輝達 (NVDA) 2024 Q1 法說會逐字稿

Good afternoon. My name is David, and I'll be your conference operator today. At this time, I'd like to welcome everyone to NVIDIA's First Quarter Earnings Call. Today's conference is being recorded. (Operator Instructions) Simona Jankowski, you may begin your conference.

午安.我叫大衛，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 第一季財報電話會議。今天的會議正在錄製中。 （操作員指示）Simona Jankowski，您可以開始會議了。

Thank you. Good afternoon, everyone, and welcome to NVIDIA's conference call for the first 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. 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 2024.

謝謝。大家下午好，歡迎參加 NVIDIA 2024 財年第一季的電話會議。執行副總裁兼財務長 Colette Kress。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 2024 財年第二季財務業績的電話會議之前進行重播。

The content of today's call is NVIDIA's property. It can't be reproduced or transcribed without our prior written consent. During this call, we may make forward-looking statements based on current expectations. These are subject to a number of significant risks and uncertainties, and our actual results may differ materially. For a discussion of factors that could affect our future financial results and business, please refer to the disclosure in today's earnings release, our most recent Forms 10-K and 10-Q and the reports that we may file on Form 8-K with the Securities and Exchange Commission. All our statements are made as of today, May 24, 2023, based on information currently available to us. Except as required by law, we assume no obligation to update any such statements.

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。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披露、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。我們的所有聲明均基於我們目前掌握的信息，截至 2023 年 5 月 24 日作出。除法律要求外，我們不承擔更新任何此類聲明的義務。

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。

And with that, let me turn the call over to Colette.

接下來，讓我把電話轉給科萊特。

Thanks, Simona. Q1 revenue was $7.19 billion, up 19% sequentially and down 13% year-on-year. Strong sequential growth was driven by record data center revenue with our gaming and professional visualization platforms emerging from channel inventory corrections.

謝謝，西蒙娜。第一季營收為 71.9 億美元，季增 19%，年減 13%。強勁的環比成長是由創紀錄的資料中心收入以及通路庫存修正中出現的遊戲和專業視覺化平台所推動的。

Starting with Data Center. Record revenue of $4.28 billion was up 18% sequentially and up 14% year-on-year on strong growth of our accelerated computing platform worldwide. Generative AI is driving exponential growth in compute requirements and a fast transition to NVIDIA accelerated computing, which is the most versatile, most energy-efficient and the lowest TCO approach to train and deploy AI. Generative AI drove significant upside in demand for our products, creating opportunities and broad-based global growth across our markets.

從資料中心開始。由於我們全球加速運算平台的強勁成長，營收創紀錄地達到 42.8 億美元，季增 18%，年增 14%。生成式 AI 正在推動運算需求呈指數級增長，並快速過渡到 NVIDIA 加速運算，這是用於訓練和部署 AI 的最通用、最節能且總體擁有成本最低的方法。生成式人工智慧推動了我們產品的需求大幅成長，為我們的市場創造了機會和廣泛的全球成長。

Let me give you some color across our 3 major customer categories: cloud service providers or CSPs, consumer Internet companies and enterprises. First, CSPs around the world are racing to deploy our flagship Hopper and Ampere architecture GPUs to meet the surge in interest from both enterprise and consumer AI applications for training and inference. Multiple CSPs announced the availability of H100 on their platforms, including private previews at Microsoft Azure, Google Cloud and Oracle Cloud Infrastructure, upcoming offerings at AWS and general availability at emerging GPU-specialized cloud providers like CoreWeave and Lambda. In addition to enterprise AI adoption, these CSPs are serving strong demand for H100 from generative AI pioneers.

讓我為您介紹我們的 3 個主要客戶類別：雲端服務供應商或 CSP、消費者網路公司和企業。首先，世界各地的 CSP 競相部署我們的旗艦級 Hopper 和 Ampere 架構 GPU，以滿足企業和消費者 AI 應用程式對訓練和推理的興趣激增。多個 CSP 宣佈在其平台上推出 H100，包括 Microsoft Azure、Google Cloud 和 Oracle Cloud Infrastructure 的私人預覽版、AWS 即將推出的產品以及 CoreWeave 和 Lambda 等新興 GPU 專業雲端供應商的全面可用性。除了企業人工智慧的採用之外，這些 CSP 還滿足了生成式人工智慧先驅對 H100 的強烈需求。

Second, consumer Internet companies are also at the forefront of adopting generative AI and deep-learning-based recommendation systems, driving strong growth. For example, Meta has now deployed its H100-powered Grand Teton AI supercomputer for its AI production and research teams.

其次，消費互聯網公司也走在採用生成式人工智慧和基於深度學習的推薦系統的前沿，推動了強勁的成長。例如，Meta 現在為其人工智慧生產和研究團隊部署了由 H100 驅動的 Grand Teton AI 超級電腦。

Third, enterprise demand for AI and accelerated computing is strong. We are seeing momentum in verticals such as automotive, financial services, health care and telecom where AI and accelerated computing are quickly becoming integral to customers' innovation road maps and competitive positioning. For example, Bloomberg announced it has a $50 billion parameter model, BloombergGPT, to help with financial natural language processing tasks such as sentiment analysis, named entity recognition, news classification and question answering.

第三，企業對人工智慧和加速運算的需求旺盛。我們看到汽車、金融服務、醫療保健和電信等垂直行業的發展勢頭，其中人工智慧和加速運算正在迅速成為客戶創新路線圖和競爭定位的組成部分。例如，彭博社宣布擁有價值 500 億美元的參數模型 BloombergGPT，以協助完成情感分析、命名實體識別、新聞分類和問答等金融自然語言處理任務。

Auto insurance company CCC Intelligent Solutions is using AI for estimating repairs. And AT&T is working with us on AI to improve fleet dispatches so their field technicians can better serve customers. Among other enterprise customers using NVIDIA AI are Deloitte for logistics and customer service, and Amgen for drug discovery and protein engineering.

汽車保險公司 CCC 智慧解決方案正在使用人工智慧來估計維修費用。 AT&T 正在與我們合作開發人工智慧，以改善車隊調度，以便他們的現場技術人員能夠更好地為客戶服務。使用 NVIDIA AI 的其他企業客戶包括負責物流和客戶服務的德勤以及負責藥物發現和蛋白質工程的安進。

This quarter, we started shipping DGX H100, our Hopper generation AI system, which customers can deploy on-prem. And with the launch of DGX Cloud through our partnership with Microsoft Azure, Google Cloud and Oracle Cloud Infrastructure, we deliver the promise of NVIDIA DGX to customers from the cloud. Whether the customers deploy DGX on-prem or via DGX Cloud, they get access to NVIDIA AI software, including NVIDIA-based command, end-to-end AI frameworks and pretrained models. We provide them with the blueprint for building and operating AI, spanning our expertise across systems, algorithms, data processing and training methods.

本季度，我們開始出貨 DGX H100，我們的 Hopper 世代人工智慧系統，客戶可以在當地部署。隨著我們與 Microsoft Azure、Google Cloud 和 Oracle Cloud Infrastructure 合作推出 DGX Cloud，我們從雲端向客戶兌現了 NVIDIA DGX 的承諾。無論客戶是在本地部署 DGX 還是透過 DGX Cloud 部署，他們都可以存取 NVIDIA AI 軟體，包括基於 NVIDIA 的命令、端對端 AI 框架和預訓練模型。我們為他們提供建置和操作人工智慧的藍圖，涵蓋我們在系統、演算法、資料處理和訓練方法方面的專業知識。

We also announced NVIDIA AI Foundations, which are model foundry services available on DGX Cloud that enable businesses to build, refine and operate custom large language models and generative AI models trained with their own proprietary data created for unique domain-specific tasks. They include NVIDIA NeMo for large language models, NVIDIA Picasso for images, video and 3D, and NVIDIA BioNeMo for life sciences. Each service has 6 elements: pretrained models, frameworks for data processing and curation, proprietary knowledge-based vector databases, systems for fine-tuning, aligning and guard railing, optimized inference engines, and support from NVIDIA experts to help enterprises fine-tune models for their custom use cases.

我們還發布了NVIDIA AI Foundations，這是DGX Cloud 上提供的模型鑄造服務，使企業能夠建立、完善和操作自訂大型語言模型和生成式AI 模型，這些模型使用為獨特的特定領域任務創建的自己的專有資料進行訓練。其中包括用於大型語言模型的 NVIDIA NeMo、用於圖像、視訊和 3D 的 NVIDIA Picasso 以及用於生命科學的 NVIDIA BioNeMo。每項服務均包含 6 個要素：預訓練模型、資料處理和管理框架、專有的基於知識的向量資料庫、微調、對齊和防護系統、優化的推理引擎以及 NVIDIA 專家幫助企業微調模型的支援用於他們的客製化用例。

ServiceNow, a leading enterprise services platform is an early adopter of DGX Cloud and NeMo. They are developing custom large language models trained on data specifically for the ServiceNow platform. Our collaboration will let ServiceNow create new enterprise-grade generative AI offerings with the thousands of enterprises worldwide running on the ServiceNow platform, including for IT departments, customer service teams, employees and developers.

ServiceNow 是一家領先的企業服務平台，是 DGX Cloud 和 NeMo 的早期採用者。他們正在開發專門針對 ServiceNow 平台的資料訓練的客製化大型語言模型。我們的合作將使 ServiceNow 為全球數千家在 ServiceNow 平台上運作的企業（包括 IT 部門、客戶服務團隊、員工和開發人員）創建新的企業級生成式人工智慧產品。

Generative AI is also driving a step function increase in inference workloads. Because of their size and complexities, these workloads require acceleration. The latest MLPerf industry benchmark released in April showed NVIDIA's inference platforms deliver performance that is orders of magnitude ahead of the industry with unmatched versatility across diverse workloads. To help customers deploy generative AI applications at scale, at GTC, we announced 4 major new inference platforms that leverage the NVIDIA AI software stack. These include L4 Tensor Core GPU for AI video, L40 for Omniverse and graphics rendering, H100 NBL for large language models and the Grace Hopper Superchip for LLMs, and also recommendation systems and vector databases. Google Cloud is the first CSP to adopt our L4 inference platform with the launch of its G2 virtual machines for generative AI inference and other workloads, such as Google Cloud Dataproc, Google AlphaFold and Google Cloud's Immersive Stream, which render 3D and AR experiences. In addition, Google is integrating our Triton Inference Server with Google Kubernetes engine and its cloud-based Vertex AI platform.

生成式人工智慧也推動推理工作負載的階躍函數增加。由於其規模和複雜性，這些工作負載需要加速。 4 月發布的最新 MLPerf 行業基準顯示，NVIDIA 推理平台的性能領先於該行業幾個數量級，並且在不同工作負載上具有無與倫比的多功能性。為了幫助客戶大規模部署生成式 AI 應用程序，我們在 GTC 上宣布了 4 個利用 NVIDIA AI 軟體堆疊的主要新推理平台。其中包括用於 AI 視訊的 L4 Tensor Core GPU、用於 Omniverse 和圖形渲染的 L40、用於大型語言模型的 H100 NBL 和用於法學碩士的 Grace Hopper Superchip，以及推薦系統和向量資料庫。 Google Cloud 是第一個採用我們的L4 推理平台的CSP，推出了用於生成AI 推理和其他工作負載的G2 虛擬機，例如穀歌 Cloud Dataproc、Google AlphaFold 和Google Cloud 的Immersive Stream，可呈現3D 和AR體驗。此外，Google正在將我們的 Triton 推理伺服器與Google Kubernetes 引擎及其基於雲端的 Vertex AI 平台整合。

In networking, we saw strong demand at both CSPs and enterprise customers for generative AI and accelerated computing, which require high-performance networking like NVIDIA's Mellanox networking platforms. Demand relating to general purpose CPU infrastructure remains soft. As generative AI applications grow in size and complexity, high-performance networks become essential for delivering accelerated computing at data center scale to meet the enormous demand of both training and inferencing. Our 400-gig Quantum-2 InfiniBand platform is the gold standard for AI-dedicated infrastructure, with broad adoption across major cloud and consumer Internet platforms such as Microsoft Azure.

在網路方面，我們看到 CSP 和企業客戶對生成式 AI 和加速運算的強烈需求，這需要像 NVIDIA 的 Mellanox 網路平台這樣的高效能網路。與通用 CPU 基礎架構相關的需求仍然疲軟。隨著生成式人工智慧應用程式規模和複雜性的成長，高效能網路對於在資料中心規模提供加速運算以滿足訓練和推理的巨大需求至關重要。我們的 400g Quantum-2 InfiniBand 平台是 AI 專用基礎架構的黃金標準，在 Microsoft Azure 等主要雲端和消費者互聯網平台中廣泛採用。

With the combination of in-network computing technology and the industry's only end-to-end data center scale optimized software stack, customers routinely enjoy a 20% increase in throughput for their sizable infrastructure investment. For multi-tenant cloud transitioning to support generative AI, our high-speed Ethernet platform with BlueField-3 DPUs and Spectrum-4 Ethernet switching offers the highest available Ethernet network performance. BlueField-3 is in production and has been adopted by multiple hyperscale and CSP customers, including Microsoft Azure, Oracle Cloud, CoreWeave, Baidu and others. We look forward to sharing more about our 400-gig spectrum for accelerated AI networking platform next week at the Computex Conference in Taiwan.

透過將網路內運算技術與業界唯一的端到端資料中心規模最佳化的軟體堆疊相結合，客戶在其龐大的基礎設施投資中通常可以享受到 20% 的吞吐量成長。對於多租戶雲端過渡以支援生成式 AI，我們配備 BlueField-3 DPU 和 Spectrum-4 乙太網路交換的高速乙太網路平台可提供最高的可用乙太網路效能。 BlueField-3 已投入生產，並已被多個超大規模和 CSP 客戶採用，包括 Microsoft Azure、Oracle Cloud、CoreWeave、百度等。我們期待下週在台灣舉行的 Computex 大會上分享更多有關我們用於加速 AI 網路平台的 400 GB 頻譜的資訊。

Lastly, our Grace data center CPU is sampling with customers. At this week's International Supercomputing Conference in Germany, the University of Bristol announced a new supercomputer based on the NVIDIA Grace CPU Superchip, which is 6x more energy efficient than the previous supercomputer. This adds to the growing momentum for Grace with both CPU-only and CPU-GPU opportunities across AI and cloud and supercomputing applications. The coming wave of BlueField-3, Grace and Grace Hopper Superchips will enable a new generation of super energy-efficient accelerated data centers.

最後，我們的 Grace 資料中心 CPU 正在向客戶提供樣品。在本週於德國舉行的國際超級計算大會上，布里斯託大學宣布推出一款基於 NVIDIA Grace CPU 超級晶片的新型超級計算機，其能源效率比之前的超級電腦高 6 倍。這增加了 Grace 在人工智慧、雲端和超級運算應用程式中的純 CPU 和 CPU-GPU 機會的成長勢頭。即將到來的 BlueField-3、Grace 和 Grace Hopper Superchips 浪潮將實現新一代超級節能加速資料中心。

Now let's move to Gaming. Gaming revenue of $2.24 billion was up 22% sequentially and down 38% year-on-year. Strong sequential growth was driven by sales of the 40 Series GeForce RTX GPUs for both notebooks and desktops. Overall end demand was solid and consistent with seasonality, demonstrating resilience against a challenging consumer spending backdrop. The GeForce RTX 40 Series GPU laptops are off to a great start, featuring 4 NVIDIA inventions: RTX Path Tracing, DLSS 3 AI rendering, Reflex ultra-low latency rendering and Max-Q energy-efficient technologies. They deliver tremendous gains in industrial design, performance and battery life for gamers and creators.

現在讓我們轉向遊戲。博彩收入為 22.4 億美元，季增 22%，年減 38%。筆記型電腦和桌上型電腦 40 系列 GeForce RTX GPU 的銷售推動了強勁的環比成長。整體終端需求穩定且與季節性一致，在充滿挑戰的消費者支出背景下展現韌性。 GeForce RTX 40 系列 GPU 筆記型電腦迎來了一個良好的開端，擁有 4 個 NVIDIA 發明：RTX 路徑追蹤、DLSS 3 AI 渲染、Reflex 超低延遲渲染和 Max-Q 節能技術。它們為遊戲玩家和創作者帶來了工業設計、性能和電池壽命方面的巨大收益。

Unlike our desktop offerings, 40 Series laptops support the NVIDIA Studio platform for software technologies, including acceleration for creative data science and AI workflows, and Omniverse, giving content creators unmatched tools and capabilities. In desktop, we ramped the RTX 4070, which joined the previously launched RTX 4090, 4080 and the 4070 Ti GPUs. The RTX 4070 is nearly 3x faster than the RTX 2070 and offers our large installed base a spectacular upgrade.

與我們的桌上型電腦產品不同，40 系列筆記型電腦支援 NVIDIA Studio 平台的軟體技術，包括創意資料科學和 AI 工作流程的加速以及 Omniverse，為內容創作者提供了無與倫比的工具和功能。在桌面領域，我們升級了 RTX 4070，加入了先前推出的 RTX 4090、4080 和 4070 Ti GPU。 RTX 4070 的速度比 RTX 2070 快近 3 倍，為我們龐大的安裝基礎提供了驚人的升級。

Last week, we launched the 60 family, RTX 4060 and 4060 Ti, bringing our newest architecture to the world's core gamers starting at just $299. These GPUs for the first time provide 2x the performance of the latest gaming console at mainstream price points. The 4060 Ti is available starting today, while the 4060 will be available in July.

上週，我們推出了 60 系列、RTX 4060 和 4060 Ti，為全球核心遊戲玩家帶來了我們最新的架構，起價僅為 299 美元。這些 GPU 首次以主流價位提供 2 倍於最新遊戲機的效能。 4060 Ti 即日起上市，4060 將於 7 月上市。

Generative AI will be transformative to gaming and content creation from development to runtime. At the Microsoft Build Developer Conference earlier this week, we showcased how Windows PCs and workstations with NVIDIA RTX GPUs will be AI powered at their core. NVIDIA and Microsoft have collaborated on end-to-end software engineering, spanning from the Windows operating system to the NVIDIA graphics drivers and NeMo LLM framework to help make Windows on NVIDIA RTX Tensor Core GPUs a supercharged platform for generative AI.

生成式人工智慧將徹底改變遊戲和內容創建從開發到運行的整個過程。在本週稍早的 Microsoft Build 開發者大會上，我們展示了配備 NVIDIA RTX GPU 的 Windows PC 和工作站如何以 AI 為核心。 NVIDIA 和 Microsoft 在端對端軟體工程方面合作，涵蓋從 Windows 作業系統到 NVIDIA 圖形驅動程式和 NeMo LLM 框架，以協助將 NVIDIA RTX Tensor Core GPU 上的 Windows 打造為生成式 AI 的強大平台。

Last quarter, we announced a partnership with Microsoft to bring Xbox PC games to GeForce NOW. The first game from this partnership, Gears 5, is now available with more set to be released in the coming months. There are now over 1,600 games on GeForce NOW, the richest content available on any cloud gaming service.

上季度，我們宣布與 Microsoft 合作，將 Xbox PC 遊戲引入 GeForce NOW。這次合作的第一款遊戲《戰爭機器 5》現已推出，更多遊戲將在未來幾個月內發布。目前，GeForce NOW 上有超過 1,600 款遊戲，是所有雲端遊戲服務中最豐富的。

Moving to Pro Visualization. Revenue of $295 million was up 31% sequentially and down 53% year-on-year. Sequential growth was driven by stronger workstation demand across both mobile and desktop form factors, with strength in key verticals such as public sector, health care and automotive. We believe the channel inventory correction is behind us. The ramp of our Ada Lovelace GPU architecture in workstations kicks off with major product cycle. At GTC, we announced 6 new RTX GPUs for laptops and desktop workstations with further rollouts planned in the coming quarters.

轉向專業視覺化。營收為 2.95 億美元，季增 31%，年減 53%。連續成長是由行動和桌面裝置的工作站需求強勁推動的，其中公共部門、醫療保健和汽車等關鍵垂直領域的實力強勁。我們相信通路庫存調整已經過去。我們的 Ada Lovelace GPU 架構在工作站中的升級從主要產品週期開始。在 GTC 上，我們宣布推出 6 款適用於筆記型電腦和桌上型工作站的全新 RTX GPU，並計劃在未來幾季進一步推出。

Generative AI is a major new workload for NVIDIA-powered workstation. Our collaboration with Microsoft transforms Windows into the ideal platform for creators and designers harnessing generative AI to elevate their creativity and productivity. At GTC, we announced NVIDIA Omniverse Cloud and NVIDIA fully managed service, running in Microsoft Azure. That includes the full suite of Omniverse applications and NVIDIA OVX infrastructure. Using this full stack cloud environment, customers can design, develop, deploy and manage industrial metaverse applications. NVIDIA Omniverse Cloud will be available starting in the second half of this year. Microsoft and NVIDIA will also connect Office 365 applications with Omniverse.

生成式 AI 是 NVIDIA 支援的工作站的一項主要新工作負載。我們與 Microsoft 的合作將 Windows 轉變為創作者和設計師利用生成式 AI 來提高創造力和生產力的理想平台。在 GTC 上，我們宣布推出在 Microsoft Azure 中執行的 NVIDIA Omniverse Cloud 和 NVIDIA 完全託管服務。其中包括全套 Omniverse 應用程式和 NVIDIA OVX 基礎架構。使用這個全端雲端環境，客戶可以設計、開發、部署和管理工業元宇宙應用程式。 NVIDIA Omniverse Cloud 將於今年下半年開始推出。微軟和 NVIDIA 也將將 Office 365 應用程式與 Omniverse 連接起來。

Omniverse Cloud is being used by companies to digitize their workflows from design and engineering to smart factories and 3D content generation for marketing. The automotive industry has been a leading early adopter of Omniverse, including companies such as BMW Group, Geely Lotus, General Motors and Jaguar Land Rover.

該公司正在使用 Omniverse Cloud 將其工作流程數位化，從設計和工程到智慧工廠以及用於行銷的 3D 內容生成。汽車產業一直是 Omniverse 的領先早期採用者，包括寶馬集團、吉利蓮花、通用汽車和捷豹路虎等公司。

Moving to Automotive. Revenue was $296 million, up 1% sequentially and up 114% from a year ago. Our strong year-on-year growth was driven by the ramp of the NVIDIA DRIVE Orin across a number of new energy vehicles. As we announced in March, our automotive design win pipeline over the next 6 years now is down at $14 billion, up from $11 billion a year ago, giving us visibility into continued growth over the coming years.

轉向汽車。營收為 2.96 億美元，季增 1%，年增 114%。我們強勁的年成長得益於 NVIDIA DRIVE Orin 在多款新能源汽車上的推廣。正如我們在 3 月宣布的那樣，我們未來 6 年的汽車設計贏得管道目前已從一年前的 110 億美元下降至 140 億美元，這讓我們能夠看到未來幾年的持續增長。

Sequentially, growth moderated as some NEV customers in China are adjusting their production schedules to reflect slower-than-expected demand growth. We expect this dynamic to linger for the rest of the calendar year. During the quarter, we expanded our partnership with BYD, the world's leading manufacturer of NEVs. Our new design win will extend BYD's use of the DRIVE Orin to its next-generation, high-volume Dynasty and Ocean series of vehicles set to start production in calendar 2024.

隨後，由於中國的一些新能源汽車客戶正在調整生產計劃，以反映低於預期的需求成長，成長放緩。我們預計這種動態將持續到今年剩餘時間。本季度，我們擴大了與全球領先的新能源汽車製造商比亞迪的合作夥伴關係。我們的新設計勝利將把比亞迪對 DRIVE Orin 的使用擴展到下一代大批量的 Dynasty 和 Ocean 系列車輛，這些車輛將於 2024 年開始生產。

Moving to the rest of the P&L. GAAP gross margins was 64.6%. Non-GAAP gross margins were 66.8%. Gross margins have now largely recovered to prior peak levels as we have absorbed higher costs and offset them by innovating and delivering higher-valued products as well as products incorporating more and more software. Sequentially, GAAP operating expenses were down 3% and non-GAAP operating expenses were down 1%. We have held OpEx at roughly the same level over the last -- past 4 quarters while working through the inventory corrections in Gaming and Professional Visualization. We now expect to increase investments in the business while also delivering operating leverage.

轉向損益表的其餘部分。 GAAP 毛利率為 64.6%。非 GAAP 毛利率為 66.8%。由於我們吸收了更高的成本，並透過創新和提供更高價值的產品以及包含越來越多軟體的產品來抵消成本，毛利率現在已基本恢復到先前的峰值水平。隨後，GAAP 營運費用下降了 3%，非 GAAP 營運費用下降了 1%。在過去四個季度中，我們在遊戲和專業視覺化領域進行庫存修正時，營運支出保持在大致相同的水平。我們現在期望增加對該業務的投資，同時提供營運槓桿。

We returned $99 million to shareholders in the form of cash dividends. At the end of Q1, we have approximately $7 billion remaining under our share repurchase authorization through December 2023.

我們以現金股利的形式向股東返還 9,900 萬美元。截至第一季末，截至 2023 年 12 月，我們的股票回購授權剩餘約 70 億美元。

Let me turn to the outlook for the second quarter of fiscal '24. Total revenue is expected to be $11 billion, plus or minus 2%. We expect this sequential growth to largely be driven by Data Center, reflecting a steep increase in demand related to generative AI and large language models. This demand has extended our Data Center visibility out a few quarters, and we have procured substantially higher supply for the second half of the year.

讓我談談 24 財年第二季的展望。總收入預計為 110 億美元，上下浮動 2%。我們預計這一環比成長將主要由資料中心推動，反映出與生成式人工智慧和大型語言模型相關的需求急劇增長。這種需求使我們的資料中心可見性延長了幾個季度，並且我們在今年下半年採購了大幅增加的供應量。

GAAP and non-GAAP gross margins are expected to be 68.6% and 70%, respectively, plus or minus 50 basis points. GAAP and non-GAAP operating expenses are expected to be approximately $2.71 billion and $1.9 billion, respectively. GAAP and non-GAAP other income and expenses are expected to be an income of approximately $90 million, excluding gains and losses from nonaffiliated investments.

GAAP 和非 GAAP 毛利率預計分別為 68.6% 和 70%，上下浮動 50 個基點。 GAAP 和非 GAAP 營運費用預計分別約為 27.1 億美元和 19 億美元。 GAAP 和非 GAAP 其他收入和支出預計約為 9,000 萬美元，不包括非關聯投資的損益。

GAAP and non-GAAP tax rates are expected to be 14%, plus or minus 1%, excluding any discrete items. Capital expenditures are expected to be approximately $300 million to $350 million. Further financial details are included in the CFO commentary and other information available on our IR website.

GAAP 和非 GAAP 稅率預計為 14%，上下浮動 1%（不包括任何離散項目）。資本支出預計約為3億至3.5億美元。更多財務細節包含在 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 of the upcoming events. Jensen will give the Computex keynote address in person in Taipei this coming Monday, May 29, local time, which will be Sunday evening in the U.S. In addition, we will be attending the BofA Global Technology Conference in San Francisco on June 6 and Rosenblatt Virtual Technology Summit on the age of AI on June 7 and the New Street Future of Transportation Virtual Conference on June 12. Our earnings call to discuss the results of our second quarter fiscal '24 is scheduled for Wednesday, August 23.

最後，讓我重點介紹一些即將舉行的活動。 Jensen 將於當地時間 5 月 29 日星期一親自在台北發表主題演講，美國時間為週日晚上。日召開人工智慧時代科技高峰會，6 月12 日召開新街未來交通虛擬會議。

Well, that covers our opening remarks. We're now going to open the call for questions. Operator, would you please poll for questions?

好的，這就是我們的開場白。我們現在要開始提問。接線員，請您投票詢問問題嗎？

(Operator Instructions)

（操作員說明）

We'll take our first question from Toshiya Hari with Goldman Sachs.

我們將回答高盛 Toshiya Hari 提出的第一個問題。

Congrats on the strong results and incredible outlook. Just one question on Data Center. Colette, you mentioned the vast majority of the sequential increase in revenue this quarter will come from Data Center. I was curious what the construct is there, if you can speak to what the key drivers are from April to July. And perhaps more importantly, you talked about visibility into the second half of the year. I'm guessing it's more of a supply problem at this point. What kind of sequential growth beyond the July quarter can your supply chain support at this point?

祝賀您取得的強勁成果和令人難以置信的前景。只是一個關於資料中心的問題。 Colette，您提到本季營收季增的絕大部分將來自資料中心。我很好奇其中的結構是什麼，如果您能談談四月到七月的關鍵驅動因素是什麼。也許更重要的是，您談到了下半年的前景。我猜現在更多的是供應問題。目前，您的供應鏈能夠支援七月季度之後什麼樣的連續成長？

Okay. So a lot of different questions there, so let me see if I can start, and I'm sure Jensen will have some following up comments. So when we talk about our sequential growth that were expected between Q1 and Q2, our generative AI large language models are driving this surge in demand, and it's broad-based across both our consumer Internet companies, our CSPs, our enterprises and our AI start-ups. It is also interest in both of our architectures, both of our Hopper latest architecture as well as our Ampere architecture. This is not surprising as we generally often sell both of our architectures at the same time.

好的。那裡有很多不同的問題，所以讓我看看是否可以開始，我確信 Jensen 會有一些後續評論。因此，當我們談論第一季和第二季之間預期的連續成長時，我們的生成式人工智慧大型語言模式正在推動需求激增，並且它在我們的消費者網路公司、我們的CSP、我們的企業和我們的人工智慧新創企業中都有廣泛的基礎-UPS。它也對我們的兩種架構感興趣，包括我們的 Hopper 最新架構和我們的 Ampere 架構。這並不奇怪，因為我們通常經常同時銷售兩種架構。

This is also a key area where deep recommendators are driving growth. And we also expect to see growth both in our computing as well as in our networking business. So those are some of the key things that we have baked in when we think about the guidance that we have provided to Q2. We also surfaced in our opening remarks that we are working on both supply today for this quarter, but we have also procured a substantial amount of supply for the second half. We have some significant supply chain flow to serve our significant customer demand that we see, and this is demand that we see across a wide range of different customers.

這也是深度推薦器推動成長的關鍵領域。我們也期望我們的計算和網路業務都能實現成長。因此，當我們考慮向第二季度提供的指導時，這些是我們考慮的一些關鍵事項。我們也在開場白中提到，我們今天正在處理本季的供應，但我們也為下半年採購了大量供應。我們有一些重要的供應鏈流程來滿足我們所看到的重要客戶需求，這是我們在廣泛的不同客戶中看到的需求。

They are building platforms for some of the largest enterprises but also setting things up at the CSPs and the large consumer Internet companies. So we have visibility right now for our data center demand that has probably extended out a few quarters, and this led us to working on quickly procuring that substantial supply for the second half.

他們正在為一些最大的企業建立平台，同時也在 CSP 和大型消費互聯網公司建立平台。因此，我們現在可以清楚地了解資料中心的需求，這種需求可能會持續幾個季度，這促使我們致力於快速採購下半年的大量供應。

I'm going to pause there and see if Jensen wants to add a little bit more.

我要暫停一下，看看 Jensen 是否想補充一點。

I thought that was great, Colette. Thank you.

我覺得那太好了，科萊特。謝謝。

Next, we'll go to C.J. Muse with Evercore ISI.

接下來，我們將與 Evercore ISI 一起前往 C.J. Muse。

I guess with Data Center essentially doubling quarter-on-quarter, 2 natural kind of questions that relate to 1 another come to mind. Number one, where are we in terms of driving acceleration into servers to support AI? And as part of that, as you deal with longer cycle times with TSMC and your other partners, how are you thinking about managing the commitments there with where you want to manage your lead times in the coming years to best kind of match that supply and demand?

我猜想，隨著資料中心數量基本上每季翻一番，我會想到兩個相互關聯的自然問題。第一，在推動伺服器加速以支援人工智慧方面，我們處於什麼位置？作為其中的一部分，當您與台積電和其他合作夥伴處理更長的周期時間時，您如何考慮管理那裡的承諾以及您希望在未來幾年管理交貨時間以最好地匹配供應和要求？

Yes, C.J., thanks for the question. I'll start backwards. The -- remember, we were in full production of both Ampere and Hopper when the ChatGPT moment came, and it helped everybody crystallize how to transition from the technology of large language models to a product and service based on a chatbot. The integration of guardrails and alignment systems with reinforcement learning human feedback, knowledge vector databases for proprietary knowledge, connection to search, all of that came together in a really wonderful way. And it's the reason why I call it the iPhone moment. All the technology came together and helped everybody realize what an amazing product it can be and what capabilities it can have.

是的，C.J.，謝謝你的提問。我將從倒退開始。請記住，當 ChatGPT 時刻到來時，我們正在全力生產 Ampere 和 Hopper，它幫助每個人明確瞭如何從大型語言模型技術過渡到基於聊天機器人的產品和服務。護欄和對準系統與強化學習人類回饋、專有知識的知識向量資料庫、搜尋連接的集成，所有這些都以非常美妙的方式結合在一起。這就是我將其稱為“iPhone 時刻”的原因。所有技術都匯集在一起，幫助每個人認識到它可以是一個多麼令人驚嘆的產品以及它可以擁有什麼功能。

And so we were already in full production. NVIDIA's supply chain flow and our supply chain is very significant as you know. And we build supercomputers in volume, and these are giant systems and we build them in volume. It includes, of course, the GPUs, but on our GPUs, the system boards have 35,000 other components. And the networking and the fiber optics and the incredible transceivers and the NICs, the SmartNICs, the switches, all of that has to come together in order for us to stand up a data center. And so we were already in full production when the moment came. We had to really significantly increase our procurement substantially for the second half, as Colette said.

所以我們已經全面投入生產。如您所知，NVIDIA 的供應鏈流程以及我們的供應鏈非常重要。我們批量建造超級計算機，這些是巨型系統，我們批量建造它們。當然，它包括 GPU，但在我們的 GPU 上，系統板還有 35,000 個其他組件。網路、光纖、令人難以置信的收發器、NIC、SmartNIC、交換機，所有這些都必須結合在一起，才能讓我們建立一個資料中心。因此，當那一刻到來時，我們已經全面投入生產。正如科萊特所說，下半年我們必須大幅增加採購量。

Now let me talk about the bigger picture and why the entire world's data centers are moving toward accelerated computing. It's been known for some time, and you've heard me talk about it, that accelerated computing is a full stack problem but -- it is full stack challenged. But if you could successfully do it in a large number of application domain that's taken us 15 years, it's sufficiently that almost the entire data center's major applications could be accelerated. You could reduce the amount of energy consumed and the amount of cost for a data center substantially by an order of magnitude. It costs a lot of money to do it because you have to do all the software and everything and you have to build all the systems and so on and so forth, but we've been at it for 15 years.

現在讓我談談更大的前景以及為什麼全世界的資料中心都在向加速運算邁進。人們已經知道加速運算是一個全端問題已經有一段時間了，而且你也聽過我談論它——它是全端挑戰。但如果能在大量的應用領域成功做到這一點，我們花了15年的時間，就足以讓幾乎整個資料中心的主要應用都加速。您可以將資料中心的能源消耗量和成本大幅減少一個數量級。做這件事要花很多錢，因為你必須做所有的軟體和一切，你必須建立所有的系統等等，但我們已經這樣做了 15 年。

And what happened is when generative AI came along, it triggered a killer app for this computing platform that's been in preparation for some time. And so now we see ourselves in 2 simultaneous transitions. The world's $1 trillion data center is nearly populated entirely by CPUs today. And I -- $1 trillion, $250 billion a year, it's growing of course. But over the last 4 years, call it $1 trillion worth of infrastructure installed, and it's all completely based on CPUs and dumb NICs. It's basically unaccelerated.

發生的事情是，當生成式人工智慧出現時，它觸發了這個計算平台的殺手級應用程序，該應用程式已經準備了一段時間。所以現在我們看到自己處於兩個同時的轉變之中。如今，全球價值 1 兆美元的資料中心幾乎全部由 CPU 組成。我——1兆美元，每年2500億美元，當然還在成長。但在過去 4 年裡，我們安裝了價值 1 兆美元的基礎設施，而且全部完全基於 CPU 和啞 NIC。基本上是沒有加速的。

In the future, it's fairly clear now with this -- with generative AI becoming the primary workload of most of the world's data centers generating information, it is very clear now that -- and the fact that accelerated computing is so energy efficient, that the budget of a data center will shift very dramatically towards accelerated computing, and you're seeing that now. We're going through that moment right now as we speak, while the world's data center CapEx budget is limited. But at the same time, we're seeing incredible orders to retool the world's data centers.

在未來，這一點現在已經相當清楚了——隨著生成式人工智慧成為世界上大多數資料中心生成資訊的主要工作負載，現在很清楚——而且事實上加速運算是如此節能，以至於資料中心的預算將急劇轉向加速計算，您現在已經看到了這一點。正如我們所說，我們現在正在經歷這個時刻，而全球資料中心的資本支出預算是有限的。但同時，我們看到了令人難以置信的重組全球資料中心的訂單。

And so I think you're starting -- you're seeing the beginning of, call it, a 10-year transition to basically recycle or reclaim the world's data centers and build it out as accelerated computing. You have a pretty dramatic shift in the spend of a data center from traditional computing and to accelerated computing with SmartNICs, smart switches, of course, GPUs and the workload is going to be predominantly generative AI.

所以我認為你正在開始——你正在看到一個為期 10 年的過渡的開始，基本上回收或回收世界上的數據中心並將其構建為加速計算。資料中心的支出從傳統運算轉向使用 SmartNIC、智慧交換器、當然還有 GPU 的加速運算，工作負載將主要是生成式 AI。

We'll move to our next question, Vivek Arya with BofA Securities.

我們將討論下一個問題，美國銀行證券公司的 Vivek Arya。

Colette, just wanted to clarify, does visibility mean data center sales can continue to grow sequentially in Q3 and Q4? Or do they sustain at Q2 level? So I just wanted to clarify that. And then, Jensen, my question is that given this very strong demand environment, what does it do to the competitive landscape? Does it invite more competition in terms of custom ASICs? Does it invite more competition in terms of other GPU solutions or other kinds of solutions? What -- how do you see the competitive landscape change over the next 2 to 3 years?

Colette，只是想澄清一下，可見性是否意味著資料中心銷售額可以在第三季和第四季繼續連續成長？或者他們能維持在第二季的水平嗎？所以我只是想澄清這一點。然後，詹森，我的問題是，鑑於這種非常強勁的需求環境，這對競爭格局有何影響？它是否會在定制 ASIC 方面引發更多競爭？它是否會在其他 GPU 解決方案或其他類型的解決方案方面引發更多競爭？您如何看待未來 2 到 3 年的競爭格局變化？

Yes, Vivek, thanks for the question. Let me see if I can add a little bit more color. We believe that the supply that we will have for the second half of the year will be substantially larger than H1. So we are expecting not only the demand that we just saw in this last quarter, the demand that we have in Q2 for our forecast but also planning on seeing something in the second half of the year. We just have to be careful here, but we're not here to guide on the second half. But yes, we do plan a substantial increase in the second half compared to the first half.

是的，維韋克，謝謝你的提問。讓我看看是否可以添加更多顏色。我們認為下半年的供應量將大大高於上半年。因此，我們不僅預計上個季度的需求、第二季的需求，還計劃在下半年看到一些情況。我們在這裡必須要小心，但我們不是來指導下半場的。但是，是的，我們確實計劃下半年比上半年大幅增加。

Regarding competition, we have competition from every direction. Start-ups, really, really well funded and innovative start-ups, countless of them all over the world. We have competitions from existing semiconductor companies. We have competition from CSPs with internal projects, and many of you know about most of these. And so we're mindful of competition all the time, and we get competition all the time.

關於競爭，我們有來自各方面的競爭。新創企業，確實是資金充足、創新的新創企業，全世界無數。我們面臨現有半導體公司的競爭。我們面臨著 CSP 內部專案的競爭，你們中的許多人都知道其中的大部分。因此，我們一直關注競爭，我們也一直面臨競爭。

NVIDIA's value proposition at the core is we are the lowest cost solution. We're the lowest TCO solution. And the reason for that is because accelerated computing is 2 things that I talk about often, which is it's a full stack problem. It's a full stack challenge. You have to engineer all of the software and all the libraries and all the algorithms, integrate them into and optimize the frameworks and optimize it for the architecture of not just one chip but the architecture of an entire data center but all the way into the frameworks, all the way into the models.

NVIDIA 的核心價值主張是我們是成本最低的解決方案。我們是 TCO 最低的解決方案。原因是因為加速運算是我經常談論的兩件事，那就是它是一個完整的堆疊問題。這是一個全端挑戰。你必須設計所有的軟體、所有的函式庫和所有的演算法，將它們整合到框架中並對其進行最佳化，不僅針對一個晶片的架構進行最佳化，而且針對整個資料中心的架構進行最佳化，一直到框架中，一直進入模型。

And the amount of engineering and distributed computing, fundamental computer science work is really quite extraordinary. It is the hardest computing as we know. And so number one, it's a full stack challenge and you have to optimize it across the whole thing and across just a mind-blowing number of stacks. We have 400 acceleration libraries. As you know, the amount of libraries and frameworks that we accelerate is pretty mind blowing.

工程和分散式運算、基礎電腦科學工作的數量確實相當驚人。據我們所知，這是最難的計算。因此，第一，這是一個完整的堆疊挑戰，您必須在整個過程中以及在數量驚人的堆疊中對其進行最佳化。我們有 400 個加速庫。如您所知，我們加速的程式庫和框架的數量相當令人興奮。

The second part is that generative AI is a large-scale problem, and it's a data center scale problem. It's another way of thinking that the computer is the data center or the data center is the computer. It's not the chip. It's the data center, and it's never happened like us before. And in this particular environment, your networking operating system, your distributed computing engines, your understanding of the architecture of the networking gear, the switches and the computing systems, the computing fabric, that entire system is your computer, and that's what you're trying to operate. And so in order to get the best performance, you have to understand full stack and understand data center scale. And that's what accelerated computing is.

第二部分是生成式人工智慧是一個大規模問題，而且是一個資料中心規模的問題。這是電腦就是資料中心或資料中心就是電腦的另一種思考方式。這不是晶片。這是資料中心，以前從未發生過像我們這樣的情況。在這個特定的環境中，你的網路作業系統，你的分散式運算引擎，你對網路設備架構的理解，交換器和計算系統，計算結構，整個系統就是你的計算機，這就是你的計算機嘗試操作。因此，為了獲得最佳效能，您必須了解完整堆疊並了解資料中心規模。這就是加速計算。

The second thing is that utilization, which talks about the amount of the types of applications that you can accelerate and the versatility of your architecture, keeps that utilization high. If you can do one thing and doing one thing only incredibly fast, then your data center is largely underutilized, and it's hard to scale that out. NVIDIA's universal GPU and the fact that we accelerate so many stacks makes our utilization incredibly high. And so number one is throughput, and that's software-intensive problems and data center architecture problem. The second is digitalization versatility problem.

第二件事是利用率，它指的是您可以加速的應用程式類型的數量以及架構的多功能性，從而保持高利用率。如果您只能做一件事，並且只以令人難以置信的速度做一件事，那麼您的資料中心基本上沒有充分利用，並且很難擴展。 NVIDIA 的通用 GPU 以及我們加速如此多堆疊的事實使我們的利用率非常高。因此，第一位是吞吐量，這是軟體密集型問題和資料中心架構問題。二是數位化通用性問題。

And the third is just data center expertise. We've built 5 data centers of our own, and we've helped companies all over the world build data centers. And we integrate our architecture into all the world's clouds. From the moment of delivery of the product to the standing up and the deployment, the time to operations of a data center is measured not -- it can -- if you're not good at it and not proficient at it, it could take months. Standing up a supercomputer, let's see, some of the largest supercomputers in the world were installed about 1.5 years ago, and now they're coming online.

第三是資料中心專業知識。我們自己建置了5個資料中心，也幫助世界各地的公司建置了資料中心。我們將我們的架構整合到世界上所有的雲端中。從產品交付的那一刻到建立和部署，資料中心的營運時間不是可以衡量的，如果你不擅長並且不精通它，那麼它可能需要幾個月。豎起一台超級計算機，讓我們看看，世界上最大的一些超級計算機是大約 1.5 年前安裝的，現在它們正在上線。

And so it's not unheard of to see a delivery to operations of about a year. Our delivery to operation's measured in weeks. And that's -- we've taken data centers and supercomputers, and we've turned it into products. And the expertise of the team in doing that is incredible.

因此，交付營運時間約為一年的情況並非聞所未聞。我們的交付以周為單位計算。那就是——我們已經採用了資料中心和超級計算機，並將其轉化為產品。團隊在這方面的專業知識令人難以置信。

And so our value proposition is in final analysis. All of this technology translates into infrastructure, the highest throughput and the lowest possible cost. And so I think our market is, of course, very, very competitive, very large, but the challenge is really, really great.

因此，我們的價值主張是最終的分析。所有這些技術都轉化為基礎設施、最高的吞吐量和最低的成本。所以我認為我們的市場當然非常非常有競爭力，非常大，但挑戰確實非常大。

Next, we go to Aaron Rakers with Wells Fargo.

接下來，我們將前往富國銀行 (Wells Fargo) 的 Aaron Rakers。

Congrats on the quarter. As we kind of think about unpacking the various different growth drivers of the Data Center business going forward, I'm curious, Colette, of just how we should think about the monetization effect of software considering that the expansion of your cloud service agreements continues to grow. I'm curious of what -- where do you think we're at in terms of that approach, in terms of the AI enterprise software suite and other drivers of software-only revenue going forward?

恭喜本季。當我們思考未來資料中心業務的各種不同成長驅動因素時，Colette，我很好奇，考慮到雲端服務協議的擴展持續不斷，我們應該如何考慮軟體的貨幣化效果。我很好奇——您認為我們在這種方法、人工智慧企業軟體套件和未來純軟體收入的其他驅動因素方面處於什麼位置？

Thanks for the question. Software is really important to our accelerated platforms. Not only do we have a substantial amount of software that we are including in our newest architecture and essentially all products that we have, we're now with many different models to help customers start their work in generative AI and accelerated computing.

謝謝你的提問。軟體對於我們的加速平台非常重要。我們不僅擁有大量包含在最新架構和基本上所有產品中的軟體，而且現在還擁有許多不同的模型來幫助客戶開始產生人工智慧和加速運算方面的工作。

So anything that we have here from DGX Cloud on providing those services, helping them build models or, as you've discussed, the importance of NVIDIA AI Enterprise, essentially, that operating system for AI, so all things should continue to grow as we go forward, both the architecture and the infrastructure as well as the -- both availability of the software and our ability to monetize that with it as well. I'll turn it over to Jensen if needs to comment.

因此，我們從DGX Cloud 獲得的有關提供這些服務、幫助他們構建模型的任何內容，或者正如您所討論的，NVIDIA AI Enterprise 的重要性，本質上是用於AI 的操作系統，因此隨著我們的發展，所有事情都應該繼續增長繼續前進，無論是架構和基礎設施，還是軟體的可用性以及我們利用它獲利的能力。如果需要發表評論，我會將其轉交給 Jensen。

Yes. We can see in real time the growth of generative AI in CSPs, both for training the models, refining the models as well as deploying the models. As Colette said earlier, inference is now a major driver of accelerated computing because generative AI is used so capably in so many applications already.

是的。我們可以即時看到 CSP 中生成式 AI 的成長，包括訓練模型、完善模型以及部署模型。正如科萊特之前所說，推理現在是加速運算的主要驅動力，因為生成式人工智慧已經在如此多的應用程式中得到如此強大的應用。

There are 2 segments that require a new stack of software, and the 2 segments are enterprise and industrials. Enterprise requires a new stack of software because many enterprises need to have all the capabilities that we've talked about, whether it's large language models, the ability to adapt them for your proprietary use case and your proprietary data and alignment to your own principles and your own operating domains. You want to have the ability to be able to do that in a high-performance computing sandbox, and we call that DGX Cloud, and to create that model.

有兩個細分市場需要新的軟體堆疊，這兩個細分市場是企業和工業。企業需要新的軟體堆棧，因為許多企業需要擁有我們討論過的所有功能，無論是大型語言模型、使其適應您的專有用例和專有數據的能力以及與您自己的原則和規則保持一致的能力您自己的操作域。您希望能夠在高效能運算沙箱（我們稱之為 DGX 雲端）中做到這一點，並建立該模型。

Then you want to deploy your chatbot or your AI in any cloud because you have services and you have agreements with multiple cloud vendors and depending on the applications, you might deploy it on various clouds. And for the enterprise, we have NVIDIA AI Foundations for helping you create custom models and we have NVIDIA AI Enterprise. NVIDIA AI Enterprise is the only accelerated stack -- GPU accelerated stack in the world that is enterprise safe and enterprise supported. There are constant patching that you have to do. There are 4,000 different packages that build up NVIDIA AI Enterprise and represents the operating engine -- end-to-end operating engine of the entire AI workflow.

然後，您希望將聊天機器人或人工智慧部署在任何雲端中，因為您擁有服務並且與多個雲端供應商簽訂了協議，並且根據應用程序，您可能會將其部署在各種雲端上。對於企業來說，我們有 NVIDIA AI Foundations 來幫助您建立自訂模型，我們還有 NVIDIA AI Enterprise。 NVIDIA AI Enterprise 是世界上唯一一個企業安全且受企業支援的加速堆疊 - GPU 加速堆疊。您必須不斷地進行修補。有 4,000 個不同的軟體包建構了 NVIDIA AI Enterprise，並代表了操作引擎——整個 AI 工作流程的端到端操作引擎。

It's the only one of its kind from data ingestion, data processing. Obviously, in order to train an AI model, you have a lot of data you have to process and package up and curate and align. And there's just a whole bunch of stuff that you have to do to the data to prepare it for training. That amount of data could consume some 40%, 50%, 60% of your computing time. And so data processing is a very big deal. And then the second aspect of it is training the model, refining the model. And the third is deploying model for inferencing.

從資料攝取、資料處理來看，它是同類中唯一的一種。顯然，為了訓練人工智慧模型，您必須處理、打包、整理和調整大量資料。為了準備訓練，您必須對資料執行一大堆操作。該資料量可能會消耗約 40%、50%、60% 的計算時間。因此，資料處理是一件非常重要的事情。第二個面向是訓練模型，完善模型。第三是部署推理模型。

NVIDIA AI Enterprise supports and patches and security patches continuously all of those 4,000 packages of software. And for an enterprise that wants to deploy their engines just like they want to deploy Red Hat Linux, this is incredibly complicated software. In order to deploy that in every cloud and as well as on-prem, it has to be secure. It has to be supported. And so NVIDIA AI Enterprise is the second part.

NVIDIA AI Enterprise 持續支援所有這 4,000 個軟體包並提供修補程式和安全性修補程式。對於想要像部署 Red Hat Linux 一樣部署引擎的企業來說，這是一個極為複雜的軟體。為了將其部署在每個雲端和本地，它必須是安全的。必須支持它。因此 NVIDIA AI Enterprise 是第二部分。

The third is Omniverse. Just as people are starting to realize that you need to align an AI to ethics, the same for robotics, you need to align the AI for physics, and aligning an AI for ethics includes a technology called reinforcement learning human feedback. In the case of industrial applications and robotics, it's reinforcement learning Omniverse feedback. And Omniverse is a vital engine for software-defined and robotic applications and industries. And so Omniverse also needs to be a cloud service platform.

第三個是全宇宙。正如人們開始意識到需要使人工智慧與道德保持一致一樣，對於機器人技術來說，您需要使人工智慧與物理保持一致，而使人工智慧與道德保持一致包括一種稱為強化學習人類反饋的技術。就工業應用和機器人技術而言，這就是強化學習 Omniverse 回饋。 Omniverse 是軟體定義和機器人應用程式及產業的重要引擎。所以Omniverse也需要成為雲端服務平台。

And so our software stack, the 3 software stacks, AI Foundations, AI Enterprise and Omniverse, runs in all of the world's clouds that we have partnerships, DGX Cloud partnerships with. Azure, we have partnerships on both AI as well as Omniverse. With GCP and Oracle, we have great partnerships in DGX Cloud for AI, and AI Enterprise is integrated into all 3 of them.

因此，我們的軟體堆疊，即 3 個軟體堆棧，AI Foundations、AI Enterprise 和 Omniverse，在我們與之建立合作夥伴關係（DGX Cloud 合作夥伴）的全球所有雲端中運作。 Azure，我們在人工智慧和 Omniverse 方面都有合作夥伴關係。我們與 GCP 和 Oracle 在 DGX Cloud for AI 方面建立了良好的合作夥伴關係，AI Enterprise 已整合到這三個雲端。

And so I think the -- in order for us to extend the reach of AI beyond the cloud and into the world's enterprise and into the world's industries, you need 2 new types of -- you need new software stacks in order to make that happen. And by putting it in the cloud, integrated into the world CSP clouds, it's a great way for us to partner with the sales and the marketing team and the leadership team of all the cloud vendors.

所以我認為，為了讓我們將人工智慧的影響範圍擴展到雲端之外，進入世界企業和世界產業，你需要兩種新型的——你需要新的軟體堆疊來實現這一目標。透過將其放入雲端中，整合到世界 CSP 雲端中，這是我們與所有雲端供應商的銷售和行銷團隊以及領導團隊合作的好方法。

Next, we'll go to Timothy Arcuri with UBS.

接下來，我們將邀請瑞銀集團的 Timothy Arcuri。

I had a question and then I had a clarification as well. So the question, first, is, Jensen, on the InfiniBand versus Ethernet argument, can you sort of speak to that debate and maybe how you see it playing out? I know you need the low latency of InfiniBand for AI. But can you sort of talk about the attach rate of your InfiniBand solutions to what you're shipping on the core compute side and maybe whether that's similarly crowding out Ethernet like you are with -- on the compute side? And then the clarification, Colette, is that there wasn't a share buyback despite you still having about $7 billion on the share repo authorization. Was that just timing?

我有一個問題，然後我也得到了澄清。因此，首先的問題是，Jensen，關於 InfiniBand 與乙太網路的爭論，您能否談談這場爭論，以及您如何看待這場爭論？我知道您需要 InfiniBand 的低延遲來實現 AI。但是您能否談談您的 InfiniBand 解決方案與您在核心運算端所提供的產品的連接速率，以及這是否會像您在計算端那樣擠壓乙太網路？然後澄清一下，科萊特，儘管您仍有約 70 億美元的股票回購授權，但並沒有進行股票回購。這只是時機嗎？

Colette, how about you go first? You take the question first.

科萊特，你先走怎麼樣？你先回答問題。

Sure. That is correct. We have $7 billion available in our current authorization for repurchases. We did not repurchase anything in this last quarter, but we do repurchase opportunistically and we'll consider that as we go forward as well.

當然。這是正確的。我們目前的回購授權中有 70 億美元。我們在上個季度沒有回購任何東西，但我們確實會機會性地回購，我們在前進時也會考慮這一點。

InfiniBand and Ethernet are -- target different applications in a data center. They both have their place. InfiniBand had a record quarter. We're going to have a giant record year. And InfiniBand has a really -- NVIDIA's Quantum InfiniBand has an exceptional road map. It's going to be really incredible.

InfiniBand 和乙太網路針對資料中心中的不同應用。他們都有自己的位置。 InfiniBand 的季度業績創歷史新高。我們將迎來創紀錄的一年。 InfiniBand 有一個真正的——NVIDIA 的 Quantum InfiniBand 有一個特別的路線圖。這將是非常令人難以置信的。

The 2 networks are very different. InfiniBand is designed for an AI factory if you will. If that data center is running a few applications for a few people for a specific use case and is doing it continuously, and that infrastructure costs you, pick a number, $500 million, the difference between InfiniBand and Ethernet could be 15%, 20% in overall throughput. And if you spent $500 million on an infrastructure and the difference is 10% to 20%, and it's $100 million, InfiniBand's basically free. That's the reason why people use it.

這兩個網路非常不同。如果你願意的話，InfiniBand 是為人工智慧工廠而設計的。如果該資料中心針對特定用例為少數人運行一些應用程序，並且持續運行，並且該基礎設施會花費您，請選擇一個數字，5 億美元，InfiniBand 和以太網之間的差異可能是15%、20 %在總吞吐量中。如果你在基礎設施上花了 5 億美元，差價是 10% 到 20%，那就是 1 億美元，InfiniBand 基本上是免費的。這就是人們使用它的原因。

InfiniBand is effectively free. The difference in data center throughput is just -- it's too great to ignore. And you're using it for that one application. And so however, if your data center is a cloud data center and it's multi-tenant, it's a bunch of little jobs, a bunch of little jobs and is shared by millions of people, then Ethernet is really the right answer. There's a new segment in the middle where the cloud is becoming a generative AI cloud. It's not an AI factory per se, but it's still a multi-tenant cloud, but it wants to run generative AI workloads.

InfiniBand 實際上是免費的。資料中心吞吐量的差異實在太大，不容忽視。您正在將其用於該應用程式。然而，如果您的資料中心是雲端資料中心並且是多租戶的，它是一堆小工作，一堆小工作並且由數百萬人共享，那麼乙太網路確實是正確的答案。中間有一個新的部分，雲端正在成為生成式人工智慧雲端。它本身不是一個人工智慧工廠，但它仍然是一個多租戶雲，但它希望運行生成式人工智慧工作負載。

This new segment is a wonderful opportunity. And at Computex -- I referred to it at the last GTC. At Computex, we're going to announce a major product line for this segment, which is for Ethernet-focused generative AI application type of clouds. But InfiniBand is doing fantastically, and we're doing record numbers quarter-on-quarter, year-on-year.

這個新的細分市場是一個絕佳的機會。在台北國際電腦展上——我在上一屆 GTC 上提到了它。在台北國際電腦展上，我們將宣布該細分市場的主要產品線，該產品線適用於以乙太網路為中心的生成式人工智慧應用類型的雲端。但 InfiniBand 的表現非常出色，我們的季度環比和同比業績都創下了紀錄。

Next, we'll go to Stacy Rasgon with Bernstein Research.

接下來，我們將前往伯恩斯坦研究中心的史黛西‧拉斯貢 (Stacy Rasgon)。

I had a question on inference versus training for generative AI. So you're talking about inference as being a very large opportunity. I guess 2 subparts of that. Is that because inference basically scales with like the usage versus like training is more of a one and done? And can you give us some sort of -- even if it's just like qualitatively, like do you think inference is bigger than training or vice versa? Like if it's bigger, how much bigger? Is it like the opportunity, is it 5x? Is it 10x? Is there anything you can give us on those 2 workloads within generative AI? Would be helpful.

我有一個關於產生人工智慧的推理與訓練的問題。所以你說推理是一個非常大的機會。我猜其中有兩個子部分。這是因為推理基本上是隨著使用情況而擴展的，而訓練則​​更像是一勞永逸的事情嗎？你能否給我們一些資訊——即使只是定性的，例如你認為推理比訓練更重要，反之亦然？比如如果它更大的話，會大多少？是不是就像機會一樣，是5x嗎？是10倍嗎？關於生成式人工智慧中的這兩個工作負載，您能給我們什麼建議嗎？會有幫助的。

Yes, I'll work backwards. You're never done with training. You're always -- every time you deploy, you're collecting new data. When you collect new data, you train with the new data. And so you're never done training. You're never done producing and processing a vector database that augments the large language model. You're never done with vectorizing all of the collected structured, unstructured data that you have. And so whether you're building a recommender system, a large language model, a vector database, these are probably the 3 major applications of -- the 3 core engines, if you will, of the future of computing as well as a bunch of other stuff.

是的，我會向後工作。你的訓練永遠不會結束。每次部署時，您總是在收集新資料。當您收集新資料時，您可以使用新資料進行訓練。所以你永遠不會完成訓練。您永遠不會完成產生和處理增強大型語言模型的向量資料庫。您永遠不會完成對您所收集的所有結構化、非結構化資料進行向量化。因此，無論您是在建立推薦系統、大型語言模型、向量資料庫，這些都可能是未來計算的 3 個核心引擎（如果您願意的話）以及一系列的 3 個主要應用程式。

But obviously, these are very -- 3 very important ones. They are always, always running. You're going to see that more and more companies realize they have a factory for intelligence -- an intelligence factory. And in that particular case, it's largely dedicated to training and processing data and vectorizing data and learning representation of the data, so on and so forth.

但顯然，這是非常——三個非常重要的。他們永遠、永遠在奔跑。你會看到越來越多的公司意識到他們有一個智慧工廠——一個智慧工廠。在這種特殊情況下，它主要致力於訓練和處理數據、向量化數據以及學習數據的表示，等等。

The inference part of it are APIs that are either open APIs that can be connected to all kinds of applications, APIs that are integrated into workflows but APIs of all kinds. There will be hundreds of APIs in a company. Some of them, they built themselves. Some of them, part that -- many of them could come from companies like ServiceNow and Adobe that we're partnering with in AI Foundations. And they'll create a whole bunch of generative AI APIs that companies can then connect into their workflows or use as an application. And of course, there'll be a whole bunch of Internet service companies.

它的推理部分是 API，這些 API 要么是可以連接到各種應用程式的開放 API，要么是整合到工作流程中的 API，而是各種 API。一家公司裡會有數百個API。其中一些是他們自己建造的。其中一些，部分 - 其中許多可能來自 ServiceNow 和 Adob​​e 等我們在人工智慧基金會中合作的公司。他們將創建一整套生成式人工智慧 API，然後公司可以將其連接到他們的工作流程或用作應用程式。當然，還會有一大堆網路服務公司。

And so I think you're seeing for the very first time, simultaneously, a very significant growth in the segment of AI factories as well as a market that -- a segment that really didn't exist before but now it's growing exponentially practically by the week for AI inference with APIs. The simple way to think about it in the end is that the world has a $1 trillion of data center installed and it used to be 100% CPUs. In the future, we know -- we've heard it in enough places, and I think this year's ISC keynote was actually about the end of Moore's Law.

因此，我認為您將第一次同時看到人工智慧工廠和市場的顯著增長，這個市場以前確實不存在，但現在實際上呈指數級增長。最終簡單的思考方式是，世界上安裝了價值 1 兆美元的資料中心，並且曾經是 100% CPU。未來，我們知道——我們已經在足夠多的地方聽到這個說法，我認為今年的 ISC 主題演講實際上是關於摩爾定律的終結。

We've seen it in a lot of places now that you can't reasonably scale out data centers with general purpose computing and that accelerated computing is the path forward. And now it's got a killer app. It's called generative AI. And so the easiest way to think about that is your $1 trillion infrastructure. Every quarter's capital, CapEx budget would lean very heavily into generative AI, into accelerated computing infrastructure, everywhere from the number of GPUs that would be used in the CapEx budget to the accelerated switches and accelerated networking chips that connect them all.

我們已經在很多地方看到，現在你無法透過通用運算合理地擴展資料中心，而加速運算才是前進的道路。現在它有了一個殺手級應用程式。這就是所謂的生成人工智慧。因此，最簡單的思考方式就是價值 1 兆美元的基礎設施。每個季度的資本、資本支出預算都將非常傾向於產生人工智慧、加速運算基礎設施，從資本支出預算中使用的 GPU 數量到連接它們的加速交換器和加速網路晶片，無處不在。

The easiest way to think about that is, over the next 4, 5, 10 years, most of that $1 trillion and then compensating, adjusting for all the growth in data center still, it will be largely generative AI. And so that's probably the easiest way to think about that, and that's training as well as inference.

最簡單的思考方式是，在未來 4 年、5 年、10 年中，這 1 兆美元中的大部分，然後補償、調整資料中心的所有成長，大部分將是生成式人工智慧。所以這可能是思考這個問題的最簡單的方法，那就是訓練和推理。

Next, we'll go to Joseph Moore with Morgan Stanley.

接下來，我們將採訪摩根士丹利的約瑟夫‧摩爾。

I wanted to follow up on that in terms of the focus on inference. It's pretty clear that this is a really big opportunity around large language models. But the cloud customers are also talking about trying to reduce cost per query by very significant amounts. Can you talk about the ramifications of that for you guys? Is that where some of the specialty inference products that you launched at GTC come in? And just how are you going to help your customers get the cost per query down?

我想在關注推理方面跟進這一點。很明顯，這是圍繞大型語言模型的一個非常大的機會。但雲端客戶也在談論嘗試大幅降低每次查詢的成本。您能談談這對你們的影響嗎？這就是你們在 GTC 上推出的一些專業推理產品的用武之地嗎？您將如何幫助您的客戶降低每次查詢的成本？

Yes, that's a great question. Whether you're -- whether -- you start by building a large language model, and you use that large language model, very large version, and you could distill them into medium, small and tiny size. And the tiny sized ones, you could put in your phone and your PC and so on and so forth. And they all have good -- they all have -- it seems surprising, but they all can do the same thing. But obviously, the zero shot or the generalizability of the large language model, the biggest one is much more versatile and it can do a lot more amazing things.

是的，這是一個很好的問題。無論你是——無論——你先建立一個大型語言模型，然後使用那個大型語言模型，非常大的版本，然後你可以將它們提煉成中型、小型和微型。而小型的，你可以放入你的手機和電腦等等。他們都有好的一面——他們都有——這似乎令人驚訝，但他們都能做同樣的事情。但顯然，零鏡頭或大語言模型的泛化性，最大的一個是更通用，它可以做很多更令人驚奇的事情。

And the large one would teach the smaller ones how to be good AIs, and so you use the large one to generate prompts to align the smaller ones and so on and so forth. And so you start by building very large ones, and then you also have to train a whole bunch of smaller ones. That's exactly the reason why we have so many different sizes of our inference. You saw that I announced L4; L40; H100 NVL, which also has H100. And then it has -- and then we have H100 HGX, and then we have H100 multinode with NVLink. And so there's a -- you could have model sizes of any kind that you like.

較大的人工智慧會教導較小的人工智慧如何成為優秀的人工智慧，因此您可以使用較大的人工智慧來產生提示來對齊較小的人工智慧，依此類推。所以你首先要建立非常大的模型，然後你還必須訓練一大堆較小的模型。這正是我們的推論有這麼多不同規模的原因。你看到我宣布了L4； L40； H100 NVL，其中也有H100。然後我們有了 H100 HGX，然後我們有了帶有 NVLink 的 H100 多節點。因此，您可以擁有任何您喜歡的模型尺寸。

The other thing that's important is these are models, but they're connected ultimately to applications. And the applications could have image in, video out, video in, text out, image in, proteins out, text in, 3D out, video in, in the future, 3D graphics out. So the input and the output requires a lot of pre and postprocessing. The pre and postprocessing can't be ignored. And this is one of the things that most of the specialized chip arguments fall apart. And it's because the length -- the model itself is only, call it, 25% of the data -- of the overall processing of inference. The rest of it is about preprocessing, postprocessing, security, decoding, all kinds of things like that.

另一件重要的事情是這些是模型，但它們最終與應用程式相關。這些應用程式可以有圖像輸入、視訊輸出、視訊輸入、文字輸出、圖像輸入、蛋白質輸出、文字輸入、3D 輸出、視訊輸入，未來還會有 3D 圖形輸出。因此輸入和輸出需要大量的預處理和後處理。預處理和後處理不容忽視。這是大多數專業晶片爭論失敗的原因之一。這是因為整個推理處理的長度（模型本身只佔資料的 25%）。剩下的部分是關於預處理、後處理、安全性、解碼以及諸如此類的事情。

And so I think the -- we -- the multi-modality aspect of inference, the multidiversity of inference that it's going to be done in the cloud on-prem, it's going to be done in multi-cloud. That's the reason why we have AI Enterprise in all the clouds. It's going to be done on-premise. It's the reason why we have a great partnership with Dell we just announced the other day called Project Helix. That's going to be integrated into third-party services. That's the reason why we have a great partnership with ServiceNow and Adobe because they're going to be creating a whole bunch of generative AI capabilities. And so there's a -- the diversity and the reach of generative AI is so broad, you need to have some very fundamental capabilities like what I just described in order to really address the whole space of it.

所以我認為——我們——推理的多模態方面，推理的多樣性，它將在本地雲中完成，它將在多雲中完成。這就是我們在所有雲端中擁有 AI Enterprise 的原因。它將在內部完成。這就是我們與戴爾建立良好合作關係的原因，我們前幾天剛剛宣布了名為 Project Helix 的計劃。這將被整合到第三方服務中。這就是我們與 ServiceNow 和 Adob​​e 建立良好合作夥伴關係的原因，因為他們將創建一整套生成式 AI 功能。因此，產生人工智慧的多樣性和影響範圍是如此廣泛，你需要具備一些非常基本的能力，就像我剛才描述的那樣，才能真正解決它的整個領域。

Next, we'll go to Harlan Sur with JPMorgan.

接下來，我們將與摩根大通一起前往哈蘭蘇爾。

Congratulations on the strong results and execution. I really appreciate more of the focus or some of the focus today on your networking products. I mean it's really an integral part to sort of maximize the full performance of your compute platforms. I think your data center networking business is driving about $1 billion of revenues per quarter, plus or minus. That's 2.5x growth from 3 years ago, right, when you guys acquired Mellanox, so very strong growth.

祝賀您所取得的強勁成果和執行力。我真的很感謝今天對你們網路產品的更多關注或部分關注。我的意思是，它確實是最大限度地提高計算平台的全部性能的不可或缺的一部分。我認為您的資料中心網路業務每季帶來約 10 億美元的收入（上下）。與 3 年前收購 Mellanox 時相比，成長了 2.5 倍，成長非常強勁。

But given the very high attach of your InfiniBand Ethernet solutions, your accelerated compute platforms, is the networking run rate stepping up in line with your compute shipments? And then what is the team doing to further unlock more networking bandwidth going forward just to keep pace with the significant increase in compute complexity, data sets, requirements for lower latency, better traffic predictability and so on?

但是，考慮到您的 InfiniBand 乙太網路解決方案、您的加速運算平台的高度附加性，網路運行速率的增加是否與您的運算出貨量一致？那麼，團隊正在採取哪些措施來進一步釋放更多的網路頻寬，以滿足計算複雜性、資料集、更低延遲、更好的流量可預測性等方面顯著增長的需求？

Yes. Harlan, I really appreciate that. Nearly everybody who thinks about AI, they think about that chip, that accelerator chip. And in fact, this is the whole point nearly completely. And I've mentioned before that accelerated computing is about the stack, about the software. And networking, remember, we announced very, very early on this networking stack called DOCA, and we have an acceleration library called Magnum IO. These 2 pieces of software are some of the crown jewels of our company. Nobody ever talks about it because it's hard to understand, but it makes it possible for us to connect tens of thousands of GPUs.

是的。哈倫，我真的很感激。幾乎每個想到人工智慧的人都會想到那個晶片，那個加速器晶片。事實上，這幾乎就是全部要點。我之前提到過，加速運算是關於堆疊、關於軟體的。至於網絡，請記住，我們非常非常早就宣布了這個名為 DOCA 的網絡堆棧，並且我們有一個名為 Magnum IO 的加速庫。這兩個軟體是我們公司皇冠上的寶石。沒有人談論它，因為它很難理解，但它使我們能夠連接數以萬計的 GPU。

How do you connect tens of thousands of GPUs if the operating system of the data center, which is the infrastructure, is not insanely great? And so that's the reason why we're so obsessed about networking in the company. And one of the great things that we have, we have Mellanox, as you know quite well, was the world's highest performance and the unambiguous leader in high-performance networking, is the reason why our 2 companies are together.

如果作為基礎設施的資料中心的作業系統不是非常出色，那麼如何連接數以萬計的GPU？這就是我們如此沉迷於公司網路的原因。正如您所熟知的，我們擁有的最偉大的事物之一是 Mellanox，它是世界上性能最高的產品，也是高效能網路領域無可爭議的領導者，這也是我們兩家公司合併的原因。

You also see that our network expands starting from NVLink, which is a computing fabric with really super low latency, and it communicates using memory references, not network packaged. And then we take NVLink. We connect it inside multiple GPUs, and I've described going beyond the GPU. And I'll talk a lot more about that at Computex in a few days. And then that gets connected to InfiniBand, which includes the NIC and the SmartNIC, BlueField-3 that we're in full production with and the switches. All of the fiber optics that are optimized end to end, these things are running at incredible line rates.

您還可以看到我們的網路從 NVLink 開始擴展，NVLink 是一種具有超低延遲的計算結構，並且它使用記憶體引用進行通信，而不是網路打包。然後我們採用 NVLink。我們將其連接到多個 GPU 內部，並且我已經描述了超越 GPU 的情況。幾天後我將在台北國際電腦展上詳細討論這一點。然後連接到 InfiniBand，其中包括 NIC 和 SmartNIC、我們正在全面生產的 BlueField-3 以及交換器。所有端對端優化的光纖都以令人難以置信的線路速率運行。

And then beyond that, if you want to connect the smart AI factory -- this AI factory into your computing fabric, we have a brand-new type of Ethernet that we'll be announcing at Computex. And so the -- this whole area of the computing fabric extending -- connecting all of these GPUs and computing units together all the way through the networking, through the switches, the software stack is insanely complicated. And so we're -- I'm delighted you understand it, and -- but we don't break it out particularly because we think of the whole thing as a computing platform as it should be.

除此之外，如果您想將智慧型 AI 工廠——這個 AI 工廠連接到您的運算結構中，我們將在 Computex 上推出一種全新類型的乙太網路。因此，計算結構的整個區域延伸——透過網路、交換器將所有這些 GPU 和計算單元連接在一起，軟體堆疊非常複雜。所以我們——我很高興你能理解它，而且——但我們並沒有特別分解它，因為我們認為整件事應該是一個計算平台。

We sell it to all of the world's data centers as components so that they can integrate it into whatever style or architecture that they would like and we can still run our software stack. That's the reason why we break it up. It's way more complicated the way that we do it, but it makes it possible for NVIDIA's computing architecture to be integrated into anybody's data center in the world from cloud of all different kinds to on-prem of all different kinds, all the way out to the edge to 5G. And so this way of doing it is really, really complicated, but it gives us incredible reach.

我們將其作為組件出售給世界上所有的資料中心，以便他們可以將其整合到他們想要的任何風格或架構中，而我們仍然可以運行我們的軟體堆疊。這就是我們分手的原因。我們的做法要複雜得多，但它使 NVIDIA 的運算架構能夠整合到世界上任何人的資料中心，從各種不同類型的雲端到各種不同類型的本地資料中心，一直到5G 的邊緣。因此，這種做法確實非常非常複雜，但它給了我們令人難以置信的影響力。

And our last question will come from Matt Ramsay with TD Cowen.

我們的最後一個問題將由 Matt Ramsay 和 TD Cowen 提出。

Congratulations, Jensen, and to the whole team. One of the things I wanted to dig into a little bit is the DGX Cloud offering. You guys have been working on this for some time behind the scenes, where you sell in the hardware to your hyperscale partners and then lease it back for your own business. And the rest of us kind of found out about it publicly a few months ago.

恭喜詹森和整個團隊。我想深入研究的一件事是 DGX Cloud 產品。你們已經在幕後為此工作了一段時間，將硬體出售給超大規模合作夥伴，然後將其租回給自己的企業。我們其他人幾個月前就公開發現了這一點。

And as we look forward over the next number of quarters, as Colette discussed, to high visibility in the Data Center business, maybe you could talk a little bit about the mix you're seeing of hyperscale customers buying for their own first-party internal workloads versus their own sort of third party, their own customers versus what of that big upside in Data Center going forward is systems that you're selling in with potential to support your DGX Cloud offerings and what you've learned since you've launched it about the potential of that business.

正如科萊特所討論的那樣，當我們展望未來幾個季度時，資料中心業務的高知名度，也許您可以談談您所看到的超大規模客戶為其自己的第一方內部購買的組合工作負載與他們自己的第三方、他們自己的客戶與資料中心未來的巨大優勢是您正在銷售的系統，有可能支援您的 DGX 雲端產品以及您自推出以來所學到的知識關於該業務的潛力。

Yes. Thanks, Matt. It's -- without being too specific about numbers, but the ideal scenario, the ideal mix is something like 10% NVIDIA DGX Cloud and 90% the CSPs' clouds. And the reason -- and our DGX Cloud is the NVIDIA stack. It's the pure NVIDIA stack. It is architected the way we like and achieves the best possible performance. It gives us the ability to partner very deeply with the CSPs to create the highest-performing infrastructure, number one.

是的。謝謝，馬特。雖然數字不太具體，但理想的場景、理想的組合是 10% 的 NVIDIA DGX 雲和 90% 的 CSP 雲。原因是－我們的 DGX Cloud 就是 NVIDIA 堆疊。這是純粹的 NVIDIA 堆疊。它按照我們喜歡的方式構建，並實現最佳性能。它使我們能夠與通訊服務提供者進行深入合作，以創建性能最高的基礎設施，這是第一個。

Number two, it allows us to partner with the CSPs to create markets. Like for example, we're partnering with Azure to bring Omniverse Cloud to the world's industries. And the world has never had a system like that, the computing stack with all the generative AI stuff and all the 3D stuff and the physics stuff, incredibly large database and really high-speed networks and low-latency networks, that kind of a virtual -- industrial virtual world has never existed before. And so we partnered with Microsoft to create Omniverse Cloud inside Azure Cloud.

第二，它使我們能夠與 CSP 合作創造市場。例如，我們正在與 Azure 合作，將 Omniverse Cloud 引入全球各行各業。世界上從未有過這樣的系統，計算堆疊包含所有生成式 AI 內容、所有 3D 內容和物理內容、令人難以置信的大型數據庫、真正的高速網絡和低延遲網絡，那種虛擬的——工業虛擬世界是前所未有的。因此，我們與 Microsoft 合作，在 Azure 雲端建立了 Omniverse Cloud。

And so it allows us, number two, to create new applications together and develop new markets together. And we go to market as one team. And we benefit by getting customers on our computing platform, and they benefit by having us in their cloud, number one. But number two, the amount of data and services and security services and all of the amazing things that Azure and GCP and OCI have, they can instantly have access to that through Omniverse Cloud.

因此，第二，它使我們能夠共同創建新的應用程式並共同開發新的市場。我們作為一個團隊進入市場。我們透過讓客戶使用我們的運算平台而受益，他們也透過讓我們進入他們的雲端而受益，這是第一。但第二，資料、服務和安全服務的數量以及 Azure、GCP 和 OCI 擁有的所有令人驚訝的東西，他們可以透過 Omniverse Cloud 立即存取這些內容。

And so it's a huge win-win. And for the customers, the way that NVIDIA's cloud works for these early applications, they could do it anywhere. So one standard stack runs in all the clouds. And if they would like to take their software and run it on the CSP's cloud themselves and manage it themselves, we're delighted by that because NVIDIA AI Enterprise, NVIDIA AI Foundations and long term, this is going to take a little longer, but NVIDIA Omniverse will run in the CSP's clouds. Okay? So our goal really is to drive architecture, to partner deeply in creating new markets and the new applications that we're doing and provide our customers with the flexibilities to run NVIDIA everywhere, including on-prem.

所以這是一個巨大的雙贏。對於客戶來說，NVIDIA 的雲端適用於這些早期應用程式的方式，他們可以在任何地方進行。因此，一個標準堆疊可以在所有雲端中運行。如果他們想使用自己的軟體並在 CSP 的雲端上自行運行並自行管理，我們對此感到很高興，因為 NVIDIA AI Enterprise、NVIDIA AI Foundations 和長期而言，這將需要更長的時間，但是NVIDIA Omniverse將在CSP 的雲端中運行。好的？因此，我們的目標實際上是推動架構發展，在創建新市場和我們正在開發的新應用程式方面進行深度合作，並為我們的客戶提供在任何地方（包括本地）運行 NVIDIA 的靈活性。

And so that -- those were the primary reasons for it. And it's worked out incredibly. Our partnership with the 3 CSPs and that we currently have DGX Cloud in and their sales force and marketing teams, their leadership teams is really quite spectacular. It works great.

所以——這些就是其主要原因。結果令人難以置信。我們與 3 個 CSP 的合作關係以及目前擁有 DGX Cloud 的合作夥伴以及他們的銷售團隊和行銷團隊以及他們的領導團隊確實非常出色。效果很好。

I'll now turn it back over to Jensen Huang for closing remarks.

現在我將把它轉回給黃仁勳做總結發言。

The computer industry is going through 2 simultaneous transitions, accelerated computing and generative AI. CPU scaling has slowed, yet computing demand is strong and now with generative AI, supercharged. Accelerated computing, a full stack and data center scale approach that NVIDIA pioneered is the best path forward. There's $1 trillion installed in the global data center infrastructure based on the general purpose computing method of the last era. Companies are now racing to deploy accelerated computing for the generative AI era.

電腦產業同時經歷兩個轉型：加速運算和產生人工智慧。 CPU 擴充速度已經放緩，但運算需求依然強勁，而且現在隨著生成式人工智慧的出現，運算需求變得更加強勁。加速運算、NVIDIA 開創的全堆疊和資料中心規模方法是最佳的前進道路。基於上一個時代​​的通用運算方法，全球資料中心基礎設施安裝了 1 兆美元。公司現在正在競相為生成人工智慧時代部署加速運算。

Over the next decade, most of the world's data centers will be accelerated. We are significantly increasing our supply to meet their surging demand. Large language models can learn information encoded in many forms. Guided by large language models, generative AI models can generate amazing content with models to fine-tune, guardrail, align to guiding principles and to ground facts, generative AI is emerging from labs and is on its way to industrial applications.

未來十年，全球大部分資料中心將加速發展。我們正在大幅增加供應以滿足他們不斷增長的需求。大型語言模型可以學習以多種形式編碼的資訊。在大型語言模型的指導下，生成式人工智慧模型可以產生令人驚嘆的內容，模型可以進行微調、護欄、與指導原則和基本事實保持一致，生成式人工智慧正在從實驗室中出現，並正在走向工業應用。

As we scale with cloud and Internet service providers, we are also building platforms for the world's largest enterprises. Whether within one of our CSP partners or on-prem with Dell Helix, whether on a leading enterprise platform like ServiceNow and Adobe or bespoke with NVIDIA AI Foundations, we can help enterprises leverage their domain expertise and data to harness generative AI securely and safely.

隨著我們與雲端和網路服務供應商一起擴展，我們也在為世界上最大的企業建立平台。無論是在我們的CSP 合作夥伴之一內，還是在Dell Helix 的本地部署中，無論是在ServiceNow 和AdAdobe 等領先企業平台上，還是與NVIDIA AI Foundations 進行定制，我們都可以幫助企業利用其領域專業知識與數據，安全可靠地利用生成式AI。

We are ramping a wave of products in the coming quarters, including H100, our Grace and Grace Hopper Superchips and our BlueField-3 and Spectrum-4 networking platform. They are all in production. They will help deliver data center scale computing that is also energy efficient and sustainable computing. Join us next week at Computex, and we'll show you what's next. Thank you.

我們將在未來幾季推出一系列產品，包括 H100、Grace 和 Grace Hopper 超級晶片以及 BlueField-3 和 Spectrum-4 網路平台。它們都在生產中。它們將幫助提供資料中心規模的計算，同時也是節能和可持續的計算。下週參加我們的台北國際電腦展，我們將向您展示接下來的內容。謝謝。

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。