輝達 (NVDA) 2022 Q3 法說會逐字稿

Good afternoon.

下午好。

My name is Saydie, and I will be your conference operator today.

我的名字是 Saydie，今天我將成為您的會議接線員。

At this time, I would like to welcome everyone to the NVIDIA's third quarter earnings call.

在這個時候，我想歡迎大家參加 NVIDIA 第三季度財報電話會議。

(Operator Instructions) Thank you.

（操作員說明）謝謝。

Simona Jankowski, you may begin your conference.

Simona Jankowski，你可以開始你的會議了。

Thank you.

謝謝你。

Good afternoon, everyone, and welcome to NVIDIA's conference call for the third quarter of fiscal 2022.

大家下午好，歡迎參加 NVIDIA 2022 財年第三季度電話會議。

With me today from NVIDIA are Jensen Huang, President and Chief Executive Officer; and Colette Kress, Executive Vice President and Chief Financial Officer.

今天和我一起來自 NVIDIA 的是總裁兼首席執行官 Jensen Huang；和 Colette Kress，執行副總裁兼首席財務官。

I'd like to remind you that our call is being webcast live on NVIDIA's Investor Relations website.

我想提醒您，我們的電話會議正在 NVIDIA 的投資者關係網站上進行網絡直播。

The webcast will be available for replay until the conference call to discuss our financial results for the fourth quarter and fiscal year 2022.

該網絡廣播將在電話會議之前進行重播，以討論我們第四季度和 2022 財年的財務業績。

The content of today's call is NVIDIA's property.

今天通話的內容是 NVIDIA 的財產。

It can't be reproduced or transcribed without our prior written consent.

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.

在本次電話會議期間，我們可能會根據當前預期做出前瞻性陳述。

These are subject to a number of significant risks and uncertainties and our actual results may differ materially.

這些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存在重大差異。

For a discussion of factors that could affect our future financial results and business, please refer to the disclosure in today's earnings release, our most recent Forms 10-K and 10-Q and the reports that we may file on Form 8-K with the Securities and Exchange Commission.

有關可能影響我們未來財務業績和業務的因素的討論，請參閱今天的收益發布中的披露、我們最近的 10-K 和 10-Q 表格以及我們可能在 8-K 表格中與證券交易委員會。

All our statements are made as of today, November 17, 2021, based on information currently available to us.

我們所有的聲明都是基於我們目前可獲得的信息，截至今天，即 2021 年 11 月 17 日。

Except as required by law, we assume no obligation to update any such statements.

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.

在本次電話會議中，我們將討論非 GAAP 財務指標。

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.

您可以在我們的 CFO 評論中找到這些非 GAAP 財務指標與 GAAP 財務指標的對賬，該評論發佈在我們的網站上。

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

有了這個，讓我把電話轉給科萊特。

Thanks, Simona.

謝謝，西蒙娜。

Q3 was an outstanding quarter with revenue of $7.1 billion and year-on-year growth of 50%.

第三季度是一個出色的季度，收入為 71 億美元，同比增長 50%。

We set records for total revenue as well as for gaming, data center and professional visualization.

我們創造了總收入以及遊戲、數據中心和專業可視化的記錄。

Starting with gaming.

從遊戲開始。

Revenue of $3.2 billion was up 5% sequentially and up 42% from a year earlier.

收入 32 億美元，環比增長 5%，同比增長 42%。

Demand was strong across the board.

需求全面強勁。

While we continue to increase desktop GPU supply, we believe channel inventories remain low.

雖然我們繼續增加桌面 GPU 供應，但我們認為渠道庫存仍然很低。

Laptop GPUs also posted strong year-on-year growth, led by increased demand for high-end RTX laptops.

由於對高端 RTX 筆記本電腦的需求增加，筆記本電腦 GPU 也實現了強勁的同比增長。

NVIDIA RTX technology is driving our biggest-ever refresh cycle with gamers, and continues to expand our base with creatives.

NVIDIA RTX 技術正在推動我們有史以來最大規模的遊戲玩家更新周期，並繼續通過創意擴大我們的基礎。

RTX introduced groundbreaking real-time ray tracing and AI-enabled super resolution capabilities, which are getting adopted at an accelerating pace.

RTX 引入了突破性的實時光線追踪和支持 AI 的超分辨率功能，這些功能正以更快的速度被採用。

More than 200 games and applications now support NVIDIA RTX, including 125 with NVIDIA DLSS.

現在有超過 200 款遊戲和應用程序支持 NVIDIA RTX，其中 125 款支持 NVIDIA DLSS。

This quarter alone, 45 new games shipped with DLSS.

僅本季度就有 45 款新遊戲搭載 DLSS。

And NVIDIA Reflex latency-reducing technology is in top eSports titles, including VALORANT, Fortnite, Apex Legends and Overwatch.

NVIDIA Reflex 減少延遲技術已應用於頂級電子競技遊戲，包括 VALORANT、Fortnite、Apex Legends 和 Overwatch。

In addition, the Reflex ecosystem continues to grow, with Reflex technology now integrated in almost 50 gaming peripherals.

此外，Reflex 生態系統不斷發展壯大，Reflex 技術現已集成到近 50 種遊戲外圍設備中。

NVIDIA Studio for creatives keeps expanding.

用於創意的 NVIDIA Studio 不斷擴展。

Last month at the Adobe Max Creativity Conference, Adobe announced 2 powerful AI features for Adobe Lightroom and the Lightroom Classic, accelerated by NVIDIA RTX GPUs.

上個月在 Adobe Max 創意大會上，Adobe 宣布了 Adobe Lightroom 和 Lightroom Classic 的 2 項強大的 AI 功能，由 NVIDIA RTX GPU 加速。

In addition, several of our partners launched new studio systems, including Microsoft, HP and ASUS.

此外，我們的一些合作夥伴推出了新的工作室系統，包括微軟、惠普和華碩。

We estimate that 1/4 of our installed base has adopted RTX GPUs.

我們估計 1/4 的安裝基礎已經採用 RTX GPU。

Looking ahead, we expect continued upgrades as well as growth from NVIDIA GeForce users, given rapidly expanding RTX support and the growing popularity of gaming, eSports, content creation and streaming.

展望未來，鑑於快速擴展的 RTX 支持以及遊戲、電子競技、內容創作和流媒體的日益普及，我們預計 NVIDIA GeForce 用戶將繼續升級和增長。

Our GPUs are capable of crypto money, but we don't have visibility into how much this impacts our overall GPU demand.

我們的 GPU 能夠提供加密貨幣，但我們無法了解這對我們的整體 GPU 需求有多大影響。

In Q3, nearly all of our Ampere architecture gaming desktop GPU shipments were Lite Hash Rate to help steer GeForce supply to gamers.

在第三季度，我們幾乎所有的 Ampere 架構遊戲台式機 GPU 出貨量都是 Lite Hash Rate，以幫助引導 GeForce 向遊戲玩家供應。

Crypto mining processor revenue was $105 million, which is included in our OEM and other.

加密採礦處理器收入為 1.05 億美元，包括在我們的 OEM 和其他產品中。

Our cloud gaming service, GeForce Now, has 2 major achievements this quarter.

我們的雲遊戲服務 GeForce Now 在本季度取得了兩項重大成就。

First, Electronic Arts brought more of its hit games to the server.

首先，Electronic Arts 將更多熱門遊戲帶到了服務器上。

And second, we announced the new GeForce Now RTX 3080 membership tier, priced at less than $100 for 6 months.

其次，我們宣布了新的 GeForce Now RTX 3080 會員等級，6 個月的價格低於 100 美元。

GeForce NOW membership has more than doubled in this last year to over 14 million gamers who are streaming content from the 30 data centers in more than 80 countries.

GeForce NOW 的會員人數在去年增加了一倍多，超過 1400 萬遊戲玩家正在流式傳輸來自 80 多個國家/地區的 30 個數據中心的內容。

Moving to Pro Visualization.

轉向專業可視化。

Q3 revenue of $577 million was up 11% sequentially and up 144% from the year ago quarter.

第三季度收入為 5.77 億美元，環比增長 11%，同比增長 144%。

The sequential rise was led by mobile workstations, with desktop workstations also growing as enterprise deployed systems to support hybrid work environment.

移動工作站引領了連續增長，桌面工作站也隨著企業部署的系統而增長，以支持混合工作環境。

Building on the strong initial ramp in Q2, Ampere architecture sales continue to grow.

在第二季度強勁的初始增長基礎上，Ampere 架構的銷售繼續增長。

Leading verticals, including media and entertainment, health care, public sector and automotive.

領先的垂直行業，包括媒體和娛樂、醫療保健、公共部門和汽車。

Last week, we announced general availability of Omniverse Enterprise, a platform for simulating physically accurate 3D world and digital trends.

上週，我們宣布全面推出 Omniverse Enterprise，這是一個模擬物理上準確的 3D 世界和數字趨勢的平台。

Initial market reception to Omniverse has been incredible.

Omniverse 最初的市場接受度令人難以置信。

Professionals at over 700 companies are evaluating the platform, including BMW, Ericsson, Lockheed Martin and Sony Pictures.

700 多家公司的專業人士正在評估該平台，包括寶馬、愛立信、洛克希德馬丁和索尼影業。

More than 70,000 individual creators have downloaded Omniverse since the open beta launch in December.

自 12 月公開測試版發布以來，已有超過 70,000 名個人創作者下載了 Omniverse。

There are approximately 40 million 3D designers in the global market.

全球市場上約有 4000 萬 3D 設計師。

Moving to Automotive.

轉向汽車。

Q3 revenue of $135 million declined 11% sequentially and increased 8% from the year ago quarter.

第三季度收入為 1.35 億美元，環比下降 11%，同比增長 8%。

The sequential decline was primarily driven by AI cockpit revenue, which has negatively been [upended] by automotive manufacturers' supply constraints.

環比下降主要是由 AI 駕駛艙收入推動的，而汽車製造商的供應限制對這一收入產生了負面影響。

We announced that self-driving truck start-up Kodiak Robotics, automaker Lotus, autonomous bus manufacturer QCraft and EV startup WM Motor have adopted the NVIDIA DRIVE Orin platform for their next-generation vehicles.

我們宣布，自動駕駛卡車初創公司 Kodiak Robotics、汽車製造商 Lotus、自動巴士製造商 QCraft 和電動汽車初創公司 WM Motor 已在其下一代車輛中採用 NVIDIA DRIVE Orin 平台。

They join a large and rapidly growing list of companies adopting and developing an NVIDIA DRIVE, including auto OEMs, Tier 1 suppliers, NAVs, trucking companies, robo-taxis and software startups.

他們加入了採用和開發 NVIDIA DRIVE 的龐大且快速增長的公司名單，其中包括汽車 OEM、一級供應商、NAV、貨運公司、機器人出租車和軟件初創公司。

Moving to data center.

搬到數據中心。

Record revenue of $2.9 billion grew 24% sequentially and 55% from the year ago quarter, with record revenue across both hyperscale and vertical industries.

創紀錄的 29 億美元收入環比增長 24%，同比增長 55%，超大規模和垂直行業的收入均創歷史新高。

Strong growth was led by hyperscale customers, fueled by continued rapid adoption of Ampere architecture tensor core GPUs for both internal and external workloads.

超大規模客戶引領了強勁增長，這得益於 Ampere 架構張量核心 GPU 持續快速用於內部和外部工作負載。

Hyperscale compute revenue doubled year-on-year, driven by the scale out of natural language processing and recommender models and computing.

由於自然語言處理和推薦模型和計算的擴展，超大規模計算收入同比翻了一番。

Vertical industry growth was also strong, led by consumer Internet and broader cloud providers.

在消費互聯網和更廣泛的雲提供商的帶動下，垂直行業的增長也很強勁。

For example, Oracle Cloud deployed NVIDIA GPUs for its launch of AI services such as tech analysis, speech recognition, computer vision and anomaly detection.

例如，甲骨文云部署了 NVIDIA GPU 來推出技術分析、語音識別、計算機視覺和異常檢測等 AI 服務。

We continue to achieve exceptional growth in inference, which again outpaced our overall Data Center growth.

我們繼續在推理方面取得非凡的增長，這再次超過了我們數據中心的整體增長。

We have transitioned our lineup of inference-focused processors to the Ampere architecture such as the A40 GPU.

我們已將專注於推理的處理器系列轉換為 Ampere 架構，例如 A40 GPU。

We also released the latest version of our Triton Inference Server software, enabling compute-intensive inference workloads such as large language models to scale across multiple GPUs and nodes with real-time performance.

我們還發布了最新版本的 Triton 推理服務器軟件，使計算密集型推理工作負載（例如大型語言模型）能夠以實時性能跨多個 GPU 和節點進行擴展。

Over 25,000 companies worldwide use NVIDIA AI inference.

全球超過 25,000 家公司使用 NVIDIA AI 推理。

A great new example is Microsoft Teams, which has nearly 250 million monthly active users.

一個很好的新例子是 Microsoft Teams，它每月有近 2.5 億活躍用戶。

It uses NVIDIA AI to convert speech to text real-time during video calls in 28 languages in a cost-effective way.

它使用 NVIDIA AI 以經濟高效的方式在 28 種語言的視頻通話期間將語音實時轉換為文本。

We reached 3 milestones to help drive more mainstream enterprise adoption of NVIDIA AI.

我們達到了 3 個里程碑，以幫助推動更多主流企業採用 NVIDIA AI。

First, we announced the general availability of NVIDIA AI Enterprise, a comprehensive software suite of AI tools and frameworks that enables the hundreds of thousands of companies running NVIDIA -- running vSphere to virtualize AI workloads on NVIDIA-Certified Systems.

首先，我們宣布 NVIDIA AI Enterprise 全面上市，這是一套全面的 AI 工具和框架軟件套件，使數十萬家運行 NVIDIA 的公司能夠通過運行 vSphere 來虛擬化 NVIDIA 認證系統上的 AI 工作負載。

Second, VMware announced a future update to vSphere with Tensor that is fully optimized for NVIDIA AI.

其次，VMware 宣布了針對 NVIDIA AI 進行全面優化的 vSphere with Tensor 的未來更新。

And this combined with NVIDIA AI Enterprise, enterprises can efficiently manage cloud-native AI development and deployment on manual screen data center servers and files with existing IT tools.

而這與 NVIDIA AI Enterprise 相結合，企業可以使用現有的 IT 工具有效地管理手動屏幕數據中心服務器和文件上的雲原生 AI 開發和部署。

And third, we expanded our LaunchPad program globally with Equinix as our first digital infrastructure partner.

第三，我們在全球範圍內擴展了 LaunchPad 計劃，Equinix 作為我們的第一個數字基礎設施合作夥伴。

NVIDIA LaunchPad is now available in 9 locations worldwide, providing enterprises with immediate access to NVIDIA software and infrastructure to help them prototype and test data science and AI workloads.

NVIDIA LaunchPad 現在可在全球 9 個地點使用，讓企業可以立即訪問 NVIDIA 軟件和基礎設施，以幫助他們對數據科學和 AI 工作負載進行原型設計和測試。

LaunchPad features NVIDIA-Certified Systems and NVIDIA DGX systems running the entire NVIDIA AI software stack.

LaunchPad 採用 NVIDIA 認證系統和 NVIDIA DGX 系統，運行整個 NVIDIA AI 軟件堆棧。

In networking, revenue was impacted as demand outstrips supply.

在網絡方面，由於供不應求，收入受到影響。

We saw momentum towards higher speed and new-generation products, including ConnectX-5 and 6. We announced the NVIDIA Quantum-2 400 gigabit per second end-to-end networking platform, consisting of the Quantum-2 switch, the ConnectX-7 network adapter and the BlueField-3 DPU.

我們看到了向更高速度和新一代產品發展的勢頭，包括 ConnectX-5 和 6。我們宣布推出 NVIDIA Quantum-2 400 Gb/秒端到端網絡平台，包括 Quantum-2 交換機、ConnectX-7網絡適配器和 BlueField-3 DPU。

The NVIDIA Quantum-2 switch is available from a wide range of leading infrastructure and system vendors around the world.

NVIDIA Quantum-2 交換機可從全球眾多領先的基礎設施和系統供應商處獲得。

Earlier this week, the latest Top500 list of supercomputers showed continued momentum for our full-stack computing approach.

本週早些時候，最新的 Top500 超級計算機列表顯示了我們全棧計算方法的持續發展勢頭。

NVIDIA technologies accelerate over 70% of the systems on the list, including over 90% of all new systems and 23 of the top 25 most energy-efficient systems.

NVIDIA 技術加速了列表中超過 70% 的系統，包括超過 90% 的所有新系統和前 25 個最節能係統中的 23 個。

Turning to GTC.

轉向 GTC。

Last week, we hosted our GPU Technology Conference, which had over 270,000 registered attendees.

上週，我們舉辦了 GPU 技術大會，有超過 270,000 名註冊與會者。

Jensen's keynote has been viewed 25 million times over the past 8 days.

在過去的 8 天裡，Jensen 的主題演講被瀏覽了 2500 萬次。

While our spring GTC focused on new chips and systems, this edition focused on software, demonstrating our full computing stack.

雖然我們的春季 GTC 專注於新的芯片和系統，但這個版本專注於軟件，展示了我們完整的計算堆棧。

Let me cover some of the highlights.

讓我介紹一些亮點。

Our vision for Omniverse came to life at GTC.

GTC 實現了我們對 Omniverse 的願景。

We significantly expanded this ecosystem and announced new capabilities.

我們顯著擴展了這個生態系統並宣布了新功能。

Omniverse Replicator is an engine for producing data to train robots, replicating augment's real-world data with massive, diverse and physically accurate synthetic data sets to help accelerate development of high-quality, high-performance AI across computing demand.

Omniverse Replicator 是一種用於生成數據以訓練機器人的引擎，它使用海量、多樣化和物理上準確的合成數據集複製增強的現實世界數據，以幫助加速跨計算需求的高質量、高性能人工智能的開發。

NVIDIA Omniverse Avatar is our platform for generating interactive AI avatars.

NVIDIA Omniverse Avatar 是我們用於生成交互式 AI 化身的平台。

It connects several core NVIDIA SDKs including speech AI, computer vision, natural language understanding, recommendation engines and simulation.

它連接了多個核心 NVIDIA SDK，包括語音 AI、計算機視覺、自然語言理解、推薦引擎和模擬。

Applications including automated customer service, virtual collaboration and content creation.

應用程序包括自動化客戶服務、虛擬協作和內容創建。

Replicator and Avatar joined several other announced features and capabilities for Omniverse, including AI, AR, VR and simulation-based technologies.

Replicator 和 Avatar 加入了 Omniverse 的其他幾項已宣布的特性和功能，包括 AI、AR、VR 和基於模擬的技術。

We introduced 65 new and updated software development kits, bringing our total to more than 150 serving industries from gaming and design to AI, cybersecurity, 5G and robotics.

我們推出了 65 個新的和更新的軟件開發套件，使我們的總數達到 150 多個服務行業，從遊戲和設計到人工智能、網絡安全、5G 和機器人技術。

One of the SDKs is our first 4 licensed AI model, NVIDIA Riva, for building conversational AI applications.

其中一個 SDK 是我們第一個獲得許可的 4 個 AI 模型 NVIDIA Riva，用於構建對話式 AI 應用程序。

Companies using Riva during the open beta include RingCentral for videoconference live caption and [MTS] for customer service chatbot.

在公測期間使用 Riva 的公司包括用於視頻會議實時字幕的 RingCentral 和用於客戶服務聊天機器人的 [MTS]。

NVIDIA Riva Enterprise will be commercially available early next year for launching.

NVIDIA Riva Enterprise 將於明年初上市並上市。

We introduced the NVIDIA NeMo Megatron framework, optimized for training large language models on NVIDIA DGX SuperPOD infrastructure.

我們引入了 NVIDIA NeMo Megatron 框架，該框架針對在 NVIDIA DGX SuperPOD 基礎架構上訓練大型語言模型進行了優化。

This combination brings together production-ready, enterprise-grade hardware and software to help vertical industries develop language and industry-specific chatbots, personal systems, content generation and summarization.

這種組合將生產就緒的企業級硬件和軟件結合在一起，以幫助垂直行業開發語言和行業特定的聊天機器人、個人系統、內容生成和摘要。

Early adopters include CD, JD.com and VinBrain.

早期採用者包括 CD、京東和 VinBrain。

We unveiled BlueField DOCA 1.2, the latest version of our DPU programming [vendor] with new cybersecurity capabilities.

我們推出了 BlueField DOCA 1.2，這是我們 DPU 編程 [供應商] 的最新版本，具有新的網絡安全功能。

DOCA is to our DPUs as CUDA is to our GPUs.

DOCA 之於我們的 DPU，就像 CUDA 之於我們的 GPU。

It enables developers to build applications and services on top of our BlueField DPU.

它使開發人員能夠在我們的 BlueField DPU 之上構建應用程序和服務。

Our new capabilities make BlueField the ideal platform for the industry to build their own zero-trust security platforms.

我們的新功能使 BlueField 成為業界構建自己的零信任安全平台的理想平台。

The leading cybersecurity companies are working with us to provision their next-generation firewall service on BlueField, including Checkpoint, Juniper, Fortinet, F5, Palo Alto Networks and VMware.

領先的網絡安全公司正在與我們合作，在 BlueField 上配置他們的下一代防火牆服務，包括 Checkpoint、Juniper、Fortinet、F5、Palo Alto Networks 和 VMware。

And we released Clara Holoscan, an edge AI computing platform for medical instruments to improve decision-making tools in areas such as robo-assisted surgery, interventional radiology and radiation therapy planning.

我們發布了 Clara Holoscan，這是一個用於醫療器械的邊緣 AI 計算平台，以改進機器人輔助手術、介入放射學和放射治療規劃等領域的決策工具。

Other new or expanded SDKs or libraries unveiled at GTC include ReOpt for AI-optimized logistics, cuQuantum for quantum computing, Morpheus for cybersecurity, Modulus for physical-based (sic) [physics-based] machine learning and cuNumeric, a data-center-scale math library to bring accelerated computing to the large and growing Python ecosystem.

在 GTC 上推出的其他新的或擴展的 SDK 或庫包括用於 AI 優化物流的 ReOpt、用於量子計算的 cuQuantum、用於網絡安全的 Morpheus、用於基於物理 (sic) [基於物理] 機器學習的 Modulus 和 cuNumeric，一個數據中心-擴展數學庫，為龐大且不斷增長的 Python 生態系統帶來加速計算。

All in, NVIDIA's computing platform continues to expand as a broadening set of SDK enable more and more GPU-accelerated applications and industry use cases.

總而言之，NVIDIA 的計算平台隨著不斷擴展的 SDK 集支持越來越多的 GPU 加速應用程序和行業用例而不斷擴展。

CUDA has been downloaded 30 million times, and our developer ecosystem is now nearing 3 million strong.

CUDA 已被下載 3000 萬次，我們的開發者生態系統現在已接近 300 萬。

The applications they develop on top of our SDK and the cloud to edge computing platform are helping to transform multitrillion dollar industries from health care to transportation to financial services, manufacturing, logistics and retail.

他們在我們的 SDK 和雲到邊緣計算平台之上開發的應用程序正在幫助將價值數万億美元的行業從醫療保健到交通運輸再到金融服務、製造、物流和零售。

In automotive, we announced NVIDIA DRIVE Concierge and DRIVE Chauffeur, AI software platforms that enhance a vehicle's performance features and safety.

在汽車領域，我們發布了 NVIDIA DRIVE Concierge 和 DRIVE Chauffeur，這兩個 AI 軟件平台可增強車輛的性能和安全性。

DRIVE Concierge built on Omniverse Avatar functions as an AI-based in-vehicle personal assistant, but enables automatic parking summoning capabilities.

基於 Omniverse Avatar 構建的 DRIVE Concierge 可用作基於 AI 的車載個人助理，但具有自動停車召喚功能。

It also enhanced safety by monitoring the driver throughout the duration of the trip.

它還通過在整個行程期間監控駕駛員來提高安全性。

DRIVE Chauffeur offers autonomous capabilities, relieving the driver of constantly having to control the car.

DRIVE Chauffeur 提供自動駕駛功能，減輕駕駛員不斷控制汽車的負擔。

It will also perform address to address driving when combined with DRIVE Hyperion 8 platform.

當與 DRIVE Hyperion 8 平台結合使用時，它還將執行尋址驅動。

For robotics, we announced Jetson AGX Orin, the world's smallest, most powerful and energy-efficient AI supercomputer for robotics, autonomous machines and embedded computing device.

對於機器人技術，我們發布了 Jetson AGX Orin，這是世界上最小、最強大、最節能的人工智能超級計算機，適用於機器人、自主機器和嵌入式計算設備。

Built on our Ampere architecture, Jetson AGX Orin provides 6x the processing of its predecessor and delivers 200 trillion operations per second, similar to a GPU-enabled server that fits into the palm of your hand.

Jetson AGX Orin 建立在我們的 Ampere 架構之上，其處理能力是其前身的 6 倍，每秒可提供 200 萬億次運算，類似於手掌大小的支持 GPU 的服務器。

Jetson AGX Orin will be available in the first quarter of calendar 2022.

Jetson AGX Orin 將於 2022 年第一季度上市。

Finally, we revealed plans to build Earth-2, the world's most powerful AI supercomputer dedicated to confronting climate change.

最後，我們公佈了建造 Earth-2 的計劃，這是世界上最強大的人工智能超級計算機，致力於應對氣候變化。

The system would be the climate change counterpart to Cambridge-1, the U.K.'s most powerful AI supercomputer that we built for health care research.

該系統將是英國最強大的人工智能超級計算機 Cambridge-1 的氣候變化對應物，我們為醫療保健研究而構建。

Earth-2 harnesses all the technologies we've embedded -- invented up to this moment.

Earth-2 利用了我們嵌入的所有技術——發明至今。

Let me discuss -- I'll provide you a brief update on our proposed acquisition of ARM.

讓我討論一下——我將向您簡要介紹我們提議收購 ARM 的最新情況。

ARM with NVIDIA is a great opportunity for the industry and customers.

ARM 與 NVIDIA 是行業和客戶的絕佳機會。

With NVIDIA's scale, capabilities and robust understating of data center computing, acceleration in AI, we can assist ARM in expanding their reach into data center, IoT and physics and advance ARM's IP for decades to come.

憑藉 NVIDIA 的規模、能力和對數據中心計算的穩健理解以及人工智能的加速，我們可以幫助 ARM 將其範圍擴展到數據中心、物聯網和物理領域，並在未來幾十年推動 ARM 的 IP。

The combination of our companies can enhance competition in the industry as we work together on further building the world of AI.

我們公司的合併可以增強行業競爭，因為我們共同努力進一步建設人工智能世界。

Regulators at the U.S. FTC have expressed concerns regarding the transaction, and we are engaged in discussions with them regarding revenues to address their concerns.

美國聯邦貿易委員會的監管機構已經表達了對該交易的擔憂，我們正在與他們就收入進行討論以解決他們的擔憂。

The transaction has been under review by China's antitrust authority pending the former case initiation.

在前一案立案之前，中國反壟斷機構正在審查該交易。

Regulators in the U.K. and the EU have declined to (inaudible) Phase 1 of the review on competition concerns.

英國和歐盟的監管機構已拒絕（聽不清）競爭問題審查的第一階段。

In the U.K., they have also voiced national security concerns.

在英國，他們也表達了對國家安全的擔憂。

We have begun the Phase 2 process in the EU and U.K. jurisdictions.

我們已經在歐盟和英國司法管轄區開始了第二階段流程。

Despite these concerns and those raised by some ARM licensees, we continue to believe in the merits and the benefits of the acquisition to ARM, to its licensees and the industry.

儘管存在這些擔憂以及一些 ARM 被許可方提出的擔憂，但我們仍然相信收購對 ARM、其被許可方和整個行業的好處和好處。

We believe these concerns and those raised by some ARM licensees, we continue to believe in the merit and benefit of the ARM acquisition.

我們相信這些擔憂以及一些 ARM 被許可方提出的擔憂，我們繼續相信 ARM 收購的優點和好處。

Moving to the rest of the P&L.

轉到損益表的其餘部分。

GAAP gross margin for the third quarter was up 260 basis points from a year earlier, primarily due to higher-end mix within desktop, notebook GeForce GPUs.

第三季度的 GAAP 毛利率比去年同期增長 260 個基點，這主要是由於台式機、筆記本電腦 GeForce GPU 的高端組合。

The year-on-year increase also benefited from a reduced impact of acquisition-related costs.

同比增長還受益於收購相關成本的影響減少。

GAAP gross margin was up 40 basis points sequentially, driven by growth in our data center Ampere architecture products, which is particularly offset by our mix in gaming.

在我們的數據中心安培架構產品增長的推動下，GAAP 毛利率環比增長 40 個基點，這尤其被我們的遊戲組合所抵消。

Non-gaming gross margin was up 150 basis points from a year earlier and up 30 basis points sequentially.

非博彩毛利率同比增長 150 個基點，環比增長 30 個基點。

Q3 GAAP EPS was $0.97, 83% from a year earlier.

第三季度 GAAP 每股收益為 0.97 美元，同比增長 83%。

Non-GAAP EPS was $1.17, up 60% from a year ago, adjusting for our stock split.

非 GAAP 每股收益為 1.17 美元，比一年前增長 60%，調整了我們的股票分割。

Q3 cash flow from operations was $1.5 billion, up from $1.3 billion a year earlier and down from $2.7 billion in the prior quarter.

第三季度運營現金流為 15 億美元，高於去年同期的 13 億美元，低於上一季度的 27 億美元。

The year-on-year increase primarily reflects higher operating income, particularly offset by prepayments for long-term supply agreements.

同比增長主要反映了較高的營業收入，特別是被長期供應協議的預付款所抵消。

Let me turn to the outlook for the fourth quarter of fiscal 2022.

讓我談談對 2022 財年第四季度的展望。

We expect sequential growth to be driven by data center and gaming.

我們預計連續增長將受到數據中心和遊戲的推動。

-- more than offsetting a decline in CMP.

- 不僅抵消了 CMP 的下降。

Revenue is expected to be $7.4 billion, plus or minus 2%.

收入預計為 74 億美元，正負 2%。

GAAP and non-GAAP gross margins are expected to be 65.3% and 67%, respectively, plus or minus 50 basis points.

GAAP 和非 GAAP 毛利率預計分別為 65.3% 和 67%，上下浮動 50 個基點。

GAAP and non-GAAP operating expenses are expected to be approximately $2.02 billion and $1.43 billion, respectively.

GAAP 和非 GAAP 運營費用預計分別約為 20.2 億美元和 14.3 億美元。

GAAP and non-GAAP other income and expenses are both expected to be an expense of approximately $60 million, excluding gains and losses from nonaffiliated investments.

GAAP 和非 GAAP 其他收入和支出預計都將是大約 6000 萬美元的支出，不包括非關聯投資的損益。

GAAP and non-GAAP tax rates are both expected to be 11%, plus or minus 1%, excluding dispute items.

GAAP 和非 GAAP 稅率預計均為 11%，正負 1%，不包括爭議項目。

Capital expenditures are expected to be approximately $250 million to $275 million.

資本支出預計約為 2.5 億至 2.75 億美元。

Further financial details are included in the CFO commentary, other information is also available on our IR website.

更多財務細節包含在 CFO 評論中，其他信息也可在我們的 IR 網站上找到。

In closing, let me highlight upcoming events for the financial community.

最後，讓我強調一下金融界即將發生的事件。

We will be attending the Credit Suisse 25th Annual Technology Conference in person on November 30.

我們將於 11 月 30 日親自參加瑞士信貸第 25 屆年度技術會議。

We will also be at the Wells Fargo Fifth Annual TMT Summit, virtually, on December 1; the UBS Global TMT Virtual Conference on December 6; and the Deutsche Bank Virtual AutoTech Conference on December 9. Our earnings call to discuss our fourth quarter and fiscal year 2022 results is scheduled for Wednesday, February 16.

我們還將在 12 月 1 日參加 Wells Fargo 第五屆年度 TMT 峰會； 12 月 6 日的瑞銀全球 TMT 虛擬會議；以及 12 月 9 日舉行的德意志銀行虛擬汽車技術會議。我們定於 2 月 16 日星期三召開財報電話會議，討論我們的第四季度和 2022 財年業績。

With that, we will now open the call for questions.

有了這個，我們現在開始提問。

Operator, will you please poll today's questions?

接線員，請您對今天的問題進行投票嗎？

(Operator Instructions) For our first question, we have Aaron Rakers from Wells Fargo.

（操作員說明）對於我們的第一個問題，我們請來富國銀行的 Aaron Rakers。

Yes.

是的。

Congratulations on the results.

祝賀結果。

I guess I wanted to ask about Omniverse.

我想我想問一下關於 Omniverse 的問題。

Obviously, a lot of excitement around that.

顯然，這引起了很多興奮。

I guess the simple question is, Jensen, how do you define success in Omniverse as we look out over the next, let's call it, 12 months?

我想一個簡單的問題是，Jensen，你如何定義 Omniverse 中的成功，因為我們展望下一個，讓我們稱之為 12 個月？

And how do we think about the subscription license opportunity for Omniverse?

我們如何看待 Omniverse 的訂閱許可機會？

I know you've talked about 40 million total 3D designers.

我知道你談到了總共 4000 萬 3D 設計師。

I think that actually doubled what you talked about back in August.

我認為這實際上使您在八月份談論的內容翻了一番。

So I'm just curious of how we, as financial analysts, should start to think about that opportunity materializing?

所以我只是好奇，作為金融分析師，我們應該如何開始考慮這個機會的實現？

Yes.

是的。

Omniverse's success will be defined by, number one, developer engagement, connections with developers around the world; two, applications being developed by enterprises; three, the connection of designers and creators among themselves.

Omniverse 的成功將首先取決於開發人員的參與度、與世界各地開發人員的聯繫；二是企業正在開發的應用；三、設計師和創作者之間的聯繫。

Those are the nearest term.

這些是最接近的術語。

And I would say that in my type of definition of success.

我會在我對成功的定義中這麼說。

Near term also should be revenues.

近期也應該是收入。

And Omniverse has real immediate applications as I demonstrated at the keynote.

正如我在主題演講中所展示的，Omniverse 具有真正的直接應用。

And I'll highlight a few of them right now.

我現在將重點介紹其中的一些。

One of them, of course, is that it serves as a way to connect 3D and digital design world.

其中之一，當然，是它作為連接 3D 和數字設計世界的一種方式。

Think of Adobe as a world.

將 Adobe 視為一個世界。

Think of Autodesk as a world because revenue is a world.

將 Autodesk 視為一個世界，因為收入就是一個世界。

These are design worlds in the sense that people are doing things in it, they're creating things in it, and it has its own database.

從某種意義上說，這些都是設計世界，人們在其中做事，他們在其中創造東西，並且它有自己的數據庫。

We made it possible for these worlds to be connected for the very first time and for it to be shared like a cloud document.

我們使這些世界第一次連接成為可能，並且可以像雲文檔一樣共享。

That's not been possible ever before.

這在以前是不可能的。

And you can now share work with each other.

現在你們可以互相分享工作了。

You can see how this work, you can collaborate.

你可以看到這是如何工作的，你可以合作。

And so in a world of remote working, Omniverse's collaboration capability is going to be really appreciated.

因此，在遠程工作的世界中，Omniverse 的協作能力將受到真正的讚賞。

And that should happen right away.

這應該馬上發生。

We would like to see that happen in very near term.

我們希望在短期內看到這種情況發生。

And that drives, of course, more PC sales, more GPU sales, more workstation sales, more server sales.

當然，這會推動更多 PC 銷售、更多 GPU 銷售、更多工作站銷售、更多服務器銷售。

The second use case is digital twins.

第二個用例是數字孿生。

And you saw -- we saw examples of how several companies, they are using Omniverse to create digital twin of a city so that they could optimize radio placements and video energy used for (inaudible).

你看到了——我們看到了幾家公司如何使用 Omniverse 創建城市的數字雙胞胎的例子，以便他們可以優化無線電佈置和用於（聽不清）的視頻能量。

You saw BMW using it for their factories.

你看到寶馬在他們的工廠中使用它。

You're going to see people using it for warehouse -- logistics warehouse to plan and to optimize their warehouses and to plan their robots.

你會看到人們將它用於倉庫——物流倉庫來規劃和優化他們的倉庫並規劃他們的機器人。

And so digital twin applications are absolutely needed.

因此，絕對需要數字孿生應用程序。

And then remember, robots have several kinds.

然後記住，機器人有好幾種。

There's the physical robots that you saw and a physical robot with the self-driving car and a physical robot to do the car itself, turning it into a robot so that it could be an intelligent assistant.

有你看到的物理機器人，還有一個物理機器人和自動駕駛汽車，還有一個物理機器人自己做汽車，把它變成一個機器人，這樣它就可以成為一個智能助手。

But I demonstrate probably the -- in my estimation, the largest application of robots in the future is avatars.

但我可能展示了——據我估計，未來機器人最大的應用是化身。

We built Omniverse Avatar to make it easy for people to integrate some amazing technology from computer vision to speech recognition, natural language understanding, gesture recognition, facial animation, speech synthesis, recommender systems, all of that integrated into one system and running in real time.

我們構建了 Omniverse Avatar，讓人們可以輕鬆地將一些驚人的技術集成到一個系統中並實時運行，從計算機視覺到語音識別、自然語言理解、手勢識別、面部動畫、語音合成、推薦系統，所有這些都集成到一個系統中並實時運行.

That avatar system is essentially a robotic system.

該化身系統本質上是一個機器人系統。

And the way that you would do that is, for example, the 25 million-or-so retail stores, restaurants, places like airports and train stations and office buildings and such, where you're going to have intelligent avatars doing a lot of assistance.

你會這樣做的方式是，例如，2500 萬左右的零售店、餐館、機場、火車站和辦公樓等地方，你將讓智能化身做很多事情幫助。

They might be doing check-out, they might be doing check-in, they might be doing customer support.

他們可能正在辦理退房手續，他們可能正在辦理入住手續，他們可能正在辦理客戶支持。

And all of that could be done with avatars, as I've demonstrated.

正如我所展示的，所有這些都可以通過化身來完成。

So the virtual robotics application, digital bots or avatars is going to be likely the largest robotics opportunity.

因此，虛擬機器人應用、數字機器人或化身可能是最大的機器人機會。

So if you look at our licensing model, the way it basically works is that inside Omniverse, each one of the main users, new users -- and the main user could be one of the 20 million creatives or 20 million designers, we have the 40 million creatives and designers around the world, when they scale Omniverse, each one of the main users would be $1,000 per user per year.

所以如果你看看我們的許可模式，它的基本運作方式是在 Omniverse 內部，每個主要用戶，新用戶——主要用戶可能是 2000 萬創意人員或 2000 萬設計師之一，我們有全球有 4000 萬創意人員和設計師，當他們擴展 Omniverse 時，每個主要用戶每年每位用戶將獲得 1000 美元。

But don't forget that intelligent beings or intelligent users that can be connected to Omniverse will likely be much larger as digital bots than humans.

但不要忘記，可以連接到 Omniverse 的智能生物或智能用戶作為數字機器人可能比人類大得多。

So I've mentioned 40 million, but there are 100 million cars.

所以我提到了 4000 萬輛，但有 1 億輛汽車。

And 100 million cars will all have -- all these have the capability to have something like an Omniverse Avatar.

1 億輛汽車都將擁有——所有這些都有能力擁有像 Omniverse Avatar 這樣的東西。

And so those 100 million cars could be $1,000 per car per year.

因此，這 1 億輛汽車每年可能每輛汽車 1,000 美元。

And in the case of the 25 million or so places where you would have a digital avatar as customer support or check-out, smart retail or smart warehouse, it's smart whatever it is.

在大約 2500 萬個地方，您可以使用數字化身作為客戶支持或結賬、智能零售或智能倉庫，無論它是什麼都是智能的。

Those avatars are also with -- each individual will have a main account.

這些化身也有——每個人都有一個主賬戶。

And so they would be $1,000 per avatar per year.

因此，他們每年每個化身的費用為 1,000 美元。

And so those are the immediate tangible opportunities for us, and I demonstrated the application now.

所以這些對我們來說是直接的切實機會，我現在演示了這個應用程序。

And then, of course, behind all of that, the -- call it, a couple of hundred million digital agents, intelligent agents, some of them human, some of robots, some of them avatars at $1,000 per agent per year.

然後，當然，在這一切的背後，有幾億數字代理、智能代理，其中一些是人類，一些是機器人，其中一些是化身，每個代理每年 1,000 美元。

Behind it are NVIDIA GPUs in PCs, NVIDIA GPUs in the cloud and NVIDIA GPUs still on Omniverse servers.

其背後是 PC 中的 NVIDIA GPU、雲中的 NVIDIA GPU 和仍在 Omniverse 服務器上的 NVIDIA GPU。

And my guess would be that the hardware part of it is probably going to be about half and then the licensing part of it is probably be about half of the time.

我的猜測是，它的硬件部分可能會佔一半左右，然後它的許可部分可能會佔一半左右。

And -- but this was really going to be one of the largest graphics opportunities that we've ever seen.

而且——但這確實將是我們見過的最大的圖形機會之一。

And the reason why it's taking so long for this to manifest is because it requires 3 fundamental technologies to come together.

之所以需要這麼長時間才能體現出來，是因為它需要 3 種基本技術結合在一起。

I guess 4 fundamental technologies come together.

我猜想有 4 種基本技術結合在一起。

First of all is to do the [bracket].

首先是做[括號]。

Second is physics simulation because we're talking about things in a world that has to be believable so it has to obey the laws of physics.

其次是物理模擬，因為我們談論的是一個必須可信的世界中的事物，因此它必須遵守物理定律。

And then third is artificial intelligence, as I've demonstrated in several stages now.

第三個是人工智能，正如我現在分幾個階段展示的那樣。

And all of it runs on top of an Omniverse computer that has to do not just AI, not just physics, not just computer graphics, but all of it.

所有這些都運行在一台 Omniverse 計算機之上，該計算機不僅要處理 AI，不僅僅是物理，不僅僅是計算機圖形，而是所有這些。

And so what long term people -- why people are so excited about it is at the highest level, what it basically means is that long term, when we engage with Internet, which is largely 2D today, long term, every query would be 3D.

那麼長期的人——為什麼人們對它如此興奮是最高水平的，它基本上意味著長期，當我們使用互聯網時，今天主要是 2D，從長期來看，每個查詢都是 3D .

And instead of just querying information, we would query and interact with people and avatars and things, places, and all of these things are in 3D.

而不僅僅是查詢信息，我們將查詢並與人和化身以及事物、地點進行交互，所有這些事物都是 3D 的。

So hopefully, one of these days, that we will try to realize it as fast as we can.

所以希望，在這些日子裡，我們會盡可能快地實現它。

Every transaction that goes over the Internet touches a GPU.

通過 Internet 進行的每筆交易都涉及 GPU。

And today, that's a very small percentage, but hopefully, one of these days it will be a very, very high percentage.

今天，這是一個非常小的百分比，但希望有一天，它會是一個非常非常高的百分比。

I hope that's helpful.

我希望這會有所幫助。

For our next question, we have Mark Lipacis from Jefferies.

對於我們的下一個問題，我們請來自 Jefferies 的 Mark Lipacis。

Mark?

標記？

Jensen, it seems like every year, there seems to be a new set of demand drivers for your accelerated processing ecosystem.

Jensen，似乎每一年，您的加速處理生態系統似乎都有一套新的需求驅動因素。

There's gaming, then neural networking and AI and the blockchain and the ray tracing.

有遊戲，然後是神經網絡和人工智能，還有區塊鍊和光線追踪。

And then like 5 or 6 years ago, you guys showed a bunch of virtual reality demos, which were really exciting at your Analyst Day.

然後就像 5 或 6 年前一樣，你們展示了一堆虛擬現實演示，這在您的分析師日非常令人興奮。

Excitement died down.

興奮消失了。

Now it seems to be resurfacing, particularly with Omniverse Avatar capability and Facebook shining light on the opportunities.

現在它似乎重新浮出水面，特別是 Omniverse Avatar 功能和 Facebook 照亮了機遇。

So the 2 questions from that are, how close is your Omniverse Avatar to morphing into like a mass market technology that everybody uses daily?

因此，其中的兩個問題是，您的 Omniverse Avatar 離轉變為每個人每天都在使用的大眾市場技術有多近？

You can talk about like -- you said that everybody is going to be a gamer, everybody going to be an Omniverse Avatar user.

你可以這樣說——你說每個人都將成為遊戲玩家，每個人都將成為 Omniverse Avatar 用戶。

And maybe the bigger picture is, is it reasonable to think about a new killer app coming out every year?

也許更大的圖景是，考慮每年推出一款新的殺手級應用是否合理？

Is there a parallel that we should think about with previous computing markets that we could think about for the computing era that we're entering right now?

我們是否應該考慮與我們現在正在進入的計算時代可以考慮的先前計算市場的相似之處？

I really appreciate that.

我真的很感激。

Chips are enablers, but chips don't create markets.

芯片是推動者，但芯片不會創造市場。

Software creates markets.

軟件創造市場。

I've explained over the years that accelerated computing is very different than general purpose computing.

多年來，我已經解釋過加速計算與通用計算有很大不同。

And the reason for that is because you can't just write a C compiler and compile quantum physics into a chip when it doesn't.

這樣做的原因是因為你不能只編寫一個 C 編譯器並將量子物理學編譯到芯片中，而實際上它沒有。

You can't just compile showing your situation and have it distributed across multiple GPUs and multiple nodes and have it (inaudible).

您不能只編譯顯示您的情況並將其分佈在多個 GPU 和多個節點上並擁有它（聽不清）。

You just -- you can't do that for computer graphics.

你只是 - 你不能為計算機圖形做那個。

You can't do that for artificial intelligence.

對於人工智能，你不能這樣做。

You can't do that for robotics.

對於機器人技術，你不能這樣做。

You can't do that for most of the interesting applications in the world.

對於世界上大多數有趣的應用程序，您無法做到這一點。

And because we really went out of theme with GPUs, that people are saying that not because it's not true, it's abundantly clear that the amount in instructional parallels will then be squeezed out of a system is, although not 0, is incredibly hard.

而且因為我們真的脫離了 GPU 的主題，人們說這不是因為它不是真的，很明顯，教學並行的數量將被擠出一個系統，儘管不是 0，但非常困難。

It's just incredibly hard.

這簡直太難了。

And there's another approach.

還有另一種方法。

And we've been advocating accelerated computing for some time, and now people really see the benefit of it.

一段時間以來，我們一直在提倡加速計算，現在人們真正看到了它的好處。

But it does require a lot of work.

但這確實需要大量的工作。

And the work basically says for every domain, for every application, we have -- for every application in a large domain ideally, you have to have a full stack.

這項工作基本上說，對於每個領域，對於我們擁有的每個應用程序——理想情況下，對於大型領域中的每個應用程序，您都必須擁有完整的堆棧。

And so whenever you want to open a new market by accelerating those applications or the domain of applications, you have to come up with a new stack.

因此，每當您想通過加速這些應用程序或應用程序領域來打開一個新市場時，您都必須想出一個新的堆棧。

And the new stack is hard because you have to understand the application, you have to understand the algorithms, the mathematics, you have to understand computer science to distribute it across, to take something that was single threaded and make it multi-threaded and make something that was done sequentially and make its process in parallel.

新堆棧很難，因為你必須了解應用程序，你必須了解算法、數學，你必須了解計算機科學才能將其分佈，將單線程的東西變成多線程的，然後按順序完成並使其過程並行的事情。

You break everything.

你打破了一切。

You break storage, you break networking, you break everything.

你破壞了存儲，你破壞了網絡，你破壞了一切。

And so it takes a fair amount of expertise, and that's why we say that over the years, over the course of 30 years, we've become a full-stack company because we've been trying to solve this problem practically through decades.

所以這需要相當多的專業知識，這就是為什麼我們說多年來，在 30 年的過程中，我們已經成為一家全棧公司，因為幾十年來我們一直在努力解決這個問題。

And so that's one.

這就是其中之一。

But the benefit, once you have the ability, then you can open new markets.

但好處是，一旦有了能力，就可以開闢新的市場。

And we played a very large role in democratizing artificial intelligence and making it possible for anybody to be able to do it.

我們在使人工智能民主化並使任何人都能做到這一點方面發揮了非常重要的作用。

Our greatest contribution is I hope when it's all said and done, that we've democratized scientific computing so that researchers and scientists, computer scientists and data scientists, scientists of all kinds, were able to get access to this incredibly powerful tool that we call computers to do -- to advance research.

我們最大的貢獻是，我希望說到底，我們已經使科學計算民主化，以便研究人員和科學家、計算機科學家和數據科學家、各種科學家能夠使用我們稱之為的這個非常強大的工具計算機要做的——推進研究。

And so every single year, we're filling up with these stacks, and we've got a whole bunch of stacks that we're working on.

所以每一年，我們都在用這些堆棧填滿，我們正在處理一大堆堆棧。

And many of them I'm working on in plain sight so that you see it coming, you just have to connect it together.

其中許多我都在顯眼地工作，以便您看到它的到來，您只需將它們連接在一起即可。

One of the areas that we spoke about this time, of course, was Omniverse.

當然，我們這次談到的領域之一是 Omniverse。

You saw the pieces that are being built in over time.

你看到了隨著時間的推移正在建造的作品。

It took half a decade to start building Omniverse, but it's built on a quarter century of work.

開始構建 Omniverse 花了五年時間，但它建立在四分之一世紀的工作之上。

In the case of the Omniverse Avatar, you can literally point to Merlin recommender; Megatron, language -- large language model; Riva, the speech AI; all of our computer vision AIs that I've been demonstrating over the years; natural speed synthesis that you see every single year with i am ai, the opening credits, how we're using, developing an AI to be able to speak in a human way so that people feel more comfortable and more engaged with the AI; face, eye tracking, Maxine.

對於 Omniverse Avatar，您可以直接指向 Merlin 推薦器；威震天，語言——大型語言模型； Riva，語音人工智能；多年來我一直在展示的所有計算機視覺 AI；你每年都會看到的自然速度合成我是人工智能，開場學分，我們如何使用，開發一個能夠以人類方式說話的人工智能，讓人們感覺更舒服，更參與人工智能；面部，眼動追踪，Maxine。

And all of these technologies all kind of tie together.

所有這些技術都結合在一起。

They were all being built in pieces from the integrated -- we have no intentions of integrating it to create what is called Omniverse Avatar.

它們都是從集成中構建的——我們無意集成它來創建所謂的 Omniverse Avatar。

And now -- then you asked the question, how quickly will we deploy this?

現在 - 然後你問了這個問題，我們將多快部署它？

I believe Omniverse Avatar will be in drive-throughs of restaurants, fast food restaurants, check-outs of restaurants, in retail stores all over the world within less than 5 years.

我相信 Omniverse Avatar 將在不到 5 年的時間內進入世界各地的餐館、快餐店、餐館結賬和零售店。

And we really use it in all kinds of different applications because there's such a great shortage of labor.

我們真的在各種不同的應用中使用它，因為勞動力非常短缺。

And there's such a wonderful way that we can now engage in Avatar.

有一種奇妙的方式，我們現在可以參與 Avatar。

And I think it could -- think it doesn't make mistakes, it never gets tired and it's always on.

而且我認為它可以 - 認為它不會犯錯誤，它永遠不會感到疲倦並且它總是在運行。

And we made it so that it's cloud-native.

我們做到了，它是雲原生的。

And so when you saw the keynote, I hope you'd agree that the interaction is continuous and the conversational form is so enjoyable.

所以當你看到主題演講時，我希望你會同意互動是連續的，對話形式是如此令人愉快。

And so anyway, I think what you highlighted is, one, accelerating computing is a full-stack challenge; two, it takes software to open new markets.

所以無論如何，我認為你強調的是，第一，加速計算是一個全棧挑戰；二是軟件打開新市場。

Chips won't open new markets.

芯片不會打開新市場。

If you build another chip, you can steal somebody's share, but you can't open new markets.

如果你製造另一個芯片，你可以竊取別人的份額，但你不能打開新市場。

And it takes software to open new markets, and we have switched with software.

並且需要軟件來打開新市場，而我們已經用軟件進行了轉換。

And that's one of the reasons why we could engage such large market opportunity.

這就是我們能夠抓住如此巨大的市場機會的原因之一。

And then lastly, with respect to Omniverse, I believe it's a near-term opportunity that we've been working on for some 3, 4, 5 years.

最後，關於 Omniverse，我相信這是一個我們已經努力了 3、4、5 年的短期機會。

For our next question, we have C.J. Muse from Evercore ISI.

對於我們的下一個問題，我們有來自 Evercore ISI 的 C.J. Muse。

Yes.

是的。

And I guess, not an Omniverse question, but I guess, Jensen, I'd like your commitment that you will not use Omniverse to target the sell-side research industry.

我想，這不是 Omniverse 的問題，但我想，Jensen，我希望你能承諾不會使用 Omniverse 來瞄準賣方研究行業。

But as my real question, can you speak to your data center visibility into 2022 and beyond?

但作為我真正的問題，您能否談談您的數據中心對 2022 年及以後的可見性？

And within this outlook, can you talk to traditional cloud versus industry verticals?

在這種前景下，您能否談談傳統雲與垂直行業？

And then perhaps emerging opportunities like Omniverse and others.

然後可能會出現 Omniverse 等新興機會。

Would love to get a sense of kind of what you're seeing today.

很想了解你今天所看到的。

And then as part of that, how you're planning to secure foundry and other supply to support that growth.

然後作為其中的一部分，您計劃如何確保代工廠和其他供應以支持這種增長。

Thank you, C.J. First of all, we have secured guaranteed supply, very large amounts of it, quite a spectacular amount of it from the world's leading foundry and substrate in packaging and testing companies that are integral part of our supply chain.

謝謝你，C.J。首先，我們已經從世界領先的封裝和測試公司的代工和基板公司獲得了有保證的供應，數量非常多，數量相當驚人，這些公司是我們供應鏈中不可或缺的一部分。

And so we have done that and feel very good about our supply situation, particularly starting in the second half of next year and going forward.

所以我們已經做到了，並且對我們的供應情況感覺非常好，特別是從明年下半年開始並展望未來。

I think this whole last year was a wake-up call for everybody to be much more mindful about not taking the supply chain for granted.

我認為去年的這一整年是一個警鐘，讓每個人都更加註意不要將供應鏈視為理所當然。

And we were fortunate to have such great partners.

我們很幸運有這麼好的合作夥伴。

But nonetheless, we've secured this with our future.

但儘管如此，我們已經用我們的未來確保了這一點。

With respect to data center, about half of our data center business comes from the cloud and cloud service providers, and the other half comes from enterprise, what we call enterprise companies.

在數據中心方面，我們的數據中心業務大約有一半來自云和雲服務提供商，另一半來自企業，我們稱之為企業公司。

They're all -- in all kinds of industries.

他們都 - 在各種行業。

And about 1% of it comes from supercomputing centers.

其中約 1% 來自超級計算中心。

So 30%-or-so, cloud; 50%-or-so, enterprise; and 1% supercomputing centers.

所以 30% 左右，雲； 50%左右，企業；和 1% 的超級計算中心。

And we expect next year, the cloud service providers to scale out their deep learning and their AI workloads really aggressively.

我們預計明年，雲服務提供商將真正積極地擴展他們的深度學習和人工智能工作負載。

And we're seeing it right now.

我們現在就看到了。

We've built a really fantastic platform, and -- number one.

我們已經建立了一個非常棒的平台，而且——第一。

Number two, the work we've been doing with TensorRT, which is the run time that goes into the server that's called Triton is one of our best pieces of work.

第二，我們一直在使用 TensorRT 進行的工作，即進入稱為 Triton 的服務器的運行時，是我們最好的工作之一。

We're just so proud of it.

我們為此感到非常自豪。

And we said nearly 4 years ago, 3.5 years ago that inference is going to be one of the great computer science challenges, and we're really prudent to be so.

近 4 年前，3.5 年前我們說過，推理將成為計算機科學的一大挑戰，我們非常謹慎地這樣做。

And the reason for that is because sometimes it's so fast, sometimes it's latency, sometimes it's interactivity and the type of models we have to inference.

其原因是因為有時它非常快，有時是延遲，有時是交互性以及我們必須推斷的模型類型。

It's just all over the math.

這只是在數學上。

It's not just computer vision or image recognition, it's all over the math.

這不僅僅是計算機視覺或圖像識別，而是數學。

And the reason for that is we have so many different types of architectures.

原因是我們有這麼多不同類型的架構。

We have so many different ways to build different applications.

我們有很多不同的方法來構建不同的應用程序。

And so the application is fabricated, and we're trying to -- we just have wonderful people working.

所以這個應用程序是虛構的，我們正在努力——我們只是有很棒的人在工作。

We're now on our eighth generation on that.

我們現在已經是第八代了。

It's adopted all over the world for the 25,000 companies who are now using NVIDIA AI.

它已被全球 25,000 家公司採用，這些公司現在正在使用 NVIDIA AI。

And recently, at GTC, we announced 2 very, very big things.

最近，在 GTC 上，我們宣布了 2 件非常非常大的事情。

One, we reminded everybody that just months before, we have tried to support not just in every generation of NVIDIA GPUs, which there are so many versions -- could you imagine, without Triton, how would you possibly deploy AI across the entire fleet of NVIDIA servers, NVIDIA GPU servers that are all over the world?

第一，我們提醒大家，就在幾個月前，我們試圖支持的不僅僅是每一代 NVIDIA GPU，它們有很多版本——你能想像，如果沒有 Triton，你怎麼可能在整個艦隊中部署人工智能？ NVIDIA服務器，NVIDIA GPU服務器遍布全球？

And so it's almost an essential tool just to operate and take advantage of all of NVIDIA's GPUs.

因此，它幾乎是操作和利用所有 NVIDIA GPU 的必備工具。

These are the latest.

這些是最新的。

Two, we support GPUs.

第二，我們支持 GPU。

And so it's no longer necessary for someone to have 2 inference servers, you can just have 1 inference server because the NVIDIA versions are already essential.

因此，不再需要有人擁有 2 台推理服務器，您可以只擁有 1 台推理服務器，因為 NVIDIA 版本已經必不可少。

Now everybody could just use Triton in every single server in the data center to be part of the inference capacity.

現在每個人都可以在數據中心的每台服務器中使用 Triton 作為推理能力的一部分。

And then we did something else that was a really big deal at GTC, which is this we call forced inference library cost bill.

然後我們做了一些在 GTC 非常重要的事情，這就是我們所說的強制推理庫成本賬單。

But basically, the most popular machine learning systems in inference models are based on trees, decision trees, and boosted gradient trees.

但基本上，推理模型中最流行的機器學習系統是基於樹、決策樹和增強梯度樹的。

And people might know it as XGBoost.

人們可能將其稱為 XGBoost。

And it's used all over the place in fraud detection, in recommender systems, and they're utilized in companies all over the world because it's just self-explanatory, you can build upon it.

它在欺詐檢測、推薦系統中被廣泛使用，並且它們被世界各地的公司使用，因為它是不言自明的，你可以在它的基礎上進行構建。

You don't worry about regressions if you build these under the trees.

如果您在樹下構建這些，您不必擔心回歸。

And we -- this GTC, we announced that we support that as well.

我們——這個 GTC，我們宣布我們也支持它。

And so all of a sudden, all of that workload that runs on GPUs not only do they run on Triton, it becomes accelerated.

突然之間，所有在 GPU 上運行的工作負載不僅在 Triton 上運行，而且變得加速了。

And then the last -- the next thing that we have announced was the tremendous interest in large language models.

最後——我們宣布的下一件事是對大型語言模型的極大興趣。

Triton now also supports multi-GPU and multi-node inference so that we could take something like an open AI, GPT-3, NVIDIA Megatron, 530B or anybody's giant model that's being developed all over the world in all these different languages and all these different domains and all these different deals of signers and what in the industry we can now inference it in real time.

Triton 現在還支持多 GPU 和多節點推理，因此我們可以採用開放式 AI、GPT-3、NVIDIA Megatron、530B 或任何人的巨型模型，這些模型正在世界各地以所有這些不同的語言和所有這些語言開發不同的領域和所有這些不同的簽名者交易，以及我們現在可以實時推斷的行業內容。

And I demonstrated it in one of the demos.

我在其中一個演示中演示了它。

There was a toy Jensen that the team built and it was able to, basically, answer questions in real time.

團隊製造了一個玩具 Jensen，它基本上能夠實時回答問題。

And so that is just a giant breakthrough.

所以這只是一個巨大的突破。

And these are the type of workloads that's going to make it possible for us to continue to scale out, basically.

基本上，這些工作負載類型將使我們能夠繼續向外擴展。

So back to your original question.

所以回到你原來的問題。

I think this year has been quite a big year for data centers.

我認為今年對於數據中心來說是非常重要的一年。

Customers are very mindful of securing their supply for their scale-out.

客戶非常注意確保其橫向擴展的供應。

And so we have a fair amount of visibility, and more visibility probably than ever of data centers.

因此，我們擁有相當多的可見性，而且數據中心的可見性可能比以往任何時候都高。

But in addition to the Triton adoption everywhere.

但除了海衛一的採用無處不在。

And then finally, our brand-new workload, which is built on top of AI and graphics and simulation, which is Omniverse.

最後是我們全新的工作負載，它建立在人工智能、圖形和模擬之上，即 Omniverse。

And you saw the examples that I gave.

你看到了我給出的例子。

These are real companies doing real work.

這些是真正的公司在做真正的工作。

And one of the areas that is -- has severe shortages around the world is customer support.

世界各地嚴重短缺的領域之一是客戶支持。

Just genuine severe shortages all over the world.

只是世界各地真正的嚴重短缺。

And we think the answer is Omniverse Avatar, and it runs in data centers.

我們認為答案是 Omniverse Avatar，它在數據中心運行。

You could adapt Omniverse Avatar to do drive-throughs or retail check-out or customer service.

您可以調整 Omniverse Avatar 來進行免下車或零售結賬或客戶服務。

And I've demonstrated that with Tokkio, a talking kiosk.

我已經用 Tokkio 演示了這一點，這是一個會說話的信息亭。

You can use it for a teleoperated customer service, and we've demonstrated that with [Maxine].

您可以將它用於遠程操作的客戶服務，我們已經用 [Maxine] 證明了這一點。

We've demonstrated how you can use it even for videoconferencing.

我們已經演示瞭如何將它用於視頻會議。

And then lastly, we demonstrated how we can use Omniverse Avatar for robotics, and for example, to create a concierge, what we call DRIVE Concierge for your car, turning it into an intelligent, it's like your customer support, intelligent agent.

最後，我們演示瞭如何將 Omniverse Avatar 用於機器人技術，例如，為您的汽車創建一個禮賓部，我們稱之為 DRIVE Concierge，將其變成智能的，就像您的客戶支持、智能代理一樣。

That's why I think Omniverse Avatar is going to be a really exciting driver for enterprises next year.

這就是為什麼我認為 Omniverse Avatar 明年將成為企業的一個真正令人興奮的驅動力。

So next year, NVIDIA is going to be -- we're seeing up a pretty, pretty terrific year for [Omniverse].

所以明年，NVIDIA 將會是——我們看到 [Omniverse] 的美好、非常棒的一年。

For our next question, we have Stacy Rasgon from Bernstein Research.

對於我們的下一個問題，我們請來了來自 Bernstein Research 的 Stacy Rasgon。

I wanted to ask 2 of them on data center, both near term and then maybe a little longer term.

我想問他們中的 2 個關於數據中心的問題，無論是短期還是長期。

On the near-term, Colette, you've suggested guidance into Q4 will be driven by data center and gaming.

在短期內，科萊特，你建議第四季度的指導將由數據中心和遊戲驅動。

And you mentioned data center first.

你首先提到了數據中心。

Does that mean that it's bigger?

這是否意味著它更大？

And if you could just help us like parse the contribution of each into Q4?

如果你能幫助我們將每個人的貢獻解析到第四季度？

And then into next year, given the commentary for the last question.

然後進入明年，給出最後一個問題的評論。

Again, it sounds like you've got like a very strong outlook for data center, both from hyperscale and enterprise.

同樣，聽起來您對超大規模和企業數據中心的前景非常看好。

And if I look at sort of the implied guidance, we give our data center for you is probably likely to grow 50% year-over-year in this fiscal year.

如果我看一下隱含的指導，我們為您提供的數據中心在本財年可能會同比增長 50%。

Would it be crazy to think given all those drivers that it could grow by a similar amount next year as well?

考慮到所有這些驅動因素，明年它也可能以類似的數量增長，這是不是很瘋狂？

Like how should we be thinking about that given all of the drivers that you've been laying out?

考慮到您一直在佈置的所有驅動程序，我們應該如何考慮這一點？

Okay.

好的。

Thanks, Stacy, for the question.

謝謝，史黛西的問題。

Let's first focus in terms of our guidance for Q4 or statements that we made were, yes, about driven by revenue growth from data center and gaming sequentially.

讓我們首先關注我們對第四季度的指導或我們所做的聲明，是的，是由數據中心和遊戲的收入連續增長推動的。

We could probably expect our data center to grow faster than our gaming probably both in terms of percentage-wise and absolute dollars.

我們可能期望我們的數據中心在百分比和絕對美元方面都比我們的遊戲增長得更快。

We also expect our key product to decline quarter-on-quarter to very negligible levels in Q4.

我們還預計我們的主要產品將在第四季度環比下降至非常微不足道的水平。

So I hope that gives you a color on Q4.

所以我希望這能給你一個關於第四季度的顏色。

Now in terms of next year, we'll certainly turn the corner into the new fiscal year.

現在就明年而言，我們肯定會轉入新的財政年度。

We certainly provide guidance one quarter out.

我們當然會提供四分之一的指導。

We've given some great discussion here about the opportunities in front of us, opportunities with the hyperscales, the opportunities with the verticals, Omniverse is a full-stack opportunity in front of us.

我們在這裡就擺在我們面前的機會、超大規模的機會、垂直領域的機會進行了一些精彩的討論，Omniverse 是擺在我們面前的全棧機會。

We are securing supply for next year not just for the current year in Q4 to allow us to really grow into so much of this opportunity going forward.

我們正在確保明年的供應，而不僅僅是今年第四季度的供應，以使我們能夠真正成長為未來的這個機會。

But at this time, we're going to wait until next year to provide guidance.

但目前，我們要等到明年才能提供指導。

For the next question, we have Vivek Arya from BofA Securities.

對於下一個問題，我們請來了美國銀行證券公司的 Vivek Arya。

Actually, I had 2 quick ones, Jensen.

實際上，我有兩個快速的，Jensen。

So Colette, you suggested the inventory purchase and supply agreements are up, I think, almost 68% year-on-year.

所以科萊特，你建議庫存採購和供應協議同比增長近 68%。

Does that provide some directional correlation with how you are preparing for growth over the next 12 to 24 months?

這是否與您如何為未來 12 到 24 個月的增長做準備提供了一些方向相關性？

So that's one question.

所以這是一個問題。

And then the bigger question, Jensen, that I have for you is, where are we in the AI adoption cycle?

然後更大的問題，詹森，我要問你的是，我們在人工智能採用周期中處於什麼位置？

What percentage of servers are accelerated in hyperscale and vertical industry today?

當今超大規模和垂直行業中加速服務器的百分比是多少？

And where can those ratios get to?

這些比率可以到達哪裡？

Thanks for the question.

謝謝你的問題。

So let's first start in terms of supply or our supply purchase agreements.

因此，讓我們首先從供應或我們的供應採購協議開始。

You have noted that we are discussing that we have made payments towards some of those commitments.

你已經註意到我們正在討論我們已經為其中的一些承諾付款。

Not only are we procuring for what we need in the quarter, what we need next year.

我們不僅採購了本季度所需的東西，而且採購了明年所需的東西。

And again, we are planning for growth next year, so we have been planning that supply purchases, we are also doing long-term supply purchases.

同樣，我們正在計劃明年的增長，所以我們一直在計劃供應採購，我們也在進行長期供應採購。

These are areas of capacity agreements and/or many of our different suppliers.

這些是產能協議和/或我們許多不同供應商的領域。

We made a payment within this quarter of approximately $1.6 billion out of total long-term capacity agreements of about $3.4 billion.

我們在本季度內支付了約 16 億美元的總長期產能協議中的約 34 億美元。

So we still have more payments to make, and we will likely continue to be purchasing longer term to support our growth that we are planning for many years to come.

因此，我們仍然需要支付更多的款項，而且我們可能會繼續購買更長期的產品，以支持我們計劃在未來多年內實現的增長。

Every single server will be GPU-accelerated some day.

總有一天，每台服務器都會被 GPU 加速。

Today, of all the clouds and all the enterprise, less than 10%.

今天，在所有云和所有企業中，不到 10%。

That kind of gives you a sense of where you are.

那種感覺讓你知道你在哪裡。

In terms of the workloads, it's also consistent with that in the sense that a lot of the workloads still only run on GPUs, which is the reason why in order for us to grow, we have to be a full-stack company.

在工作負載方面，這也是一致的，因為很多工作負載仍然只在 GPU 上運行，這就是為什麼我們要成長，我們必須成為一家全棧公司。

And we have to go find applications.

我們必須去尋找應用程序。

We don't have to find them, we have plenty of them.

我們不必找到它們，我們有很多。

Focus on applications that require acceleration or benefits tremendously from acceleration that if they were to get a million x speedup, which sounds insane, but it's not.

專注於需要加速或從加速中獲得巨大收益的應用程序，如果他們要獲得一百萬倍的加速，這聽起來很瘋狂，但事實並非如此。

Mathematically, I can prove it to you.

數學上，我可以證明給你看。

And historically, I can even demonstrate to you that in many, many areas, we have seen knowing their speedups and have completely revolutionized those industries.

從歷史上看，我什至可以向你證明，在很多很多領域，我們已經看到了他們的加速並徹底改變了這些行業。

Computer graphics is, of course, one of them.

當然，計算機圖形學就是其中之一。

Omniverse would not be possible without.

沒有 Omniverse 是不可能的。

And so the work that we're doing with digital biology, protein synthesis, which is likely going to be one of the large industries in the world that doesn't exist today at all, protein engineering.

所以我們在數字生物學方面所做的工作，蛋白質合成，這很可能是世界上今天根本不存在的大型產業之一，蛋白質工程。

And the protein economy is likely going to be very, very large.

蛋白質經濟可能會非常非常大。

You can't do that unless you are able to get a million x speedup in -- I see the relation of protein dynamics.

除非您能夠獲得一百萬倍的加速，否則您無法做到這一點——我看到了蛋白質動力學的關係。

And so those are -- and not to mention some of the most imperative problems that we have to go and engage, climate science needs a million x, billion x speedup.

所以這些是 - 更不用說我們必須解決的一些最緊迫的問題，氣候科學需要一百萬倍，十億倍的加速。

And we are at a point where we can actually tackle it.

我們正處於可以真正解決它的地步。

And so in each one of these cases, we have to find -- we have to focus our resources to go and accelerate those applications, and that translates to growth.

因此，在每一種情況下，我們都必須找到——我們必須集中資源來加速這些應用程序，這會轉化為增長。

Until then, they'll run on GPUs.

在那之前，它們將在 GPU 上運行。

And if you look at a lot of today's speech synthesis and speech recognition systems, they're still using a fairly traditional or [minister] traditional deep learning approaches for CGI.

而且，如果您查看當今的許多語音合成和語音識別系統，它們仍在使用相當傳統的或 [部長] 傳統的 CGI 深度學習方法。

NVIDIA's Riva is the world's first, I believe, that is end-to-end deep neural network.

我相信 NVIDIA 的 Riva 是世界上第一個端到端的深度神經網絡。

And we've worked with many companies in helping them advance there so that they could move their clouds to neural-based approaches.

我們已經與許多公司合作，幫助他們在這方面取得進展，以便他們可以將他們的雲遷移到基於神經的方法。

But that's one of the reasons why we do it, so that we could provide a reference.

但這是我們這樣做的原因之一，以便我們可以提供參考。

But we can also license it to enterprises around the world so that they could adapt it for their own use cases.

但我們也可以將其授權給世界各地的企業，以便他們根據自己的用例進行調整。

And so one application after another, we have to get it accelerated.

因此，一個又一個應用程序，我們必須加快速度。

One domain after another, we have to get it accelerated.

一個接一個的領域，我們必須讓它加速。

One of the ones that I was very excited about and something that we've been working on for so long is EDA, even our own industry, Electronic Design Automation.

其中一個讓我非常興奮並且我們一直在研究的東西是 EDA，甚至是我們自己的行業，電子設計自動化。

For the very first time, we announced the EDA using computer science computing, whether because of the artificial intelligence capability because EDA is a very large combinatorial optimization program.

我們第一次宣布使用計算機科學計算的 EDA，無論是因為人工智能能力，因為 EDA 是一個非常大的組合優化程序。

And using artificial intelligence, we could really improve the design quality and design time.

並且使用人工智能，我們可以真正提高設計質量和設計時間。

So we're seeing all the major EDA vendors, from chip design to simulation to PCB design and optimization, design synthesis moving towards artificial intelligence and GPU acceleration in a very significant way.

因此，我們看到所有主要的 EDA 供應商，從芯片設計到仿真，再到 PCB 設計和優化，設計合成正以非常重要的方式向人工智能和 GPU 加速發展。

And then we see that with mechanical CAD and traditional CAD application.

然後我們在機械 CAD 和傳統 CAD 應用程序中看到了這一點。

Now also jumping on to GPU acceleration.

現在也開始使用 GPU 加速。

And again, very significant speedups.

再次，非常顯著的加速。

And so I'm super excited about the work that we're doing in each one of these domains because every time you do it, you open up a brand-new market.

因此，我對我們在這些領域中的每一個領域所做的工作感到非常興奮，因為每次你這樣做，你都會打開一個全新的市場。

And customers who have never used them (inaudible), now can because ultimately people don't buy chips.

從未使用過它們的客戶（聽不清）現在可以使用，因為最終人們不會購買芯片。

They're trying to solve problems.

他們正在努力解決問題。

And without a full fact, without software expertise, we can't really commence the enabling technology, what the chip provides, and ultimately resolving the question for me.

如果沒有完整的事實，沒有軟件專業知識，我們就無法真正開始啟用技術、芯片提供的功能，並最終為我解決問題。

Your final question comes from the line of Timothy Arcuri from UBS.

您的最後一個問題來自瑞銀集團的 Timothy Arcuri。

Colette, I had a question about gross margin.

Colette，我有一個關於毛利率的問題。

Are there any margin headwinds maybe on the wafer pricing side that we should sort of think about normalizing out because gross margin is pretty flat between fiscal Q4 to fiscal -- sorry, between fiscal Q2 and fiscal Q4.

晶圓定價方面是否有任何利潤逆風，我們應該考慮一下正常化，因為毛利率在第四財季到財年之間相當平緩——抱歉，在第二財季和第四財季之間。

But I imagine that's kind of masking a strong underlying margin growth, especially as data center has been actually driving that growth.

但我認為這掩蓋了強勁的潛在利潤率增長，尤其是在數據中心實際上一直在推動這種增長的情況下。

So I'm wondering if maybe there are some underlying factors that are sort of gaining gross margin.

所以我想知道是否可能有一些潛在因素正在增加毛利率。

Yes.

是的。

So we have always been working on our gross margin and being able to absorb a lot of the cost changes along the way, architecture-to-architecture, year-to-year.

因此，我們一直在努力提高我們的毛利率，並且能夠吸收沿途的大量成本變化，架構到架構，每年。

So that's always baked in to our gross margins.

所以這總是影響我們的毛利率。

Our gross margins right now are largely stable.

我們目前的毛利率基本穩定。

Our incremental revenue, for example, what we're expecting next quarter will likely align to our current gross margin levels that we finished in terms of Q3.

例如，我們的增量收入，例如，我們對下一季度的預期可能與我們在第三季度完成的當前毛利率水平保持一致。

Our largest driver always continues to be mix.

我們最大的驅動力始終是混合的。

We have a lot of different mix that has vision related to high-end AR and RTX solutions, for example, and the software that's embedded in solutions have allowed us to increase the gross margin.

例如，我們有許多與高端 AR 和 RTX 解決方案相關的不同組合，並且嵌入在解決方案中的軟件使我們能夠提高毛利率。

As we look forward, long term, software is sold separately.

正如我們所期待的那樣，從長遠來看，軟件是單獨出售的。

It can be another damper of gross margin increases in the future.

這可能是未來毛利率增長的另一個抑制因素。

But cost changes, cost increases have generally been a part of our gross margin for years.

但是成本變化，成本增加多年來通常是我們毛利率的一部分。

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

我現在將把電話轉回給 Jensen Huang 做閉幕詞。

Thank you.

謝謝你。

We had an outstanding quarter.

我們有一個出色的季度。

Demand for NVIDIA AI is strong, with hyperscalers and cloud services deploying at scale and enterprises broadening adoption.

對 NVIDIA AI 的需求強勁，超大規模和雲服務大規模部署，企業擴大採用。

We now count more than 25,000 companies that are using NVIDIA AI.

我們現在統計了超過 25,000 家使用 NVIDIA AI 的公司。

And with NVIDIA AI Enterprise Software Suite, our collaboration with VMware and our collaboration with Equinix to place NVIDIA LaunchPads across the world, every enterprise has an easy on-ramp to NVIDIA AI.

借助 NVIDIA AI 企業軟件套件、我們與 VMware 的合作以及與 Equinix 的合作，我們將 NVIDIA LaunchPad 部署到世界各地，每個企業都可以輕鬆進入 NVIDIA AI。

Gaming and pro viz are surging.

遊戲和專業人士正在蓬勃發展。

RTX opportunity continues to expand with the growing markets of gamers, creators, designers and now, professionals building home workstations.

RTX 機會隨著遊戲玩家、創作者、設計師以及現在構建家庭工作站的專業人士市場的不斷增長而繼續擴大。

We are working hard to increase supply for the overwhelming demand this holiday season.

我們正在努力增加供應，以滿足這個假期旺季的巨大需求。

Last week, GTC showcased the expanding universe of NVIDIA-accelerated computing.

上週，GTC 展示了不斷擴大的 NVIDIA 加速計算領域。

In combination with AI and data-center-scale computing, the model we pioneered is on the cusp of producing million x speedups that will revolutionize many important fields, already, AI, and upcoming, robotics, digital biology and what I hope, climate science.

結合人工智能和數據中心規模的計算，我們開創的模型正處於產生數百萬倍加速的風口浪尖，這將徹底改變許多重要領域，包括人工智能，以及即將到來的機器人技術、數字生物學以及我希望的氣候科學.

GTC highlighted our full-stack expertise in action.

GTC 強調了我們在行動中的全棧專業知識。

Built on CUDA and our acceleration libraries in data processing, in simulation, graphics, artificial intelligence, marketplace and domain-specific software is needed to solve customer problems.

建立在 CUDA 和我們在數據處理、模擬、圖形、人工智能、市場和特定領域軟件方面的加速庫是解決客戶問題所必需的。

We also showed how software opens new growth opportunities for us, but the chips are the enablers, but it's the software that opens new growth opportunities.

我們還展示了軟件如何為我們打開新的增長機會，但芯片是推動者，但正是軟件打開了新的增長機會。

NVIDIA has 150 SDKs now, addressing many of the world's largest end markets.

NVIDIA 目前擁有 150 個 SDK，面向世界上許多最大的終端市場。

One of the major themes of this GTC was Omniverse, our simulation platform for virtual worlds and digital tools.

本次 GTC 的主要主題之一是 Omniverse，這是我們用於虛擬世界和數字工具的模擬平台。

Our body of work and expertise in graphics, physics simulation, AI, robotics, and full stack computing made Omniverse possible.

我們在圖形、物理模擬、人工智能、機器人和全棧計算方面的工作和專業知識使 Omniverse 成為可能。

At GTC, we showed how Omniverse is used to reinvent collaborative design, customer service avatars and video conferences and digital twins of factories, processing plants and even entire cities.

在 GTC，我們展示了 Omniverse 如何用於重塑協作設計、客戶服務化身和視頻會議以及工廠、加工廠甚至整個城市的數字雙胞胎。

This is just the tip of the iceberg of what's to come.

這只是即將發生的事情的冰山一角。

We look forward to updating you on our progress next quarter.

我們期待在下個季度向您通報我們的進展情況。

Thank you.

謝謝你。

I will now turn it over to Jensen for closing remarks.

我現在將把它交給 Jensen 做結束語。

Well, I think we just heard the closing remarks.

好吧，我想我們剛剛聽到了閉幕詞。

Thank you so much for joining us.

非常感謝您加入我們。

We look forward to seeing everybody at the conferences that we have planned over the next 2 months, and I'm sure we'll talk before the end of next earnings.

我們期待在未來 2 個月內計劃的會議上見到大家，我相信我們會在下一次財報結束前進行討論。

Thanks again, everybody.

再次感謝大家。

This concludes today's conference call.

今天的電話會議到此結束。

Thank you all for participating.

謝謝大家的參與。

You may now disconnect.

您現在可以斷開連接。