輝達 (NVDA) 2021 Q1 法說會逐字稿

Good afternoon. My name is Josh, and I will be your conference operator today. At this time, I would like to welcome everyone to NVIDIA's Financial Results Conference call. (Operator Instructions) Thank you.

午安.我叫喬希，今天我將擔任您的會議主持人。現在，我歡迎大家參加 NVIDIA 的財務業績電話會議。 （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. Good afternoon, everyone, and welcome to NVIDIA's conference call for the first quarter of fiscal 2021. With me on the call today from NVIDIA are Jensen Huang, President and Chief Executive Officer; and Colette Kress, Executive Vice President and Chief Financial Officer. I'd like to remind you that our call is being webcast live on NVIDIA's Investor Relations website. The webcast will be available for replay until the conference call to discuss our financial results for the second quarter of fiscal 2021. The content of today's call is NVIDIA's property. It can't be reproduced or transcribed without our prior written consent.

謝謝。大家下午好，歡迎參加 NVIDIA 2021 財年第一季電話會議。今天與我一起參加電話會議的還有 NVIDIA 總裁兼執行長黃仁勳；以及執行副總裁兼財務長 Colette Kress。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路直播。網路直播將可重播，直至電話會議討論 2021 財年第二季的財務業績。今天電話會議的內容屬於 NVIDIA 的財產。未經我們事先書面同意，不得複製或轉錄。

During this call, we may make forward-looking statements based on current expectations. These are subject to a number of significant risks and uncertainties, and our actual results may vary materially. For a discussion of factors that could affect our future financial results and business, please refer to the disclosure in today's earnings release, our most recent Form 10-K and 10-Q and the reports that we may file on Form 8-K with the Securities and Exchange Commission. All our statements are made as of today, May 21, 2020, based on information currently available to us. Except as required by law, we assume no obligation to update any such statements.

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。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發生重大差異。有關可能影響我們未來財務表現和業務的因素的討論，請參閱今天的收益報告中的披露內容、我們最新的 10-K 和 10-Q 表格以及我們可能向美國證券交易委員會提交的 8-K 表格報告。我們所有的聲明都是根據我們目前掌握的資訊截至 2020 年 5 月 21 日做出的。除法律要求外，我們不承擔更新任何此類聲明的義務。

During this call, we will discuss non-GAAP financial measures. You can find a reconciliation of these non-GAAP financial measures to GAAP financial measures in our CFO commentary, which is posted on our website.

在本次電話會議中，我們將討論非公認會計準則財務指標。您可以在我們網站上發布的 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 Jensen.

說完這些，讓我把電話轉給詹森。

Thanks, Simona. Before Colette describes our quarterly results, I'd like to thank those who are on the front lines of this crisis, first responders, health care workers, service providers, who inspires every day with their bravery and selflessness. I also want to acknowledge the incredible efforts of our colleagues here at NVIDIA. Despite many challenges, they have barely broken stride during one of the busiest periods in our history.

謝謝，西蒙娜。在科萊特描述我們的季度業績之前，我想感謝那些在這場危機中奮戰在前線的人們，急救人員、醫護人員、服務提供者，他們的勇敢和無私每天都激勵著我們。我還要感謝 NVIDIA 同事們所做的巨大努力。儘管面臨許多挑戰，但在我們歷史上最繁忙的時期之一，他們幾乎沒有停下腳步。

Our efforts related to the virus are focused in 3 areas. First, we're taking care of our families and communities. We've pooled in raises by 6 months to put more money in our employees' hands, and NVIDIA and our people have donated thus far more than $10 million to those in need.

我們應對病毒的努力主要集中在三個方面。首先，我們要照顧我們的家庭和社區。我們已將加薪金額集中到 6 個月，以便為員工提供更多資金，NVIDIA 和我們的員工迄今已向有需要的人們捐贈了超過 1000 萬美元。

Second, we're using NVIDIA's unique capabilities to fight the virus. A great deal of science being done on COVID-19 uses NVIDIA technology for acceleration when every second counts. Some of the many examples, including sequencing the virus, analyzing drug candidates, imaging the virus at molecular resolution with cryo-electron microscopy and identifying elevated body temperature with AI cameras.

第二，我們正在利用NVIDIA的獨特功能來對抗病毒。在 COVID-19 研究中，大量科學研究都利用 NVIDIA 技術來加速分秒必爭的研究。其中包括對病毒進行測序、分析候選藥物、使用低溫電子顯微鏡以分子分辨率對病毒進行成像以及使用人工智慧相機識別體溫升高。

And third, because COVID-19 won't be the last killer virus, we need to be ready for the next outbreak. NVIDIA technology is essential for the scientific community to develop an end-to-end computational defense system, a system that can detect early, accelerate the development of a vaccine, contain the spread of disease and continuously test and monitor.

第三，由於 COVID-19 不會是最後一種致命病毒，我們需要為下一次疫情爆發做好準備。 NVIDIA 技術對於科學界開發端到端運算防禦系統至關重要，該系統能夠以及早發現、加速疫苗開發、遏制疾病傳播並持續進行測試和監測。

We are racing to deploy the NVIDIA Clara computational health care platforms, Clara Parabricks can accelerate genomics analysis from days to minutes. Clara Imaging will continue to partner with leading research institutes to develop state-of-the-art AI models to detect infections, and Clara Guardian will connect AI to cameras and microphones and hospitals to help overloaded staff watch over patients.

我們正在競相部署 NVIDIA Clara 計算醫療平台，Clara Parabricks 可以將基因組學分析時間從幾天縮短到幾分鐘。 Clara Imaging 將繼續與領先的研究機構合作，開發最先進的 AI 模型來檢測感染，而 Clara Guardian 將把 AI 連接到攝影機、麥克風和醫院，以幫助超負荷的工作人員照顧患者。

We completed the acquisition of Mellanox on April 27. Mellanox is now NVIDIA's networking brand and business unit and will be reported as part of our data center market platform, and Israel is now one of NVIDIA's major technology centers.

我們於4月27日完成了對Mellanox的收購。 Mellanox現在是NVIDIA的網路品牌和業務部門，並將作為我們資料中心市場平台的一部分進行報道，而以色列現在是NVIDIA的主要技術中心之一。

The new NVIDIA has a much larger footprint in data center computing, end-to-end and full-stack expertise in data center architectures and tremendous scale to accelerate innovation. NVIDIA Mellanox are a perfect combination and position us for the major forces shaping the IT industry today, data center scale computing and AI.

新的 NVIDIA 在資料中心運算領域擁有更大的影響力，在資料中心架構方面擁有端到端和全端專業知識，並擁有加速創新的龐大規模。 NVIDIA Mellanox 是一個完美的組合，使我們成為塑造當今 IT 產業、資料中心規模運算和人工智慧的主要力量。

From micro service cloud applications to machine learning and AI, accelerated computing and high-performance networking are critical to modern data centers. Previously, a CPU compute node was the unit of computing. Going forward, the new unit of computing is an entire data center. The basic computing elements are now storage servers, CPU servers and GPU servers, and are composed and orchestrated by hyperscale applications that are serving millions of users simultaneously. Connecting these computing elements together is the high-performance Mellanox networking. This is the era of data center scale computing. And together, NVIDIA Mellanox can architect end to end. Mellanox is an extraordinary company, and I'm thrilled that we're now one force to invent the future together.

從微服務雲端應用到機器學習和人工智慧，加速運算和高效能網路對於現代資料中心至關重要。以前，CPU 運算節點是計算的單位。展望未來，新的計算單元是整個資料中心。現在的基本運算元素是儲存伺服器、CPU 伺服器和 GPU 伺服器，由同時為數百萬用戶提供服務的超大規模應用程式組成和編排。將這些運算元素連接在一起的是高效能 Mellanox 網路。這是資料中心規模計算的時代。 NVIDIA Mellanox 可以協同建置端對端架構。 Mellanox 是一家非凡的公司，我很高興我們現在能夠成為共同創造未來的力量。

Now let me turn the call over to Colette.

現在讓我把電話轉給科萊特。

Thanks, Jensen. Against the backdrop of the extraordinary events unfolding around the globe, we had a very strong quarter. Q1 revenue was $3.08 billion, up 39% year-on-year, down 1% sequentially and slightly ahead of our outlook, reflecting upside in our data center and gaming platforms.

謝謝，詹森。在全球發生許多非同尋常事件的背景下，我們本季的業績表現十分強勁。第一季營收為 30.8 億美元，年成長 39%，季減 1%，略高於我們的預期，反映了我們的資料中心和遊戲平台的上升空間。

Starting with gaming. Revenue of $1.34 billion was up 27% year-on-year and down 10% sequentially. We are pleased with these results, which exceeded expectations in the quarter, marked by the unprecedented challenge of the COVID-19.

從遊戲開始。營收為 13.4 億美元，年增 27%，季減 10%。我們對這些結果感到滿意，這些結果超出了本季度的預期，儘管面臨前所未有的 COVID-19 挑戰。

Let me give you some color. Early in Q1, as the epidemic unfolded, demand in China was impacted with iCafes closing for an extended period. As the virus spread globally, much of the world started working and learning from home, and gameplay surged. Globally, we have seen 50% rise in gaming hours played on our GeForce platform, driven both by more people playing and more gameplay per user.

讓我給你一些顏色。第一季度初，隨著疫情蔓延，中國的需求受到影響，網咖長期關閉。隨著病毒在全球蔓延，世界上許多地方開始在家工作和學習，遊戲玩法激增。在全球範圍內，我們發現 GeForce 平台上的遊戲時間增加了 50%，這得益於遊戲人數的增加和每個用戶的遊戲時間的增加。

With many retail outlets closed, demand for our products has shifted quite efficiently to e-tail channels globally. Gaming laptops revenue accelerated to its fastest year-on-year growth in 6 quarters. We are working with our OEMs, channel partners to meet the growing needs of the professionals and students engaged in working, learning and playing at home. In early April, our global OEM partners announced a record new 100 NVIDIA GeForce-powered laptops with availability starting in Q1 and the most to ship in Q2. These laptops are the first to use our high-end GeForce RTX 2080 SUPER and 2070 SUPER GPUs, which have been available for desktop since last summer.

由於許多零售店關閉，我們產品的需求已相當有效地轉移到全球電子零售通路。遊戲筆記型電腦收入加速成長，創下六個季度以來最快的年增長。我們正在與我們的 OEM、通路合作夥伴合作，以滿足在家工作、學習和娛樂的專業人士和學生日益增長的需求。 4 月初，我們的全球 OEM 合作夥伴宣布將推出創紀錄的 100 款搭載 NVIDIA GeForce 的筆記型電腦，這些筆記型電腦將於第一季開始發售，並將於第二季發售。這些筆記型電腦率先採用我們的高階 GeForce RTX 2080 SUPER 和 2070 SUPER GPU，這些 GPU 自去年夏天起就已在桌上型電腦上推出。

In addition, OEMs are bringing to market laptops based on the RTX 2060 GPU at just $999, a price point that enables a larger audience to take advantage of the power and features of RTX, including its unique ray tracing and AI capabilities. These launches are well-timed as mobile and remote computing needs accelerate.

此外，OEM 廠商還以僅 999 美元的價格向市場推出了基​​於 RTX 2060 GPU 的筆記型電腦，這個價格可以讓更多的用戶享受到 RTX 的強大功能和特性，包括其獨特的光線追蹤和 AI 功能。隨著行動和遠端運算需求的加速，這些發布的時機恰到好處。

The global rise in gaming also lifted sales of NVIDIA Nintendo Switch and our console business, driving strong growth both sequentially and year-over-year. We collaborated with Microsoft and Mojang to bring RTX ray tracing to Minecraft, the world's most popular game with over 100 million gamers monthly and over 100 billion total views on YouTube. Minecraft with RTX looks astounding with realistic shadows and reflections. Light that reflects, refracts and scatters through surfaces as naturalistic effects like fog. Reviews for it are off the charts. Ars Technica called it a jaw-dropping stunner, and PC World said it was glorious to behold.

全球遊戲產業的興起也提升了 NVIDIA Nintendo Switch 和我們的遊戲機業務的銷量，推動了環比和同比的強勁成長。我們與微軟和 Mojang 合作，將 RTX 光線追蹤引入 Minecraft，這是世界上最受歡迎的遊戲，每月擁有超過 1 億遊戲玩家，YouTube 上的總觀看次數超過 1000 億次。帶有 RTX 的 Minecraft 具有逼真的陰影和反射，看起來令人驚嘆。光線透過表面反射、折射和散射，產生類似霧的自然效果。對它的評價非常高。 Ars Technica 稱其為令人驚嘆的傑作，而 PC World 則稱其為令人嘆為觀止的傑作。

Our RTX technology stands apart, not only with our 2-year lead in ray tracing but with its use of AI to speed up and enhance games using the Tensor Core silicon on our RTX class GPUs. We introduced the next version of our AI algorithm called Deep Learning Super Sampling. In real time, DLSS 2.0 can fill the missing bits from every frame, doubling performance. It represents a major step function from the original, and it can be trained on nongaming-specific images, making it universal and easy to implement.

我們的 RTX 技術脫穎而出，不僅因為我們在光線追蹤方面領先 2 年，還因為我們使用 RTX 級 GPU 上的 Tensor Core 矽片利用 AI 來加速和增強遊戲。我們推出了人工智慧演算法的下一個版本，稱為深度學習超級取樣。 DLSS 2.0 可以即時填滿每一幀的缺位，使效能加倍。它代表了原始函數的一個主要步驟，並且可以在非遊戲特定的圖像上進行訓練，使其通用且易於實現。

The value and momentum of our RTX GPUs continue to grow. We have a significant upgrade opportunity over the next year with the rise and tide of RTX-enabled games, including major blockbusters like Minecraft and Cyberpunk. Let me also touch on our game streaming service, GFN, which exited beta this quarter. It gives gamers access to more than 650 games with another 1,500 in line to get onboarded. These include Epic Games, Fortnite, which is the most played game on GFN; and other popular titles such as CONTROL, Destiny 2 and League of Lighting in the fall. Since launching in February, GFN has added 2 million users around the world, with both sign-ups and hours of gameplays boosted by stay-at-home measures. GFN expands our market reach to the billions of gamers with underpowered devices. It is the most publisher-friendly, developer-friendly game streaming service with the greatest number of games and the only one that supports ray tracing.

我們的 RTX GPU 的價值和發展勢頭持續成長。隨著 RTX 遊戲（包括《我的世界》和《Cyber​​punk》等大片）的興起和流行，我們在未來一年將擁有重大的升級機會。我還要談談我們的遊戲串流服務 GFN，本季已結束測試階段。它為玩家提供了 650 多款遊戲，另有 1,500 款遊戲正在排隊等待加入。其中包括 Epic Games 的 Fortnite，這是 GFN 上玩得最多的遊戲；以及秋季推出的《CONTROL》、《天命 2》和《閃電聯盟》等其他熱門遊戲。自 2 月推出以來，GFN 在全球已增加了 200 萬用戶，居家措施的實施也增加了註冊用戶和遊戲時間。 GFN 將我們的市場範圍擴大到數十億擁有功能不足設備的玩家。它是最適合發行商、開發者的遊戲串流服務，擁有最多的遊戲數量，也是唯一支援光線追蹤的服務。

Moving to Pro Visualization. Revenue was $307 million, up 15% year-on-year and down 7% sequentially. Year-on-year revenue growth accelerated in Q1 driven by laptop workstations and Turing adoption.

轉向專業視覺化。營收為 3.07 億美元，年增 15%，季減 7%。在筆記型電腦工作站和圖靈的推動下，第一季營收年增。

We are seeing continued momentum in our ecosystem for RTX ray tracing. We now have RTX support for all major rendering visualization and design software packages, including Autodesk Maya, Dassault's CATIA, Pixar's RenderMan, Chaos Group's V-Ray and many others. Autodesk has announced that the latest release of VRED, its automotive 3D visualization software, supports NVIDIA RTX GPUs. This enables designers to take advantage of RTX to produce more like-life designs in a fraction of the time versus CPU-based systems. Over 45 leading creative and design applications now take advantage of RTX, driving a sustained upgrade opportunity for Quadro-powered systems while also expanding their reach.

我們看到 RTX 光線追蹤在我們的生態系統中持續保持強勁發展勢頭。我們現在為所有主要的渲染視覺化和設計軟體包提供 RTX 支持，包括 Autodesk Maya、Dassault 的 CATIA、Pixar 的 RenderMan、Chaos Group 的 V-Ray 等等。 Autodesk 宣布其汽車 3D 視覺化軟體 VRED 的最新版本支援 NVIDIA RTX GPU。這使得設計師能夠利用 RTX 在比基於 CPU 的系統少得多的時間內製作出更逼真的設計。目前，已有超過 45 款領先的創意和設計應用程式利用 RTX，為 Quadro 驅動的系統帶來持續的升級機會，同時擴大其覆蓋範圍。

We see strong demand in verticals, including health care, media and entertainment and higher education, among others. Higher health care demand was fueled in part by COVID-19 related research at Siemens, Oxford and Caption Health. Caption Health received FDA clearance for an update to its AI-guided ultrasound, making it easier to perform diagnostics-quality cardiac ultrasounds. And in media and entertainment, demand increased as companies like Disney deployed remote workforce initiatives.

我們看到垂直領域的需求強勁，包括醫療保健、媒體和娛樂以及高等教育等。醫療保健需求的增加部分得益於西門子、牛津和 Caption Health 對 COVID-19 的相關研究。 Caption Health 已獲得 FDA 批准更新其 AI 導引超音波檢查，從而更容易進行診斷品質的心臟超音波檢查。在媒體和娛樂領域，隨著迪士尼等公司部署遠端勞動力計劃，需求也在增加。

Turning to automotive and robotic autonomous machines. Automotive revenue was $155 million, down 7% year-on-year and down 5% sequentially. The automotive industry is seeing a significant impact from the pandemic, and we expect that to affect our revenue in the second quarter as well, likely declining about 40% from Q1.

轉向汽車和機器人自主機器。汽車業務收入為 1.55 億美元，年減 7%，季減 5%。汽車產業正受到疫情的嚴重影響，我們預計這也會影響我們第二季的收入，可能比第一季下降約 40%。

Despite the near-term challenges, our important work continues. We believe that every machine that moves someday will have autonomous capabilities. During the quarter, Xpeng introduced the P7, an all-electric sports sedan with innovative Level 3 automated driving features, powered by the NVIDIA DRIVE AGX Xavier AI compute platform. Our open, programmable, software-defined platform enables Xpeng to run its proprietary software while also delivering over-the-air updates for new driving features and capabilities. Production deliveries of the P7 with NVIDIA DRIVE begin next month.

儘管面臨近期挑戰，但我們的重要工作仍在繼續。我們相信，未來某一天，每台運轉的機器都將具備自主能力。本季度，小鵬汽車推出了 P7，這是一款全電動運動轎車，具有創新的 3 級自動駕駛功能，由 NVIDIA DRIVE AGX Xavier AI 計算平台提供支援。我們的開放、可程式化、軟體定義的平台使小鵬汽車能夠運行其專有軟體，同時也提供新駕駛功能和性能的無線更新。搭載 NVIDIA DRIVE 的 P7 將於下個月開始生產交付。

Our Ampere architecture will power our next-generation NVIDIA DRIVE platform called Orin, delivering more than 6x the performance of Xavier Solutions and 4x better power efficiency. With Ampere scalability, the DRIVE platform will extend from driverless robotaxis all the way down to in windshield driver assistant systems sipping just a few watts of power. Customers appreciate the top-to-bottom platform all based on a single architecture, letting them build one software-defined platform for every vehicle in their fleet.

我們的 Ampere 架構將為我們的下一代 NVIDIA DRIVE 平台 Orin 提供動力，其效能是 Xavier 解決方案的 6 倍以上，功率效率是 Xavier 解決方案的 4 倍。憑藉 Ampere 的可擴展性，DRIVE 平台將從無人駕駛機器人計程車一直擴展到僅需幾瓦功率的擋風玻璃駕駛員輔助系統。客戶欣賞基於單一架構的自上而下的平台，這使得他們能夠為車隊中的每輛車建立一個軟體定義的平台。

Lastly, in the area of robotics, we announced that BMW Group has selected the new NVIDIA as a robotics platforms to automate their factories, utilizing logistic robots built on advanced AI computing and visualization technologies.

最後，在機器人領域，我們宣布寶馬集團選擇新的 NVIDIA 作為機器人平台來實現其工廠自動化，並利用基於先進的 AI 運算和視覺化技術構建的物流機器人。

Turning to data center. Quarterly revenue was a record $1.14 billion, up 80% year-on-year and up 18% sequentially, crossing the $1 billion mark for the first time. Announced last week, the A100 is the first Ampere architecture GPU. Although just announced, A100 is in full production, contributed meaningful to Q1 revenue and demand is strong. Overall, data center demand was solid throughout the quarter. It was also broad-based across hyperscale and vertical industry customers as well as across workloads, including training, inference and high-performance computing. We continue to have solid visibility into Q2.

轉向數據中心。季度營收創歷史新高，達11.4億美元，年增80%，季增18%，首度突破10億美元大關。 A100 於上週發布，是第一款安培架構 GPU。雖然剛發布，但A100已全面投入生產，對第一季的營收貢獻顯著，需求強勁。整體而言，整個季度資料中心的需求都很強勁。它也廣泛適用於超大規模和垂直行業客戶以及工作負載，包括訓練、推理和高效能運算。我們對第二季的前景仍然有清晰的認識。

The A100 offers the largest leap in performance to date over our 8 generations of GPUs, boosting performance by up to 20x over its predecessor. It is exceptionally versatile, serving as a universal accelerator for the most important high-performance workloads, including AI training and inference as well as data analytics, scientific computing and cloud graphics.

A100 實現了我們 8 代 GPU 迄今為止最大的效能飛躍，效能比其前代產品提高了 20 倍。它功能極其多樣，可作為最重要的高效能工作負載的通用加速器，包括人工智慧訓練和推理以及資料分析、科學運算和雲端圖形。

Beyond its leap performance and versatility, the A100 introduces new elastic computing technologies that make it possible to bring rightsized computing power to every job. A multi-instance GPU capability allows each A100 to be partitioned into as many as 7 smaller GPU instances. Conversely, multiple A100 interconnected by our third-generation NVLink can operate as one giant GPU for ever larger training tasks. This makes the A100 ideal for both training and for inference. The A100 will be deployed by the world's leading cloud service providers and system builders, including Alibaba cloud, Amazon Web Services, Baidu Cloud, Dell Technologies, Google Cloud platform, HPE and Microsoft Azure, among others. It is also getting adopted by several supercomputing centers, including the National Energy Research Scientific Computing Center and the Jülich Supercomputing Centre in Germany and Argonne National Laboratory.

除了飛躍的效能和多功能性之外，A100 還引入了新的彈性運算技術，可以為每項工作帶來合適的運算能力。多執行個體 GPU 功能允許將每個 A100 分割為多達 7 個較小的 GPU 執行個體。相反，透過我們的第三代 NVLink 互連的多個 A100 可以作為一個巨大的 GPU 運行，以執行更大的訓練任務。這使得 A100 成為訓練和推理的理想選擇。 A100 將由全球領先的雲端服務供應商和系統建構商部署，包括阿里雲、亞馬遜網路服務、百度雲、戴爾科技、谷歌雲端平台、HPE 和微軟 Azure 等。它也被多個超級計算中心採用，包括國家能源研究科學計算中心、德國於利希超級計算中心和阿貢國家實驗室。

We launched and shipped the DGX A100, our third-generation DGX and the most advanced AI system in the world. The DGX A100 is configurable from 1 to 56 independent GPUs to deliver elastic software-defined data center infrastructure for the most demanding workloads from AI training and inference to data analytics.

我們推出並交付了 DGX A100，這是我們的第三代 DGX，也是世界上最先進的 AI 系統。 DGX A100 可設定 1 到 56 個獨立 GPU，為從 AI 訓練和推理到資料分析的最嚴苛的工作負載提供彈性軟體定義資料中心基礎架構。

We announced 2 products for edge AI: the EGX A100 for larger commercial off-the-shelf servers; and the EGX Jetson Xavier NX for micro-edge servers. Supported by full AI optimized cloud, native and secure software, the EGX platform is built for AI computing at the edge. With the EGX, hospitals, retail stores, farms and factories can securely carry out real-time processing of the massive amounts of data streaming from trillions of edge sensors. NVIDIA EGX makes it possible to securely, deploy and manage and update fleets of servers remotely. EGX is also ideal for the massive computational challenge of 5G networks, which we are working on with our partners like Ericsson and Mavenir.

我們發布了兩款用於邊緣 AI 的產品：適用於大型商用現貨伺服器的 EGX A100；以及用於微邊緣伺服器的 EGX Jetson Xavier NX。 EGX 平台由完全 AI 優化的雲端、原生和安全軟體支持，專為邊緣 AI 運算而建置。借助 EGX，醫院、零售店、農場和工廠可以安全地即時處理來自數萬億個邊緣感測器的大量資料流。 NVIDIA EGX 可以安全地遠端部署、管理和更新伺服器群。 EGX 也非常適合 5G 網路的大規模運算挑戰，我們正與愛立信和 Mavenir 等合作夥伴合作應對這項挑戰。

Additionally, we announced CUDA 11 and other important software harnessing the A100's performance and universatility (sic) [universality] to accelerate 3 of the most complex and fast-growing workloads: recommendation systems, conversational AI and data science.

此外，我們還宣布了 CUDA 11 和其他重要軟體，利用 A100 的效能和通用性來加速 3 個最複雜和成長最快的工作負載：推薦系統、對話式人工智慧和資料科學。

First, NVIDIA Merlin is a deep recommendator (sic) [recommender] application framework that enables developers to quickly build state-of-the-art recommendation systems, leveraging our pretrained models. With billions of users and trillions of items on the Internet, deep recommendators are the critical engine powering virtually every internet service.

首先，NVIDIA Merlin 是一個深度推薦器（原文如此）[推薦器]應用程式框架，使開發人員能夠利用我們預先訓練的模型快速建立最先進的推薦系統。網路上有數十億用戶和數萬億項內容，深度推薦器幾乎是所有網路服務的關鍵引擎。

Second, NVIDIA Jarvis is a GPU-accelerated application framework that makes it easy for developers to create, deploy and run end-to-end real-time conversational AI applications that understand terminology unique to each company and its customers using both vision and speech. Demand for these applications are surging. Amid the shift to working from home, telemedicine and remote learning.

其次，NVIDIA Jarvis 是一個 GPU 加速的應用程式框架，它使開發人員可以輕鬆創建、部署和運行端到端即時對話式 AI 應用程序，這些應用程式可以使用視覺和語音來理解每個公司及其客戶獨有的術語。這些應用程式的需求正在激增。隨著人們逐漸轉向在家工作、遠距醫療和遠距學習。

And third, in the field of data science and data analytics, we announced that we are bringing end-to-end GPU acceleration to Apache Spark, an analytics engine for big data processing that uses more than 500,000 data scientists worldwide. Native GPU acceleration for the entire Spark pipeline, from extracting, transforming and loading the data to training to inference, delivers the performance and the scale needed to finally connect the potential of big data with the power of AI. Adobe has achieved a 7x performance improvement and a 90% cost savings in an initial test using GPU-accelerated data analytics with Spark.

第三，在資料科學和資料分析領域，我們宣佈為 Apache Spark 帶來端對端 GPU 加速，Apache Spark 是一個大資料處理分析引擎，全球有超過 50 萬名資料科學家使用它。從提取、轉換和加載資料到訓練和推理，整個 Spark 管道的原生 GPU 加速提供了最終將大數據的潛力與 AI 的力量相結合所需的效能和規模。 Adobe 在使用 Spark 進行 GPU 加速資料分析的初步測試中實現了 7 倍的效能提升和 90% 的成本節省。

Our accelerated computing platform continues to gain momentum, underscored by the tremendous success of GTC Digital, our annual GPU technology conference, which shifted this spring to an online format. More than 55,000 online developers and AI research registered for the online event, which includes hundreds of hours of free content from AI practitioners and industry experts who leverage NVIDIA's platforms.

我們的加速運算平台持續獲得發展勢頭，這得益於我們年度 GPU 技術會議 GTC Digital 的巨大成功，該會議於今年春季改為線上形式。超過 55,000 名線上開發人員和 AI 研究人員註冊參加了此次線上活動，其中包括利用 NVIDIA 平台的 AI 從業者和行業專家提供的數百小時免費內容。

Our ecosystem is now 1.8 million developers strong. Times like these truly test a computing platform's metal in the utility it brings to scientist racing for solutions. Researchers around the world are deploying our GPU computing platform in the fight against COVID-19. Scientists are combining AI simulation to detect changes in pneumonia cases, sequence, the virus and seek effective biomolecular compounds for a vaccine or treatment.

我們的生態系統目前擁有 180 萬名開發人員。這樣的時刻真正考驗了運算平台的實用性，它能為那些正在努力尋找解決方案的科學家帶來什麼。世界各地的研究人員正在部署我們的 GPU 運算平台來對抗 COVID-19。科學家正在結合人工智慧模擬來檢測肺炎病例、序列和病毒的變化，並尋找用於疫苗或治療的有效生物分子化合物。

The first breakthrough came from researchers at the University of Texas at Austin and National Institute of Health, who used the GPU-accelerated application to create the first 3D atomic scale map of virus using NVIDIA GPUs. This was followed by researchers at Oak Ridge National Laboratory who screened 8,000 compounds to identify 77 promising drug targets using the world's fastest supercomputer, Summit, which is powered by more than 27,000 NVIDIA GPUs.

第一個突破來自德克薩斯大學奧斯汀分校和美國國立衛生研究院的研究人員，他們使用 GPU 加速應用程序，利用 NVIDIA GPU 創建了第一個病毒的 3D 原子尺度地圖。隨後，橡樹嶺國家實驗室的研究人員使用世界上最快的超級電腦 Summit（由 27,000 多個 NVIDIA GPU 驅動）篩選了 8,000 種化合物，以確定 77 種有前景的藥物標靶。

The V100 GPUs at Oak Ridge are in high demand as they can analyze 17 million compound protein combinations in a day. They'll help understand the virus spread pattern, the University of California, San Diego, researchers ported their microbiomic analysis software to GPUs in the San Diego supercomputing cluster of 500x analysis speed up from what some people are more susceptible to the virus.

橡樹嶺國家實驗室的 V100 GPU 需求量很大，因為它們一天可以分析 1700 萬種複合蛋白質組合。它們將有助於了解病毒的傳播模式，加州大學聖地牙哥分校的研究人員將他們的微生物組分析軟體移植到聖地牙哥超級運算叢集的 GPU 上，分析速度提高了 500 倍，從而了解哪些人更容易感染病毒。

Okay. Moving to the rest of the P&L. Q1 GAAP gross margins was 65.1% and non-GAAP was 65.8%, up sequentially and year-on-year, primarily driven by GeForce GPU product mix and higher data center sales. Q1 GAAP operating expenses were $1.03 billion, and non-GAAP operating expenses were $821 million, up 10% and 9% year-on-year, respectively. Q1 GAAP EPS was $1.47, up 130% from a year earlier, and non-GAAP EPS was $1.80, up 105% from a year ago. Q1 cash flow from operations was $909 million.

好的。轉到損益表的其餘部分。第一季 GAAP 毛利率為 65.1%，非 GAAP 毛利率為 65.8%，較上季和年成長，主要得益於 GeForce GPU 產品組合和資料中心銷售額的增加。第一季 GAAP 營運費用為 10.3 億美元，非 GAAP 營運費用為 8.21 億美元，分別年增 10% 和 9%。第一季 GAAP EPS 為 1.47 美元，年增 130%，非 GAAP EPS 為 1.80 美元，較去年同期成長 105%。第一季經營活動現金流為 9.09 億美元。

Before I turn to the outlook, let me make a few comments on our Mellanox acquisition. Beyond the strong strategic and cultural fit that Jensen has discussed, Mellanox has exceptionally strong financial profile. The company reported revenue of $429 million in its March quarter, accelerating to 40% year-on-year growth, with GAAP and non-GAAP gross margins in the mid- to high 60% range. We expect the acquisition to be immediately accretive to non-GAAP gross margins, non-GAAP earnings per share and free cash flow. We aim to retain the full Mellanox team and accelerate investments in our combined road map as we jointly innovate on our shared vision for the future of accelerated computing.

在談到前景之前，我先對 Mellanox 的收購發表一些評論。除了 Jensen 討論過的強大的策略和文化契合度之外，Mellanox 還擁有異常強大的財務狀況。該公司報告稱，其 3 月當季營收為 4.29 億美元，年增 40%，GAAP 和非 GAAP 毛利率均在 60% 左右。我們預計此次收購將立即增加非公認會計準則毛利率、非公認會計準則每股收益和自由現金流。我們的目標是保留整個 Mellanox 團隊，並加快對我們合併路線圖的投資，共同創新我們對加速運算未來的共同願景。

With that, let me turn to the outlook of the second quarter of fiscal 2021, which includes a full quarter contribution from Mellanox. We have assumed in our outlook the potential ongoing impact from COVID-19. We expect our automotive platform sales to be down 40% on a sequential basis and Pro Viz to decline sequentially. In gaming, while we will likely see ongoing impact from the partial operations or closures of iCafes and retail stores, we expect that to be largely offset by a shift to e-tail channels. Overall, the precise magnitude of the impact is difficult to predict, given uncertainties around the reopening of the economy. Overall, we expect second quarter revenue to be $3.65 billion, plus or minus 2%. The contribution of Mellanox revenue is likely to be in the low teens percentage range of our total Q2 revenue. We are providing this breakout to help with comparability between Q1 and Q2. But going forward, it will become an integrated part of our data center market platform.

接下來，讓我來談談 2021 財年第二季的展望，其中包括 Mellanox 整個季度的貢獻。我們在展望中假設了 COVID-19 可能帶來的持續影響。我們預計我們的汽車平台銷售額將環比下降 40%，Pro Viz 銷售額也將環比下降。在遊戲領域，雖然我們可能會看到網咖和零售店部分營運或關閉帶來的持續影響，但我們預計這種影響將在很大程度上被轉向電子零售通路所抵消。總體而言，鑑於經濟重新開放的不確定性，影響的具體程度很難預測。整體而言，我們預計第二季營收為 36.5 億美元，上下浮動 2%。 Mellanox 的營收貢獻很可能占我們第二季總營收的百分之十幾。我們提供此分類是為了幫助比較 Q1 和 Q2。但展望未來，它將成為我們資料中心市場平台不可或缺的一部分。

GAAP and non-GAAP gross margins are expected to be 58.6% and 66%, respectively, plus or minus 50 basis points. The sequential decline in GAAP gross margins primarily reflects an increase in acquisition-related costs, most of which are nonrecurring.

預計 GAAP 和非 GAAP 毛利率分別為 58.6% 和 66%，上下浮動 50 個基點。 GAAP 毛利率的連續下降主要反映了收購相關成本的增加，其中大部分是非經常性成本。

GAAP and non-GAAP operating expenses are expected to be approximately $1.52 billion, and $1.04 billion, respectively. The sequential change in GAAP operating expenses reflects an increase in stock-based compensation and acquisition-related costs. GAAP and non-GAAP operating expenses for the full year are expected to be approximately $5.7 billion and $4.1 billion, respectively. For the full year, stock-based compensation and acquisition-related costs also influence.

預計 GAAP 和非 GAAP 營運費用分別約為 15.2 億美元和 10.4 億美元。依照美國通用會計準則計算的營業費用的環比變化反映了股票薪酬和收購相關成本的增加。預計全年 GAAP 和非 GAAP 營運費用分別約為 57 億美元和 41 億美元。對於全年而言，股票薪酬和收購相關成本也會產生影響。

GAAP and non-GAAP OI&E are both expected to be an increase of approximately $50 million and $45 million, respectively. GAAP and non-GAAP tax rates are both expected to be 9%, plus or minus 1%, excluding discrete items. Capital expenditures are expected to be approximately $225 million to $250 million. Further financial details are included in the CFO commentary and other information available on our IR website.

預計 GAAP 和非 GAAP OI&E 將分別增加約 5,000 萬美元和 4,500 萬美元。預計 GAAP 和非 GAAP 稅率均為 9%，上下浮動 1%，不包括單一項目。預計資本支出約2.25億至2.5億美元。進一步的財務細節包含在財務長評論和我們 IR 網站上提供的其他資訊中。

New this quarter, we have also posted an investor presentation summarizing our results and key highlights. In closing, let me highlight upcoming events for the financial community. Next Thursday, May 28, we will webcast a presentation and Q&A with Jensen on our recent product announcement moderated by Evercore. We will also be at Cowen's TMT Conference on May 27; Morgan Stanley's Cloud Secular Winners Conference on June 1; BoFa's Technology Conference on June 2; Needham's Fourth Automotive Technology Conference on June 3 and Nasdaq Investor Conference on June 16.

本季度，我們也發布了一份投資者介紹，總結了我們的業績和主要亮點。最後，讓我重點介紹一下金融界即將發生的事件。下週四，即 5 月 28 日，我們將在 Evercore 主持下，透過網路直播的方式與 Jensen 一起就我們最近的產品發布會進行演示和問答。我們也將於 5 月 27 日參加 Cowen 的 TMT 會議；摩根士丹利將於 6 月 1 日舉辦雲端長期贏家會議；6月2日 BoFa技術發布會；6 月 3 日舉行 Needham 第四屆汽車技術會議，6 月 16 日舉行納斯達克投資者會議。

Operator, we will now open for questions. Can you please poll for questions, please.

接線員，我們現在開始回答問題。您能否進行民意調查以提出問題？

(Operator Instructions) Aaron Rakers with Wells Fargo.

（操作員指示）富國銀行的 Aaron Rakers。

Congratulations on a solid quarter. Colette, I'm curious of your commentary around visibility in the data center side, that that's comments over the last couple of quarters, how would you characterize your visibility today relative to maybe what it was last quarter? And how do we think about the visibility in the context of trends maybe into the back half of the calendar year.

恭喜本季業績穩健。科萊特，我很好奇您對資料中心方面可見性的評論，這是過去幾個季度的評論，您如何描述今天的可見性相對於上個季度的可見性？我們如何看待今年下半年趨勢的可見度。

Thanks, Will, for the question. You are correct. We have indicated a couple of quarters ago that we were starting to see improved visibility after we came out of the digestion period in the prior overall fiscal year. As we move into Q2, we still have visibility and solid visibility into our Q2 results for overall data centers. So at this time, I'd say they are relatively about the same of what we had seen going into the Q1 period. And we think that is a true indication of their excitement about our platform and most particularly our excitement regarding A100, and that's launched and its additional products.

謝謝威爾提出這個問題。你是對的。我們在幾個季度前就曾表示，在走出上一財年的消化期後，我們開始看到可見度的提高。隨著進入第二季度，我們仍然對第二季度整體資料中心的業績具有清晰的可見度。因此，目前我認為它們與我們在第一季看到的情況基本相同。我們認為這真實地表明了他們對我們的平台的興奮，尤其是對 A100 及其附加產品的興奮。

Now regarding the second half of the year, as you know, we have seen broad-based growth in both the hyperscale and the vertical industries, both of them in terms of at record levels. In our Q1 results. And we see in terms of inferencing continuing to grow as well, as well as we're also expanding in terms of edge AI. Our strong demand of the A100 products, including the Delta Board, but also in terms of our DGXs, was just starting an initial ramp. However, we do guide only 1 quarter at a time. So it's still a little bit too early for us to give a true certainty in terms of the macro situation that's in front of us. But again, we feel very good about the demand for A100.

現在談到下半年，正如您所知，我們看到超大規模和垂直行業都出現了廣泛的增長，並且都達到了創紀錄的水平。在我們的第一季業績中。我們看到推理方面也在持續成長，同時邊緣人工智慧也在不斷擴展。我們對 A100 產品（包括 Delta Board）以及 DGX 的需求強勁，這才剛開始成長。但是，我們每次只指導一個季度。因此，就我們面臨的宏觀情勢而言，現在下定論還為時過早。但我們再次對 A100 的需求感到非常樂觀。

Your next question comes from Stacy Rasgon with Bernstein Research.

您的下一個問題來自伯恩斯坦研究公司的 Stacy Rasgon。

I first wanted to follow-up on your gaming commentary. You sort of mentioned a couple of offsets. COVID potentially still a headwind, e-tail or tailwind and maybe offsetting each other. Were you trying to suggest that those did offset completely and gaming was kind of flattish into Q2? Because I know it has a typical seasonal pattern switches typically up. I guess what were you trying to say with those kind of factors? And what are the kinds of things we should be thinking about when it comes to seasonality, Colette, into Q2 around that business segment?

我首先想跟進一下你的遊戲評論。您提到了一些偏移量。 COVID 可能仍是逆風、電子零售或順風，並且可能相互抵消。您是否想說這些因素確實完全抵消了，並且遊戲在第二季度表現平平？因為我知道它有一個典型的季節性模式，通常會向上轉變。我猜你透過這些因素想表達什麼？那麼，科萊特，當談到季節性時，我們應該考慮哪些事情，圍繞著該業務部門進入第二季？

So let me start, and I'll see if Jensen also wants to add on to it. I think you're talking about our sequential between Q1 and Q2. Some of the...

那麼讓我開始吧，我會看看 Jensen 是否也想補充一些內容。我認為您談論的是第一季和第二季之間的連續情況。其中一些...

Yes. That's right.

是的。這是正確的。

Right. Some of the pieces that we had seen related to COVID-19 in Q1 may carry over into Q2. COVID-19, in fact, had an impact in terms of our retail channels as well as our iCafes. However, as we discussed, efficiently, moved to overall e-tail. We have normally been seasonally down in desktop between Q1 and Q2, and that will likely happen. But we do see the strength in terms of laptops and overall consoles as we move for Q1 to Q2. So in summary, we do expect grow sequentially between Q1 and Q2 for our overall gaming business. And I'll turn it over to Jensen to see if he has additional commentary.

正確的。我們在第一季看到的一些與 COVID-19 相關的現象可能會延續到第二季​​。事實上，COVID-19 對我們的零售通路和網咖產生了影響。然而，正如我們所討論的，有效地轉向了整體電子零售。通常情況下，我們的桌面業務在第一季和第二季之間會出現季節性下滑，這種情況很可能會發生。但隨著第一季進入第二季度，我們確實看到了筆記型電腦和整體遊戲機的強勁表現。總而言之，我們確實預計我們的整體遊戲業務將在第一季和第二季之間連續成長。我會把它交給詹森，看看他是否還有其他評論。

No, that was great. That was fantastic.

不，那太棒了。太棒了。

Yes. I guess just to follow up on that, though, if it's growing. I mean like in prior years, we've seen it grow like very strong double digits. Obviously, the mix of the business was different back then. But do you think that the kind of -- I mean are we thinking kind of it's up somewhat? You don't -- is there any chance that it could be up like on -- for what we've seen in terms of like typical levels in the past? Like can you give us any sense of magnitude, that would be really helpful?

是的。我想，如果它在成長的話，就繼續跟進。我的意思是，就像前幾年一樣，我們看到它實現了非常強勁的兩位數成長。顯然，當時的業務組合有所不同。但是您是否認為——我的意思是我們是否認為它有所上升？您不知道——有沒有可能它能像——我們過去所見的典型水平那樣上升？例如，您能否提供我們一些真正有幫助的量級概念？

Yes. I think when we think about that sequential growth, we'll probably be in the low -- moving up to probably the mid-single digits in terms of -- that's what our guidance right now, and we'll just have to see how the quarter goes.

是的。我認為，當我們考慮連續成長時，我們可能會處於低位 - 可能上升到中等個位數 - 這就是我們目前的指導，我們只需要看看本季的進展。

Yes. That's very helpful.

是的。這非常有幫助。

Stacy, the thing that I would add is this. I would say, I think the guidance is exactly what Colette mentioned. But if you look at the big picture, there's a few dynamics that are working really well in our favor. First, of course, is that RTX and ray tracing is just the home run. Minecraft was phenomenal. We have 33 games in the pipe that has already been announced or shipping. Just about every game developers signed on to RTX and ray tracing, and I think it's a foregone conclusion that this is the next generation. This is the way computer graphics is going to be in the future. And so I think RTX is a home run.

史黛西，我想補充的是這個。我想說，我認為指導正是科萊特所提到的。但如果從整體來看，有一些動態對我們非常有利。首先，當然是 RTX 和光線追蹤才是本壘打。 Minecraft 非常棒。我們有 33 款遊戲正在籌備中，其中一些已經宣布或發售。幾乎每個遊戲開發商都簽署了 RTX 和光線追蹤協議，我認為這是下一代技術已是必然的。這就是未來電腦圖形學的發展方向。所以我認為 RTX 是一支全壘打。

The second, the notebooks that we create is just doing great. We got 100 notebooks in gaming. We have 75 notebooks designed for either mobile workstations or what we call NVIDIA studio for designers and creators. And the timing was just perfect. With everybody needing to stay at home, the ability to have a mobile gaming platform and a mobile workstation, it was just perfect timing. And then, of course, you guys know quite well that our Nintendo Switch is doing fantastic. There are 3 -- the top 3 games in the world. The top games in the world today are Fortnite, Minecraft and Animal Crossing. All 3 games are NVIDIA platforms. And so I think we have all the dynamics working in our favor. And then we just got to see how it turns out.

第二，我們創建的筆記本表現非常好。我們有 100 台遊戲筆記本。我們有 75 款筆記型電腦專為行動工作站或我們稱之為設計師和創作者的 NVIDIA 工作室而設計。而且時機也恰到好處。由於每個人都需要待在家裡，能夠擁有一個行動遊戲平台和一個行動工作站，這真是一個完美的時機。當然，你們都很清楚我們的 Nintendo Switch 表現非常出色。有 3 款——世界排名前 3 名的遊戲。當今世界最熱門的遊戲是 Fortnite、Minecraft 和 Animal Crossing。這3款遊戲均為NVIDIA平台。所以我認為我們擁有一切對我們有利的動力。然後我們只需要看看結果如何。

Your next question comes from Joe Moore with Morgan Stanley.

您的下一個問題來自摩根士丹利的喬·摩爾。

I wanted to ask about the rollout of Ampere how quickly does that roll in the various segments between hyperscale as well as on the DGX side as well as on the HPC side. And is it a smooth transition? Is there -- I remember when you launched Volta, there was a little bit of a transitional pause. Just can you tell us how you see that ramping up with the different customer segments?

我想問一下 Ampere 的推出情況，它在超大規模、DGX 方面以及 HPC 方面的各個細分市場的推出速度有多快。這是一個平穩的過渡嗎？我記得當你推出 Volta 時，有一點過渡停頓。您能否告訴我們，您如何看待這項舉措對不同客戶群的影響？

Yes. Thanks a lot, Joe. So first of all, taking a step back. Accelerated computing is now common sense in data centers. It wasn't the case when we first launched Volta. If you went back to Volta, Volta was the first generation that the deep learning training in a really serious way, and it was really focused on training. It was focused on training and high-performance computing. We didn't come until later with the inference version called T4.

是的。非常感謝，喬。首先，退一步來說。加速運算如今已成為資料中心的常識。當我們第一次推出 Volta 時並非如此。如果你回顧一下 Volta，你會發現 Volta 是第一代以真正嚴肅的方式進行深度學習訓練的產品，它真正專注於訓練。它專注於培訓和高效能運算。直到後來我們才有了稱為 T4 的推理版本。

But over the course of the last 5 years, we've been accelerating workloads that are now diversifying in data centers. If you take a look at most of the hyperscalers, machine learning is now pervasive. Deep learning is now pervasive. The notion of accelerated deep learning and machine learning using our GPUs is now common sense. It didn't used to be. People still saw it as something esoteric. But today, data centers all over the world expect a very significant part of their data center being accelerated with GPUs. The number of workloads that we've accelerated since in the last 5 years have expanded tremendously, whether it's imaging or video or conversational AI or deep recommender systems that probably unquestionably, at this point, the most important machine learning model in the world. And so the number of applications we now accelerate is quite diverse. And so that's really -- that's contributed greatly to the ramp of Ampere. When we came -- when we started to introduce Ampere to the data center, it was very commonsensical to them that they would adopt it. They have a large amount of workload that's already accelerated by NVIDIA GPUs.

但在過去五年裡，我們一直在加速資料中心的工作負載多樣化。如果你觀察大多數超大規模企業，你會發現機器學習現在已經無所不在。深度學習現已無所不在。使用我們的 GPU 加速深度學習和機器學習的概念現在已成為常識。以前不是這樣的。人們仍然將其視為深奧的東西。但如今，世界各地的資料中心都期望其資料中心的很大一部分能夠透過 GPU 加速。過去 5 年來，我們加速的工作負載數量大幅增加，無論是影像、影片、對話式人工智慧或深度推薦系統，毫無疑問，目前這些是世界上最重要的機器學習模型。因此，我們現在加速的應用程式數量相當多樣化。所以這確實對安培的成長做出了巨大貢獻。當我們開始將 Ampere 引入資料中心時，他們自然會採用它。他們有大量的工作負載，這些工作負載已經透過 NVIDIA GPU 加速。

And as you know, our GPUs are architecturally compatible from generation to generation. We're forward compatible or backwards compatible. Everything that runs on T4 runs on A100, everything that runs on V100 runs on A100. And so I think the transition is going to be really, really smooth. On the other hand, because V100 and T4 -- which, by the way, V100 and T4 had a great quarter. It was sequentially up. And then on top of that, we grew with the A100 shipment. A100 -- or excuse me, V100 and T4 are now quite broadly adopted in hyperscalers for their AI services, in cloud computing, in a vertical industries, which is almost roughly half of our overall HPC business. All the way out to the edge, which had a great quarter. Much smaller part, of course -- supercomputing is important, but it's a very small part of the high-performance computing. But that's also -- we also shipped A100 to supercomputing centers. And so I think the general sense of it -- the summary of it is that the number of workloads for accelerated computing has continued to grow, the adoption of machine learning and AI and all the cloud and hyperscalers has grown. The common sense of using acceleration is now a foregone conclusion. And so I think we're ramping into a very receptive market with a really fantastic -- with a really fantastic product.

如您所知，我們的 GPU 在架構上是代代相容的。我們向前相容或向後相容。 T4 上運行的所有內容都在 A100 上運行，V100 上運行的所有內容都在 A100 上運行。所以我認為過渡將會非常非常順利。另一方面，因為 V100 和 T4——順便說一下，V100 和 T4 本季表現非常出色。是依序上升的。除此之外，我們也隨著 A100 的出貨量而成長。 A100——或者不好意思，V100 和 T4 現在被超大規模企業廣泛用於其 AI 服務、雲端運算和垂直行業，這幾乎占我們整體 HPC 業務的一半。一路走到邊緣，度過了一個美好的季度。當然，超級運算很重要，但它只是高效能運算中非常小的一部分。但這也是——我們還將 A100 運送到超級運算中心。所以我認為它的整體意義——總結起來就是加速運算的工作負載數量持續增長，機器學習和人工智慧以及所有雲端和超大規模的採用都在增長。使用加速度的常識現在已是必然。所以我認為我們正在以一款非常棒的產品打入一個接受度很高的市場。

Your next question comes from Vivek Arya with Bank of America.

您的下一個問題來自美國銀行的 Vivek Arya。

Congratulations on the strong growth and execution. Just a quick clarification. Colette, 66% kind of the new baseline for gross margin? And then the question, Jensen, for you, is give us a sense for how much inference as a workload and payer as a product are expected to contribute? I'm just curious where you are in terms of growing in the inference and edge AI market? And where are we kind of in the journey of Ampere penetration?

恭喜您取得強勁的成長和執行力。只需簡單澄清一下。 Colette，66% 是毛利率的新基準嗎？那麼，詹森，你的問題是，讓我們了解推理作為工作量和付款人作為產品預計會貢獻多少？我只是好奇您在推理和邊緣 AI 市場的成長如何？那麼我們在安培滲透之旅中處於什麼位置呢？

So let me start on the first question regarding the gross margin and our gross margin as we look into Q2. We are guiding Q2 non-GAAP gross margins at 66%. This is -- would be another record gross margin quarter just as we finished an overall record level, even as we are continuing right now to ramp our overall Ampere architecture within that. The Q2 also incorporates Mellanox. Mellanox has a very similar overall margins to our overall data center margins as well. But we see this new baseline as a great transition and likely to see some changes as we go forward. However, it's still a little early to see where these gross margins will go. But we're very pleased with the overall guidance right now at 66% for Q2.

因此，讓我先從有關毛利率以及我們第二季的毛利率的第一個問題開始。我們預計第二季非 GAAP 毛利率為 66%。這將是另一個創紀錄的毛利率季度，正如我們完成了整體創紀錄的水平一樣，儘管我們現在正在繼續提升我們的整體安培架構。 Q2 也採用了 Mellanox。 Mellanox 的整體利潤率與我們的整體資料中心利潤率非常相似。但我們認為這個新基準是一個巨大的轉變，並且隨著我們前進可能會看到一些變化。然而，現在判斷這些毛利率將會達到何種水準還為時過早。但我們對目前第二季的整體預期非常滿意，為 66%。

Accelerated computing is just at the beginning of its journey. If you look at -- I would characterize it as several segments. First is hyperscaler AI microservices, which is all the services that we enjoy today that has AI. Whenever you shop on the web, it recommends a product. When you're watching a movie, it recommends a movie or it recommends a song. All of those -- or recommends news or recommends a friend or recommends a website, the first 10 websites that they recommend. All of these recommenders that are powering the Internet are all based on machine learning today. It's the reason why they're collecting so much data. The more data they can collect, the more they could predict your preference, and that predicting your preference is the core to a personalized Internet. It used to be largely based on CPU approaches. But going forward, it's all based on deep learning approaches. The results are much more superior and a few percentage change in preference prediction accuracy could result in tens of billions of dollars of economics.

加速計算才剛開始其旅程。如果你看一下——我會將其分為幾個部分。首先是超大規模 AI 微服務，也就是我們今天所享受的所有具有 AI 的服務。每當您在網上購物時，它都會推薦一種產品。當你正在看電影時，它會推薦一部電影或推薦一首歌曲。所有這些——或者推薦新聞、推薦朋友、推薦網站，他們推薦的前 10 個網站。如今，所有這些為互聯網提供支援的推薦系統都是基於機器學習的。這就是他們收集如此多數據的原因。他們收集的數據越多，就越能預測你的偏好，而預測你的偏好正是個人化網路的核心。它過去主要基於 CPU 方法。但展望未來，一切都將基於深度學習方法。結果更加優越，偏好預測準確度的幾個百分點的變化就可能帶來數百億美元的經濟效益。

And so this is very, very big deal. And the shift towards deep learning in hyperscale micro services or AI micro services is still ramping. Second is cloud. And as you know, cloud is a $100 billion market segment of it today, growing about 40% into $1 trillion opportunity. This -- cloud computing is the single largest IT industry transformation that we have ever seen. The 2 powers that is really -- the force -- the 2 forces that is really driving our data center business is AI and cloud computing. We're perfectly, perfectly positioned to benefit from these 2 powerful forces. So the second is cloud computing. And that journey is -- has a long ways to go. Then the third is industrial edge. In the future, today -- it's not the case today. But the combination of IoT, 5G, industrial 5G and artificial intelligence, it's going to turn every single industry into a tech industry. And whether it's logistics or warehousing or manufacturing or farming, construction, industrial, every single industry will become a tech industry. And there'll be trillions of sensors, and they'll be connected to little micro data centers.

所以這是一件非常非常重要的事情。超大規模微服務或人工智慧微服務向深度學習的轉變仍在不斷加速。第二是雲。如你所知，雲端運算目前是一個價值 1,000 億美元的細分市場，預計將成長 40% 左右，達到 1 兆美元的市場機會。這是我們迄今所見過的最大的 IT 產業變革。真正推動我們資料中心業務發展的兩大力量是人工智慧和雲端運算。我們完全有能力從這兩股強大的力量中受益。第二個是雲端運算。而這段旅程還有很長的路要走。第三是產業優勢。將來，今天——情況已經不再如此。但物聯網、5G、工業 5G 和人工智慧的結合將把每個產業變成科技業。無論是物流、倉儲、製造、農業、建築、工業，每個產業都將成為科技業。將會有數萬億個感測器，它們將連接到小型微型資料中心。

And those data centers will be in the millions. They'll be distributed all over the edge. And that journey has just barely started. We announced 3 very important partners in 3 domains. And they're the lead partners that we felt that people would know, but we have several hundred partners that are working with us on edge AI. We announced Walmart for smart retail. We announced the U.S. Postal Service, the world's largest mail sorting service and logistics service. And then we announced this last quarter, BMW, who is working with us to transform their factory into a robotics, automated factory of the future. And so these 3 applications are great examples of the next phase of artificial intelligence and where Ampere is going to ramp into. And that is just really at its early stages. And so I think it's fair to say that we're really well positioned in the 2 fundamental forces of IT today, data center scale computing and artificial intelligence. And the segments that it's going to make a real impact are all gigantic markets. Hyperscale AI, cloud and edge AI.

這些數據中心的數量將達到數百萬。它們將分佈在整個邊緣。而這段旅程才剛開始。我們宣布了三個領域的三個非常重要的合作夥伴。我們認為他們是大家所熟知的主要合作夥伴，但我們有數百個合作夥伴與我們一起在邊緣人工智慧領域合作。我們宣布推出沃爾瑪智慧零售。我們宣布成立美國郵政服務，這是全球最大的郵件分揀服務和物流服務。然後我們在上個季度宣布，寶馬正在與我們合作，將他們的工廠改造成未來的機器人自動化工廠。因此，這 3 個應用程式是人工智慧下一階段的絕佳範例，也是安培即將進軍的領域。而這其實還處於早期階段。因此，我認為可以公平地說，我們在當今 IT 的兩大基本力量——資料中心規模運算和人工智慧——中佔據了有利地位。它將產生真正影響的領域都是巨大的市場。超大規模 AI、雲端和邊緣 AI。

Your next question comes from C.J. Muse with Evercore.

您的下一個問題來自 Evercore 的 C.J. Muse。

I guess if I could ask 2. Colette, can you help us with what you think the growth rate for Mellanox could look like in calendar '20? And then Jensen, a bigger picture question for you and really not specific to health care, more broad-based. But how do you think about the long-lasting impact of COVID on worldwide demand for AI?

我想我可以問 2. 科萊特，您能否幫助我們了解一下您認為 20 年 Mellanox 的增長率會是多少？然後詹森，我想問您一個更大的問題，這個問題實際上並不具體針對醫療保健，而是更廣泛的範圍。但是您如何看待 COVID 對全球人工智慧需求的長期影響？

C.J., can you help me? You cut out in the middle of your sentence to me. Can you repeat the first part of it for me?

C.J.，你能幫助我嗎？你對我說的話中途打斷了。你能為我重複第一部分嗎？

No, sorry about that. I'm curious if you could provide a little handholding on what we should think about for growth for Mellanox in calendar '20?

不，很抱歉。我很好奇，您是否可以提供一些指導，讓我們了解在 20 年期間我們應該如何考慮 Mellanox 的成長？

At this time, it's a little early for us. And as you know, we generally just go 1 quarter out, and we're excited to bring the Mellanox team on board so we can start beginning the future of building products together. For the overall margin, their overall performance over the last couple of quarters, they had a great last year. They had a great March quarter as well. And we're just going to have to stay tuned to see equally with them what the second half of the year looks for them. Okay?

這個時間對我們來說還有些早。如您所知，我們通常只推出一個季度的產品，我們很高興能夠讓 Mellanox 團隊加入我們，這樣我們就可以開始共同打造產品的未來。就整體利潤率以及過去幾季的整體表現而言，去年他們的表現非常出色。他們在三月的季度也表現出色。我們只要繼續關注他們，就能知道今年下半年他們會有什麼樣的表現。好的？

Yes, C.J., thanks for the question. This pandemic is really quite tragic, and it's reshaping industries and markets. And I think it's going to be structural. I think it's going to remain. And I think your question is really good because now it's a good time to think about where to double down. There's a few areas that I believe are going to be structurally changed. And I think that once I say it, it'll be very sensible. The first is that the world's enterprise digital transformation and moving to the cloud, that is going to accelerate. Every single company can't afford to rely just on on-prem IT. They have to be much more resilient. And having a hybrid cloud computing infrastructure is going to provide them the resilience they need. And so that's one. And when the world moves and accelerates into this $1 trillion IT infrastructure transformation, which is now $100 billion into that journey, it's growing 40% a year, I wouldn't be surprised to see that accelerate. And so cloud computing AI is going to accelerate because of that. The second is the importance of creating a computational defense system. The defense systems of most nations today are based on radar. And yet in the future, our defense systems are going to detect things that are unseeable. It's going to be infectious disease.

是的，C.J.，謝謝你的提問。這次疫情確實非常悲慘，它正在重塑產業和市場。我認為這將是結構性的。我認為它會保留下來。我認為你的問題非常好，因為現在是思考加倍投入的好時機。我認為有幾個領域將會發生結構性變化。而且我認為，一旦我說出來，就會非常明智。第一，全球企業數位轉型和雲端化進程將會加速。每家公司都無法承受僅依賴內部部署 IT 的後果。他們必須更加堅韌。擁有混合雲端運算基礎設施將為他們提供所需的彈性。這就是其中之一。當世界加速進入這個價值 1 兆美元的 IT 基礎設施轉型時，目前這一進程已耗資 1,000 億美元，每年增長 40%，我不會對這一加速感到驚訝。因此，雲端運算人工智慧將會加速發展。第二是建立計算防禦系統的重要性。當今大多數國家的防禦系統都是以雷達為基礎的。然而在未來，我們的防禦系統將會偵測到那些看不見的東西。這會是一種傳染病。

And I think every nation and government and scientific lab is now gearing up to think about what does it take to create a national defense system for each country that is based on computational methods? And NVIDIA is an accelerated computing company. We take something that otherwise would take a year in the case of Oak Ridge, and they filter 1 billion compounds in a day. And that's what you need to do. You need to find a way to have an accelerated computational defense system that allows you to find insight, detect early warning ASAP. And then, of course, the computational system has to go through the entire range from mitigation to containment to living within the monitoring. And so scientific labs are going to be gearing up. National labs are going to be gearing up. The third part is AI and robotics. We're going to have to have the ability to be able to do our work remotely.

我認為每個國家、政府和科學實驗室現在都在思考如何為每個國家創建一個基於計算方法的國家防禦系統？而NVIDIA是一家加速運算公司。我們所做的一些事情，在橡樹嶺國家實驗室則需要一年的時間，而他們一天就能過濾 10 億種化合物。這就是你需要做的。您需要找到一種方法來建立加速計算防禦系統，以便您能夠盡快發現洞察力並偵測預警。然後，當然，計算系統必須經歷從緩解到遏制再到監測的整個過程。因此科學實驗室將加緊努力。國家實驗室即將加緊努力。第三部分是人工智慧和機器人技術。我們必須具備遠距工作的能力。

NVIDIA has a lot of robots that are helping us in our labs. And without those robots helping us in our labs, we'll have a hard time getting our work done. And so we need to have remote autonomous capability for -- to handle all of these -- either dangerous circumstances to disinfect environments, to fumigate environments autonomously, to clean environments, to be able to interact with people where as little as possible in the event of an outbreak. All kinds of robotics applications are being dreamed up right now to help society forward in the case of another outbreak. And then lastly, I think more and more people are going to work permanently from home. There's a strong movement of companies that are going to support a larger percentage of people working from home. And when people work from home, it's going to clearly increase the single best home entertainment, which is video games. I think video games is going to represent a much larger segment of the overall entertainment budget of society.

NVIDIA 有許多機器人在實驗室裡為我們提供協助。如果沒有這些機器人在實驗室幫助我們，我們將很難完成工作。因此，我們需要具備遠端自主能力來處理所有這些情況，無論是危險情況，還是對環境進行消毒，自主熏蒸，清潔環境，在疫情爆發時能夠盡可能少地與人互動。人們目前正在構思各種機器人應用，以便在疫情再次爆發時幫助社會前進。最後，我認為越來越多的人將永久在家工作。很多公司正在大力支持更多員工在家工作。當人們在家工作時，最好的家庭娛樂方式——電子遊戲——顯然會增加。我認為電子遊戲將佔據社會整體娛樂預算的更大份額。

And so these are some of the trends, I would say. I would say cloud computing, AI. I would say national labs, a computational defense system, robotics and working from home are structural changes that are going to be here to stay. And these dynamics are really good for us.

我想說，這些都是一些趨勢。我想說的是雲端運算、人工智慧。我想說國家實驗室、計算防禦系統、機器人技術和在家工作都是會持續存在的結構性變化。這些動態對我們來說確實很有利。

Your next question comes from Toshiya Hari with Goldman Sachs.

您的下一個問題來自高盛的 Toshiya Hari。

I had one for Colette and then one for Jensen as well, if I may. Colette, I wanted to come back to the gross margin question. You're guiding July essentially flat sequentially, despite what I'm guessing is better mixed with non-ops coming in and automotive guided down 40% sequentially. I guess the question is, what are some of the offsets that are pulling down gross margins in the current quarter? And sort of related to that, how should we be thinking about the cadence and OpEx going forward, given the 6-month pull in that you guys talked about on the compensation side? And then one quick one for Jensen. I was hoping you could comment on the current trade landscape between the U.S. and China. I feel like you guys shouldn't be impacted in a material way directly nor indirectly. But at the same time, given the critical role you play in scientific computing, I can sort of see a scenario where some people may claim that you guys contribute to efforts outside of the U.S. So if you can kind of speak on that -- speak to that, that will be helpful.

如果可以的話，我為 Colette 準備了一個，然後也為 Jensen 準備了一個。科萊特，我想回到毛利率問題。儘管我猜測非營運業務的營收和汽車業務的營收季減 40%，但您預測 7 月營收將與上一季基本持平。我想問題是，哪些抵銷因素導致本季毛利率下降？與此相關的是，考慮到你們在薪酬方面談到的 6 個月的拉動，我們應該如何考慮未來的節奏和營運支出？接下來是 Jensen 的簡短發言。我希望您能對當前中美貿易狀況發表評論。我覺得你們不應該受到直接或間接的物質影響。但同時，鑑於你們在科學計算中發揮的關鍵作用，我可以想像有些人可能會聲稱你們為美國以外的努力做出了貢獻。所以，如果你能就此談談，那將會很有幫助。

Thanks, Toshiya, for your question. So regarding our gross margins in the second quarter, our second quarter guide at 66% is up sequentially from even a record level in terms of what we had in terms of Q1. This next record that we hope to achieve with our overall guidance is even with including our overall Ampere architecture. So typically, when we transition to a new architectures, margins can somewhat be a little bit lower on the onset but tend to kind of move up and trend up over time. Additionally, as you articulated, our automotive is lower. But also, we're going to see growth in some of our platforms in gaming such as consoles, which may offset those 2. But overall, there's nothing structural to really highlight other than our mix in business and the ramp of Ampere and its transition.

謝謝 Toshiya 提出的問題。因此，就我們第二季的毛利率而言，我們預計第二季的毛利率為 66%，與第一季相比，這一水平甚至達到了創紀錄的水平。我們希望透過我們的整體指導實現的下一個記錄甚至包括我們的整體安培架構。因此，通常情況下，當我們過渡到新的架構時，利潤率一開始可能會稍微低一些，但隨著時間的推移，利潤率往往會上升。此外，正如您所說，我們的汽車價格較低。但同時，我們也將看到一些遊戲平台（例如遊戲機）的成長，這可能會抵消上述兩者。但總的來說，除了我們的業務組合以及 Ampere 的成長和轉型之外，沒有任何結構性的東西值得真正強調。

Let's see, the trade tension. We've been living in this environment for some time, Toshiya. And as you know, the trade tension has been in the background for coming up on a year, probably gotten longer. And China's high-performance computing systems are largely based on Chinese electronics anyhow. And so that's -- I think our condition won't materially change going forward.

讓我們看看貿易緊張局勢。我們已經在這個環境中生活了一段時間了，Toshiya。正如你們所知，貿易緊張局勢已經持續了一年，甚至可能持續更久。而且中國的高效能運算系統很大程度上都是基於中國電子產品。所以——我認為我們的狀況未來不會發生實質改變。

So Toshiya, let me respond to your second question that you had for me, which was regarding to our OpEx and our decision to pull forward our overall local into Q2. This is something that we've normally done later in the year. We felt it was prudent during the current COVID-19. Although our employees are quite safe. We just wanted to make sure that their family members also were safe and had the opportunity to have cash upfront. It is about a couple of months, about 4 months earlier than normal, and it is incorporated in our guidance for Q2.

那麼 Toshiya，讓我來回答您提出的第二個問題，該問題涉及我們的營運支出以及我們將整體本地業務提前到第二季的決定。這是我們通常在每年晚些時候做的事情。我們認為在目前 COVID-19 疫情期間這是謹慎的做法。儘管我們的員工相當安全。我們只是想確保他們的家人也安全並且有機會預先獲得現金。這比正常情況早了大約幾個月，大約 4 個月，並且已納入我們對第二季的指導中。

Your next question comes from Mark Lipacis with Jefferies.

您的下一個問題來自 Jefferies 的 Mark Lipacis。

A question coming back to the A100. I'm trying to understand how this kind of fits into the evolution of your solution set over time and the evolution of the demand for the applications. Is -- and I guess if I think about it going back, you had a solution, which is largely training based. And then you kind of introduced solutions that were targeted more inferencing. And now you have a solution, it sounds to my understanding that it solves both inferencing and training efficiently. And so I guess I'm wondering is 3 years, 5 years, 10 years down the line, is this part of the kind of general purpose computing or acceleration framework that you had talked about in the past, Jensen, where Ampere is kind of like an Ampere-class product? Or is this -- would you still -- should we still expect to see inferencing-specific solutions in the market and then training-specific solutions and then an Ampere solution for a different class application? If you could provide a framework for thinking about Ampere in those context, I think that would be helpful.

回到 A100 的一個問題。我試圖了解這種做法如何適應您的解決方案集隨時間的演變以及應用程式需求的演變。是的——我想如果我回想一下，你有一個解決方案，它主要是基於培訓的。然後您引入了針對更多推理的解決方案。現在您有了一個解決方案，根據我的理解，它可以有效地解決推理和訓練問題。所以我想知道 3 年後、5 年後、10 年後，這是否是您過去談到的通用計算或加速框架的一部分，Jensen，其中 Ampere 有點像安培級產品？還是 — — 您是否仍然 — — 我們是否仍應期望在市場上看到推理專用解決方案，然後是訓練專用解決方案，然後是針對不同類別應用的安培解決方案？如果您能提供一個在那些背景下思考安培的框架，我認為這將會很有幫助。

Yes. Thanks for the call, Mark. Good Question. I think the -- if you take a step back, currently in our data centers, the current setup in data centers, starting from probably all the way back, 6, 7 years ago, but really accelerating in the last 5 years and then really accelerating in the last couple of years, we learned our way into it. There are 3 classes of workloads, and they kind of came into acceleration over time. The first class of workload that we discovered was -- the major workload was deep learning training. Deep learning training. And the ideal setup for that today prior to Ampere or yesterday prior to Ampere is the V100 SXM with NVLink, 8 GPUs on one board, and that architecture is called scale up. It's like a supercomputer architecture. It's like a -- it's like a weather simulation architecture. You're trying to build the largest possible computing node you can for one operating system called scale up. And the second thing that we learned along the way was then cloud computing started to grow because researchers around the world needed to get access to an accelerated platform for developing their machine learning algorithms. And because they have a different degree of budget, and they want to get into it, a little bit more lightly and have the ability to scale up to larger nodes, the perfect model for that was actually a V100 PCI Express, not SXM, but PCI Express that allows you to offer 1 GPU all the way up to many GPUs.

是的。謝謝您的來電，馬克。好問題。我認為——如果你退一步來看，目前在我們的資料中心，資料中心的當前設置，可能從 6、7 年前就開始了，但在過去的 5 年裡真正加速，然後在過去幾年裡真正加速，我們已經學會瞭如何進入這個領域。工作負載有 3 類，隨著時間的推移，它們的負載逐漸增加。我們發現的第一類工作負荷是－主要工作負荷是深度學習訓練。深度學習訓練。如今在 Ampere 之前或之前在 Ampere 之前的理想設置是帶有 NVLink 的 V100 SXM，一塊板上有 8 個 GPU，這種架構稱為縱向擴展。它就像一個超級電腦架構。它就像一個——它就像一個天氣模擬架構。您正在嘗試為一個作業系統建立盡可能最大的運算節點，稱為擴充。在這個過程中，我們了解到的第二件事是雲端運算開始發展，因為世界各地的研究人員需要訪問加速平台來開發他們的機器學習演算法。因為他們的預算不同，他們想更輕鬆地進入這個領域，並有能力擴展到更大的節點，因此，完美的車型實際上是 V100 PCI Express，而不是 SXM，而是 PCI Express，它允許您提供 1 個 GPU 一直到多個 GPU。

And so that versatility, V100 PCI Express, not as scalable in performance as the V100 SXMs, but it was much more flexible for rentals. Cloud renting was really quite ideal. And then we started to get into inference, and we're on our seventh generation of TensorRT, TensorRT 7.0. Along the way, we've been able to accelerate more and more. And today, we largely accelerate every deep learning inference computational graph that's out there. And the ideal GPU for that was something that has the reduced precision, which is called (inaudible), reduced precision not with electronics that is focused more for inference -- and because inference is a scale-out application, where you have millions of queries, and each one of the queries are quite small versus scale up, where you have 1 training job and that 1 training job is running for a day.

因此，V100 PCI Express 的多功能性在性能上不如 V100 SXM 那樣具有可擴展性，但對於租賃來說更加靈活。雲端租賃確實非常理想。然後我們開始進行推理，我們現在使用的是第七代 TensorRT，即 TensorRT 7.0。一路走來，我們的速度不斷加快。今天，我們大大加速了現有的每一個深度學習推理計算圖。而理想的 GPU 是具有較低精度的 GPU，也就是所謂的（聽不清），較低精度不是針對更側重於推理的電子設備 - 因為推理是一個橫向擴展的應用程序，其中有數百萬個查詢，並且每個查詢相對於縱向擴展而言都非常小，其中您有 1 個訓練作業，並且 1 個訓練作業要運行一天。

It could be running for days and sometimes even weeks. And so scale-up application is for 1 user that uses it for a long period of time on a very large machine. Scale out, it's for millions of users, each one of them have a very small query and that query could last hundreds of milliseconds and where ideally, you like to get it done in hundreds of milliseconds. And so notice, I've said 3 different architecture in a data center today. Most data centers today has a storage server, has CPU servers, and it has scale-up acceleration service with Voltas has scaled out servers with GeForce and then it has scale cloud computing, flexible servers based on V100. And so the ability to predict workload is so hard, and therefore, the utilization of these systems will be spiky. And so we created an architecture that allows for 3 things.

它可能運行幾天，有時甚至幾週。因此，擴大規模的應用程式適用於 1 個用戶在非常大的機器上長期使用它。橫向擴展，它適用於數百萬用戶，每個用戶都有一個非常小的查詢，並且該查詢可能持續數百毫秒，理想情況下，您希望在數百毫秒內完成它。請注意，我今天談到了資料中心的 3 種不同架構。如今大多數資料中心都有儲存伺服器、CPU 伺服器，並且具有 Voltas 的擴展加速服務、使用 GeForce 的擴展伺服器，然後具有基於 V100 的擴展雲端運算和靈活伺服器。因此，預測工作量的能力非常困難，因此這些系統的使用率將會急劇上升。因此我們創造了一個可以實現 3 件事的架構。

So things -- the 3 characteristics of Ampere are: number one, it is the greatest generational leap in history. I mean I don't remember a generation where we increased throughput for training and inference by 20x. And it's just a gigantic -- for training and for inference, it is a gigantic leap forward. The second, it's the first architecture that is unified. We could use this to the computational -- the computation engine of Ampere accelerates the moment the data comes into the data center. From data processing, it's called [ETO]; the engine, which many of you probably know, it's the single most important computational engine in the world today for big data. It used to be Hadoop, but now it's Spark. Spark is used all over the world, 16,000 customers. We finally have the ability to accelerate that.

因此，安培的三個特點是：第一，它是歷史上最偉大的世代飛躍。我的意思是，我不記得有哪一代人將訓練和推理的吞吐量提高了 20 倍。對於訓練和推理來說，這是一個巨大的飛躍。第二，這是第一個統一的架構。我們可以將其用於計算——安培的計算引擎可以在資料進入資料中心時加速。從資料處理上來說，它被稱為[ETO]；你們中的許多人可能都知道這個引擎，它是當今世界上最重要的大數據計算引擎。以前是Hadoop，現在是Spark。 Spark 在全球各地使用，擁有 16,000 個客戶。我們最終有能力加速這一進程。

And then it's -- Ampere is also good for training, deep learning, machine learning, extra boost as well as deep learning, all the way out to inference. And so we now have a unified acceleration platform for the entire workload. And then the third thing is it's the first GPU ever, the first acceleration platform ever that's elastic. You could reconfigure it. You could configure it for either scale up or you can configure it for scale out. When you configure it for scale up, you're gaining a whole bunch of GPUs together using NVLink, and it creates this 1 gigantic GPU. When you want to scale it out, that same computation node becomes 56 small GPUs. Each one of those 56 partitions, each 1 is more powerful than Volta.

然後 - Ampere 也適用於訓練、深度學習、機器學習、額外提升以及深度學習，一直到推理。因此，我們現在擁有一個適用於整個工作負載的統一加速平台。第三件事是，它是有史以來第一個 GPU，第一個具有彈性的加速平台。您可以重新配置它。您可以將其配置為縱向擴展，也可以將其配置為橫向擴展。當您將其配置為擴大規模時，您將使用 NVLink 獲得一大堆 GPU，並創建這個巨大的 GPU。當您想要擴充它時，同一個運算節點會變成 56 個小型 GPU。這 56 個分區中的每一個都比 Volta 更強大。

I mean it's really quite extraordinary. And so Ampere is a breakthrough on all of these fronts for performance for the fact that it unifies the workload, and you can now have 1 acceleration cluster; and then number three, it's elastic. You could use in the cloud, you could use for inference, you could use it for training. And so the versatility of Ampere is the thing that I'm most excited about. And now you could have 1 acceleration cluster that serves all of your needs. That's very helpful.

我的意思是這確實非常特別。因此，Ampere 在所有這些效能方面都取得了突破，因為它統一了工作負載，現在您可以擁有 1 個加速叢集；第三，它有彈性。您可以在雲端中使用它，可以用它來進行推理，也可以用它來進行訓練。因此，Ampere 的多功能性是最讓我興奮的。現在您可以擁有 1 個滿足您所有需求的加速叢集。這非常有幫助。

Your next question comes from Timothy Arcuri with UBS.

您的下一個問題來自瑞銀的 Timothy Arcuri。

Actually I had 2, I guess, Jensen, first for you. Just on the data center business, things have been very strong recently. Obviously, there's always concerns that customers are pulling in CapEx, but it sounds like you have pretty good visibility into July. But I guess last time, most folks also thought that your kind of attrition really was so low that you would be immune into any digestion, but that wasn't the case. So I guess I'm wondering, if things are different now with A100 and whatnot, but my question is how do you handicap your ability to this time, maybe get through any digestion on the CapEx side? And then I guess, second question, Colette, stock comp had been running like 220 a quarter, and the guidance implies that it goes to like 460 a quarter. So it goes up a lot. Is that all executive retention? And is that sort of the right level as you look into 2021?

事實上，我想我有 2 個，Jensen，首先是給你的。就資料中心業務而言，最近情況非常強勁。顯然，人們總是擔心客戶會拉動資本支出，但聽起來你對七月的前景有相當好的預見性。但我想上次大多數人也認為你的損耗真的很低，以至於你不會受到任何消化的影響，但事實並非如此。所以我想知道，現在有了 A100 和其他東西，情況是否有所不同，但我的問題是，您這次如何限制自己的能力，也許可以解決資本支出方面的任何消化問題？然後我想，第二個問題，科萊特，股票補償每季約為 220，而指導意見暗示每季將達到 460。所以它漲了很多。這就是所有高階主管的保留嗎？展望 2021 年，這是否是正確的水準？

Colette, did you want to handle that first? And then I'll do the...

科萊特，你想先處理這個嗎？然後我會做...

Sure. So let me help you out on the overall GAAP adjustments, so the delta between our GAAP OpEx and our non-GAAP OpEx. If you look at it for the full year and what we guided, we probably have about $1.55 billion associated with GAAP level expenses. Keep in mind, there is more in there than just our stock-based compensation. We have also incorporated the accounting that we will do for the overall Mellanox, and a really good portion of those costs are associated with the amortization of intangibles and also in terms of acquisition-related costs and deal fees and onetime items. So our stock-based compensation includes what we need for NVIDIA and also the onboarding of Mellanox. There is some retention with the overall onboarding of Mellanox. But for the most part, it is just working them in to the year for 3 quarters, which is influencing the stock-based compensation.

當然。因此，讓我協助您進行整體 GAAP 調整，即我們的 GAAP 營運支出和非 GAAP 營運支出之間的差異。如果從全年來看以及我們給予的指導，我們可能會有大約 15.5 億美元的 GAAP 等級費用。請記住，這其中不僅僅只有基於股票的薪酬。我們還納入了整個 Mellanox 的會計核算，其中很大一部分成本與無形資產攤銷以及收購相關成本、交易費用和一次性項目有關。因此，我們的股票薪酬包括 NVIDIA 和 Mellanox 的入職培訓所需的薪酬。 Mellanox 的整體加入有一定的保留。但在大多數情況下，這只是將其計入一年中的三個季度，這會影響股票薪酬。

Tim, there are several differences between our condition then and our condition today. So the first -- the first difference is the diversity of workload we now accelerate. Back then, we were early in our inference. We were still early in our inference, and most of the data center acceleration was used for deep learning. And so today, the versatility stands from data processing to deep learning and the number of -- the number of different types of AI models that's being trained for deep learning is growing tremendously from detecting, from training video, from training a model to detecting unsafe video. The natural language understanding the conversational AI to now a gigantic movement towards deep recommender systems. And so the number of different models that are being trained is growing. The size of the models are gigantic. Recommendation systems are gigantic. They're training on models that are hundreds. The data sizes, hundreds of terabytes. Terabytes, hundreds of terabytes. And it would take tens of -- hundreds of servers to hold all of the data that is needed to train these recommender systems. And so the diversity of -- from data analytics to training all the different models to the influence of all different models. We didn't inference recurring elements at a time, which is probably the most important model today. Text language models, speech models are all recurrent, Euronet models. And so those models were early for us at the time. So number one is the diversity of workload. The second is the acceleration of -- to cloud computing. I think that accelerated cloud computing is a movement that is going to be a multiyear if not a decade-long transition. From where we are today, it's only $100 billion industry segment of the IT industry. It's going to be $1 trillion someday, and that movement is just starting. We're also much more diversified out of the clouds. At the time, cloud was largely where our acceleration went for deep learning. And today, hyperscale only represents about half. And so we've diversified significantly out of cloud, not out of cloud, but including vertical industries. And a lot of that has to do with edge AI and inference. And as I mentioned earlier, we're working with Walmart and BMW and USPS, and that's just the tip of the iceberg. And so I think the conditions are a little different. And then what I would say lastly is Ampere. I mean we are -- we've ramped a few weeks. Even though it was quite significant, it was a great ramp. The demand is fantastic. It is the best ramp we've ever had. The demand is the strongest we've ever had in data centers. And we're starting to ramp of a multiyear ramp. And so -- those are some of the differences. I think the conditions are very different.

提姆，我們當時的狀況和現在的狀況有幾個不同之處。因此，第一個差異是我們現在加速的工作量的多樣性。當時，我們的推論還處於早期階段。我們的推理還處於早期階段，大部分資料中心加速都用於深度學習。因此，今天，多功能性從資料處理延伸到深度學習，用於深度學習訓練的不同類型的人工智慧模型的數量正在從檢測、訓練影片、訓練模型到檢測不安全視訊等大幅增長。自然語言理解對話式人工智慧現在已經朝著深度推薦系統邁進了一大步。因此，正在訓練的不同模型的數量正在增加。這些模型的尺寸非常巨大。推薦系​​統非常龐大。他們正在對數百個模型進行訓練。數據大小達數百 TB。數 TB，數百 TB。並且需要數十到數百台伺服器來保存訓練這些推薦系統所需的所有資料。因此，從資料分析到訓練所有不同模型，再到所有不同模型的影響，都是多樣化的。我們沒有一次推論重複的元素，這可能是當今最重要的模型。文字語言模型，語音模型都是循環的，Euronet模型。所以這些模型對於我們當時來說還處於早期階段。因此，首要的是工作量的多樣性。第二是加速雲端運算的發展。我認為加速雲端運算是一場需要數年甚至十年的轉變。從目前的情況來看，它只是 IT 產業中價值 1000 億美元的產業部分。有一天它會達到 1 兆美元，而這項運動才剛開始。我們在雲端的業務也更加多元。當時，雲端是我們加速深度學習的主要途徑。而今天，超大規模僅佔一半左右。因此，我們在雲端運算領域實現了顯著的多元化，不僅是雲端運算，還包括垂直產業。其中很多與邊緣人工智慧和推理有關。正如我之前提到的，我們正在與沃爾瑪、寶馬和美國郵政合作，而這只是冰山一角。所以我認為情況有些不同。最後我想說的是安培。我的意思是我們已經加快了幾個星期的步伐。儘管它很重要，但它是一個很棒的坡道。需求量非常大。這是我們曾經擁有過的最好的坡道。我們對資料中心的需求是有史以來最強的。我們正開始實現多年的成長。所以——這些就是一些差異。我認為情況非常不同。

Your next question comes from Harlan Sur with JPMorgan.

您的下一個問題來自摩根大通的 Harlan Sur。

Jensen, the team has showed the importance of networking, networking fabric and the Mellanox acquisition, like, for example, when you guys move from Volta DGX-1 to Volta DGX-2, you guys didn't change the GPU chipset. But by adding a custom networking fabric chip and more Mellanox network interface cards, among other things, you guys drove a pretty significant improvement in performance per GPU. But now when we think about scaling out compute acceleration to data center skilled implementation, how does Mellanox' Ethernet switching platforms differ from those provided by other large networking OEMs, some of whom have been your long-term partners? And then how does the Cumulus acquisition fit into the switching and networking strategy as well?

Jensen，團隊已經展示了網路、網路結構和 Mellanox 收購的重要性，例如，當你們從 Volta DGX-1 轉移到 Volta DGX-2 時，你們並沒有改變 GPU 晶片組。但透過添加客製化的網路結構晶片和更多的 Mellanox 網路介面卡等，你們推動了每個 GPU 效能的顯著提升。但現在，當我們考慮將運算加速擴展到資料中心熟練實作時，Mellanox 的乙太網路交換平台與其他大型網路 OEM 提供的平台有何不同（其中一些是您的長期合作夥伴）？那麼 Cumulus 的收購如何適應交換和網路策略呢？

Yes. Great. Thanks a lot, Harlan. I appreciate the question. So DGX, you know this is our third-generation DGX and it's really successful. People love it. It's the most advanced AI instrument in the world. If you're a serious AI researcher, this is your instrument. And in the DGX, there are 8 A100s and there are 9 Mellanox NICs, the highest speed NICs they have. And so we have a great appreciation for high-performance networking. High-performance networking and high-performance computing go hand-in-hand. And the reason for that is because the problems we're trying to solve no longer fit in one computer, no matter how big it is. And so it has to be distributed. And when you distribute a computational workload of such intense scale, the communications overhead becomes one of its greatest bottlenecks, which is the reason why Mellanox is so valuable. There's reason why this company is so precious and really a jewel and one of a kind. And so -- and it's not just about the link speed. It's not mostly.

是的。偉大的。非常感謝，哈蘭。我很感謝你提出這個問題。所以 DGX，你知道這是我們的第三代 DGX，它非常成功。人們喜歡它。這是世界上最先進的人工智慧儀器。如果您是嚴肅的人工智慧研究人員，這就是您的工具。在 DGX 中，有 8 個 A100 和 9 個 Mellanox NIC，這是他們擁有的最高速度 NIC。因此，我們非常重視高效能網路。高效能網路和高效能運算齊頭並進。原因在於，無論一台電腦有多大，我們試圖解決的問題都不再適合它。因此它必須被分發。當你分配如此大規模的運算工作負載時，通訊開銷就會成為最大的瓶頸之一，這也是 Mellanox 如此有價值的原因。這就是為什麼這家公司如此珍貴，真正是一顆寶石，獨一無二。所以——這不僅僅與連結速度有關。大部分情況並非如此。

I mean we just have a deep appreciation for software. It's a combination of architecture and software and electronics design, chip design. And in that combination, Mellanox is just world-class. And that's the reason why they're in 60% of the world's supercomputers. That's why they're in 100% of the AI supercomputers. And their understanding of large-scale distributed computing is second to none. Now in the world of -- and I just talked about scale up. And you're absolutely right.

我的意思是我們對軟體有著深刻的理解。它是架構、軟體、電子設計、晶片設計的結合。就這組合而言，Mellanox 堪稱世界一流。這就是為什麼它們出現在全球 60% 的超級電腦中。這就是為什麼它們存在於 100% 的人工智慧超級電腦中。他們對大規模分散式計算的理解是首屈一指的。現在的世界——我剛才談到了擴大規模。你說得完全正確。

Now the question is why scale out? And the reason for that is this. This is the reason why they're doing so well. The movement towards disaggregated microservice applications where containers, microservice containers are distributed all over the data center and orchestrated so that the workload could be distributed across a very large hyperscale data center. That architecture -- and you probably know the 3 most important application in my estimation in the world today, number one, would be TensorFlow and PyTorch; number two would be Spark; and number three would be Kubernetes. And you could rank it however you desire.

現在的問題是為什麼要擴大規模？原因如下。這就是他們做得如此出色的原因。向分解的微服務應用程式的轉變，其中容器、微服務容器分佈在整個資料中心並進行協調，以便工作負載可以分佈在非常大的超大規模資料中心。該架構——您可能知道，在我看來，當今世界上最重要的 3 個應用程序，第一名是 TensorFlow 和 PyTorch；第二名是 Spark；第三個是 Kubernetes。您可以按照自己想要的方式對其進行排名。

And these 3 applications, in the case of Kubernetes, it's a brand-new type of application where the application is broken up with a small pieces and orchestrated across an entire data center. And because it's broken up into small pieces and orchestrate across the entire data center, the networking between the compute nodes becomes the bottleneck again. And that's the reason why they're doing so well. By increasing the network performance by offloading the communications of the CPUs, you increase the throughput of a data center tremendously. And so it's the reason why they had a record quarter last quarter. It's the reason why they've been growing 27% per year. And their stock was back, their integration into the hyperscale cloud companies, they're low latency, they're incredibly low latency of their link makes them really unique, even whether it's Ethernet or InfiniBand in both cases.

而這 3 個應用程序，就 Kubernetes 而言，是一種全新類型的應用程序，其中應用程式被分解成小塊並在整個資料中心進行編排。由於它被分解成小塊並在整個資料中心進行協調，因此計算節點之間的網路再次成為瓶頸。這就是他們表現如此出色的原因。透過卸載 CPU 的通訊來提高網路效能，您可以大幅提高資料中心的吞吐量。這就是他們上個季度創下紀錄的原因。這就是他們每年增長 27% 的原因。他們的股票又回來了，他們與超大規模雲端公司的整合，他們的低延遲，他們的連結的極低延遲使他們真正獨一無二，即使兩種情況下都是乙太網路或 InfiniBand。

And so they're -- it's a really fantastic stack. And then lastly, Cumulus, we would like to integrate -- we would like to innovate in this world where the world is moving away from just a CPU as a compute node. The new computing unit, a software developer is writing a piece of software that runs on the entire data center. In the future, going forward, the computing, the fundamental computing unit is an entire data center. It's so incredible. It's just utterly incredible. You write an application, 1 human could write an application, and it would literally activate an entire data center. And in that world, we would like to be able to innovate from end to end, from networking storage, security. Everything has to be secured in the future so that we can reduce the attack surface down to practically nothing.

所以他們——這是一個非常棒的堆疊。最後，Cumulus，我們希望整合——我們希望在這個不再僅僅使用 CPU 作為運算節點的世界中進行創新。新的計算單元，軟體開發人員正在編寫一個在整個資料中心運行的軟體。在未來，計算，最基本的計算單元是整個資料中心。這真是太不可思議了。這真是太不可思議了。你編寫一個應用程序，一個人就可以編寫一個應用程序，它實際上可以啟動整個資料中心。在那個世界裡，我們希望能夠從網路儲存到安全進行端到端的創新。未來一切都必須得到保障，這樣我們才能將攻擊面縮小到幾乎為零。

And so networking storage, security are all completely offloaded, all incredibly low latency, all incredibly high performance and all the way to compute, all the way through the switch. And then the second thing is we'd like to be able to innovate across the entire stack. You know that NVIDIA is just supremely obsessed about software stacks. And the reason for that is because software creates markets. You can't create new markets like we're talking about, whether it's computational health care or autonomous driving or robotic or conversational AI or recommender systems or edge AI. All of that requires software stacks. It takes software to create markets. And so our obsession about software and creating open platforms for the ecosystem and all of our developer partners, Cumulus plays perfectly into that. They are -- they pioneered the open networking stack. And they pioneered, in a lot of ways, software-defined data centers. And so we're super, super excited about the team. And now we have the ability to innovate in a data center scale world from end to end and then from top to bottom of the entire stack.

因此，網路儲存、安全性都完全卸載，所有延遲都極低，所有效能都極高，並且全程透過交換器進行計算。第二件事是我們希望能夠在整個堆疊中進行創新。你知道 NVIDIA 對軟體堆疊極為著迷。原因在於軟體創造了市場。你無法創造我們所談論的新市場，無論是計算醫療保健、自動駕駛、機器人、對話式人工智慧、推薦系統或邊緣人工智慧。所有這些都需要軟體堆疊。需要軟體來創造市場。因此，我們沉迷於軟體，並為生態系統和所有開發合作夥伴創建開放平台，Cumulus 完美地體現了這一點。他們是——開放網路堆疊的先驅。他們在許多方面開創了軟體定義的資料中心。所以我們對這個團隊感到非常非常興奮。現在，我們有能力在資料中心規模的世界中從端到端、從整個堆疊的頂部到底部進行創新。

Your next question comes from William Stein with SunTrust.

您的下一個問題來自 SunTrust 的 William Stein。

Jensen, I'd like to focus on something you said. I think it was in one of your earlier responses, you said something about a very significant part of data centers are now accelerated with GPUs. I'm sort of curious how to interpret that. If we think about sort of the evolution of compute architecture going from almost entirely, let's say, [REX and REXs] CPUs to some future day where we have many more accelerators and maybe a much smaller number of CPUs relative to those. Maybe you can talk to us about where we are in terms of that architectural shift and where you think it goes sort of longer term, where we are in the position of that?

詹森，我想集中討論你所說的一些事情。我認為這是您之前的回覆之一，您提到現在很大一部分的資料中心都是透過 GPU 加速。我有點好奇該如何解釋這一點。如果我們考慮一下運算架構的演變，比如說從幾乎完全由 [REX 和 REXs] CPU 組成，到未來某一天我們擁有更多的加速器，而相對於這些加速器來說，CPU 的數量可能會少得多。也許您可以和我們談談我們在架構轉變方面所處的位置，以及您認為它的長期發展方向，以及我們目前所處的位置？

Yes. I appreciate the question. And this, for computer architecture geeks and people who follow history, you know well that in the entire history of time, there are only 2 computing architectures that has made it so far, which is one of them is x86. The other one's ARM in any reasonable way. And if you get an ARM computer, you get an x86 computer, you can program it. And in fact, there's no such thing as an accelerated computing platform until we came along.

是的。我很感謝你提出這個問題。對於電腦架構愛好者和關注歷史的人來說，你們都很清楚，在整個歷史中，只有兩種運算架構能夠走到今天，其中之一就是 x86。另一個的 ARM 以任何合理的方式。如果你有一台 ARM 計算機，你就有一台 x86 計算機，你可以對其進行程式設計。事實上，在我們出現之前，根本不存在加速運算平台。

And today, we're the only computing -- accelerated computing platform that you could really largely address. We're in every cloud. We're in every computer company. We're in every country. We have every single size, and we accelerate applications from computer graphics to video games to scientific computing to workstations to machine learning to robotics. This journey took 20-some-odd years. Inside our company, it took 20-some-odd years. And we've been focused on accelerated computing since the beginning of our company. And we made a general purpose. We made a general purpose really starting with an endeavor cost Cg, C for graphics, and then it became CUDA. And we've been working on accelerated computing for quite a long time.

今天，我們是唯一能夠真正解決大規模運算問題的加速運算平台。我們在每一朵雲裡。我們存在於每一家計算機公司。我們的業務遍佈每個國家。我們擁有各種規模的產品，我們加速從電腦圖形到視訊遊戲到科學計算到工作站到機器學習到機器人技術的應用程式。這段旅程耗時二十多年。在我們公司內部，這花了二十多年的時間。自公司成立以來，我們一直專注於加速運算。我們制定了一個總體目標。我們真正從一項努力開始，做出了一個通用目標，成本是 Cg，C 代表圖形，然後它變成了 CUDA。我們致力於加速運算已經很久了。

And I think at this point, it's a foregone conclusion that accelerated computing has reached the tipping point and is well beyond it. The number of developers this year that support -- that we supported was almost 2 million developers around the world, and it's growing what appears to be exponentially. And so I think accelerated computing is now well established. NVIDIA-accelerated computing is well established. It's common sense, and people who are designing data centers expect to put accelerated computing in it. The question is how much? How much accelerated computing do you use? And what part of the date in your pipeline do you do it? And the big -- the gigantic breakthrough, of course, we know well now, and NVIDIA is recognized as one of the 3 pillars that ignited the modern AI, the big bang of modern AI.

我認為，就目前情況而言，加速計算已經達到臨界點，並且遠遠超出了臨界點，這是必然的結論。今年，我們支持的開發人員數量已接近 200 萬，而且呈指數級增長。所以我認為加速計算現在已經很成熟了。 NVIDIA 加速運算已經非常成熟。這是常識，設計資料中心的人希望在其中加入加速計算。問題是多少？您使用了多少加速運算？您在管道的哪個階段進行約會？當然，我們現在都知道這是一個巨大的突破，NVIDIA 被公認為點燃現代人工智慧、現代人工智慧大爆炸的三大支柱之一。

And the other 2 pillar, of course, is deep learning algorithm and the abundance of data. And so the 3 -- these 3 ingredients came together and was -- people use NVIDIA accelerated computing largely for training. But over time, we expanded training to have a lot more models. And as I mentioned earlier, the single most important model of machine learning today is the recommender system. It's the most important model because it's the only way that you and I could use the Internet in any reasonable way. It's the only way that you and I could use a shopping website or a video web -- a video app or a music app or book or news or anything. And so it is the engine of the Internet from the consumer's perspective. On the company perspective, it is the engine of commerce. Without the recommender system, there's no way they could possibly make money.

另外兩個支柱當然是深度學習演算法和豐富的數據。因此，這 3 個要素結合在一起，人們主要使用 NVIDIA 加速運算進行訓練。但隨著時間的推移，我們擴大了訓練範圍，擁有了更多的模型。正如我之前提到的，當今機器學習最重要的模型是推薦系統。這是最重要的模型，因為這是你我以合理方式使用網路的唯一方式。這是你和我使用購物網站或影片網站（影片應用程式或音樂應用程式或書籍或新聞或任何東西）的唯一方式。從消費者的角度來看，它就是網路的引擎。從公司角度來看，它是商業的引擎。如果沒有推薦系統，他們就不可能賺錢。

And so their accuracy in predicting user preferences is core to everything they do. You just go up and down the list of every company. And that engine is gigantic. It is just a gigantic engine. And from the data processing part of it, which is the reason why we went and spent 3 years on Spark and RAPIDS, which made Spark possible and all the work that we did on NVLink and all that stuff was really focused on big data analytics. The second is all of the training of the deep learning models and the inference. So the number of applications, the footprint, the accelerated computing has grown tremendously, and its importance has grown tremendously because of the applications are the most important applications of these companies. And so I think when I mentioned -- when I said that, that acceleration is still growing, it is. But the major workloads, the most important workloads of the world's most important companies are now -- solidly require acceleration. And so I'm looking forward to a really exciting ramp for Ampere for all the reasons that I just mentioned.

因此，預測使用者偏好的準確性是他們所做一切的核心。你只需瀏覽每家公司的清單即可。而且那個引擎非常巨大。它只是一個巨大的引擎。從資料處理部分來看，這就是我們花了 3 年時間研發 Spark 和 RAPIDS 的原因，這使得 Spark 成為可能，我們在 NVLink 上所做的所有工作以及所有這些工作實際上都集中在大數據分析上。第二是所有深度學習模型的訓練和推理。因此，應用程式的數量、佔用空間和加速運算都大幅成長，其重要性也大幅成長，因為這些應用程式是這些公司最重要的應用程式。所以我認為當我提到——當我說那一點時，加速度仍在增長，事實確實如此。但現在，世界上最重要的公司的主要工作量、最重要的工作量都迫切需要加速。因此，基於我剛才提到的所有原因，我期待安培能夠取得真正令人興奮的成長。

Your next question comes from John Pitzer with Crédit Suisse.

您的下一個問題來自瑞士信貸的約翰‧皮策。

Just 2 quick ones. Colette, I hate to ask something as mundane as OpEx, but just given the full year guide, there's sort of a lot to unpack. And you talked about some of it like the raises. I mean I think you also probably have some COVID plus or minuses in that. I think there's an extra week this year as well. And then, of course, there's Mellanox and how you're thinking about investing in that asset.

只需簡單兩句話。科萊特，我不想問像營運支出這樣平凡的問題，但僅從全年指南來看，就有很多事情需要解決。您還談到了加薪等一些問題。我的意思是，我認為你可能也會從中受益於 COVID 的一些好處或壞處。我認為今年也會多出一周。當然，還有 Mellanox，以及您如何考慮投資該資產。

I guess I'm just kind of curious, when we look at the full year guide, is there something structural going on OpEx as you try to take advantage of all these opportunities? Or can we use it as sort of a guidepost to how you're thinking about revenue for the back half of the year as well? How do I understand that? And then, Jensen, just a quick one for you. It kind of makes sense to me that COVID is accelerating activity in sort of HPC and hyperscale and maybe even in certain verticals like health care. But in the other verticals, has the sort of shelter in place kind of hurt engagement? And could we actually come out of COVID with some pent-up demand in those vertical markets?

我想我只是有點好奇，當我們查看全年指南時，當您嘗試利用所有這些機會時，營運支出是否發生了一些結構性變化？或者我們可以將其作為指導，了解您對下半年收入的看法嗎？我該如何理解這一點？然後，Jensen，我只想快速問你一個問題。對我來說，COVID 正在加速 HPC 和超大規模領域的活動，甚至可能加速醫療保健等某些垂直領域的活動，這很有道理。但在其他垂直領域，這種就地避難的措施是否會損害參與？我們真的能從新冠疫情中釋放出這些垂直市場中一些被壓抑的需求嗎？

Okay. Thanks, John, for the question. Let's start from the first perspective on the overall OpEx for the year. We've guided the non-GAAP at approximately $4.1 billion for the year. Yes, that incorporates 3 full quarters of Mellanox, Mellanox and its employees. We have about close to 3,000 Mellanox employees coming on board. You are correct. We have a 53rd week in this quarter -- excuse me, not this quarter, this year.

好的。謝謝約翰提出這個問題。讓我們從第一個角度開始了解今年的整體營運支出。我們預計今年的非公認會計準則營收約為 41 億美元。是的，這涵蓋了 Mellanox 的 3 個完整季度、Mellanox 及其員工。我們有大約 3,000 名 Mellanox 員工加入。你是對的。本季我們已經有第 53 週了——抱歉，不是這個季度，而是今年。

And that is -- has been outlined in SEC filings, and you should expect that as well. We pulled forward a little bit our focal by several months in order to take care of our employees. And then lastly though, we are investing in our business. We see some great opportunities. You've seen some good results from our investment, and there's more to do. We are hiring and investing in those businesses. So there's nothing different structurally, but just this onset of Mellanox and are investing together, I think, we'll produce long-term great results.

這一點已在美國證券交易委員會 (SEC) 的文件中概述，您也應該預料到這一點。為了照顧我們的員工，我們將焦點提前了幾個月。最後，我們正在對我們的業務進行投資。我們看到了一些絕佳的機會。您已經看到了我們的投資所取得的一些良好成果，但我們還有更多工作要做。我們正在招募員工並投資這些企業。因此，從結構上來說沒有什麼不同，但只要 Mellanox 的出現和共同投資，我認為我們將產生長期的偉大成果。

And as usual, John, you know that we're investing into the IT industry's largest opportunities, cloud computing and AI. And then after these 2 opportunities is edge AI. And so we're looking down the fairway with some pretty extraordinary opportunities. But as usual, we're thoughtful about the rate of investment, and we're well-managed. And NVIDIA's leadership team are excellent managers, and you could count on us to continue to do that. Simona, what was John's question? Could you just give me one hint? I haven't...

像往常一樣，約翰，你知道我們正在投資 IT 產業最大的機會——雲端運算和人工智慧。這兩個機會之後是邊緣人工智慧。因此，我們在球道上看到了一些非常難得的機會。但和往常一樣，我們對投資率很謹慎，而且管理得很好。 NVIDIA 的領導團隊都是優秀的管理者，您可以相信我們會繼續這樣做。西蒙娜，約翰的問題是什麼？你能給我一點提示嗎？我還沒有...

Just the idea of engagement levels in verticals, just with shelter in place. Has that hampered...

這只是垂直領域的參與程度的想法，只是有庇護所。這是否妨礙了…

Oh yes. Right. Yes, right. Yes, right. A few -- some of the industries have been affected. We already mentioned automotive industry. The automotive industry has been grounded to a halt. Manufacturing hasn't largely stopped. And you saw that in our guidance. We expect automotive to be down 40% quarter-to-quarter. It's not going to remain that way. It's going to come back. And nobody knows what level is going to come back to you and how long, but it's going to come back. And there's no question in my mind that the automotive industry, they're hunkered down right now, but they will absolutely invest in the future of autonomous vehicles.

哦是的。正確的。是的，對。是的，對。一些行業受到了影響。我們已經提到了汽車產業。汽車產業已陷入停滯。製造業並未基本停止。您在我們的指導中看到了這一點。我們預計汽車銷量將環比下降 40%。情況不會一直這樣。它會回來的。沒有人知道它會以什麼水平回到你身邊以及需要多長時間，但它會回來。我毫不懷疑，汽車產業現在正處於停滯狀態，但他們絕對會投資於自動駕駛汽車的未來。

They have to, or they'll be extinct. It's not possible not to have autonomous capability in the future of everything that moves. Not so that it could just completely drive without you. That a nice benefit, too. But mostly because of safety and comfort and just a joy of what seems like the car is reading your mind. And of course, you're still responsible for driving it and -- but it just seems to be coasting down the road, reading your mind and helping you. And so I think the future of autonomous vehicles is a certainty. People recognize the incredible economics that the pioneer, Tesla, is enjoying. And the industry is going to go after it.

它們必須這麼做，否則就會滅絕。未來所有運動物體都不可能不具備自主能力。但這並不意味著它完全不需要你就能正常行駛。這也是一個很好的好處。但主要是因為安全和舒適，以及汽車似乎能讀懂你心思的樂趣。當然，你仍然有責任駕駛它——但它似乎只是在路上滑行，讀懂你的想法並幫助你。所以我認為自動駕駛汽車的未來是確定的。人們認識到先驅特斯拉正在享受的令人難以置信的經濟效益。整個產業都會追隨它。

The future car companies are going to be software-defined companies and technology companies. And they would love to have an economic that allows them to enjoy the installed base of their fleets. And so they're going to go after it. And so this is -- I'm certain that this is going to come back. And well, I have every confidence is going to come back. And let's see, the energy sectors are -- have been impacted. The retail sector has been impacted. There's -- those aren't large industries for us. The impact in some of the industries is accelerating their focus in robotics. Like, for example, on the one hand, BMW has obviously impacted in manufacturing, which is the reason why they're moving so rapidly towards robotics. They have to figure out a way to get robotics into their factories. So same thing with retail. You're going to see a lot more robotic support in retail, you're going to see a lot more robotic support in warehouses, in logistics. And so during this time, when the market -- when the industry is disrupted and impacted, it allows the market leaders to really lean into investing into the future. And so when they come back, they'll be coming back stronger than ever.

未來的汽車公司是軟體定義的公司、科技公司。他們希望擁有一種經濟的方式，讓他們能夠享受其車隊的安裝基礎。所以他們將會繼續追求它。所以這是——我確信這會回來的。嗯，我完全有信心我會回來的。讓我們看看，能源產業已經受到了影響。零售業受到了影響。對我們來說，這些都不是大型產業。一些行業受到的影響正在加速他們對機器人技術的關注。例如，一方面，寶馬在製造業中產生了明顯的影響，這就是他們如此迅速地向機器人技術邁進的原因。他們必須想辦法將機器人引進工廠。零售業也是如此。你將會看見零售業、倉庫和物流領域中更多的機器人支援。因此，在這段時間裡，當市場——當產業受到干擾和影響時，市場領導者才真正傾向於投資未來。所以當他們回來時，他們會比以前更強大。

And your next question comes from Matt Ramsay with Cowen.

您的下一個問題來自 Cowen 的 Matt Ramsay。

Two different topics, Jensen. Well, first of all, congrats on Ampere. It's a heck of a product. The first question...

兩個不同的主題，詹森。嗯，首先，恭喜 Ampere。這真是一個非常棒的產品。第一個問題...

Thank you, Matt. I'm so proud.

謝謝你，馬特。我感到非常自豪。

The first question is it might have been a little bit hard to talk when the deal was pending about this topic, but now that it's closed, maybe you could talk a little bit about opportunities to innovate on and customize the Mellanox stack and the balance of having an industry standard. And the second one is E3 canceled, Computex moved around. At the same time, there's obviously stay-at-home gaming demand. Just how you think about gaming product, launch logistics? And any comments on there would be really helpful.

第一個問題是，當交易懸而未決時，談論這個話題可能有點困難，但現在交易已經結束，也許您可以談論創新和定制 Mellanox 堆疊的機會以及擁有行業標準的平衡。第二個是 E3 取消，Computex 延期。同時，居家遊戲的需求也明顯存在。您如何看待遊戲產品的發布物流？任何評論都會非常有幫助。

Yes. Thanks a lot, Matt. I appreciate your questions. I'll go backwards because it's kind of cool. On the one hand, I do miss that we can't engage the developers face to face. It's just so much fun. GTC is doing all their work and the hundreds of papers that are presented, I learned so much each time. And frankly, I really enjoyed the analyst meeting that we have. And so there's all kinds of stuff that I missed about the physical GTC, but here's the amazing thing.

是的。非常感謝，馬特。我很感謝你的提問。我會倒退，因為這樣很酷。一方面，我確實很懷念我們不能與開發人員面對面交流。這真是太有趣了。 GTC 正在進行他們的所有工作，並發表了數百篇論文，每次我都學到了很多東西。坦白說，我真的很享受我們舉行的分析師會議。因此，我錯過了很多有關實體 GTC 的內容，但這裡有一件令人驚奇的事情。

We had almost -- the GTC kitchen keynote. I did it from my kitchen just right behind me, and the kitchen keynote has been viewed almost 4 million times. And the video is incredible. And so I think our reach is -- could be quite great. And so I'm not too -- we've got an amazing marketing team, and we just -- we've got great people. They're going to find a way to reach our gamers. And whenever we launch something next, you know that gamers are going to be and our customers are going to be -- our end markets are going to be really excited to see it. And so I'm very confident that we're going to do just fine. Matt, what was the question before? I should never do backwards.

我們幾乎已經完成了——GTC 廚房主題演講。我是在我身後的廚房裡做的這個演講，這個廚房主題演講已經被觀看了近 400 萬次。影片真是令人難以置信。所以我認為我們的影響力可能會非常大。所以我並不是太——我們有一個很棒的行銷團隊，我們只是——我們擁有優秀的人才。他們會找到一種方法來接觸我們的玩家。無論我們何時推出新產品，您都知道遊戲玩家和我們的客戶都會非常興奮——我們的終端市場都會非常興奮地看到它。因此我非常有信心我們會做得很好。馬特，之前的問題是什麼？我絕不應該倒退。

Just the industry standard versus customization of Mellanox opportunity.

只是業界標準與 Mellanox 機會的客製化。

I see. Okay. Yes. There's -- we work so closely with Mellanox over the years. And on the day that we announced GTC, you could see the number of products that we have working together. The product synergies are really incredible, and the product synergies include a lot of software development that went in and a lot of architectural development that went in. DGX comes with 9 Mellanox, Matt, as I mentioned. If you look at our data center, we ship -- before we ship DGXs to the customers, we ship it to our own engineers. And the reason for that is because every single product in our company has AI in it.

我懂了。好的。是的。多年來，我們與 Mellanox 一直密切合作。在我們宣布 GTC 的那天，您可以看到我們合作的產品數量。產品綜效確實令人難以置信，產品綜效包括大量的軟體開發和大量的架構開發。正如我所提到的，DGX 附帶 9 Mellanox，Matt。如果你看看我們的資料中心，我們會先發——在將 DGX 發送給客戶之前，我們會將其發送給我們自己的工程師。原因在於我們公司的每款產品都蘊含著人工智慧。

From Jarvis to Metropolis to Merlin to DRIVE to Clara to Isaac to -- right? All of our products has AI in it, and we're accelerating frameworks for all of the AI industry. And Ampere comes with a brand-new numerical format called Tensor Float 32. And TF32 is just a fantastic [medium medical] format and the performance is incredible. And we had to get it integrated in with the industry standard frameworks. And now Tensor Float comes standard with Tensor Flow -- with TF -- NVIDIA TF32, and PyTorch come standard with TF32. And so we need our own large-scale data center. And so the first customer we ship to was ourselves. And then we started shipping as quickly as we could to all of the customers. You saw that in our data center, in our supercomputer.

從賈維斯 (Jarvis) 到大都會 (Metropolis) 到梅林 (Merlin) 到 DRIVE 到克拉拉 (Clara) 到艾薩克 (Isaac) 到 — — 對吧？我們的所有產品都包含人工智慧，我們正在加速整個人工智慧產業的框架建立。 Ampere 帶有一種全新的數值格式，稱為 Tensor Float 32。 TF32 是一種非常棒的格式，其性能令人難以置信。我們必須將其與行業標準框架相結合。現在，Tensor Float 已成為 Tensor Flow 的標準配置——帶有 TF——NVIDIA TF32，而 PyTorch 也已成為 TF32 的標準配置。因此我們需要自己的大型資料中心。因此，我們出貨的第一位客戶就是我們自己。然後我們開始盡快向所有客戶發貨。您可以在我們的資料中心、超級電腦中看到這一點。

We have 170 -- 170 state-of-the-art, brand-new Mellanox switches. And almost 1,500 -- 200 gigabit per second Mellanox mix. And 15 kilometers of cables, fiber optic cables. And that is one of the most powerful supercomputers in the world today, and it's based on Ampere. And so we have a great deal of work that we did there together. We announced our first edge computer between us and Mellanox in this new card, we call it the EGX A100. It integrates Ampere and it integrates Mellanox' CX-6 Dx, which is designed for 5G telcos and edge computing. It's incredible security and has a single route of trust, and it's virtualized. And so basically, we -- this EGX A100, when you put it into a standard center x86 server, turns that server into a cloud computer in a box.

我們擁有 170 台最先進的全新 Mellanox 交換器。以及幾乎每秒 1,500 - 200 千兆位元的 Mellanox 混合。還有15公里的纜線、光纖電纜。這是當今世界上最強大的超級電腦之一，它基於安培 (Ampere) 架構。因此我們在那裡一起做了很多工作。我們在這款新卡上發布了我們與 Mellanox 合作的首款邊緣計算機，我們稱之為 EGX A100。它整合了 Ampere 並整合了 Mellanox 的 CX-6 Dx，專為 5G 電信和邊緣運算而設計。它的安全性令人難以置信，並且具有單一信任路線，而且是虛擬化的。所以基本上，當你將這個 EGX A100 放入標準中心 x86 伺服器時，它會將該伺服器變成盒子裡的雲端電腦。

The entire capability of a cloud, of a state-of-the-art cloud, which is cloud native, it's secure, it has incredible AI processing, it's now completely hyperconverged inside 1 box. The technology that made EGX A100 is really quite remarkable. And so you could see all the different product synergies that we have in working together. We could have done Spark acceleration without the collaboration with Mellanox. They worked on this piece of networking software called UCX. We worked on [nickel] together. It made possible the infrastructure for large-scale distributor computing. I mean just the list goes on and on and on. And so we -- the 2 teams have great chemistry. The culture -- it's a great culture fit. I love working with them. And right out of the chute, you saw all of the great product synergies that are made possible because of the combination.

雲端的全部功能、最先進的雲端的全部功能、雲端原生的、安全的、具有令人難以置信的人工智慧處理能力，現在它在一個盒子內完全超融合。 EGX A100 所採用的技術確實非常了不起。因此，您可以看到我們在合作中產生的所有不同產品的協同效應。即使沒有與 Mellanox 合作，我們也可以完成 Spark 加速。他們致力於開發一款名為 UCX 的網路軟體。我們一起研究[鎳]。它使大規模分銷商計算的基礎設施成為可能。我的意思是這個清單可以一直列下去。因此我們——兩隊有著很好的化學反應。這種文化——非常契合。我喜歡和他們一起工作。一開始，您就看到了透過這種結合而產生的所有出色的產品協同效應。

That is all the time we have for questions. I'll turn the call back to Jensen Huang for closing remarks.

我們回答問題的時間就這麼多了。我將把電話轉回給黃仁勳，請他做最後發言。

It's coming. Let me just -- thank you. We had a great and busy quarter. With our announcements, we highlighted several initiatives. First, computing is moving to data center scale where computing and networking go hand in hand. The acquisition of Mellanox gives us deep expertise and scale to innovate from end to end. Second, AI is the most powerful technology force of our time. Our Ampere generation offers several breakthroughs. It is the largest ever generational leap 20x in training and inference throughput; the first unified acceleration platform for data analytics, machine learning, deep learning, training and inference; and the first elastic accelerator that can be configured for scale-up applications like training to scale-out applications like inference.

它來了。讓我——謝謝你。我們度過了一個美好而忙碌的季度。我們在公告中強調了幾項措施。首先，計算正在轉向資料中心規模，計算和網路齊頭並進。收購 Mellanox 為我們提供了深厚的專業知識和規模，可以實現端到端的創新。第二，人工智慧是我們這個時代最強大的科技力量。我們的安培世代實現了多項突破。這是有史以​​來最大的一次飛躍，訓練和推理吞吐量提高了 20 倍；第一個用於資料分析、機器學習、深度學習、訓練和推理的統一加速平台；第一個彈性加速器可以配置為從訓練等擴展應用到推理等擴展應用。

Ampere is fast, it's universal and it's elastic. It's going to re-architect the modern data center. Third, we are opening large new markets with AI software application framework, such as Clara for health care, DRIVE for autonomous vehicles, Isaac for robotics, Jarvis for conversational AI, Metropolis for edge IoT, AERIAL for 5G and Merlin with the very important recommender systems. And then finally, we have built up multiple engines of accelerated computing growth. RTX computer graphics, artificial intelligence, and data center scale computing from cloud to edge. I look forward to updating you on our progress next quarter. Thanks, everybody.

安培速度快、通用且有彈性。它將重新建構現代資料中心。第三，我們正在利用人工智慧軟體應用框架來開拓新的大型市場，例如用於醫療保健的 Clara、用於自動駕駛汽車的 DRIVE、用於機器人技術的 Isaac、用於對話式人工智慧的 Jarvis、用於邊緣物聯網的 Metropolis、用於 5G 的 AERIAL 以及具有非常重要的推薦系統的 Merlin。最後，我們建立了多個加速運算成長的引擎。 RTX 電腦圖形、人工智慧和資料中心從雲端到邊緣的規模運算。我期待著下個季度向您通報我們的進展。謝謝大家。

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

今天的電話會議到此結束。您現在可以斷開連線。