NVIDIA Corporation (NASDAQ:NVDA) Q1 2025 Earnings Conference Call May 22, 2024 5:00 PM ET

英伟达公司（纳斯达克股票代码：NVDA）2025 年第一季度收益电话会议 2024 年 5 月 22 日 下午 5:00 ET

Company Participants 公司参与者

Simona Jankowski - Vice President, Investor Relations

Simona Jankowski - 副总裁，投资者关系

Colette Kress - Executive Vice President and Chief Financial Officer

柯蕾特·克雷斯 - 执行副总裁兼首席财务官

Jensen Huang - President and Chief Executive Officer

黄仁勋 - 总裁兼首席执行官

Conference Call Participants

电话会议参与者

Stacy Rasgon - Bernstein 斯泰西·拉斯贡 - 伯恩斯坦

Timothy Arcuri - UBS

蒂莫西·阿库里 - 瑞银

Vivek Arya - Bank of America Securities

Vivek Arya - 美国银行证券

Joseph Moore - Morgan Stanley

Joseph Moore - 摩根士丹利

Toshiya Hari - Goldman Sachs

Toshiya Hari - 高盛

Matthew Ramsay - TD Cowen

马修·拉姆齐 - TD Cowen

Mark Lipacis - Evercore ISI

马克·利帕西斯 - Evercore ISI

Blayne Curtis - Jefferies

布莱恩·柯蒂斯 - 杰富瑞

Srini Pajjuri - Raymond James

William Stein - Truist Securities

威廉·斯坦 - Truist Securities

C.J. Muse - Cantor Fitzgerald

C.J. Muse - 康特菲茨杰 erald Fitzgerald

Operator 操作员

Good afternoon. My name is Regina and I will be your conference operator today. At this time, I would like to welcome everyone to NVIDIA's First Quarter Earnings Call. All lines have been placed on mute to prevent any background noise. After the speaker's remarks, there will be a question-and-answer session. [Operator Instructions] Thank you.

下午好。我是 Regina，今天将担任您的电话会议操作员。此时，我想欢迎大家参加英伟达的第一季度收益电话会议。为防止任何背景噪音，所有线路均已静音。在发言人讲话后，将进行问答环节。【操作员指示】谢谢。

Simona Jankowski, you may begin your conference.

辛莫娜·詹科夫斯基，您可以开始您的会议。

Simona Jankowski 西莫娜·扬科夫斯基

Thank you. Good afternoon, everyone, and welcome to NVIDIA's conference call for the first quarter of fiscal 2025. With me today from NVIDIA are Jen-Hsun Huang, President and Chief Executive Officer, and Colette Kress, Executive Vice President and Chief Financial Officer.

谢谢。大家下午好，欢迎参加英伟达 2025 财年第一季度电话会议。今天和我一起出席的有英伟达的总裁兼首席执行官黄仁勋，以及执行副总裁兼首席财务官科莱特·克雷斯。

I'd like to remind you that 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 second quarter of fiscal 2025. The content of today's call is NVIDIA's property. It can't be reproduced or transcribed without our prior written consent.

我想提醒您，我们的电话会议正在 NVIDIA 投资者关系网站上进行现场网络广播。该网络广播将可供重播，直到讨论我们 2025 财年第二季度财务业绩的电话会议结束。今天电话会议的内容属于 NVIDIA，未经我们事先书面同意，不得复制或转录。

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.

在本通话中，我们可能根据当前预期进行前瞻性声明。这些声明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。

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.

有关可能影响我们未来财务业绩和业务的因素的讨论，请参阅今天的收益发布中的披露，我们最近的 10-K 和 10-Q 表格以及我们可能向证券交易委员会提交的 8-K 表格中的报告。

All our statements are made as of today, May 22, 2024, 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.

所有我们的声明均基于我们当前掌握的信息，截至 2024 年 5 月 22 日。除非法律要求，我们不承担更新任何此类声明的义务。在本次通话中，我们将讨论非通用会计准则财务指标。您可以在我们首席财务官评论中找到这些非通用会计准则财务指标与通用会计准则财务指标的调和情况，该评论已发布在我们的网站上。

Let me highlight some upcoming events. On Sunday, June 2nd, ahead of the Computex Technology Trade Show in Taiwan, Jensen will deliver a keynote which will be held in-person in Taipei as well as streamed live. And on June 5th, we will present at the Bank of America Technology Conference in San Francisco.

让我强调一些即将到来的活动。6 月 2 日星期日，在台湾举行 Computex 科技贸易展之前，Jensen 将在台北亲自发表主题演讲，并进行现场直播。6 月 5 日，我们将在旧金山参加美国银行科技会议。

With that let me turn the call over to Colette.

让我把电话交给科莱特。

Colette Kress 柯蕾特·克雷斯

Thanks, Simona. Q1 was another record quarter. Revenue of $26 billion was up 18% sequentially and up 262% year-on-year and well above our outlook of $24 billion.

谢谢，Simona。Q1 是又一个创纪录的季度。260 亿美元的收入环比增长 18%，同比增长 262%，远高于我们的预期 240 亿美元。

Starting with Data Center. Data Center revenue of $22.6 billion was a record, up 23% sequentially and up 427% year-on-year, driven by continued strong demand for the NVIDIA Hopper GPU computing platform. Compute revenue grew more than 5x and networking revenue more than 3x from last year.

从数据中心开始。数据中心收入为 226 亿美元，创下纪录，环比增长 23%，同比增长 427%，这得益于对英伟达霍珀 GPU 计算平台持续强劲需求的推动。计算收入较去年增长超过 5 倍，网络收入增长超过 3 倍。

Strong sequential data center growth was driven by all customer types, led by enterprise and consumer internet companies. Large cloud providers continue to drive strong growth as they deploy and ramp NVIDIA AI infrastructure at scale and represented the mid-40s as a percentage of our Data Center revenue.

强劲的数据中心增长受到所有类型客户的推动，以企业和消费者互联网公司为主导。大型云服务提供商继续推动强劲增长，随着它们在规模上部署和推广英伟达人工智能基础设施，代表我们数据中心收入的中 40%左右。

Training and inferencing AI on NVIDIA CUDA is driving meaningful acceleration in cloud rental revenue growth, delivering an immediate and strong return on cloud provider's investment. For every $1 spent on NVIDIA AI infrastructure, cloud providers have an opportunity to earn $5 in GPU instant hosting revenue over four years. NVIDIA's rich software stack and ecosystem and tight integration with cloud providers makes it easy for end customers up and running on NVIDIA GPU instances in the public cloud.

在 NVIDIA CUDA 上进行 AI 的培训和推理，推动了云租赁收入增长的显著加速，为云服务提供商的投资带来了即时和强劲的回报。在 NVIDIA AI 基础设施上每花费 1 美元，云服务提供商有机会在四年内获得 5 美元的 GPU 即时托管收入。NVIDIA 丰富的软件堆栈和生态系统以及与云服务提供商的紧密集成，使最终客户能够轻松在公共云中使用 NVIDIA GPU 实例。

For cloud rental customers, NVIDIA GPUs offer the best time to train models, the lowest cost to train models and the lowest cost to inference large language models. For public cloud providers, NVIDIA brings customers to their cloud, driving revenue growth and returns on their infrastructure investments. Leading LLM companies such as OpenAI, Adept, Anthropic, Character.AI, Cohere, Databricks, DeepMind, Meta, Mistral, xAI, and many others are building on NVIDIA AI in the cloud.

对于云租户，NVIDIA GPU 提供了训练模型的最佳时间、训练模型的最低成本以及推理大型语言模型的最低成本。对于公共云服务提供商，NVIDIA 带来了客户，推动了收入增长和基础设施投资的回报。领先的 LLM 公司，如 OpenAI、Adept、Anthropic、Character.AI、Cohere、Databricks、DeepMind、Meta、Mistral、xAI 等许多公司正在基于 NVIDIA 云中的人工智能构建应用。

Enterprises drove strong sequential growth in Data Center this quarter. We supported Tesla's expansion of their training AI cluster to 35,000 H100 GPUs. Their use of NVIDIA AI infrastructure paved the way for the breakthrough performance of FSD Version 12, their latest autonomous driving software based on Vision.

本季度企业推动了数据中心强劲的环比增长。我们支持特斯拉将其培训 AI 集群扩展到 35,000 个 H100 GPU。他们使用的 NVIDIA AI 基础设施为 FSD Version 12 的突破性性能铺平了道路，这是他们基于视觉的最新自动驾驶软件。

Video Transformers, while consuming significantly more computing, are enabling dramatically better autonomous driving capabilities and propelling significant growth for NVIDIA AI infrastructure across the automotive industry. We expect automotive to be our largest enterprise vertical within Data Center this year, driving a multibillion revenue opportunity across on-prem and cloud consumption.

视频变压器虽然消耗了大量计算资源，但却使自动驾驶能力显著提升，并推动了 NVIDIA AI 基础设施在汽车行业的显著增长。我们预计汽车行业将成为我们数据中心中最大的企业垂直市场，为本地和云端消费带来数十亿美元的收入机会。

Consumer Internet companies are also a strong growth vertical. A big highlight this quarter was Meta's announcement of Llama 3, their latest large language model, which was trained on a cluster of 24,000 H100 GPUs. Llama 3 powers Meta AI, a new AI assistant available on Facebook, Instagram, WhatsApp and Messenger. Llama 3 is openly available and has kickstarted a wave of AI development across industries.

消费互联网公司也是一个增长强劲的领域。本季度的一个重要亮点是 Meta 宣布推出 Llama 3，他们最新的大型语言模型，该模型是在一组 24,000 个 H100 GPU 上进行训练的。Llama 3 驱动 Meta AI，这是一款新的 AI 助手，可在 Facebook、Instagram、WhatsApp 和 Messenger 上使用。Llama 3 是公开可用的，并已在各行业掀起了一波 AI 开发热潮。

As generative AI makes its way into more consumer Internet applications, we expect to see continued growth opportunities as inference scales both with model complexity, as well as with the number of users and number of queries per user, driving much more demand for AI compute.

随着生成式人工智能进入更多消费者互联网应用程序，我们预计随着模型复杂性和用户数量以及每个用户的查询数量的增加，推理规模将继续增长，从而推动对人工智能计算的需求大幅增加。

In our trailing four quarters, we estimate that inference drove about 40% of our Data Center revenue. Both training and inference are growing significantly. Large clusters like the ones built by Meta and Tesla are examples of the essential infrastructure for AI production, what we refer to as AI factories.

在我们过去的四个季度中，我们估计推理驱动了我们数据中心收入的约 40%。培训和推理都在显著增长。像 Meta 和特斯拉构建的大型集群是 AI 生产的基本基础设施示例，我们称之为 AI 工厂。

These next-generation data centers host advanced full-stack accelerated computing platforms where the data comes in and intelligence comes out. In Q1, we worked with over 100 customers building AI factories ranging in size from hundreds to tens of thousands of GPUs, with some reaching 100,000 GPUs.

这些下一代数据中心托管先进的全栈加速计算平台，数据输入，智能输出。在第一季度，我们与 100 多家客户合作建立了各种规模的人工智能工厂，从数百个到数万个 GPU，有些甚至达到了 10 万个 GPU。

From a geographic perspective, Data Center revenue continues to diversify as countries around the world invest in Sovereign AI. Sovereign AI refers to a nation's capabilities to produce artificial intelligence using its own infrastructure, data, workforce and business networks.

从地理角度来看，数据中心收入继续多样化，因为世界各国在主权人工智能方面进行投资。主权人工智能是指一个国家利用自己的基础设施、数据、劳动力和商业网络生产人工智能的能力。

Nations are building up domestic computing capacity through various models. Some are procuring and operating Sovereign AI clouds in collaboration with state-owned telecommunication providers or utilities. Others are sponsoring local cloud partners to provide a shared AI computing platform for public and private sector use.

各国正在通过各种模式建设国内计算能力。一些国家与国有电信提供商或公用事业单位合作，采购和运营主权人工智能云。其他国家正在资助本地云合作伙伴，为公共和私营部门提供共享人工智能计算平台。

For example, Japan plans to invest more than $740 million in key digital infrastructure providers, including KDDI, Sakura Internet, and SoftBank to build out the nation's Sovereign AI infrastructure. France-based, Scaleway, a subsidiary of the Iliad Group, is building Europe's most powerful cloud native AI supercomputer.

例如，日本计划投资超过 7.4 亿美元在关键数字基础设施提供商，包括 KDDI、樱互联网和 SoftBank，以建设该国的主权人工智能基础设施。总部位于法国的 Scaleway，是 Iliad 集团的子公司，正在建设欧洲最强大的云原生人工智能超级计算机。

In Italy, Swisscom Group will build the nation's first and most powerful NVIDIA DGX-powered supercomputer to develop the first LLM natively trained in the Italian language. And in Singapore, the National Supercomputer Center is getting upgraded with NVIDIA Hopper GPUs, while Singtel is building NVIDIA's accelerated AI factories across Southeast Asia.

在意大利，瑞士电信集团将建造该国第一台也是最强大的由 NVIDIA DGX 驱动的超级计算机，以开发首批使用意大利语进行本地训练的LLM。而在新加坡，国家超级计算中心正在升级为 NVIDIA Hopper GPU，新加坡电信则正在东南亚建设 NVIDIA 加速的人工智能工厂。

NVIDIA's ability to offer end-to-end compute to networking technologies, full-stack software, AI expertise, and rich ecosystem of partners and customers allows Sovereign AI and regional cloud providers to jumpstart their country's AI ambitions. From nothing the previous year, we believe Sovereign AI revenue can approach the high single-digit billions this year. The importance of AI has caught the attention of every nation.

英伟达提供端到端的计算到网络技术、全栈软件、人工智能专业知识以及丰富的合作伙伴和客户生态系统，使得主权人工智能和地区云服务提供商能够启动其国家的人工智能雄心。从去年的零起步，我们相信主权人工智能的收入今年可以接近高个位数的十亿美元。人工智能的重要性已经引起了每个国家的关注。

We ramped new products designed specifically for China that don't require an export control license. Our Data Center revenue in China is down significantly from the level prior to the imposition of the new export control restrictions in October. We expect the market in China to remain very competitive going forward.

我们推出了专门为中国设计的新产品，这些产品不需要出口许可证。我们在中国的数据中心收入较十月新出口管制限制实施前显著下降。我们预计中国市场未来将继续保持竞争激烈。

From a product perspective, the vast majority of compute revenue was driven by our Hopper GPU architecture. Demand for Hopper during the quarter continues to increase. Thanks to CUDA algorithm innovations, we've been able to accelerate LLM inference on H100 by up to 3x, which can translate to a 3x cost reduction for serving popular models like Llama 3.

从产品角度来看，绝大部分计算收入是由我们的 Hopper GPU 架构推动的。本季度对 Hopper 的需求持续增长。多亏了 CUDA 算法创新，我们已经能够将 H100 上的LLM推理加速高达 3 倍，这可以转化为为提供像 Llama 3 这样的热门模型节约 3 倍成本。

We started sampling the H200 in Q1 and are currently in production with shipments on track for Q2. The first H200 system was delivered by Jensen to Sam Altman and the team at OpenAI and powered their amazing GPT-4o demos last week. H200 nearly doubles the inference performance of H100, delivering significant value for production deployments.

我们在第一季度开始对 H200 进行取样，目前已经开始生产，并计划在第二季度按时发货。第一台 H200 系统由 Jensen 交付给 OpenAI 的 Sam Altman 团队，并在上周为他们令人惊叹的 GPT-4o 演示提供动力。H200 的推理性能几乎是 H100 的两倍，为生产部署提供了显著价值。

For example, using Llama 3 with 700 billion parameters, a single NVIDIA HGX H200 server can deliver 24,000 tokens per second, supporting more than 2,400 users at the same time. That means for every $1 spent on NVIDIA HGX H200 servers at current prices per token, an API provider serving Llama 3 tokens can generate $7 in revenue over four years.

例如，使用具有 7000 亿参数的 Llama 3，单个 NVIDIA HGX H200 服务器可以每秒传输 24,000 个令牌，同时支持超过 2,400 个用户。这意味着以当前价格每个令牌在 NVIDIA HGX H200 服务器上花费 1 美元，为 Llama 3 令牌提供服务的 API 提供商可以在四年内产生 7 美元的收入。

With ongoing software optimizations, we continue to improve the performance of NVIDIA AI infrastructure for serving AI models. While supply for H100 prove, we are still constrained on H200. At the same time, Blackwell is in full production. We are working to bring up our system and cloud partners for global availability later this year. Demand for H200 and Blackwell is well ahead of supply and we expect demand may exceed supply well into next year.

随着持续的软件优化，我们不断提高 NVIDIA AI 基础设施的性能，用于提供 AI 模型。虽然 H100 的供应有所证明，但我们在 H200 上仍受限。与此同时，Blackwell 已全面投产。我们正在努力与系统和云合作伙伴合作，计划在今年晚些时候全球推出。H200 和 Blackwell 的需求远远超过供应，我们预计需求可能会持续超过供应，直至明年。

Grace Hopper Superchip is shipping in volume. Last week at the International Supercomputing Conference, we announced that nine new supercomputers worldwide are using Grace Hopper for a combined 200 exaflops of energy-efficient AI processing power delivered this year.

格雷斯·霍珀超级芯片正在大量发货。上周在国际超级计算大会上，我们宣布全球有九台新超级计算机正在使用格雷斯·霍珀，今年共提供 200 艾克斯佛洛普的高效能人工智能处理能力。

These include the Alps Supercomputer at the Swiss National Supercomputing Center, the fastest AI supercomputer in Europe. Isambard-AI at the University of Bristol in the UK and JUPITER in the Julich Supercomputing Center in Germany.

这些包括瑞士国家超级计算中心的阿尔卑斯超级计算机，欧洲最快的人工智能超级计算机。英国布里斯托大学的 Isambard-AI 和德国尤利希超级计算中心的 JUPITER。

We are seeing an 80% attach rate of Grace Hopper in supercomputing due to its high energy efficiency and performance. We are also proud to see supercomputers powered with Grace Hopper take the number one, the number two, and the number three spots of the most energy-efficient supercomputers in the world.

由于其高能效和性能，我们看到格雷斯·霍珀在超级计算中的附加率达到了 80%。我们也很自豪地看到由格雷斯·霍珀提供动力的超级计算机占据了世界上能效最高的超级计算机的第一、第二和第三名。

Strong networking year-on-year growth was driven by InfiniBand. We experienced a modest sequential decline, which was largely due to the timing of supply, with demand well ahead of what we were able to ship. We expect networking to return to sequential growth in Q2. In the first quarter, we started shipping our new Spectrum-X Ethernet networking solution optimized for AI from the ground up.

强劲的网络年度增长受 InfiniBand 驱动。我们经历了适度的环比下降，这在很大程度上是由于供应时间的安排，需求远远超过我们能够发货的量。我们预计网络在第二季度将恢复环比增长。在第一季度，我们开始发货全面针对人工智能优化的新 Spectrum-X 以太网网络解决方案。

It includes our Spectrum-4 switch, BlueField-3 DPU, and new software technologies to overcome the challenges of AI on Ethernet to deliver 1.6x higher networking performance for AI processing compared with traditional Ethernet.

它包括我们的 Spectrum-4 交换机、BlueField-3 DPU 和新软件技术，以克服 AI 在以太网上的挑战，为 AI 处理提供比传统以太网高 1.6 倍的网络性能。

Spectrum-X is ramping in volume with multiple customers, including a massive 100,000 GPU cluster. Spectrum-X opens a brand-new market to NVIDIA networking and enables Ethernet only data centers to accommodate large-scale AI. We expect Spectrum-X to jump to a multibillion-dollar product line within a year.

Spectrum-X 正在与多个客户扩大规模，包括一个庞大的 100,000 GPU 集群。Spectrum-X 为 NVIDIA 网络开辟了全新市场，并使以太网数据中心能够容纳大规模人工智能。我们预计 Spectrum-X 将在一年内跃升为一个价值数十亿美元的产品线。

At GTC in March, we launched our next-generation AI factory platform, Blackwell. The Blackwell GPU architecture delivers up to 4x faster training and 30x faster inference than the H100 and enables real-time generative AI on trillion-parameter large language models.

在三月的 GTC 上，我们推出了我们的下一代 AI 工厂平台 Blackwell。Blackwell GPU 架构的训练速度比 H100 快 4 倍，推理速度快 30 倍，并且能够在万亿参数的大型语言模型上实现实时生成式 AI。

Blackwell is a giant leap with up to 25x lower TCO and energy consumption than Hopper. The Blackwell platform includes the fifth-generation NVLink with a multi-GPU spine and new InfiniBand and Ethernet switches, the X800 series designed for a trillion parameter scale AI.

Blackwell 是一个巨大的飞跃，比 Hopper 的总拥有成本和能耗低 25 倍。Blackwell 平台包括第五代 NVLink，带有多 GPU 骨干和新的 InfiniBand 和 Ethernet 交换机，X800 系列专为万亿参数规模的人工智能设计。

Blackwell is designed to support data centers universally, from hyperscale to enterprise, training to inference, x86 to Grace CPUs, Ethernet to InfiniBand networking, and air cooling to liquid cooling. Blackwell will be available in over 100 OEM and ODM systems at launch, more than double the number of Hopper's launch and representing every major computer maker in the world. This will support fast and broad adoption across the customer types, workloads and data center environments in the first year shipments.

Blackwell 旨在全面支持数据中心，从超大规模到企业级，从培训到推理，从 x86 到 Grace CPU，从以太网到 InfiniBand 网络，从空气冷却到液冷。Blackwell 将在超过 100 家 OEM 和 ODM 系统中推出，是 Hopper 推出数量的两倍以上，代表了世界上每个主要计算机制造商。这将在首年发货中支持快速和广泛的采用，涵盖各种客户类型、工作负载和数据中心环境。

Blackwell time-to-market customers include Amazon, Google, Meta, Microsoft, OpenAI, Oracle, Tesla, and xAI. We announced a new software product with the introduction of NVIDIA Inference Microservices or NIM.

Blackwell 的上市时间客户包括亚马逊、谷歌、Meta、微软、OpenAI、甲骨文、特斯拉和 xAI。我们宣布推出了一款新的软件产品，即 NVIDIA 推理微服务或 NIM。

NIM provides secure and performance-optimized containers powered by NVIDIA CUDA acceleration in network computing and inference software, including Triton Inference Server and TensorRT LLM with industry-standard APIs for a broad range of use cases, including large language models for text, speech, imaging, vision, robotics, genomics and digital biology.

NIM 提供由 NVIDIA CUDA 加速驱动的安全且性能优化的容器，在网络计算和推理软件中包括 Triton 推理服务器和 TensorRT LLM，具有行业标准 API，适用于广泛的用例，包括文本、语音、图像、视觉、机器人、基因组学和数字生物学的大型语言模型。

They enable developers to quickly build and deploy generative AI applications using leading models from NVIDIA, AI21, Adept, Cohere, Getty Images, and Shutterstock and open models from Google, Hugging Face, Meta, Microsoft, Mistral AI, Snowflake and Stability AI. NIMs will be offered as part of our NVIDIA AI enterprise software platform for production deployment in the cloud or on-prem.

它们使开发人员能够快速构建和部署生成式人工智能应用程序，使用来自 NVIDIA、AI21、Adept、Cohere、Getty Images 和 Shutterstock 的领先模型，以及来自 Google、Hugging Face、Meta、Microsoft、Mistral AI、Snowflake 和 Stability AI 的开放模型。NIMs 将作为我们的 NVIDIA AI 企业软件平台的一部分提供，用于在云端或本地进行生产部署。

Moving to gaming and AI PCs. Gaming revenue of $2.65 billion was down 8% sequentially and up 18% year-on-year, consistent with our outlook for a seasonal decline. The GeForce RTX Super GPUs market reception is strong and end demand and channel inventory remained healthy across the product range.

转向游戏和人工智能电脑。游戏收入为 26.5 亿美元，按季度下降 8%，同比增长 18%，与我们对季节性下降的预期一致。GeForce RTX Super GPU 的市场反应强劲，终端需求和渠道库存在整个产品范围内保持健康。

From the very start of our AI journey, we equipped GeForce RTX GPUs with CUDA Tensor Cores. Now with over 100 million of an installed base, GeForce RTX GPUs are perfect for gamers, creators, AI enthusiasts and offer unmatched performance for running generative AI applications on PCs.

从我们 AI 之旅的一开始，我们就为 GeForce RTX GPU 配备了 CUDA Tensor Cores。现在拥有超过 1 亿的安装基数，GeForce RTX GPU 非常适合游戏玩家、创作者、AI 爱好者，并为在 PC 上运行生成式 AI 应用程序提供无与伦比的性能。

NVIDIA has full technology stack for deploying and running fast and efficient generative AI inference on GeForce RTX PCs. TensorRT LLM now accelerates Microsoft's Phi-3-Mini model and Google's Gemma 2B and 7B models as well as popular AI frameworks, including LangChain and LlamaIndex. Yesterday, NVIDIA and Microsoft announced AI performance optimizations for Windows to help run LLMs up to 3x faster on NVIDIA GeForce RTX AI PCs.

NVIDIA 拥有完整的技术堆栈，可在 GeForce RTX PC 上部署和运行快速高效的生成式 AI 推断。TensorRT LLM现在加速 Microsoft 的 Phi-3-Mini 模型以及 Google 的 Gemma 2B 和 7B 模型，以及流行的 AI 框架，包括 LangChain 和 LlamaIndex。昨天，NVIDIA 和 Microsoft 宣布了针对 Windows 的 AI 性能优化，以帮助在 NVIDIA GeForce RTX AI PC 上运行LLMs快 3 倍。

And top game developers, including NetEase Games, Tencent and Ubisoft are embracing NVIDIA Avatar Character Engine to create lifelike avatars to transform interactions between gamers and nonplayable characters.

顶尖游戏开发者，包括网易游戏、腾讯和育碧，正在采用 NVIDIA Avatar Character Engine，创建逼真的角色扮演，改变玩家与非玩家角色之间的互动。

Moving to ProVis. Revenue of $427 million was down 8% sequentially and up 45% year-on-year. We believe generative AI and Omniverse industrial digitalization will drive the next wave of professional visualization growth. At GTC, we announced new Omniverse Cloud APIs to enable developers to integrate Omniverse industrial digital twin and simulation technologies into their applications.

转向 ProVis。 4.27 亿美元的收入环比下降 8%，同比增长 45%。 我们相信生成式人工智能和 Omniverse 工业数字化将推动下一波专业可视化增长。 在 GTC 上，我们宣布了新的 Omniverse 云 API，以便开发人员将 Omniverse 工业数字孪生体和仿真技术集成到他们的应用程序中。

Some of the world's largest industrial software makers are adopting these APIs, including ANSYS, Cadence, 3DEXCITE at Dassault Systemes, Brand and Siemens. And developers can use them to stream industrial digital twins with spatial computing devices such as Apple Vision Pro. Omniverse Cloud APIs will be available on Microsoft Azure later this year.

一些全球最大的工业软件制造商正在采用这些 API，包括 ANSYS、Cadence、Dassault Systemes 旗下的 3DEXCITE、Brand 和西门子。开发人员可以使用它们来通过空间计算设备（如 Apple Vision Pro）流式传输工业数字孪生体。Omniverse Cloud API 将于今年晚些时候在 Microsoft Azure 上提供。

Companies are using Omniverse to digitalize their workflows. Omniverse power digital twins enable Wistron, one of our manufacturing partners to reduce end-to-end production cycle times by 50% and defect rates by 40%. And BYD, the world's largest electric vehicle maker, is adopting Omniverse for virtual factory planning and retail configurations.

公司正在使用 Omniverse 来数字化他们的工作流程。 Omniverse 动力数字孪生使得我们的制造合作伙伴之一 Wistron 能够将端到端生产周期时间缩短 50%，缺陷率降低 40%。而全球最大的电动汽车制造商比亚迪正在采用 Omniverse 进行虚拟工厂规划和零售配置。

Moving to automotive. Revenue was $329 million, up 17% sequentially and up 11% year-on-year. Sequential growth was driven by the ramp of AI cockpit solutions with global OEM customers and strength in our self-driving platforms. Year-on-year growth was driven primarily by self-driving. We supported Xiaomi in the successful launch of its first electric vehicle, the SU7 sedan built on the NVIDIA DRIVE Orin, our AI car computer for software-defined AV fleets.

转向汽车领域。收入为 3.29 亿美元，环比增长 17%，同比增长 11%。环比增长主要受全球原始设备制造商客户 AI 驾驶舱解决方案的推出和自动驾驶平台的强劲增长推动。同比增长主要受自动驾驶的推动。我们支持小米成功推出首款电动汽车 SU7 轿车，该车采用了英伟达 DRIVE Orin，我们的用于软件定义自动驾驶车队的 AI 汽车电脑。

We also announced a number of new design wins on NVIDIA DRIVE Thor, the successor to Orin, powered by the new NVIDIA Blackwell architecture with several leading EV makers, including BYD, XPeng, GAC's Aion Hyper and Neuro. DRIVE Thor is slated for production vehicles starting next year.

我们还宣布了一系列在 NVIDIA DRIVE Thor 上的新设计胜利，这是 Orin 的继任者，由新的 NVIDIA Blackwell 架构提供支持，与包括比亚迪、小鹏、广汽蔚来和 Neuro 在内的几家领先的电动汽车制造商合作。 DRIVE Thor 计划从明年开始投入生产车辆。

Okay. Moving to the rest of the P&L. GAAP gross margin expanded sequentially to 78.4% and non-GAAP gross margins to 78.9% on lower inventory targets. As noted last quarter, both Q4 and Q1 benefited from favorable component costs. Sequentially, GAAP operating expenses were up 10% and non-GAAP operating expenses were up 13%, primarily reflecting higher compensation-related costs and increased compute and infrastructure investments.

好的。继续讨论损益表的其他部分。根据通用会计准则，毛利率按顺序扩大至 78.4%，非通用会计准则的毛利率扩大至 78.9%，这主要是由于库存目标降低。正如上季度所指出的，Q4 和 Q1 都受益于有利的零部件成本。按顺序计算，通用会计准则的营业费用增加了 10%，非通用会计准则的营业费用增加了 13%，主要反映了较高的与薪酬相关的成本以及计算和基础设施投资的增加。

In Q1, we returned $7.8 billion to shareholders in the form of share repurchases and cash dividends. Today, we announced a 10-for-1 split of our shares with June 10th as the first day of trading on a split-adjusted basis. We are also increasing our dividend by 150%.

在第一季度，我们以股份回购和现金股息的形式向股东返还了 78 亿美元。今天，我们宣布以 10 比 1 的比例拆分我们的股份，6 月 10 日将是拆分调整后的交易首日。我们还将股息提高了 150%。

Let me turn to the outlook for the second quarter. Total revenue is expected to be $28 billion, plus or minus 2%. We expect sequential growth in all market platforms. GAAP and non-GAAP gross margins are expected to be 74.8% and 75.5%, respectively, plus or minus 50 basis points, consistent with our discussion last quarter.

让我来谈谈第二季度的展望。预计总收入将达到 280 亿美元，加减 2%。我们预计所有市场平台都将实现环比增长。预计根据通用会计准则和非通用会计准则计算的毛利率分别为 74.8%和 75.5%，加减 50 个基点，与上一季度的讨论保持一致。

For the full year, we expect gross margins to be in the mid-70s percent range. GAAP and non-GAAP operating expenses are expected to be approximately $4 billion and $2.8 billion, respectively. Full year OpEx is expected to grow in the low 40% range.

对于整个年度，我们预计毛利率将在中 70%的范围内。 预计根据通用会计准则和非通用会计准则，运营费用分别约为 40 亿美元和 28 亿美元。 预计全年运营费用将增长在低 40%的范围内。

GAAP and non-GAAP other income and expenses are expected to be an income of approximately, excuse me, approximately $300 million, excluding gains and losses from nonaffiliated investments. GAAP and non-GAAP tax rates are expected to be 17%, plus or minus 1%, excluding any discrete items. Further financial details are included in the CFO commentary and other information available on our IR website.

预计根据普通会计准则（GAAP）和非普通会计准则（non-GAAP）的其他收入和费用将约为收入，抱歉，约为 300 亿美元，不包括来自非关联投资的收益和损失。预计 GAAP 和非 GAAP 税率将为 17%，加减 1%，不包括任何离散项目。财务细节请参阅首席财务官评论以及我们 IR 网站上提供的其他信息。

I would like to now turn it over to Jensen as he would like to make a few comments.

我现在想把话题转给 Jensen，因为他想发表几点看法。

Jensen Huang 黄仁勋

Thanks, Colette. The industry is going through a major change. Before we start Q&A, let me give you some perspective on the importance of the transformation. The next industrial revolution has begun.

谢谢，Colette。这个行业正在经历重大变革。在我们开始问答环节之前，让我给你一些关于转型重要性的看法。下一次工业革命已经开始。

Companies and countries are partnering with NVIDIA to shift the trillion-dollar installed base of traditional data centers to accelerated computing and build a new type of data center, AI factories, to produce a new commodity, artificial intelligence.

AI will bring significant productivity gains to nearly every industry and help companies be more cost and energy efficient while expanding revenue opportunities. CSPs were the first generative AI movers. With NVIDIA, CSPs accelerated workloads to save money and power. The tokens generated by NVIDIA Hopper drive revenues for their AI services. And NVIDIA cloud instances attract rental customers from our rich ecosystem of developers.

人工智能将为几乎所有行业带来显著的生产力增长，并帮助公司更具成本效益和能源效率，同时拓展收入机会。CSPs 是第一批推动 AI 发展的公司。与 NVIDIA 合作，CSPs 加速工作负载以节省成本和能源。由 NVIDIA Hopper 生成的代币为他们的 AI 服务带来收入。而 NVIDIA 云实例吸引了来自我们丰富的开发者生态系统的租户客户。

Strong and accelerated demand -- accelerating demand for generative AI training and inference on Hopper platform propels our Data Center growth. Training continues to scale as models learn to be multimodal, understanding text, speech, images, video and 3D and learn to reason and plan.

强劲且加速的需求——在霍珀平台上生成式人工智能训练和推理的需求加速推动了我们数据中心的增长。随着模型学习成为多模态，理解文本、语音、图像、视频和 3D，并学会推理和规划，训练持续扩展。

Our inference workloads are growing incredibly. With generative AI, inference, which is now about fast token generation at massive scale, has become incredibly complex. Generative AI is driving a from-foundation-up full stack computing platform shift that will transform every computer interaction.

我们的推理工作负载增长得令人难以置信。随着生成式人工智能的发展，推理工作，现在已经是在大规模快速生成令人难以置信的令牌，变得异常复杂。生成式人工智能正在推动一种从基础开始的全栈计算平台转变，将改变每一次计算机交互。

From today's information retrieval model, we are shifting to an answers and skills generation model of computing. AI will understand context and our intentions, be knowledgeable, reason, plan and perform tasks.

从今天的信息检索模型，我们正在转向计算的答案和技能生成模型。人工智能将理解上下文和我们的意图，具备知识，推理，规划和执行任务。

We are fundamentally changing how computing works and what computers can do, from general purpose CPU to GPU accelerated computing, from instruction-driven software to intention-understanding models, from retrieving information to performing skills, and at the industrial level, from producing software to generating tokens, manufacturing digital intelligence.

我们正在从通用 CPU 转向 GPU 加速计算，从指令驱动软件转向意图理解模型，从检索信息转向执行技能，以及在工业层面，从生产软件转向生成代币，制造数字智能，从而根本改变计算方式和计算机的功能。

Token generation will drive a multiyear build-out of AI factories. Beyond cloud service providers, generative AI has expanded to consumer Internet companies and enterprise, Sovereign AI, automotive, and health care customers, creating multiple multibillion-dollar vertical markets.

令牌生成将推动多年建设人工智能工厂。除了云服务提供商，生成式人工智能已扩展到消费互联网公司和企业、主权人工智能、汽车和医疗客户，创造了多个价值数十亿美元的垂直市场。

The Blackwell platform is in full production and forms the foundation for trillion-parameter scale generative AI. The combination of Grace CPU, Blackwell GPUs, NVLink, Quantum, Spectrum, mix and switches, high-speed interconnects and a rich ecosystem of software and partners let us expand and offer a richer and more complete solution for AI factories than previous generations.

Blackwell 平台已全面投产，为万亿参数规模的生成式人工智能奠定了基础。Grace CPU、Blackwell GPU、NVLink、Quantum、Spectrum、混合和交换机、高速互连以及丰富的软件和合作伙伴生态系统的结合，使我们能够比以往任何一代更全面地扩展并提供更丰富完整的人工智能工厂解决方案。

Spectrum-X opens a brand-new market for us to bring large-scale AI to Ethernet-only data centers. And NVIDIA NIMs is our new software offering that delivers enterprise-grade optimized generative AI to run on CUDA everywhere, from the cloud to on-prem data centers to RTX AI PCs through our expansive network of ecosystem partners. From Blackwell to Spectrum-X to NIMs, we are poised for the next wave of growth. Thank you.

Spectrum-X 为我们打开了一个全新的市场，使我们能够将大规模人工智能引入仅使用以太网的数据中心。而 NVIDIA NIMs 是我们的新软件产品，提供企业级优化的生成式人工智能，可在从云端到本地数据中心再到 RTX 人工智能个人电脑上的 CUDA 上运行，通过我们庞大的生态伙伴网络。从 Blackwell 到 Spectrum-X 再到 NIMs，我们已准备好迎接下一波增长。谢谢。

Simona Jankowski 西莫娜·扬科夫斯基

Thank you, Jensen. We will now open the call for questions. Operator, could you please poll for questions?

谢谢，詹森。我们现在将开放提问环节。操作员，请您进行提问调查好吗？

Question-and-Answer Session

问答环节

Operator 操作员

[Operator Instructions] Your first question comes from the line of Stacy Rasgon with Bernstein. Please go ahead.

【操作员指示】您的第一个问题来自伯恩斯坦的 Stacy Rasgon。请提问。

Stacy Rasgon 斯泰西·拉斯贡

Hi, guys. Thanks for taking my questions. My first one, I wanted to drill a little bit into the Blackwell comment that it's in full production now. What does that suggest with regard to shipments and delivery timing if that product is -- doesn't sound like it's sampling anymore. What does that mean when that's actually in customers' hands if it's in production now?

嗨，各位。感谢您们回答我的问题。我的第一个问题是，我想深入了解一下 Blackwell 的评论，即它现在已经全面投产。如果该产品已经投产，那么这意味着发货和交付时间会如何？听起来似乎不再是样品。如果产品现在已经投产，那么当产品实际交到客户手中时，这意味着什么？

Jensen Huang 黄仁勋

We will be shipping. Well, we've been in production for a little bit of time. But our production shipments will start in Q2 and ramp in Q3, and customers should have data centers stood up in Q4.

我们将开始发货。嗯，我们已经生产了一段时间。但我们的生产发货将从第二季度开始，并在第三季度加速，客户应该在第四季度建立数据中心。

Stacy Rasgon 斯泰西·拉斯贡

Got it. So this year, we will see Blackwell revenue, it sounds like?

明白了。所以今年，我们会看到 Blackwell 的收入，是这样吗？

Jensen Huang 黄仁勋

We will see a lot of Blackwell revenue this year.

今年我们将看到很多布莱克韦尔的收入。

Operator 操作员

Our next question will come from the line of Timothy Arcuri with UBS. Please go ahead.

我们下一个问题将来自瑞银的 Timothy Arcuri。请继续。

Timothy Arcuri 蒂莫西·阿库里

Thanks a lot. I wanted to ask, Jensen, about the deployment of Blackwell versus Hopper just between the systems nature and all the demand for GB that you have. How does the deployment of this stuff differ from Hopper? I guess I ask because liquid cooling at scale hasn't been done before, and there's some engineering challenges both at the node level and within the data center. So do these complexities sort of elongate the transition? And how do you sort of think about how that's all going? Thanks.

非常感谢。我想问一下，关于 Blackwell 与 Hopper 的部署，涉及到系统性质和您所面临的所有 GB 需求。这些部署方式与 Hopper 有何不同？我猜我问这个问题是因为大规模液冷技术以前从未实施过，节点级别和数据中心内部都存在一些工程挑战。这些复杂性是否会延长过渡期？您如何看待这一切？谢谢。

Jensen Huang 黄仁勋

Yes. Blackwell comes in many configurations. Blackwell is a platform, not a GPU. And the platform includes support for air cooled, liquid cooled, x86 and Grace, InfiniBand, now Spectrum-X and very large NVLink domain that I demonstrated at GTC, that I showed at GTC. And so for some customers, they will ramp into their existing installed base of data centers that are already shipping Hoppers. They will easily transition from H100 to H200 to B100. And so Blackwell systems have been designed to be backwards compatible, if you will, electrically, mechanically. And of course, the software stack that runs on Hopper will run fantastically on Blackwell. We also have been priming the pump, if you will, with the entire ecosystem, getting them ready for liquid cooling. We've been talking to the ecosystem about Blackwell for quite some time. And the CSPs, the data centers, the ODMs, the system makers, our supply chain beyond them, the cooling supply chain base, liquid cooling supply chain base, data center supply chain base, no one is going to be surprised with Blackwell coming and the capabilities that we would like to deliver with Grace Blackwell 200. GB200 is going to be exceptional.

是的。Blackwell 有许多配置。Blackwell 是一个平台，而不是一个 GPU。该平台包括支持空气冷却、液冷、x86 和 Grace、InfiniBand、现在的 Spectrum-X 以及我在 GTC 展示的非常大的 NVLink 域。因此，对于一些客户来说，他们将逐步过渡到已经在运送 Hoppers 的现有数据中心基础上。他们将轻松地从 H100 过渡到 H200 再到 B100。因此，Blackwell 系统被设计为在电气上、机械上具有向后兼容性。当然，在 Hopper 上运行的软件堆栈将在 Blackwell 上运行得非常出色。我们也一直在为整个生态系统做准备，让他们为液冷做好准备。我们已经和生态系统谈论 Blackwell 相当长一段时间了。CSP、数据中心、ODM、系统制造商、我们的供应链以及冷却供应链基础、液冷供应链基础、数据中心供应链基础，没有人会对 Blackwell 的到来以及我们希望通过 Grace Blackwell 200 提供的功能感到惊讶。GB200 将是非常出色的。

Operator 操作员

Our next question will come from the line of Vivek Arya with Bank of America Securities. Please go ahead.

我们下一个问题将来自美国银行证券的 Vivek Arya。请提问。

Vivek Arya

Thanks for taking my question. Jensen, how are you ensuring that there is enough utilization of your products and that there isn't a pull-ahead or holding behavior because of tight supply, competition or other factors? Basically, what checks have you built in the system to give us confidence that monetization is keeping pace with your really very strong shipment growth?

谢谢您回答我的问题。詹森，您如何确保产品有足够的利用率，并且不会因为供应紧张、竞争或其他因素而提前拉动或持有行为？基本上，您在系统中建立了哪些检查来让我们确信，货币化与您非常强劲的出货增长保持同步？

Jensen Huang 黄仁勋

Well, I guess, there's the big picture view that I'll come to, and then, but I'll answer your question directly. The demand for GPUs in all the data centers is incredible. We're racing every single day. And the reason for that is because applications like ChatGPT and GPT-4o, and now it's going to be multi-modality and Gemini and its ramp and Anthropic and all of the work that's being done at all the CSPs are consuming every GPU that's out there. There's also a long line of generative AI startups, some 15,000, 20,000 startups that in all different fields from multimedia to digital characters, of course, all kinds of design tool application -- productivity applications, digital biology, the moving of the AV industry to video, so that they can train end-to-end models, to expand the operating domain of self-driving cars. The list is just quite extraordinary. We're racing actually. Customers are putting a lot of pressure on us to deliver the systems and stand it up as quickly as possible. And of course, I haven't even mentioned all of the Sovereign AIs who would like to train all of their regional natural resource of their country, which is their data to train their regional models. And there's a lot of pressure to stand those systems up. So anyhow, the demand, I think, is really, really high and it outstrips our supply. Longer term, that's what -- that's the reason why I jumped in to make a few comments. Longer term, we're completely redesigning how computers work. And this is a platform shift. Of course, it's been compared to other platform shifts in the past. But time will clearly tell that this is much, much more profound than previous platform shifts. And the reason for that is because the computer is no longer an instruction-driven only computer. It's an intention-understanding computer. And it understands, of course, the way we interact with it, but it also understands our meaning, what we intend that we asked it to do and it has the ability to reason, inference iteratively to process a plan and come back with a solution. And so every aspect of the computer is changing in such a way that instead of retrieving prerecorded files, it is now generating contextually relevant intelligent answers. And so that's going to change computing stacks all over the world. And you saw a build that, in fact, even the PC computing stack is going to get revolutionized. And this is just the beginning of all the things that -- what people see today are the beginning of the things that we're working in our labs and the things that we're doing with all the startups and large companies and developers all over the world. It's going to be quite extraordinary.

嗯，我想，有一个我将要谈到的宏观视角，但我会直接回答你的问题。所有数据中心对 GPU 的需求是惊人的。我们每天都在竞速。原因是像 ChatGPT 和 GPT-4o 这样的应用，现在将会是多模态和Gemini以及其扩展和 Anthropic 以及所有 CSPs 正在进行的工作都在消耗着每一块 GPU。此外，还有一长串生成式人工智能初创公司，大约有 15,000、20,000 家初创公司，涉及各种领域，从多媒体到数字角色，当然还有各种设计工具应用——生产力应用、数字生物学、AV 行业向视频的转变，以便他们可以训练端到端模型，扩展自动驾驶汽车的操作领域。这个清单实在是非常了不起。我们实际上正在竞速。客户正在给我们施加很大的压力，要求我们尽快交付系统并启动。当然，我甚至还没有提到所有主权 AI，他们想要训练他们国家的所有区域自然资源，也就是他们的数据，以训练他们的区域模型。 有很大的压力来建立这些系统。总之，需求非常非常高，超过了我们的供应。从长远来看，这就是我发表一些评论的原因。从长远来看，我们正在彻底重新设计计算机的工作方式。这是一个平台转变。当然，它已经被比作过去的其他平台转变。但时间将清楚地表明，这比以往的平台转变更加深刻。原因在于计算机不再仅仅是一个指令驱动的计算机。它是一个理解意图的计算机。它理解我们与之互动的方式，当然，它也理解我们的意图，我们要求它做什么，并且具有推理的能力，迭代地进行推理以制定计划并提出解决方案。因此，计算机的每个方面都在发生变化，不再仅仅是检索预先录制的文件，而是生成具有上下文相关智能答案。这将改变全球各地的计算堆栈。你看到了一个构建，事实上，即使是 PC 计算堆栈也将得到革命性改变。 这只是我们在实验室里进行的工作、与全球各地的初创公司、大公司和开发人员合作的事情的开端。这将是非常了不起的。

Operator 操作员

Our next question will come from the line of Joe Moore with Morgan Stanley. Please go ahead.

我们下一个问题将来自摩根士丹利的乔·摩尔。请开始。

Joseph Moore 约瑟夫·摩尔

Great. Thank you. I understand what you just said about how strong demand is. You have a lot of demand for H200 and for Blackwell products. Do you anticipate any kind of pause with Hopper and H100 as you sort of migrate to those products? Will people wait for those new products, which would be a good product to have? Or do you think there's enough demand for H100 to sustain growth?

太好了。谢谢。我明白你刚才说的需求有多强劲。你对 H200 和 Blackwell 产品有很大的需求。在你转向这些产品的过程中，你是否预计 Hopper 和 H100 会有任何暂停？人们会等待这些新产品吗，这将是一个很好的产品吗？或者你认为 H100 有足够的需求来维持增长？

Jensen Huang 黄仁勋

We see increasing demand of Hopper through this quarter. And we expect to be -- we expect demand to outstrip supply for some time as we now transition to H200, as we transition to Blackwell. Everybody is anxious to get their infrastructure online. And the reason for that is because they're saving money and making money, and they would like to do that as soon as possible.

我们看到本季度对 Hopper 的需求不断增加。我们预计在过渡到 H200、过渡到 Blackwell 期间，需求将在一段时间内超过供应。每个人都急于让他们的基础设施上线。原因是因为他们在节省和赚钱，他们希望尽快实现这一目标。

Operator 操作员

Our next question will come from the line of Toshiya Hari with Goldman Sachs. Please go ahead.

我们下一个问题将来自高盛的 Toshiya Hari。请提问。

Toshiya Hari

Hi. Thank you so much for taking the question. Jensen, I wanted to ask about competition. I think many of your cloud customers have announced new or updates to their existing internal programs, right, in parallel to what they're working on with you guys. To what extent did you consider them as competitors, medium to long term? And in your view, do you think they're limited to addressing most internal workloads or could they be broader in what they address going forward? Thank you.

你好。非常感谢您回答问题。詹森，我想问一下关于竞争的问题。我认为您的许多云客户已经宣布了新的或更新现有的内部程序，对吗，与他们与您们合作的工作同时进行。在中长期内，您认为他们在多大程度上是竞争对手？在您看来，您认为他们仅限于解决大多数内部工作负载，还是在未来可能会更广泛地解决其他问题？谢谢。

Jensen Huang 黄仁勋

We're different in several ways. First, NVIDIA's accelerated computing architecture allows customers to process every aspect of their pipeline from unstructured data processing to prepare it for training, to structured data processing, data frame processing like SQL to prepare for training, to training to inference. And as I was mentioning in my remarks, that inference has really fundamentally changed, it's now generation. It's not trying to just detect the cat, which was plenty hard in itself, but it has to generate every pixel of a cat. And so the generation process is a fundamentally different processing architecture. And it's one of the reasons why TensorRT LLM was so well received. We improved the performance in using the same chips on our architecture by a factor of three. That kind of tells you something about the richness of our architecture and the richness of our software.

我们在几个方面有所不同。首先，NVIDIA 的加速计算架构使客户能够处理从非结构化数据处理到准备训练的每个流程，再到结构化数据处理、类似 SQL 的数据框处理以准备训练，再到训练到推理的每个方面。正如我在讲话中提到的，推理已经发生了根本性的变化，现在是一代新的推理。不再仅仅是检测猫，这本身已经相当困难，而是要生成猫的每个像素。因此，生成过程是一种根本不同的处理架构。这也是为什么 TensorRT LLM 受到如此好评的原因之一。我们通过相同芯片在我们的架构上提高了三倍的性能。这在一定程度上说明了我们架构的丰富性以及我们软件的丰富性。

So one, you could use NVIDIA for everything, from computer vision to image processing, the computer graphics to all modalities of computing. And as the world is now suffering from computing cost and computing energy inflation because general-purpose computing has run its course, accelerated computing is really the sustainable way of going forward. So, accelerated computing is how you're going to save money in computing, is how you're going to save energy in computing. And so, the versatility of our platform results in the lowest TCO for their data center.

因此，您可以使用 NVIDIA 进行各种任务，从计算机视觉到图像处理，从计算机图形学到所有计算模式。由于通用计算已经走到尽头，世界现在正遭受着计算成本和计算能源通货膨胀的困扰，加速计算真的是未来可持续发展的方式。因此，加速计算是您在计算方面节省金钱的方式，也是您在计算方面节省能源的方式。因此，我们平台的多功能性导致了数据中心的最低总体拥有成本。

Second, we're in every cloud. And so for developers that are looking for a platform to develop on, starting with NVIDIA is always a great choice. And we're on-prem, we're in the cloud. We're in computers of any size and shape. We're practically everywhere. And so, that's the second reason. The third reason has to do with the fact that we build AI factories. And this is becoming more an apparent to people that AI is not a chip problem only. It starts, of course, with very good chips and we build a whole bunch of chips for our AI factories, but it's a systems problem.

其次，我们在每一片云中。因此，对于寻找开发平台的开发人员来说，从 NVIDIA 开始总是一个很好的选择。我们在本地，我们在云端。我们存在于各种大小和形状的计算机中。我们几乎无处不在。这就是第二个原因。第三个原因与我们建立 AI 工厂有关。人们越来越意识到 AI 不仅仅是一个芯片问题。当然，它始于非常优秀的芯片，我们为我们的 AI 工厂构建了大量芯片，但这是一个系统问题。

In fact, even AI is now a systems problem. It's not just one large language model. It's a complex system of a whole bunch of large language models that are working together. And so the fact that NVIDIA builds this system causes us to optimize all of our chips to work together as a system, to be able to have software that operates as a system, and to be able to optimize across the system. And just to put it in perspective in simple numbers, if you had a $5 billion infrastructure and you improved the performance by a factor of two, which we routinely do, when you improve the infrastructure by a factor of two, the value too is $5 billion.

事实上，即使是人工智能现在也是一个系统问题。它不仅仅是一个大型语言模型。它是一整套大型语言模型的复杂系统，它们一起协同工作。因此，英伟达构建这个系统导致我们优化所有芯片以作为一个系统共同运作，能够拥有作为一个系统运行的软件，并能够在整个系统中进行优化。简单来说，如果你有一个价值 50 亿美元的基础设施，并将性能提高了两倍，我们经常这样做，当你将基础设施提高了两倍时，价值也将提高到 50 亿美元。

All the chips in that data center doesn't pay for it. And so, the value of it is really quite extraordinary. And this is the reason why today, performance matters everything. This is at a time when the highest performance is also the lowest cost because the infrastructure cost of carrying all of these chips cost a lot of money. And it takes a lot of money to fund the data center, to operate the data center, the people that goes along with it, the power that goes along with it, the real estate that goes along with it, and all of it adds up. And so the highest performance is also the lowest TCO.

那个数据中心里的所有芯片都没有为其付费。因此，它的价值确实非常非凡。这就是为什么今天性能至关重要的原因。这是一个性能最高的时代，也是成本最低的时代，因为携带所有这些芯片的基础设施成本很高。资助数据中心、运营数据中心、与之相关的人员、与之相关的电力、与之相关的房地产都需要大量资金，所有这些加起来。因此，最高性能也是最低总拥有成本。

Operator 操作员

Our next question will come from the line of Matt Ramsay with TD Cowen. Please go ahead.

我们下一个问题将由 TD Cowen 的 Matt Ramsay 提出。请开始。

Matthew Ramsay 马修·拉姆齐

Thank you very much. Good afternoon, everyone. Jensen, I've been in the data center industry my whole career. I've never seen the velocity that you guys are introducing new platforms at the same combination of the performance jumps that you're getting, I mean, 5x in training. Some of the stuff you talked about at GTC up to 30x in inference. And it's an amazing thing to watch but, it also creates an interesting juxtaposition where the current generation of product that your customers are spending billions of dollars on, it's going to be not as competitive with your new stuff, very, very much more quickly than the depreciation cycle of that product.

非常感谢。大家下午好。詹森，我一直在数据中心行业工作。我从未见过你们推出新平台的速度，以及性能提升的组合，我是说，在培训方面提高了 5 倍。在 GTC 上讨论的一些内容，推理方面提高了 30 倍。这是一件令人惊叹的事情，但也产生了一个有趣的对比，即您的客户正在花费数十亿美元购买的当前产品一代，将会比您的新产品更快地失去竞争力，远远快于该产品的折旧周期。

So, I'd like you to -- if you wouldn't mind speak a little bit about how you're seeing that situation evolve itself with customers. As you move to Blackwell, you're going to have very large installed bases, obviously software compatible, but large installed bases of product that's not nearly as performant as your new generation stuff. And it'd be interesting to hear what you see happening with customers along that path. Thank you.

所以，我想让你——如果你不介意的话，稍微谈谈你如何看待客户与情况的发展。随着你们转向布莱克韦尔，你们将拥有非常庞大的已安装基础，显然是软件兼容的，但产品的已安装基础远不及你们的新一代产品。很有趣听听你对客户在这条道路上发生的事情有什么看法。谢谢。

Jensen Huang 黄仁勋

Yes. I really appreciate it. Three points that I'd like to make. If you're 5% into the build-out versus if you're 95% into the build-out, you're going to feel very differently. And because you're only 5% into the build-out anyhow, you build as fast as you can. And when Blackwell comes, it's going to be terrific. And then after Blackwell, as you mentioned, we have other Blackwells coming. And then there's a short -- we're in a one-year rhythm as we've explained to the world. And we want our customers to see our road map for as far as they like, but they're early in their build-out anyways and so they had to just keep on building, okay. And so, there's going to be a whole bunch of chips coming at them, and they just got to keep on building and just, if you will, performance average your way into it.

是的。我非常感激。我想提出三点。如果您的建设进度是 5%，与 95%相比，您会有非常不同的感受。而且因为您只完成了 5%的建设，无论如何，您都要尽快建设。当 Blackwell 到来时，情况将会很棒。然后在 Blackwell 之后，正如您提到的，我们还有其他的 Blackwell 即将到来。然后有一个短暂的——我们正如我们向世界解释的那样，我们处于一年的节奏中。我们希望我们的客户可以看到我们的路线图，尽可能远，但他们的建设仍处于早期阶段，所以他们必须继续建设，好吗。因此，将有大量的芯片面临他们，他们必须继续建设，并且，如果可以的话，通过平均性能的方式逐步适应。

So that's the smart thing to do. They need to make money today. They want to save money today. And time is really, really valuable to them. Let me give you an example of time being really valuable, why this idea of standing up a data center instantaneously is so valuable and getting this thing called time to train is so valuable. The reason for that is because the next company who reaches the next major plateau gets to announce a groundbreaking AI. And the second one after that gets to announce something that's 0.3% better. And so the question is, do you want to be repeatedly the company delivering groundbreaking AI or the company delivering 0.3% better? And that's the reason why this race, as in all technology races, the race is so important.

这就是明智的做法。他们需要今天赚钱。他们想要今天省钱。时间对他们来说真的非常宝贵。让我举个时间非常宝贵的例子，为什么立即建立数据中心的想法如此宝贵，以及获得这个被称为“时间训练”的东西是如此宝贵。原因是因为下一个达到下一个重要里程碑的公司将宣布一项突破性的人工智能。在那之后的第二家公司将宣布一项比之前好 0.3%的东西。所以问题是，你想要一再成为发布突破性人工智能的公司，还是发布比之前好 0.3%的公司？这就是为什么这场比赛，就像所有技术比赛一样，比赛如此重要。

And you're seeing this race across multiple companies because this is so vital to have technology leadership, for companies to trust the leadership and want to build on your platform and know that the platform that they're building on is going to get better and better. And so, leadership matters a great deal. Time to train matters a great deal. The difference between time to train that is three months earlier just to get it done, in order to get time to train on three-months project, getting started three months earlier is everything. And so it's the reason why we're standing up Hopper systems like mad right now because the next plateau is just around the corner. And so that's the second reason.

你会看到多家公司都在竞相进行这场竞赛，因为拥有技术领导力对公司至关重要，公司需要信任领导层，愿意在你的平台上构建，并且知道他们正在构建的平台会变得越来越好。因此，领导力非常重要。培训时间也非常重要。提前三个月进行培训与仅仅为了完成而提前三个月进行培训之间的差异，对于开始进行为期三个月的项目而言，提前三个月就是一切。这就是为什么我们现在像疯狂一样建立 Hopper 系统的原因，因为下一个高峰就在眼前。这就是第二个原因。

The first comment that you made is really a great comment, which is how is it that we're doing -- we're moving so fast and advancing so quickly? Because we have all the stacks here. We literally build the entire data center and we can monitor everything, measure everything, optimize across everything. We know where all the bottlenecks are. We're not guessing about it. We're not putting up PowerPoint slides that look good. We're actually -- we also like our PowerPoint slides look good, but we're delivering systems that perform at scale. And the reason why we know they perform at scale is because we built it all here.

你所提出的第一条评论真的是一条很棒的评论，这是因为我们在做什么——我们移动得如此之快，进步如此之快？因为我们拥有所有的技术栈。我们实际上构建了整个数据中心，我们可以监控一切，衡量一切，优化所有方面。我们知道所有的瓶颈在哪里。我们不是在猜测。我们也不是只是放一些看起来不错的幻灯片。我们实际上——我们也喜欢我们的幻灯片看起来不错，但我们提供的系统在规模上运行。我们知道它们在规模上运行的原因是因为我们在这里构建了所有这些。

Now one of the things that we do that's a bit of a miracle is that we build entire AI infrastructures here, but then we disaggregated and integrated into our customers' data centers however they liked, but we know how it's going to perform and we know where the bottlenecks are. We know where we need to optimize with them and we know where we have to help them improve their infrastructure to achieve the most performance. This deep intimate knowledge at the entire data center scale is fundamentally what sets us apart today. We build every single chip from the ground up. We know exactly how processing is done across the entire system. And so, we understand exactly how it's going to perform and how to get the most out of it with every single generation. So, I appreciate. Those are the three points.

现在我们做的一件奇迹般的事情之一是，我们在这里构建整个人工智能基础设施，然后将其解聚并集成到我们的客户数据中心中，以他们喜欢的方式，但我们知道它将如何表现，我们知道瓶颈在哪里。我们知道我们需要与他们优化的地方，我们知道我们必须帮助他们改善基础设施以实现最佳性能。在整个数据中心规模上拥有这种深入的了解，基本上是我们今天的差异所在。我们从头开始构建每一颗芯片。我们确切地知道整个系统如何进行处理。因此，我们完全了解它将如何表现以及如何在每一代产品中充分利用它。所以，我感激。这就是三点。

Operator 操作员

Your next question will come from the line of Mark Lipacis with Evercore ISI. Please go ahead.

您接下来的问题将来自 Evercore ISI 的 Mark Lipacis。请提问。

Mark Lipacis 马克·利帕西斯

Hi. Thanks for taking my question. Jensen, in the past, you've made the observation that general-purpose computing ecosystems typically dominated each computing era. And I believe the argument was that they could adapt to different workloads, get higher utilization, drive cost of compute cycle down. And this is a motivation for why you were driving to a general-purpose GPU CUDA ecosystem for accelerated computing. And if I mischaracterized that observation, please do let me know. So the question is, given that the workloads that are driving demand for your solutions are being driven by neural network training and inferencing, which on the surface seem like a limited number of workloads, then it might also seem to lend themselves to custom solutions. And so then the question is about does the general purpose computing framework become more at risk or is there enough variability or a rapid enough evolution on these workloads that support that historical general purpose framework? Thank you.

嗨。感谢您回答我的问题。詹森，在过去，您曾观察到通用计算生态系统通常主导每个计算时代。我相信您的观点是，它们能够适应不同的工作负载，提高利用率，降低计算周期成本。这也是您推动通用 GPU CUDA 生态系统用于加速计算的动机。如果我对这一观察有误解，请告诉我。因此，问题是，鉴于推动对您解决方案需求的工作负载是由神经网络训练和推断驱动的，这些工作负载表面上似乎是有限的，那么它们也可能适合定制解决方案。那么问题是，通用计算框架是否更容易受到威胁，或者这些工作负载的变化足够大或者演变足够快，以支持历史上的通用框架？谢谢。

Jensen Huang 黄仁勋

Yes. NVIDIA's accelerated computing is versatile, but I wouldn't call it general-purpose. Like for example, we wouldn't be very good at running the spreadsheet. That was really designed for general-purpose computing. And so there is a -- the control loop of an operating system code probably isn't fantastic for general-purpose compute, not for accelerated computing. And so I would say that we're versatile, and that's usually the way I describe it. There's a rich domain of applications that we're able to accelerate over the years, but they all have a lot of commonalities. Maybe some deep differences, but commonalities. They're all things that I can run in parallel, they're all heavily threaded. 5% of the code represents 99% of the run-time, for example. Those are all properties of accelerated computing. The versatility of our platform and the fact that we design entire systems is the reason why over the course of the last 10 years or so, the number of start-ups that you guys have asked me about in these conference calls is fairly large. And every single one of them, because of the brittleness of their architecture, the moment generative AI came along or the moment the fusion models came along, the moment the next models are coming along now. And now all of a sudden, look at this, large language models with memory because the large language model needs to have memory so they can carry on a conversation with you, understand the context. All of a sudden, the versatility of the Grace memory became super important. And so each one of these advances in generative AI and the advancement of AI really begs for not having a widget that's designed for one model. But to have something that is really good for this entire domain, properties of this entire domain, but obeys the first principles of software, that software is going to continue to evolve, that software is going to keep getting better and bigger. We believe in the scaling of these models. There's a lot of reasons why we're going to scale by easily a million times in the coming few years for good reasons, and we're looking forward to it and we're ready for it. And so the versatility of our platform is really quite key. And it's not -- if you're too brittle and too specific, you might as well just build an FPGA or you build an ASIC or something like that, but that's hardly a computer.

是的。NVIDIA 的加速计算功能多才多艺，但我不会称其为通用目的。比如，我们在运行电子表格方面可能并不擅长。那是真正为通用计算而设计的。因此，操作系统代码的控制循环可能并不适合通用计算，而适合加速计算。所以我会说我们是多才多艺的，通常是这样描述的。多年来，我们能够加速的应用程序领域丰富，但它们都有很多共同点。也许有一些深刻的差异，但也有共同点。它们都是我可以并行运行的东西，都是高度线程化的。例如，5%的代码代表了 99%的运行时间。这些都是加速计算的特性。我们平台的多才多艺以及我们设计整个系统的事实，是过去 10 年左右，你们在这些电话会议中问我关于的初创公司数量相当庞大的原因。 由于它们的架构脆弱，每一个模型在生成式人工智能出现时或融合模型出现时，或者下一个模型出现时，都会变得脆弱。现在突然之间，看看这个，带有记忆的大型语言模型，因为大型语言模型需要具有记忆，这样它们才能与您进行对话，理解上下文。突然之间，Grace 记忆的多功能性变得非常重要。因此，生成式人工智能的每一次进步和人工智能的进步都迫使我们不再设计专门用于某一模型的小部件。而是要拥有一些真正适用于整个领域、符合整个领域属性的东西，但要遵循软件的第一原则，即软件将继续发展，软件将变得越来越好、越来越大。我们相信这些模型的扩展。有很多原因让我们有望在未来几年内轻松扩展一百万倍，这是有充分理由的，我们期待并已做好准备。因此，我们平台的多功能性真的非常关键。 如果你太脆弱和太具体，你可能就只能构建一个 FPGA 或者构建一个 ASIC 之类的东西，但那几乎不算是一台计算机。

Operator 操作员

Our next question will come from the line of Blayne Curtis with Jefferies. Please go ahead.

我们下一个问题将来自 Jefferies 的 Blayne Curtis。请开始。

Blayne Curtis 布莱恩·柯蒂斯

Thanks for taking my question. Actually kind of curious, I mean, being supply constrained, how do you think about , I mean, you came out with a product for China, H20. I'm assuming there'd be a ton of demand for it, but obviously, you're trying to serve your customers with the other Hopper products. Just kind of curious how you're thinking about that in the second half. You could elaborate any impact, what you're thinking for sales as well as gross margin.

谢谢您回答我的问题。实际上有点好奇，我是说，由于供应受限，您如何看待，我是说，您推出了一款针对中国市场的产品，H20。我假设会有大量需求，但显然，您也在努力为客户提供其他 Hopper 产品。只是好奇您如何考虑这个问题在下半年。您可以详细说明任何影响，您对销售以及毛利的想法。

Jensen Huang 黄仁勋

I didn't hear your questions. Something bleeped out.

我没有听到你的问题。有什么东西发出了嘟嘟声。

Simona Jankowski 西莫娜·扬科夫斯基

H20 and how you're thinking about allocating supply between the different Hopper products.

H20 以及您如何考虑在不同的 Hopper 产品之间分配供应。

Jensen Huang 黄仁勋

Well, we have customers that we honor and we do our best for every customer. It is the case that our business in China is substantially lower than the levels of the past. And it's a lot more competitive in China now because of the limitations on our technology. And so those matters are true. However, we continue to do our best to serve the customers in the markets there and to the best of our ability, we'll do our best. But I think overall, the comments that we made about demand outstripping supply is for the entire market and particularly so for H200 and Blackwell towards the end of the year.

嗯，我们尊重并尽力为每位客户提供最好的服务。我们在中国的业务水平明显低于过去。由于技术限制，中国市场现在更加竞争激烈。这些问题是事实。然而，我们仍然尽力为当地市场的客户提供最好的服务，尽力而为。但我认为总体而言，我们关于需求超过供应的评论适用于整个市场，尤其是对于 H200 和 Blackwell 在年底时更是如此。

Operator 操作员

Our next question will come from the line of Srini Pajjuri with Raymond James. Please go ahead.

我们下一个问题将来自雷蒙德詹姆斯的 Srini Pajjuri。请提问。

Srini Pajjuri

Thank you. Jensen, actually more of a clarification on what you said. GB 200 systems, it looks like there is a significant demand for systems. Historically, I think you've sold a lot of HGX boards and some GPUs and the systems business was relatively small. So I'm just curious, why is it that now you are seeing such a strong demand for systems going forward? Is it just the TCO or is it something else or is it just the architecture? Thank you.

谢谢。詹森，实际上更多是对你说的内容进行澄清。GB 200 系统，看起来对系统有很大的需求。从历史上看，我认为你们卖出了很多 HGX 板卡和一些 GPU，系统业务相对较小。所以我很好奇，为什么现在你们看到系统需求如此强劲？是仅仅因为 TCO 还是其他原因，还是仅仅是架构？谢谢。

Jensen Huang 黄仁勋

Yes. I appreciate that. In fact, the way we sell GB200 is the same. We disaggregate all of the components that make sense and we integrate it into computer makers. We have 100 different computer system configurations that are coming this year for Blackwell. And that is off the charts. Hopper, frankly, had only half, but that's at its peak. It started out with way less than that even. And so you're going to see liquid cooled version, air cooled version, x86 visions, Grace versions, so on and so forth. There's a whole bunch of systems that are being designed. And they're offered from all of our ecosystem of great partners. Nothing has really changed. Now of course, the Blackwell platform has expanded our offering tremendously. The integration of CPUs and the much more compressed density of computing, liquid cooling is going to save data centers a lot of money in provisioning power and not to mention to be more energy efficient. And so it's a much better solution. It's more expansive, meaning that we offer a lot more components of a data center and everybody wins. The data center gets much higher performance, networking from networking switches, networking. Of course, NICs, we have Ethernet now so that we can bring NVIDIA AI to a large-scale NVIDIA AI to customers who only operate only know how to operate Ethernet because of the ecosystem that they have. And so Blackwell is much more expansive. We have a lot more to offer our customers this generation around.

是的。我很感激。事实上，我们销售 GB200 的方式是一样的。我们将所有有意义的组件进行分解，然后集成到计算机制造商那里。今年为 Blackwell 推出了 100 种不同的计算机系统配置。这是空前的。Hopper，坦率地说，只有一半，但那是它的巅峰。它甚至一开始比这还少。因此，您将看到液冷版本、空气冷却版本、x86 版本、Grace 版本等等。有很多正在设计的系统。它们来自我们众多伙伴的生态系统。没有真正发生变化。现在当然，Blackwell 平台极大地扩展了我们的产品线。CPU 的集成和更加紧凑的计算密度，液冷技术将为数据中心节省大量的能源成本，更加节能。因此，这是一个更好的解决方案。它更加广泛，意味着我们提供了数据中心更多的组件，每个人都受益。数据中心获得了更高的性能，从网络交换机到网络。 当然，NICs，我们现在有以太网，这样我们可以将 NVIDIA AI 带给那些只懂得如何操作以太网的大规模 NVIDIA AI 客户，因为他们拥有的生态系统。因此，Blackwell 更加广泛。这一代我们有更多可以提供给客户的东西。

Operator 操作员

Our next question will come from the line William Stein with Truist Securities. Please go ahead.

我们下一个问题将由 Truist Securities 的 William Stein 提出。请开始。

William Stein 威廉·斯坦

Great. Thanks for taking my question. Jensen, at some point, NVIDIA decided that while there are reasonably good CPUs available for data center operations, your ARM-based Grace CPU provides some real advantage that made that technology worth delivering to customers, perhaps related to cost or power consumption or technical synergies between Grace and Hopper, Grace and Blackwell. Can you address whether there could be a similar dynamic that might emerge on the client side, whereby while there are very good solutions, you've highlighted that Intel and AMD are very good partners and deliver great products in x86, but there might be some, especially in emerging AI workloads, some advantage that NVIDIA can deliver that others have more of a challenge?

很好。感谢您回答我的问题。詹森，在某个时候，英伟达决定，虽然数据中心操作有相当不错的 CPU 可用，但您基于 ARM 架构的 Grace CPU 提供了一些真正有利的优势，使得这项技术值得交付给客户，也许与成本、功耗或 Grace 与 Hopper、Grace 与 Blackwell 之间的技术协同性有关。您能否谈谈在客户端是否可能出现类似的动态，即虽然有非常好的解决方案，您已经强调英特尔和 AMD 是非常好的合作伙伴，并在 x86 架构上提供出色的产品，但在新兴的人工智能工作负载中，英伟达可能提供一些其他人难以应对的优势？

Jensen Huang 黄仁勋

Well, you mentioned some really good reasons. It is true that for many of the applications, our partnership with x86 partners are really terrific and we build excellent systems together. But Grace allows us to do something that isn't possible with the configuration, the system configuration today. The memory system between Grace and Hopper are coherent and connected. The interconnect between the two chips, calling it two chips is almost weird because it's like a superchip. The two of them are connected with this interface that's like a terabytes per second. It's off the charts. And the memory that's used by Grace is LPDDR. It's the first data center-grade low-power memory. And so we save a lot of power on every single node. And then finally, because of the architecture, because we can create our own architecture with the entire system now, we could create something that has a really large NVLink domain, which is vitally important to the next-generation large language models for inferencing. And so you saw that GB200 has a 72-node NVLink domain. That's like 72 Blackwells connected together into one giant GPU. And so we needed Grace Blackwells to be able to do that. And so there are architectural reasons, there are software programming reasons and then there are system reasons that are essential for us to build them that way. And so if we see opportunities like that, we'll explore it. And today, as you saw at the build yesterday, which I thought was really excellent, Satya announced the next-generation PCs, Copilot+ PC, which runs fantastically on NVIDIA's RTX GPUs that are shipping in laptops. But it also supports ARM beautifully. And so it opens up opportunities for system innovation even for PCs.

嗯，你提到了一些非常好的理由。确实，对于许多应用程序来说，我们与 x86 合作伙伴的合作非常出色，我们一起构建了优秀的系统。但是 Grace 让我们能够做一些在当前系统配置下不可能的事情。Grace 和 Hopper 之间的内存系统是一致的和连接的。这两个芯片之间的互连，称之为两个芯片几乎有点奇怪，因为它就像一个超级芯片。它们之间通过每秒几 TB 的接口连接。这是超出常规的。Grace 使用的内存是 LPDDR。这是第一个数据中心级低功耗内存。因此，我们在每个节点上节省了大量功耗。最后，由于架构的原因，因为我们现在可以创建自己的整个系统架构，我们可以创建一个具有非常大 NVLink 域的东西，这对于下一代大型语言模型的推理非常重要。因此，你看到 GB200 有一个 72 节点的 NVLink 域。这就像 72 个 Blackwell 连接在一起形成一个巨大的 GPU。因此，我们需要 Grace Blackwells 来实现这一点。 因此，有建筑原因，有软件编程原因，还有系统原因，这些对我们构建它们的方式至关重要。因此，如果我们看到这样的机会，我们会进行探索。就像你昨天在发布会上看到的那样，我认为那真的很出色，萨提亚宣布了下一代 PC，Copilot+ PC，在 NVIDIA 的 RTX GPU 上运行得非常出色，这些 GPU 已经在笔记本电脑上发货。但它也很好地支持 ARM。因此，即使是对 PC 来说，它也为系统创新打开了机会。

Operator 操作员

Our last question comes from the line of C.J. Muse with Cantor Fitzgerald. Please go ahead.

我们最后一个问题来自康特菲茨杰伊·穆斯。请提问。

C.J. Muse

Good afternoon. Thank you for taking the question. I guess, Jensen, a bit of a longer-term question. I know Blackwell hasn't even launched yet, but obviously, investors are forward-looking and amidst rising potential competition from GPUs and custom ASICs, how are you thinking about NVIDIA's pace of innovation and your million-fold scaling over the last decade, truly impressive. CUDA, Varsity, Precision, Grace, Cohere and Connectivity. When you look forward, what frictions need to be solved in the coming decade? And I guess, maybe more importantly, what are you willing to share with us today?

下午好。感谢您提问。我猜，詹森，一个稍长期的问题。我知道 Blackwell 甚至还没有推出，但显然，投资者是前瞻性的，在 GPU 和定制 ASICs 潜在竞争不断增加的情况下，您如何看待英伟达创新的速度以及过去十年里您的百万倍扩展，确实令人印象深刻。CUDA、Varsity、Precision、Grace、Cohere 和 Connectivity。展望未来，未来十年需要解决哪些摩擦？我猜，也许更重要的是，您今天愿意与我们分享什么？

Jensen Huang 黄仁勋

Well, I can announce that after Blackwell, there's another chip. And we are on a one-year rhythm. And so and you can also count that -- count on us having new networking technology on a very fast rhythm. We're announcing Spectrum-X for Ethernet. But we're all in on Ethernet, and we have a really exciting road map coming for Ethernet. We have a rich ecosystem of partners. Dell announced that they're taking Spectrum-X to market. We have a rich ecosystem of customers and partners who are going to announce taking our entire AI factory architecture to market. And so for companies that want the ultimate performance, we have InfiniBand computing fabric. InfiniBand is a computing fabric, Ethernet is a network. And InfiniBand, over the years, started out as a computing fabric, became a better and better network. Ethernet is a network and with Spectrum-X, we're going to make it a much better computing fabric. And we're committed -- fully committed to all three links, NVLink computing fabric for single computing domain to InfiniBand computing fabric, to Ethernet networking computing fabric. And so we're going to take all three of them forward at a very fast clip. And so you're going to see new switches coming, new NICs coming, new capability, new software stacks that run on all three of them. New CPUs, new GPUs, new networking NICs, new switches, a mound of chips that are coming. And all of it, the beautiful thing is all of it runs CUDA. And all of it runs our entire software stack. So you invest today on our software stack, without doing anything at all, it's just going to get faster and faster and faster and faster. And if you invest in our architecture today, without doing anything, it will go to more and more clouds and more and more data centers and everything just runs. And so I think the pace of innovation that we're bringing will drive up the capability, on the one hand, and drive down the TCO on the other hand. And so we should be able to scale out with the NVIDIA architecture for this new era of computing and start this new industrial revolution where we manufacture not just software anymore, but we manufacture artificial intelligence tokens and we're going to do that at scale. Thank you.

嗯，我可以宣布，在 Blackwell 之后，还有另一款芯片。我们按照一年的节奏进行。因此，您也可以期待我们以非常快的节奏推出新的网络技术。我们宣布了以太网的 Spectrum-X。但我们全力以赴支持以太网，并且我们为以太网制定了一份非常令人兴奋的路线图。我们拥有丰富的合作伙伴生态系统。戴尔宣布他们将推出 Spectrum-X。我们拥有丰富的客户和合作伙伴生态系统，他们将宣布推出我们整个 AI 工厂架构。因此，对于希望获得最佳性能的公司，我们有 InfiniBand 计算布局。InfiniBand 是一种计算布局，以太网是一种网络。多年来，InfiniBand 起初是一种计算布局，逐渐发展成为一种更好的网络。以太网是一种网络，而有了 Spectrum-X，我们将使其成为一种更好的计算布局。我们承诺全力支持所有三种连接，从单一计算领域的 NVLink 计算布局到 InfiniBand 计算布局，再到以太网网络计算布局。因此，我们将以非常快的速度推进这三种技术。 因此，您将看到新的交换机、新的网卡、新的功能、在所有这些设备上运行的新软件堆栈。新的 CPU、新的 GPU、新的网络网卡、新的交换机，一堆即将到来的芯片。而美妙之处在于，所有这些都支持 CUDA。所有这些都支持我们的整个软件堆栈。因此，如果您今天投资我们的软件堆栈，什么都不用做，它将变得越来越快。如果您今天投资我们的架构，什么都不用做，它将覆盖更多云和更多数据中心，一切都将运行。因此，我认为我们带来的创新速度将在一方面提高能力，在另一方面降低总体拥有成本。因此，我们应该能够通过 NVIDIA 架构扩展这个新计算时代，并开启这个新的工业革命，我们不再只生产软件，而是生产人工智能代币，我们将大规模地做到这一点。谢谢。

Operator 操作员

That will conclude our question-and-answer session and our call for today. We thank you all for joining and you may now disconnect.

这将结束我们的问答环节和今天的电话会议。感谢大家的参与，现在可以挂断电话了。