的多个阶段使用它，运行你能想象到的每一种模型，无论是扩散模型、自回归模型、状态空间模型还是某种混合版本，你能想到的每一种注意力机制，小型或大型模型，你的投资将在更长的时间内得到保留、有用和高效。所以我认为我们在新世界中的优势确实非常非凡，这可以解释为什么我们的增长实际上在加速。它已经很大了，但现在还在加速。让我们看看，你实际上问到了 Groq。对 Groq 3 超级兴奋。我们实现了创纪录的 token 交互率，极低延迟的性能生成。团队做得非常好。我们花了最后几个月融合 NVLink 架构，它将成为核心，它将成为核心引擎。然后对于希望实现超高交互性、超高速 token 生成的服务，你知道，吞吐量会低很多，每 token 成本会更高，但你可以将其与高 ASP 服务联系起来。所以对于这些公司，你可以加上我们的一个 Groq 加速器。我对此超级兴奋。但世界上绝大多数数据中心将只是 Vera Rubin NVLink 72。

Yeah, thanks, CJ. The AI lifecycle is getting way more complex than it used to be, and it’s playing into NVIDIA’s architecture much, much more greatly than it used to be. And so you could kind of see it as 4 phases. You know, there’s the first phase, which is preparing all of the data that you need. Some of it is synthetic, some of it is real, some of it is human labor and human labeled and generated, pre-trained the models. And then there’s post-training, the third phase, and then there’s the agentic inference. And agentic inference is extremely complicated. And so every one of those phases are complicated. The thing that’s really great about the NVIDIA architecture, and we created this with NVLink 72, it was a big surprise on the world when we first created the first, the world’s first rack-scale architecture. It was hardly easy, and it was very challenging building the first generation. We’re now in our third generation of NVLink 72 rack-scale systems. We had to reinvent the entire supply chain, reinvent systems, reinvent the technology, redistribute our software, refactor our software. Everything, every aspect of it was hard. But what it allowed us to do was to create one fungible system that allows us to transition from data creation, data preparation, the pre-training, the post-training, to agentic inference. The benefits to customers is incredible. And the reason for that is because you’ve just spent, you know, and we just mentioned each gigawatt of technology and NVIDIA’s revenue exposure in the Hopper timeframe with Hopper plus InfiniBand and now Vera Rubin and CPU and 3 types of different networking because it takes that many types of networking to address the entire world’s data center, not to mention the scale-in security networking and the scale-across multi-campus networking. So you could, you could argue 5 different types of networking systems. And then of course Groq. And all of that increased our revenue contribution or revenue opportunity per gigawatt to $40 billion. So each gigawatt of data center increased from, say, $30 billion about 5 years ago to now $60 billion today. Of course, the productivity is tremendous. The performance is incredible in comparison. But you’re talking about a $60 billion investment. And to the extent that you could, you could use it across multiple phases of the AI lifecycle, run every single type of model you can imagine running on it, whether it’s diffusion or autoregressive or state space or some hybrid version of that, every version of attention mechanism you can think of, small or large models, the investment that you make will be preserved and useful and productive for a lot longer time. And so I think our advantage in this new world is really quite extraordinary, and it could explain why it is that our growth is actually accelerating. It was already large, but now it’s accelerating. Let’s see, you actually asked about Groq. Super excited about Groq 3. We achieved a record token interactive interactivity rate, extremely low latency performance generation. The team is doing fantastically. We spent the last several months fusing the NVLink architecture, which will be the core, and it’ll be the core engine. And then for services that would like to have super high interactivity, super high-speed token generation done, you know, the throughput is going to be a lot lower, the cost per token will be higher, but you could associate it with, you know, high ASP services. And so for those companies, you could bolt on one of our Groq accelerators. I’m super excited about that. But the vast majority of the world’s data centers will just be Vera Rubin NVLink 72.

下一个问题来自 Bernstein Research 的 Stacy Rasgon。您的线路已接通。

Your next question comes from the line of Stacy Rasgon with Bernstein Research. Your line is open.

嗨，大家好，谢谢回答我的问题。所以 2028 财年 70% 的增长，我猜那大约是 2027 日历年，所以那大约是，我不知道，比之前的展望增加了 2000 亿美元。如果我反推，之前的展望是 3 年内 1 万亿美元。所以这可能多出 2000 亿美元。我想知道，您能否谈谈不同产品（Avira、CPU、Groq 等）对这一增长的贡献。另外，您提到了第一季度生效的涨价。所以我假设其中一些是定价因素。我还好奇，也许我在这里塞了太多问题，但我还好奇，这只是一个受限制的数字。如果没有限制，会是多少？

Hi guys, thanks for taking my question. So the 70% growth in fiscal ’28, which I guess is sort of calendar ’27, so that’s something like, I don’t know, a $200 billion uptick versus the prior outlook. If I back it out, the prior outlook was $1 trillion over the 3 years. So this is probably $200 billion more. I was just wondering, If you could talk us through the contributors to that increase across the different products, Avira and CPUs and Groq and everything else. And also, you talked about your price increase that takes effect in Q1. So I assume some of this is pricing. And I guess I’m also curious, maybe I’m squeezing too many questions in here, but I’m also curious, just it’s a constrained number. What would it be if it wasn’t constrained?

不受约束的需求会高得多。今年我们同比增长了 100%。不受约束的需求非常可观。因此，我们必须努力获得更多产能。我们拥有庞大的供应链，一个非常庞大的供应链，还有出色的合作伙伴。我们已经获得了大量供应，但我们需要更多。具体来说，大多数人只看到超大规模云服务商，那只是工作的一半，只是图景的一半。另一半是我们所说的 AC，即所有企业、新云、主权 AI。这部分对所有人来说都是不可见的。原因在于他们不购买定制芯片，也不一次只买一颗芯片。他们真正需要的是为他们构建的整个工厂平台。所以这是我们创造巨大价值的领域。当然，在超大规模领域，增长也非常惊人。你知道他们现在有 2 万亿美元的积压订单。你知道当他们启用 NVIDIA Compute 时，他们的收入会增加，盈利贡献也会增加。Compute 如今非常赚钱，而且直接转化为收入的增长。因此，无论是在超大规模云服务商那里，都有一场竞赛，想要让更多的 NVIDIA Compute 上线。但你看不到的是，在超大规模之外还有巨大的机会。另一部分是，这也是我们为你规划的原因，在 Hopper 的情况下，每吉瓦大约是 180 亿美元。对于 Grace Blackwell，每吉瓦大约是 250 亿美元。对于 Vera Rubin，每吉瓦大约是 400 亿美元。每一代的生产力都成倍增长。因此，客户希望尽快进入下一代。与此同时，由于 NVIDIA 的 Compute 生产力如此之高，他们生成的 token、出租的 GPU 小时数利润极高。如你所知，他们的利润率非常出色。所以这一切都在同时发生。我认为大背景是我们正在经历这次平台转变，它影响到每一家计算机公司，而世界上每个行业都在使用计算机。因此，每个行业都受到影响，每家公司都受到影响。这种新的计算方式是智能的，不是基于文件检索，而是生成式的，生成智能，这需要算力。但你得到的结果是惊人的，好得多。所以我们在世界各地都看到了这一点。每个人都想成为 AI 革命的一部分。每个人都必须参与这次计算转变，每个人都必须建设基础设施。

The unconstrained would be a lot higher. We grew 100% year over year this year. The unconstrained is significant. So we're just going to have to work hard to get more capacity. We have a large supply chain, a really gigantic supply chain, and incredible partners. We've secured a lot of supply, but we just need a lot more. To break it down, the way to think about that is most people see just hyperscalers, and that's half of the work. That's half of the picture. The other half of the picture is what we call AC, and that's all the enterprise, the neo clouds, the sovereign AIs. That part of the world is invisible to everybody. The reason is because they don't buy custom chips, they don't buy chips one at a time. They really need an entire factory platform built for them. So that's a space where we add just a tremendous amount of value. Now, of course, back in this hyperscale space, that's growing incredibly too. You know that they now have backlogs of $2 trillion. You know that when they stand up NVIDIA Compute, their revenues go up, their earnings contribution go up. Compute is profitable, very profitable today, and compute directly translates into increased revenues. So there's a race to bring more NVIDIA compute online, both at the hyperscalers. But what you don't see is just really tremendous opportunities outside of the hyperscalers. The other part of it is, and it's the reason why we mapped it out for you, in the case of Hopper, we were at about $18 billion per gigawatt. For Grace Blackwell, we're about $25 billion per gigawatt. And for Vera Rubin, it's about $40 billion per gigawatt. The productivity is X factors increase in each generation. So customers want to race to the next generation as fast as they can. Meanwhile, because NVIDIA's compute is so productive, the tokens they're generating, the GPU hours they're renting out is insanely profitable. As you know, their margins are fantastic. So all of that is just simultaneously happening. I think the big picture is that we're going through this platform shift and it affects every computer company and every industry in the world uses computers. So every industry is affected, every company is affected. This new way of doing computing is intelligent. It's not based on retrieval of files. It's now generative, generating intelligence, and that requires compute. But the results you get are phenomenal, tremendously better. So we're just seeing that across the world. Everybody wants to be part of the AI revolution. Everybody will have to be part of this computing shift, and everybody has to build infrastructure.

下一个问题来自美国银行的 Vivek Arya。请讲。

Your next question comes from the line of Vivek Arya with Bank of America Securities. Your line is open.

感谢回答我的问题，也感谢你提供了多年的透明度和所有承诺与保证。当我加总 CFO 评论中的所有内容时，我得到大约 5000 亿美元的数字，显然是在未来几年内。所以我对此有几个问题。第一，这是否意味着你未来几年的生态系统投资总额在这个范围内，还是说还有其他股权或其他投资可能还在后面？这是第一个问题。第二，在 2028 财年，我们是否应该考虑其中特定的现金部分？然后 Jensen，很多这些投资旨在帮助前沿实验室，尤其是 OpenAI 和 Anthropic。但他们两家都在设计自己的定制芯片。事实上，OpenAI 就在最近几天谈到了 Alpamayo，并声称它比 Blackwell 更好等等。那么你如何平衡这种动态：你想在生态系统中大量投资，但该生态系统的一部分想要开发竞争性解决方案？谢谢。

Thanks for taking my question, and thanks for providing all the transparency and all the commitments and guarantees that you have for a number of years. When I just add up everything that's in the CFO commentary, I get to a number of about $500 billion or so, obviously over the next several years. So I had a few questions related to that. First, is that the takeaway that the sum of all your ecosystem investments over the next several years is in that ballpark, or are there other equity or other investments that could still be ahead? That's one. Secondly, if there is a specific cash part of that that we should think about in fiscal '28. And then Jensen, a lot of these investments are designed to help the frontier labs, especially OpenAI and Anthropic. But both of them are designing their own custom chips. In fact, OpenAI just, in the last few days, spoke about Alpamayo and their claims about being better than Blackwell and so forth. So how are you balancing this dynamic where you want to invest a lot in the ecosystem, but part of that ecosystem wants to develop competitive solutions? Thank you.

嗯，我们在构建非常不同的东西。许多 XPU 是针对单一云或单一服务的推理专用芯片，而 NVIDIA 是一个平台，一个完整的 AI 工厂平台，涵盖整个 AI 生命周期，你可以在任何云中使用。它存在于每个云中。你可以在任何地方运行。我们会帮助你在任何地方部署。所以我们构建了非常不同的东西。所有 AI 服务在某个时候都会希望走向全球，而那些数据中心不一定只由他们自己建造。而且，我完全预期——所以他们会运行，我认为他们会在世界各地运行在 NVIDIA 上。当然，我认为我们的技术——我 100% 相信我们的技术对他们来说将继续是卓越的，而且使用我们技术的经济性，无论是从数据处理到训练到后训练到智能体式处理，我们的技术对他们来说都将是卓越的。他们会使用它。所以我很自信他们会在很长一段时间内成为我们的客户和合作伙伴。话虽如此，退一步看，投资这两家公司——或者我们投资了几家 AI 实验室——投资这些公司是千载难逢的机会。我想我唯一的遗憾是我没有更早、更多地投资。这两家公司很可能很快就会上市，其他公司也会跟进。这些将成为历史上最具影响力的科技公司。所以我很高兴成为他们的朋友，很高兴与他们合作，很高兴他们在 NVIDIA 架构之上构建生态系统，很高兴他们指望我们扩大规模。我 100% 相信，在相当长的一段时间内，他们会在很多计算中使用 NVIDIA Compute。所以我感觉很好。

Well, we're building something very different. Whereas many of these XPUs are inference-specific chips for one cloud or one service, NVIDIA is a platform, an entire AI factory platform that spans the entire AI lifecycle that you can use in any cloud. It's in every cloud. You can run anywhere. We'll help you set it up anywhere. So we built something very different. All of the AI services at some point are gonna want to go around the world, and those data centers won't necessarily be just built by them. And also, I fully expect — and so they're going to run, and I think they're going to run on NVIDIA all around the world. And of course, I think our technology — I have 100% confidence that our technology will continue to be extraordinary for them, and that the economics of using our technology, whether it's from data processing to training to post-training to agentic processing, our technology is going to be extraordinary for them. They're going to use it. So I'm very confident that they're going to be customers and partners of ours for a very long time. Now, having said that, taking a step backwards, investing in these 2 companies — or there are several AI labs that we've invested in — investing in these companies are once-in-a-generation opportunity. I think the only regret that I have is that I didn't invest more and sooner. And both of the 2 companies will likely go public soon, and others will follow. And these will be some of the most consequential technology companies in history. So I'm delighted to be their friend. I'm delighted to partner with them. I'm delighted that they're building an ecosystem on top of the NVIDIA architecture. I'm delighted that they're counting on us to scale up. And I have 100% confidence that, through quite a long period of time, they're going to be utilizing NVIDIA compute for a lot of their computing. So I feel great about it.

Vivek，让我再补充一点关于承诺以及这些承诺中的一部分，即我们的供应承诺。这至关重要。这对于今天以及明年整个 Vera Rubin 的产能提升至关重要。你可以看到，这些承诺中最大的部分集中在前三年，我们将利用这些来构建我们需要的产品。这也给了我们对收入增长的信心，因为我们已经就供应和所需产能达成了大量承诺。

So Vivek, let me add a little bit more regarding the commitments and the portion within those commitments, which is our supply commitments. This is essential. This is essential for the raising of Vera Rubin today as well as all next year. You can see that those commitments, the biggest parts of them are in the first 3 years, and we will use that to build the products that we need. This is what also gives us the confidence in terms of our growth in revenue, given how much we have already aligned in commitment in terms of our supply as well as capacity that we would need.

下一个问题来自瑞银的 Timothy Arcuri。请讲。

Your next question comes from the line of Timothy Arcuri with UBS. Your line is open.

嗨，非常感谢。Jensen，我想问一下关于开源的问题。有很多讨论说这些模型可能会在美国的工作负载中占据更多份额。你显然通过 Nemotron 占据了有利位置，但另一方面，很多终端需求是由那些大型前沿模型公司驱动的。所以有很多投资者认为开源模型对这些公司的增长是负面的。那么你如何看待这个问题？你认为开源模型的兴起对 NVIDIA 是有利的，还是最终是负面的？谢谢。

Hi, thanks a lot. Jensen, I wanted to ask about open source. There’s a lot of talk about that these models could gain share for workload in the US. You’re obviously well positioned with Nemotron, but on the other hand, a lot of the end demand is being driven by these big frontier model companies. So there’s a lot of investors that equate open models as being negative for the growth of those. Companies. So how do you sort of put and take that? Do you see the rise of open models as being good for NVIDIA or ultimately negative? Thanks.

世界将同时需要闭源模型和开源模型，而闭源模型和开源模型的使用量都在飞速增长。几乎可以说，几乎所有开源模型都运行在 NVIDIA 上。原因在于 NVIDIA 在全球的足迹是最广的，我们的架构是最通用的。它无处不在，从 PC 和边缘设备（比如表现优异的 DGX Spark），一直到机器人和工作站，以及你的本地数据中心。开源模型表现非常出色。你知道，闭源模型，我们知道也表现非常出色。前沿实验室的规模、销售额都在飙升，利润率非常可观。它们正在产生盈利的 token，唯一的限制就是算力。开源模型也是如此。而我们在开源模型方面的地位非常好，因为 CUDA 生态系统确实无处不在。开源模型也是全球几乎所有 AI 初创公司和企业的基石，对它们至关重要。原因在于你应该尽可能租用智能、强大的智能、聪明的智能，这就是为什么我们自己也租用，并且我鼓励员工尽可能多地使用云服务。但每个大公司、每个国家、每个初创公司都需要构建自己领域特定的、专有的 AI，自己的专有优势。而开源模型达到前沿水平，使得这一切成为可能。前沿模型至关重要的一个领域是网络安全。你可以看到，许多网络安全公司借助前沿模型，能够构建分布式、大规模分布式、持续运行的自主网络安全系统来进行防御。这些公司正在涌现，有一些非常了不起的公司。没有开源模型，它们就无法做到。因此，开源模型既取得了巨大成功，终于达到了前沿水平，同时也对美国经济至关重要，对世界经济至关重要，对企业构建自己的专有 AI 至关重要。两者缺一不可，它们都将取得非凡的成功。最后，如你所知，我们在所有 AI 模型的市场覆盖上，我们是唯一的——我认为我们是唯一的平台，我们相当确定我们是唯一运行所有前沿模型的平台。无论是闭源还是开源，大多数模型都是在 NVIDIA 上构建的，所以它们在 NVIDIA 上运行得很好。因此，任何模型的成功都让我们感到高兴。只要模型成功，我就非常开心。闭源和开源模型都会成功，它们同时推动着我们的销售。

The world will need both closed models and open models, and both closed models and open models are skyrocketing in use. Near most, I would say nearly all open models run on NVIDIA. And the reason for that is because NVIDIA’s footprint around the world is the highest. And our architecture is the most fungible. It’s everywhere. It’s in PCs and edge devices like DGX Spark, which is doing great, all the way to robots and workstations and your on-prem data centers. Open models are doing incredibly well. You know, closed models, we, we know are doing incredibly well. The Frontier Labs, their scales, their sales are skyrocketing. Their margins are fantastic. They’re, they’re generating profitable tokens. They’re only limited by the amount of compute. That is equally true for open models. And Our position in open models is very good, you know, because the CUDA ecosystem is literally everywhere. The open model — open models are also foundational to just about every AI startup and every enterprise company around the world. It’s vital to them. And the reason for that is because you should rent intelligence, strong intelligence, smart intelligence, wherever you can, which is the reason why You know, we rent it, and I encourage my employees to use the cloud services as much as they can. But every major company and every — surely every country and every startup needs to build their domain-specific, their proprietary AI, their proprietary alpha. And the open models reaching frontier levels. Has made it possible, has enabled them to all do that. One of the areas where frontier models is vital is cybersecurity. You see the number of cybersecurity companies that are enabled by frontier models so that they could have distributed, massively distributed, continuously running autonomous cybersecurity systems to defend. Um, those companies are emerging. There’s some amazing companies. They couldn’t do it without open models. And so open models is, is both incredibly successful and has finally reached the frontier, but it’s also vital to the American economy. It’s vital to the world economy. It’s vital to companies to build their own proprietary AI. You can’t do without one or the other. Both are going to be extraordinarily successful. And lastly, as you know, our market footprint of all AI models, we’re the only — I think we’re the only platform, we’re fairly certain we’re the only platform that runs every frontier model. And whether it’s closed or open, most of them were built on NVIDIA. And so they run great on NVIDIA. And, and so we’re delighted by, by any model succeeding. So long as models succeed, I’m very happy. And both closed and open models are going to succeed, and they’re, they’re both simultaneously driving our sales.

下一个问题来自 Melius Research 的 Ben Reitzes。请讲。

Your next question comes from the line of Ben Reitzes with Melius Research. Your line is open.

是的，嘿，谢谢。Jensen，我想换个角度问一个关于需求的问题。你知道，你提到明年需求将增长 100%，我想了解一下推动这一增长的几个因素，甚至更长远的情况。这里有两个概念：递归自我改进，显然在 Anthropic 和 OpenAI 那里进展顺利，也就是 AI 自我改进。甚至 OpenAI 说他们可能在今年年底达到 AGI。那么，随着 RSI 和 AGI 的发展，行业需求会发生什么？它会进一步加速吗？当这些事情发生时，对 NVIDIA 意味着什么？你如何看待这作为需求催化剂？谢谢。

Yeah, hey, thanks. I wanted to ask you a question, Jensen, about demand in a different way. You know, you talked about demand growing 100% next year, and I wanted to kind of get a sense for a couple things driving that and even beyond that. And there’s 2 concepts here. There’s recursive self-improvement, which apparently at Anthropic and OpenAI is going very well with, you know, AI that improves itself. And even OpenAI said they could hit AGI by the end of this year. And, you know, with the developments in RSI as well as AGI, what happens to industry demand? Does it inflect further? And what does it mean for NVIDIA when that — when those things take place? And how are you looking at that as a demand catalyst? Thanks.

感谢你的提问。需求将进一步加速。如今，绝大多数 AI 是由人类提示的。我相信上个月已经跨过了一个临界点，大多数 AI 现在都是智能体式的。但未来，每个公司都会有一大堆智能体。你知道，我们大约有 4 万名员工。未来，我们将有 40 万个智能体，400 万个智能体。这些智能体将持续运行，在后台运行。如果你认识任何构建边缘个人 AI 智能体的人，他们会在 DGX Spark 这样的设备上运行。你知道，我认识很多人，他们在 DGX 工作站上运行，这是我们构建的不可思议的工作站，你可以从戴尔购买，它们非常出色。这些 AI 智能体在 DGX 工作站上 24/7 运行，因为你总有事情让它做。所以，当世界转向智能体式、完全智能体式的系统时，你将拥有持续运行的智能体，与其他持续运行的智能体协作。它们将在后台工作，改善你的公司，改善你的生活。在很多方面，我们现在已经有点递归了。你可以说这是粗粒度的。但每次你运行一个智能体，它都会反思下次如何做得更好，并更新技能文件。所以技能文档（markdown）在每次运行结束时都会更新。下次运行时，它会变得更好。这是一种松散的、粗粒度的自我改进。所以你到处都能看到这种情况。因此，在很多方面，对于许多任务，我们可以说我们已经实现了 AGI。我认为所有这些里程碑，在这一点上都有点无意义。我认为对行业最重要的是一，AI 现在正在做生产性的、有用的工作；二，AI 正在产生盈利的 token；三，如果我们有更多算力，我们就能产生更多盈利的 token，从而为所有服务带来更多利润。这正是我们目前所处的阶段，这就是为什么每个人都在全力以赴。

I appreciate that. It’s going to inflect further. Today, the vast majority of AI is prompted by people. I believe that this last month it has crossed. Most AI are now agentic. But in the future, in the future, every company will have a whole bunch of agents. You know, we have, we have 40,000 employees roughly. In the future, we’ll have 400,000 agents, 4 million agents. And those agents are running continuously. They’re running in the background. If you have anybody who builds, if you know anybody who builds edge, you know, personal AI agents and they run it on their, like a DGX Spark. And, you know, I, I know a lot of people who, who run it on DGX stations, this incredible workstation that, that we’ve built and you can buy it from Dell and, and they’re, they’re, they’re, they’re incredible. And these AI agents running on a DGX station runs 24/7 because you got stuff for that to do, for it to do all the time. And so the, when, when the world goes to agentic, fully agentic, agentic systems, you’re gonna have agents running all the time, working with other agents running all the time. And those will just be working in the background, improving your company, you know, improving your lives. Um, in a lot of ways, we’re, we’re kind of recursive at this point, at this point. And you could argue it’s, it’s coarse-grained. But every time you run through an agent, it, it reflects on how it could do a better job next time, and it updates the skill file. And so the skills document, the markdown, is updated at the end of every single one of them. And so next time you run it, it’s going to get better. It’s a, it’s a close — it’s a, you know, kind of a loosely coarse-grained self-improvement. And so you see that all over, always. And so in a lot of ways, and for many tasks, we could say that we’ve already achieved AGI. I think all of those milestones and all those, you know, they’re kind of senseless at this point. I think the most important thing that matters for the industry is that one, AI is now doing productive and useful work. 2, AI is generating profitable tokens. And 3, if we had more compute, we could generate more profitable tokens, which results in more profit for all of the services. This is the exact phase where we’re at, which is the reason why everybody’s leaning in.

下午好。感谢您回答我的问题。如果您考虑您提到的 100% 的增长，即您预期的无约束需求增长，以及您预计能实现的 70% 的供应，您能否谈谈一些最严重的限制因素，或者按顺序排列一下，无论是数据中心电力和机壳可用性、DRAM、晶圆代工可用性等。如果您能帮助我们了解其中哪些是最大的，那将非常有帮助。谢谢。

Good afternoon. Thank you for taking my question. If you think about the 100% growth you talked about in terms of the plus unconstrained demand growth you’re expecting, the 70% you expect to fulfill in terms of supply, can you maybe talk about some of the — or rank order some of the most acute constraints, whether that be things like data center power and shell availability, DRAM, wafer foundry, availability, etc. If you can maybe help us understand, you know, which are the biggest among those, that would be very helpful. Thank you.

我本可以说点有趣的事，但我不打算说了。去年，最有趣的事情之一就是弄清楚我去哪里吃饭、和谁吃饭，然后他们的股价第二天就翻倍了。我认为答案是，我们的整个供应链都面临挑战，每个人都在全力以赴。更多的产能正在不断上线，这是优势之一。今年将要发生的是，产能不会一下子全部上线，而是每天都会增加。良率会提高，我们会进行良率改进。我们会与每个供应商努力合作。所以我们有很多很多时间每天努力工作。目前，我们有 70% 的供应——实际上我们有的供应超过 70%，但大约是 70%。我们的需求远高于此，我们必须努力工作，否则我们会让客户失望。我们不想让客户失望，我们愿意为他们努力工作。所以我需要整个供应链的帮助。但他们都知道这一点。我告诉你们的关于明年需求的情况，与我告诉他们的完全一致。每个人都在同一张乐谱上，我尽可能保持透明，因为我们谈论的是大数字。

There’s something funny I could say, but I’m just not going to. Last year, one of the funnest things to do was to figure out where I go for dinner and who I have dinner with, and their stock price doubles the next day. I think the answer is that our entire supply chain is challenged, and everybody is really running flat out. More capacity is coming online all the time, which is one of the advantages. What’s going to happen this year is not that it comes online in an instant, but it’s going to come online every day. Yields are going to improve. We’re going to be doing yield improvement. We’re going to work hard with every one of our suppliers. So we have lots and lots of time to work hard every day. At this moment, we have supply for 70% — we have more supply than 70%, but about 70%. Our demand is much higher than that, and we’ve got to work hard or we’re going to be disappointing customers. We would like not to disappoint our customers, and we like to work hard for them. So I’m going to need the help of the entire supply chain to help me out here. But they all know that. What I’m telling you about our needs for next year is exactly consistent with what I’ve told them. Everybody’s on the exact same song sheet, and I’m trying to be as transparent as we can because we’re talking about big numbers.

最后一个问题来自富国银行的 Aaron Rakers。您的线路已接通。

Your final question comes from the line of Aaron Rakers with Wells Fargo. Your line is open.

是的，感谢您回答问题。我想回到千兆瓦的问题，即 25 到 40，并尝试理解，我想 Jensen，您在最近的一些会议上说过，这还将进一步扩大。所以，当我们考虑超越 Vera Rubin 的路径时，我们考虑 Vera Rubin Ultra 等等。我们是否应该这样理解：400 亿美元会变成 600 亿、800 亿，然后，我猜，在这个问题之下，我们如何考虑您扩大产能部署的能力？它是线性函数，还是有什么东西能解锁您在 2027 财年供应更多需求的能力？

Yeah, thanks for taking the question. I want to go back to the gigawatts, the 25 to 40, and maybe try and understand, like, I think Jensen, you said at some recent conferences that that’s going to further scale. So, as we think about the path even beyond Vera Rubin, we think about Vera Rubin Ultra and so on and so forth. Like, should we really conceptualize like $40 billion goes to $60 billion, $80 billion, and then yeah, I guess, you know, underneath of that question is how do we kind of think about your ability to scale the capacity deployments? You know, is it a linear function or is there something that kind of unlocks your ability to supply more demand as we look through fiscal ’27?

好问题。

Great question.

2028 年，抱歉。

2028, sorry.

是的，好问题。而且很简单。我们的目标是在一块土地上放尽可能多的算力吗？我们的目标是在 1 千兆瓦或更少的情况下放入更多的算力吗？显然，我们希望得到光速般的答案。完美的答案实际上是每千兆瓦无限大。如果我们真的能把一万亿美元的算力放进 1 千兆瓦和一块土地里，那将是一个奇妙的结果。答案在方向上就是如此。我们从摩尔定律时代的通用计算世界开始。我们可能是，选一个您喜欢的数字，但我会说通用计算每千兆瓦大约 50 亿美元或 30 亿美元。然后到了 Hopper，是 180 亿美元。现在 Grace Blackwell 是 250 亿美元。接下来，Vera Rubin 是 400 亿美元。之后还会更高。这非常好。这对行业来说太棒了。对客户来说也太棒了。只要它的生产力持续增长，耐用性和可互换性持续增长，那么人们就愿意投资于能产生收入、产生利润并帮助他们如此快速地收回回报的资产。我前几天听说投资资本回报率现在不到一年，而我们谈论的是 500 亿美元的数据中心。这告诉您 NVIDIA 技术的生产力和盈利能力。我要感谢大家今天参加我们的会议。

Yeah, great question. And very simple. Is our goal to put as much compute on a plot of land? Is our goal to put more compute into 1 gigawatt or less? Obviously, we would like the speed of light answer. The perfect answer is actually infinity per gigawatt. If we could literally get a trillion dollars of compute into 1 gigawatt and 1 piece of land, it would be a fantastic outcome. The answer is directionally in that direction. We started in the world of general-purpose computing during Moore’s Law. We were probably, pick your favorite number, but I’m going to go with something like $5 billion, $3 billion per gigawatt of compute with general-purpose computing. Then eventually with Hopper, it was $18 billion. Now Grace Blackwell is $25. Next, Vera Rubin is $40. And after that, it’s going to be higher. That’s excellent. That’s fantastic for the industry. It’s fantastic for customers. So long as the productivity of it continues to grow, the durability and the fungibility continues to grow, then people are happy to invest in assets that generate revenues, generate profits, and help them recoup their returns so incredibly fast. I heard the other day that return on investment capital is now less than a year, and we’re talking about $50 billion data centers. That tells you something about the productivity of NVIDIA’s technology and the rentability of it. I want to thank all of you for joining us today.

目前没有更多问题。Toshiya Hari，我把电话交还给您。

There are no further questions at this time. Toshiya Hari, I turn the call back over to you.

谢谢。在结束之前，请注意 Jensen 将于 9 月 10 日在旧金山举行的 Goldman Sachs Communicopia 和技术会议上参加主题演讲炉边谈话。他还将于 10 月 21 日在 GTC Berlin 发表主题演讲。我们讨论 2027 财年第三季度业绩的财报电话会议定于 11 月 17 日举行。感谢您今天参加我们的会议。接线员，请结束通话。

Thank you. Before we close, please note that Jensen will be participating in a keynote fireside chat at the Goldman Sachs Communicopia and Technology Conference in San Francisco on September 10th. He’ll also be giving a keynote at GTC Berlin on October 21st. Our earnings call to discuss the results of our 3rd quarter of fiscal 2027 is scheduled for November 17th. Thank you for joining us today. Operator, please close the call.

[接线员结束语]

[Operator Closing Remarks]