輝達 (NVDA) 2017 Q3 法說會逐字稿

Good afternoon. My name is Victoria, and I will be your conference operator today. Welcome to NVIDIA financial results conference call.

午安.我叫維多利亞，今天將擔任本次電話會議的接線生。歡迎參加英偉達財務業績電話會議。

(Operator Instructions)

（操作說明）

I will now turn the call over to Arnab Chanda, Vice President of Investor Relations. You may begin your conference.

現在我將把電話轉交給投資者關係副總裁阿納布·錢達先生。您可以開始您的會議了。

Thank you. Good afternoon, everyone, and welcome to NVIDIA's conference call for the third quarter of FY2017. With me on the call today from NVIDIA our Jen-Hsun 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2017財年第三季業績電話會議。今天與我一起參加會議的還有英偉達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 the 17th of November 2016. The webcast will be available for replay up until next quarter's conference call to discuss Q4 financial results. The content of today's call is NVIDIA's property. It cannot 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2016年11月17日。網路直播回放將持續提供至下一季財報電話會議召開前。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 forward-looking statements 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 of our statements are made as of today, the 10th of November 2016, 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，我們可能會基於目前預期做出前瞻性陳述。這些前瞻性陳述受許多重大風險和不確定因素的影響，而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，請參閱今日發布的盈利報告、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。所有陳述均截至2016年11月10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 your reconciliation of these non-GAAP financial measures to GAAP financial measures in our CFO commentary which is posted on our website. With that, let me turn the call over to Colette.

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。接下來，我將把電話會議交給科萊特。

Thanks, Arnab. Revenue reached a record in the third quarter exceeding $2 billion for the first time. Driving this was success in our Pascal-based gaming platform and growth in our data center platform reflecting the role of NVIDIA's GPU as the engine of AI computing. Q3 revenue increased 54% from a year earlier to $2 billion and was up 40% from the previous quarter. Strong year-on-year gains were achieved across all four of our platforms. Gaming, professional visualization, data center, and automotive. The GPU business was up 53% to $1.7 billion, and the Tegra processor business increased 87% to $241 million.

謝謝 Arnab。第三季營收創下歷史新高，首度突破 20 億美元大關。這主要得益於基於 Pascal 架構的遊戲平台取得成功，以及資料中心平台的成長，後者體現了 NVIDIA GPU 作為 AI 運算引擎的重要角色。第三季營收年增 54% 至 20 億美元，季增 40%。我們四大平台——遊戲、專業視覺化、資料中心和汽車——均實現了強勁的年成長。 GPU 業務成長 53% 至 17 億美元，Tegra 處理器業務成長 87% 至 2.41 億美元。

Let's start with our gaming platform. Gaming revenue crossed the $1 billion mark and increased 63% year on year to a record $1.24 billion fueled by our Pascal-based GPUs. Demand was strong in every geographic region across desktop and notebook and across the full gaming audience from GTX 1050 to the Titan X. GeForce gaming PC notebooks recorded significant gains. Our continued growth in the GTX gaming GPUs reflects the unprecedented performance and efficiency gains in the Pascal architecture. It delivers seamless play on games and richly immersive VR experiences.

我們先來看看遊戲平台。遊戲業務營收突破10億美元大關，年增63%，創下12.4億美元的新紀錄，主要得益於我們基於Pascal架構的GPU。從GTX 1050到Titan X，無論在桌上型電腦或筆記型電腦領域，所有地區的市場需求都十分強勁，涵蓋了所有遊戲使用者群體。 GeForce遊戲筆記型電腦的銷售也取得了顯著成長。 GTX遊戲GPU的持續成長反映了Pascal架構前所未有的效能和效率提升。它能夠帶來流暢的遊戲體驗和沈浸式的VR體驗。

In Q3 for desktops, we launched the GTX 1050 and the 1050 Ti bringing eSports and VR capabilities at great value. For notebooks, we introduced GTX 1080, 1070, and 1060 giving gamers a major leap forward in performance and efficiency in a mobile experience. The fundamentals of the gaming market remain strong. The production value of blockbuster games continues to increase.

第三季度，我們針對桌上型電腦市場推出了 GTX 1050 和 1050 Ti，以極具競爭力的價格為電競和 VR 用戶帶來卓越效能。在筆記型電腦市場，我們推出了 GTX 1080、1070 和 1060，為遊戲玩家帶來行動遊戲體驗中效能和效率的顯著提升。遊戲市場的基本面依然強勁，大作的製作水準也持續提升。

Gamers are upgrading to higher-end GPUs to enjoy highly anticipated fall titles like Battlefield 1, Gears of War 3, Call of Duty: Infinite Warfare, and eSports is attracting a new generation of gamers to the PC. League of Legends is played by over 100 million gamers each month. And, there is now a Twitch audience of more than 300 million who follow eSports. VR and AR will redefine entertainment and gaming. A great experience requires a high performance GPU, and we believe we are still in the early innings of these evolving markets. Pascal represents not only the biggest innovation gains we've made in a single GPU generation in a decade, it's also our best executed product rollout.

為了暢玩備受期待的秋季大作，例如《戰地1》、《戰爭機器3》和《決勝時刻：無限戰爭》，玩家們紛紛升級到更高階的GPU。電子競技也吸引新一代玩家加入PC平台。 《英雄聯盟》每月擁有超過1億的活躍玩家。此外，Twitch上關注電競的觀眾也已超過3億人。 VR和AR將重新定義娛樂和遊戲。卓越的遊戲體驗需要高效能的GPU，而我們相信，這些新興市場仍處於發展初期。 Pascal不僅代表了我們十年來在單代GPU中最大的創新成果，也是我們迄今為止執行最成功的產品發表。

Moving to professional visualization, Quadro revenue grew 9% from a year ago to $207 million driven by growth in the high end of the markets for Realtime rendering and mobile workstations. We are seeing strong customer interest in the Pascal-based P6000 among digital entertainment leaders like: Pixar, Disney, and ILM, architectural, engineering, and construction companies like Japan's SHIMIZU, and automotive companies like Hyundai.

在專業視覺化領域，Quadro 的營收年增 9%，達到 2.07 億美元，這主要得益於即時渲染和行動工作站等高端市場的成長。我們看到，基於 Pascal 架構的 P6000 顯示卡受到了眾多客戶的青睞，其中包括皮克斯、迪士尼和工業光魔等數位娛樂巨頭，日本清水建設等建築、工程和施工公司，以及現代汽車等汽車公司。

Next, data center. Revenue nearly tripled from a year ago and was up 59% sequentially to $240 million. Growth was strong across all fronts in AI and supercomputing for hyperscale as well as for GRID virtualization and supercomputing. GPU deep learning is revolutionizing AI and is poised to impact every industry worldwide. Hyperscale companies like Facebook, Microsoft, and Baidu are using it to solve problems for their billions of consumers.

接下來是資料中心。營收較去年同期成長近三倍，較上季成長59%，達到2.4億美元。人工智慧和超大規模超級運算以及網格虛擬化和超級運算等各個領域均實現了強勁成長。 GPU深度學習正在革新人工智慧，並有望影響全球各行各業。 Facebook、微軟和百度等超大規模公司正在利用這項技術為數十億用戶解決實際問題。

Cloud GPU computing has shown explosive growth. Amazon Web Services, Microsoft Azure, and Ali cloud are deploying NVIDIA GPUs for AI data analytics and HPC. AWS most recently announced its new EC2 P2 Instance which scales up to 16 GPUs to accelerate a wide range of AI applications including image and video recognition, unstructured data analytics, and video transcoding. We saw strong growth in AI training. For AI inference, we announced the Tesla P4 and P40 to serve power-efficient and high performance workloads, respectively.

雲端GPU運算呈現爆炸性成長。亞馬遜雲端服務（AWS）、微軟Azure和阿里雲都在部署NVIDIA GPU用於AI資料分析和高效能運算（HPC）。 AWS最近發布了全新的EC2 P2實例，該實例最多可擴展至16個GPU，以加速包括影像和視訊辨識、非結構化資料分析和視訊轉碼在內的各種AI應用。我們看到AI訓練領域成長強勁。針對AI推理，我們分別發布了Tesla P4和P40，以滿足節能和高效能工作負載的需求。

Shipments began in Q3 for the DGX-1 AI super computer. Early users include major universities like Stanford, UC Berkeley, and NYU, leading research groups such as OpenAI, the German Institute of Artificial Intelligence, and the Swiss Artificial Intelligence Lab as well as multinationals like SAP. So far this year, our GPU technology conference program has reached 18,000 developers and ecosystem partners underscoring the broad enthusiasm for AI. Complementing our major spring event in Silicon Valley, we have organized GPCs in seven cities on four continents. They drew sellout audiences in Beijing, Taipei, Tokyo, and Seoul, as well as Amsterdam, Melbourne, and Washington DC, with Mumbai still to come. Along with 400 sessions and labs, we provided training in AI skills to nearly 2,000 individuals through our Deep Learning Institute construction program.

DGX-1 AI 超級電腦已於第三季開始出貨。早期用戶包括史丹佛大學、加州大學柏克萊分校和紐約大學等知名大學，OpenAI、德國人工智慧研究所和瑞士人工智慧實驗室等頂尖研究機構，以及 SAP 等跨國公司。今年迄今為止，我們的 GPU 技術大會已吸引了 18,000 名開發者和生態系統合作夥伴參與，充分體現了人們對人工智慧的熱情。除了在矽谷舉辦的春季大型活動外，我們還在四大洲的七個城市舉辦了 GPU 技術大會 (GPC)。北京、台北、東京、首爾、阿姆斯特丹、墨爾本和華盛頓特區的 GPC 均座無虛席，孟買的 GPC 也即將舉行。除了 400 場研討會和實驗室活動外，我們還透過深度學習學院建設計畫為近 2,000 人提供了人工智慧技能培訓。

We also have begun partnering with key global companies to enable the adoption of AI. To implement AI in manufacturing, we announced a collaborative with Japan's FANUC focused on robots and automated factories, and in the transportation sector, more than 80 OEMs, Tier 1s, and startups are using our GPUs for their work on self-driving cars.

我們也開始與全球主要公司合作，以推動人工智慧的應用。為了在製造業中實施人工智慧，我們宣布與日本發那科公司合作，專注於機器人和自動化工廠；在交通運輸領域，已有超過80家原始設備製造商、一級供應商和新創公司正在使用我們的GPU進行自動駕駛汽車的研發工作。

Our GRID graphics virtualization business continues to achieve extremely strong growth. Adoption is accelerating across a variety of industries particularly manufacturing, automotive, engineering, and education. Among customers added this quarter were John Hopkins University and GE global India.

我們的 GRID 圖形虛擬化業務持續保持強勁成長。該技術在各行各業的應用正在加速，尤其是在製造業、汽車業、工程業和教育業。本季新增客戶包括約翰霍普金斯大學和通用電氣印度公司。

And, finally, in automotive, revenue increased to a record $127 million, up 61% year over year and up 7% sequentially from premium infotainment products. NVIDIA is developing an end-to-end AI computing platform for autonomous driving. This allows car makers to collect and label data, train their own deep neural networks on the video GPUs in the data center, and then process them in the car with DRIVE PX 2.

最後，在汽車領域，營收成長至創紀錄的1.27億美元，年增61%，季增7%，主要得益於高階資訊娛樂產品。 NVIDIA正在開發一個用於自動駕駛的端到端人工智慧運算平台。該平台允許汽車製造商收集和標註數據，在數據中心的視頻GPU上訓練自己的深度神經網絡，然後使用DRIVE PX 2在車內處理。

We have also been developing a cloud-to-car HD mapping system with mapping companies all over the world. Two such partnerships were announced this quarter. We are working with Baidu to create a cloud-to-car development platform with HD maps, Level 3 autonomous vehicles, and automated parking. We are also partnering with TomTom to develop an AI-based, cloud-to-car mapping system that enables real-time, in-car localization to mapping.

我們也與全球各地的地圖公司合作，開發雲端高清地圖系統。本季我們宣布了兩項此類合作。我們正與百度合作，打造一個雲端高清地圖開發平台，用於支援L3級自動駕駛汽車和自動泊車。此外，我們也與TomTom合作，開發一個基於人工智慧的雲端地圖系統，實現車載即時定位與地圖同步。

We've developed an integrated, scalable AI platform with capabilities ranging from automated highway driving to fully autonomous driving operation. We are extending the DRIVE PX2 architecture to scale in performance and power consumption. It will range from DRIVE PX2 auto cruise with a single SSE for self-driving on highways up to multiple DRIVE PX2 computers capable of enabling fully autonomous driving.

我們開發了一個整合式、可擴展的人工智慧平台，其功能涵蓋從高速公路自動駕駛到完全自動駕駛的各個方面。我們正在擴展 DRIVE PX2 架構，以提升其效能並降低功耗。該平台將支援從單一 SSE 的 DRIVE PX2 自動巡航（用於高速公路自動駕駛）到多個 DRIVE PX2 電腦（能夠實現完全自動駕駛）的各種功能。

We also announced a single-chip AI supercomputer called Xavier with over 7 billion transistors. Xavier incorporates our next GPU architecture, a custom CPU design, and a new computer vision accelerator. Xavier will deliver performance equivalent to today's full DRIVE PX2 board, and its two Parker SoCs and two Pascal GPUs while only consuming a fraction of the energy.

我們還發布了一款名為 Xavier 的單晶片人工智慧超級計算機，它擁有超過 70 億個電晶體。 Xavier 整合了我們新一代的 GPU 架構、客製化的 CPU 設計以及全新的電腦視覺加速器。 Xavier 的性能將與目前完整的 DRIVE PX2 開發板及其兩顆 Parker SoC 和兩顆 Pascal GPU 相當，而能耗卻僅為後者的幾分之一。

Finally, Tesla motors announced last month that all its factory-produced vehicles: the Model S, the Model X, and upcoming Model 3 feature a new autopilot system powered by the NVIDIA DRIVE PX2 platform and will be capable of fully autonomous operation via future software updates. This system delivers over 40 times the processing power of the previous technology and runs a new, neural network for vision, sonar, and data processing. Beyond our four platforms, our OEM and IP business was $186 million, down 4% year on year.

最後，特斯拉汽車公司上個月宣布，其所有工廠生產的車型，包括Model S、Model X以及即將推出的Model 3，均配備了基於NVIDIA DRIVE PX2平台的新型自動駕駛系統，並將透過未來的軟體更新實現完全自動駕駛。此系統的處理能力是上一代技術的40倍以上，並運行著用於視覺、聲吶和資料處理的全新神經網路。除了這四大平台之外，我們的OEM和IP業務收入為1.86億美元，比去年同期下降4%。

Now, turning to the rest of the income statement. GAAP gross margin for Q3 was a record 59%, and non-GAAP gross margin was a record 59.2%. These reflect the strength of our GeForce gaming GPUs, the success of our platform approach, and strong demand for Deep Learning. GAAP operating expenses were $544 million including $66 million in stock-based compensation and other charges.

現在來看損益表的其餘部分。第三季GAAP毛利率創下59%的歷史新高，非GAAP毛利率也創下59.2%的歷史新高。這反映了我們GeForce遊戲GPU的強勁表現、平台策略的成功以及市場對深度學習的強勁需求。 GAAP營運費用為5.44億美元，其中包括6,600萬美元的股權激勵支出和其他費用。

Non-GAAP operating expenses were $478 million, up 11% from one year earlier. This reflects headcount-related costs for our growth initiatives as well as investments in sales and marketing. We intend to continue to invest in Deep Learning to capture this once-in-a-lifetime opportunity. Thus, we would expect the operating expense growth rate to be sustained over the next several quarters.

非GAAP營運費用為4.78億美元，較上年同期成長11%。這反映了我們成長計劃中與人員相關的成本，以及在銷售和行銷方面的投資。我們計劃繼續投資深度學習，以把握這一千載難逢的機會。因此，我們預計未來幾季營運費用成長率將保持穩定。

GAAP operating income was $639 million. Non-GAAP operating income more than doubled to $708 million. Non-GAAP operating margins were over 35% this quarter. For FY18, we intend to return $1.25 billion to shareholders through ongoing quarterly cash dividends and share repurchases. We also announced a 22% increase in our quarterly cash dividend to $0.14 per share.

以美國通用會計準則（GAAP）計算，營業收入為6.39億美元。以非美國通用會計準則（Non-GAAP）計算，營業收入翻了一番多，達到7.08億美元。本季非美國通用會計準則營業利益率超過35%。 2018財年，我們計劃透過持續的季度現金分紅和股票回購，向股東返還12.5億美元。此外，我們也宣布將季度現金分紅提高22%，至每股0.14美元。

Now turning to the outlook for the fourth-quarter of FY17, we expect revenue to be $2.1 billion, plus or minus 2%. Our GAAP and non-GAAP gross margin are expected to be 59% and 59.2%, respectively, plus or minus 50 basis points. GAAP operating expenses are expected to be $572 million. Non-GAAP operating expenses are expected to be approximately $500 million. And, GAAP and non-GAAP tax rates for the fourth quarter of FY17 are both expected to be 20%, plus or minus 1%. With that, Operator, I'm going to turn it back to you and see if we can take some questions.

現在來看看2017財年第四季的展望。我們預計營收為21億美元，上下浮動2%。 GAAP和非GAAP毛利率預計分別為59%和59.2%，上下浮動50個基點。 GAAP營運費用預計為5.72億美元，非GAAP營運費用預計約5億美元。此外，2017財年第四季的GAAP和非GAAP稅率預計均為20%，上下浮動1%。好了，接線員，現在把時間交給您，看看我們是否可以回答一些問題。

(Operator Instructions)

（操作說明）

Mark Lipacis, Jefferies.

馬克‧利帕西斯，傑富瑞集團。

Thanks for taking my questions and congratulations on a great quarter. I think to start out, Jen-Hsun, maybe if you could help us understand -- the data center business tripled year over year. What's going on in that business that's enabling that to happen? if you could maybe talk about if it's on the technology side or the end market side? And, maybe as part of that, you can help us deconstruct the revenues and what's really driving that growth? And, I had a follow-up, too. Thanks.

感謝您回答我的問題，也恭喜您本季業績優異。 Jen-Hsun，我想先請您幫我們了解一下—資料中心業務年增了兩倍。是什麼因素促成了這一成長？您能否談談是技術面還是終端市場方面？另外，您能否幫我們分析一下收入組成以及真正推動成長的因素？我還有一個後續問題。謝謝。

A couple things. First of all, GPU computing is more important than ever. There's so many different types of applications that require GPU computing today, and it's permeating all over enterprise. There are several applications that we're really driving. One of them is graphics virtualization, application virtualization. Partnering with VMware and Citrix, we have essentially taken very compute-intensive, very graphics-intensive applications, virtualizing it and putting it into the data center.

有幾點要說明。首先，GPU 運算比以往任何時候都更重要。如今，許多不同類型的應用程式都需要 GPU 運算，而且它正在滲透到企業的各個角落。我們正在大力推進幾個應用領域。其中之一就是圖形虛擬化和應用程式虛擬化。透過與 VMware 和 Citrix 合作，我們成功地將運算密集型和圖形密集型應用程式虛擬化，並將其部署到資料中心。

The second is computational sciences. Using our GPU for general-purpose scientific computing, and scientific computing, as you know, is not just for scientists. It's running equations and using numerics is a tool that is important to a large number of industries. And then, third, one of the most exciting things that we're doing because of deep learning we've really ignited a wave of AI innovation all over the world. These several applications -- graphics application, virtualization, computational science, and data science has really driven our opportunity in the data center.

第二點是計算科學。我們利用GPU進行通用科學計算，而科學計算，如您所知，並非科學家的專屬領域。它涉及運行方程式和運用數值方法，對眾多行業都至關重要。第三點，也是我們目前最令人興奮的進展之一，是深度學習在全球掀起了一股人工智慧創新浪潮。圖形應用、虛擬化、計算科學和資料科學等諸多應用領域，大大拓展了我們在資料中心的機會。

The thing that made it possible though -- the thing that really made it possible was really the transformation from our Company from a graphics processor to a general-purpose processor, and then on top of that -- probably the more important part of that is transforming from a chip Company to a platform Company. What makes application and graphics virtualization possible is a complicated stack of software we call GRID, and you have heard me talk about it for several years now. And, second, in the area of numerics and computational sciences, CUDA, our rich library of applications and libraries on top of numerics -- numerical libraries on top of CUDA and all the tools that we have invested in the ecosystem we have worked with all the developers all around the world that now know how to use CUDA to develop applications makes that part of our business possible.

然而，真正讓這一切成為可能的，是我們公司從圖形處理器製造商轉型為通用處理器製造商，更重要的是，我們從晶片公司轉型為平台公司。實現應用程式和圖形虛擬化的關鍵在於我們稱為 GRID 的複雜軟體棧，我已經講了好幾年了。其次，在數值運算領域，CUDA，我們豐富的基於 CUDA 的應用和函式庫，以及我們投入到生態系統中的所有工具，讓我們與世界各地的開發者合作，使他們能夠使用 CUDA 開發應用，這些都為我們的業務發展提供了支持。

And then, third, our deep learning toolkit, the NVIDIA deep learning toolkit has made it possible for all frameworks in the world to get GPU acceleration. And, with GPU acceleration the benefit is incredible. It's not 20% it's not 50%. It's 20 times, 50 times. That translates to most importantly for researchers the ability to gain access to insight much, much faster. Instead of months, it could be days. It's essentially like having a time machine. And, secondarily, for IT managers it translates to lower energy consumption, and most importantly, it translates to a substantial reduction in data center cost. Whereas you have a rack of servers with GPUs, it replaces an entire basketball court of cluster of off-the-shelf servers. And so, a pretty big deal. A great value proposition.

第三，我們的深度學習工具包－NVIDIA深度學習工具包－讓全球所有框架都能獲得GPU加速。 GPU加速帶來的優勢是巨大的。不是20%，也不是50%，而是20倍、50倍。這對研究人員來說，最重要的是能夠更快地獲得洞見。原本需要幾個月的時間，現在可能只需要幾天。這簡直就像擁有了一台時光機。其次，對IT經理來說，這意味著更低的能耗，最重要的是，它能大幅降低資料中心的成本。原本只需要一機架配備GPU的伺服器，就能取代相當於一個籃球場大小的現成伺服器叢集。所以，這意義非凡，極具價值。

Vivek Arya, Bank of America Merrill Lynch

Vivek Arya，美國銀行美林

Thanks for taking my question and congratulations on the consistent growth and execution. Jen-Hsun, one more on the data center business. It has obviously grown very strongly this year, but in the past, it has been lumpy. For example, when I go back to your FY15, it grew 60% to 70% year on year. Last year, it grew about 7%. This year, it is growing over 100%. How should we think about the diversity of customers and the diversity of applications to help us forecast how the business can grow over the next one or two years?

感謝您回答我的問題，並祝賀貴公司持續成長和高效執行。 Jen-Hsun，關於資料中心業務，我還有一個問題。顯然，今年資料中心業務成長非常強勁，但過去成長並不穩定。例如，回顧2015財年，該業務年增了60%到70%。去年，成長率約為7%。而今年，成長率超過了100%。我們該如何看待客戶和應用程式的多元化，才能更好地預測未來一兩年內該業務的成長？

I think embedded in your question, in fact, are many of the variables that influence our business. Especially in the beginning, several years ago when we started working on GPU computing and bringing this capability into data centers. We relied on supercomputing centers in the beginning, and then, we relied on remote workstations, data center workstations, if you will, virtualized workstations. And, then, increasingly we started relying on -- we started seeing demand from hyperscale data centers as they used our GPUs for deep learning and to develop their networks. And now, we're starting to see data centers take advantage of our new GPUs, P40 and P4 to apply to operate, to use the networks for inferencing in a large-scale way. So, I think we are moving, if you will, our data center business in multiple trajectories.

我認為，你的問題其實包含了許多影響我們業務的因素。尤其是在幾年前，我們開始研發GPU運算並將其引入資料中心的時候。起初，我們依賴超級運算中心，後來，我們轉向遠端工作站，也就是資料中心工作站，或者說是虛擬化工作站。之後，我們開始越來越依賴超大規模資料中心，因為他們使用我們的GPU進行深度學習並開發他們的網路。現在，我們開始看到資料中心利用我們新的GPU P40和P4來運行網絡，大規模地進行推理。所以，我認為我們的資料中心業務正在朝著多個方向發展。

The first trajectory is the number of applications we can run. Our GPU now has the ability with one architecture to run all of those applications that I mentioned from graphics virtualization to scientific computing to AI. Second, we used to be in data centers, but now we're in data centers, supercomputing centers, as well as hyperscale data centers. And then, third, the number of applications -- industries that we effect is growing. It used to start with supercomputing. Now, we have supercomputing. We have automotive. We have oil and gas. We have energy discovery. We have financial services industry. We have, of course, one of the largest industries in the world, consumer Internet Cloud services. And so, we're starting to see applications in all of those different dimensions.

第一個發展趨勢是我們能夠運行的應用程式數量。我們的GPU現在憑藉單一架構就能運行我所提到的所有應用，從圖形虛擬化到科學運算再到人工智慧。第二個趨勢是，我們過去主要服務於資料中心，但現在我們不僅服務於資料中心，還服務於超級運算中心以及超大規模資料中心。第三個趨勢是，我們影響的應用領域——也就是產業數量——正在不斷成長。最初，我們的應用主要集中在超級運算領域。現在，我們服務超級運算、汽車、石油天然氣、能源勘探、金融服務等行業。當然，我們服務於全球最大的產業之一—消費網路雲端服務。因此，我們開始看到我們在所有這些不同領域都有應用。

I think the combination of those three things: the number of applications, the number of platforms and locations by which we have success. And then, of course, the number of industries that we affect. The combination of that should give us more upward directory in a consistent way. But, I think really, the mega point though is really the size of the industries we are now able to engage. In no time in the history of our Company have we ever been able to engage industries of this magnitude. And so, that's the exciting part, I think, in the final analysis.

我認為這三點結合起來至關重要：應用程式的數量、我們成功的平台和地區數量，當然還有我們影響的行業數量。這三者的結合應該能讓我們持續獲得更大的發展。但我認為，真正關鍵的是我們現在能夠涉足的產業規模。在我們公司歷史上，我們從未涉足過如此龐大的產業。所以，歸根結底，我認為這才是最令人興奮的地方。

Toshiya Hari, Goldman Sachs

Toshiya Hari，高盛

Great. Thanks for taking my question and congratulations on a very strong quarter. Jen-Hsun, you've been on the road quite a bit over the past few months, and I'm sure you've had the opportunity to connect with many of your important customers and partners. Can you maybe share with us what you learned from the multiple trips? And, how your view on the Company's long-term growth trajectory changed, if at all?

太好了。感謝您回答我的問題，也恭喜您本季業績非常出色。 Jen-Hsun，您過去幾個月一直在出差，我相信您一定有機會與許多重要的客戶和合作夥伴交流。您能否和我們分享您從這些行程中學到了什麼？另外，您對公司長期成長軌跡的看法是否有所改變？

Yes. Thanks a lot, Toshi. First of all, the reason why I've been on the road for almost two months solid is because at the request and the demand, if you will, from developers all over the world for a better understanding of GPU computing and getting access to our platform and learning about all of the various applications that GPUs can now accelerate. The demand is just really great. We no longer could do GTC, which is our developer conference, essentially our developer conference. We can no longer do GTC just here in Silicon Valley, and so we this year decided to take it on the road. And, we went to China, went to Taiwan, went to Japan, went to Korea. We had one in Australia, and also one in India and Washington DC and Amsterdam for Europe.

是的，非常感謝，Toshi。首先，我之所以連續近兩個月奔波在外，是因為世界各地的開發者都強烈要求我們更了解GPU運算，並希望能夠使用我們的平台，學習GPU現在可以加速的各種應用程式。需求真的非常旺盛。我們已經無法再像以前那樣只在矽谷舉辦GTC開發者大會了，所以我們今年決定把它搬到各地。我們去了中國、台灣、日本、韓國，也在澳洲、印度、華盛頓特區和阿姆斯特丹舉辦了一場。

So, we pretty much covered the world with our first global developer conference. I would say probably the two themes that came out of it, is that GPU acceleration, the GPU has really reached a tipping point. That it is so available everywhere. It's available on PCs. It's available from every computer company in the world. It's in the cloud. It's in the data center. It's in laptops. GPU is no longer a niche component. As they say, it's a large-scale, massively available general-purpose computing platform. So I think people realize now the benefits of GPU and that the incredible speedup or cost reduction -- basically, the opposite sides of a coin that you can get with GPUs. So, GPU computing.

所以，我們的首屆全球開發者大會幾乎涵蓋了全世界。我認為大會湧現的兩個主題是：GPU加速，GPU已經真正達到了一個臨界點。它無所不在。個人電腦上都有，全球所有電腦公司都在提供GPU。雲端、資料中心、筆記型電腦裡都有GPU。 GPU不再是小眾組件。正如他們所說，它是一個大規模、大量可用的通用運算平台。所以我認為人們現在意識到了GPU的優勢，以及它帶來的驚人速度提升或成本降低——基本上，GPU的兩面性。所以，GPU計算。

Number two, is AI. Just the incredible enthusiasm around AI, and the reason for that, of course, for everybody who knows already about AI what I'm going to say is pretty clear. But, there's a large number of applications, problems, challenges where a numerical approach is not available. A laws-of-physics-based, equation-based approach is not available. These problems are very complex. Oftentimes, the information is incomplete, and there's no laws of physics around it. For example, what's the laws of physics of what I look like? What's the laws of physics for recommending tonight's movie? So, there's no laws of physics involved.

第二點是人工智慧。人們對人工智慧的熱情令人難以置信，當然，對於已經了解人工智慧的人來說，我接下來要說的內容顯而易見。但是，在許多應用、問題和挑戰中，數值方法並不適用。基於物理定律或方程式的方法也不行。這些問題非常複雜。通常情況下，資訊並不完整，也沒有任何物理定律可以解釋它們。例如，我的外表遵循什麼物理定律？推薦今晚的電影遵循什麼物理定律？所以，這些問題並不涉及物理定律。

The question is how do you solve those kind of incomplete problems? There's no laws-of-physics equation that you can program into a car that causes the car to drive and drive properly. These are artificial intelligence problems. Search is an artificial intelligence problem. Recommendations is an artificial intelligence problem. So, now that GPU deep learning has ignited this capability, and it has made it possible for machines to learn from a large amount of data and to determine the features by itself -- to compute the features to recognize. GPU deep learning has really ignited this wave of AI revolution. So, I would say the second thing that is just incredible enthusiasm around the world is learning how to use GPU deep learning. How to use it to solve AI-type problems, and to do so in all of the industries that we know from healthcare to transportation to entertainment to enterprise to you name it.

問題在於如何解決這類不完整的問題？你不可能把任何物理定律的方程式編程到汽車裡，讓它自動行駛並保持良好的行駛狀態。這些都是人工智慧問題。搜尋是人工智慧問題，推薦也是人工智慧問題。如今，GPU深度學習的出現激發了這種能力，使機器能夠從海量資料中學習，並自主確定特徵——計算用於識別的特徵。 GPU深度學習真正點燃了這場人工智慧革命的浪潮。因此，我認為全球範圍內另一個令人無比興奮的現像是學習如何使用GPU深度學習。如何利用它來解決人工智慧類型的問題，並將其應用於我們所熟知的各個行業，從醫療保健到交通運輸，從娛樂到企業，等等。

Atif Malik, Citigroup.

阿提夫·馬利克，花旗集團。

Hi. Thanks for taking my question and congratulations. You mentioned that Maxwell upgrade was about 30% of your (technical difficulty) exactly two years. Should we be thinking about a two-year time (inaudible)?

您好。感謝您回答我的問題，也恭喜您。您提到Maxwell升級大約佔您（技術難題）的30%，剛好持續了兩年。我們是否應該考慮兩年的時間（聽不清楚）？

Atif, first of all, there were several places where you cut out, and this is one of those artificial intelligence problems. Because what I heard incomplete information, but I'm going to infer from some of the important words that I did hear, and I'm going to apply in this case human intelligence to see if I can predict what it is that you were trying to ask. The baseline, the basis of your question was that Maxwell -- in the past, Maxwell GPU during that generation, we saw an upgrade cycle about every two or three years. And, we had an install base of some 60 million, 80 million gamers during that time and several years have now gone by. The question is what would be the upgrade cycle for Pascal, and what would it look like?

阿提夫，首先，你剛才說的有些地方斷斷續續的，這其實是人工智慧問題。因為我聽到的資訊並不完整，但我會根據我聽到的一些關鍵資訊進行推斷，並運用人類的判斷力來嘗試預測你真正想問的是什麼。你問題的基本想法是，在過去的Maxwell架構GPU時代，我們大約每兩到三年就會看到一次升級週期。當時，Maxwell GPU的裝置量達到了6,000萬到8,000萬，而現在幾年過去了。問題是，Pascal架構的升級週期會是多久，又會是什麼樣的呢？

There are several things that have changed that I think it's important to know that could affect the Pascal upgrade. First of all, the increase in adoption, the number of units has grown, and the number of the ASP has grown. And, I think the reason for that is several-fold. I think, one, the number of gamers in the world is growing. Everybody that is effectively born in the last 10, 15 years are likely to be a gamer and so long as they have access to electricity and the Internet, they are very likely a gamer. The quality of games has grown significantly.

有幾項變化我認為很重要，它們可能會影響 Pascal 架構的升級。首先，Pascal 架構的普及率提高了，產品數量和平均售價 (ASP) 都成長了。我認為這有幾個原因。首先，全球遊戲玩家數量正在成長。過去 10 到 15 年出生的人幾乎都是遊戲玩家，只要他們能用上電和網絡，就很有可能成為遊戲玩家。其次，遊戲品質也顯著提升。

One of the factors of production value of games that has been possible is because the PC and the two game consoles, Xbox and PlayStation, and in the future, in the near future, the Nintendo Switch. All of these architectures are common in the sense that they all use modern GPUs. They all use programmable shading, and they all have basically similar features. They have very different design points. They have different capabilities, but they have very similar architectural features. As a result of that, game developers can target a much larger install base with one common code base. As a result, they can increase the production quality, the production value of the games.

遊戲製作價值得以提升，其中一個重要因素是PC、Xbox和PlayStation兩大遊戲主機，以及即將推出的Nintendo Switch。這些平台架構的共通之處在於它們都採用了現代GPU，都支援可程式著色，而且基本功能相似。儘管它們的設計理念和效能各有不同，但架構特徵卻非常相似。因此，遊戲開發者可以使用同一套程式碼庫面向更廣泛的使用者群體，從而提升遊戲的製作品質和價值。

The second -- and one of the things that you might have noticed that recently, PlayStation and Xbox both announced 4K versions. Basically, the Pro versions of their game console. That's really exciting for the gaming industry. It's really exciting for us because what's going to happen is the production value of games will amp up, and as a result, it would increase the adoption of higher-end GPUs. I think that that's a very important positive. That's probably the second one. The first one being the number of gamers is growing. Second is game production value continues to grow.

第二點－你可能也注意到了，PlayStation 和 Xbox 最近都發布了 4K 版本，也就是他們遊戲主機的 Pro 版本。這對遊戲產業來說確實令人振奮。對我們來說尤其如此，因為這意味著遊戲的製作水準將會大幅提升，進而推動高階 GPU 的普及。我認為這是一個非常重要的利多因素。這大概是第二點。第一點是玩家數量的成長。第二點是遊戲製作水準的持續提升。

And then the third is gaming is no longer just about gaming. Gaming is part sports, part gaming, and part social. There's a lot of people who play games just so they can hang out with their other friends who are playing games. It's a social phenomenon. And then, of course, because games are -- the quality of games, the complexity of games, and some such as League of Legends, such as StarCraft; the real-time simulation, the real-time strategy component of it, the agility, the hand-eye coordination part of it, the incredible teamwork part of it is so great that it has become a sport. Because there are so many people in gaming, because it's a fun thing to do, and it's hard to do so it's hard to master. And the size of the industry is large. It has become a real sporting event.

第三點是，遊戲不再只是遊戲。它融合了體育、競技和社交元素。很多人玩遊戲只是為了和同樣在玩遊戲的朋友們一起消磨時光。這是一種社交現象。當然，遊戲本身——遊戲的品質、遊戲的複雜性，以及像《英雄聯盟》和《星海爭霸》這樣的遊戲——其實時模擬、即時戰略元素、對操作的敏捷性、手眼協調能力以及令人驚嘆的團隊合作，都使其發展成為一項體育運動。因為遊戲玩家眾多，因為它既有趣又有挑戰性，所以精通它也並非易事。而且，遊戲產業規模龐大。它已經成為一項真正的體育賽事。

And, one of the things that I'll predict is that one of these days, I believe that gaming would likely be the world's largest sport industry. And, the reason for that is because it's the largest industry. There are more people who play games and now enjoy games and watch other people play games than there are people who play football, for example. So, I think it stands to reason that eSports will be the largest sporting industry in the world, and that's just a matter of time before it happens. So, I think all of these factors have been driving both the increase in the size of the market for us as well as the ASP of the GPUs for us.

我預測，總有一天，遊戲很可能會成為全球最大的運動產業。原因很簡單，因為它本身就是最大的產業。玩遊戲、看別人玩遊戲的人數，甚至比踢足球的人數還要多。所以，我認為電子競技成為全球最大的運動產業是必然的，這只是時間問題。我認為，所有這些因素都推動了我們市場規模的成長，也推高了我們GPU的平均售價。

Steven Chin, UBS.

Steven Chin，瑞銀集團。

Hi, thanks for taking my questions. Jen-Hsun, first question if I could on your comments regarding the GRID systems. You mentioned some accelerating demands in the manufacturing and automotive verticals? Just wondering if you had any thoughts on what inning you are currently in, in terms of seeing a strong ramp-up towards a full run rate for those areas? And, especially for the broader corporate enterprise and market vertical, also? And, as a quick follow-up on the gaming side, was wondering if you had any thoughts on whether or not there is still a big gap between the ramp-up of Pascal supply and the pent-up demand for those new products? Thank you.

您好，感謝您回答我的問題。 Jen-Hsun，首先我想問一個關於您之前提到的GRID系統的問題。您提到製造業和汽車產業的需求正在加速成長？我想了解一下，就目前而言，您認為這些領域的產能提升速度如何？特別是對於更廣泛的企業和市場領域，您的看法是什麼？另外，關於遊戲方面，我想問一下，您認為Pascal架構的產能提升速度與市場對這些新產品的潛在需求之間是否存在較大的差距？謝謝。

Sure. I would say that we're probably in the first at-bat of the first inning of GRID. The reason for that is this. We prepared ourselves. We went to spring training camp. We came up through the farm league or something like that. I'm not really a baseball player, but I heard some people talk about it. So, I think we're probably at the first at-bat of the first inning. The reason why I'm excited about it is because I believe in the future applications are virtualized in the data center or in the cloud.

當然。我覺得我們現在大概處於GRID計畫第一局第一棒的位置。原因如下：我們做了充分的準備。我們參加了春訓營，從農場聯盟一路打上來。我其實不太懂棒球，但我聽一些人聊過。所以，我覺得我們現在大概處於GRID計畫第一局第一棒的位置。我之所以感到興奮，是因為我相信未來應用程式會在資料中心或雲端虛擬化運作。

On first principles, I believe that data applications will be virtualized, and that you will be able to enjoy these applications irrespective of whether you're using a PC, a chrome notebook, a Mac, or a Linux workstation. It simply won't matter. And yet, on the other hand, I believe that in the future applications will become increasingly GPU-accelerated. How do you put something in the cloud that have no GPUs, and how do you GPU-accelerate these applications that are increasingly GPU-accelerated? The answer is, of course, is putting GPUs in the cloud and putting GPUs in data center. That's what GRID is all about. It's about virtualization. It's about putting GPUs in large-scale data centers and be able to virtualize the applications so that we can enjoy it on any computer, on any device, and putting computing closer to the data.

從根本上講，我相信數據應用將會虛擬化，無論您使用的是PC、Chrome筆記型電腦、Mac還是Linux工作站，都能享受這些應用。這根本無關緊要。然而，另一方面，我相信未來應用將越來越依賴GPU加速。那麼，如何將沒有GPU的應用程式部署到雲端？又該如何加速這些越來越依賴GPU的應用呢？答案當然是在雲端和資料中心部署GPU。這正是GRID的核心所在。它關乎虛擬化，關乎在大型資料中心部署GPU，並實現應用程式的虛擬化，從而使我們可以在任何電腦、任何裝置上流暢運行，真正做到計算更貼近資料。

I think we're just in the beginning of that, and that could explain why GRID is finally after a long period of time of building the ecosystem, building the infrastructure, developing all the software, getting the quality of service to be really exquisite, working with the ecosystem partners, it has really taken off. And I could surely expect to see it continue to grow at the rate that we're seeing for some time.

我認為我們才剛起步，這或許可以解釋為什麼 GRID 在經歷了漫長的生態系統建設、基礎設施搭建、軟體開發、服務品質優化以及與生態系統合作夥伴的緊密協作之後，終於取得了突破性進展。而且我完全可以預見，在未來一段時間內，它將繼續以目前的速度成長。

In terms of Pascal, we are still ramping. Production is fully ramped in the sense that all of our products are fully qualified. They are on the market. They have been certified and qualified with OEMs. However, demand is still fairly high so we're going to continue to work hard. Our manufacturing partner, TSMC, is doing a great job for us. The yields are fantastic for 2016 FinFET, and they're just doing a fantastic job supporting us. We're just going to keep running at it.

就 Pascal 架構而言，我們仍在加速產能爬坡。從某種意義上說，我們的產能已經全面提升，所有產品都已通過全面認證，並已上市。這些產品已經獲得了 OEM 廠商的認證和認可。然而，市場需求依然相當旺盛，因此我們將繼續努力。我們的製造合作夥伴台積電 (TSMC) 為我們提供了卓越的支援。 2016 年 FinFET 的良率非常出色，他們為我們提供了極大的幫助。我們將繼續全力以赴。

Joe Moore, Morgan Stanley.

喬摩爾，摩根士丹利。

Thank you very much. Great quarter by the way and still amazed how good this is. Can you talk a little bit about the size of the inference opportunity? Obviously, you have done really well in training. I assume penetrating inference is reasonably early on, but can you talk about how you see GPUs competitively versus FPGAs on that side of it, and how big you think that opportunity could become? Thank you.

非常感謝。順便說一句，你們這個季度表現出色，仍然讓我感到非常驚訝。您能否談談推理領域的市場潛力？顯然，你們在訓練方面做得非常好。我假設你們現在才剛開始涉足推理領域，但你們能否談談你們如何看待GPU在推理方面與FPGA的競爭優勢，以及你們認為這個領域的市場潛力有多大？謝謝。

Sure, I'll start backwards. I'll start backwards and answer the FPGA question first. FPGA is good at a lot of things, and anything that you could do on an FPGA if the market opportunity is large, you could always -- it's always better to develop an ASIC. And, FPGA is what you use when the volume is not large. FPGA is what you use when you are not certain about the functionality you want to put into something. FPGA is largely useful when the volume is not large. Because you could build an ASIC -- you could build a full-custom chip that obviously could deliver more performance. Not 20% more performance but 10 times better performance and better energy efficiency than you could using FPGAs. I think that's a well-known fact.

好的，我先從倒敘開始。我先回答FPGA的問題。 FPGA有很多優點，如果市場機會很大，任何可以用FPGA實現的功能，你總是可以－－總是可以－－開發ASIC來做。 FPGA適用於產量不大的情況。當你不確定想要實現的功能時，才會使用FPGA。 FPGA在產量不大的情況下非常有用。因為你可以建造ASIC——你可以建造一個完全客製化的晶片，顯然可以提供更高的性能。不是提升20%，而是比FPGA高10倍的效能和更高的能源效率。我想這是眾所周知的事實。

Our strategy is very different than any of that. Our strategy is really about building a computing platform. Our GPU is not a specific function thing anymore. It's a general-purpose parallel processor. CUDA can do molecular dynamics. It could do fluid dynamics. It could do partial differential equations. It could do linear algebra. It could do artificial intelligence. It could be used for seismic analysis. It could be used for computer graphics, even computer graphics. And so, our GPU is incredibly flexible, and it's really designed for, it's designed specifically for parallel throughput computing. And, by combining it with the CPU, we have created a computing platform that is both good at sequential information, sequential instruction processing as well as very high throughput data processing. And so, we have created a computing architecture that's good at both of those things.

我們的策略與上述任何策略都截然不同。我們的策略實際上是建立一個計算平台。我們的GPU不再是特定功能的產物，而是一個通用平行處理器。 CUDA可以進行分子動力學計算、流體動力學計算、偏微分方程求解、線性代數運算、人工智慧計算、地震分析，甚至電腦圖形學計算。因此，我們的GPU極為靈活，並且是專為平行吞吐量計算而設計的。透過將其與CPU結合，我們創建了一個既擅長順序資訊處理（順序指令處理）又擅長高吞吐量資料處理的運算平台。因此，我們創建了一個在這兩方面都表現出色的運算架構。

The reason why we believe that's important is because several things. We want to build a computing platform that is useful to a large industry. You could use it for AI. You could use it for search. You could use it for video transcoding. You could use it for energy discovery. You could use it for health. You could use it for finance. You could use it for robotics. You could use it for all these different things.

我們認為這很重要，原因有幾點。我們想建立一個對整個產業都有用的運算平台。它可以用於人工智慧、搜尋引擎、視訊轉碼、能源勘探、醫療健康、金融、機器人技術等等，用途非常廣泛。

On the first principles, we're trying to build a computing platform. It's a computing architecture. And, not a dedicated application thingy. Most of the customers that we're calling on, most of the markets that we are addressing, and the areas that we have highlighted are all computer users. They need to use and deploy a computing platform. It has the benefit of being able to rapidly improve their AI networks.

從根本上講，我們試圖建立一個運算平台，一個運算架構，而不是一個專用的應用程式。我們接觸的大多數客戶、我們瞄準的大多數市場以及我們重點關注的領域都是電腦使用者。他們需要使用和部署一個運算平台，該平台能夠幫助他們快速改進人工智慧網路。

AI is still in the early days. It's the early days of early days, and GPU deep learning is going through innovations at a very fast clip. Our GPU allows people to learn to develop new networks and deploy new networks as quickly as possible. So, I think the way to think about it is think of our GPU as a computing platform.

人工智慧仍處於早期階段，甚至可以說是早期階段的早期階段，而GPU深度學習正以驚人的速度不斷創新。我們的GPU能夠幫助人們學習開發新的網絡，並以最快的速度部署新的網路。因此，我認為應該把我們的GPU看成是一個運算平台。

In terms of the market opportunity, the way I would look at it is this. The way I would look at is there are something along the lines of 5 million to 10 million hyperscale data center nodes. I think, as you have heard me say this before, I think that training is a new set of HPC clusters that have been added into these data centers. And then, the next thing that's going to happen is you're going to see GPUs being added to a lot of these 5 million to 10 million nodes so that you could accelerate every single query that will likely come into the data center will be an AI query in the future. I think GPUs have an opportunity to see a fairly large hyperscale installed base.

就市場機會而言，我的看法是這樣的：目前大約有 500 萬到 1000 萬個超大規模資料中心節點。正如我之前所說，我認為訓練需要在這些資料中心新增一批高效能運算 (HPC) 叢集。接下來，我們將看到這 500 萬到 1000 萬個節點中的許多節點都配備 GPU，以便加速未來進入資料中心的每一個查詢——這些查詢很可能都是 AI 查詢。我認為 GPU 有望在超大規模資料中心擁有相當龐大的裝置量。

But, beyond that there is the enterprise market. Still although, a lot of computing is done in the cloud, a great deal of computing especially the type of computing that we're talking about here that requires a lot of data -- and we're a data throughput machine -- the type of computers that we're talking about tends to be one of being in enterprise. And, I believe a lot of the enterprise market is going to go towards AI; and the type of things that we are looking for in the future is to simplify our business processors using AI, to find business intelligence or insight using AI, to optimize our supply chain using AI, to optimize our forecasting using AI, to optimize the way that we find and surprise and delight customers, digital customers or customers in digital using AI. So, all of these parts of the business operations of large companies, I think AI can really enhance.

但除此之外，還有企業市場。儘管現在許多計算都在雲端完成，尤其是我們在這裡討論的那種需要大量數據的計算——而我們本身就是一台數據吞吐量機器——我們討論的這類計算機往往是企業級的。我相信，企業市場很大一部分將會轉向人工智慧；我們未來追求的是利用人工智慧簡化業務流程，利用人工智慧獲取商業智慧或洞察，利用人工智慧優化供應鏈，利用人工智慧優化預測，以及利用人工智慧優化我們尋找客戶、為數位客戶或數位化客戶帶來驚喜和愉悅的方式。因此，我認為人工智慧可以真正提升大型企業業務營運的各個層面。

And then, the third -- so hyperscale, enterprise computing, and then the third is something very, very new. It's called IoT. IoT -- we're going to have 1 trillion things connected to the Internet over time, and they are going to be measuring things from vibration, to sound, to images, to temperature, to air pressure, to -- you name it. These things are going to be all over the world, and we are going to measure and we are going to be constantly measuring and monitoring their activity. And, using the only thing that we can imagine that can help to add value to that and find insight from that is really AI using deep learning. We could have these new types of computers, and they will likely be on-premise or near the location of the cluster of things that you have. And, monitor all of these devices and keep -- prevent them from failing or adding intelligence to it so that they add more value to what it is that people have them do. So, I think the size of the marketplace that we are addressing is really larger than any time in our history. And, probably the easiest way to think about it is we're now a computing platform Company. We are simply a computing platform Company, and our focus is GPU computing and one of the major applications is AI.

然後，第三個面向──超大規模企業運算，以及一個非常全新的領域，叫做物聯網（IoT）。物聯網意味著，隨著時間的推移，將有1兆個設備連接到互聯網，它們將測量從振動、聲音、影像、溫度、氣壓到幾乎所有你能想到的資訊。這些設備將遍布全球，我們將持續不斷地測量和監控它們的活動。而我們目前能想到的唯一能夠幫助我們從中增值並從中獲得洞察的方法，就是利用深度學習的人工智慧。我們可以擁有這些新型計算機，它們很可能部署在用戶本地或設備集群附近。這些電腦將監控所有這些設備，防止它們發生故障，並賦予它們智能，從而為用戶創造更多價值。因此，我認為我們所面對的市場規模比以往任何時候都要大。或許最簡單的理解方式是，我們現在是一家運算平台公司。我們是一家純粹的運算平台公司，專注於GPU運算，而人工智慧是其主要應用之一。

Craig Ellis, B. Riley and Company.

克雷格·艾利斯，B. Riley and Company。

Thanks for taking the question and congratulations on the stellar execution. Jen-Hsun, I wanted to go back to the automotive business. In the past, the Company has mentioned that the revenues consist of display and then on the auto-pilot side both consulting and product revenues. But, I think much more intensively on the consulting side for now. But, as we look ahead to Xavier and the announcement that you had made intra-quarter that, that's coming late next year, how should we expect that the revenue mix would evolve? Not just from consulting to product, but from Parker towards Xavier?

感謝您回答這個問題，並祝賀您出色地完成了任務。 Jen-Hsun，我想回到汽車業務的話題。公司之前提到過，收入主要來自顯示屏，而自動駕駛方麵包括諮詢和產品收入。但我認為目前諮詢收入佔比更高。展望未來，您在本季中宣布了Xavier專案將於明年年底啟動，我們應該如何預期收入結構會發生變化？不僅是從顧問收入轉向產品收入，還包括從Parker計畫轉向Xavier計畫？

I don't know that I have really granular breakdowns for you, Craig, partly because I'm just not sure. But, I think the dynamics are that self-driving cars is probably the single-most disruptive event -- the most disruptive dynamic that's happening in the automotive industry. It's almost impossible for me to imagine that in five years time, a reasonably capable car will not have autonomous capability at some level. And, a very significant level at that. I think what Tesla has done by launching and having on the road in the very near future here full autonomous driving capability using AI, that has sent a shockwave through the automotive industry. It's basically five years ahead.

克雷格，我不太清楚具體細節，所以可能沒辦法給你詳細分析。但我認為，自動駕駛汽車可能是汽車產業最具顛覆性的事件——最具變革性的動態。我幾乎無法想像，五年後，一輛性能尚可的汽車不會具備一定程度的自動駕駛能力，而且是相當高的水平。特斯拉推出並即將上路的、利用人工智慧實現完全自動駕駛的車型，這無疑為汽車產業帶來了巨大衝擊。它基本上領先了五年。

Anybody who's talking about 2021, that's just a non-starter anymore. I think that, that's probably the most significant bit in the automotive industry. Anybody who was talking about autonomous capabilities and 2020 and 2021 is at the moment reevaluating in a very significant way. So, I think that, of course, will change how our business profile will ultimately look. It depends on those factors. Our autonomous vehicle strategy is relatively clear, but let me explain it anyway.

任何還在談論2021年的人，現在都已經無從談起了。我認為，這可能是汽車產業最重要的轉捩點。任何之前在談論自動駕駛能力以及2020年和2021年的人，目前都在進行非常深入的重新評估。所以，我認為這當然會改變我們最終的業務格局。這取決於這些因素。我們的自動駕駛汽車策略相對清晰，但我還是想解釋一下。

Number one, we believe that autonomous vehicles is not a detection problem, it's an AI computing problem. That it's not just about detecting objects. It's about perception of the environment around you. It's about reasoning about what to do -- what is happening and what to do -- and to take action based on that reasoning. And, to be continuously learning. So, I think that AI computing requires a fair amount of computation, and anybody who thought that it would take only one or two watt -- basically, the amount of energy -- one-third the energy of a cell phone. I think it's unfortunate, and it is not going to happen any time soon.

首先，我們認為自動駕駛汽車並非簡單的偵測問題，而是人工智慧運算問題。它不僅僅是檢測物體，而是感知周圍環境，推理並根據當前情況採取行動，並且持續學習。因此，我認為人工智慧運算需要大量的運算資源。任何認為它只需要一兩瓦——也就是手機三分之一的電力——的想法都是錯誤的，而且短期內也不可能實現。

So, I think people now recognize that AI computing is a very software-rich problem, and it is a supremely exciting AI problem. And, that deep learning and GPUs could add a lot of value, and it is going to happen in 2017, it's not going to happen in 2021. I think number one. Number two, our strategy is to apply, to deploy a one-architecture platform that is open that car companies could work on to leverage our software stack and create their network, their artificial intelligence network. And, that we would address everything from highway cruising, excellent highway cruising, all the way to full autonomous to trucks to shuttles. And, using one computing architecture, we could apply it for radar-based systems, radar plus cameras, radar plus cameras plus Lidars. We could use it for all kinds of sensor fusion environments. So, I think our strategy is really resonating well with the industry as people now realize that we need the computation capability five years earlier. That's not a detection problem, but it's an AI computing problem and that software is really intensive. But, these three observations, I think, has put us in a really good position.

所以，我認為人們現在認識到，人工智慧運算是一個非常依賴軟體的問題，也是一個極其令人興奮的人工智慧問題。深度學習和GPU可以帶來巨大的價值，而且這將在2017年實現，而不是2021年。我認為，第一點是，第二點，我們的策略是部署一個開放的單一架構平台，汽車公司可以利用我們的軟體堆疊來建立自己的網絡，即人工智慧網路。我們將解決從高速公路巡航（包括卓越的高速公路巡航）到完全自動駕駛、卡車和接駁車等各種應用場景。而且，使用單一的計算架構，我們可以將其應用於基於雷達的系統、雷達加攝影機、雷達加攝影機加雷射雷達等。我們可以將其用於各種感測器融合環境。因此，我認為我們的策略與業界產生了良好的共鳴，因為人們現在意識到我們需要提前五年實現所需的運算能力。這不是一個檢測問題，而是一個人工智慧運算問題，而且軟體開發非常耗費資源。但我認為這三點觀察結果使我們處於非常有利的地位。

Mitch Steves, RBC Capital Markets.

Mitch Steves，加拿大皇家銀行資本市場。

Hi. Thanks for taking my question. Great quarter across the board. I did want to return to the automotive segment because the data center segment has been talked about at length. With the new Drive PX platform increasing potentially the ASPs, how do we think about the ASPs for automotive going forward? And, if I recall, you had about $30 million in backlog in terms of cars? I'm not sure if it's possible to get an update there as well?

您好。感謝您回答我的問題。本季整體業績非常出色。我想回到汽車領域，因為資料中心領域已經討論了很多。新的 Drive PX 平台可能會提高平均售價 (ASP)，那麼我們該如何看待汽車領域未來的平均售價呢？如果我沒記錯的話，你們的汽車訂單積壓額約為 3000 萬美元？能否也提供一下這方面的最新資訊？

Our architecture for Drive PX is scalable. You could start from one Parker SoC, and that allows you to have surround camera. It allows you to use AI for highway cruising. And, if you would like to have even more cameras so that your functionality could be used more frequently in more conditions, you could always add more processors. So, we go from one to four processors. And, if it's a fully autonomous, driverless car -- a driverless taxi, for example, you might need more than even four of our processors. You might need eight processors. You might need 12 processors. And, the reason for that is because you need to reduce the circumstance by which auto-pilot doesn't work, doesn't turn on, excuse me, doesn't engage. And, because you don't have a driver in the car at all. I think that depending on the application that you have, we will have a different configuration, and it's scalable. It ranges from a few hundred dollars to a few thousand dollars so I think it just depends on what configuration people are trying to deploy. Now for a few thousand dollars, the productivity of that vehicle is incredible as you can simply do the math. It's much more available. The cost of operations is reduced. And, a few thousand dollars is surely almost nothing in the context of that use case.

我們的 Drive PX 架構具有可擴充性。您可以從一個 Parker SoC 開始，它支援環景攝影機，並允許您使用 AI 進行高速公路巡航。如果您希望擁有更多相機，以便在更多場景下更頻繁地使用其功能，您可以隨時添加更多處理器。因此，我們可以從一個處理器擴展到四個處理器。如果是完全自動駕駛汽車——例如無人駕駛計程車——您可能需要超過四個處理器。您可能需要八個處理器，甚至十二個處理器。這是因為您需要盡可能減少自動駕駛功能無法啟動或無法啟用的情況。而且，因為車內根本沒有駕駛。我認為，根據您的應用程式場景，我們將提供不同的配置，並且它是可擴展的。價格從幾百美元到幾千美元不等，因此我認為這取決於用戶想要部署的配置。現在，只需花費數千美元，這輛車的生產力就非常驚人，你只需簡單計算一下就能明白。它的普及率更高，營運成本也更低。而且，在這種使用場景下，幾千美元幾乎可以忽略不計。

Harlan Sur, JPMorgan.

哈蘭‧蘇爾，摩根大通。

Good afternoon. Congratulations on the solid execution and growth. Looking at some of your cloud customers' new services offerings, you mentioned AWS EC2 P2 platform. You have Microsoft Azure's Cloud Services platforms. It's interesting because they are ramping new instances primarily using your K80 accelerator platform which means that the Maxwell base and the recently introduced Pascal-based adoption curves are still way ahead of the team which obviously is a great setup as it relates to the continued strong growth going forward. Can you just help us understand why the long design and cycle times for these accelerators? And, when do you expect the adoption curve for the Maxwell-based accelerators to start to kick in with some of your Cloud customers?

午安.祝賀貴公司取得穩健的執行和成長。在查看貴公司一些雲端客戶的新服務產品時，您提到了 AWS EC2 P2 平台。貴公司也擁有 Microsoft Azure 雲端服務平台。有趣的是，他們主要使用貴公司的 K80 加速器平台來快速部署新實例，這意味著基於 Maxwell 架構和最近推出的基於 Pascal 架構的加速器平台的採用率仍然遠高於貴公司，這顯然有利於貴公司未來持續強勁的成長。能否請您解釋一下這些加速器的設計和開發週期為何如此長？另外，您預期基於 Maxwell 架構的加速器何時才能在貴公司的雲端客戶中開始普及？

Harlan, good question. And, it's exactly the reason why having started almost five years ago in working with all of these large-scale data centers is what it takes. The reason for that is because several things have to happen. Applications have to be developed. They're hyperscale. They are enterprise -- their data center-level software has to accommodate this new computing platform. The neural networks have to be developed and trained and ready for deployment. The GPUs have to be tested against every single data center and every single server configuration that they have, and it takes that type of time to deploy at the scales that we are talking about. So, I think that, that's number one.

哈蘭，問得好。這正是為什麼近五年前就開始與這些大型資料中心合作的原因。原因在於，很多事情都需要完成。首先，應用程式必須開發出來。它們是超大規模的，是企業級的——它們的資料中心級軟體必須能夠適應這種新的運算平台。其次，神經網路必須開發、訓練並做好部署準備。最後，GPU 必須針對每個資料中心和每種伺服器配置進行測試，而要達到我們所說的這種規模，就需要花費大量的時間。所以，我認為這是首要的。

The good news is that between Kepler and Maxwell and Pascal, the architecture is identical. Even though the underlying architecture has been improved dramatically and the performance increases dramatically, the software layer is the same. So, the adoption rate of our future generations is going to be much, much faster, and you will see that. It takes that long to integrate our software and our architecture and our GPUs into all of the data centers around the world. It takes a lot of work. It takes a long time.

好消息是，從開普勒到麥克斯韋再到帕斯卡，架構完全相同。儘管底層架構得到了顯著改進，效能也大幅提升，但軟體層卻保持不變。因此，我們未來幾代產品的普及速度將會快得多，這一點您很快就會看到。將我們的軟體、架構和GPU整合到世界各地的資料中心需要很長時間。這需要大量的工作，也需要很長時間。

Romit Shah, Nomura.

Romit Shah，野村證券。

Yes, thank you. Jen-Hsun, I just wanted to ask regarding the auto-pilot win. We know that you displaced Mobileye, and I was just curious if you could talk about why Tesla chose your GPU? And, what you can give us in terms of the ramp and timing, and how would a ramp like this affect automotive gross margin?

是的，謝謝。 Jen-Hsun，我只是想問關於自動駕駛系統競標成功的問題。我們知道你們取代了Mobileye，我只是好奇你們能否談談特斯拉為什麼選擇你們的GPU？另外，你們能否透露一下產能爬坡和時間安排方面的情況，以及這樣的爬坡會對汽車業務的毛利率產生怎樣的影響？

I think there are three things that we offer today. The first thing is that it's not a detection problem, it's an AI computing problem. And, a computer has processors and the architecture is coherent and you can program it. You could write software. You can compile to it. It's an AI computing problem, and our GPU computing architecture has the benefit of 10 years of refinement. In fact, this year is the 10-year anniversary of our first GPGPU, our first CUDA GPU called G8, and we been working on this for 10 years. And so, the number one is autonomous driving, autonomous vehicles is an AI computing problem. It's not a detection problem.

我認為我們今天提供的有三點。首先，這不是一個檢測問題，而是一個人工智慧運算問題。電腦擁有處理器，其架構是連貫的，你可以對其進行編程，編寫軟體，並進行編譯。這是一個人工智慧運算問題，而我們的GPU運算架構受益於十年的不斷改進。事實上，今年是我們首款GPGPU（首款CUDA GPU，名為G8）發表十週年，我們為此投入了十年。因此，第一點是自動駕駛，自動駕駛汽車是人工智慧運算問題，而不是一個偵測問題。

Second, car companies realize that they need to deliver ultimately a service. That the service is a network of cars by which they continuously improve. It's like phones. It's like set-top boxes. You have to maintain and serve that customer because they are interested in the service of autonomous driving. It's not a functionality. Autonomous driving is always being improved with better maps and better driving behavior and better perception capability and better AI, so the software component of it and the ability for car companies to own their own software once they develop it on our platform is a real positive. Real positive to the point where it's enabling, or it's essential for the future of the driving fleets.

其次，汽車公司意識到，他們最終需要提供的是一種服務。這種服務是一個不斷改進的汽車網路。這就像電話，就像機上盒。你必須維護並服務好客戶，因為他們感興趣的是自動駕駛服務本身，而不是一項功能。自動駕駛技術一直在不斷改進，包括更完善的地圖、更優化的駕駛行為、更強的感知能力和更先進的人工智慧。因此，自動駕駛的軟體部分，以及汽車公司在我們平台上開發並擁有自己的軟體的能力，都是非常有利的。這種有利性甚至可以說是推動未來車隊發展的關鍵。

And then, the third -- to be able to continue to do OTA on them. And, third, is simply the performance and energy level. I don't believe it's actually possible at this moment in time to deliver an AI computing platform of the performance level that is required to do autonomous driving at an energy efficiency level that is possible in a car and to put all the functionality together in a reasonable way. I believe DRIVE PX2 is the only viable solution on the planet today. So, because Tesla had a great intention to deliver this level of capability to the world five years ahead of anybody else, we were a great partner for them. So, those are probably the three reasons.

第三點是能夠繼續進行OTA升級。第三點就是性能和能耗水準。我認為目前還不可能打造出一個既能滿足自動駕駛所需的高性能AI運算平台，同時又能確保汽車的能源效率，並將所有功能以合理的方式整合在一起。我相信DRIVE PX2是目前地球上唯一可行的解​​決方案。特斯拉雄心勃勃地想要比任何人都提前五年將這種能力帶給全世界，因此我們是他們理想的合作夥伴。以上大概就是三個原因。

Matt Ramsay, Canaccord Genuity.

Matt Ramsay，Canaccord Genuity。

Thank you very much. Good afternoon. Jen-Hsun, I make an interesting observation about your commentary that your Company has gone from a graphic accelerator Company to a computing platform Company, and I think that's fantastic. One of the things that I wonder as maybe AI and deep learning acceleration standardize on your platform, what you are seeing and hearing in the Valley about startup activity? And, folks that are trying to innovate around the platform that you are bringing up both complementary to what you are doing, and potentially really long-term competitive to what you are doing? Would love to hear your perspectives on that. Thanks.

非常感謝。午安. Jen-Hsun，關於您提到的貴公司從圖形加速器公司轉型為計算平台公司，我有一個有趣的觀察，我認為這非常棒。隨著人工智慧和深度學習加速技術可能逐漸在您的平台上實現標準化，我想了解一下矽谷的創業活動。另外，那些圍繞著您平台進行創新的人們，他們既與您現有的業務互補，也可能在長期發展中與您形成競爭關係。我很想聽聽您的看法。謝謝。

Yes, Matthew, I really appreciate that. We see a large number of AI startups around the world. There's a very large number here in the United States, of course. There's quite a significant number in China. There's a very large number in Europe. There's a large number in Canada. It's pretty much a global event. The number of software companies that have now jumped on to using GPU deep learning and taking advantage of the computing platform that we have taken almost seven years to build, and it's really quite amazing. We are tracking about 1,500.

是的，馬修，我非常感謝。我們看到世界各地湧現大量的AI新創公司。當然，美國的數量尤其龐大。中國也有相當可觀的數量。歐洲數量也很多。加拿大也有很多。這幾乎是一個全球性的趨勢。現在，許多軟體公司都開始使用GPU深度學習，並利用我們花了近七年時間打造的運算平台，這真的非常驚人。我們追蹤到大約有1500家這樣的公司。

We have a program called Inception, and Inception is our startup support program, if you will. They can get access to our early technology. They can get access to our expertise, our computing platform, and all that we've learned about deep learning we can share with many of these startups. They are trying to use deep learning in industries from cybersecurity to genomics to consumer applications, computational finance, to IoT, robotics, and self-driving cars. The number of startups out there is really quite amazing.

我們有一個名為「Inception」的項目，可以理解為我們的創業扶持計劃。他們可以獲得我們早期的技術資源、專業知識、運算平台，以及我們在深度學習領域累積的所有經驗，這些都可以與許多新創公司分享。他們正嘗試將深度學習應用於網路安全、基因組學、消費應用、運算金融、物聯網、機器人和自動駕駛汽車等各個產業。新創公司的數量著實驚人。

So, our deep learning platform is a really unique advantage for them because it's available in a PC so you can -- almost anybody with even a couple hundred dollars of spending money can get a startup going with a [video] GPU that can do deep learning. It's available from system builders and server OEMs all over the world: HP, Dell, Cisco, IBM, system builders, small system builders, local system builders all over the world. And very importantly, it's available in cloud data centers all over the world so Amazon AWS, Microsoft's Azure cloud has a really fantastic implementation ready to scale out. You have got the IBM cloud. You have got Alibaba cloud. So, if you have a few dollars an hour for computing, you pretty much can get a company started and use the NVIDIA platform in all of these different places. So, it's an incredibly productive platform because of its performance. It works with every framework in the world. It's available basically everywhere, and so as a result of that, we've given artificial intelligence startups anywhere on the planet the ability to jump on and create something. The availability, if you will, the democratization of deep learning -- NVIDIA's GPU deep learning is really quite enabling for startups.

因此，我們的深度學習平台對他們來說是一項真正獨特的優勢，因為它可以在個人電腦上運行，幾乎任何人，即使只有幾百美元的預算，也能用一塊可以進行深度學習的GPU啟動一個新創公司。全球各地的系統整合商和伺服器OEM廠商都有提供這項服務：惠普、戴爾、思科、IBM，以及世界各地的小型和本地系統整合商。更重要的是，它在全球各地的雲端資料中心都可使用，例如亞馬遜AWS、微軟Azure雲端平台都提供了非常出色的可擴展部署。此外，還有IBM雲和阿里雲。所以，只要你每小時有幾美元的運算預算，你幾乎就可以在所有這些不同的平台上啟動一家公司並使用NVIDIA平台。因此，由於其卓越的性能，這是一個極其高效的平台。它兼容全球所有框架，幾乎無處不在。正因如此，我們讓全球各地的AI新創公司都能輕鬆上手，創造價值。如果要說深度學習的普及化，那就是英偉達的 GPU 深度學習技術對新創公司來說確實非常有利。

David Wong, Wells Fargo.

David Wong，富國銀行。

Thanks very much. It was really impressive that 60% growth in your gaming revenues. So, does this imply that there was a 60% jump in [cards] that are being been sold by [online] retailers and retail stores? Or, does the growth reflect new channels through which NVIDIA gaming products are getting to customers?

非常感謝。你們遊戲業務收入成長60%確實令人印象深刻。那麼，這是否意味著線上零售商和實體零售店的顯示卡銷售量成長了60%？還是說，這項成長反映了NVIDIA遊戲產品觸達客戶的新管道？

It's largely the same channels. Our channel has been pretty stable for some time. We have a large network. I appreciate your question. It's one of our great strengths, if you will. We cultivated over two decades a network of partners who take the GeForce platform out to the world. You could access our GPUs. You can access GeForce and be part of the GeForce PC gaming platform from literally anywhere on the planet. So, that's a real advantage, and we're really proud of them.

渠道基本相同。我們的通路一直都很穩定。我們擁有龐大的網路。感謝您的提問。這正是我們的一大優勢。二十多年來，我們精心打造了一個合作夥伴網絡，將 GeForce 平台推廣到世界各地。無論您身處地球何處，都可以使用我們的 GPU，並加入 GeForce PC 遊戲平台。這的確是一項巨大的優勢，我們為此感到非常自豪。

I guess you could also say that Nintendo contributed a fair amount to that growth, and over the next -- as you know, the Nintendo architecture and the Company tends to stick with an architecture for a very long time so we've worked with them now for almost two years. Several hundred engineering years have gone into the development of this incredible game console. I really believe when everybody sees it and enjoy it, they are going to be amazed by it. It's like nothing they've ever played with before, and of course, the brand -- their franchise and their game content is incredible. I think this is a relationship that will likely last two decades, and I'm super-excited about it.

我想你也可以說任天堂對這一增長做出了相當大的貢獻，而且正如你所知，任天堂的架構和公司往往會長期堅持使用同一種架構，所以我們和他們合作至今已經快兩年了。這款令人驚嘆的遊戲機凝聚了數百年的工程心血。我深信，當大家親眼見到並體驗到它時，一定會為之驚嘆。它與他們之前玩過的任何遊戲機都截然不同，當然，任天堂的品牌——他們的遊戲系列和遊戲內容也同樣令人嘆為觀止。我認為這種合作關係很可能會持續二十年，我對此感到無比興奮。

We have no more time for questions.

我們沒有時間回答問題了。

Thank you very much for joining us today. I would leave you with several thoughts that, first, we're seeing growth across all of our platforms from gaming to Pro graphics, to cars to data centers. The transformation of our Company from a chip Company to a computing platform Company is really gaining traction, and you can see that you can see the results of our work as a result of things like GameWorks and GFE and Driveworks. All of the AI that goes on top of that. Our graphics virtualization remoting platform called GRID to the NVIDIA GPU deep learning toolkit are just really examples of how we have transformed a Company from a chip to a computing platform Company.

非常感謝您今天蒞臨本次會議。我想和大家分享幾點：首先，我們看到公司所有平台都在成長，從遊戲到專業圖形，從汽車到資料中心。公司從晶片公司向運算平台公司的轉型正在穩步推進，您可以看到，GameWorks、GFE 和 Driveworks 等產品就是我們轉型成果的體現。此外，還有基於這些產品的 AI 技術。我們的圖形虛擬化遠端平台 GRID 以及 NVIDIA GPU 深度學習工具包，都充分展現了我們如何成功轉型，從晶片公司發展成為計算平台公司。

In no time in the history of our Company have we enjoyed and addressed as exciting large market as we have today. Whether it's artificial intelligence, self-driving cars, the gaming market as it continues to grow and evolve, and virtual reality. And, of course, we all know now very well that GPU deep learning has ignited a wave of AI innovation all over the world, and our strategy and the thing that we've been working on for the last seven years is building an end-to-end AI computing platform. An end-to-end AI computing platform. Starting from GPUs that we have optimized and evolved and enhanced for deep learning to system architectures to algorithms for deep learning, to tools necessary for developers to frameworks, and the work that we do with all of the framework developers and AI researchers around the world, to servers to the cloud to data centers to ecosystems and working with ISVs and startups and all the way to evangelizing and teaching people how to use deep learning to revolutionize the software that they build. And, we call that the Deep Learning Institute, the NVIDIA DLI. These are some of the high-level points that I hope that you got, and I look forward to talking to you again next quarter.

在公司歷史上，我們從未像今天這樣擁有如此龐大且令人興奮的市場。無論是人工智慧、自動駕駛汽車、持續成長和發展的遊戲市場，還是虛擬現實，我們都擁有巨大的發展空間。當然，我們現在都非常清楚，GPU深度學習已經在全球掀起了一股人工智慧創新浪潮。過去七年來，我們一直致力於建立一個端到端的人工智慧運算平台。這個平台涵蓋了從我們針對深度學習優化、改進和增強的GPU，到系統架構、深度學習演算法、開發人員所需的工具、框架，以及我們與全球框架開發人員和人工智慧研究人員的合作，直至伺服器、雲端、資料中心、生態系統，以及與獨立軟體開發商（ISV）和新創公司的合作，最終目標是推廣和培訓人們如何利用深度學習來我們稱之為深度學習研究院（NVIDIA DLI）。希望以上是一些您已經理解的要點，期待下個季度再次與您交流。

This concludes today's conference call. You may now disconnect. We thank you for your participation.

今天的電話會議到此結束。您可以斷開連線了。感謝您的參與。