輝達 (NVDA) 2022 Q3 法說會逐字稿

Good afternoon.

午安.

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

我叫 Saydie，今天我將擔任您的會議主持人。

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

現在，我歡迎大家參加 NVIDIA 第三季財報電話會議。

(Operator Instructions) Thank you.

（操作員指示）謝謝。

Simona Jankowski, you may begin your conference.

Simona Jankowski，您可以開始您的會議了。

Thank you.

謝謝。

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.

今天與我一起出席的還有 NVIDIA 總裁兼執行長黃仁勳；以及執行副總裁兼財務長 Colette Kress。

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

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。

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

網路直播將可供重播，直至電話會議討論我們 2022 年第四季和財年的財務表現。

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

今天電話會議的內容屬於 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.

在本次電話會議中，我們可能會根據目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。

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表現和業務的因素的討論，請參閱今天的收益報告中的披露內容、我們最新的 10-K 表和 10-Q 表以及我們可能向美國證券交易委員會提交的 8-K 表報告。

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根據我們目前掌握的資訊截至今天（2021 年 11 月 17 日）做出的。

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，我們將討論非公認會計準則財務指標。

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.

您可以在我們網站上發布的 CFO 評論中找到這些非 GAAP 財務指標與 GAAP 財務指標的對帳表。

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

說完這些，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%.

第三季表現出色，營收達 71 億美元，年增 50%。

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

我們創下了總收入以及遊戲、資料中心和專業視覺化的記錄。

Starting with gaming.

從遊戲開始。

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

營收為 32 億美元，季增 5%，年增 42%。

Demand was strong across the board.

全方面需求強勁。

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 的供應，但我們認為通路庫存仍然很低。

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

受高階 RTX 筆記型電腦需求成長的推動，筆記型電腦 GPU 也實現了強勁的同比成長。

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

NVIDIA RTX 技術正在推動我們與玩家一起實現有史以來最大的更新周期，並繼續透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 推出了突破性的即時光線追蹤和 AI 支援的超解析度功能，這些功能正在加速被採用。

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

目前已有超過 200 款遊戲和應用程式支援 NVIDIA RTX，其中 125 款支援 NVIDIA DLSS。

This quarter alone, 45 new games shipped with DLSS.

光是本季就有 45 款新遊戲搭載 DLSS 發行。

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

NVIDIA Reflex 降低延遲技術已應用於 VALORANT、Fortnite、Apex Legends 和 Overwatch 等頂級電競遊戲。

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，Reflex 技術現已整合到近 50 種遊戲週邊設備中。

NVIDIA Studio for creatives keeps expanding.

NVIDIA Studio 創意空間不斷擴大。

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 Adob​​e Max 創意大會上，Adobe 宣布了適用於 Adob​​e Lightroom 和 Lightroom Classic 的兩項強大的 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，我們的安裝基數中有 1/4 已經採用了 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 RTX 支援的快速擴展以及遊戲、電子競技、內容創作和串流媒體的日益普及，我們預計 NVIDIA GeForce 用戶將繼續升級並實現成長。

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 支援加密貨幣，但我們無法預見這對我們整體 GPU 需求有多大的影響。

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，我們幾乎所有的安培架構遊戲桌上型 GPU 出貨量都是精簡哈希率，以幫助引導 GeForce 供應給遊戲玩家。

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

加密挖礦處理器收入為 1.05 億美元，其中包括我們的 OEM 和其他收入。

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

我們的雲端遊戲服務GeForce Now本季取得了兩大成就。

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

首先，藝電公司將更多熱門遊戲引入該伺服器。

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

其次，我們宣布推出新的 GeForce Now RTX 3080 會員等級，6 個月價格不到 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.

去年，GeForce NOW 的會員人數增加了一倍多，達到 1400 多萬遊戲玩家，他們從遍布 80 多個國家的 30 個資料中心串流內容。

Moving to Pro Visualization.

轉向專業視覺化。

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

第三季營收為 5.77 億美元，季增 11%，年增 144%。

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

這一連續成長是由行動工作站推動的，隨著企業部署系統來支援混合工作環境，桌面工作站也在成長。

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

在第二季強勁成長的基礎上，安培架構的銷售額持續成長。

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，這是一個用於模擬物理上精確的 3D 世界和數位趨勢的平台。

Initial market reception to Omniverse has been incredible.

Omniverse 的初始市場反應非常好。

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

超過 700 家公司的專業人士正在評估該平台，其中包括寶馬、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.

全球市場約有4000萬名3D設計師。

Moving to Automotive.

轉向汽車產業。

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

第三季營收為 1.35 億美元，季減 11%，年增 8%。

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

連續下滑主要是由於人工智慧座艙收入受到汽車製造商供應限制的負面影響。

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，自動駕駛卡車新創公司 Kodiak Robotics、汽車製造商 Lotus、自動駕駛巴士製造商 QCraft 和電動汽車新創公司 WM Motor 在其下一代汽車中採用 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 的眾多公司行列，而這個名單還在迅速增長，其中包括汽車原始設備製造商、一級供應商、導航設備製造商、卡車運輸公司、機器人出租車和軟體新創公司。

Moving to data center.

移至資料中心。

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 億美元營收比上一季成長 24%，比去年同期成長 55%，超大規模和垂直產業的營收均創下歷史新高。

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

強勁的成長是由超大規模客戶引領的，這得益於安培架構張量核心 GPU 在內部和外部工作負載方面的持續快速採用。

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

超大規模運算收入年增一倍，這得益於自然語言處理和推薦模型及計算的擴展。

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

在消費者互聯網和更廣泛的雲端供應商的推動下，垂直行業成長也強勁。

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，Oracle Cloud 部署了 NVIDIA GPU 來推出技術分析、語音辨識、電腦視覺和異常偵測等 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卓越成長，這再次超過了我們整體資料中心的成長。

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過渡到 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 推理伺服器軟體，使大型語言模型等運算密集型推理工作負載能夠跨多個 GPU 和節點進行即時擴展。

Over 25,000 companies worldwide use NVIDIA AI inference.

全球有超過 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.

一個很好的新例子就是 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 種語言的視訊通話過程中即時將​​語音轉換為文字。

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

我們達到了三個里程碑，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，我們宣布了 NVIDIA AI Enterprise 的全面上市，這是一套全面的 AI 工具和框架軟體套件，可讓運行 NVIDIA 的數十萬家公司（運行 vSphere）在 NVIDIA 認證系統上虛擬化 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 vSphere 未來將透過 Tensor 進行更新，將針對 NVIDIA AI 進行全面最佳化。

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.

結合NVIDIA AI Enterprise，企業可以利用現有的IT工具有效地管理人工螢幕資料中心伺服器和檔案上的雲端原生AI開發和部署。

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

第三，我們將 Equinix 作為我們的第一個數位基礎設施合作夥伴，在全球擴展了我們的 LaunchPad 計畫。

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 現已在全球 9 個地點開放，為企業提供 NVIDIA 軟體和基礎設施的即時存取權，幫助他們製作原型並測試資料科學和 AI 工作負載。

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

LaunchPad 具有 NVIDIA 認證系統和運行整個 NVIDIA AI 軟體堆疊的 NVIDIA DGX 系統。

In networking, revenue was impacted as demand outstrips supply.

在網路方面，由於需求超過供應，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邁進的勢頭，包括 ConnectX-5 和 6。

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

全球眾多領先的基礎設施和系統供應商均提供 NVIDIA Quantum-2 交換器。

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

本週早些時候，最新的超級電腦 500 強榜單顯示，我們的全端運算方法繼續保持強勁勢頭。

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 技術可加速榜單上超過 70% 的系統，其中包括超過 90% 的所有新系統和前 25 名的節能係統中的 23 個系統。

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，註冊與會者超過 27 萬名。

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

在過去 8 天裡，Jensen 的主題演講已經被觀看了 2500 萬次。

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

我們的春季 GTC 專注於新晶片和系統，而本屆 GTC 則專注於軟體，展示了我們的完整運算堆疊。

Let me cover some of the highlights.

讓我介紹一下其中的一些亮點。

Our vision for Omniverse came to life at GTC.

我們對 Omniverse 的願景在 GTC 上得以實現。

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的功能。

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 是一個用於生成數據來訓練機器人的引擎，它透過大量、多樣化和物理上精確的合成數據集複製增強的真實世界數據，以幫助加速開發滿足運算需求的高品質、高效能人工智慧。

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

NVIDIA Omniverse Avatar 是我們用來產生互動式 AI 頭像的平台。

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、電腦視覺、自然語言理解、推薦引擎和類比。

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

應用程式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 公佈的其他幾項功能和能力，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 個新的和更新的軟體開發套件，總數達到 150 多個，服務於遊戲和設計到人工智慧、網路安全、5G 和機器人等行業。

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

其中一個 SDK 是我們的第一個 4 授權 AI 模型 NVIDIA Riva，用於建立對話式 AI 應用程式。

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

在公開測試期間使用 Riva 的公司包括用於視訊會議即時字幕的 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 將於明年初正式上市。

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

我們推出了 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最佳化。

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.

這個組合將可投入生產的企業級硬體和軟體整合在一起，幫助垂直行業開發特定語言和行業的聊天機器人、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，具有新的網路安全功能。

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

DOCA 對我們的 DPU 來說就像 CUDA 對我們的 GPU 一樣。

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 上建立應用程式和服務。

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 成為業界建立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.

領先的網路安全公司正在與我們合作，在 BlueField 上提供他們的下一代防火牆服務，其中包括 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.

我們也發布了用於醫療器材的邊緣AI運算平台Clara Holoscan，以改善機器人輔助手術、介入放射學和放射治療計畫等領域的決策工具。

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.

GTC 上發布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、用於網路安全的 Morpheus、用於基於物理的機器學習的 Modulus 和 cuNumeric，這是一個數據中心規模的數學庫，旨在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，隨著不斷擴展的 SDK 集支援越來越多的 GPU 加速應用程式和產業用例，NVIDIA 的運算平台也在不斷擴展。

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

CUDA 的下載量已達 3,000 萬次，而我們​​的開發者生態系統現已接近 300 萬人。

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 和雲端到邊緣運算平台上開發的應用程式正在幫助從醫療保健到交通運輸到金融服務、製造業、物流和零售業等數萬億美元的產業轉型。

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，我們宣布推出 NVIDIA DRIVE Con​​cierge 和 DRIVE Chauffeur，這兩款 AI 軟體平台可增強汽車的性能和安全性。

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 Con​​cierge 可作為基於 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.

它還透過整個行程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，讓駕駛者無需隨時控制汽車。

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

與DRIVE Hyperion 8平台結合使用時，它也會執行位址到位址驅動。

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.

在機器人領域，我們發布了 Jetson AGX Orin，這是全球最小、功能最強大、能源效率最高的 AI 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 基於我們的 Ampere 架構構建，其處理能力是其前代產品的 6 倍，每秒可進行 200 萬億次運算，類似於一台掌中支援 GPU 的伺服器。

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

Jetson AGX Orin 將於 2022 年第一季上市。

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

最後，我們透露了建造地球2號的計劃，這是世界上最強大的人工智慧超級計算機，專門用於應對氣候變遷。

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.

該系統將成為劍橋 1 號（英國最強大的人工智慧超級計算機，我們為醫療保健研究而建造）的氣候變遷對應系統。

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

地球 2 利用了我們迄今為止發明的所有技術。

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擬議的 ARM 收購計劃的最新進展。

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

ARM 與 NVIDIA 的合作對於業界和客戶來說都是絕佳的機會。

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、能力以及對資料中心運算、AI 加速的深入了解，我們可以協助 ARM 擴大其在資料中心、物聯網和物理學領域的影響力，並在未來幾十年推動 ARM 的 IP 發展。

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

我們兩家公司的合併可以增強產業競爭力，共同致力於進一步建立人工智慧世界。

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對這筆交易表示了擔憂，我們正在與他們就收入問題進行討論，以解決他們的擔憂。

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

在前述案件立案之前，此項交易一直在接受中國反壟斷機構的審查。

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拒絕（聽不清楚）對競爭問題進行第一階段審查。

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的進程。

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 授權商提出的擔憂，我們仍然相信此次收購對於 ARM、其授權商以及整個行業都具有價值和益處。

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，儘管這些擔憂以及一些 ARM 授權持有者提出的擔憂，我們仍然相信 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.

第三季的 GAAP 毛利率較上年同期上升了 260 個基點，主要得益於桌上型電腦和筆記型電腦 GeForce GPU 的高端組合。

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

年比成長也受惠於收購相關成本影響的減少。

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.

受資料中心 Ampere 架構產品成長的推動，GAAP 毛利率較上季成長 40 個基點，但遊戲產品組合的增幅尤其抵銷了這一成長。

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

非博彩毛利率較上年同期上升150個基點，較上一季上升30個基點。

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

第三季 GAAP EPS 為 0.97 美元，比去年同期成長 83%。

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

根據股票分割調整後，非公認會計準則每股收益為 1.17 美元，較去年同期成長 60%。

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.

第三季經營現金流為 15 億美元，高於去年同期的 13 億美元，但低於上一季的 27 億美元。

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

同比成長主要反映了更高的營業收入，特別是抵消了長期供應協議的預付款。

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

讓我來談談2022財年第四季的展望。

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

我們預計資料中心和遊戲將推動連續成長。

-- more than offsetting a decline in CMP.

——足以抵銷 CMP 的下滑。

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

預計營收為 74 億美元，上下浮動 2%。

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

預計 GAAP 和非 GAAP 毛利率分別為 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.

根據 GAAP 和非 GAAP 計算，預計營業費用分別約為 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.

根據 GAAP 和非 GAAP 標準，其他收入和支出預計均為約 6,000 萬美元（不包括非關聯投資的收益和損失）。

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

不包括爭議項目，預計 GAAP 和非 GAAP 稅率均為 11%，上下浮動 1%。

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

預計資本支出約 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.

進一步的財務細節包含在財務長評論中，其他資訊也可在我們的 IR 網站上找到。

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

最後，讓我重點介紹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.

我們也將於 12 月 1 日以線上方式參加富國銀行第五屆年度 TMT 高峰會； 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。

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

現在，我們將開始提問。

Operator, will you please poll today's questions?

接線員，您可以調查一下今天的問題嗎？

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

(操作員指示) 我們的第一個問題來自富國銀行的 Aaron Rakers。

Yes.

是的。

Congratulations on the results.

恭喜你所取得的成果。

I guess I wanted to ask about Omniverse.

我想問的是有關 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?

我想簡單的問題是，Jensen，當我們展望未來 12 個月時，您如何定義 Omniverse 的成功？

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

我們如何看待 Omniverse 的訂閱授權機會？

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

我知道您談到了總共 4000 萬名 3D 設計師。

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

我認為這實際上是您八月談論的內容的兩倍。

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，身為金融分析師，我們該如何開始思考這個機會的實現？

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 的成功取決於以下幾點：第一，開發者的參與度以及與世界各地開發者的連結；二是企業正在開發的應用程式；三、設計師與創作者之間的連結。

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.

我會根據我對成功的定義來這麼說。

Near term also should be revenues.

近期也應該有收入。

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那樣，Omniverse 具有真正的直接應用。

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

我現在就重點介紹其中的幾點。

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

其中之一當然是它是連接 3D 和數位設計世界的一種方式。

Think of Adobe as a world.

將 Adob​​e 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，創造事物，它有自己的資料庫。

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.

我們首次讓這些世界連接起來，並像雲端文件一樣共享。

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.

現在你們就可以互相分享工作了。

You can see how this work, you can collaborate.

您可以了解這項工作是如何進行的，並且可以進行協作。

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

因此，在遠距工作的世界中，Omniverse 的協作能力將受到真正的讚賞。

And that should happen right away.

這應該會立即發生。

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

我們希望這能在短期內實現。

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

當然，這會推動更多的 PC 銷售、更多的 GPU 銷售、更多的工作站銷售、更多的伺服器銷售。

The second use case is digital twins.

第二個用例是數位孿生。

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).

我們看到了一些公司的例子，他們正在使用 Omniverse 創建城市的數位孿生，以便他們可以優化廣播位置和視訊能量的使用（聽不清楚）。

You saw BMW using it for their factories.

您會看到寶馬將它在其工廠中應用。

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.

因此數位孿生應用是絕對必要的。

And then remember, robots have several kinds.

然後記住，機器人有幾種。

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.

其中包括您看到的實體機器人，帶有自動駕駛汽車的實體機器人，以及可以控制汽車本身的實體機器人，將汽車轉變為機器人，使其成為智慧助理。

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

但我可能展示的是——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.

我們建立了 Omniverse Avatar，讓人們可以輕鬆整合一些令人驚嘆的技術，從電腦視覺到語音辨識、自然語言理解、手勢辨識、臉部動畫、語音合成、推薦系統，所有這些都整合到一個系統中並即時運行。

That avatar system is essentially a robotic system.

這個化身係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.

你可以透過例如 2500 萬個左右的零售商店、餐廳、機場、火車站和辦公大樓等場所的智慧化身來提供大量協助。

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，可能正在辦理入住手續，可能正在提供客戶支援。

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

正如我所演示的，所有這些都可以透過化身來實現。

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、數位機器人或化身可能會成為最大的機器人機會。

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.

因此，如果你看一下我們的許可模式，它的基本工作方式是，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，我們在全球擁有 4000 萬創意人員和設計師，當他們擴大 Omniverse 的規模時，每個主要用戶每年的費用為 1,000 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.

但別忘了，能夠連接到 Omniverse 的智慧生物或智慧用戶作為數位機器人的規模可能會比人類大得多。

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

我提到了 4000 萬輛，但汽車數量有 1 億輛。

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

1 億輛汽車都將配備——所有這些汽車都能夠擁有類似 Omniverse Avatar 的東西。

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

因此，這 1 億輛汽車每輛每年的成本可能是 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，你可以使用數位化身作為客戶支援或結帳、智慧零售或智慧倉庫，無論它是什麼，它都是智慧的。

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

這些頭像也帶有——每個人都會有一個主帳戶。

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

所以每個頭像每年的費用是 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都是直接的切實機會，我現在演示了該應用程式。

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.

當然，在這一切的背後，還有幾億個數位代理、智慧代理，其中一些是人類，有些是機器人，有些是化身，每個代理每年的費用為 1,000 美元。

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

背後是PC中的NVIDIA GPU、雲端中的NVIDIA GPU以及仍在Omniverse伺服器上的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，硬體部分可能佔一半，許可證部分可能佔一半。

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

而且——但這確實是我們所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實現這一目標，是因為它需要三項基礎技術的結合。

I guess 4 fundamental technologies come together.

我認為四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.

第二是物理模擬，因為我們談論的事物必須可信，所以它必須遵守物理定律。

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

第三是人工智慧，我已經分幾個階段進行了展示。

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 Omniverse 電腦上，這台電腦不僅要處理人工智慧、物理、電腦圖形學，還要處理所有這些。

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.

那麼從長遠來看，人們為什麼對此如此興奮，這是最高層次的，它的基本含義是，從長遠來看，當我們使用互聯網時，今天互聯網基本上是 2D 的，從長遠來看，每個查詢都將是 3D 的。

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.

我們不只是查詢訊息，還可以查詢並與人、化身、事物、地點進行交互，而所有這些事物都是 3D 的。

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

所以希望有一天我們能夠盡快實現它。

Every transaction that goes over the Internet touches a GPU.

每筆透過網路進行的交易都會涉及 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，這個比例非常小，但希望有一天，這個比例會變得非常非常高。

I hope that's helpful.

我希望這有幫助。

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

下一個問題是來自 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.

詹森，似乎每年，您的加速處理生態系統似乎都會出現一組新的需求驅動因素。

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

有遊戲，然後是神經網路、人工智慧、區塊鏈和光線追蹤。

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.

大約五、六年前，你們在分析師日上展示了一系列虛擬實境演示，非常令人興奮。

Excitement died down.

興奮感漸漸消退。

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

現在它似乎正在重新浮出水面，尤其是隨著 Omniverse Avatar 功能和 Facebook 所帶來的機會。

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，由此產生的兩個問題是，您的 Omniverse Avatar 距離轉變為每個人日常使用的大眾市場技術還有多遠？

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

您可以談論——您說每個人都會成為遊戲玩家，每個人都會成為 Omniverse Avatar 用戶。

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

也許從更大的角度來看，每年考慮推出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?

我們是否應該思考以前的計算市場與我們現在正在進入的計算時代的相似之處？

I really appreciate that.

我真的很感激。

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

晶片是推動因素，但晶片不會創造市場。

Software creates markets.

軟體創造市場。

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.

原因在於你不能只寫一個 C 編譯器並將量子物理學編譯到晶片中，而晶片卻不行。

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

您不能只是編譯顯示您的情況並將其分佈在多個 GPU 和多個節點上並讓它（聽不清楚）。

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

你只是——你不能對電腦圖形學做到這一點。

You can't do that for artificial intelligence.

你無法對人工智慧做到這一點。

You can't do that for robotics.

你無法對機器人做到這一點。

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，你無法做到這一點。

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.

而且由於我們確實超越了 GPU 的主題，人們之所以這麼說，並不是因為它不是事實，而是很明顯，從系統中擠出的教學並行量雖然不是 0，但卻非常困難。

It's just incredibly hard.

這實在是太難了。

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.

我們一直在倡導加速運算，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.

這項工作基本上是針對每個領域、每個應用程式而言的——理想情況下，對於大領域中的每個應用程序，你必須有一個完整的堆疊。

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，每當您想透過加速這些應用程式或應用程式領域來開拓新市場時，您都必須提出一個新的堆疊。

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.

新的堆疊很難，因為你必須了解應用程序，必須了解演算法、數學，必須了解計算機科學，以便將其分佈到各個地方，將單線程變為多線程，使某些事情按順序完成並使其過程並行。

You break everything.

你把一切都破壞了。

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.

因此，這需要相當多的專業知識，這就是為什麼我們說，多年來，在過去的 30 年裡，我們已經成為一家全端公司，因為幾十年來，我們一直在努力實際解決這個問題。

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，一旦你有能力，你就可以開拓新的市場。

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

我們在人工智慧民主化和讓任何人都能使用人工智慧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.

我希望我們最大的貢獻是，當一切都說完了，我們已經實現了科學計算的民主化，以便研究人員和科學家、計算機科學家和數據科學家，各種科學家都能夠使用這種我們稱之為計算機的強大工具來推動研究。

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.

所以每年，我們都會在堆疊中填滿這些，而且我們還有大量的堆疊需要處理。

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

其中許多都是我正在研究的，以便您能夠看到它們的存在，您只需將它們聯繫在一起即可。

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

當然，我們這次討論的領域之一就是 Omniverse。

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

您看到了隨著時間的推移而不斷建造的各個部分。

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

Omniverse 的建設花了五年時間，但卻是二十五年辛勤努力的結果。

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.

在 Omniverse Avatar 的情況下，您可以直接指向 Merlin 推薦器；威震天，語言－大型語言模型； Riva，語音人工智慧；我多年來一直在展示的所有電腦視覺人工智慧；每年在《我是人工智慧》中都會看到的自然速度合成，片頭字幕，我們如何使用、開發一種能夠以人類的方式說話的人工智慧，以便人們對人工智慧感到更舒服、更投入；臉部、眼球追蹤、Maxine。

And all of these technologies all kind of tie together.

所有這些技術都是相互連結在一起的。

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

它們都是從整合中分塊建構的——我們無意將它們整合起來以創建所謂的 Omniverse Avatar。

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

現在——您問的問題是，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，不到五年的時間，Omniverse Avatar 將會出現在世界各地的餐廳、快餐店、餐廳收銀台和零售店中。

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

由於勞動力嚴重短缺，我們確實將其用於各種不同的應用。

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

現在我們可以以非常美妙的方式參與《阿凡達》。

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

我認為它可以——認為它不會犯錯，永遠不會疲倦並且始終處於開啟狀態。

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

我們使其成為雲端原生的。

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

因此，當您看到主題演講時，我希望您會同意互動是連續的，並且對話形式非常令人愉悅。

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.

無論如何，我認為你強調的是，第一，加速運算是一個全端挑戰；二、需要軟體來開拓新市場。

Chips won't open new markets.

晶片不會開拓新的市場。

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

如果你製造了另一種晶片，你可以竊取別人的份額，但你無法開拓新的市場。

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

開拓新市場需要軟體，我們已經轉向使用軟體了。

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，我相信這是一個近期的機會，我們已經為此努力了 3、4、5 年。

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

下一個問題請來的是 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，這不是一個關於 Omniverse 的問題，但 Jensen，我希望你承諾不會使用 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?

但我真正的問題是，您能談談您的資料中心在 2022 年及以後的可見性嗎？

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

在這種展望中，您能談談傳統雲端與垂直產業嗎？

And then perhaps emerging opportunities like Omniverse and others.

然後也許還有像 Omniverse 和其他公司這樣的新興機會。

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

很想了解一下您今天所看到的情況。

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和其他供應以支持這一增長。

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，C.J。

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.

所以我們已經這樣做了，並且對我們的供應狀況感到非常滿意，特別是從明年下半年開始以及以後。

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.

我認為整個過去的一年都給每個人敲響了警鐘，要大家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.

我們很榮幸能有如此優秀的合作夥伴。

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

但儘管如此，我們已經用我們的未來來確保這一點。

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.

關於資料中心，我們的資料中心業務大約有一半來自雲端和雲端服務供應商，另一半來自企業，我們稱之為企業公司。

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

他們遍佈各行各業。

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%左右是雲； 50%左右，企業；以及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.

我們預計明年雲端服務供應商將積極擴展其深度學習和人工智慧工作負載。

And we're seeing it right now.

我們現在就看到了它。

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，我們在 TensorRT 上所做的工作，即進入名為 Triton 的伺服器的運行時間，是我們最好的工作之一。

We're just so proud of it.

我們對此感到非常自豪。

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.

我們在大約 4 年前、3.5 年前就說過，推理將成為電腦科學的重大挑戰之一，我們對此非常謹慎。

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.

原因在於，有時它非常快，有時它有延遲，有時它有互動性以及我們必須推理的模型類型。

It's just all over the math.

這全是數學問題。

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，而是整個數學。

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

原因在於我們擁有許多不同類型的架構。

We have so many different ways to build different applications.

我們有很多不同的方法來建立不同的應用程式。

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

因此，該申請是偽造的，我們正在嘗試——我們只是有優秀的人在工作。

We're now on our eighth generation on that.

如今，我們已是第八代了。

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

目前，全球有 25,000 家公司正在使用 NVIDIA AI。

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

最近，在 GTC 上，我們宣布了兩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?

首先，我們提醒大家，就在幾個月前，我們嘗試不僅支援每一代 NVIDIA GPU，因為它有這麼多的版本——你能想像，如果沒有 Triton，你如何可能在遍布全球的 NVIDIA 伺服器、NVIDIA GPU 伺服器上部署 AI？

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.

二，我們支援 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，不再需要擁有 2 個推理伺服器，只需要 1 個推理伺服器，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.

現在每個人都可以或許正在資料中心的每台辦事器中使用 Triton 作為推理才能的一部分。

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

然後我們在 GTC 上做了另一件非常重要的事情，這就是我們所說的強制推理庫成本帳單。

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，推理模型中最受歡迎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.

人們可能知道它叫 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.

它被廣泛應用於欺詐檢測、推薦系統等各個領域，並被世界各地的公司所採用，因為它是不言自明的，你可以在其基礎上進行構建。

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

如果您在樹下建造這些，那麼您就不必擔心回歸。

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

而且我們 — — 在本次 GTC 上，我們宣布我們也支持這一點。

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

因此，突然之間，所有在 GPU 上運行的工作負載不僅可以在 Triton 上運行，而且還會加速。

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人們對大型語言模型的巨大興趣。

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 現在還支援多 GPU 和多節點推理，這樣我們就可以採用開放式 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各種不同的語言、各種不同的領域和各種不同的簽名者交易進行開發，而我們現在可以在行業中實時推理它。

And I demonstrated it in one of the demos.

我在一個演示中演示了這一點。

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

該團隊製作了一個玩具 Jensen，它基本上可以即時回答問題。

And so that is just a giant breakthrough.

這是一個巨大的突破。

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，這些類型的工作負載可以讓我們繼續擴展。

So back to your original question.

那麼回到你最初的問題。

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

我認為今年對資料中心來說是相當重要的一年。

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

客戶非常注意確保其擴展業務的供應。

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

因此，我們擁有相當高的可見性，而且對資料中心的可見性可能比以往任何時候都要高。

But in addition to the Triton adoption everywhere.

但除了 Triton 以外，到處都有採用。

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

最後，我們的全新工作負載建立在 AI、圖形和模擬之上，即 Omniverse。

And you saw the examples that I gave.

你們也看到了我所舉的例子。

These are real companies doing real work.

這些都是真正的公司，在做真正的工作。

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

全球範圍內嚴重短缺的領域之一就是客戶支援。

Just genuine severe shortages all over the world.

世界各地確實存在嚴重短缺。

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

我們認為答案是 Omniverse Avatar，它在資料中心運作。

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

您可以調整 Omniverse Avatar 來實現免下車服務、零售結帳或客戶服務。

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

我已經用會說話的資訊亭 Tokkio 示範了這一點。

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

您可以將其用於遠端客戶服務，我們已經透過 [Maxine] 進行了演示。

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

我們已經演示瞭如何使用它進行視訊會議。

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，我們示範如何將 Omniverse Avatar 用於機器人技術，例如為您的汽車創建禮賓服務，我們稱之為 DRIVE Con​​cierge，將其變成智能，就像您的客戶支援、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 明年將成為企業真正令人興奮的推動力。

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

因此，明年，NVIDIA 將會——我們預計 Omniverse 將迎來相當相當輝煌的一年。

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

下一個問題請來自伯恩斯坦研究公司的史黛西‧拉斯岡 (Stacy Rasgon) 來回答。

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

我想問他們兩個關於資料中心的問題，都是近期的，然後也許是稍長期的。

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

科萊特，就短期而言，您建議第四季度的指引將由資料中心和遊戲驅動。

And you mentioned data center first.

您首先提到了資料中心。

Does that mean that it's bigger?

這是否意味著它更大了？

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

您能幫我們分析一下每個人對 Q4 的貢獻嗎？

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

然後進入明年，對最後一個問題進行評論。

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

再次，聽起來您對資料中心的前景非常樂觀，無論是從超大規模還是企業角度來看。

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引，我們為您提供的資料中心在本財年可能比去年同期成長 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，認為明年也能實現類似的成長，這太瘋狂了？

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

例如，考慮到您列出的所有驅動因素，我們應該如何考慮這個問題？

Okay.

好的。

Thanks, Stacy, for the question.

謝謝史黛西提出這個問題。

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.

讓我們先關註一下我們對第四季的指導或我們所做的聲明，是的，這些聲明是關於資料中心和遊戲收入連續成長的。

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

我們可能會預期我們的資料中心的成長速度將比我們的遊戲成長速度更快，無論是從百分比還是絕對美元來看。

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

我們也預計，我們的主要產品的銷量在第四季度將環比下降至非常小的水平。

So I hope that gives you a color on Q4.

我希望這能讓您了解 Q4 的情況。

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

現在就明年而言，我們肯定會進入新的財政年度。

We certainly provide guidance one quarter out.

我們當然會提供一個季度前的指導。

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.

我們在這裡對我們面臨的機會、超大規模的機會、垂直領域的機會進行了精彩的討論，Omniverse 是我們面臨的全端機會。

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的供應，以便我們可以真正抓住未來的機會。

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

但目前，我們要等到明年才提供指導。

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

下一個問題請到美國銀行證券公司的 Vivek Arya 來回答。

Actually, I had 2 quick ones, Jensen.

實際上，我有兩個很快的問題，詹森。

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

所以，科萊特，您說庫存購買和供應協議上漲了，我認為，年比上漲了近 68%。

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

這是否與您為未來 12 至 24 個月的成長做準備的方式提供了一些方向性的關聯？

So that's one question.

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?

那麼，Jensen，我想問你的一個更大的問題是，我們處於人工智慧採用週期的哪個階段？

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

目前超大規模和垂直行業中有多少比例的伺服器實現了加速？

And where can those ratios get to?

那麼這些比例又能達到什麼程度呢？

Thanks for the question.

謝謝你的提問。

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

因此，我們首先從供應或供應購買協議開始。

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

您注意到我們正在討論我們已經對其中一些承諾進行了付款。

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

我們不僅採購本季所需的物資，也採購明年所需的物資。

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.

再次，我們計劃明年實現成長，因此我們一直在規劃供應採購，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/或我們的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.

我們在本季支付了約 16 億美元，而長期容量協議總額約為 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，我們仍需要支付更多款項，我們可能會繼續進行長期購買，以支持我們未來多年的成長計劃。

Every single server will be GPU-accelerated some day.

將來某一天，每個伺服器都將實現 GPU 加速。

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

如今，在所有的雲端和所有的企業中，這一比例還不到 10%。

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

這會讓你了解自己身在何處。

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.

就工作負載而言，這也與此一致，因為許多工作負載仍然只在 GPU 上運行，這就是為什麼我們為了發展，必須成為一家全端公司。

And we have to go find applications.

我們必須去尋找應用程式。

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

我們不需要去尋找它們，我們已經有很多了。

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受益匪淺的應用程序，如果它們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.

從數學上來說，我可以向你證明這一點。

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，我甚至可以向你們證明，在許多領域，我們已經看到了它們的加速，並且徹底改變了這些產業。

Computer graphics is, of course, one of them.

當然，電腦圖形學就是其中之一。

Omniverse would not be possible without.

如果沒有它，Omniverse 就不可能實現。

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.

因此，我們在數位生物學、蛋白質合成領域所做的工作很可能將成為當今世界上尚未存在的大型產業之一，即蛋白質工程。

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

蛋白質經濟可能會變得非常非常龐大。

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

除非你能夠獲得一百萬倍的加速，否則你是無法做到這一點的——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.

這些都是——更不用說我們必須去解決的一些最迫切的問題，氣候科學需要百萬倍、十億倍的加速。

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

現在我們確實可以解決這個問題了。

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──我們必須集中我們的資源去加速這些應用，這意味著成長。

Until then, they'll run on GPUs.

在此之前，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.

如果你觀察當今的許多語音合成和語音辨識系統，你會發現它們仍然在使用相當傳統或傳統的深度學習方法來實現 CGI。

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

我認為，NVIDIA 的 Riva 是世界上第一個端到端的深度神經網路。

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步，以便他們可以將他們的雲端轉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.

但這正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，以便他們能夠根據自己的使用情況進行調整。

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

因此，我們必須加速一個又一個的應用。

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

一個又一個領域，我們必須加速它。

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.

令我非常興奮並且我們長期致力於的領域之一是 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.

我們首次宣布EDA使用計算機科學計算，無論是因為人工智慧能力還是因為EDA是一個非常龐大的組合優化程式。

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

利用人工智慧，我們可以真正提高設計品質和縮短設計時間。

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，從晶片設計到模擬到 PCB 設計和優化、設計綜合都在以非常顯著的方式向人工智慧和 GPU 加速邁進。

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

然後我們看到機械 CAD 和傳統 CAD 應用。

Now also jumping on to GPU acceleration.

現在也跳到 GPU 加速。

And again, very significant speedups.

再次，速度顯著提高。

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，我對我們在每個領域所做的工作感到非常興奮，因為每次你這樣做，你就會開闢一個全新的市場。

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

而那些從未使用過它們的客戶（聽不清楚），現在可以使用了，因為最終人們不會購買晶片。

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和軟體專業知識，我們就無法真正開始使用支援技術，晶片也無法提供這些技術，也無法最終為我解決問題。

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

您的最後一個問題來自瑞銀的蒂莫西·阿庫裡。

Colette, I had a question about gross margin.

科萊特，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.

在晶圓定價方面是否存在利潤阻力，我們應該考慮將其正常化，因為第四財季與第二財季和第四財季之間的毛利率相當持平。

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

但我想這掩蓋了強勁的潛在利潤率成長，尤其是資料中心實際上推動了這一成長。

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

所以我想知道是否存在一些潛在因素可以提高毛利率。

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，我們一直致力於提高毛利率，並且能夠吸收每年、不同架構間產生的大量成本變化。

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

所以這始終是包含在我們的毛利率中的。

Our gross margins right now are largely stable.

我們目前的毛利率基本上穩定。

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，我們預計下個季度的增量收入可能與我們第三季完成的當前毛利率水準一致。

Our largest driver always continues to be mix.

我們最大的驅動力始終是混合。

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，我們有很多與高端 AR 和 RTX 解決方案相關的視覺組合，而嵌入在解決方案中的軟體使我們能夠提高毛利率。

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

展望未來，從長遠來看，軟體將單獨出售。

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

這可能會成為未來毛利率成長的另一個阻礙。

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

但多年來，成本變化和成本增加一直是我們毛利率的一部分。

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

現在我將把電話轉回給黃仁勳 (Jensen Huang) 作結束語。

Thank you.

謝謝。

We had an outstanding quarter.

我們度過了一個出色的季度。

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

隨著超大規模企業和雲端服務大規模部署以及企業擴大採用，對 NVIDIA AI 的需求強勁。

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

目前，我們統計有超過 25,000 家公司正在使用 NVIDIA AI。

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.

透過 NVIDIA AI 企業軟體套件、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，每家企業都可以輕鬆使用 NVIDIA AI。

Gaming and pro viz are surging.

遊戲和專業視覺正在蓬勃發展。

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

隨著遊戲玩家、創作者、設計師以及現在建立家庭工作站的專業人士的市場不斷增長，RTX 機會不斷擴大。

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季節的巨大需求。

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

上週，GTC 展示了不斷擴展的 NVIDIA 加速運算領域。

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.

結合人工智慧和資料中心規模計算，我們首創的模型即將產生數百萬倍的加速，這將徹底改變許多重要領域，包括人工智慧、即將到來的機器人技術、數位生物學以及我希望的氣候科學。

GTC highlighted our full-stack expertise in action.

GTC 重點強調了我們實際的全端專業知識。

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.

基於 CUDA 和我們的數據處理、模擬、圖形、人工智慧、市場和領域特定軟體的加速庫來解決客戶問題。

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.

我們也展示了軟體如何為我們開闢新的成長機會，晶片是推動因素，而軟體才是開闢新成長機會的關鍵。

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

NVIDIA 現擁有 150 個 SDK，涵蓋全球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位工具的類比平台。

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

我們在圖形、實體模擬、人工智慧、機器人和全端運算方面的工作和專業知識使得 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 上，我們展示瞭如何使用 Omniverse 重塑協作設計、客戶服務化身和視訊會議以及工廠、加工廠甚至整個城市的數位孿生。

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

這只是未來事件的冰山一角。

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

我們期待下個季度向您通報我們的進展。

Thank you.

謝謝。

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

現在我將把發言權交給 Jensen 來做結束語。

Well, I think we just heard the closing remarks.

好吧，我想我們剛剛聽完了結束語。

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.

我們期待在未來兩個月內計劃的會議上見到大家，我相信我們會在下次收益報告結束前進行交談。

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.

感謝大家的參與。

You may now disconnect.

您現在可以斷開連線。