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.

下午好。我的名字是維多利亞，我是你今天的會議接線員。歡迎參加 NVIDIA 的財務業績電話會議。 （操作員說明）我現在將電話轉給投資者關係副總裁 Simona Jankowski，開始您的會議。

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.

謝謝你。大家下午好，歡迎參加 NVIDIA 2018 財年第三季度電話會議。今天與我一起參加電話會議的是 NVIDIA 總裁兼首席執行官 Jensen Huang；和 Colette Kress，執行副總裁兼首席財務官。

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

我想提醒您，我們的電話會議正在 NVIDIA 的投資者關係網站上進行網絡直播。它也在被記錄。您可以在 2017 年 11 月 16 日之前通過電話收聽重播。該網絡廣播將在下個季度的電話會議之前進行重播，以討論第四季度和 2018 財年全年財務業績。今天通話的內容是 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 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.

在本次電話會議期間，我們可能會根據當前預期做出前瞻性陳述。這些受到許多重大風險和不確定性的影響，我們的實際結果可能存在重大差異。有關可能影響我們未來財務業績和業務的因素的討論，請參閱今天的收益發布中的披露、我們最近的 10-K 和 10-Q 表格以及我們可能在 8-K 表格中與證券交易委員會。我們所有的聲明都是基於我們目前可獲得的信息，截至今天，即 2017 年 11 月 9 日。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 GAAP 財務指標。您可以在我們的 CFO 評論中找到這些非 GAAP 財務指標與 GAAP 財務指標的對賬，該評論發佈在我們的網站上。

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

有了這個，讓我把電話轉給科萊特。

Thanks, Simona. 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。我們有一個出色的季度，在我們的 4 個市場平台中的每一個都有創紀錄的收入。每一項利潤指標都達到創紀錄水平，反映了我們模型的槓桿作用。數據中心收入為 5.01 億美元，比一年前翻了一番還多，我們的 Volta 平台的大力採用和我們的推理產品組合的早期牽引力。

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.

第三季度收入達到 26.4 億美元，同比增長 32%，環比增長 18%，遠高於我們預期的 23.5 億美元。從報告部分的角度來看，GPU 收入比去年增長了 31%，達到 22.2 億美元。 Tegra 處理器收入增長 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.

讓我們從我們的遊戲業務開始。遊戲收入為 15.6 億美元，同比增長 25%，環比增長 32%。我們看到所有地區和形式因素的需求強勁。我們基於 Pascal 的 GPU 仍然是遊戲玩家的首選平台，我們對 GeForce GTX 10 系列產品的強勁需求就是明證。我們推出了上週上市的 GeForce GTX 1070 Ti。它補充了我們強大的假期產品陣容，從入門級 GTX 1050 到我們的旗艦 GTX 1080 Ti。

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].

一波好遊戲即將迎來假期，激發了市場的熱情。本月早些時候，我們與動視合作將《命運 2》帶到了 PC 上。 PlayerUnknown's Battlegrounds，俗稱PUBG，仍然是今年最成功的遊戲之一。我們與 PUBG 緊密合作，以確保 GeForce 是玩遊戲的最佳方式，包括為 2000 萬玩家帶來皮影戲亮點。上週末，《使命召喚：二戰》強勢登場，《星球大戰前線 II》即將上映。

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.

電子競技仍然是遊戲市場中最重要的長期增長動力之一，其粉絲群現已超過 3.5 億。上週末，英雄聯盟世界總決賽在北京國家體育場鳥巢舉行，2008年奧運會正是在這裡舉行。超過 40,000 名粉絲參加了現場直播，在線觀眾將打破去年 18 種語言的 4300 萬粉絲的記錄。

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 銷售也受益於持續的加密貨幣挖掘。我們通過 OEM 業務中的專用板和 GeForce GTX 板滿足了其中的一些需求，儘管很難量化。我們對加密貨幣市場的態度保持靈活。它是不穩定的，不會也不會分散我們對核心遊戲市場的關注。最後，Nintendo Switch 主機自 3 月推出以來繼續保持增長勢頭，也為增長做出了貢獻。

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.

搬到數據中心。我們的數據中心業務有一個出色的季度。由於新 Volta 架構的強大牽引力，收入為 5.01 億美元，比去年增加了一倍多，本季度增長 20%。 Tesla V100 GPU 的出貨量從第二季度開始，並在第三季度大幅增長，主要受雲服務提供商和高性能計算需求的推動。正如我們之前提到的，Volta 提供的深度學習性能是一年前推出的 Pascal 架構的 10 倍，遠遠超過摩爾定律。

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 已被各大服務器 OEM 和雲提供商廣泛採用。在中國，阿里巴巴、百度和騰訊宣布他們正在將 V100 整合到他們的數據中心和雲服務器服務基礎設施中。在美國，Amazon Web Services 宣布 V100 推理現已在其 4 個地區推出。甲骨文云剛剛在其基礎設施產品中添加了 Tesla P100 GPU，併計劃擴展到 V100 GPU。我們也期待其他主要雲提供商的 V100 支持。

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，所有主要服務器 OEM 都宣布支持 V100，Dell EMC、Hewlett Packard Enterprise、IBM 和 Supermicro 正在將其整合到服務器中。而中國頂級的服務器OEM廠商，華為、浪潮和聯想都採用了我們的HGX服務器架構，構建了具有V100 GPU的新一代加速數據中心。

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.

我們為 AI 推理市場提供的新產品也獲得了動力。最近推出的 TensorRT 3 可編程推理加速平台為我們打開了一個新的市場機會，提高了 AI 推理的性能並降低了成本——與 CPU 相比降低了幾個數量級。它支持每個主要的深度學習框架、每個網絡架構和任何級別的網絡複雜性。超過 1,200 家公司已經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.

在本季度，我們宣布 NVIDIA GPU Cloud 容器註冊表 (NGC) 現在可通過 Amazon 的雲獲得，並將很快得到其他雲平台的支持。 NGC 通過免費訪問全面、易用、全面優化的深度學習軟件堆棧，幫助開發人員開始深度學習開發。它支持即時訪問最廣泛使用的 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.

我們還繼續看到 HPC 業務的強勁增長。下一代超級計算機，如美國能源部的 Sierra 和 Summit 系統預計將於明年上線，利用 Volta 行業領先的性能，我們的管道很強大。過去幾週對我們來說異常忙碌。我們在北京、慕尼黑、台北、特拉維夫和華盛頓舉辦了 5 場主要的 GPU 技術會議，下個月將在東京舉行另一場。強烈表明 GPU 加速計算的重要性日益增加，今年將有超過 22,000 名開發人員、數據科學家和其他人參加我們的 GTC，包括在矽谷舉行的主要活動。這在短短 5 年內增長了 10 倍。其他關鍵指標也顯示出類似的收益。同期，NVIDIA GPU 開發者的數量增長了 15 倍，達到 64.5 萬，而今年 CUDA 下載量增長了 5 倍，達到 180 萬。

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.

轉向專業可視化。第三季度收入增長至 2.39 億美元，同比增長 15%，環比增長 2%，主要受高端實時渲染、模擬和更強大的移動工作站需求的推動。隨著對 Quadro P5000 和 P6000 GPU 驅動的專業 VR 解決方案的需求，國防和汽車行業強勁增長。在主要客戶中，奧迪和寶馬正在汽車展廳部署 VR。美國陸軍、海軍和國土安全部正在使用 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，我們宣布搶先體驗智能 VR 協作平台 NVIDIA Holodeck。 Holodeck 使設計師、開發人員和他們的客戶能夠在高度逼真、協作和物理模擬的環境中從世界任何地方虛擬地聚集在一起。未來的更新將解決對在虛擬環境中開發深度學習技術不斷增長的需求。

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.

在汽車領域，收入增長至 1.44 億美元，同比增長 13%，比上一季度略有增長。在本季度的主要發展中，我們宣布了 DRIVE PX Pegasus，這是世界上第一台支持 5 級無人駕駛汽車的人工智能計算機。 Pegasus 每秒將提供超過 320 萬億次操作，是其前身的 10 倍以上。它由車牌大小的超級計算機中的 4 個高性能 AI 處理器提供動力。超過 25 家公司正在使用 NVIDIA DRIVE 來開發全自動機器人出租車，而 DRIVE PX Pegasus 將成為量產之路。它專為行業最高安全等級ASIL D認證而設計，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.

我們還推出了 DRIVE IX SDK，用於在車內提供智能體驗。 DRIVE IX 為汽車公司提供了一個平台，可以創建並始終參與 AI 副駕駛。它使用深度學習網絡來跟踪頭部運動和注視，並使用先進的語音識別、唇讀和自然語言理解與駕駛員進行對話。我們相信這將為下一代信息娛樂系統設定標準，而這個市場剛剛開始發展。

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採埃孚將於明年使用 NVIDIA DRIVE PX 平台部署一支自動送貨卡車測試車隊。 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.

現在轉向損益表的其餘部分。第三季度 GAAP 毛利率為 59.5%，非 GAAP 毛利率為 59.7%，環比和同比均增長，反映了增值平台的持續增長。 GAAP 運營費用為 6.74 億美元，非 GAAP 運營費用為 5.7 億美元，與我們的預期一致，同比增長 19%。投資我們的關鍵市場機會對我們的未來至關重要，包括遊戲、人工智能和自動駕駛汽車。

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.

GAAP 營業收入達到創紀錄的 8.95 億美元，比一年前增長 40%。非美國通用會計準則營業收入為 10.1 億美元，同比增長 42%。 GAAP 淨收入達到創紀錄的 8.38 億美元，每股收益為 1.33 美元，同比分別增長 55% 和 60%。非美國通用會計準則淨收入為 8.33 億美元，每股收益為 1.33 美元，分別同比增長 46% 和 41%，反映了收入實力以及毛利率和營業利潤率的增長。

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.

本財年到目前為止，我們通過季度股息和股票回購相結合的方式向股東返還了 11.6 億美元。我們已宣布將我們的季度股息從 0.01 美元增加到年化 0.60 美元，這與我們的 18 財年第四季度股息一起生效。我們還很高興地宣布，我們打算在 2019 財年通過季度股息和股票回購向股東返還 12.5 億美元。我們的季度運營現金流達到創紀錄水平，首次超過 10 億美元，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.

現在轉向 2018 財年第四季度的展望。我們預計收入為 26.5 億美元，正負 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 運營費用預計分別約為 7.22 億美元和 6 億美元。 GAAP 和非 GAAP OI&E 預計都是名義上的。 GAAP 和非 GAAP 稅率預計均為 17.5%，正負 1%，不包括離散項目。更多財務細節包含在 CFO 評論和我們網站上提供的其他信息中。

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

我們現在將打開問題的電話。 （操作員說明）操作員，我們會——請大家一起提問？謝謝你。

(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。

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，3 個月前，您將 7 月季度描述為您的數據中心業務的過渡季度。很明顯，你們在 10 月份的表現非常好。但是，如果你能談談數據中心未來幾個季度的前景，特別是在推理方面。我知道你們對這個機會感到非常興奮。因此，如果您可以分享客戶反饋以及您對明年推理的期望，那就太好了。

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非常強勁地增加 Volta，並且我們在前一個季度開始了斜坡。從那時起，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，各大雲提供商都宣布支持 Volta，我們將提供 Volta 供他們內部使用深度學習以及外部使用公共雲服務。我們還宣布，世界上所有主要的服務器計算機製造商現在都支持 Volta，並且正在將 Volta 推向市場。惠普、戴爾、IBM、思科和華為在中國，浪潮在中國，聯想都宣布他們將構建服務器——圍繞 Volta GPU 的服務器系列。所以我認為我們——這個斜坡只是支持我們公司為世界各地的數據中心以及世界各地的雲服務提供商構建 GPU 加速服務器的第一部分。這些 GPU 服務器的應用程序現已發展到許多市場。我已經談到了我們 Tesla GPU 的主要部分。我經常談論其中的 5 個。第一個是高性能計算，市場規模為 110 億美元左右。它是 IT 行業中增長較快的部分之一，因為越來越多的人使用高性能計算來進行產品開發或尋找洞察力或預測市場或其他任何事情。今天，我們代表了世界 500 強超級計算機中的 15%。我反复說過，我完全相信這一點，而且我認為未來每台超級計算機都會以某種方式加速，這一點變得越來越真實。所以這對我們來說是一個相當重要的增長機會。第二個是深度學習訓練，它非常非常像高性能計算。你需要進行大規模的計算。您正在執行數万億次迭代。模型越來越大。每一年，我們用它訓練的數據量都在增加。快速計算平台與不快速計算平台之間的差異可能意味著建造一個價值 2000 萬美元的數據中心或用於訓練的高性能計算服務器之間的差異是 2 億美元。所以我們節省的錢和我們提供的能力真的是，價值是難以置信的。第三部分，也就是你剛才提到的部分，與推理有關，當你完成了這個網絡的開發後，你必須把它放到超大規模數據中心來支持數十億的數據。消費者每天在互聯網上進行的查詢。這對我們來說是一個全新的市場。今天，世界上 100% 的推理都是在 CPU 上完成的。我們最近宣布，實際上是上個季度，TensorRT 3 推理加速平台與我們的 Tensor Core GPU 指令集架構相結合，我們能夠將網絡加速 100 倍。現在考慮一下想像一下，無論您有多少工作量，如果您可以將我們平台的使用速度提高 100 倍，您可以節省多少。另一種思考方式是因為網絡的數量越來越大，而且現在非常複雜。而且我們知道地球上的每個網絡都將在我們的架構上運行，因為他們今天在我們的架構上接受過培訓。因此，無論是 CNN、RNN、GAN、自動編碼器還是它們的所有變體，無論您需要支持的精度、網絡的規模如何，我們都有能力支持它們。因此，您可以擴展超大規模數據中心以支持更多流量，也可以大幅降低成本或同時降低成本。我們數據中心的第四部分是提供所有這些功能，我剛才提到的，無論是 HPC、訓練還是推理，並將其從內到外，並在公共雲中提供。現在有成千上萬的初創企業——都是因為人工智能而開始的。每個人都認識到這種新計算模型的重要性。由於有了這個新工具，這個新功能，過去所有這些無法解決的問題現在都可以有趣地解決了。所以你可以看到初創企業在西部、東部到處湧現，而且只是——有數千家。而這些公司也不會——寧願不使用他們稀缺的財務資源去建立高性能計算中心，或者他們沒有能力像這些互聯網公司那樣構建一個高性能平台能夠。因此，這些雲提供商、雲平台對他們來說只是一個極好的資源，因為他們可以按小時租用它。我們與它一起創建，我提到所有云服務提供商都將它推向市場，同時，我們在雲中創建了一個註冊表，將這些非常複雜的軟件堆棧容器化。這些軟框架中的每一個都具有我們不同版本的 GPU、不同的加速層和不同的優化技術，我們已經為市場上的每個版本和每種類型的框架都容器化了所有這些。我們把它放在註冊表中——稱為 NVIDIA GPU Cloud 的雲註冊表。因此，您只需將其下載到我們經過認證和測試的雲服務提供商中，只需單擊一下，您就可以進行深度學習。然後是最後一個——這就是雲服務提供商。如果你 - 猜測的方式 - 估計顯然有數百億美元投資於這些人工智能初創企業。他們籌集的大部分投資資金最終將不得不用於高性能計算，無論是他們自己構建還是在雲中租用。所以我認為這對我們來說是一個數十億美元的機會。最後，這可能是所有機會中最大的一個，那就是垂直行業。無論是正在開發超級計算機為自動駕駛汽車做準備的汽車公司，還是正在利用人工智能進行更好的診斷的醫療保健公司——疾病診斷，製造公司——在線檢查，機器人，大型物流公司。 Colette 前面提到過 DHL。但思考的方式是所有這些計劃——所有這些公司都在計劃通過這個龐大的交付系統網絡向你交付產品，這是世界上最大的計劃問題。而且無論是 Uber、滴滴、Lyft、亞馬遜、DHL、UPS 還是 FedEx，它們都存在高性能計算問題，現在正在轉向深度學習。所以這些對我們來說真的是令人興奮的機會。所以最後一個只是垂直行業。我的意思是，所有這些細分市場，我們現在都可以開始解決了，因為我們已經將我們的 GPU 放在了雲中，我們所有的 OEM 都在將這些平台推向市場，我們現在有能力使用一個通用平台解決高性能計算和深度學習訓練以及推理問題。所以我認為——我們一直堅信數據中心加速計算的興奮，我認為這只是一切的開始。

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

您的下一個問題來自 Bernstein Research 的 Stacy Rasgon。

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?

我對您進入第四季度的遊戲季節性有疑問。通常會漲一點。我想知道，我猜你是否看到任何驅動因素會導致缺乏正常的季節性趨勢，因為它的連續性和同比性有多強？我想作為一個相關問題，您是否認為第四季度的 Volta 銷量超過了第三季度？

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.

讓我們來看看。有——我會先回答最後一個，然後再朝著第一個方向努力。我認為我們提供的指導讓我們感到滿意。但如果你想想 Volta，它才剛剛起步，它將進入我談到的市場機會。所以我希望我們繼續增長，並且有充分的證據表明我們服務的市場，我們正在與 Volta 合作的市場是非常大的市場。因此，有很多理由對 Volta 未來的增長機會充滿希望。我們已經啟動了泵。因此，雲服務提供商要么宣布推出 Volta，要么宣布即將推出 Volta。他們都在競相將 Volta 加入他們的雲，因為客戶要求它。原始設備製造商是——我們已經為原始設備製造商準備了泵，其中一些現在正在取樣，其中一些正在競相讓 Volta 在市場上投入生產。所以我認為基礎，需求就在那裡。由於摩爾定律不再可擴展，因此迫切需要加速計算，然後我們已經啟動了泵。所以需求就在那裡。有需求——需求就在那裡，將 Volta 推向市場的基礎已經準備就緒。關於遊戲，是什麼推動了我們的遊戲業務？請記住，我們的遊戲業務一次只賣給數以百萬計的人。推動我們遊戲業務的有幾件事。如您所知，電子競技令人難以置信，令人難以置信的充滿活力，並且是什麼驅動 - 電子競技如此獨特的原因是因為人們想要獲勝並且擁有更好的裝備會有所幫助。他們期望的延遲非常低，性能降低了延遲，他們希望能夠盡可能快地做出反應。人們想贏，他們想確保他們使用的裝備不是他們沒有贏的原因。我們的第二個增長動力是內容，即內容的質量。男孩，如果你看《使命召喚》或《命運 2》或《絕地求生》，內容看起來簡直太棒了。 AAA 內容看起來很棒。視頻遊戲真正獨特的一件事是，為了享受內容和內容的保真度，充分發揮生產價值的質量，你需要最好的裝備。它與流媒體視頻非常不同，與觀看流媒體視頻的電影非常不同，它就是這樣。但對於電子遊戲來說，當然不是。因此，當 AAA 遊戲在今年晚些時候問世時，它有助於推動平台的採用。最後，越來越多的社交正在成為遊戲增長動力的重要組成部分。人們——他們認識到這些電子遊戲是多麼美麗，因此他們想與人們分享他們最精彩的時刻。他們想分享他們發現的關卡。他們想為裡面的驚人圖形拍照。它是 YouTube 和觀看其他人玩視頻遊戲的人（這些廣播公司）的主要驅動力之一，實際上是主要驅動力。現在有了我們的 Ansel，世界上第一款遊戲內虛擬現實、環繞聲和數碼相機，我們可以拍照並與人們分享。所以我認為所有這些不同的驅動因素都在幫助我們的遊戲業務，我對第四季度持樂觀態度。看起來這將是一個很棒的季度。

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

您的下一個問題來自 Evercore 的 C.J. Muse。

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.

我希望潛入一個近期和一個長期的問題。在短期內，您談到了 Volta 的健康需求方面。好奇您是否在供應方面看到任何形式的限制，無論是晶圓還是訪問高帶寬內存等等。然後更長期的問題真的圍繞著 CUDA，你已經談到這是你們進入這一年的可持續競爭優勢。現在我們已經超越了 HPC 和超大規模訓練，更多地進入了推理和 GPU 即服務，並且您已經在世界各地託管了 GTC，想知道您是否可以推斷出您是如何看到這種優勢以及您是如何看到的它在一年中不斷發展，以及您如何將 CUDA 視為 AI 標準。

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. 好吧，我們構建的一切都很複雜。 Volta 是人類製造的最大的處理器，210 億個晶體管，3D 封裝，地球上最快的存儲器，所有這些都在幾百瓦，這基本上說它是世界上最節能的計算形式曾經知道。一個 Volta 可以替換數百個 CPU。所以它是節能的。它節省了大量資金。它可以非常非常快地完成這項工作，這也是 GPU 加速計算現在如此流行的原因之一。關於我們架構的前景，如您所知，我們是一家單一架構的公司，這非常重要。其原因是因為在這個架構之上創建瞭如此多的軟件和工具。在推理方面——在訓練方面，我們擁有一整套軟件和優化編譯器和數字庫，這些庫完全針對一種稱為 CUDA 的架構進行了優化。在推理方面，優化編譯器採用來自所有這些框架的這些龐大的計算圖，並且這些計算圖變得越來越大，並且它們的數值精度因一種類型的網絡而異——從一種類型的應用程序到另一個。您對自動駕駛汽車的數值精度要求，在危及生命的情況下，檢測在哪裡——計算過馬路的人數，計算某物與試圖追踪——在各種天氣中檢測和追踪非常微妙的東西條件是一個非常非常不同的問題。所以數字 - 網絡的類型一直在變化。它們一直在變大。對於不同的應用，數值精度是不同的。我們有不同的計算——計算機性能水平和能量可用性水平，這些推理編譯器可能是世界上最複雜的軟件之一。因此，我們有一個單一的架構可供優化，無論是用於數值的 HPC——分子動力學、計算化學、生物學和天體物理學，一直到訓練到推理，這一事實給了我們巨大的影響力。這就是為什麼 NVIDIA 可以成為一家擁有 11,000 名員工的公司，並且可以說，其業績水平是該公司的 10 倍。這樣做的原因是因為我們有一個單一的架構——隨著時間的推移會產生收益，而不是 3、4、5 種不同的架構，在這些架構中，您的軟件組織被分解為所有這些不同的小型次臨界質量塊。所以這對我們來說是一個巨大的優勢，對這個行業來說也是一個巨大的優勢。因此，支持 CUDA 的人都知道，下一代架構只會從中受益，並順應技術進步為他們提供和負擔的機會。好的。因此，坦率地說，我認為這是一個呈指數級增長的優勢，我對此感到興奮。

Your next question comes from the line of 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 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?

祝賀您取得了驕人的成績和始終如一的執行力。 Jensen，在過去的幾個月裡，我們看到了很多來自英特爾、賽靈思和其他公司的公告，這些公告描述了人工智能市場的其他方法。我的問題是客戶如何決定是使用 GPU、FPGA 還是 ASIC，對嗎？什麼是 - 從長遠來看，什麼可以保持您的競爭優勢？當他們考慮解決問題的推理部分時，您在培訓市場中的地位是否也會讓您有所幫助？

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，維維克。所以首先，我們有一個架構，人們知道我們對 GPU 的承諾，我們對 CUDA 的承諾，我們對在我們的 GPU 上運行的所有軟件堆棧的承諾，500 個應用程序中的每一個，每個數字求解器，每個 CUDA 編譯器，每個計算平台中每個操作系統的每個工具鏈，我們完全致力於它。只要我們有生之年，我們就會一直支持該軟件，因此，他們對 CUDA 的投資所帶來的收益將繼續增加。我——你不知道有多少人給我發了關於他們如何從字面上取出他們的舊 GPU，放入一個新 GPU 並且不費吹灰之力，事情比他們以前做的事情快 2 倍、3 倍、4 倍的筆記，難以置信對客戶的價值。事實上，我們非常專注並完全致力於這一架構，並且以堅定不移的方式讓每個人都信任我們，並且知道只要我們活著，我們就會支持它。這就是架構策略的好處。當您有 4 或 5 種不同的架構需要支持時，您提供給客戶並要求他們選擇他們最喜歡的一種，您實際上是在說您不確定哪一種是最好的。我們都知道沒有人能夠永遠支持 5 種架構。結果，必須付出一些代價，如果客戶選擇了錯誤的，那將是非常不幸的。如果有 5 種架構，那麼隨著時間的推移，肯定有 80% 的架構是錯誤的。所以我認為我們的優勢在於我們非常專注。關於 FPGA，我認為 FPGA 有自己的位置，我們在 NVIDIA 使用 FPGA 來製作原型，但 FPGA 是一種芯片設計。它能夠成為芯片——它非常擅長成為任何芯片的柔性基板，這就是它的優勢。我們的優勢是我們有一個編程環境，編寫軟件比設計芯片要容易得多。如果它在我們關注的領域內，例如，我們不關注網絡數據包處理，但我們非常關注深度學習。我們非常專注於高性能和並行數值分析。如果我們專注於這些領域，我們的平台確實是無與倫比的。所以你就是這麼想的。我希望這會有所幫助。

Your next question comes from Atif Malik with Citi.

您的下一個問題來自 Atif Malik 和 Citi。

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.

Colette，在最後一次電話會議上，你提到加密貨幣在 7 月季度的 OEM 線中為 1.5 億美元。你能量化 10 月季度的加密貨幣數量和 1 月季度的預期嗎？從長遠來看，我們為什麼認為加密不會影響未來的遊戲需求？如果你能談談 NVIDIA 在不同模式方面採取的步驟等等。

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，在我們的結果中，在 OEM 結果中，我們特定的加密 [boards] 相當於大約 7000 萬美元的收入，這與我們上一季度看到的 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 - 嗯，首先，謝謝你。從長遠來看，考慮這一點的方式是，加密對我們來說很小，但不是 0。而且我相信加密會存在一段時間，就像今天一樣。將會有新的貨幣出現。現有貨幣將升值。對挖掘這些新興貨幣加密算法的興趣將繼續發生。所以我認為有一段時間，我們將看到加密貨幣將是我們業務的一小部分，但不是 0，很小但不是 0。 - 當你在我們公司的整體背景下考慮加密時，要記住的是，我們是世界上最大的 GPU 計算公司。我們的整體 GPU 業務非常龐大，我們有多個細分市場。還有數據中心，我已經談到了數據中心內的 5 個不同部分。有 ProVis，甚至其中有多個部分。無論是渲染或計算機輔助設計，還是工作站、筆記本電腦或數據中心中的廣播，架構都大相徑庭。當然，您知道我們擁有高性能計算。你知道我們有自動機器業務、自動駕駛汽車和機器人技術。你當然知道，我們有遊戲。因此，這些不同的細分市場都非常大並且還在不斷增長。所以我的感覺是——儘管加密貨幣將繼續存在，但它會保持小規模，但不會為 0。

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?

只是跟進最後一個問題。你提到一些加密市場已經轉向傳統遊戲。是什麼驅動了它？是否缺乏專用加密產品的可用性？還是僅僅是對面向遊戲的加密解決方案有偏好？

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，我很感謝你這麼問。這就是原因。所以發生的事情是，當加密貨幣——當一種貨幣——數字貨幣市場變得非常大時，它會誘使某人為其構建定制的 ASIC。當然，比特幣就是一個完美的例子。比特幣作為一種特殊的芯片形式非常容易設計。但隨後發生的事情是幾個不同的參與者開始壟斷市場，結果，它將所有人趕出採礦市場，並鼓勵一種新的貨幣發展——出現。而新貨幣，讓人們開採它的唯一方法是，如果它很難開採，那就很難開採，好吧，你必須付出一些努力。但是，您希望很多人嘗試挖掘它。因此，最適合它的平台，理想的數字平台——新興的數字貨幣原來是 CUDA GPU。原因是市場上有數億個 NVIDIA GPU。如果您想創建一種新的加密貨幣算法，那麼針對我們的 GPU 進行優化是非常理想的。這很難做到。這很難做到，因此，您需要大量的計算才能做到這一點。然而，市場上有足夠多的 GPU，它是一個如此開放的平台，以至於有人進入並開始挖礦的能力是非常低的進入門檻。所以這是這些數字貨幣的周期，這就是為什麼我說GPU的數字貨幣加密使用，GPU的加密使用會很小但在一段時間內不會為0。它很小，因為當它變大時，有人會去構建一個定制的 ASIC。但是，如果有人構建了一個定制的 ASIC，就會出現一種新興的加密貨幣，所以會起起落落。

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

您的下一個問題來自 Craig Ellis 和 B. Riley 的對話。

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，祝賀數據中心的年收入達到 20 億美元。這是一個巨大的里程碑。我想就您對數據中心合作夥伴的一些評論提出問題，因為當我回顧過去 5 年時，我只是沒有看到您現在在服務器之間在市場上擁有的勢頭的任何先例合作夥伴、白盒合作夥伴、正在部署、託管等的超大規模合作夥伴。所以我的問題是關於我們在過去 2 年中每年看到的同比翻番，合作夥伴的擴張對數據中心的增長意味著什麼？然後，如果我可以再偷偷摸摸一下。剛剛在遊戲平台上宣布的 2 款新產品，1070 Ti 和 TITAN Xp 上的珍藏版。這些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。讓我們來看看。我們從未創造出一種產品得到行業的廣泛支持，並且連續 9 個季度增長，同比增長一倍，並且與我們正在尋找的規模合作夥伴關係。我們以前從未創造過這樣的產品，我認為原因有很多。首先，CPU 擴展確實已經結束。那隻是物理定律。摩爾定律的終結只是物理定律。然而，軟件開發的世界和世界——計算可以幫助解決的問題比以往任何時候都增長得更快。以前沒有人見過像亞馬遜這樣的大規模規劃問題。以前沒有人見過像滴滴這樣的大規模規劃問題。每週數以百萬計的出租車數量簡直是驚人的。所以以前沒有人見過像這樣的大問題，像這樣的大規模問題。因此，使用 GPU 進行高性能計算和加速計算已成為公認的前進道路。所以我認為這是最重要參數的最高級別。其次是人工智能及其在解決我們歷史上認為無法解決的問題中的出現和應用。解決無法解決的問題是一個真正的實現。我的意思是，這種情況發生在我們所知道的幾乎每個行業，無論是互聯網服務提供商、醫療保健、製造、運輸和物流，你只要說出它的名字，金融服務。所以我認為人工智能是一個真正的工具，深度學習是一個真正的工具，可以幫助解決世界上一些無法解決的問題。我認為，我們對高性能計算和這一單一架構的奉獻，我們在深度學習方面的 7 年領先優勢，以及我們對這種新計算方法重要性的早期認識，無論是它的時機，事實上，它自然而然地完美契合了我們所擁有的技能，而且我認為，這種方法令人難以置信的效果確實為我們的建築創造了完美的條件。所以我想——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.

您的下一個問題來自 Chris Caso 和 Raymond James。

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?

我對汽車市場及其前景有疑問。有趣的是，隨著其他細分市場的增長速度如此之快，汽車現在在收入中所佔的比例越來越小。當然，設計吸引力似乎非常積極。您能否談談汽車收入何時上升，何時我們可以看到它恢復到類似的收入百分比？是不是增長得更快了？你認為這有可能在明年發生，其中一些設計勝利會出來嗎？或者這是我們應該做的——我們要等幾年？

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.

我很感激，克里斯。因此，考慮這一點的方式是，正如你所知，我們確實，真的減少了對信息娛樂的重視，即使這是我們收入的主要部分，所以我們可以從字面上招募數百名工程師，包括我們的處理器'現在正在建設中，有 2,000 到 3,000 名工程師，為這個市場開發我們的自主機器和人工智能平台，以利用我們擁有的地位，並追求即將發生的這場驚人的革命。我碰巧相信，有朝一日，所有移動的東西都將是自主的，它可能是公共汽車、卡車、班車、汽車。總有一天，所有移動的東西都將是自主的。它可能是一種運輸工具。可能是在倉庫周圍移動的小機器人。它可能是給你送披薩。我們覺得那些——這是一個令人難以置信的、難以置信的巨大挑戰，也是一個巨大的計算問題，我們決定全身心投入其中。在接下來的幾年裡，如果你看看我們今天的 DRIVE PX 平台，就會發現有 200 多家公司正在開發它。 125 家初創公司正在研究它。這些公司是地圖公司。他們是 1 級。他們是原始設備製造商。他們是班車公司、汽車公司、貨運公司、出租車公司。在上個季度，我們宣布擴展我們的 DRIVE PX 平台，以包括 DRIVE PX Pegasus，它現在是世界上第一個用於機器人出租車的汽車級全 ASIL D 平台。所以我認為我們的位置非常好，而且這項投資已被證明是有史以來最好的投資之一。因此，我認為就收入而言，我的預期是，來年，我們將享受到超級計算機帶來的收入，客戶必須購買這些超級計算機來訓練他們的網絡，模擬所有這些自動駕駛汽車的駕駛和開發他們的自動駕駛汽車。我們將在明年看到相當大量的開發系統被出售。我認為，在那之後的一年，你會看到自動駕駛出租車的興起，而我們每輛自動駕駛出租車的經濟效益都是幾千美元。然後開始，我想說，從 2020 年末到 2021 年，你將開始看到第一輛全自動自動駕駛汽車，人們稱之為 4 級汽車，開始上路。這就是我的看法。明年是模擬環境、開發系統、超級計算機，然後是機器人出租車，再過一兩年將是所有的自動駕駛汽車。

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

您的下一個問題來自於 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.

我想，我有一個關於毛利率的兩部分問題。科萊特，我記得，我不知道，也許 3 年前，3.5 年前的一個分析師日，你們談論的是 50 年代中期的毛利率，其中包括英特爾的付款。現在你的數字達到了 60%，不包括這個。我想 - 如果你能談談數據中心業務和其他一些業務的組合如何推動毛利率向前發展。也許，Jensen，你可以談談 - 你提到 Volta 在晶體管數量方面是如此巨大的芯片。當您明年將其投入遊戲時，您如何考慮從該產品中扣除成本以及對毛利率的影響。

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，馬特，這個問題。是的，多年來我們一直在穩步提高毛利率。但這是整個模型的演變，我們銷售的增值平台模型，包括我們所做的整個工作生態系統，我們在我們推向市場的許多平台中啟用的軟件。數據中心就是其中之一。我們的 ProVis，另一個，如果您考慮一下我們在遊戲和生態系統的整體擴展方面所做的所有工作。因此，這一直在繼續增加我們的毛利率。就每個季度而言，混合更像是一種聲明，我們在產品方面有不同的組合。所以他們中的一些人有一點季節性。根據其中一些平台何時上市，我們可以在其中一些子集中進行混合更改。這仍然是我們的重點，因為我們盡可能地提高毛利率，你可以從我們對第四季度的指導中看到，我們對這個指導感到滿意，我們也會增加它。

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，就產量提高而言，考慮的方式是我們以多種方式進行。首先，我為我們在 VLSI 中擁有的技術團隊感到無比自豪，他們讓我們為這些全新的節點做好了準備，無論是在所有電路就緒、封裝、內存就緒的過程中。準備情況是如此令人難以置信——對我們來說非常重要，因為我們正在創建的這些處理器真的非常非常難。它們是世界上最大的東西。所以我們一槍搞定。因此，團隊竭盡全力為我們做好準備。當我們真正流片一個產品時，我們肯定知道我們可以建造它。所以我們公司的技術團隊是世界一流的，絕對是世界一流的。沒有什麼比得上的。然後，一旦我們投入生產，我們就可以提高產品的產量。隨著產量的提高，我們肯定會從成本中受益。但這並不是真正的重點。我的意思是，歸根結底，我們真正的重點是繼續改進處理器之上的軟件堆棧。原因是我們的每一個處理器都攜帶著大量的內存、系統、網絡和整個數據中心。我們的大多數數據中心產品，如果我們可以將數據中心的吞吐量再提高 50%，或者在我們的案例中，通常我們會將某些東西從 2 倍提高到 4 倍，那麼考慮的方式就是十億美元數據中心剛剛將其生產力提高了 2 倍。我們在 CUDA 之上所做的所有軟件工作以及我們在優化編譯器和圖形分析方面所做的令人難以置信的工作，所有這些東西突然轉化為對我們客戶的價值，不是以美元來衡量的，而是以億萬美元來衡量的。這就是加速計算的真正優勢。

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

您的下一個問題來自 Hans Mosesmann 和 Rosenblatt 的觀點。

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，您能否評論一下本周有關英特爾的一些問題，以及他們對進入圖形領域的重新興趣以及他們與 AMD 在芯片級的關係？

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，那裡有很多新聞。我想我帶走的一些東西，首先，Raja 離開 AMD 對 AMD 來說是一個巨大的損失。英特爾可能承認 GPU 現在非常非常重要。而現代 GPU 並不是圖形加速器。現代 GPU，我們只是在其中留下了 G 一詞——字母 G。但是這些處理器是特定領域的並行加速器，而且它們非常複雜。它們是當今地球上任何人製造的最複雜的處理器。這就是 IBM 將我們的處理器用於世界上最大的超級計算機的原因。這就是為什麼世界上每一個雲、每一個——每一個主要的雲、每一個主要的服務器製造商都採用 NVIDIA GPU 的原因：這很難做到。在它之上進行的軟件工程的數量也很重要。因此，如果你看看我們做事的方式，我們會計劃一個大約 5 年後的路線圖。構建新一代大約需要 3 年時間，我們同時構建多個 GPU。最重要的是，大約有 5,000 名工程師致力於系統軟件和數字庫、求解器和編譯器、圖形分析以及雲平台和虛擬化堆棧，以使這種計算架構對我們服務的所有人有用。因此，當您從這個角度考慮時，這只是一項艱鉅的任務；可以說，這是當今世界上任何處理器中最重要的任務。這就是為什麼我們能夠將應用程序加速 100 倍的原因。您不會走進來並擁有一個新的小部件和幾個晶體管，然後突然將應用程序加速 100 或 50 倍或20. 除非您進行我們所做的創新類型，否則這是不可想像的。最後，關於他們共同構建的芯片，我認為現在不用說 Pascal GeForce 的能效和 Max-Q 設計技術以及我們創建的所有軟件確實設置了一個新的設計點為行業。現在可以使用最先進的 GeForce 處理器構建最先進的遊戲筆記本電腦，並且能夠提供比 4K 遊戲機高出數倍的遊戲體驗，並且在 18毫米薄。 Pascal 和 Max-Q 的結合確實提高了標準，我認為這就是它的精髓所在。

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

不幸的是，我們已經沒有時間了。演示者，我現在將把電話轉給你，讓你結束髮言。

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.

我們有另一個很棒的季度。遊戲是增長最快的娛樂行業之一，我們為假期做好了準備。人工智能在全球許多行業中變得越來越普遍，我們希望引領所有主要雲提供商和計算機製造商部署 Volta。我們正在構建自動駕駛的未來。我們預計，使用我們的技術的機器人出租車將在短短幾年內上路。我們期待下週在 SC17 見到你們中的許多人，並感謝你們加入我們。

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

今天的電話會議到此結束。您現在可以斷開連接。