輝達 (NVDA) 2018 Q3 法說會逐字稿

Good afternoon. My name is Victoria, and I'm your conference operator for today. Welcome to NVIDIA's financial results conference call. (Operator Instructions) I'll now turn the call over to Simona Jankowski, Vice President of Investor Relations, to begin your conference.

午安.我是維多利亞，今天的電話會議主持人。歡迎參加英偉達財務業績電話會議。 （主持人提示）現在我將把電話轉交給投資者關係副總裁西蒙娜·揚科夫斯基，由她開始今天的會議。

Thank you. Good afternoon, everyone and welcome to NVIDIA's conference call for the third quarter of fiscal 2018. 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.

謝謝。大家下午好，歡迎參加英偉達2018財年第三季業績電話會議。今天與我一同參加會議的英偉達代表有：總裁兼執行長黃仁勳，以及執行副總裁兼財務長科萊特·克雷斯。

I'd like to remind you that our call is being webcast live on NVIDIA's Investor Relations website. It is also being recorded. You can hear a replay by telephone until November 16, 2017. The webcast will be available for replay up until next quarter's conference call to discuss Q4 and full year fiscal 2018 financial results. The contents of today's call is NVIDIA's property. It can't be reproduced or transcribed without our prior written consent.

我想提醒各位，本次電話會議正在英偉達投資者關係網站進行網路直播，並已錄音。您可以透過電話收聽錄音回放，有效期至2017年11月16日。網路直播重播將持續提供至下一季電話會議，屆時我們將討論2018財年第四季及全年財務表現。本次電話會議的內容歸英偉達所有，未經我們事先書面同意，不得複製或轉錄。

During this call, we may make forward-looking statements based on current expectations. These are subject to a number of significant risks and uncertainties, and our actual results may differ materially. For a discussion of factors that could affect our future financial results and business, please refer to the disclosure in today's earnings release, our most recent Forms 10-K and 10-Q and the reports that we may file on Form 8-K with the Securities and Exchange Commission. All our statements are made as of today, November 9, 2017, 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的預期做出前瞻性陳述。這些陳述受許多重大風險和不確定因素的影響，我們的實際結果可能與預期有重大差異。有關可能影響我們未來財務表現和業務的因素的討論，請參閱今天發布的盈利報告中的披露資訊、我們最新的10-K表和10-Q表，以及我們可能向美國證券交易委員會提交的8-K表報告。我們所有陳述均截至2017年11月9日，並基於我們目前掌握的資訊。除法律要求外，我們不承擔更新任何此類陳述的義務。

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（非GAAP）財務指標。您可以在我們網站上發布的《財務長評論》中找到這些非GAAP財務指標與GAAP財務指標的調節表。

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

那麼，現在讓我把電話交給科萊特。

Thanks, Simona. We had an excellent quarter with record revenue in each of our 4 market platforms. And every measure of profit hit record levels, reflecting the leverage of our model. Data center revenue of $501 million more than doubled from a year ago and the strong adoption of our Volta platform and early traction with our inferencing portfolio.

謝謝，西蒙娜。我們本季業績斐然，四大市場平台均創下營收新高。各項利潤指標也都達到歷史新高，充分體現了我們商業模式的優勢。資料中心營收達 5.01 億美元，較去年同期成長一倍多；此外，Volta 平台廣受歡迎，推理產品組合也取得了初步成功。

Q3 revenue reached $2.64 billion, up 32% from a year earlier, up 18% sequentially and well above our outlook of $2.35 billion. From a reporting segment perspective, GPU revenue grew 31% from last year to $2.22 billion. Tegra processor revenue rose 74% to $419 million.

第三季營收達26.4億美元，年增32%，季增18%，遠超我們先前23.5億美元的預期。從業務板塊來看，GPU營收年增31%至22.2億美元；Tegra處理器營收成長74%至4.19億美元。

Let's start with our gaming business. Gaming revenue was $1.56 billion, up 25% year-on-year and up 32% sequentially. We saw robust demand across all regions and form factors. Our Pascal-based GPUs remained the platform of choice for gamers as evidenced by our strong demand for GeForce GTX 10-Series products. We introduced the GeForce GTX 1070 Ti, which became available last week. It complements our strong holiday lineup ranging from the entry-level GTX 1050 to our flagship GTX 1080 Ti.

我們先來看看遊戲業務。遊戲業務收入達15.6億美元，較去年成長25%，較上季成長32%。所有地區和各種規格的產品需求都十分強勁。基於Pascal架構的GPU仍是遊戲玩家的首選平台，GeForce GTX 10系列產品的強勁需求便是最好的證明。我們推出了GeForce GTX 1070 Ti，該產品已於上週上市。它完善了我們強大的假期產品陣容，該陣容涵蓋了從入門級GTX 1050到旗艦級GTX 1080 Ti的各種產品。

A wave of great titles is arriving for the holidays, driving enthusiasm in the market. We collaborated with Activision to bring Destiny 2 to the PC earlier in the month. PlayerUnknown's Battlegrounds, popularly known as PUBG, continues to be one of the year's most successful titles. We are closely aligned with PUBG to ensure that GeForce is the best way to play the game, including bringing shadow play highlights to its 20 million players. Last weekend, Call of Duty: WWII had a strong debut, and Star Wars Battlefront II will be out [soon].

一系列精彩遊戲即將登陸假日季，點燃了市場熱情。本月初，我們與動視合作，將《命運2》帶到了PC平台。廣受歡迎的《絕地求生》（PUBG）依然是今年最成功的遊戲之一。我們與PUBG緊密合作，確保GeForce顯示卡是體驗這款遊戲的最佳方式，包括為2,000萬玩家帶來精彩重播功能。上週末，《決勝時刻：二戰》強勢先發，《星際大戰：前線2》也即將上市。

eSports remains one of the most important secular growth drivers in the gaming market with a fan base that now exceeds 350 million. Last weekend, the League of Legends World Championship was held in Beijing's National Stadium, the Bird's Nest, where the 2008 Olympics games were held. More than 40,000 fans attended live, and online viewers were set to break last year's record of 43 million following in 18 languages.

電子競技仍是遊戲市場最重要的長期成長動力之一，其粉絲群已超過3.5億。上週末，英雄聯盟全球總決賽在北京國家體育場（鳥巢）舉行，也是2008年奧運的舉辦地點。超過4萬名現場觀眾到場觀賽，而線上觀看人數預計將打破去年4,300萬的紀錄，直播語言涵蓋18種。

GPU sales also benefited from continued cryptocurrency mining. We met some of this demand with a dedicated board in our OEM business and a portion with GeForce GTX boards, though it's difficult to quantify. We remain nimble in our approach to the cryptocurrency market. It is volatile, does not and will not distract us from focusing on our core gaming market. Lastly, Nintendo Switch console continues to gain momentum since launching in March and also contributed to growth.

GPU銷售也受惠於加密貨幣挖礦的持續成長。我們透過OEM業務中的專用顯示卡滿足了部分需求，並透過GeForce GTX顯示卡滿足了一部分需求，但具體比例難以量化。我們對加密貨幣市場仍保持靈活應對。加密貨幣市場波動性較大，不會分散我們專注於核心遊戲市場的精力。最後，自3月上市以來，任天堂Switch遊戲機持續成長，也為整體成長做出了貢獻。

Moving to data center. Our data center business had an outstanding quarter. Revenue of $501 million more than doubled from last year and rose 20% on the quarter amid strong traction of the new Volta architecture. Shipments of the Tesla V100 GPU began in Q2 and ramped significantly in Q3 driven primarily by demand from cloud service providers and high-performance computing. As we have noted before, Volta delivers 10x the deep learning performance of our Pascal architecture, which has been introduced just a year earlier, far outpacing Moore's Law.

轉向資料中心業務。我們的資料中心業務本季表現出色。營收達到 5.01 億美元，較去年同期翻了一番多，環比成長 20%，主要得益於全新 Volta 架構的強勁表現。 Tesla V100 GPU 的出貨量從第二季開始，並在第三季大幅成長，這主要得益於雲端服務供應商和高效能運算領域的需求。正如我們之前提到的，Volta 架構的深度學習效能是僅一年前推出的 Pascal 架構的 10 倍，遠遠超越了摩爾定律的預測。

The V100 is being broadly adopted with every major server OEM and cloud provider. In China, Alibaba, Baidu and Tencent announced that they are incorporating V100 in their data centers and cloud server service infrastructures. In the U.S., Amazon Web Services announced that V100 inferences are now available in 4 of its regions. Oracle Cloud has just added Tesla P100 GPUs to its infrastructure offerings and plans to expand to the V100 GPUs. We expect support from V100 from other major cloud providers as well.

V100 正被各大伺服器 OEM 廠商和雲端服務供應商廣泛採用。在中國，阿里巴巴、百度和騰訊宣布將在其資料中心和雲端伺服器服務基礎設施中整合 V100。在美國，亞馬遜雲端服務 (AWS) 宣布其 4 個區域現已支援 V100 推理。 Oracle 雲端剛剛在其基礎設施產品中新增了 Tesla P100 GPU，並計劃擴展到 V100 GPU。我們預計其他主要雲端服務供應商也將支援 V100。

In addition, all major server OEMs announced support for the V100, Dell EMC, Hewlett Packard Enterprise, IBM and Supermicro are incorporating it in servers. And China's top server OEMs, Huawei, Inspur and Lenovo have adopted our HGX server architecture to build a new generation of accelerated data centers with V100 GPUs.

此外，所有主流伺服器OEM廠商都已宣布支援V100，戴爾EMC、惠普企業、IBM和超微等廠商已將其整合到伺服器中。而中國頂尖的伺服器OEM廠商華為、浪潮和聯想也已採用我們的HGX伺服器架構，利用V100 GPU建構新一代加速資料中心。

Our new offerings for the AI inference market are also gaining momentum. The recently launched TensorRT 3 programmable inference acceleration platform opens a new market opportunity for us, improving the performance and reducing the cost of AI inferencing in order -- by orders of magnitude compared with CPUs. It supports every major deep learning framework, every network architecture and any level of network complexity. More than 1,200 companies are already using our inference platform including Amazon, Microsoft, Facebook, Google, Alibaba, Baidu, JD.com, iFLYTEK, Hikvision and Tencent.

我們面向人工智慧推理市場的新產品也正蓬勃發展。近期推出的TensorRT 3可程式推理加速平台為我們開闢了新的市場機遇，與CPU相比，它能顯著提升人工智慧推理的效能並大幅降低成本。該平台支援所有主流深度學習框架、所有網路架構以及任何複雜程度的網路。目前已有超過1200家公司在使用我們的推理平台，包括亞馬遜、微軟、Facebook、谷歌、阿里巴巴、百度、京東、科大訊飛、海康威視和騰訊。

During the quarter, we announced that the NVIDIA GPU Cloud container registry, or NGC, is now available through Amazon's cloud and will be supported soon by other cloud platforms. NGC helps developers get started with deep learning development through no-cost access to a comprehensive, easy-to-use, fully optimized deep learning software stack. It enables instant access to the most widely used GPU-accelerated frameworks.

本季度，我們宣布 NVIDIA GPU Cloud 容器註冊表 (NGC) 現已在亞馬遜雲端上線，並將很快支援其他雲端平台。 NGC 為開發者提供免費存取全面、易用且完全優化的深度學習軟體棧，幫助他們快速上手深度學習開發。它支援即時存取最常用的 GPU 加速框架。

We also continue to see robust growth in our HPC business. Next-generation supercomputers, such as the U.S. Department of Energy's Sierra and Summit systems expected to come online next year, leverage Volta's industry-leading performance, and our pipeline is strong. The past weeks have been exceptionally busy for us. We have hosted 5 major GPU Technology Conferences in Beijing, Munich, Taipei, Tel Aviv and Washington with another next month in Tokyo. In a strong indication of the growing importance of GPU-accelerated computing, more than 22,000 developers, data scientists and others will come this year to our GTCs, including the main event in Silicon Valley. That's up 10x in just 5 years. Other key metrics show similar gains. Over the same period, the number of NVIDIA GPU developers has grown 15x to 645,000, and the number of CUDA downloads this year are up 5x to 1.8 million.

我們的高效能運算 (HPC) 業務也持續保持強勁成長。下一代超級計算機，例如預計明年上線的美國能源部 Sierra 和 Summit 系統，將充分利用 Volta 業界領先的性能，而我們的專案儲備也十分充足。過去幾週我們異常忙碌。我們在北京、慕尼黑、台北、特拉維夫和華盛頓舉辦了五場大型 GPU 技術大會 (GTC)，下個月也將在東京舉辦一場。 GPU 加速運算的重要性日益凸顯，今年將有超過 22,000 名開發者、資料科學家和其他人士參加我們的 GTC，其中包括在矽谷舉辦的主會場。短短五年內，與會者人數增加了十倍。其他關鍵指標也呈現類似的成長。同期，NVIDIA GPU 開發者數量增加了 15 倍，達到 645,000 人；今年 CUDA 下載量增加了 5 倍，達到 180 萬次。

Moving to professional visualization. Third quarter revenue grew to $239 million, up 15% from a year ago and up 2% sequentially driven by demand for high-end real-time rendering, simulation and more powerful mobile workstations. The defense and automotive industries grew strongly as the demand for professional VR solutions driven by Quadro P5000 and P6000 GPUs. Among key customers, Audi and BMW are deploying VR in auto showrooms. And the U.S. Army, Navy and Homeland Security are using VR for mission training.

轉向專業視覺化領域。第三季營收成長至2.39億美元，年增15%，季增2%，主要得益於市場對高階即時渲染、模擬以及更強大的行動工作站的需求。國防和汽車產業成長強勁，這主要得益於Quadro P5000和P6000 GPU對專業VR解決方案的需求。在主要客戶中，奧迪和寶馬正在汽車展廳部署VR技術。此外，美國陸軍、海軍和國土安全部也正在使用VR進行任務訓練。

Last month, we announced early access to NVIDIA Holodeck, the intelligent VR collaboration platform. Holodeck enables designers, developers and their customers to come together virtually from anywhere in the world in a highly realistic, collaborative and physically simulated environment. Future updates will address the growing demand for the development of deep learning techniques in virtual environments.

上個月，我們宣布提前推出 NVIDIA Holodeck，這是一款智慧 VR 合作平台。 Holodeck 讓設計師、開發人員及其客戶能夠在高度逼真、協作且實體模擬的虛擬環境中，從世界任何地方進行虛擬協作。未來的更新將滿足日益增長的在虛擬環境中開發深度學習技術的需求。

In automotive, revenue grew to $144 million, up 13% year-over-year and up slightly from last quarter. Among key developments this quarter, we announced DRIVE PX Pegasus, the world's first AI computer for enabling Level 5 driverless vehicles. Pegasus will deliver over 320 trillion operations per second, more than 10x its predecessor. It's powered by 4 high-performance AI processors in a supercomputer that is the size of a license plate. NVIDIA DRIVE is being used by over 25 companies to develop fully autonomous robotaxis, and DRIVE PX Pegasus will become the path to production. It is designed for ASIL D certification, the industry's highest safety level and will be available in the second half of 2018.

在汽車業務方面，營收成長至 1.44 億美元，年增 13%，較上季略有成長。本季的一項重要進展是，我們發布了 DRIVE PX Pegasus，這是全球首款用於實現 L5 級自動駕駛汽車的 AI 電腦。 Pegasus 的運算速度將超過每秒 320 兆次，是其前代產品的 10 倍以上。它由 4 個高性能 AI 處理器組成，體積僅相當於一塊車牌大小。 NVIDIA DRIVE 已被超過 25 家公司用於開發全自動無人駕駛計程車，而 DRIVE PX Pegasus 將成為實現量產的關鍵。該產品旨在獲得 ASIL D 認證，這是業界最高的安全等級，並將於 2018 年下半年上市。

We also introduced the DRIVE IX SDK for delivering intelligent experiences inside the vehicle. DRIVE IX provides a platform for car companies to create and always engage AI co-pilot. It uses deep learning networks to track head movement and gaze, and it will have a conversation with the driver using advanced speech recognition, lipreading and natural language understanding. We believe this will set the standard for the next generation of infotainment systems, a market that is just beginning to develop.

我們也推出了 DRIVE IX SDK，旨在打造車內智慧體驗。 DRIVE IX 為汽車公司提供了一個平台，用於建立並持續啟用 AI 副駕駛功能。它利用深度學習網路追蹤頭部運動和視線，並透過先進的語音識別、唇語識別和自然語言理解技術與駕駛員進行對話。我們相信，這將為下一代車載資訊娛樂系統樹立標桿，而這個市場目前正處於起步階段。

Finally, we announced that DHL, the world's largest mail and package delivery service, and ZF, one of the world's leading automotive suppliers, will deploy a test fleet of autonomous delivery trucks next year using the NVIDIA DRIVE PX platform. DHL will outfit electric light trucks with the ZF ProAI self-driving system based on our technology.

最後，我們宣布，全球最大的郵件和包裹遞送服務公司DHL和全球領先的汽車供應商之一ZF將於明年部署一支基於NVIDIA DRIVE PX平台的自動駕駛送貨車隊進行測試。 DHL將為電動輕型卡車配備基於我們技術的ZF ProAI自動駕駛系統。

Now turning to the rest of the income statement. Q3 GAAP gross margins was 59.5% and non-GAAP was 59.7%, both up sequentially and year-over-year, reflecting continued growth in value-added platforms. GAAP operating expenses were $674 million, and non-GAAP operating expenses were $570 million, consistent with our outlook and up 19% year-on-year. Investing in our key market opportunities is essential to our future, including gaming, AI and self-driving cars.

現在來看損益表的其餘部分。第三季GAAP毛利率為59.5%，非GAAP毛利率為59.7%，皆較上季與年成長，反映出加值平台業務的持續成長。 GAAP營業費用為6.74億美元，非GAAP營業費用為5.7億美元，與我們的預期一致，年增19%。投資我們的關鍵市場機會對我們的未來至關重要，這些機會包括遊戲、人工智慧和自動駕駛汽車。

GAAP operating income was a record $895 million, up 40% from a year ago. Non-GAAP operating income was $1.01 billion, up 42% from a year ago. GAAP net income was a record $838 million, and EPS was $1.33, up 55% and 60%, respectively, from a year earlier. Non-GAAP net income was $833 million, and EPS was $1.33, up 46% and 41%, respectively from a year earlier, reflecting revenue strength as well as gross margin and operating margin expansion.

以美國通用會計準則（GAAP）計算，營業收入創下8.95億美元的歷史新高，較去年同期成長40%。以非美國通用會計準則（Non-GAAP）計算，營業收入為10.1億美元，較去年同期成長42%。以美國通用會計準則（GAAP）計算，淨收入創下8.38億美元的歷史新高，每股盈餘（EPS）為1.33美元，分別較去年同期成長55%和60%。以非美國通用會計準則（Non-GAAP）計算，淨收入為8.33億美元，每股收益為1.33美元，分別同比增長46%和41%，這反映了營收的強勁增長以及毛利率和營業利潤率的提升。

We have returned $1.16 billion to shareholders so far this fiscal year through a combination of quarterly dividends and share repurchases. We have announced an increase to our quarterly dividend of $0.01 to an annualized $0.60 effective with our Q4 fiscal year '18 dividend. We are also pleased to announce that we intend to return another $1.25 billion to shareholders for fiscal 2019 through quarterly dividends and share repurchases. Our quarterly cash flow from operations reached record levels, surpassing $1 billion for the first time to $1.16 billion.

本財年至今，我們已透過季度分紅和股票回購的方式向股東返還了11.6億美元。我們宣布，自2018財年第四季分紅起，季分紅將增加0.01美元，年化分紅將達到0.60美元。同時，我們也欣然宣布，計劃在2019財年透過季度分紅和股票回購的方式再向股東返還12.5億美元。我們的季度經營活動現金流創下歷史新高，首次突破10億美元大關，達到11.6億美元。

Now turning to the outlook for the fourth quarter of fiscal 2018. We expect revenue to be $2.65 billion, plus or minus 2%. GAAP and non-GAAP gross margins are expected to be 59.7% and 60%, respectively, plus or minus 50 basis points. GAAP and non-GAAP operating expenses are expected to be approximately $722 million and $600 million, respectively. GAAP and non-GAAP OI&E are both expected to be nominal. GAAP and non-GAAP tax rates are both expected to be 17.5%, plus or minus 1%, excluding discrete items. Further financial details are included in the CFO Commentary and other information available on our website.

現在展望2018財年第四季。我們預計營收為26.5億美元，上下浮動2%。 GAAP和非GAAP毛利率預計分別為59.7%和60%，上下浮動50個基點。 GAAP和非GAAP營運費用預計分別約為7.22億美元和6億美元。 GAAP及非GAAP經營損益預計均為小幅變動。 GAAP和非GAAP稅率預計均為17.5%，上下浮動1%（不含特殊項目）。更多財務詳情請參閱財務長評論以及我們網站上的其他資訊。

We will now open the call for questions. (Operator Instructions) Operator, we will -- would you please pool for questions? Thank you.

現在開始提問環節。 （操作員指示）操作員，請大家踴躍提問好嗎？謝謝。

(Operator Instructions) Your first question comes from the line of Toshiya Hari with Goldman Sachs.

（操作員說明）您的第一個問題來自高盛的 Toshiya Hari 的線路。

Jensen, 3 months ago you described the July quarter as a transition quarter for your data center business. And clearly, you guys have ramped very well into October. But if you can talk a little bit about the outlook for the next couple of quarters in data center and particularly on the inferencing side. I know you guys are really excited about that opportunity. So if you can share customer feedback and what your expectations are into the next year in inferencing, that would be great.

Jensen，三個月前你曾將7月份的季度描述為你們資料中心業務的過渡季度。顯然，你們在10月份的業務成長勢頭非常強勁。但如果你能談談未來幾季資料中心業務，特別是推理方面的展望就太好了。我知道你們對這個機會充滿熱情。所以，如果你能分享一些客戶回饋以及你們對明年推理業務的預期，那就再好不過了。

Yes, as you know, we started ramping very strongly Volta this last quarter, and we started the ramp the quarter before. And since then, every major cloud provider, from Amazon, Microsoft, Google to Baidu, Alibaba, Tencent and even recently, Oracle, has announced support for Volta and we'll be providing Volta for their internal use of deep learning as well as external public cloud services. We also announced that every major server computer maker in the world has now supported Volta and in the process of taking Volta out to market. HP and Dell and IBM and Cisco and Huawei in China, Inspur in China, Lenovo, have all announced that they will be building servers -- families of servers around the Volta GPU. And so I think we -- this ramp is just the first part of supporting the build out of GPU-accelerated servers from our company for data centers all over the world as well as cloud service providers all over the world. The applications for these GPU servers has now grown to many markets. I've spoken about the primary segments of our Tesla GPUs. There are 5 of them that I talk about regularly. The first one is high-performance computing where the market is $11 billion or so. It is one of the faster-growing parts of the IT industry because more and more people are using high-performance computing for doing their product development or looking for insights or predicting the market or whatever it is. And today, we represent about 15% of the world's top 500 supercomputers. And I've repeatedly said and I believe this completely and I think it's becoming increasingly true that every single supercomputer in the future will be accelerated somehow. So this is a fairly significant growth opportunity for us. The second is deep learning training, which is very, very much like high-performance computing. And you need to do computing at a very large scale. You're performing trillions and trillions of iterations. The models are getting larger and larger. Every single year, the amount of data that we're training with it is increasing. And the difference between a computing platform that's fast versus not could mean the difference between building a $20 million data center or high-performance computing servers for training to $200 million. And so the money that we save and the capability we provide is really, the value is incredible. The third segment, and this is the segment that you just mentioned, has to do with inference, which is when you're done with developing this network, you have to put it down into the hyperscale data centers to support the billions and billions of queries that consumers make to the Internet every day. And this is a brand-new market for us. 100% of the world's inference is done on CPUs today. We announced very recently, this last quarter in fact, that TensorRT 3 inference acceleration platform and in combination with our Tensor Core GPU instruction set architecture, we're able to speed up networks by a factor of 100. Now the way to think about that is imagine whatever amount of workload that you've got, if you could speed up using our platform by a factor of 100, how much you can save. The other way to think about that is because the amount of -- the networks are getting larger and larger, and they're so complex now. And we know that every network on the planet will run on our architecture because they were trained on our architecture today. And so whether it's CNNs or RNNs or GANs or autoencoders or all of the variations of those, irrespective of the precision that you need to support, the size of the network, we have the ability to support them. And so you could either scale out your hyperscale data center to support more traffic or you can reduce your cost tremendously or simultaneously, both. The fourth segment of our data center is providing all of that capability, what I just mentioned, whether it's HPC, training or inference and turning it inside out and making it available in the public cloud. There are thousands of start-ups now that are in -- are started because of AI. Everybody recognizes the importance of this new computing model. And as a result of this new tool, this new capability, all these unsolvable problems in the past are now interestingly solvable. And so you can see start-ups cropping up all over the west, all over the east, and there's just -- there are thousands of them. And these companies don't either -- would rather not use their scarce financial resources to go build high-performance computing centers, or they don't have the skill to be able to build out a high-performance platform the way these Internet companies can. And so these cloud providers, cloud platforms are just a fantastic resource for them because they could rent it by the hour. We created in conjunction with that, and I mentioned all the cloud service providers have taken it to market, in conjunction with that, we created a registry in the cloud that containerizes these really complicated software stacks. Every one of these soft frameworks with the different versions of our GPUs and different acceleration layers and different optimization techniques, we've containerized all of that for every single version and every single type of framework in the marketplace. And we put that up in the registry -- cloud registry called the NVIDIA GPU Cloud. And so all you had to do was download that into the cloud service provider that we've got certified and tested for, and with just one click, you're doing deep learning. And then the last -- and so that's the cloud service providers. If you -- the way to guess that -- estimate that is there are obviously tens of billions of dollars being invested in these AI start-ups. And some large proportion of their investment fund raise will ultimately have to go towards high-performance computing, whether they build it themselves or they rent it in the cloud. And so I think that's a multibillion dollar opportunity for us. And then lastly, this is probably the largest of all the opportunities, which is the vertical industries. Whether it's automotive companies that are developing their supercomputers to get ready for self-driving cars or health care companies that are now taking advantage of artificial intelligence to do better diagnostics of -- diagnosis of disease, to manufacturing companies to -- for in-line inspection, to robotics, large logistics companies. Colette mentioned earlier DHL. But the way to think about that is all of these planning -- all of these companies doing planning to deliver products to you through this large network of delivery systems, it is the world's largest planning problem. And whether it's Uber or Didi or Lyft or Amazon or DHL or UPS or FedEx, they all have high-performance computing problems that are now moving to deep learning. And so those are really exciting opportunities for us. And so the last one is just vertical industries. I mean, all of these segments, we're now in a position to start addressing because we've put our GPUs in the cloud, all of our OEMs are in the process of taking these platforms out to market and we have the ability now to address high-performance computing and deep learning training as well as inference using one common platform. And so I think the -- we've been steadfast with the excitement of accelerated computing for data centers, and I think this is just the beginning of it all.

是的，如您所知，我們在上個季度開始大力推進Volta的部署，而此前一個季度我們也已經開始推進。自那以後，從亞馬遜、微軟、谷歌到百度、阿里巴巴、騰訊，甚至最近的甲骨文，所有主流雲端服務供應商都宣布支援Volta，我們將為他們的內部深度學習應用以及外部公有雲服務提供Volta。我們也宣布，全球所有主流伺服器製造商都已支援Volta，並正在將其推向市場。惠普、戴爾、IBM、思科、華為（中國）、浪潮（中國）、聯想等都宣布將圍繞Volta GPU建置伺服器－一系列伺服器產品。因此，我認為此次部署只是我們支援全球資料中心和雲端服務供應商建置GPU加速伺服器的第一步。這些GPU伺服器的應用領域如今已擴展到許多市場。我之前已經談到我們Tesla GPU的主要應用領域。我經常提到其中的五個領域。第一個是高效能運算，市場規模約110億美元。它是IT行業成長最快的領域之一，因為越來越多的人使用高效能運算來進行產品開發、尋找洞察、預測市場等等。如今，我們佔據了全球前500強超級電腦的約15%。我曾多次說過，而且我完全相信這一點，我認為未來每一台超級電腦都將以某種方式加速運行，而且這種趨勢正變得越來越明顯。因此，這對我們來說是一個相當大的成長機會。第二個是深度學習訓練，它與高效能運算非常相似。你需要進行大規模的計算。你需要執行數萬億次迭代。模型越來越大。每年，我們用於訓練的資料量都在增加。一個快速的運算平台與一個慢的運算平台之間的差異，可能意味著建造一個價值2000萬美元的資料中心或高效能運算訓練伺服器的成本，最終可能高達2億美元。因此，我們節省的資金和提供的能力，其價值確實令人難以置信。第三個部分，也就是您剛才提到的部分，與推理有關。當您完成網頁開發後，必須部署到超大規模資料中心，以支援消費者每天向網路發出的數十億次查詢。這對我們來說是一個全新的市場。目前，全球 100% 的推理都是在 CPU 上完成的。我們最近，實際上是上個季度，宣布了 TensorRT 3 推理加速平台，該平台與我們的 Tensor Core GPU 指令集架構相結合，能夠將網路速度提升 100 倍。您可以想像一下，無論您有多少工作負載，如果使用我們的平台可以將速度提升 100 倍，您可以節省多少成本。另一種理解方式是，網路規模越來越大，也越來越複雜。我們知道，地球上所有網路都將在我們的架構上運行，因為它們今天都是基於我們的架構進行訓練的。因此，無論是卷積神經網路 (CNN)、循環神經網路 (RNN)、生成對抗網路 (GAN)、自編碼器，還是它們的各種變體，無論您需要支援的精度如何，網路規模如何，我們都有能力支援。因此，您可以擴展超大規模資料中心以支援更多流量，也可以大幅降低成本，或兩者兼顧。我們資料中心的第四個部分就是提供我剛才提到的所有能力，無論是高效能運算 (HPC)、訓練或推理，並將其徹底整合到公有雲中。現在有成千上萬家新創公司——都是因為人工智慧而成立的。每個人都認識到這種新型計算模型的重要性。由於這種新工具、這種新能力，過去所有無法解決的問題現在都變得觸手可及。因此，您可以看到新創公司在世界各地湧現，數量成千上萬。這些公司也不願意——他們寧願不把有限的財力投入到高效能運算中心建設中，或者他們沒有能力像網路公司那樣建立高效能平台。因此，這些雲端服務供應商和雲端平台對他們來說簡直是絕佳的資源，因為他們可以按小時租用。為此，我們創建了一個雲端註冊表，正如我之前提到的，所有雲端服務提供者都已將其推向市場。此註冊表將這些極為複雜的軟體堆疊容器化。我們針對市場上所有版本的GPU、不同的加速層和不同的最佳化技術，對所有軟體框架進行了容器化，並將其上傳到名為NVIDIA GPU Cloud的雲端註冊表中。用戶只需將其下載到我們已認證和測試過的雲端服務供應商處，一鍵即可進行深度學習。最後，我們來看看雲端服務供應商。如果你——我猜——估計一下，顯然有數百億美元正投資於這些人工智慧新創公司。而他們籌集的資金中很大一部分最終都必須用於高效能運算，無論他們是自己建置還是租用雲端資源。所以我認為這對我們來說是一個價值數十億美元的機會。最後，這可能是所有機會中最大的，那就是垂直產業。無論是正在開發超級電腦為自動駕駛汽車做準備的汽車公司，還是正在利用人工智慧進行更精準疾病診斷的醫療保健公司，亦或是正在利用人工智慧進行線上檢測的製造公司、機器人技術公司，以及大型物流公司（科萊特之前提到了DHL）。但我們應該這樣理解：所有這些公司都在進行規劃，透過龐大的配送網路將產品送到你手中，這才是世界上最大的規劃難題。無論是 Uber、滴滴、Lyft、亞馬遜、DHL、UPS 或 FedEx，它們都面臨著高效能運算的挑戰，而這些挑戰現在正轉向深度學習。因此，這對我們來說是令人振奮的機會。最後一點是垂直行業。我的意思是，所有這些細分領域，我們現在都具備了應對的條件，因為我們已將 GPU 部署到雲端，所有 OEM 廠商都在將這些平台推向市場，我們現在能夠利用一個通用平台來解決高效能運算、深度學習訓練和推理等問題。因此，我認為——我們一直對資料中心加速運算充滿熱情，而我認為這只是個開始。

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

你的下一個問題來自伯恩斯坦研究公司的史黛西·拉斯貢。

I had a question on your gaming seasonality into Q4. It's usually up a bit. I was wondering, do you see any, I guess, drivers that would drive a lack of normal seasonal trends given how strong it's been sequentially and year-over-year? And I guess as a related question, do you see your Volta volumes in Q4 exceeding Q3?

我有個關於你們遊戲業務第四季季節性波動的問題。通常情況下，第四季度遊戲業務都會略有成長。我想知道，鑑於今年遊戲業務環比和同比都表現強勁，你們認為有哪些因素會導致正常的季節性波動減弱？另外，我想問一下，你們第四季的Volta銷售量會超過第三季嗎？

Let's see. There's -- I'll answer the last one first and then work towards the first one. I think the guidance that we provided, we feel comfortable with. But if you think about Volta, it is just in the beginning of the ramp, and it's going to ramp into the market opportunities I talked about. And so my hope is that we continue to grow, and there's every evidence that the markets that we serve, that we're addressing with Volta is -- are very large markets. And so there's a lot of reasons to be hopeful about the future growth opportunities for Volta. We've primed the pump. So cloud service providers are either announce the availability of Volta or they announced the soon availability of Volta. They're all racing to get Volta to their cloud because customers are clamoring for it. The OEMs are -- we've primed the pump with the OEMs, and some of them are sampling now and some of them are racing to get Volta to production in the marketplace. And so I think the foundation, the demand is there. The urgent need for accelerated computing is there because Moore's Law is not scaling anymore, and then we've primed the pump. So the demand is there. There's a need -- the need is there, and the foundations for getting Volta to market is primed. With respect to gaming, what drives our gaming business? Remember, our gaming business is sold one at a time to millions and millions of people. And what drives our gaming business is several things. As you know, eSports is incredibly, incredibly vibrant, and what drives -- the reason why eSports is so unique is because people want to win and having better gear helps. The latency that they expect is incredibly low, and performance drives down latency and they want to be able to react as fast as they can. People want to win, and they want to make sure that the gear that they use is not the reason why they didn't win. The second growth driver for us is content, the quality of content. And boy, if you look at Call of Duty or Destiny 2 or PUBG, the content just looks amazing. The AAA content looks amazing. And one of the things that's really unique about video games is that in order to enjoy the content and the fidelity of the content, the quality of the production value at its fullest, you need the best gear. It's very different than streaming video, it's very different than watching movies where streaming videos, it is what it is. But for video games, of course, it's not. And so when AAA titles comes out in the later part of the year, it helps to drive platform adoption. And then lastly, increasingly, social is becoming a huge part of the growth dynamics of gaming. People are -- they recognize how beautiful these video games are, and so they want to share their brightest moments with people. They want to share the levels they discover. They want to take pictures of the amazing graphics that's inside. And it is one of the primary drivers, the leading driver, in fact, of YouTube and people watching other people play video games, these broadcasters. And now with our Ansel, the world's first in-game virtual reality and surround and digital camera, we have the ability to take pictures and share that with people. And so I think all of these different drivers are helping our gaming business, and I'm optimistic about Q4. It looks like it's going to be a great quarter.

嗯……我先回答最後一個問題，然後再回答第一個。我認為我們提供的指導意見是可以接受的。但如果你仔細想想Volta，它目前還處於起步階段，未來將會迎來我之前提到的市場機會。所以我希望我們能夠繼續成長，而且種種跡象表明，我們服務的市場，也就是我們用Volta所針對的市場，都是非常龐大的市場。因此，我們有充分的理由對Volta未來的成長前景充滿信心。我們已經做好了充分的準備。雲端服務提供者要么已經宣布Volta正式上線，要么已經宣布即將上線。他們都在爭分奪秒地將Volta部署到他們的雲端，因為客戶的需求非常迫切。我們也與OEM廠商進行了溝通，其中一些廠商正在進行樣品測試，另一些廠商則正在競相將Volta投入量產並推向市場。所以我認為，市場需求已經具備了。對加速運算的迫切需求源自於摩爾定律不再適用，而我們已經為此做好了準備。因此，需求是存在的，Volta 架構的市場化基礎也已就緒。就遊戲而言，是什麼驅動著我們的遊戲業務？請記住，我們的遊戲業務是逐一銷售給數百萬用戶的。驅動我們遊戲業務的因素有很多。眾所周知，電子競技非常蓬勃發展，其獨特之處在於，人們渴望勝利，而更好的裝備至關重要。他們期望的延遲極低，而效能的提升可以降低延遲，他們希望能夠以最快的速度做出反應。人們渴望勝利，他們希望確保自己使用的裝備不會成為失敗的原因。我們的第二個成長驅動力是內容，是內容的品質。看看《決勝時刻》、《天命2》或《絕地求生》，你會發現這些遊戲的內容簡直令人驚艷。這些 AAA 級遊戲的內容更是精彩絕倫。電子遊戲的獨特之處在於，為了充分體驗遊戲內容及其製作水準，你需要最好的設備。這與串流影音和電影截然不同，串流影音的體驗是固定的，而電子遊戲則不然。因此，當3A級大作在年底發售時，有助於推動平台普及。最後，社交正日益成為遊戲產業成長的重要驅動力。人們意識到這些電子遊戲的精美畫面，因此他們想要與他人分享遊戲中的精彩瞬間，分享他們探索的關卡，並拍攝遊戲中令人驚嘆的畫面。事實上，這正是YouTube以及人們觀看其他玩家玩遊戲（即主播）的主要驅動力之一。現在，有了我們的 Ansel——全球首款遊戲內虛擬實境環繞數位相機——我們就能拍照並與他人分享。我認為所有這些因素都在推動我們的遊戲業務發展，我對第四季充滿信心。看起來這將會是一個非常棒的季度。

Your next question comes from the line of C.J. Muse from Evercore.

你的下一個問題來自 Evercore 樂團的 C.J. Muse 的一句歌詞。

I was hoping to sneak in a near-term and a longer-term question. On the near term, you talked about the health on demand side for Volta. Curious if you're seeing any sort of restrictions on the supply side, whether it's wafers or access to high-bandwidth memory, et cetera. And then the longer-term question really revolves around CUDA, and you've talked about that as being a sustainable competitive advantage for you guys entering the year. And now that we've moved beyond HPC and hyperscale training to more into inference and GPU as a service and you've hosted GTC around the world, curious if you could extrapolate on how you're seeing that advantage and how you've seen it evolve over the year and how you're thinking about CUDA as the AI standard.

我原本想問一個近期和一個長期的問題。關於近期，您談到了Volta的需求端狀況。我想知道您是否看到供應端有任何限制，例如晶圓供應或高頻寬記憶體的獲取等等。而長期問題則主要圍繞在CUDA展開，您曾提到CUDA是您今年以來可持續的競爭優勢。現在，我們已經超越了高效能運算（HPC）和超大規模訓練，更多地轉向推理和GPU即服務，而且你們也在全球各地舉辦了GTC大會，我想請您進一步闡述一下您是如何看待這一優勢的，以及它在過去一年中是如何演變的，以及您如何看待CUDA作為人工智能標準的未來。

Yes, thanks a lot, C.J. Well, everything that we build is complicated. Volta is the single largest processor that humanity has ever made, 21 billion transistors, 3D packaging, the fastest memories on the planet and all of that in a couple hundred watts, which basically says it's the most energy-efficient form of computing that the world has ever known. And one single Volta replaces hundreds of CPUs. And so it's energy-efficient. It saves an enormous amount of money. And it gets this job done really, really fast, which is one of the reasons why GPU-accelerated computing is so popular now. With respect to the outlook for our architecture, as you know, we are a one-architecture company, and it's so vitally important. And the reason for that is because there are so much software and so much tools created on top of this one architecture. On the inference side -- on the training side, we have a whole stack of software and optimizing compilers and numerics libraries that are completely optimized for one architecture called CUDA. On the inference side, the optimizing compilers that takes these large, huge computational graphs that come out of all of these frameworks, and these computational graphs are getting larger and larger and their numerical precision differs from one type of network to another -- from one type of application to another. Your numerical precision requirements for a self-driving car, where lives are at stake, to detecting where -- counting the number of people crossing the street, counting something versus trying to track -- detect and track something very subtle in all kinds of weather conditions is a very, very different problem. And so numeric -- the type of networks are changing all the time. They're getting larger all the time. The numerical precision is different for different applications. And we have different computing -- computer performance levels as well as energy availability levels that these inference compilers are likely to be some of the most complex software in the world. And so the fact that we have one singular architecture to optimize for, whether it's HPC for numeric -- molecular dynamics and computational chemistry and biology and astrophysics, all the way to training to inference gives us just enormous leverage. And that's the reason why NVIDIA could be an 11,000-people company and arguably, performing at a level that is 10x that. And the reason for that is because we have one singular architecture that's -- that is accruing benefits over time instead of 3, 4, 5 different architectures where your software organization is broken up into all these different small subcritical mass pieces. And so it's a huge advantage for us, and it's a huge advantage for the industry. So people who support CUDA knows that the next-generation architecture will just get a benefit and go for the ride that technology advancement provides them and affords them. Okay. So I think it's an advantage that is growing exponentially, frankly, and I'm excited about it.

是的，非常感謝，C.J.。我們製造的所有東西都很複雜。 Volta 是人類迄今為止製造的最大的處理器，擁有 210 億個電晶體、3D 封裝、全球最快的內存，而所有這些功能都整合在幾百瓦的功耗中，這基本上意味著它是世界上最節能的計算方式。一個 Volta 處理器就能取代數百個 CPU。因此，它非常節能，節省了大量資金，而且完成任務的速度非常快，這也是 GPU 加速運算現在如此受歡迎的原因之一。關於我們架構的前景，如您所知，我們是一家只使用單一架構的公司，這一點至關重要。原因在於，大量的軟體和工具都是基於這項架構而開發的。在推理和訓練方面，我們擁有一整套軟體、最佳化編譯器和數值庫，它們都針對名為 CUDA 的單一架構進行了全面最佳化。在推理方面，最佳化編譯器需要處理來自各種框架的龐大計算圖，而這些計算圖的規模越來越大，其數值精度也因網路類型和應用類型而異。例如，自動駕駛汽車的數值精度要求（事關生命安全）與偵測過馬路的人數（統計數字）和在各種天氣條件下追蹤極為細微的目標（追蹤）截然不同，前者是需要精確計算的。因此，網路類型也在不斷變化，規模也在不斷擴大，不同應用所需的數值精度也各不相同。此外，電腦效能和能源可用性水準也在不斷變化，這些推理編譯器很可能成為世界上最複雜的軟體之一。因此，我們擁有一個單一的架構進行最佳化，無論是用於數值計算的高效能運算（HPC），例如分子動力學、計算化學、生物學和天文物理學，還是從訓練到推理，這都賦予了我們巨大的優勢。這就是為什麼英偉達（NVIDIA）這家擁有11,000名員工的公司，其業績水準卻可能達到其規模的十倍。原因在於，我們擁有一個單一的架構，它能夠隨著時間的推移不斷累積優勢，而不是像其他公司那樣，使用三四個甚至五個不同的架構，導致軟體組織被分割成許多各自為政、效率低下的模組。這對我們來說是一個巨大的優勢，對整個產業也是如此。因此，支援CUDA的人都知道，下一代架構將會從中受益，並將充分利用技術進步帶來的優勢。坦白說，我認為這是一個正在指數級增長的優勢，我對此感到非常興奮。

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

你的下一個問題來自美國銀行的 Vivek Arya。

Congratulations on the strong results and the consistent execution. Jensen, in the last few months, we have seen a lot of announcements from Intel, from Xilinx and others describing other approaches to the AI market. My question is how does a customer make that decision whether to use a GPU or an FPGA or an ASIC, right? What is -- what can remain your competitive differentiator over the longer term? And does your position in the training market also then maybe give you a leg up when they consider solution for the inference part of the problem?

恭喜你們取得如此優異的成績和始終如一的執行力。 Jensen，在過去的幾個月裡，我們看到英特爾、賽靈思和其他公司發布了許多公告，介紹了人工智慧市場的其他解決方案。我的問題是，客戶如何決定要使用GPU、FPGA還是ASIC？你們的競爭優勢是什麼？長遠來看，什麼才能維持你們的競爭優勢？你們在訓練市場的地位是否也能在他們考慮推理部分的解決方案時，為你們帶來優勢？

Yes, thank you, Vivek. So first of all, we have one architecture and people know that our commitment to our GPUs, our commitment to CUDA, our commitment to all of the software stacks that run on top of our GPUs, every single one of the 500 applications, every numerical solver, every CUDA compiler, every tool chain across every single operating system in every single computing platform, we are completely dedicated to it. We support the software for as long as we shall live, and as a result of that, the benefits to their investment in CUDA just continues to accrue. I -- you have no idea how many people send me notes about how they literally take out their old GPU, put in a new GPU and without lifting a finger, things got 2x, 3x, 4x faster than what they were doing before, incredible value to customers. The fact that we are singularly focused and completely dedicated to this one architecture and in an unwavering way allows everybody to trust us and know that we will support it for as long as we shall live. And that is the benefit of an architectural strategy. When you have 4 or 5 different architectures to support, that you offer to your customers and you ask them to pick the one that they like the best, you're essentially saying that you're not sure which one is the best. And we all know that nobody's going to be able to support 5 architectures forever. And as a result, something has to give, and it would be really unfortunate for a customer to have chosen the wrong one. And if there's 5 architectures, surely, over time, 80% of them will be wrong. And so I think that our advantage is that we're singularly focused. With respect to FPGAs, I think FPGAs have their place, and we use FPGAs here in NVIDIA to prototype things and -- but FPGA is a chip design. It's able to be a chip for -- it's incredibly good at being a flexible substrate to be any chip, and so that's its advantage. Our advantage is that we have a programming environment, and writing software is a lot easier than designing chips. And if it's within the domain that we focus on, like, for example, we're not focused on network packet processing, but we are very focused on deep learning. We're very focused on high performance and parallel numerics analysis. If we're focused on those domains, our platform is really quite unbeatable. And so that's how you think through that. I hope that was helpful.

是的，謝謝你，Vivek。首先，我們只有一個架構，大家都知道我們對GPU、CUDA以及所有運行在我們GPU上的軟體堆疊的承諾，包括所有500個應用程式、每個數值解算器、每個CUDA編譯器、每個工具鏈，涵蓋所有作業系統和所有運算平台，我們都全力以赴。我們將終身支援這些軟體，因此，用戶在CUDA上的投資收益將持續成長。你根本想像不到有多少人給我發訊息，說他們只是簡單地更換了GPU，無需任何操作，速度就提升了2倍、3倍甚至4倍，這對客戶來說價值非凡。我們始終專注於這項架構，堅定不移，正是這一點讓每個人都信任我們，並知道我們將終身支持它。這就是架構策略的優勢。當你需要支援四、五種不同的架構，並讓客戶從中挑選他們最喜歡的架構時，實際上你是在說你也不確定哪一種才是最好的。我們都知道，沒有人能夠永遠支持五種架構。因此，必然要有所取捨，而客戶選錯架構將會非常遺憾。如果有五種架構，隨著時間的推移，其中肯定有 80% 的架構會被淘汰。所以我認為我們的優勢在於我們專注於單一領域。關於 FPGA，我認為 FPGA 有其用武之地，我們在 NVIDIA 也使用 FPGA 來進行原型設計——但 FPGA 本質上是一種晶片設計。它可以作為晶片——它非常擅長作為靈活的基板來製造任何晶片，這就是它的優勢所在。我們的優勢在於我們擁有一個程式設計環境，編寫軟體比設計晶片要容易得多。如果這在我們關注的領域內，例如，我們不專注於網路資料包處理，而是專注於深度學習。我們非常專注於高效能並行數值分析。如果我們專注於這些領域，我們的平台就真的非常強大。這就是你的思路。希望這能有所幫助。

Your next question comes from Atif Malik with Citi.

下一個問題來自花旗銀行的阿提夫‧馬利克。

Colette, on the last call you mentioned crypto was $150 million in the OEM line in the July quarter. Can you quantify how much crypto was in the October quarter and expectations in the January quarter directionally? And just longer term, why should we think that crypto won't impact the gaming demand in the future? If you can just talk about the steps NVIDIA has taken with respect to having a different mode and all that.

科萊特，上次電話會議你提到，7 月季度加密貨幣在 OEM 產品線的投資額為 1.5 億美元。你能否量化一下 10 月份季度的加密貨幣投資額，以及對 1 月份季度的預期？從長遠來看，我們為什麼認為加密貨幣不會在未來影響遊戲需求？能否談談英偉達在推出不同模式等方面所採取的措施？

So in our results, in the OEM results, our specific crypto [boards] equated to about $70 million of revenue, which is the comparable to the $150 million that we saw last quarter.

因此，在我們的業績中，在 OEM 業績中，我們特定的加密 [闆卡] 帶來了約 7000 萬美元的收入，這與我們上個季度看到的 1.5 億美元相當。

Yes, longer term, Atif -- well, first of all, thank you for that. The -- longer term, the way to think about that is, is crypto is small for us but not 0. And I believe that crypto will be around for some time, kind of like today. There will be new currencies emerging. Existing currencies will grow in value. The interest in mining these new emerging currency crypto algorithms that emerge are going to continue to happen. And so I think for some time, we're going to see that crypto will be a small but not 0, small but not 0 part of our business. The -- when you think about crypto in the context of our company overall, the thing to remember is that we're the largest GPU computing company in the world. And our overall GPU business is really sizable and we have multiple segments. And there's data center and I've already talked about the 5 different segments within data center. There's ProVis and even that has multiple segments within it. Whether it's rendering or computer-aided design or broadcast in a workstation, in a laptop or in a data center, the architectures are rather different. And of course, you know that we have high-performance computing. You know that we have autonomous machine business, self-driving cars and robotics. And you know, of course, that we have gaming. And so these different segments are all quite large and growing. And so my sense is that as -- although crypto will be here to stay, it will remain small but not 0.

是的，從長遠來看，Atif——首先，非常感謝你的演講。從長遠來看，加密貨幣對我們來說雖然佔比不大，但並非完全沒有意義。我相信加密貨幣會像現在這樣存在一段時間。新的加密貨幣會不斷湧現，現有加密貨幣的價值也會持續成長。人們對這些新興加密貨幣演算法的挖礦興趣將會持續下去。因此，我認為在一段時間內，加密貨幣在我們公司業務中將佔據一小部分，但並非完全沒有意義。 ——當你從公司整體的角度來考慮加密貨幣時，需要記住的是，我們是全球最大的GPU運算公司。我們的GPU業務規模龐大，涵蓋多個業務部門。例如資料中心，我已經談到了資料中心業務下的五個不同細分領域。還有ProVis，即使是ProVis也包含多個細分領域。無論是工作站、筆記型電腦或資料中心中的渲染、電腦輔助設計或廣播，其架構都截然不同。當然，我們還有高效能運算、自主機器業務、自動駕駛汽車和機器人。當然，我們還有遊戲。這些不同的細分領域都相當龐大且不斷成長。因此，我的感覺是——儘管加密貨幣會繼續存在，但它的規模仍然很小，但不會為零。

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

你的下一個問題來自摩根士丹利的喬摩爾。

Just following up on that last question. You mentioned that some of the crypto market had moved to traditional gaming. What drives that? Is there a lack of availability of the specialized crypto product? Or is it just that there's a preference being driven for the gaming-oriented crypto solutions?

我接著上一個問題問一下。您提到部分加密貨幣市場已經轉向傳統遊戲領域。是什麼原因導致這種情況發生？是因為缺乏專門的加密貨幣產品嗎？還是僅僅因為人們更傾向於遊戲相關的加密貨幣解決方案？

Yes, Joe, I appreciate you asking that. Here's the reason why. So what happens is, is when a crypto -- when a currency -- digital currency market becomes very large, it entices somebody to build a custom ASIC for it. And of course, Bitcoin is the perfect example of that. Bitcoin is incredibly easy to design as a specialized chip form. But then what happens is a couple of different players starts to monopolize the marketplace and as a result, it chases everybody out of the mining market and it encourages a new currency to evolve -- to emerge. And the new currency, the only way to get people to mine it is if it's hard to mine, it's hard to mine, okay, you got to put some effort into it. However, you want a lot of people to try to mine it. And so therefore, the platform that is perfect for it, the ideal platform for digital -- new emerging digital currencies turns out to be a CUDA GPU. And the reason for that is because there are several hundred million NVIDIA GPUs in the marketplace. If you want to create a new cryptocurrency algorithm, optimizing for our GPUs is really quite ideal. It's hard to do. It's hard to do, therefore, you need a lot of computation to do it. And yet there's enough GPUs in the marketplace, it's such an open platform that the ability for somebody to get in and start mining is very low barriers to entry. And so it's the cycles of these digital currencies, and that's the reason why I say that digital currency crypto usage of GPUs, crypto usage of GPUs will be small but not 0 for some time. And it's small because when it gets big, somebody will go and build a custom ASIC. But if somebody builds a custom ASIC, there will be a new emerging cryptocurrency, so ebbs and flows.

是的，喬，感謝你的提問。原因如下。當一種加密貨幣－或數位貨幣市場規模龐大時，就會吸引一些人為其客製化專用積體電路（ASIC）。比特幣就是一個完美的例子。比特幣的專用晶片設計起來非常容易。但隨後，少數幾家公司開始壟斷市場，結果導致其他人退出挖礦市場，並促使一種新的貨幣出現。而要吸引人們挖這種新貨幣，唯一的方法就是提高挖礦難度。挖礦難度越高，就越需要投入精力。這樣才能吸引更多的人嘗試挖礦。因此，最適合這種新興數位貨幣的平台，也就是理想的平台，就是CUDA GPU。原因在於市面上有數億塊NVIDIA GPU。如果你想開發一種新的加密貨幣演算法，針對GPU進行最佳化確實是理想之選。但這很難做到。正因為如此，你需要大量的運算資源。然而，市場上GPU數量充足，而且這是一個非常開放的平台，任何人都可以輕鬆進入挖礦領域。這就是數位貨幣的周期性波動，這也是我為什麼說，在一段時間內，數位貨幣對GPU的需求量會很小，但不會是零。之所以需求量小，是因為一旦需求量大起來，就會有人去開發客製化的ASIC礦機。但如果有人開發了客製化的ASIC礦機，就會出現新的加密貨幣，所以市場總是有起有落。

Your next question comes from the line of Craig Ellis with B. Riley.

你的下一個問題來自 Craig Ellis 和 B. Riley 的系列。

Jensen, congratulations on data center annualizing at $2 billion. It's a huge milestone. I wanted to follow up with a question on some of your comments regarding data center partners because as I look back over the last 5 years, I just don't see any precedent for the momentum that you have in the marketplace right now between your server partners, white box partners, hyperscale partners that are deploying it, hosted, et cetera. And so my question is relative to the doubling that we've seen year-on-year in each of the last 2 years, what does that partner expansion mean for data center's growth? And then if I could sneak one more in. 2 new products just announced in the gaming platform, 1070 Ti and a Collector's Edition on TITAN Xp. What do those mean for the gaming platform?

Jensen，恭喜資料中心年收入達到20億美元，這是一個巨大的里程碑。我想就您之前關於資料中心合作夥伴的一些評論再問一個問題，因為回顧過去五年，我從未見過像現在這樣在伺服器合作夥伴、白盒合作夥伴、部署和託管等各個領域的超大規模合作夥伴之間取得如此巨大的發展勢頭。所以我的問題是，鑑於過去兩年我們每年都看到合作夥伴數量翻番，這種合作夥伴的擴張對資料中心的成長意味著什麼？另外，如果可以的話，我還想問一個問題。遊戲平台剛剛發布了兩款新產品：1070 Ti 和 TITAN Xp 的典藏版。這些產品對遊戲平台意味著什麼？

Yes, Craig, thanks a lot. Let's see. We have never created a product that is as broadly supported by the industries and has grown 9 consecutive quarters, it has doubled year-over-year and with partnerships of the scale that we're looking at. We have just never created a product like that before, and I think the reason for that is several folds. The first is that it is true that CPU scaling has come to an end. That's just laws of physics. The end of Moore's Law is just laws of physics. And yet the world for software development and the world -- the problems that computing can help solve is growing faster than any time before. Nobody's ever seen a large-scale planning problem like Amazon before. Nobody's ever seen a large-scale planning problem like Didi before. The number of millions of taxi rides per week is just staggering. And so nobody's ever seen large problems like these before, large-scale problems like these before. And so high-performance computing and accelerated computing using GPUs has become recognized as the path forward. And so I think that that's at the highest level of the most important parameter. Second is artificial intelligence and its emergence and applications to solving problems that we historically thought were unsolvable. Solving the unsolvable problems is a real realization. I mean, this is happening across just about every industry we know, whether it's Internet service providers to health care to manufacturing to transportation and logistics, you just name it, financial services. And so I think artificial intelligence is a real tool, deep learning is a real tool that can help solve some of the world's unsolvable problems. And I think that our dedication to high-performance computing and this one singular architecture, our 7-year head start, if you will, in deep learning and our early recognition of the importance of this new computing approach, both the timing of it, the fact that it was naturally a perfect fit for the skills that we have and then the incredibly -- the incredible effectiveness of this approach, I think, has really created the perfect conditions for our architecture. And so I think -- I really appreciate you noticing that, but this is definitely the most successful product line in the history of our company.

是的，克雷格，非常感謝。我們來看看。我們從未打造過一款產品，能夠獲得如此廣泛的行業支持，並連續九個季度實現成長，年增長率翻番，而且我們還在洽談如此大規模的合作。我們以前從未打造過這樣的產品，我認為原因有以下幾點。首先，CPU 的效能擴展確實已經達到極限。這是由物理定律決定的。摩爾定律的終結也是物理定律決定的。然而，軟體開發領域乃至整個世界——運算能夠幫助解決的問題——正以前所未有的速度成長。以前從未有人遇到過像亞馬遜這樣的大規模規劃問題。以前也從未有人遇到過像滴滴這樣的大規模規劃問題。每週數百萬次的計程車出遊量令人震驚。因此，以前從未有人遇到過如此龐大的問題，如此大規模的問題。因此，高效能運算和使用 GPU 的加速運算已被公認為未來的發展方向。所以我認為這是最重要的參數中的最高層級。其次是人工智慧及其興起，以及它在解決我們過去認為無法解決的問題方面的應用。解決這些看似無法解決的問題，如今已成現實。我的意思是，這種情況幾乎發生在我們所知的每個行業，無論是網路服務提供者、醫療保健、製造業、運輸物流，還是金融服務等等。因此，我認為人工智慧和深度學習都是真正能夠幫助解決一些世界難題的工具。我認為，我們對高效能運算的投入，以及我們獨特的架構，我們在深度學習領域長達七年的領先優勢，以及我們對這種新型計算方法重要性的早期認識，再加上它恰逢其時，與我們擁有的技能完美契合，以及這種方法令人難以置信的有效性，都為我們的架構創造了完美的條件。所以我覺得——我真的很感謝你注意到這一點，但這絕對是我們公司歷史上最成功的產品線。

Your next question comes from the line of Chris Caso with Raymond James.

你的下一個問題來自克里斯卡索 (Chris Caso) 與雷蒙德詹姆斯 (Raymond James) 的合作系列。

I have a question on the automotive market and the outlook there. And interestingly, with the other segments growing as quickly as they are, auto is becoming a smaller percentage of revenue now. And certainly, the design traction seems very positive. Can you talk about the ramp in terms of when the auto revenue, when we could see that as getting back to a similar percentage of revenue? Is that growing more quickly? Do you think that is likely to happen over the next year with some of these design wins coming out? Or is that something we should -- we'll be waiting for over several years?

我有一個關於汽車市場及其前景的問題。有趣的是，隨著其他細分市場快速成長，汽車業務的收入佔比正在下降。當然，設計方面的進展似乎非常積極。您能否談談汽車業務收入何時才能恢復到先前的比例？它的成長速度會更快嗎？您認為隨著一些設計項目的推出，這種情況會在未來一年內發生嗎？還是我們需要等幾年？

I appreciate that, Chris. So the way to think about that is as you know, we've really, really reduced our emphasis on infotainment even though that's the primary part of our revenues, so that we could take, literally, hundreds of engineers and including the processors that we're building now, a couple of 2,000, 3,000 engineers, working on our autonomous machine and artificial intelligence platform for this marketplace to take advantage of the position we have and to go after this amazing revolution that's about to happen. I happen to believe that everything that moves will be autonomous someday, and it could be a bus, a truck, a shuttle, a car. Everything that moves will be autonomous someday. It could be a delivery vehicle. It could be little robots that are moving around warehouses. It could be delivering a pizza to you. And we felt that those -- this was such an incredibly, incredibly great challenge and such a great computing problem that we decided to dedicate ourselves to it. Over the next several years, and if you look at our DRIVE PX platform today, there's over 200 companies that are working on it. 125 start-ups are working on it. And these companies are mapping companies. They're Tier 1s. They're OEMs. They're shuttle companies, car companies, trucking companies, taxi companies. And this last quarter, we announced an extension of our DRIVE PX platform to include DRIVE PX Pegasus, which is now the world's first auto-grade, full ASIL D platform for robotaxis. And so I think our position is really excellent, and the investment has proven to be one of the best ever. And so I think in terms of revenues, my expectation is that this coming year, we'll enjoy revenues as a result of the supercomputers that customers will have to buy for training their networks, for simulating the -- all these autonomous vehicles driving and developing their self-driving cars. And we'll see fairly large quantities of development systems being sold this coming year. The year after that, I think, is the year when you're going to see the robotaxis ramping, and our economics in every robotaxi is several thousand dollars. And then starting, I would say, late 2020 to 2021, you're going to start to see the first fully automatic autonomous cars, what people call Level 4 cars, starting to hit the road. And so that's kind of how I see it. Just next year is simulation environments, development systems, supercomputers, and then the year after that is robotaxis and then a year or 2 after that will be all the self-driving cars.

克里斯，我明白你的意思。如你所知，我們已經大幅降低了對車載資訊娛樂系統的投入，儘管這是我們收入的主要來源。這樣一來，我們就能投入數百名工程師，包括我們正在研發的處理器工程師在內，大約兩三千名工程師，致力於開發面向市場的自主機器和人工智慧平台，從而充分利用我們現有的市場地位，迎接即將到來的這場變革。我堅信，未來所有移動的交通工具都將自動化，無論是公車、卡車、接駁車或汽車。所有移動的交通工具都將自動化。它可以是送貨車輛，也可以是倉庫裡穿梭的小型機器人，甚至可以是送披薩給你的機器人。我們認為，這是一個極其巨大的挑戰，也是一個極其重要的計算難題，因此我們決定全心投入其中。未來幾年，如果您看看我們目前的 DRIVE PX 平台，您會發現有超過 200 家公司正在使用它，其中包括 125 家新創公司。這些公司涵蓋了地圖公司、一級供應商、原始設備製造商 (OEM)、班車公司、汽車公司、貨運公司和計程車公司。上個季度，我們宣布擴展 DRIVE PX 平台，新增 DRIVE PX Pegasus，這是目前全球首個自動駕駛計程車的全汽車級、ASIL D 級平台。因此，我認為我們目前的地位非常優越，這項投資也已被證明是迄今為止最明智的投資之一。就收入而言，我預計明年我們將受益於客戶購買超級電腦來訓練其網路、模擬所有這些自動駕駛車輛的行駛以及開發自動駕駛汽車所帶來的收入。我們預計明年將有大量的開發系統售出。我認為，再過一年，無人駕駛計程車就會開始大規模普及，每輛無人駕駛計程車的成本大約在幾千美元。然後，從2020年底到2021年初，你會看到第一批全自動自動駕駛汽車，也就是人們所說的L4級自動駕駛汽車，開始上路。這就是我的預測。明年主要會是模擬環境、開發系統和超級電腦的研發階段，再過一年左右就會是無人駕駛計程車普及，再過一兩年，所有自動駕駛汽車都會投入使用。

Your next question comes from the line of Matt Ramsay with Canaccord Genuity.

你的下一個問題來自 Canaccord Genuity 的 Matt Ramsay。

I have, I guess, a 2-part question on gross margin. Colette, I remember, I don't know, maybe 3 years ago, 3.5 years ago at an analyst day, you guys were talking about gross margins in the mid-50s and that was inclusive of the Intel payment. And now you're hitting numbers at 60% excluding that. I want -- if you could talk a little bit about how mix of the data center business and some others drives gross margin going forward. And maybe, Jensen, you could talk a little bit about -- you mentioned Volta being such a huge chip in terms of transistor count. How you're thinking about taking costs out of that product as you ramp it into gaming next year and the effects on gross margin.

我有一個關於毛利率的問題，可以分成兩個部分。 Colette，我記得，大概三年前，或者三年半前，在一次分析師日活動上，你們討論的毛利率是50%左右，而且這其中包含了英特爾的付款。現在，剔除這部分付款後，你們的毛利率已經達到了60%。我想請你們談談資料中心業務和其他業務的組合如何影響未來的毛利率。還有，Jensen，你剛才提到Volta晶片的電晶體數量非常龐大。你們打算如何降低這款晶片的成本，以便明年將其推廣到遊戲領域，以及這些措施對毛利率的影響。

Okay. Thanks, Matt, for the question. Yes, we've been on a steady stream of increasing the gross margins over the years. But this is the evolution of the entire model, the model of the value-added platforms that we sell and inclusive of the entire ecosystem of work that we do, the software that we enable in so many of these platforms that we bring to market. Data center is one of them. Our ProVis, another one and if you think about all of our work that we have in terms of gaming and that overall expansion of the ecosystem. So this has been continuing to increase our gross margin. Mix is more of a statement in terms of each quarter we have a different mix in terms of our products. So some of them have a little bit of seasonality. And depending on when some of those platforms come to market, we can have a mix change within some of those subsets. It's still going to be our focus as we go forward in terms of growing gross margins as best as we can, you can see in terms of our guidance into Q4, which we feel comfortable with that guidance that we will increase it as well.

好的。謝謝Matt的提問。是的，這些年來我們的毛利率一直在穩定成長。但這其實是整個商業模式演進的結果，是我們銷售的加值平台模式，以及我們所進行的整個生態系統，包括我們為眾多推向市場的平台提供的軟體。資料中心就是其中之一。我們的ProVis是另一個例子。想想我們在遊戲領域的所有工作，以及整個生態系統的擴展。因此，這持續提升了我們的毛利率。產品組合更多的是一種表述，因為我們每季的產品組合都不同。有些產品會受到季節性因素的影響。而且，根據某些平台上市的時間，我們某些子產品的組合也會有所改變。未來，我們仍將專注於盡可能提高毛利率，這一點從我們對第四季度的業績預期中可以看出，我們對這一預期充滿信心，並預計會繼續提高毛利率。

Yes, with respect to yield enhancement, the way to think about that is we do it in several ways. The first thing is I'm just incredibly proud of the technology group that we have in VLSI, and they get us ready for these brand new nodes, whether it's in the process readiness with all the circuit readiness, the packaging, the memory readiness. The readiness is so incredible -- incredibly important for us because these processors that we're creating are really, really hard. They're the largest things in the world. And so we get one shot at it. And so the team does everything they can to essentially prepare us. And by the time that we tape-out a product for real, we know for certain that we can build it. And so the technology team in our company is just world-class, absolutely world-class. There's nothing like it. Then once we go into production, we have the benefit of ramping up the products. And as yields improve, we'll surely benefit from the cost. But that's not really where the focus is. I mean, in the final analysis, the real focus for us is continue to improve the software stack on top of our processors. And the reason for that is each one of our processors carry with it an enormous amount of memory and systems and networking and the whole data center. Most of our data center products, if we can improve the throughput of a data center by another 50% or, in our case, often times we'll improve something from 2x to 4x, the way to think about that is that billion-dollar data center just improved its productivity by a factor of 2. And all of the software work that we do on top of CUDA and the incredible work that we do with optimizing compilers and graph analytics, all of that stuff then all of a sudden translates to value to our customers, not measured by dollars, but measured by hundreds of millions of dollars. And that's really the leverage of accelerated computing.

是的，關於良率提升，我們採取了多種方式。首先，我為我們VLSI（超大規模積體電路）部門的技術團隊感到無比自豪，他們讓我們為這些全新的製程節點做好了充分準備，無論是製程準備、電路準備、封裝或記憶體準備。這種準備工作非常出色——對我們來說至關重要，因為我們製造的這些處理器難度極高，是世界上體積最大的產品。我們只有一次機會。因此，團隊竭盡所能地做好準備。等到產品真正流片時，我們就能確保能成功生產。我們公司的技術團隊是世界一流的，絕對是世界一流的，獨一無二。一旦投入生產，我們就能快速提升產量。隨著良率的提高，成本自然也會降低。但這並非我們關注的重點。我的意思是，歸根結底，我們真正的重點是持續改進處理器之上的軟體堆疊。原因在於，我們每個處理器都承載著大量的記憶體、系統、網路以及整個資料中心。我們的大多數資料中心產品，如果能將資料中心的吞吐量提升 50%，或者像我們這樣，通常能提升 2 到 4 倍，那就意味著一個價值數十億美元的資料中心的生產力翻了一番。我們在 CUDA 之上所做的所有軟體工作，以及我們在優化編譯器和圖分析方面所做的卓越工作，所有這些最終都會轉化為客戶的價值，這種價值並非以美元來衡量，而是以數億美元來衡量。這才是加速計算的真正槓桿作用。

Your next question comes from the line of Hans Mosesmann with Rosenblatt.

你的下一個問題來自漢斯·摩西曼和羅森布拉特的系列。

Jensen, can you comment on some of the issues this week regarding Intel and their renewed interest in getting into the graphics space and their relationship at the chip level with AMD?

Jensen，您能否就本周有關英特爾及其重新對圖形領域產生的興趣，以及他們與 AMD 在晶片層面的關係的一些問題發表一些評論？

Yes, thanks, Hans. Yes, listen, there's a lot of news out there. I guess some of the things I take away, first of all, Raja leaving AMD is a great loss for AMD. And it's a recognition by Intel probably that the GPU is just incredibly, incredibly important now. And the modern GPU is not a graphics accelerator. The modern GPU, we just left the word G in there -- the letter G in there. But these processors are domain-specific parallel accelerators, and they're enormously complex. They're the most complex processors built by anybody on the planet today. And that's the reason why IBM uses our processors for the world's largest supercomputers. That's the reason why every single cloud, every single -- every major cloud, every major server maker in the world has adopted NVIDIA GPUs: It's just incredibly hard to do. The amount of software engineering that goes on top of it is significant as well. And so if you look at the way we do things, we plan a road map about 5 years out. It takes about 3 years to build a new generation, and we build multiple GPUs at the same time. And on top of that, there are some 5,000 engineers working on systems software and numerics libraries and solvers and compilers and graph analytics and cloud platforms and virtualization stacks in order to make this computing architecture useful to all of the people that we serve. And so when you think about it from that perspective, it's just an enormous undertaking; arguably, the most significant undertaking of any processor in the world today. And that's the reason why we're able to speed up applications by a factor of 100. You don't walk in and have a new widget and a few transistors and all of a sudden speed up applications by a factor of 100 or 50 or 20. That's just something that's inconceivable unless you do the type of innovation that we do. And then lastly, with respect to the chip that they built together, I think it goes without saying now that the energy efficiency of Pascal GeForce and the Max-Q design technology and all of the software that we created has really set a new design point for the industry. It is now possible to build a state-of-the-art gaming notebook with the most leading-edge GeForce processors and be able to deliver gaming experiences that are many times greater than a console in 4K and have that be in a laptop that's 18 millimeters thin. The combination of Pascal and Max-Q has really raised the bar, and I think that that's really the essence of it.

是的，謝謝，漢斯。是的，聽著，現在新聞很多。我想我總結的幾點是，首先，Raja離開AMD對AMD來說是一個巨大的損失。這或許也顯示英特爾意識到GPU現在極為重要。現代GPU並非圖形加速器。我們只是保留了「G」這個字——保留了字母G。但這些處理器是特定領域的平行加速器，它們極為複雜。它們是當今地球上任何人製造的最複雜的處理器。這就是為什麼IBM使用我們的處理器來建立世界上最大的超級電腦。這也是為什麼世界上每一個雲端平台，每個主要的雲端平台，每個主要的伺服器製造商都採用了NVIDIA GPU：這真的非常困難。其上的軟體工程量也相當大。所以，如果你看看我們的做事方式，我們會制定一個大約五年的路線圖。開發新一代處理器大約需要三年時間，而且我們同時生產多個GPU。除此之外，還有大約5,000名工程師致力於系統軟體、數值庫、求解器、編譯器、圖分析、雲端平台和虛擬化堆疊的開發，以確保這項運算架構能夠服務我們所有的使用者。因此，從這個角度來看，這的確是一項浩大的工程；可以說，它是當今世界上任何處理器研發項目中最為重要的工程。正因如此，我們才能將應用程式的運行速度提升100倍。你不可能隨便增加一個新零件和幾個電晶體，就立刻將應用程式的運行速度提升100倍、50倍甚至20倍。除非你進行像我們這樣的創新，否則這是不可想像的。最後，關於他們共同打造的晶片，我認為現在無需贅言，Pascal GeForce 的能源效率、Max-Q 設計技術以及我們開發的所有軟體，確實為整個產業樹立了新的設計標竿。現在，我們可以打造一款搭載最前沿 GeForce 處理器的頂級遊戲筆記型電腦，在 4K 解析度下提供遠超遊戲主機的出色遊戲體驗，而這一切都可以在僅 18 毫米厚的筆記型電腦中實現。 Pascal 和 Max-Q 的結合真正提高了行業標準，我認為這才是其精髓所在。

Unfortunately, we have run out of time. Presenters, I'll now turn the call over to you for closing remarks.

很遺憾，時間到了。各位發言人，現在我把發言權交給你們，請作總結發言。

We had another great quarter. Gaming is one of the fastest-growing entertainment industries, and we are well positioned for the holidays. AI is becoming increasingly widespread in many industries throughout the world, and we're hoping to lead the way with all major cloud providers and computer makers moving to deploy Volta. And we're building the future of autonomous driving. We expect robotaxis, using our technology, to hit the road in just a couple of years. We look forward to seeing many of you at SC17 next week, and thank you for joining us.

我們又度過了一個精彩的季度。遊戲是成長最快的娛樂產業之一，我們為即將到來的假期季節做好了充分準備。人工智慧正在全球眾多產業中日益普及，我們希望引領潮流，推動所有主流雲端服務供應商和電腦製造商部署Volta平台。同時，我們也在建構自動駕駛的未來。我們預計，採用我們技術的無人駕駛計程車將在短短幾年內投入使用。我們期待下週在SC17大會上與各位相聚，感謝你們的參與。

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

今天的電話會議到此結束。您可以掛斷電話了。