輝達 (NVDA) 2024 Q3 法說會逐字稿

Good afternoon. My name is Jael, and I will be your conference operator today. At this time, I would like to welcome everyone to NVIDIA's third quarter earnings call. (Operator Instructions) Thank you. Simona Jankowski, you may now begin your conference.

午安.我叫 Jael，今天我將擔任你們的會議操作員。此時此刻，我謹歡迎大家參加 NVIDIA 第三季財報電話會議。 （操作員說明）謝謝。 Simona Jankowski，您現在可以開始會議了。

Thank you. Good afternoon, everyone, and welcome to NVIDIA's conference call for the third quarter of fiscal 2024. With me today from NVIDIA are Jensen Huang, President and Chief Executive Officer; and Colette Kress, Executive Vice President and Chief Financial Officer.

謝謝。大家下午好，歡迎參加 NVIDIA 2024 財年第三季電話會議。今天與我一起出席的有 NVIDIA 總裁兼首席執行官黃仁勳 (Jensen Huang)；執行副總裁兼財務長 Colette Kress。

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 fourth quarter and fiscal 2024. 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，我們的電話會議正在 NVIDIA 投資者關係網站上進行網路直播。此網路廣播將在討論我們第四季和 2024 財年財務業績的電話會議之前進行重播。今天電話會議的內容屬於 NVIDIA 的財產。未經我們事先書面同意，不得複製或轉錄。

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 statements are made as of today, November 21, 2023, based on information currently available to us. Except as required by law, we assume no obligation to update any forward-looking statements.

在這次電話會議中，我們可能會根據目前的預期做出前瞻性陳述。這些都受到許多重大風險和不確定性的影響，我們的實際結果可能會有重大差異。有關可能影響我們未來財務表現和業務的因素的討論，請參閱今天的收益發布中的揭露、我們最新的表格 10-K 和 10-Q，以及我們可能在表格 8-K 上提交的報告證券交易委員會。所有聲明均基於我們目前掌握的信息，截至 2023 年 11 月 21 日作出。除法律要求外，我們不承擔更新任何前瞻性聲明的義務。

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.

在本次電話會議中，我們將討論非公認會計準則財務指標。您可以在我們網站上發布的財務長評論中找到這些非 GAAP 財務指標與 GAAP 財務指標的調整表。

With that, let me turn the call over to Colette.

現在，讓我把電話轉給科萊特。

Thanks, Simona. Q3 was another record quarter. Revenue of $18.1 billion was up 34% sequentially and up more than 200% year-on-year and well above our outlook of $16 billion. Starting with data center. The continued ramp of the NVIDIA HGX platform based on our Hopper Tensor Core GPU architecture, along with InfiniBand end-to-end networking, drove record revenue of $14.5 billion, up 41% sequentially and up 279% year-on-year.

謝謝，西蒙娜。第三季又是創紀錄的季度。營收為 181 億美元，比上一季成長 34%，年成長超過 200%，遠高於我們 160 億美元的預期。從資料中心開始。基於 Hopper Tensor Core GPU 架構的 NVIDIA HGX 平台的持續增長，加上 InfiniBand 端到端網絡，推動收入創紀錄地達到 145 億美元，環比增長 41%，同比增長 279%。

NVIDIA HGX with InfiniBand together are essentially the reference architecture for AI supercomputers and data center infrastructures. Some of the most exciting generative AI applications are built and run on NVIDIA, including Adobe, Firefly, ChatGPT, Microsoft 365 Copilot, CoAssist, Now Assist with ServiceNow and Zoom AI Companion. Our data center compute revenue quadrupled from last year and networking revenue nearly tripled. Investment in infrastructure for training and inferencing large language models, deep learning recommender systems and generative AI applications is fueling strong broad-based demand for NVIDIA accelerated computing. Inferencing is now a major workload for NVIDIA AI computing.

NVIDIA HGX 與 InfiniBand 本質上是 AI 超級電腦和資料中心基礎設施的參考架構。一些最令人興奮的生成式 AI 應用程式都是在 NVIDIA 上建置和運行的，包括 Adob​​e、Firefly、ChatGPT、Microsoft 365 Copilot、CoAssist、Now Assist with ServiceNow 和 Zoom AI Companion。我們的資料中心計算收入比去年增長了四倍，網路收入幾乎增長了兩倍。對訓練和推理大型語言模型、深度學習推薦系統和生成式 AI 應用的基礎設施的投資正在推動對 NVIDIA 加速運算的強勁廣泛需求。推理現在是 NVIDIA AI 計算的主要工作負載。

Consumer Internet companies and enterprises drove exceptional sequential growth in Q3, comprising approximately half of our data center revenue and outpacing total growth. Companies like Meta are in full production with deep learning recommender systems and also investing in generative AI to help advertisers optimize images and text. Most major consumer Internet companies are racing to ramp up generative AI deployment. The enterprise wave of AI adoption is now beginning. Enterprise software companies such as Adobe, Databricks, Snowflake and ServiceNow are adding AI copilots and assistants to their platforms. And broader enterprises are developing custom AI for vertical industry applications such as Tesla and autonomous driving.

消費互聯網公司和企業在第三季度推動了驚人的環比成長，約占我們資料中心收入的一半，並且超過了整體成長。像 Meta 這樣的公司已經全面投入使用深度學習推薦系統，並投資生成式人工智慧來幫助廣告商優化圖像和文字。大多數主要消費互聯網公司都在競相加強生成式人工智慧的部署。企業採用人工智慧的浪潮現已開始。 Adobe、Databricks、Snowflake 和 ServiceNow 等企業軟體公司正在為其平台添加人工智慧副駕駛和助理。更廣泛的企業正在為特斯拉和自動駕駛等垂直行業應用開發客製化人工智慧。

Cloud service providers drove roughly the other half of our data center revenue in the quarter. Demand was strong from all hyperscale CSPs as well as from a broadening set of GPU-specialized CSPs globally that are rapidly growing to address the new market opportunities in AI.

本季度，雲端服務供應商大約貢獻了我們資料中心收入的一半。所有超大規模 CSP 以及全球範圍內不斷擴大的 GPU 專用 CSP 的需求都很強勁，這些 CSP 正在快速成長，以應對人工智慧領域的新市場機會。

NVIDIA H100 Tensor Core GPU instances are now generally available in virtually every cloud with instances and high demand. We have significantly increased supply every quarter this year to meet strong demand and expect to continue to do so next year. We will also have a broader and faster product launch cadence to meet a growing and diverse set of AI opportunities.

NVIDIA H100 Tensor Core GPU 執行個體現在幾乎在每個具有執行個體和高需求的雲端中都可用。今年每季我們都大幅增加供應，以滿足強勁的需求，並預計明年將繼續這樣做。我們還將有更廣泛、更快的產品發布節奏，以滿足不斷成長和多樣化的人工智慧機會。

Toward the end of the quarter, the U.S. government announced a new set of export control regulations for China and other markets, including Vietnam and certain countries in the Middle East. These regulations require licenses for the export of a number of our products including our Hopper and Ampere 100 and 800 series and several others. Our sales to China and other affected destinations derived from products that are now subject to licensing requirements have consistently contributed approximately 20% to 25% of data center revenue over the past few quarters. We expect that our sales to these destinations will decline significantly in the fourth quarter, though we believe they'll be more than offset by strong growth in other regions.

本季末，美國政府宣布了一套針對中國和其他市場（包括越南和某些中東國家）的新出口管制法規。這些法規要求我們的許多產品獲得出口許可證，包括我們的 Hopper 和 Ampere 100 和 800 系列以及其他幾種產品。在過去的幾個季度中，我們對中國和其他受影響目的地的銷售源自於目前需要遵守授權要求的產品，一直貢獻了資料中心收入的約 20% 至 25%。我們預計第四季度對這些目的地的銷售額將大幅下降，但我們相信其他地區的強勁成長將足以抵消這些下降。

The U.S. government designed the regulation to allow the U.S. industry to provide data center compute products to markets worldwide, including China, continuing to compete worldwide as the regulations encourage, promote U.S. technology leadership, spurs economic growth and support U.S. jobs. For the highest performance levels, the government requires licenses. For lower performance levels, the government requires a streamlined prior notification process. And for products even lower performance levels, the government does not require any notice at all. Following the government's clear guidelines, we are working to expand our data center product portfolio to offer compliant solutions for each regulatory category, including products for which the U.S. government does not wish to have advanced notice before each shipment.

美國政府設計該法規的目的是允許美國產業向包括中國在內的全球市場提供資料中心運算產品，在法規鼓勵、提升美國技術領先地位、刺激經濟成長和支持美國就業的同時，繼續在全球範圍內競爭。為了達到最高的性能水平，政府需要許可證。對於較低的績效水平，政府要求簡化事先通知流程。而對於性能水準較低的產品，政府根本不需要任何通知。遵循政府的明確指導方針，我們正在努力擴展我們的資料中心產品組合，為每個監管類別提供合規的解決方案，包括美國政府不希望在每次發貨前提前通知的產品。

We are working with some customers in China and the Middle East to pursue licenses from the U.S. government. It is too early to know whether these will be granted for any significant amount of revenue.

我們正在與中國和中東的一些客戶合作，尋求美國政府的許可。現在判斷是否會獲得大量收入還為時過早。

Many countries are awakening to the need to invest in sovereign AI infrastructure to support economic growth and industrial innovation. With investments in domestic compute capacity, nations can use their own data to train LLMs and support their local generative AI ecosystems. For example, we are working with India's government and largest tech companies, including Infosys, Reliance and Tata to boost their sovereign AI infrastructure. And French private cloud provider, Scaleway is building a regional AI cloud based on NVIDIA H100, InfiniBand and NVIDIA AI Enterprise software to fuel advancement across France and Europe. National investment in compute capacity is a new economic imperative, and serving the sovereign AI infrastructure market represents a multibillion-dollar opportunity over the next few years.

許多國家開始意識到投資主權人工智慧基礎設施的必要性，以支持經濟成長和產業創新。透過對國內運算能力的投資，各國可以使用自己的數據來培訓法學碩士並支援當地的生成式人工智慧生態系統。例如，我們正在與印度政府和最大的科技公司（包括 Infosys、Reliance 和 Tata）合作，以加強他們的主權人工智慧基礎設施。法國私有雲供應商 Scaleway 正在建立基於 NVIDIA H100、InfiniBand 和 NVIDIA AI Enterprise 軟體的區域 AI 雲，以推動法國和歐洲的進步。國家對運算能力的投資是新的經濟需求，服務主權人工智慧基礎設施市場代表未來幾年數十億美元的機會。

From a product perspective, the vast majority of revenue in Q3 was driven by the NVIDIA HGX platform based on our Hopper GPU architecture with lower contribution from the prior generation Ampere GPU architecture. The new L40S GPU built for industry standard servers began to ship, supporting training and inference workloads across a variety of customers.

從產品角度來看，第三季的絕大多數收入是由基於我們的 Hopper GPU 架構的 NVIDIA HGX 平台推動的，上一代 Ampere GPU 架構的貢獻較低。為業界標準伺服器建置的新型 L40S GPU 開始出貨，支援各種客戶的訓練和推理工作負載。

This was also the first revenue quarter of our GH 200 Grace Hopper Secret chip, which combines our ARM-based Grace GPU with a Hopper GPU. Grace and Grace Hopper are ramping into a new multibillion-dollar product line. Grace Hopper instances are now available at GPU-specialized cloud providers and coming soon to Oracle Cloud. Grace Hopper is also getting significant traction with supercomputing customers, initial system shipments to Los Alamos National Lab and the Swiss National Supercomputing Center, took place in the third quarter.

這也是我們的 GH 200 Grace Hopper Secret 晶片的第一個收入季度，該晶片將我們基於 ARM 的 Grace GPU 與 Hopper GPU 結合在一起。格蕾絲·霍珀 (Grace Hopper) 和格蕾絲·霍珀 (Grace Hopper) 正在進軍價值數十億美元的新產品線。 Grace Hopper 執行個體現已在 GPU 專業雲端供應商提供，很快就會在 Oracle Cloud 上推出。 Grace Hopper 也受到了超級運算客戶的極大關注，第三季向洛斯阿拉莫斯國家實驗室和瑞士國家超級運算中心出貨了初始系統。

The U.K. government announced it will build 1 of the world's fastest AI supercomputer called Isambard-AI with almost 5,500 Grace Hopper Superchips. German supercomputing center, Jülich, also announced that it will build its next-generation AI supercomputer with close to 24,000 Grace Hopper Superchips and Quantum-2 InfiniBand, making it the world's most powerful AI supercomputer with over 90 exaflops of AI performance.

英國政府宣布將建造一台世界上最快的人工智慧超級電腦 Isambard-AI，配備近 5,500 個 Grace Hopper 超級晶片。德國超級運算中心Jülich也宣布，將採用近24,000顆Grace Hopper Superchips和Quantum-2 InfiniBand打造下一代AI超級計算機，使其成為全球最強大的AI超級計算機，擁有超過90 exaflops的AI性能。

All in, we estimate that the combined AI compute capacity of all the supercomputers built on Grace Hopper across the U.S., Europe and Japan next year will exceed 200 exaflops with more wins to come. Inference is contributing significantly to our data center demand as AI is now in full production for deep learning recommenders, chatbots, copilots and text-to-image generation. And this is just the beginning.

總而言之，我們估計明年美國、歐洲和日本所有基於 Grace Hopper 構建的超級電腦的 AI 運算能力總和將超過 200 exaflops，並且還會取得更多勝利。推理對我們的資料中心需求做出了重大貢獻，因為人工智慧現在已全面投入生產，用於深度學習推薦器、聊天機器人、副駕駛和文字到圖像生成。而這只是個開始。

NVIDIA AI offers the best inference performance and versatility and thus, the lower power and cost of ownership. We are also driving a fast cost reduction curve. With the release of NVIDIA TensorRT LLM, we now achieved more than 2x the inference performance or half the cost of inferencing LLMs by NVIDIA GPUs.

NVIDIA AI 提供最佳的推理效能和多功能性，從而降低功耗和擁有成本。我們也正在推動快速降低成本曲線。隨著 NVIDIA TensorRT LLM 的發布，我們現在實現了 NVIDIA GPU 推理 LLM 的 2 倍以上的推理效能或一半的成本。

We also announced the latest member of the Hopper family, the H200, which will be the first GPU to offer HBM3e, faster, larger memory to further accelerate generative AI and LLMs. It moves inference speed up to another 2x compared to H100 GPUs for running LLMs like Llama 2. Combined, TensorRT LLM and H200 increased performance or reduced cost by 4x in just 1 year without customers changing their stack. This is a benefit of CUDA and our architecture compatibility. Compared to the [A100], H200 delivers an 18x performance increase for infancy models like GPT-3, allowing customers to move to larger models and with no increase in latency. Amazon Web Services, Google Cloud, Microsoft Azure and Oracle Cloud will be among the first CSPs to offer H200-based instances starting next year.

我們也發布了 Hopper 系列的最新成員 H200，它將成為首款提供 HBM3e、更快、更大記憶體的 GPU，以進一步加速生成式 AI 和法學碩士。與運行Llama 2 等LLM 的H100 GPU 相比，它的推理速度提高了2 倍。TensorRT LLM 和H200 相結合，在短短1 年內將性能提高了4 倍，或在客戶無需更改堆疊的情況下將成本降低了4 倍。這是 CUDA 和我們的架構相容性的好處。與 [A100] 相比，H200 為 GPT-3 等初級模型提供了 18 倍的效能提升，允許客戶遷移到更大的模型，並且不會增加延遲。從明年開始，Amazon Web Services、Google Cloud、Microsoft Azure 和 Oracle Cloud 將成為首批提供基於 H200 執行個體的 CSP。

At last week's Microsoft Ignite, we deepened and expanded our collaboration with Microsoft across the entire stock. We introduced an AI foundry service for the development and tuning of custom generative AI enterprise applications running on Azure. Customers can bring their domain knowledge and proprietary data and we help them build their AI models using our AI expertise and software stack in our DGX Cloud, all with enterprise-grade securities and support. SAP and Amdocs are the first customers of the NVIDIA AI foundry service on Microsoft Azure. In addition, Microsoft will launch new confidential computing instances based on the H100.

在上週的 Microsoft Ignite 上，我們加深並擴大了與微軟在整個股票領域的合作。我們推出了 AI 鑄造服務，用於開發和調整在 Azure 上執行的自訂生成 AI 企業應用程式。客戶可以帶來他們的領域知識和專有數據，我們使用我們的 AI 專業知識和 DGX 雲端中的軟體堆疊來幫助他們建立 AI 模型，所有這些都具有企業級的安全和支援。 SAP 和 Amdocs 是 Microsoft Azure 上 NVIDIA AI Foundry 服務的首批客戶。此外，微軟也將推出基於H100的新機密運算實例。

The H100 remains the top-performing and most versatile platform for AI training and by a wide margin, as shown in the latest MLPerf industry benchmark results. Our training cluster included more than 10,000 H100 GPUs or 3x more than in June, reflecting very efficient scaling. Efficient scaling is a key requirement in generative AI because LLMs are growing by an order of magnitude every year. Microsoft Azure achieved similar results on the nearly identical cluster, demonstrating the efficiency of NVIDIA AI in public cloud deployments.

正如最新的 MLPerf 產業基準測試結果所示，H100 仍然是效能最佳、用途最廣泛的人工智慧訓練平台，並且遙遙領先。我們的訓練集群包括超過 10,000 個 H100 GPU，比 6 月增加了 3 倍，反映出非常高效的擴展。高效擴展是產生人工智慧的關鍵要求，因為法學碩士每年都在以一個數量級成長。 Microsoft Azure 在幾乎相同的叢集上也取得了類似的結果，展示了 NVIDIA AI 在公有雲部署中的效率。

Networking now exceeds a $10 billion annualized revenue run rate. Strong growth was driven by exceptional demand for InfiniBand, which grew fivefold year-on-year. InfiniBand is critical to gaining the scale and performance needed for training LLMs.

目前，網路業務的年收入運行率已超過 100 億美元。強勁的成長是由對 InfiniBand 的巨大需求推動的，該需求同比增長了五倍。 InfiniBand 對於獲得法學碩士培訓所需的規模和性能至關重要。

Microsoft made this very point last week highlighting that Azure uses over 29,000 miles of InfiniBand cabling, enough to circle the globe. We are expanding NVIDIA networking into the Ethernet space. Our new Spectrum-X end-to-end Ethernet offering with technologies, purpose-built for AI will be available in Q1 next year with support from leading OEMs, including Dell, HPE and Lenovo. Spectrum-X can achieve 1.6x higher networking performance for AI communication compared to traditional ethernet offerings.

微軟上週就強調了這一點，強調 Azure 使用超過 29,000 英里的 InfiniBand 佈線，足以繞地球一圈。我們正在將 NVIDIA 網路擴展到乙太網路領域。我們全新的 Spectrum-X 端到端乙太網路產品以及專為 AI 打造的技術將於明年第一季推出，並得到戴爾、HPE 和聯想等領先 OEM 廠商的支援。與傳統乙太網路產品相比，Spectrum-X 的 AI 通訊網路效能可提高 1.6 倍。

Let me also provide an update on our software and services offerings, where we are starting to see excellent adoption. We are on track to exit the year at an annualized revenue run rate of $1 billion for recurring software, support and services offerings. We see 2 primary opportunities for growth over the intermediate term with our DGX Cloud service and with our NVIDIA AI Enterprise software. Each reflects the growth of enterprise AI training and enterprise AI inference, respectively.

我還要介紹我們的軟體和服務產品的最新情況，我們已經開始看到這些軟體和服務產品的出色採用。我們預計以 10 億美元的經常性軟體、支援和服務產品年化收入結束今年。我們透過 DGX 雲端服務和 NVIDIA AI Enterprise 軟體看到了中期成長的兩個主要機會。每一個都分別反映了企業人工智慧訓練和企業人工智慧推理的成長。

Our latest DGX Cloud customer announcement was this morning as part of an AI research collaboration with Genentech; the biotechnology pioneer also plans to use our BioNeMo LLM framework to help accelerate and optimize their AI drug discovery platform. We now have enterprise AI partnerships with Adobe, Dropbox, Getty, SAP, ServiceNow, Snowflake and others to come.

我們最新的 DGX Cloud 客戶公告是今天早上發布的，作為與 Genentech 人工智慧研究合作的一部分；這家生物技術先驅還計劃使用我們的 BioNeMo LLM 框架來幫助加速和優化他們的 AI 藥物發現平台。我們現在與 Adob​​e、Dropbox、Getty、SAP、ServiceNow、Snowflake 以及其他公司建立了企業人工智慧合作夥伴關係。

Okay. Moving to gaming. Gaming revenue of $2.86 billion was up 15% sequentially and up more than 80% year-on-year, with strong demand in the important back-to-school shopping season, with NVIDIA RTX ray tracing and AI technologies now available at price points as low as $299. We enter the holidays with the best ever lineup for gamers and creators. Gaming has doubled relative to pre-COVID levels, even against the backdrop of lackluster PC market performance. This reflects the significant value we've brought to the gaming ecosystem with innovations like RTX and DLSS.

好的。轉向遊戲。遊戲收入為 28.6 億美元，環比增長 15%，同比增長超過 80%，重要的返校購物季需求強勁，NVIDIA RTX 光線追蹤和 AI 技術目前的價格為低至 299 美元。我們為遊戲玩家和創作者帶來了有史以來最好的陣容。即使在 PC 市場表現低迷的背景下，遊戲遊戲數量也比新冠疫情前的水平翻了一番。這反映了我們透過 RTX 和 DLSS 等創新為遊戲生態系統帶來的巨大價值。

The number of games and applications supporting these technologies has exploded in that period, driving upgrade and attracting new buyers. The RTX ecosystem continues to grow. There are now over 475 RTX-enabled games and applications. Generative AI is quickly emerging as the new killer app for high-performance PCs, NVIDIA RTX GPUs define the most performant AI PCs and workstations. We just released TensorRT LLM for Windows, which speeds on-device LLM inference up by 4x. With an installed base of over 100 million, NVIDIA RTX is the natural platform for AI application developers.

在此期間，支援這些技術的遊戲和應用程式數量呈爆炸式增長，推動了升級並吸引了新的買家。 RTX 生態系持續發展。現在有超過 475 個支援 RTX 的遊戲和應用程式。生成式 AI 正迅速成為高效能 PC 的新殺手級應用，NVIDIA RTX GPU 定義了效能最高的 AI PC 和工作站。我們剛剛發布了適用於 Windows 的 TensorRT LLM，它將裝置上的 LLM 推理速度提高了 4 倍。 NVIDIA RTX 擁有超過 1 億的安裝量，是人工智慧應用程式開發人員的天然平台。

Finally, our GeForce NOW cloud gaming service continues to build momentum. Its library of PC games surpassed 1,700 titles, including the launches of Alan Wake 2, Baldur's Gate 3, Cyberpunk 2077, Phantom Liberty and Starfield.

最後，我們的 GeForce NOW 雲端遊戲服務持續保持強勁勢頭。其 PC 遊戲庫超過 1,700 款遊戲，包括《心靈殺手 2》、《博德之門 3》、《Cyber​​punk 2077》、《幻影自由》和《Starfield》。

Moving to ProViz. Revenue of $416 million was up 10% sequentially and up 108% year-on-year. NVIDIA RTX is the workstation platform of choice for professional design, engineering and simulation use cases, and AI is emerging as a powerful demand driver. Early applications include inference for AI imaging in health care and edge AI in smart spaces and the public sector. We launched a new line of desktop workstations based on NVIDIA RTX Ada Lovelace generation GPUs and ConnectX, SmartNICs, offering up to 2x the AI processing, ray tracing and graphics performance of the previous generations. These powerful new workstations are optimized for AI workloads such as fine-tuning AI models, training smaller models and running inference locally.

轉向 ProViz。營收為 4.16 億美元，季增 10%，年增 108%。 NVIDIA RTX 是專業設計、工程和類比用例的首選工作站平台，而人工智慧正在成為強大的需求驅動力。早期應用包括醫療保健中的人工智慧成像推理以及智慧空間和公共部門中的邊緣人工智慧。我們推出了基於 NVIDIA RTX Ada Lovelace 世代 GPU 和 ConnectX、SmartNIC 的全新桌面工作站系列，其 AI 處理、光線追蹤和圖形效能是前幾代產品的 2 倍。這些功能強大的新工作站針對人工智慧工作負載進行了最佳化，例如微調人工智慧模型、訓練較小的模型和在本地運行推理。

We continue to make progress on Omniverse, our software platform for designing, building and operating 3D virtual worlds. Mercedes-Benz is using Omniverse-powered digital twins to plan, design, build and operate its manufacturing and assembly facilities, helping it increase efficiency and reduce defects. [Axon] is also incorporating Omniverse into its manufacturing process, including end-to-end simulation for the entire robotics and automation pipeline, saving time and cost. We announced 2 new Omniverse cloud services for automotive, digitalization available on Microsoft Azure, a virtual factory simulation engine and autonomous vehicle simulation engine.

我們繼續在 Omniverse 上取得進展，這是我們用於設計、建構和操作 3D 虛擬世界的軟體平台。梅賽德斯-奔馳正在使用 Omniverse 支援的數位孿生來規劃、設計、建造和營運其製造和組裝設施，幫助其提高效率並減少缺陷。 [Axon] 也將 Omniverse 納入其製造流程，包括整個機器人和自動化管道的端到端仿真，從而節省時間和成本。我們宣布了 2 項新的 Omniverse 汽車雲端服務，可在 Microsoft Azure 上使用數位化、虛擬工廠模擬引擎和自動駕駛汽車模擬引擎。

Moving to automotive. Revenue was $261 million, up 3% sequentially and up 4% year-on-year, primarily driven by continued growth in self-driving platforms based on NVIDIA DRIVE Orin SoC and the ramp of AI cockpit solutions with global OEM customers. We extended our automotive partnership with Foxconn to include NVIDIA DRIVE Thor, our next-generation automotive SoC. Foxconn has become the ODM for EVs. Our partnership provides Foxconn with a standard AV sensor and computing platform for their customers to easily build a state-of-an-art safe and secure software-defined car.

轉向汽車領域。營收為 2.61 億美元，季增 3%，年增 4%，主要得益於基於 NVIDIA DRIVE Orin SoC 的自動駕駛平台的持續成長以及全球 OEM 客戶的 AI 座艙解決方案的不斷增長。我們擴大了與富士康的汽車合作關係，納入了我們的下一代汽車 SoC NVIDIA DRIVE Thor。富士康已成為電動車的 ODM。我們的合作夥伴關係為富士康提供了標準的 AV 感測器和運算平台，讓其客戶輕鬆建構最先進的安全可靠的軟體定義汽車。

Now we're going to move to the rest of the P&L. GAAP gross margin expanded to 74% and non-GAAP gross margin to 75%, driven by higher data center sales and lower net inventory reserves, including a 1 percentage point benefit from the release of previously reserved inventory related to the Ampere GPU architecture products. Sequentially, GAAP operating expenses were up 12% and non-GAAP operating expenses were up 10%, primarily reflecting increased compensation and benefits.

現在我們將討論損益表的其餘部分。受資料中心銷售額增加和淨庫存儲備減少的推動，GAAP 毛利率擴大至74%，非GAAP 毛利率擴大至75%，其中包括與Ampere GPU 架構產品相關的先前保留庫存的釋放，帶來1 個百分點的收益。隨後，GAAP 營運費用增加了 12%，非 GAAP 營運費用增加了 10%，主要反映了薪資和福利的增加。

Let me turn to the fourth quarter of fiscal 2024. Total revenue is expected to be $20 billion, plus or minus 2%. We expect strong sequential growth to be driven by data center with continued strong demand for both compute and networking. Gaming will likely decline sequentially as it is now more aligned with notebook seasonality. GAAP and non-GAAP gross margins are expected to be 74.5% and 75.5%, respectively, plus or minus 50 basis points. GAAP and non-GAAP operating expenses are expected to be approximately $3.17 billion and $2.2 billion, respectively. GAAP and non-GAAP Other income and expenses are expected to be an income of approximately $200 million, excluding gains and losses from nonaffiliated investments. GAAP and non-GAAP tax rates are expected to be 15%, plus or minus 1%, excluding any discrete items. Further financial information are included in the CFO commentary and other information available on our IR website.

讓我談談2024財年第四季。總營收預計為200億美元，上下浮動2%。我們預計強勁的環比成長將由資料中心以及對運算和網路的持續強勁需求推動。遊戲可能會連續下降，因為它現在更符合筆記型電腦的季節性。 GAAP 和非 GAAP 毛利率預計分別為 74.5% 和 75.5%，上下浮動 50 個基點。 GAAP 和非 GAAP 營運費用預計分別約為 31.7 億美元和 22 億美元。 GAAP 和非 GAAP 其他收入和支出預計約為 2 億美元，不包括非關聯投資的損益。 GAAP 和非 GAAP 稅率預計為 15%，上下浮動 1%（不包括任何離散項目）。更多財務資訊包含在 CFO 評論和我們的 IR 網站上提供的其他資訊中。

In closing, let me highlight some upcoming events for the financial community. We will attend the UBS Global Technology Conference in Scottsdale, Arizona on November 28, the Wells Fargo TMT Summit in Rancho Palos Verdes, California on November 29; the Arete Virtual Tech Conference on December 7 and the JPMorgan Healthcare Conference in San Francisco on January 8. Our earnings call to discuss the results of our fourth quarter and fiscal 2024 is scheduled for Wednesday, February 21.

最後，讓我強調一下金融界即將發生的一些事件。我們將參加11月28日在亞利桑那州斯科茨代爾舉行的瑞銀全球科技大會，以及11月29日在加州蘭喬帕洛斯維迪斯舉行的富國銀行TMT高峰會； 12 月 7 日舉行的 Arete 虛擬技術會議和 1 月 8 日在舊金山舉行的摩根大通醫療保健會議。我們定於 2 月 21 日星期三召開財報電話會議，討論第四季度和 2024 財年的業績。

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

我們現在開始提問。接線員，請您投票提問好嗎？

(Operator Instructions) Your first question comes from the line of Vivek Arya of Bank of America.

（操作員說明）您的第一個問題來自美國銀行的 Vivek Arya。

Just, Colette, wanted to clarify what China contributions are you expecting in Q4? And then Jensen, the main question is for you. Where do you think we are in the adoption curve in terms of your shipments into the generative AI market? Because when I just look at the trajectory of your data center, its growth, it will be close to nearly 30% of all the spending in data center next year. So what metrics are you keeping an eye on to inform you that you can continue to grow? Just where are we in the adoption curve of your products into the generative AI market?

只是，科萊特想澄清一下您期望中國在第四季做出哪些貢獻？然後詹森，主要問題是問你的。您認為就生成型人工智慧市場的出貨量而言，我們處於採用曲線的哪個位置？因為當我看一下資料中心的發展軌跡時，它的成長將接近明年資料中心所有支出的近 30%。那麼，您正在關注哪些指標來告知可以繼續成長呢？你們的產品進入生成人工智慧市場的採用曲線處於什麼位置？

So first, let me start with your question, Vivek, on export controls and the impact that we are seeing in our Q4 outlook and guidance that we provided. We had seen historically over the last several quarters that China and some of the other impacted destinations to be about 20% to 25% of our data center revenue. We are expecting in our guidance for that to decrease substantially as we move into Q4.

首先，讓我從你的問題開始，Vivek，關於出口管制以及我們在第四季度展望和我們提供的指導中看到的影響。從歷史上看，過去幾個季度，中國和其他一些受影響的目的地約占我們資料中心收入的 20% 至 25%。我們預計，隨著進入第四季度，這一數字將大幅下降。

The export controls will have a negative effect on our China business, and we do not have good visibility into the magnitude of that impact even over the long term. We are, though, working to expand our data center product portfolio to possibly offer new regulation-compliant solutions that do not require a license. These products, they may become available in the next coming months. However, we don't expect their contribution to be material or meaningful as a percentage of the revenue in Q4.

出口管制將對我們的中國業務產生負面影響，即使從長遠來看，我們也無法清楚地了解這種影響的嚴重程度。不過，我們正在努力擴展我們的資料中心產品組合，以可能提供不需要許可證的新的符合法規的解決方案。這些產品可能會在未來幾個月內上市。然而，我們預計他們的貢獻佔第四季度收入的百分比不會很大或有意義。

Generative AI is the largest TAM expansion of software and hardware that we've seen in several decades. The -- at the core of it, what's really exciting is that what was largely a retrieval based computing approach, almost everything that you do is retrieved off of storage somewhere, has been augmented now, added with a generative method, and it's changed almost everything. You could see that text to text, text to image, text to video, text to 3D, text to protein, text to chemicals, these are things that were processed and typed in by humans in the past, and these are now generative approaches.

生成式人工智慧是我們幾十年來見過的最大的軟體和硬體 TAM 擴充。其核心，真正令人興奮的是，主要是一種基於檢索的計算方法，幾乎所有你所做的事情都是從某個地方的存儲中檢索出來的，現在已經得到了增強，添加了生成方法，並且它幾乎發生了變化一切。你可以看到文字到文字、文字到圖像、文字到影片、文字到 3D、文字到蛋白質、文字到化學物質，這些是過去由人類處理和輸入的東西，而現在這些都是生成方法。

The way that we access data is changed. It used to be based on explicit queries. It is now based on natural language queries, intention queries, semantic queries. And so we're excited about the work that we're doing with SAP and Dropbox and many others that you're going to hear about.

我們存取資料的方式發生了變化。它曾經基於顯式查詢。現在是基於自然語言查詢、意圖查詢、語意查詢。因此，我們對與 SAP 和 Dropbox 以及您將聽到的許多其他合作夥伴所做的工作感到非常興奮。

And 1 of the areas that is really impactful is the software industry, which is about $1 trillion or so has been building tools that are manually used over the last couple of decades. And now there's a whole new segment of software called copilots and assistants. Instead of manually used, these tools will have copilots to help you use it. And so instead of licensing software, we will continue to do that, of course, but we will also hire copilots and assistants to help us use the -- use the software.

真正有影響力的領域之一是軟體產業，在過去的幾十年裡，該產業花費了約 1 兆美元來建立手動使用的工具。現在出現了一個全新的軟體部分，稱為副駕駛和助理。這些工具將有副駕駛來幫助您使用，而不是手動使用。因此，當然，我們將繼續這樣做，而不是許可軟體，但我們還將僱用副駕駛和助理來幫助我們使用該軟體。

We'll connect all of these copilots and assistants into teams of AIs, which is going to be the modern version of software, modern version of enterprise business software. And so the transformation of software and the way that software is done is driving the hardware underneath. And you could see that it's transforming hardware in 2 ways. One is something that's largely independent of generative AI. There's 2 trends. One is related to accelerated computing. General purpose computing is too wasteful of energy and cost. And now that we have much, much better approaches called accelerated computing, you could save an order of magnitude of energy. You could save an order of magnitude of time or you can save an order of magnitude of cost by using acceleration. And so accelerated computing is transitioning, if you will, general purpose computing into this new approach.

我們將把所有這些副駕駛和助理連接成人工智慧團隊，這將是現代版本的軟體、現代版本的企業業務軟體。因此，軟體和軟體完成方式的轉變正在驅動底層的硬體。您可以看到它以兩種方式改變硬體。其中之一在很大程度上獨立於生成式人工智慧。有2個趨勢。一是與加速計算有關。通用計算太浪費能源和成本了。現在我們有了更好的方法，稱為加速計算，您可以節省一個數量級的能源。透過使用加速，您可以節省一個數量級的時間，也可以節省一個數量級的成本。因此，如果您願意的話，加速計算正在將通用計算轉變為這種新方法。

And that has been augmented by a new class of data centers. This is the traditional data centers that you were just talking about, where we represent about 1/3 of that. But there's a new class of data centers. And this new class of data centers, unlike the data centers of the past where you have a lot of applications running used by a great many people that are different tenants that are using the same infrastructure and that data center stores a lot of files, these new data essentials are very few applications, if not, one application used by basically one tenant. And it processes data. It trains models, and it generates tokens. It generates AI. And we call these new data centers AI factories.

新型資料中心進一步增強了這一點。這就是你剛才談到的傳統資料中心，我們大約佔其中的1/3。但出現了一類新的資料中心。這種新型資料中心與過去的資料中心不同，過去的資料中心有很多應用程式運行，由許多人使用，這些人是使用相同基礎設施的不同租戶，並且資料中心儲存大量文件，這些新的資料要素是很少的應用程序，如果不是的話，基本上一個租戶使用一個應用程式。它處理數據。它訓練模型並產生令牌。它產生人工智慧。我們將這些新的資料中心稱為人工智慧工廠。

We're seeing AI factories being built out everywhere in just about every country. And so if you look at the way -- where we are in the expansion, the transition into this new computing approach, the first wave, you saw with large language model start-ups, generative AI start-ups and consumer Internet companies. And we're in the process of ramping that. Meanwhile, while that's being ramped, you see that we're starting to partner with enterprise software companies who would like to build chatbots and copilots and assistants to augment the tools that they have on their platforms.

我們看到幾乎每個國家都在建造人工智慧工廠。因此，如果你看看我們在擴張、過渡到這種新的計算方法（第一波浪潮）中所處的位置，你會看到大型語言模型新創公司、生成式人工智慧新創公司和消費者互聯網公司。我們正在加大力度。同時，在這種情況不斷增加的同時，您會看到我們開始與企業軟體公司合作，他們希望建立聊天機器人、副駕駛和助理，以增強他們平台上的工具。

You're seeing GPU-specialized CSPs cropping up all over the world, and they're dedicated to doing really one thing, which is processing AI. You're seeing sovereign AI infrastructures, people -- countries that now recognize that they have to utilize their own data, keep their own data, keep their own culture, process that data and develop their own AI. You see that in India, several -- about a year ago in Sweden. You're seeing it in Japan. Last week, a big announcement in France.

您會看到 GPU 專用的 CSP 在世界各地湧現，它們真正致力於做一件事，那就是處理人工智慧。你會看到主權人工智慧基礎設施、人民——國家現在認識到他們必須利用自己的數據、保留自己的數據、保留自己的文化、處理數據並開發自己的人工智慧。你可以在印度看到這樣的情況──大約一年前在瑞典。你在日本就看到了。上週，法國發布了一項重大公告。

But the number of sovereign AI clouds that are being built is really quite significant. And my guess is that almost every major region will have -- and surely, every major country will have their own AI cloud. And so I think you're seeing just new developments as the generative AI wave propagates through every industry, every company, every region. And so we're at the beginning of this inflection, this computing transition.

但正在建置的主權人工智慧雲端的數量確實相當可觀。我的猜測是，幾乎每個主要地區都會有——當然，每個主要國家都會有自己的人工智慧雲。因此，我認為，隨著生成式人工智慧浪潮在每個產業、每個公司、每個地區傳播，你會看到新的發展。因此，我們正處於這種拐點、這種計算轉變的開始。

Your next question comes from the line of Aaron Rakers of Wells Fargo.

你的下一個問題來自富國銀行的 Aaron Rakers。

Yes. I wanted to ask about kind of the networking side of the business. Given the growth rates that you've now cited, I think it's 155% year-over-year and strong growth sequentially, it looks like that business is like almost approaching a $2.5 billion to $3 billion quarterly level. I'm curious of how you see Ethernet involved -- evolving and maybe how you would characterize your differentiation of Spectrum-X relative to the traditional Ethernet stack as we start to think about that becoming part of the networking narrative above and maybe beyond just InfiniBand as we look into next year.

是的。我想詢問有關業務網絡方面的情況。鑑於您現在引用的成長率，我認為同比增長率為 155%，並且連續強勁增長，看起來該業務幾乎接近 25 億至 30 億美元的季度水平。我很好奇您如何看待乙太網路的發展，也許您會如何描述 Spectrum-X 相對於傳統乙太網路堆疊的差異化，因為我們開始考慮它成為上述網路敘事的一部分，甚至可能超越 InfiniBand當我們展望明年時。

Yes. Thanks for the question. Our networking business is already at a $10 billion plus run rate. And it's going to get much larger. And as you mentioned, we added a new networking platform to our networking business recently. The vast majority of the dedicated large-scale AI factories standardized on InfiniBand. And the reason for that is not only because of its data rate and not only just the latency, but the way that it moves traffic around the network is really important. The way that you process AI and the -- a multi-tenant hyperscale Ethernet environment, the traffic pattern is just radically different. And with InfiniBand and with software-defined networks, we could do congestion control, adaptive routing, performance isolation and noise isolation, not to mention, of course, the data rate and the low latency that -- and the very low overhead of InfiniBand that's natural part of InfiniBand.

是的。謝謝你的提問。我們的網路業務運行速度已經超過 100 億美元。而且它會變得更大。正如您所提到的，我們最近在我們的網路業務中新增了一個新的網路平台。絕大多數專用大型AI工廠都在InfiniBand上進行標準化。原因不僅是因為它的數據速率和延遲，而且它在網路中移動流量的方式非常重要。處理人工智慧的方式和多租戶超大規模乙太網路環境的流量模式完全不同。借助 InfiniBand 和軟體定義網絡，我們可以進行擁塞控制、自適應路由、效能隔離和噪音隔離，當然，更不用說資料速率和低延遲，以及 InfiniBand 的極低開銷InfiniBand 的自然組成部分。

And so InfiniBand is not so much just a network. It's also a computing fabric. We put a lot of software-defined capabilities into the fabric, including computation. We'll do floating point calculations and computation right on the switch and right in the fabric itself. And so that's the reason why that difference in Ethernet versus InfiniBand where InfiniBand versus Ethernet for AI factories is so dramatic.

因此，InfiniBand 不僅僅是一個網路。它也是一種計算結構。我們在結構中加入了許多軟體定義的功能，包括計算。我們將在交換器上和結構本身中進行浮點計算和計算。這就是為什麼乙太網路與 InfiniBand 之間的差異如此巨大的原因，其中 InfiniBand 與人工智慧工廠的乙太網路之間的差異如此巨大。

And the difference is profound, and the reason for that is because you've just invested in a $2 billion infrastructure for AI factories. A 20%, 25%, 30% difference in overall effectiveness, especially as you scale up, is measured in hundreds of millions of dollars of value. And if you were renting that infrastructure over the course of 4 or 5 years, it really, really adds up. And so InfiniBand's value proposition is undeniable for AI factories.

差異是深遠的，原因是你剛剛投資了 20 億美元的人工智慧工廠基礎設施。整體效率 20%、25%、30% 的差異（尤其是當規模擴大時）會帶來數億美元的價值。如果您在 4 到 5 年內租用該基礎設施，那麼它確實會增加。因此，InfiniBand 的價值主張對於人工智慧工廠來說是不可否認的。

However, as we move AI into enterprise, this is enterprise computing where we'd like to enable every company to be able to build their own custom AIs. We're building custom AIs in our company based on our proprietary data, our proprietary type of skills. For example, the -- recently, we spoke about one of the models that we're creating. It's called ChipNeMo. We're building many others. There'll be tens, hundreds of custom AI models that we create inside our company.

然而，當我們將人工智慧引入企業時，這就是企業運算，我們希望使每個公司都能夠建立自己的客製化人工智慧。我們正在公司根據我們的專有數據和專有技能類型建立客製化人工智慧。例如，最近，我們談到了我們正在創建的模型之一。它的名字叫ChipNeMo。我們正在建造許多其他的。我們將在公司內部創建數十、數百個自訂人工智慧模型。

And our company is -- for all of our employee use, that doesn't have to be as high performance as the AI factories we use to train the models. And so we would like the AI to be able to run an Ethernet environment. And so what we've done is we invented this new platform that extends Ethernet. It doesn't replace the Ethernet. It's 100% compliant with Ethernet, and it's optimized for east-west traffic, which is where the computing fabric is. It adds to Ethernet with an end-to-end solution with BlueField as well as our Spectrum switch that allows us to perform some of the capabilities that we have in InfiniBand, not all but some, and we achieve excellent results.

我們公司對於所有員工的使用來說，不必像我們用來訓練模型的人工智慧工廠那樣高效能。因此我們希望人工智慧能夠運行乙太網路環境。因此，我們所做的就是發明了這個擴展乙太網路的新平台。它不會取代乙太網路。它 100% 符合乙太網路標準，並且針對東西向流量（計算結構所在的位置）進行了最佳化。它透過 BlueField 以及我們的 Spectrum 交換器為乙太網路添加了端對端解決方案，使我們能夠執行 InfiniBand 中的一些功能（不是全部，而是部分功能），並且我們取得了出色的結果。

And the way we go to market is we go to market with our large enterprise partners who already offer our computing solution. And so HP, Dell and Lenovo have the NVIDIA AI stack, the NVIDIA AI Enterprise software stack. And now they integrate with BlueField as well as bundle -- take to market their Spectrum switch, and they'll be able to offer enterprise customers all over the world with their vast sales force and vast network of resellers the -- in a fully integrated, if you will, fully optimized at least end-to-end AI solution. And so that's basically bringing AI to Ethernet for the world's enterprise.

我們進入市場的方式是與已經提供我們的計算解決方案的大型企業合作夥伴一起進入市場。因此，惠普、戴爾和聯想擁有 NVIDIA AI 堆疊、NVIDIA AI Enterprise 軟體堆疊。現在，他們與 BlueField 以及捆綁產品整合——將他們的 Spectrum 交換器推向市場，他們將能夠通過其龐大的銷售隊伍和龐大的經銷商網絡為世界各地的企業客戶提供——以完全集成的方式如果願意的話，至少全面優化端到端的人工智慧解決方案。這基本上就是為全球企業將人工智慧引入乙太網路。

Your next question comes from the line of Joe Moore of Morgan Stanley.

你的下一個問題來自摩根士丹利的喬摩爾。

Great. I wondered if you could talk a little bit more about Grace Hopper and how you see the ability to leverage kind of the microprocessor, how you see that as a TAM expander. And what applications do you see using Grace Hopper versus more traditional H100 applications?

偉大的。我想知道您是否可以多談談 Grace Hopper，以及您如何看待利用微處理器的能力，以及如何將其視為 TAM 擴展器。與更傳統的 H100 應用程式相比，您認為使用 Grace Hopper 的應用程式有哪些？

Yes. Thanks for your question. Grace Hopper is in production in high-volume production now. We're expecting next year just with all of the design wins that we have in high-performance computing and AI, AI infrastructures. We are on a very, very fast ramp with our first data center CPU to a multibillion-dollar product line. This is going to be a very large product line for us.

是的。謝謝你的提問。 Grace Hopper 現已投入大批量生產。我們預計明年我們將在高效能運算和人工智慧、人工智慧基礎設施方面取得所有設計成果。我們正以非常非常快的速度從第一個資料中心 CPU 發展到價值數十億美元的產品線。這對我們來說將是一個非常龐大的產品線。

The capability of Grace Hopper is really quite spectacular. It has the ability to create computing nodes that simultaneously has very fast memory as well as very large memory. In the areas of vector databases or semantic search, what is called RAG, retrieval augmented generation, so that you could have a generative AI model, be able to refer to proprietary data or factful data before it generates a response, that data is quite large.

格蕾絲·霍珀的能力確實相當驚人。它能夠創建同時具有非常快的記憶體和非常大的記憶體的計算節點。在向量資料庫或語意搜尋領域，所謂的RAG，檢索增強生成，這樣你就可以擁有一個生成式人工智慧模型，能夠在生成回應之前參考專有數據或事實數據，數據相當大。

And you could also have applications or generative models where the context length is very high. You basically stored an entire book into its system memory before you ask the questions. And so the context length could be quite large. This way, the generative models have the ability to still be able to naturally interact with you on one hand; on the other hand, be able to refer to factual data, proprietary data or domain-specific data, your data and be contextually relevant and reduce hallucination. And so the -- that particular use case, for example, is really quite fantastic for Grace Hopper.

您還可以擁有上下文長度非常高的應用程式或生成模型。在提問之前，您基本上將整本書儲存到系統記憶體中。因此上下文長度可能會相當大。這樣，生成模型一方面仍然能夠與你自然地互動；另一方面，它仍然能夠與你互動。另一方面，能夠引用事實數據、專有數據或特定領域的數據、您的數據並與上下文相關並減少幻覺。因此，例如，這個特定的用例對於 Grace Hopper 來說確實非常出色。

It also serves the customers that really care to have a different CPU than x86. Maybe it's European supercomputing centers or European companies who would like to build up their own ARM ecosystem and like to build up a stack or CSPs that have decided that they would like to pivot to ARM because their own custom CPUs are based on ARM. There are a variety of different reasons that drives the success of Grace Hopper, but we're off to just an extraordinary start. This is a home run product.

它還為那些真正想要擁有不同於 x86 的 CPU 的客戶提供服務。也許是歐洲超級運算中心或歐洲公司希望建立自己的 ARM 生態系統並希望建立堆疊或 CSP 決定轉向 ARM，因為他們自己的客製化 CPU 是基於 ARM 的。格蕾絲·霍珀 (Grace Hopper) 的成功有多種不同的原因，但我們只是一個非凡的開始。這是一款全壘打產品。

Your next question comes from the line of Tim Arcuri of UBS.

你的下一個問題來自瑞銀集團的提姆·阿庫裡。

I wanted to ask a little bit about the visibility that you have on revenue. I know there's a few moving parts. I guess, on one hand, the purchase commitments went up a lot again. But on the other hand, the China bands would arguably pull in when you can fill the demand beyond China. So I know we're not even into 2024 yet, and it doesn't sound like, Jensen, you think that next year would be a peak in your data center revenue, but I just wanted to sort of explicitly ask you that. Do you think that data center can grow even into 2025?

我想問一下你們的收入可見度。我知道有一些活動部件。我想，一方面，購買承諾又增加了很多。但另一方面，當你能滿足中國以外的需求時，中國樂團可能會加入。所以我知道我們還沒有進入 2024 年，詹森，你並不認為明年會是你的資料中心收入的峰值，但我只是想明確地問你這個問題。您認為資料中心能夠發展到 2025 年嗎？

Absolutely believe that data center can grow through 2025. And there are, of course, several reasons for that. We are expanding our supply quite significantly. We have already [wanted] the broadest and largest and most capable supply chain in the world.

絕對相信資料中心能夠在 2025 年之前實現成長。當然，這有幾個原因。我們正在大幅擴大供應。我們已經[想要]世界上最廣泛、最大、最有能力的供應鏈。

Remember, people think that the GPU is a chip, but the HGX, H100, the Hopper HGX, has 35,000 parts. It weighs 70 pounds. Eight of the chips are Hopper. The other 35,000 are not. It is -- it has -- even its passive components are incredible, high-voltage parts, high-frequency parts, high-current parts. It is a supercomputer and therefore, the only way to test the supercomputer's with another supercomputer. Even the manufacturing of it is complicated. The testing of it is complicated. The shipping of it is complicated and installation is complicated. And so every aspect of our HGX supply chain is complicated.

請記住，人們認為 GPU 是一個晶片，但 HGX、H100、Hopper HGX 有 35,000 個零件。它重 70 磅。其中八個籌碼是 Hopper 的。其他 35,000 人則不然。它是——它已經——甚至它的被動元件都令人難以置信，高壓部件、高頻部件、高電流部件。它是一台超級計算機，因此是用另一台超級電腦測試超級計算機的唯一方法。就連它的製造也很複雜。它的測試很複雜。其運輸複雜，安裝複雜。因此，我們 HGX 供應鏈的各個方面都很複雜。

And the remarkable team that we have here has really scaled out the supply chain incredibly, not to mention all of our HGXs are connected with NVIDIA networking, and the networking, the transceivers, the NICs, the cables, the switches, the amount of complexity there is just incredible. And so I'm just -- first of all, I'm just super proud of the team for scaling up this incredible supply chain. We are absolutely world class.

我們這裡的出色團隊確實令人難以置信地擴展了供應鏈，更不用說我們所有的 HGX 都與 NVIDIA 網路連接，而且網路、收發器、NIC、電纜、交換器的複雜性簡直令人難以置信。所以我只是 - 首先，我為團隊擴大了這個令人難以置信的供應鏈感到非常自豪。我們絕對是世界一流的。

But meanwhile, we're adding new customers and new products. So we have new supply. We have new customers, as I was mentioning earlier. Different regions are standing up GPU specialist clouds, sovereign AI clouds coming up from all over the world as people realize that they can't afford to export their country's knowledge, their country's culture for somebody else to then resell AI back to them. They have to -- they should. They have the skills and fairly with us, in combination, we can help them do that, build up their national AI. And so the first thing that they have to do is create their AI cloud, national AI cloud.

但同時，我們正在增加新客戶和新產品。所以我們有新的供應。正如我之前提到的，我們有新客戶。不同地區正在建立 GPU 專業雲、主權人工智慧雲，這些雲來自世界各地，因為人們意識到他們無力將自己國家的知識和文化出口給其他人，然後再將人工智慧轉售給他們。他們必須——他們應該。他們擁有技能，並且與我們公平地結合起來，我們可以幫助他們做到這一點，建立他們的國家人工智慧。所以他們要做的第一件事就是創建他們的人工智慧雲，國家人工智慧雲。

You're also seeing us now growing into enterprise. The enterprise market has 2 paths. One path -- or if I could say 3 paths. The first path, of course, is the off-the-shelf AI. And there are -- of course, ChatGPT is a fabulous off-the-shelf AI. There'll be others. There's also a proprietary AI because these software companies like ServiceNow and SAP, and there are many, many others, can't afford to have their company's intelligence be outsourced to somebody else. And they are about building tools. And on top of their tools, they should build custom and proprietary and domain-specific copilots and assistants that they can then rent to their customer base.

您也看到我們現在正在成長為企業。企業市場有兩條路徑。一條路徑——或者如果我可以說 3 條路徑的話。第一條路當然是現成的人工智慧。當然，ChatGPT 是一個出色的現成人工智慧。還會有其他人。還有一個專有的人工智慧，因為像 ServiceNow 和 SAP 這樣的軟體公司，還有很多其他公司，無法負擔將他們公司的智慧外包給其他人的費用。它們是關於建造工具的。在他們的工具之上，他們應該建立客製化的、專有的和特定領域的副駕駛和助理，然後將其出租給客戶群。

This is -- they're sitting on a gold mine. Almost every major tools company in the world is sitting on gold mine, and they recognize that. They have to go build their own custom AIs. We have a new service called an AI Foundry, where we leverage NVIDIA's capabilities to be able to serve them in that.

這是——他們坐在一座金礦上。世界上幾乎所有主要工具公司都坐擁金礦，他們也意識到這一點。他們必須去建立自己的客製化人工智慧。我們有一項名為 AI Foundry 的新服務，我們利用 NVIDIA 的功能為他們服務。

And then the next one is enterprises building their own custom AIs, their own custom chatbots, their own custom RAGs. And this capability is spreading all over the world. And the way that we're going to serve that marketplace is with the entire stacks of systems, which includes our compute, our networking and our switches running our software stack called NVIDIA AI Enterprise, taking it through our market partners, HP, Dell and Lenovo, so on and so forth.

接下來是企業建構自己的客製化人工智慧、自己的客製化聊天機器人、自己的客製化 RAG。而這種能力正在全世界傳播。我們為該市場提供服務的方式是使用整個系統堆疊，其中包括我們的計算、網路和運行我們稱為 NVIDIA AI Enterprise 的軟體堆疊的交換機，並透過我們的市場合作夥伴 HP、Dell 和聯想等等。

And so we're just -- we're seeing the waves of generative AI starting from the start-ups and CSPs moving to consumer Internet companies, moving to enterprise software platforms, moving to enterprise companies. And then -- and ultimately, one of the areas that you guys have seen us spend a lot of energy on has to do with industrial generative AI. This is where NVIDIA AI and NVIDIA Omniverse comes together. And that is a really, really exciting work. And so I think the -- we're at the beginning of a, basically across the board, industrial transition to generative AI, to accelerated computing. This is going to affect every company, every industry, every country.

所以我們只是——我們看到生成式人工智慧的浪潮從新創公司和通訊服務供應商開始轉向消費者網路公司，轉向企業軟體平台，轉向企業公司。然後，最終，你們看到我們花費大量精力的領域之一與工業生成人工智慧有關。這就是 NVIDIA AI 和 NVIDIA Omniverse 的結合之處。這是一項非常非常令人興奮的工作。所以我認為，我們正處於向生成式人工智慧和加速運算的工業轉型的開始，基本上是全面的。這將影響每家公司、每個產業、每個國家。

Your next question comes from the line of Toshiya Hari of Goldman Sachs.

你的下一個問題來自高盛的 Toshiya Hari。

I wanted to clarify something with Colette real quick, and then I had a question for Jensen as well. Colette, you mentioned that you'll be introducing regulation-compliant products over the next couple of months, yet the contribution to Q4 revenue should be relatively limited. Is that a timing issue? And could it be a source of reacceleration in growth for data center in April and beyond? Or are the price points such that the contribution to revenue going forward should be relatively limited?

我想盡快向 Colette 澄清一些事情，然後我也向 Jensen 提出了一個問題。 Colette，您提到您將在接下來的幾個月內推出符合法規的產品，但對第四季度收入的貢獻應該相對有限。這是時間問題嗎？這是否會成為 4 月及以後資料中心成長重新加速的來源？或者價格點對未來收入的貢獻應該相對有限？

And then the question for Jensen, the AI foundry service announcement from last week, I just wanted to ask about that and hopefully have you expand on it. How is the monetization model going to work? Is it primarily services and software revenue? How should we think about the long-term opportunity set? And is this going to be exclusive to Microsoft? Or do you have plans to expand to other partners as well?

然後是 Jensen 的問題，上週發布的人工智慧代工服務公告，我只是想問一下這個問題，希望你能對此進行擴展。獲利模式將如何運作？主要是服務和軟體收入嗎？我們該如何思考長期機會？這是微軟獨有的嗎？或者您還計劃擴展到其他合作夥伴嗎？

Thanks, Toshiya, on the question regarding potentially new products that we could provide to our China customers. It's a significant process to both design and develop these new products. As we discussed, we're going to make sure that we are in full discussions with the U.S. government of our intent in these products as well. Given our state about where we are in the quarter, we're already several weeks into the quarter. That's just going to take some time for us to go through and discussing with our customers their needs and desires of these 2 products that we have.

謝謝 Toshiya，關於我們可以向中國客戶提供的潛在新產品的問題。設計和開發這些新產品都是一個重要的過程。正如我們所討論的，我們將確保我們也與美國政府充分討論我們對這些產品的意圖。鑑於我們本季的狀況，本季已經過去幾週了。我們需要一些時間來與客戶討論並討論他們對我們這兩種產品的需求和願望。

Moving forward, whether that's medium term or long term, it's just hard to say both the ideas of what we can produce with the U.S. government and with the interest of our China customers. So we stay still focused on finding that right balance for our China customers, but it's hard to say at this time.

展望未來，無論是中期或長期，很難說我們能夠與美國政府一起生產什麼，同時也符合我們中國客戶的利益。因此，我們仍然專注於為中國客戶找到適當的平衡，但目前還很難說。

Toshiya, thanks for the question. There is a glaring opportunity in the world for AI foundry, and it makes so much sense. First, every company has its core intelligence. It makes up our company, our data, our domain expertise. In the case of many companies, we create tools. And most of the software companies in the world are tool platforms. And those tools are used by people today. And in the future, it's going to be used by people augmented with a whole bunch of AIs that we hire.

俊哉，謝謝你的提問。人工智慧代工廠面臨巨大的機遇，而且意義重大。首先，每個公司都有自己的核心智力。它構成了我們的公司、我們的數據和我們的領域專業知識。對於許多公司來說，我們創建工具。而世界上大部分的軟體公司都是工具平台。如今人們正在使用這些工具。未來，它將被我們僱用的一大堆人工智慧增強的人們使用。

And these AI platforms just got to go across the world, and you'll see. And we've already announced a few, SAP, ServiceNow, Dropbox, Getty. Many others are coming. And the reason for that is because they have their own proprietary AI. They want their own proprietary AI. They can't afford to outsource their intelligence and hand out their data and hand out their flywheel for other companies to build the AI for them. And so they come to us.

這些人工智慧平台必須走向世界各地，你就會看到。我們已經宣布了一些，SAP、ServiceNow、Dropbox、Getty。還有很多人也來了。原因是他們擁有自己專有的人工智慧。他們想要自己專有的人工智慧。他們無法承擔外包情報、分發數據和飛輪的費用，讓其他公司為他們建立人工智慧。所以他們來找我們。

We have several things that are really essential in the foundry just as TSMC is a foundry. You have to have AI technology. And as you know, we have just an incredible depth of AI capability, AI technology capability. And then second, you have to have the best practice, known practice, the skills of processing data through the invention of AI models to create AIs that are guardrails, fine-tuned, so on and so forth -- that are safe, so on and so forth.

就像台積電是一家代工廠一樣，我們有幾件事對代工廠來說是非常重要的。你必須擁有人工智慧技術。如您所知，我們擁有令人難以置信的深度人工智慧能力、人工智慧技術能力。其次，你必須擁有最佳實踐、已知實踐、透過發明人工智慧模型來處理資料的技能，以創建具有護欄、微調等等的人工智慧——安全的等等。等等。

And the third thing is you need factories, and that's what DGX Cloud is. Our AI models are called AI Foundations. Our process, if you will, our CAD system for creating AIs are called NeMo, and they run on NVIDIA's factories we call DGX Cloud. Our monetization model is that, with each one of our partners, they rent a sandbox on DGX Cloud where we work together. They bring their data. They bring their domain expertise. We'd bring our researchers and engineers. We help them build their custom AI. We help them make that custom AI incredible.

第三件事是你需要工廠，這就是 DGX Cloud。我們的人工智慧模型稱為人工智慧基礎。如果你願意的話，我們用來建立 AI 的 CAD 系統稱為 NeMo，它們在 NVIDIA 的工廠（我們稱為 DGX Cloud）上運作。我們的獲利模式是，與我們的每位合作夥伴一起在 DGX Cloud 上租用一個沙箱，我們在那裡一起工作。他們帶來了數據。他們帶來了他們的領域專業知識。我們會帶來我們的研究人員和工程師。我們幫助他們建立客製化人工智慧。我們幫助他們打造令人難以置信的客製化人工智慧。

Then that customer AI becomes theirs, and they deploy it on a run time that is enterprise grade, enterprise optimized or outperformance optimized, runs across everything NVIDIA. We have a giant installed base in the cloud on-prem anywhere. And it's secure, securely patched, constantly patched and optimized and supported. And we call that NVIDIA AI Enterprise.

然後，客戶 AI 就成為他們的了，他們將其部署在企業級、企業優化或效能優化的運行時上，並在 NVIDIA 的所有產品上運行。我們在任何地方的雲端都擁有龐大的安裝基礎。而且它是安全的、安全修補的、不斷修補、優化和支援的。我們稱之為 NVIDIA AI Enterprise。

NVIDIA AI Enterprise is $4,500 per GP per year. That's our business model. Our business model is basically a license. Our customers then, with that basic license, can build their monetization model on top of. In a lot of ways, we're wholesale. They become retail. They could have a per -- they could have subscription license based. They could per instance or they could do per usage. There's a lot of different ways that they could take to create their own business model, but ours is basically like a software license, like an operating system. And so our business model is help you create your custom models. You run those custom models on NVIDIA AI Enterprise. And it's off to a great start. NVIDIA AI Enterprise is going to be a very large business for us.

NVIDIA AI Enterprise 的價格為每位 GP 每年 4,500 美元。這就是我們的商業模式。我們的商業模式基本上是一個許可證。然後，我們的客戶憑藉該基本許可證，可以在此基礎上建立他們的獲利模型。在很多方面，我們都是批發的。他們成為零售業。他們可以有一個基於訂閱的許可證。他們可以按實例執行，也可以按使用執行。他們可以採取很多不同的方式來創建自己的商業模式，但我們的方式基本上就像軟體許可證，就像作業系統一樣。因此，我們的商業模式是幫助您建立自訂模型。您可以在 NVIDIA AI Enterprise 上執行這些自訂模型。這是一個好的開始。 NVIDIA AI Enterprise 對我們來說將是一項非常龐大的業務。

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

你的下一個問題來自伯恩斯坦研究中心的史黛西·拉斯貢（Stacy Rasgon）。

Colette, I wanted to know, if it weren't for the China restrictions, would the Q4 guide have been higher or are you supply constrained in just reshipping stuff that would have gone to China elsewhere? And I guess along those lines, can you give us a feeling for where your lead times are right now in data center? And does the China redirection such as this, is it lowering those lead times because you've got parts that are sort of immediately available to ship?

科萊特，我想知道，如果沒有中國的限制，第四季度的指導價會更高嗎？或者你們的供應是否受到限制，只能將本來可以運往中國其他地方的東西轉運？我想沿著這些思路，您能否讓我們了解您目前在資料中心的交貨時間？像這樣的中國重定向是否會縮短交貨時間，因為您有可以立即發貨的零件？

Yes. Stacy, let me see if I can help you understand. Yes, they are still situations where we are working on both improving our supply each and every quarter. We've done a really solid job of ramping every quarter, which has defined our revenue.

是的。史黛西，讓我看看能否幫助你理解。是的，我們仍然在每個季度努力改善供應。我們在每個季度的成長方面做得非常紮實，這決定了我們的收入。

But with the absence of China, for our outlook for Q4, sure, there could have been some things that we are not supply constrained that we could have sold to China but we no longer can. So could our guidance have been a little higher in our Q4? Yes. We are still working on improving our supply and plan on continuing and growing all throughout next year as well towards that.

但由於中國缺席，就我們對第四季的前景而言，當然，可能有一些我們不受供應限制的東西，我們本來可以賣給中國，但現在不能了。那麼我們第四季的指導是否可以更高一點？是的。我們仍在努力改善我們的供應，並計劃在明年繼續成長並實現這一目標。

Your next question comes from the line of Matt Ramsay of TD Cowen.

您的下一個問題來自 TD Cowen 的 Matt Ramsay。

Congrats everybody on the results. Jensen, I had a two-part question for you, and it comes off of sort of one premise. And the premise is I still get a lot of questions from investors thinking about AI training as being NVIDIA's dominant domain and that somehow as inference, even large model inference takes more and more of the TAM that the market will become more competitive. You'll be less differentiated, et cetera, et cetera. So I guess the 2 parts of the question are, number one, maybe you could spend a little bit of time talking about the evolution of the inference workload as we move to LLMs and how your company is positioned for that rather than smaller model inference. And second, up until a month or 2 ago, I never really got any questions at all about the data processing piece of the AI workload. So the pieces of manipulating the data before training, between training and inference, after inference, and I think that's a large part of the workload now, maybe you could talk about how CUDA is enabling acceleration of those pieces of the workload.

祝賀大家的結果。詹森，我有一個由兩部分組成的問題要問你，它來自一個前提。前提是，我仍然收到投資者的許多問題，他們認為人工智慧訓練是NVIDIA 的主導領域，並且在某種程度上作為推理，即使是大型模型推理也會佔用越來越多的TAM，市場將變得更具競爭力。你的差異化程度將會降低，等等。所以我想問題的兩個部分是，第一，也許你可以花一點時間討論當我們轉向法學碩士時推理工作負載的演變，以及你的公司如何定位於此，而不是較小的模型推理。其次，直到一兩個月前，我從未真正收到任何關於人工智慧工作負載的資料處理部分的問題。因此，在訓練之前、訓練和推理之間、推理之後操作資料的部分，我認為這是現在工作量的很大一部分，也許您可以談談 CUDA 如何加速這些工作量。

Sure. Inference, inference is complicated. It's actually incredibly complicated. If you -- we, this quarter, announced one of the most exciting new engines, optimizing compilers called TensorRT LLM. The reception has been incredible. You go to GitHub. It's been downloaded a ton, a whole lot of stars integrated into stacks and frameworks all over the world almost instantaneously. And there are several reasons for that, obviously.

當然。推理，推理很複雜。它實際上非常複雜。如果您—我們本季宣布了最令人興奮的新引擎之一，即名為 TensorRT LLM 的最佳化編譯器。接待情況令人難以置信。你去GitHub。它已經被大量下載，大量的 star 幾乎在瞬間就整合到了世界各地的堆疊和框架中。顯然，這有幾個原因。

We could create TensorRT LLM because CUDA's programmable. If CUDA and our GPUs were not so programmable, it would really be hard for us to improve software stacks at the pace that we do. TensorRT LLM on the same GPU, without anybody touching anything, improves the performance by a factor of 2.

我們可以建立 TensorRT LLM，因為 CUDA 是可編程的。如果 CUDA 和我們的 GPU 不具備如此可程式性，我們就很難以現在的速度改進軟體堆疊。同一 GPU 上的 TensorRT LLM，無需任何人接觸任何東西，即可將效能提高 2 倍。

And then on top of that, of course, the pace of our innovation is so high. H200 increases it by another factor of 2. And so our inference performance, another way of saying inference cost, just reduced by a factor of 4 within about a year's time. And so that's really, really hard to keep up with.

當然，最重要的是，我們的創新步伐非常快。 H200 又將其提高了 2 倍。因此，我們的推理表現（推理成本的另一種說法）在大約一年的時間內降低了 4 倍。所以這真的很難跟上。

Now the reason why everybody likes our inference engine is because our installed base. And we've been dedicated to our installed base for 20 years, 20-plus years. We have an installed base that is not only largest in every single cloud. It's in every -- available from every enterprise system maker. It's used by companies of just about every industry. And any time you see an NVIDIA GPU, it runs our stack. It's architecturally compatible. It's something we've been dedicated to for a very long time. We're very disciplined about it. We make it, if you will -- architecture compatibility is job one.

現在每個人都喜歡我們的推理引擎的原因是因為我們的安裝基礎。 20 年來，我們一直致力於我們的客戶群。我們擁有的安裝基礎不僅在每個雲端中都是最大的。每個企業系統製造商都提供這種服務。幾乎每個行業的公司都在使用它。每當您看到 NVIDIA GPU 時，它都會運行我們的堆疊。它在架構上是相容的。這是我們長期以來一直致力於的事情。我們對此非常有紀律。如果你願意的話，我們做到了——架構相容性是首要任務。

And that has conveyed to the world the certainty of our platform stability. NVIDIA's platform stability, certainty is the reason why everybody builds on us first and the reason why everybody optimizes on us first. All the engineering and all the work that you do, all the invention of technologies that you build on top of NVIDIA accrues to the -- and benefits everybody that uses our GPUs, and we have such a large installed base large -- millions and millions of GPUs in cloud, 100 million GPUs from people's PCs, just about every workstation in the world, and they're all architecturally compatible.

這向世界傳達了我們平台穩定性的確定性。 NVIDIA 平台的穩定性、確定性是每個人首先在我們基礎上建置的原因，也是每個人首先在我們基礎上優化的原因。您所做的所有工程和所有工作以及您在 NVIDIA 基礎上構建的所有技術發明都會為使用我們 GPU 的每個人帶來好處，並且我們擁有如此龐大的安裝基礎 - 數以百萬計雲中的GPU 、人們PC 上的1 億個GPU、世界上幾乎每個工作站，而且它們在架構上都是相容的。

And so if you're an inference platform and you're deploying an inference application, you are basically an application provider. And as a software application provider, you're looking for a large installed base.

因此，如果您是推理平台並且正在部署推理應用程序，那麼您基本上就是一個應用程式提供者。作為軟體應用程式供應商，您正在尋找龐大的安裝基礎。

Data processing, before you could train a model, you have to curate the data. You have to dedupe the data. Maybe you have to augment the data with synthetic data. So you process the -- clean the data, align the data, normalize the data. All of that data is measured not in bytes and megabytes. It's measured in terabytes and petabytes. And the amount of data processing that you do before data engineering, before that you do training is quite significant. It could represent 30%, 40%, 50% of the amount of work that you ultimately do. In what you -- in ultimately creating a data-driven machine learning service. And so data processing is just a massive part.

資料處理，在訓練模型之前，您必須整理資料。您必須對資料進行重複資料刪除。也許您必須使用合成資料來擴充資料。因此，您需要處理——清理資料、對齊資料、標準化資料。所有這些數據都不是以位元組和兆位元組為單位來衡量的。它以 TB 和 PB 為單位。在資料工程之前、進行訓練之前進行的資料處理量非常大。它可能代表您最終完成的工作量的 30%、40%、50%。最終創建數據驅動的機器學習服務。所以資料處理只是一個重要的部分。

We accelerate Spark. We accelerate Python. One of the coolest things that we just did that is called cuDF pandas, without one line of code, pandas, which is the single most successful data science framework in the world, Pandas now is accelerated by NVIDIA CUDA and just out of the box without a line of code. And so the acceleration is really quite terrific, and people are just incredibly excited about. And pandas was designed for one purpose and one purpose only, really data processing for data science. And so NVIDIA CUDA gives you all of that.

我們加速 Spark。我們加速Python。我們剛剛做的最酷的事情之一就是 cuDF pandas，無需一行程式碼 pandas，它是世界上最成功的資料科學框架，Pandas 現在由 NVIDIA CUDA 加速，開箱即用，無需任何程式碼一行程式碼。因此，加速度確實非常驚人，人們對此感到非常興奮。 pandas 的設計目的只有一個，也就是真正用於資料科學的資料處理。 NVIDIA CUDA 為您提供了這一切。

Your final question comes from the line of Harlan Sur of JPMorgan.

你的最後一個問題來自摩根大通的 Harlan Sur。

If you look at the history of the tech industry, right, those companies that have been successful have always been focused on ecosystem, silicon, hardware, software, strong partnerships and just as importantly, right, an aggressive cadence of new products, more segmentation over time. The team recently announced a more aggressive new product cadence and data center from 2 years to now every year with higher levels of segmentation, training, optimization, inferencing, CPU, GPU, DPU networking. How do we think about your R&D OpEx growth that looks to support a more aggressive and expanding forward road map? But more importantly, what is the team doing to manage and drive execution through all of this complexity?

如果你看看科技業的歷史，你會發現，那些成功的公司一直專注於生態系統、晶片、硬體、軟體、強大的合作夥伴關係，同樣重要的是，新產品的積極節奏、更多的細分隨著時間的推移。該團隊最近宣布了更積極的新產品節奏和資料中心，從兩年到現在每年都有更高水準的細分、訓練、最佳化、推理、CPU、GPU、DPU 網路。我們如何看待你們的研發營運支出成長，以支持更積極、更擴展的未來路線圖？但更重要的是，團隊正在採取哪些措施來管理和推動執行所有這些複雜性？

Gosh, boy, that's just really excellent. You just wrote NVIDIA's business plan. And you described our strategy. First of all, there is a fundamental reason why we accelerate our execution. And the reason for that is because it fundamentally drives down cost. When -- the combination of TensorRT LLM and H200 reduced the cost for our customers for large model inference by a factor of 4, and so that includes, of course, our speeds and feeds, but mostly, it's because of our software. Mostly, the software benefits because of the architecture. And so we want to accelerate our road map for that reason.

天哪，孩子，這真是太棒了。您剛剛寫了 NVIDIA 的商業計劃。您描述了我們的策略。首先，我們加快執行力有一個根本原因。原因是它從根本上降低了成本。 TensorRT LLM 和 H200 的組合將我們客戶的大型模型推理成本降低了 4 倍，當然，這包括我們的速度和提要，但主要是因為我們的軟體。大多數情況下，軟體受益於架構。因此，我們希望加快我們的路線圖。

The second reason is to expand the reach of generative AI. The world's number of data center configurations, this is kind of the amazing thing. NVIDIA is in every cloud, but not one cloud is the same. NVIDIA is working with every single cloud service provider and not one of their networking control plane security posture is the same. Everybody's platform is different, and yet we're integrated into all of their stacks, all of their data centers, and we work incredibly well with all of them.

第二個原因是擴大生成人工智慧的影響範圍。全球資料中心配置的數量，這是一件令人驚奇的事。 NVIDIA 存在於每一種雲中，但沒有一種雲是相同的。 NVIDIA 正在與每家雲端服務供應商合作，但他們的網路控制平面安全狀況沒有一個是相同的。每個人的平台都不同，但我們整合到他們的所有堆疊、所有資料中心中，我們與所有這些平台的合作都非常好。

And not to mention, we then take the whole thing, and we create AI factories that are stand-alone. We take our platform. We can put them into supercomputers. We can put them into enterprise. Bringing AI to enterprise is something -- generative AI to enterprise is something nobody has ever done before. And we're right now in the process of going to market with all of that.

更不用說，我們然後採取整個事情，我們創建獨立的人工智慧工廠。我們採取我們的平台。我們可以將它們放入超級電腦中。我們可以把它們投入到企業中。將人工智慧引入企業是一件好事——將生成式人工智慧引入企業是以前從未有人做過的事情。我們現在正在將所有這些產品推向市場。

And so the complexity includes, of course, all the technologies and segments and the pace. It includes the fact that we are architecturally compatible across every single one of those. It includes all of the domain-specific libraries that we create. The reason why you -- every computer company without thinking can integrate NVIDIA into their road map and take it to market and the reason for that is because there's market demand for it. There's market demand in health care. There's market demand in manufacturing. There's market demand, and of course, in AI, in financial services and supercomputing and quantum computing. The list of markets and segments that we have domain-specific libraries is incredibly broad.

因此，複雜性當然包括所有技術、細分市場和速度。它包括這樣一個事實：我們在架構上與其中每一個都相容。它包括我們創建的所有特定於網域的庫。每家電腦公司不假思索就能將 NVIDIA 整合到他們的路線圖中並將其推向市場，原因是因為有市場需求。醫療保健有市場需求。製造業有市場需求。當然，人工智慧、金融服務、超級運算和量子運算都有市場需求。我們擁有特定領域庫的市場和細分市場清單非常廣泛。

And then finally, now we have an end-to-end solution for data centers. InfiniBand network -- InfiniBand networking, Ethernet networking, x86, ARM, just about every permutation combination of solutions, technology solutions and software stacks provided. And that translates to having the largest number of ecosystem software developers, the largest ecosystem of system makers, the largest and broadest distribution partnership network, and ultimately, the greatest reach. And that takes -- surely, that takes a lot of energy.

最後，現在我們有了資料中心的端到端解決方案。 InfiniBand 網路－InfiniBand 網路、乙太網路、x86、ARM，幾乎提供了解決方案、技術解決方案和軟體堆疊的所有排列組合。這意味著擁有最多數量的生態系統軟體開發人員、最大的系統製造商生態系統、最大和最廣泛的分銷合作夥伴網絡，以及最終的最大影響力。這需要——當然，這需要大量的能量。

But the thing that really holds it together, and this is a great decision that we made decades ago, which is everything is architecturally compatible. When you -- when we develop a domain-specific language that runs on one GPU, it runs on every GPU. When we optimize TensorRT for the cloud, we optimized it for enterprise. When we do something that brings in a new feature, a new library, a new feature or a new developer, they instantly get the benefit of all of our reach.

但真正將它們結合在一起的是我們幾十年前做出的一個偉大決定，一切在架構上都是相容的。當我們開發一種在一個 GPU 上運行的特定領域語言時，它就可以在每個 GPU 上運作。當我們針對雲端優化 TensorRT 時，我們也針對企業進行了最佳化。當我們做一些事情帶來新功能、新庫、新功能或新開發人員時，他們會立即從我們所有的影響中受益。

And so that discipline, that architecture compatible discipline that has lasted more than a couple of decades now is one of the reasons why NVIDIA is still really, really efficient. I mean we're 28,000 people large and serving just about every single company, every single industry, every single market around the world.

因此，這種與架構相容的原則已經持續了幾十年，這也是 NVIDIA 仍然非常非常有效率的原因之一。我的意思是，我們擁有 28,000 名員工，為全球幾乎每家公司、每個產業、每個市場提供服務。

Thank you. I will now turn the call back over to Jensen Huang for closing remarks.

謝謝。現在我將把電話轉回黃仁勳做總結發言。

Our strong growth reflects the broad industry platform transition from general purpose to accelerated computing and Generative AI. Large language model start-ups, consumer Internet companies and global cloud service providers are the first movers. The next waves are starting to build. Nations and regional CSPs are building AI clouds to serve local demand. Enterprise software companies like Adobe and Dropbox, SAP and ServiceNow are adding AI copilots and assistants to their platforms. And enterprises in the world's largest industries are creating custom AIs to automate and boost productivity.

我們的強勁成長反映出廣泛的產業平台從通用轉變為加速運算和產生人工智慧的轉變。大型語言模式新創公司、消費網路公司和全球雲端服務供應商是先驅。下一波浪潮正在開始形成。國家和地區通訊服務提供商正在建立人工智慧雲端來滿足當地需求。 Adobe 和 Dropbox、SAP 和 ServiceNow 等企業軟體公司正在為其平台添加人工智慧副駕駛和助理。全球最大行業的企業正在創建客製化人工智慧來實現自動化並提高生產力。

The generative AI era is in full steam and has created the need for a new type of data center and AI factory, optimized for refining data and training and inference and generating AI. AI factory workloads are different and incremental to legacy data center workloads supporting IT tasks. AI factories run copilots and AI assistants, which are significant software TAM expansion and are driving significant new investment, expanding the $1 trillion traditional data center infrastructure installed base, empowering the AI industrial revolution.

生成式人工智慧時代正在如火如荼地進行，這就產生了對新型資料中心和人工智慧工廠的需求，這些資料中心和人工智慧工廠針對提煉資料、訓練和推理以及生成人工智慧進行了優化。 AI 工廠工作負載與支援 IT 任務的傳統資料中心工作負載不同，並且是增量的。人工智慧工廠運行副駕駛和人工智慧助理，這是重要的軟體 TAM 擴展，正在推動重大新投資，擴大 1 兆美元的傳統資料中心基礎設施安裝基礎，為人工智慧工業革命賦能。

NVIDIA H100 HGX with InfiniBand and the NVIDIA AI software stack define an AI factory today. As we expand our supply chain to meet the world's demand, we are also building new growth drivers for the next wave of AI. We highlighted 3 elements to our new growth strategy that are hitting their stride, CPU, networking and software and services.

配備 InfiniBand 的 NVIDIA H100 HGX 和 NVIDIA AI 軟體堆疊定義了當今的 AI 工廠。在我們擴大供應鏈以滿足世界需求的同時，我們也正在為下一波人工智慧浪潮打造新的成長動力。我們強調了新成長策略中正在取得進展的 3 個要素：CPU、網路以及軟體和服務。

Grace is NVIDIA's first data center CPU. Grace and Grace Hopper are in full production and ramping into a new multibillion-dollar product line next year. Irrespective of the CPU choice, we can help customers build an AI factory. NVIDIA networking now exceeds a $10 billion annualized revenue run rate. InfiniBand grew fivefold year-over-year and is positioned for excellent growth ahead as the networking of AI factories.

Grace 是 NVIDIA 首款資料中心 CPU。 Grace 和 Grace Hopper 已全面投入生產，並於明年投入價值數十億美元的新產品線。無論選擇哪種CPU，我們都可以幫助客戶建立人工智慧工廠。 NVIDIA 網路年化收入現已超過 100 億美元。 InfiniBand 年成長五倍，並有望作為人工智慧工廠的網路在未來實現出色的成長。

Enterprises are also racing to adopt AI, and Ethernet is the standard networking. This week, we announced an Ethernet for AI platform for enterprises. NVIDIA Spectrum-X is an end-to-end solution of BlueField SuperNIC, Spectrum-4 Ethernet switch and software that boosts Ethernet performance by up to 1.6x for AI workloads. Dell, HPE and Lenovo have joined us to bring a full generative AI solution of NVIDIA AI computing, networking and software to the world's enterprises.

企業也競相採用人工智慧，而乙太網路是標準網路。本週，我們發布了面向企業的人工智慧平台乙太網路。 NVIDIA Spectrum-X 是 BlueField SuperNIC、Spectrum-4 乙太網路交換器和軟體的端對端解決方案，可將 AI 工作負載的乙太網路效能提高高達 1.6 倍。戴爾、慧與和聯想攜手我們，為全球企業帶來 NVIDIA AI 運算、網路和軟體的完整生成式 AI 解決方案。

NVIDIA software and services is on track to exit the year at an annualized run rate of $1 billion. Enterprise software platforms like ServiceNow and SAP need to build and operate proprietary AI. Enterprises need to build and deploy custom AI copilots. We have the AI technology, expertise and scale to help customers build custom models. With their proprietary data on NVIDIA DGX Cloud and deploy the AI applications on enterprise-grade NVIDIA AI Enterprise, NVIDIA is essentially an AI foundry. NVIDIA's GPUs, CPUs, networking, AI foundry services and NVIDIA AI Enterprise software are all growth engines in full throttle.

NVIDIA 軟體和服務今年預計將以 10 億美元的年增長率結束。 ServiceNow 和 SAP 等企業軟體平台需要建置和營運專有的人工智慧。企業需要建置和部署客製化的人工智慧副駕駛。我們擁有人工智慧技術、專業知識和規模來幫助客戶建立客製化模型。憑藉在 NVIDIA DGX Cloud 上的專有數據，並在企業級 NVIDIA AI Enterprise 上部署 AI 應用程序，NVIDIA 本質上是 AI 代工廠。 NVIDIA 的 GPU、CPU、網路、AI 代工服務和 NVIDIA AI Enterprise 軟體都是全速成長的引擎。

Thanks for joining us today. We look forward to updating you on our progress next quarter.

感謝您今天加入我們。我們期待向您通報下季的最新進展。

This concludes today's conference call. You may now disconnect.

今天的電話會議到此結束。您現在可以斷開連線。