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.

下午好。我的名字是維多利亞，今天我將成為您的會議接線員。歡迎參加 NVIDIA 財務業績電話會議。

(Operator Instructions)

（操作員說明）

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

我現在將把電話轉給投資者關係副總裁 Arnab Chanda。你可以開始你的會議了。

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.

謝謝你。大家下午好，歡迎參加 NVIDIA 2017 財年第三季度電話會議。今天與我一起接受 NVIDIA 總裁兼首席執行官黃仁勳的電話；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 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。在 2016 年 11 月 17 日之前，您可以通過電話收聽重播。該網絡廣播將在下一季度討論第四季度財務業績的電話會議之前進行重播。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 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.

在本次電話會議期間，我們可能會根據當前預期做出前瞻性陳述。這些前瞻性陳述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 表格中提交的報告證券交易委員會。我們所有的聲明都是在今天，即 2016 年 11 月 10 日，根據我們目前掌握的信息作出的。除法律要求外，我們不承擔更新任何此類聲明的義務。

During this call, we will discuss non-GAAP financial measures. You can find 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 GAAP 財務指標。您可以在我們網站上發布的 CFO 評論中找到這些非 GAAP 財務指標與 GAAP 財務指標的對賬。有了這個，讓我把電話轉給科萊特。

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，阿納布。第三季度收入首次超過 20 億美元，創歷史新高。推動這一點的是我們基於 Pascal 的遊戲平台的成功以及我們數據中心平台的增長，這反映了 NVIDIA 的 GPU 作為人工智能計算引擎的作用。第三季度收入同比增長 54% 至 20 億美元，比上一季度增長 40%。我們所有四個平台均實現了強勁的同比增長。遊戲、專業可視化、數據中心和汽車。 GPU 業務增長 53% 至 17 億美元，Tegra 處理器業務增長 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.

讓我們從我們的遊戲平台開始。在我們基於 Pascal 的 GPU 的推動下，遊戲收入突破 10 億美元大關，同比增長 63%，達到創紀錄的 12.4 億美元。每個地理區域的台式機和筆記本電腦以及從 GTX 1050 到 Titan X 的所有遊戲觀眾的需求都非常強勁。GeForce 遊戲 PC 筆記本電腦錄得顯著增長。我們在 GTX 遊戲 GPU 方面的持續增長反映了 Pascal 架構中前所未有的性能和效率提升。它提供無縫的遊戲體驗和豐富的沉浸式 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.

在第三季度，我們針對台式機推出了 GTX 1050 和 1050 Ti，為電子競技和 VR 功能帶來了巨大的價值。對於筆記本電腦，我們推出了 GTX 1080、1070 和 1060，讓遊戲玩家在移動體驗的性能和效率上實現了重大飛躍。遊戲市場的基本面依然強勁。重磅遊戲產值持續提升。

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.

遊戲玩家正在升級到更高端的 GPU，以享受備受期待的秋季遊戲，例如《戰地 1》、《戰爭機器 3》、《使命召喚：無限戰爭》，而電子競技正在為 PC 吸引新一代遊戲玩家。每個月有超過 1 億玩家玩英雄聯盟。而且，現在有超過 3 億關注電子競技的 Twitch 觀眾。 VR 和 AR 將重新定義娛樂和遊戲。出色的體驗需要高性能 GPU，我們相信我們仍處於這些不斷發展的市場的早期階段。 Pascal 不僅代表了十年來我們在單代 GPU 中取得的最大創新成果，也是我們執行效果最好的產品推出。

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.

轉向專業可視化，由於實時渲染和移動工作站高端市場的增長，Quadro 的收入比一年前增長了 9% 至 2.07 億美元。我們看到，客戶對基於 Pascal 的 P6000 表現出濃厚的興趣，其中包括：皮克斯、迪士尼和 ILM 等數字娛樂領導者，日本 SHIMIZU 等建築、工程和建築公司，以及現代等汽車公司。

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.

其次，數據中心。收入比一年前增長了近兩倍，環比增長 59% 至 2.4 億美元。人工智能和超大規模的超級計算以及 GRID 虛擬化和超級計算的所有方面都增長強勁。 GPU 深度學習正在徹底改變人工智能，並有望影響全球每個行業。 Facebook、微軟和百度等超大規模公司正在使用它為數十億消費者解決問題。

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GPU計算呈現爆發式增長。 Amazon Web Services、Microsoft Azure 和阿里雲正在部署 NVIDIA GPU 用於 AI 數據分析和 HPC。 AWS 最近宣布了其新的 EC2 P2 實例，該實例可擴展到 16 個 GPU，以加速各種 AI 應用程序，包括圖像和視頻識別、非結構化數據分析和視頻轉碼。我們看到了人工智能培訓的強勁增長。對於 AI 推理，我們發布了 Tesla P4 和 P40，分別用於處理節能和高性能工作負載。

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 超級計算機於第三季度開始出貨。早期用戶包括斯坦福大學、加州大學伯克利分校和紐約大學等主要大學，OpenAI、德國人工智能研究所和瑞士人工智能實驗室等領先研究團體以及 SAP 等跨國公司。今年到目前為止，我們的 GPU 技術會議計劃已吸引 18,000 名開發人員和生態系統合作夥伴，突顯了人們對 AI 的廣泛熱情。作為我們在矽谷舉辦的主要春季活動的補充，我們在四大洲的七個城市組織了 GPC。他們在北京、台北、東京和首爾以及阿姆斯特丹、墨爾本和華盛頓特區吸引了座無虛席的觀眾，而孟買還在繼續。除了 400 場課程和實驗室外，我們還通過我們的深度學習學院建設計劃為近 2,000 人提供了 AI 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.

我們還開始與主要的全球公司合作，以實現人工智能的採用。為了在製造業中實施人工智能，我們宣布與日本的 FANUC 合作，專注於機器人和自動化工廠，在交通運輸領域，超過 80 家 OEM、一級供應商和初創公司正在使用我們的 GPU 來開發自動駕駛汽車。

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 圖形虛擬化業務繼續實現極其強勁的增長。各種行業的採用正在加速，特別是製造業、汽車、工程和教育。本季度新增的客戶包括約翰霍普金斯大學和 GE 全球印度。

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，收入增長至創紀錄的 1.27 億美元，同比增長 61%，與高端信息娛樂產品相比，環比增長 7%。英偉達正在為自動駕駛開發端到端的人工智能計算平台。這使汽車製造商能夠收集和標記數據，在數據中心的視頻 GPU 上訓練他們自己的深度神經網絡，然後在汽車中使用 DRIVE PX 2 對其進行處理。

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.

我們還一直在與世界各地的地圖公司一起開發雲到汽車的高清地圖系統。本季度宣布了兩個這樣的合作夥伴關係。我們正在與百度合作，打造一個包含高清地圖、3 級自動駕駛汽車和自動泊車的雲到汽車開發平台。我們還與 TomTom 合作開發基於 AI 的雲到汽車地圖系統，該系統可實現實時的車內地圖定位。

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.

我們開發了一個集成的、可擴展的人工智能平台，其功能從高速公路自動駕駛到全自動駕駛操作。我們正在擴展 DRIVE PX2 架構以擴展性能和功耗。它的範圍從 DRIVE PX2 自動巡航（帶有用於在高速公路上自動駕駛的單個 SSE）到多台能夠實現完全自動駕駛的 DRIVE PX2 計算機。

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 的單芯片人工智能超級計算機，擁有超過 70 億個晶體管。 Xavier 整合了我們的下一代 GPU 架構、定制 CPU 設計和新的計算機視覺加速器。 Xavier 將提供與當今完整的 DRIVE PX2 板及其兩個 Parker SoC 和兩個 Pascal GPU 相當的性能，同時僅消耗一小部分能量。

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輛：Model S、Model X 和即將推出的 Model 3 都配備了由 NVIDIA DRIVE PX2 平台提供支持的新自動駕駛系統，並將能夠通過未來的軟件實現完全自動駕駛。更新。該系統的處理能力是以前技術的 40 多倍，並運行一個用於視覺、聲納和數據處理的新神經網絡。除了我們的四個平台，我們的 OEM 和 IP 業務為 1.86 億美元，同比下降 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，轉向損益表的其餘部分。第三季度 GAAP 毛利率為創紀錄的 59%，非 GAAP 毛利率為創紀錄的 59.2%。這些反映了我們 GeForce 遊戲 GPU 的實力、我們平台方法的成功以及對深度學習的強烈需求。 GAAP 運營費用為 5.44 億美元，其中包括 6600 萬美元的股票薪酬和其他費用。

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.

非美國通用會計準則運營費用為 4.78 億美元，比一年前增長 11%。這反映了我們的增長計劃以及在銷售和營銷方面的投資與員工人數相關的成本。我們打算繼續投資於深度學習，以抓住這個千載難逢的機會。因此，我們預計未來幾個季度的運營費用增長率將保持不變。

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.

GAAP 營業收入為 6.39 億美元。非美國通用會計準則營業收入翻了一番多，達到 7.08 億美元。本季度非 GAAP 營業利潤率超過 35%。 2018 財年，我們打算通過持續的季度現金股息和股票回購向股東返還 12.5 億美元。我們還宣布將季度現金股息提高 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.

現在轉向 FY17 第四季度的前景，我們預計收入為 21 億美元，上下浮動 2%。我們的 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 運營費用預計為 5.72 億美元。非公認會計原則的運營費用預計約為 5 億美元。而且，2017 財年第四季度的 GAAP 和非 GAAP 稅率預計均為 20%，正負 1%。有了這個，接線員，我將把它轉給你，看看我們是否可以回答一些問題。

(Operator Instructions)

（操作員說明）

Mark Lipacis, Jefferies.

馬克·利帕西斯，傑富瑞。

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.

感謝您提出我的問題並祝賀您度過了一個美好的季度。我想首先，Jen-Hsun，也許你能幫助我們理解——數據中心業務同比增長了兩倍。該業務中發生了什麼使這種情況發生？如果您可以談論它是在技術方面還是在終端市場方面？而且，也許作為其中的一部分，您可以幫助我們解構收入以及真正推動這種增長的因素是什麼？而且，我也進行了跟進。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.

幾件事。首先，GPU 計算比以往任何時候都更加重要。今天有很多不同類型的應用程序需要 GPU 計算，而且它正在滲透到整個企業。我們真正推動了幾個應用程序。其中之一是圖形虛擬化、應用程序虛擬化。通過與 VMware 和 Citrix 合作，我們基本上採用了計算密集型、圖形密集型應用程序，將其虛擬化並將其放入數據中心。

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.

第二個是計算科學。使用我們的 GPU 進行通用科學計算，如您所知，科學計算不僅適用於科學家。它運行方程式並使用數字是對許多行業都很重要的工具。然後，第三，我們正在做的最激動人心的事情之一是因為深度學習，我們真的在全世界掀起了一股人工智能創新浪潮。這幾個應用程序——圖形應用程序、虛擬化、計算科學和數據科學真正推動了我們在數據中心的機會。

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，讓這一切成為可能的事情——真正讓這一切成為可能的事情是我們公司從圖形處理器到通用處理器的轉變，然後最重要的是——可能更重要的部分是從芯片公司轉型為平台公司。使應用程序和圖形虛擬化成為可能的是我們稱為 GRID 的複雜軟件堆棧，您已經聽我談論它好幾年了。其次，在數值和計算科學領域，CUDA，我們豐富的應用程序庫和基於數值的庫——基於 CUDA 的數值庫以及我們在與所有人合作的生態系統中投入的所有工具現在知道如何使用 CUDA 開發應用程序的世界各地的開發人員使我們業務的這一部分成為可能。

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 深度學習工具包讓世界上所有的框架都可以得到 GPU 加速。而且，借助 GPU 加速，其優勢令人難以置信。不是 20%，也不是 50%。是20次，50次。這對研究人員來說最重要的是能夠更快地獲得洞察力。可能是幾天，而不是幾個月。它本質上就像擁有一台時光機。其次，對於 IT 經理來說，它可以降低能耗，最重要的是，它可以顯著降低數據中心成本。雖然您有一個帶有 GPU 的服務器機架，但它取代了整個籃球場的現成服務器集群。所以，非常重要。一個偉大的價值主張。

Vivek Arya, Bank of America Merrill Lynch

Vivek Arya，美國銀行美林證券

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?

感謝您提出我的問題，並祝賀持續增長和執行。 Jen-Hsun，另一位數據中心業務。今年顯然增長非常強勁，但在過去，它一直很不穩定。例如，當我回到你們的 FY15 時，它同比增長了 60% 到 70%。去年，它增長了約 7%。今年，它的增長率超過了 100%。我們應該如何考慮客戶的多樣性和應用程序的多樣性，以幫助我們預測未來一兩年業務的增長方式？

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.

事實上，我認為在你的問題中嵌入了許多影響我們業務的變量。尤其是在幾年前，當我們開始研究 GPU 計算並將這種能力引入數據中心時。我們一開始依靠超級計算中心，然後，我們依靠遠程工作站、數據中心工作站，如果你願意的話，還有虛擬化工作站。然後，我們開始越來越依賴——我們開始看到超大規模數據中心的需求，因為他們使用我們的 GPU 進行深度學習和開發他們的網絡。現在，我們開始看到數據中心利用我們的新 GPU、P40 和 P4 來應用運行，以大規模使用網絡進行推理。所以，如果你願意的話，我認為我們正在以多條軌跡移動我們的數據中心業務。

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.

第一個軌跡是我們可以運行的應用程序數量。我們的 GPU 現在能夠以一種架構運行我提到的所有應用程序，從圖形虛擬化到科學計算再到 AI。其次，我們曾經在數據中心，但現在我們在數據中心、超級計算中心以及超大規模數據中心。然後，第三，應用程序的數量——我們影響的行業正在增長。它曾經從超級計算開始。現在，我們有了超級計算。我們有汽車。我們有石油和天然氣。我們有能源發現。我們有金融服務業。當然，我們擁有世界上最大的行業之一，消費者互聯網雲服務。因此，我們開始看到所有這些不同維度的應用程序。

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.

我認為這三件事的結合：應用程序的數量、平台的數量和我們取得成功的位置。然後，當然，我們影響的行業數量。兩者的結合應該以一致的方式為我們提供更多向上的目錄。但是，我認為，真正的重點是我們現在能夠從事的行業的規模。在我們公司的歷史上，我們從未能夠參與如此規模的行業。所以，我認為，歸根結底，這是令人興奮的部分。

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?

偉大的。感謝您提出我的問題，並祝賀一個非常強勁的季度。 Jen-Hsun，在過去的幾個月裡，您一直在路上，我相信您有機會與您的許多重要客戶和合作夥伴建立聯繫。您能否與我們分享您從多次旅行中學到的東西？而且，您對公司長期增長軌蹟的看法如何改變（如果有的話）？

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，東芝。首先，我之所以在路上堅持了將近兩個月，是因為應世界各地開發人員的要求和需求，以便更好地了解 GPU 計算並訪問我們的平台並了解 GPU 現在可以加速的所有各種應用程序。需求真的很大。我們不能再做 GTC，這是我們的開發者大會，本質上是我們的開發者大會。我們不能再僅僅在矽谷做 GTC，所以今年我們決定把它帶到路上。而且，我們去了中國，去了台灣，去了日本，去了韓國。我們在澳大利亞有一個，在歐洲也有一個在印度、華盛頓特區和阿姆斯特丹。

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.

因此，我們的第一次全球開發者大會幾乎覆蓋了整個世界。我想說的可能是它產生的兩個主題，就是 GPU 加速，GPU 真的達到了一個臨界點。它無處不在。它可以在 PC 上使用。世界上每家計算機公司都可以使用它。它在雲端。它在數據中心。它在筆記本電腦中。 GPU 不再是一個小眾組件。正如他們所說，它是一個大規模、大量可用的通用計算平台。所以我認為人們現在意識到 GPU 的好處以及令人難以置信的加速或降低成本——基本上，你可以通過 GPU 獲得硬幣的兩面。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，是人工智能。只是圍繞人工智能的令人難以置信的熱情，當然，對於每個已經了解人工智能的人來說，我要說的是非常清楚的原因。但是，在大量應用、問題和挑戰中，數值方法不可用。基於物理定律、基於方程的方法不可用。這些問題非常複雜。通常情況下，信息是不完整的，並且沒有圍繞它的物理定律。例如，我長什麼樣的物理定律是什麼？推薦今晚的電影的物理定律是什麼？因此，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.

問題是你如何解決那些不完整的問題？沒有物理定律方程可以編程到汽車中，從而使汽車正常行駛。這些都是人工智能問題。搜索是一個人工智能問題。推薦是一個人工智能問題。因此，現在 GPU 深度學習已經點燃了這種能力，它使機器能夠從大量數據中學習並自行確定特徵——計算要識別的特徵。 GPU 深度學習真正點燃了這波 AI 革命的浪潮。所以，我想說的第二件事就是世界各地令人難以置信的熱情是學習如何使用 GPU 深度學習。如何使用它來解決 AI 類型的問題，並在我們所知道的所有行業中這樣做，從醫療保健到交通運輸到娛樂再到企業等等。

Atif Malik, Citigroup.

Atif Malik，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)?

你好。感謝您提出我的問題和祝賀。您提到 Maxwell 升級大約是您（技術難度）的 30%，正好是兩年。我們是否應該考慮兩年時間（聽不清）？

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?

Atif，首先，有幾個地方你刪掉了，這是人工智能問題之一。因為我聽到的信息不完整，但我將從我確實聽到的一些重要詞中推斷出來，我將在這種情況下應用人類智能，看看我是否可以預測你正在嘗試的是什麼問。基線，您問題的基礎是 Maxwell——在過去，Maxwell GPU 在這一代中，我們看到大約每兩三年就有一個升級週期。而且，我們在那段時間擁有大約 6000 萬、8000 萬遊戲玩家的安裝基礎，而現在已經過去了幾年。問題是 Pascal 的升級週期是什麼，它會是什麼樣子？

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.

有幾件事發生了變化，我認為重要的是要知道這可能會影響 Pascal 升級。首先，採用率的提高、單位數量的增長以及 ASP 的數量有所增長。而且，我認為原因是多方面的。我認為，第一，世界上游戲玩家的數量正在增長。實際上出生在過去 10 年、15 年的每個人都可能是遊戲玩家，只要他們能夠獲得電力和互聯網，他們就很可能是遊戲玩家。遊戲質量顯著提高。

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.

已經成為可能的遊戲產值因素之一是因為PC和兩個遊戲機Xbox和PlayStation，以及在未來，在不久的將來，Nintendo Switch。所有這些架構都是通用的，因為它們都使用現代 GPU。它們都使用可編程著色，並且它們都具有基本相似的功能。它們有非常不同的設計點。它們具有不同的功能，但具有非常相似的架構特徵。因此，遊戲開發人員可以使用一個通用代碼庫來瞄準更大的安裝基礎。結果，他們可以提高遊戲的製作質量和生產價值。

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.

第二個——也是你最近可能注意到的一件事，PlayStation 和 Xbox 都宣布了 4K 版本。基本上，他們的遊戲機的 Pro 版本。這對遊戲行業來說真的很令人興奮。這對我們來說真的很令人興奮，因為即將發生的事情是遊戲的產值將會增加，因此，它將增加更高端 GPU 的採用率。我認為這是一個非常重要的積極因素。這大概是第二個了。第一個是遊戲玩家的數量正在增長。二是遊戲產值持續增長。

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.

第三個是遊戲不再只是遊戲。遊戲是運動、遊戲和社交的一部分。有很多人玩遊戲只是為了和其他正在玩遊戲的朋友一起出去玩。這是一種社會現象。然後，當然，因為遊戲是——遊戲的質量，遊戲的複雜性，還有一些比如英雄聯盟，比如星際爭霸；實時模擬，它的實時戰略部分，它的敏捷性，它的手眼協調部分，它令人難以置信的團隊合作部分非常棒，以至於它已經成為一項運動。因為玩遊戲的人太多，因為這是一件有趣的事情，而且很難做到，所以很難掌握。而且行業規模很大。它已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.

而且，我預測的一件事是，在這些日子裡，我相信游戲可能會成為世界上最大的體育產業。而且，這樣做的原因是因為它是最大的行業。例如，現在玩遊戲、享受遊戲並觀看其他人玩遊戲的人比踢足球的人還要多。所以，我認為電子競技將成為世界上最大的體育產業是有道理的，而這只是時間問題。因此，我認為所有這些因素都在推動我們市場規模的增長以及 GPU 的平均售價。

Steven Chin, UBS.

瑞銀的史蒂文·欽。

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，感謝您提出我的問題。 Jen-Hsun，第一個問題，我能否就您對 GRID 系統的評論提出問題。您提到了製造和汽車垂直行業的一些加速需求？只是想知道您是否對您目前所處的局有任何想法，就看到這些區域的全面運行率強勁增長而言？而且，尤其是對於更廣泛的企業和垂直市場，還？而且，作為遊戲方面的快速跟進，想知道您是否對 Pascal 供應量的增加與對這些新產品的被壓抑的需求之間是否仍有很大差距有任何想法？謝謝你。

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。我會說我們可能處於 GRID 第一局的第一局。原因是這樣的。我們自己做好了準備。我們去了春訓營。我們是通過農場聯盟或類似的東西來的。我不是一個真正的棒球運動員，但我聽到一些人談論它。所以，我認為我們可能處於第一局的第一局。我之所以對此感到興奮，是因為我相信未來的應用程序將在數據中心或云中進行虛擬化。

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.

首先，我相信數據應用程序將被虛擬化，無論您使用的是 PC、Chrome 筆記本電腦、Mac 還是 Linux 工作站，您都將能夠享受這些應用程序。根本沒關係。然而，另一方面，我相信未來的應用程序將越來越多地被 GPU 加速。你如何將沒有 GPU 的東西放到雲中，你如何對這些越來越多地被 GPU 加速的應用程序進行 GPU 加速？答案當然是把 GPU 放在雲端，把 GPU 放在數據中心。這就是 GRID 的全部意義所在。這是關於虛擬化的。它是關於將 GPU 置於大型數據中心並能夠虛擬化應用程序，以便我們可以在任何計算機、任何設備上享受它，並使計算更接近數據。

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.

我認為我們才剛剛開始，這可以解釋為什麼 GRID 最終在構建生態系統、構建基礎設施、開發所有軟件、將服務質量變得非常精緻之後，與生態系統合作夥伴合作，它真的起飛了。我當然可以期待看到它繼續以我們在一段時間內看到的速度增長。

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.

就帕斯卡而言，我們仍在加速。從某種意義上說，我們所有的產品都完全合格，因此生產已全面提升。它們在市場上。它們已通過 OEM 的認證和認證。然而，需求仍然相當高，所以我們將繼續努力。我們的製造合作夥伴台積電為我們做得很好。 2016 FinFET 的產量非常好，他們在支持我們方面做得非常出色。我們將繼續努力。

Joe Moore, Morgan Stanley.

摩根士丹利的喬摩爾。

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，很棒的季度，但仍然對這有多好感到驚訝。你能談談推理機會的大小嗎？顯然，你在訓練中做得很好。我認為穿透推理在早期是合理的，但您能否談談您如何看待 GPU 與 FPGA 在這方面的競爭，以及您認為這個機會有多大？謝謝你。

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.

當然，我會倒退。我將從倒退開始，先回答 FPGA 問題。 FPGA 擅長很多事情，如果市場機會很大，你可以在 FPGA 上做的任何事情，你總是可以 - 開發 ASIC 總是更好。而且，FPGA是你在體積不大的時候使用的。當您不確定要放入某物的功能時，您會使用 FPGA。當體積不大時，FPGA 非常有用。因為你可以構建一個ASIC——你可以構建一個完全定制的芯片，顯然可以提供更高的性能。與使用 FPGA 相比，性能不是提高 20%，而是性能和能效提高 10 倍。我認為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.

我們的策略與任何其他策略都大不相同。我們的戰略實際上是建立一個計算平台。我們的 GPU 不再是特定功能的東西。它是一個通用並行處理器。 CUDA可以做分子動力學。它可以做流體動力學。它可以做偏微分方程。它可以做線性代數。它可以做人工智能。它可用於地震分析。它可以用於計算機圖形，甚至計算機圖形。因此，我們的 GPU 非常靈活，而且它真的是專為並行吞吐量計算而設計的。並且，通過將它與CPU結合起來，我們創建了一個既擅長順序信息、順序指令處理又擅長高吞吐量數據處理的計算平台。因此，我們創建了一個擅長這兩方面的計算架構。

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.

我們之所以認為這很重要，是因為有幾件事。我們想建立一個對大行業有用的計算平台。你可以將它用於人工智能。你可以用它來搜索。您可以將其用於視頻轉碼。你可以用它來發現能量。您可以將其用於健康。你可以用它來理財。您可以將其用於機器人技術。您可以將它用於所有這些不同的事情。

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.

根據首要原則，我們正在嘗試構建一個計算平台。這是一個計算架構。而且，不是專門的應用程序。我們拜訪的大多數客戶、我們正在處理的大多數市場以及我們強調的領域都是計算機用戶。他們需要使用和部署一個計算平台。它的好處是能夠快速改進他們的人工智能網絡。

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.

人工智能仍處於早期階段。這是早期的早期階段，GPU 深度學習正在以非常快的速度進行創新。我們的 GPU 使人們能夠學習開發新網絡並儘快部署新網絡。所以，我認為考慮它的方式是將我們的 GPU 視為一個計算平台。

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。我的看法是大約有 500 萬到 1000 萬個超大規模數據中心節點。我認為，正如您之前聽到我所說的那樣，我認為培訓是一組新的 HPC 集群，已添加到這些數據中心中。然後，接下來會發生的事情是，您將看到 GPU 被添加到這 500 萬到 1000 萬個節點中，這樣您就可以加速可能進入數據中心的每一個查詢。未來的AI查詢。我認為 GPU 有機會看到相當大的超大規模安裝基礎。

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.

但是，除此之外還有企業市場。儘管如此，大量的計算是在雲中完成的，大量的計算，尤其是我們在這裡談論的需要大量數據的計算類型——我們是一個數據吞吐量機器——類型我們談論的計算機往往是企業中的計算機之一。而且，我相信很多企業市場都將轉向人工智能；我們在未來尋找的事物類型是使用人工智能簡化我們的業務處理器，使用人工智能找到商業智能或洞察力，使用人工智能優化我們的供應鏈，使用人工智能優化我們的預測，優化方式我們發現並讓客戶、數字客戶或使用人工智能的數字客戶感到驚喜和愉悅。所以，所有這些大公司的業務運營部分，我認為AI真的可以提升。

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——超大規模的企業計算，第三個是非常非常新的東西。它被稱為物聯網。物聯網——隨著時間的推移，我們將有 1 萬億個事物連接到互聯網，它們將測量從振動、聲音、圖像、溫度、氣壓到——你能想到的東西。這些事情將遍布世界各地，我們將進行衡量，我們將不斷衡量和監控他們的活動。而且，使用我們能想像到的唯一可以幫助增加價值並從中獲得洞察力的東西就是真正使用深度學習的人工智能。我們可以擁有這些新型計算機，它們可能會在本地或您擁有的集群的位置附近。並且，監控所有這些設備並保持 - 防止它們出現故障或為其添加智能，以便它們為人們讓它們所做的事情增加更多價值。因此，我認為我們正在解決的市場規模確實比我們歷史上的任何時候都要大。而且，考慮它的最簡單方法可能是我們現在是一家計算平台公司。我們只是一個計算平台公司，我們的重點是GPU計算，主要應用之一是人工智能。

Craig Ellis, B. Riley and Company.

Craig Ellis、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?

感謝您提出問題並祝賀出色的執行。 Jen-Hsun，我想回到汽車行業。過去，公司曾提到收入包括展示，然後在自動駕駛方麵包括諮詢和產品收入。但是，我現在更深入地考慮諮詢方面。但是，當我們展望 Xavier 以及您在季度內宣布將於明年年底發布的公告時，我們應該如何預期收入組合會演變？不僅僅是從諮詢到產品，而是從帕克到澤維爾？

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，我不知道我對你有真正的細粒度故障，部分原因是我不確定。但是，我認為動態是自動駕駛汽車可能是最具破壞性的事件——汽車行業正在發生的最具破壞性的動態。我幾乎無法想像，在五年內，一輛性能相當不錯的汽車不會在某種程度上具備自動駕駛能力。而且，這是一個非常重要的水平。我認為特斯拉通過在不久的將來推出並在道路上使用人工智能的完全自動駕駛能力所做的事情，已經在汽車行業引發了衝擊波。基本上提前了五年。

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.

任何談論 2021 年的人，都不再是首發。我認為，這可能是汽車行業中最重要的一點。任何談論自主能力以及 2020 年和 2021 年的人目前都在以一種非常重要的方式重新評估。所以，我認為這當然會改變我們的業務概況最終的樣子。這取決於那些因素。我們的自動駕駛汽車戰略比較明確，但還是讓我解釋一下。

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.

第一，我們認為自動駕駛汽車不是檢測問題，而是人工智能計算問題。這不僅僅是檢測物體。這是關於你周圍環境的感知。它是關於要做什麼的推理——正在發生的事情和要做什麼——並根據這種推理採取行動。而且，要不斷學習。所以，我認為人工智能計算需要大量的計算，任何認為它只需要一到兩瓦的人——基本上，能量的量——是手機能量的三分之一。我認為這很不幸，而且不會很快發生。

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能計算是一個非常軟件豐富的問題，而且是一個非常令人興奮的人工智能問題。而且，深度學習和 GPU 可以增加很多價值，這將在 2017 年發生，不會在 2021 年發生。我認為是第一。第二，我們的策略是應用，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件堆棧並創建他們的網絡，他們的人工智能網絡。而且，我們將解決從高速公路巡航、出色的高速公路巡航到全自動駕駛到卡車再到穿梭巴士的所有問題。而且，使用一種計算架構，我們可以將其應用於基於雷達的系統、雷達加攝像頭、雷達加攝像頭加激光雷達。我們可以將它用於各種傳感器融合環境。所以，我認為我們的戰略在業界引起了很好的共鳴，因為人們現在意識到我們需要五年前的計算能力。這不是檢測問題，而是人工智能計算問題，而且軟件真的很密集。但是，我認為，這三個觀察結果使我們處於一個非常好的位置。

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?

你好。感謝您提出我的問題。全面的好季度。我確實想回到汽車領域，因為數據中心領域已經被詳細討論過了。隨著新的 Drive PX 平台潛在地增加 ASP，我們如何看待未來汽車的 ASP？而且，如果我記得，你有大約 3000 萬美元的汽車積壓訂單？我不確定是否也可以在那裡獲得更新？

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 架構是可擴展的。您可以從一個 Parker SoC 開始，這樣您就可以擁有環繞攝像頭。它允許您使用人工智能進行高速公路巡航。而且，如果您想擁有更多攝像頭，以便在更多條件下更頻繁地使用您的功能，您可以隨時添加更多處理器。所以，我們從一到四個處理器。而且，如果它是一輛完全自主的無人駕駛汽車——例如一輛無人駕駛出租車，你可能需要甚至超過四個我們的處理器。您可能需要八個處理器。您可能需要 12 個處理器。而且，這樣做的原因是因為您需要減少自動駕駛儀無法工作、無法開啟、對不起、無法參與的情況。而且，因為你的車裡根本沒有司機。我認為根據您擁有的應用程序，我們將有不同的配置，並且它是可擴展的。它從幾百美元到幾千美元不等，所以我認為這取決於人們試圖部署的配置。現在只需幾千美元，這輛車的生產力就令人難以置信，因為您可以簡單地進行數學計算。它更可用。運營成本降低。而且，在該用例的上下文中，幾千美元肯定幾乎是微不足道的。

Harlan Sur, JPMorgan.

摩根大通的哈蘭蘇爾。

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?

下午好。祝賀紮實的執行和成長。查看您的一些雲客戶的新服務產品時，您提到了 AWS EC2 P2 平台。您擁有 Microsoft Azure 的雲服務平台。這很有趣，因為他們主要使用您的 K80 加速器平台來增加新實例，這意味著 Maxwell 基礎和最近引入的基於 Pascal 的採用曲線仍然遙遙領先於團隊，這顯然是一個很好的設置，因為它與持續的強勁增長有關往前走。您能幫我們理解為什麼這些加速器的設計和周期時間長嗎？而且，您預計基於 Maxwell 的加速器的採用曲線何時開始受到您的一些雲客戶的歡迎？

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，好問題。而且，這正是近五年前開始與所有這些大型數據中心合作的原因所在。這樣做的原因是因為有幾件事必鬚髮生。必須開發應用程序。它們是超大規模的。他們是企業——他們的數據中心級軟件必須適應這個新的計算平台。必須開發和訓練神經網絡並準備好部署。 GPU 必須針對他們擁有的每一個數據中心和每一個服務器配置進行測試，並且在我們正在談論的規模上部署需要這種類型的時間。所以，我認為，這是第一。

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，在開普勒、麥克斯韋和帕斯卡之間，架構是相同的。儘管底層架構得到了極大的改進，性能也有了顯著的提升，但軟件層是一樣的。因此，我們未來幾代人的採用率將會非常非常快，你會看到的。將我們的軟件、架構和 GPU 集成到全球所有數據中心需要很長時間。這需要很多工作。這需要很長時間。

Romit Shah, Nomura.

Romit Shah，野村。

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，我只是想問一下關於自動駕駛勝利的問題。我們知道您取代了 Mobileye，我只是想知道您能否談談 Tesla 選擇您的 GPU 的原因嗎？而且，您可以在斜坡和時間方面給我們什麼，以及這樣的斜坡將如何影響汽車毛利率？

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三樣東西。首先，這不是檢測問題，而是 AI 計算問題。而且，計算機具有處理器，架構是一致的，您可以對其進行編程。你可以寫軟件。你可以編譯到它。這是一個 AI 計算問題，我們的 GPU 計算架構有 10 年細化的好處。事實上，今年是我們第一個 GPGPU 的 10 週年紀念日，我們的第一個 CUDA GPU 稱為 G8，我們為此工作了 10 年。所以，第一是自動駕駛，自動駕駛汽車是一個人工智能計算問題。這不是檢測問題。

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服務。該服務是一個汽車網絡，他們通過它不斷改進。就像電話一樣。這就像機頂盒。您必須維護和服務該客戶，因為他們對自動駕駛服務感興趣。這不是一個功能。自動駕駛一直在通過更好的地圖、更好的駕駛行為、更好的感知能力和更好的人工智能得到改進，因此它的軟件組件以及汽車公司一旦在我們的平台上開發它就擁有自己的軟件的能力是一個真正的積極因素。真正積極到它正在啟用的地步，或者它對駕駛車隊的未來至關重要。

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.

然後，第三個 - 能夠繼續對它們進行 OTA。第三，簡單來說就是性能和能量水平。我不相信現在真的有可能提供一個性能水平的人工智能計算平台，該平台可以在汽車中實現自動駕駛所需的能效水平，並將所有功能放在一起合理的方式。我相信 DRIVE PX2 是當今地球上唯一可行的解決方案。因此，由於特斯拉非常希望比其他任何人提前五年向世界提供這種水平的能力，因此我們是他們的絕佳合作夥伴。所以，這可能是三個原因。

Matt Ramsay, Canaccord Genuity.

馬特拉姆齊，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。下午好。 Jen-Hsun，我對您的評論做了一個有趣的觀察，即您的公司已經從圖形加速器公司變成了計算平台公司，我認為這太棒了。我想知道的一件事是，人工智能和深度學習加速可能會在您的平台上標準化，您在矽谷所看到和聽到的關於創業活動的信息是什麼？而且，那些試圖圍繞你提出的平台進行創新的人，既能補充你正在做的事情，又可能對你正在做的事情產生真正的長期競爭力？很想听聽你對此的看法。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，我真的很感激。我們在世界各地看到了大量的人工智能初創公司。當然，在美國這裡有很多人。在中國有相當數量的。歐洲的數量非常多。加拿大有很多。這幾乎是一個全球性的事件。現在已經開始使用 GPU 深度學習並利用我們花了近七年時間構建的計算平台的軟件公司的數量，這真的很了不起。我們正在跟踪大約 1,500 個。

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 的計劃，如果您願意的話，Inception 是我們的啟動支持計劃。他們可以使用我們的早期技術。他們可以訪問我們的專業知識、我們的計算平台，以及我們可以與許多初創公司分享的所有關於深度學習的知識。他們正試圖在網絡安全、基因組學、消費者應用程序、計算金融、物聯網、機器人和自動駕駛汽車等行業中使用深度學習。那裡的初創公司數量確實非常驚人。

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.

所以，我們的深度學習平台對他們來說是一個非常獨特的優勢，因為它可以在 PC 上使用，所以你可以——幾乎任何人只要花費幾百美元就可以使用可以做深度學習的 [視頻] GPU 來創業學習。它可從世界各地的系統製造商和服務器 OEM 處獲得：惠普、戴爾、思科、IBM，世界各地的系統製造商、小型系統製造商、本地系統製造商。非常重要的是，它可以在世界各地的雲數據中心使用，因此亞馬遜 AWS，微軟的 Azure 云有一個非常棒的實現，可以擴展。您已經擁有了 IBM 雲。你有阿里雲。因此，如果您每小時有幾美元用於計算，您幾乎可以創辦一家公司並在所有這些不同的地方使用 NVIDIA 平台。因此，由於其性能，它是一個非常高效的平台。它適用於世界上的每個框架。它基本上隨處可用，因此，我們為地球上任何地方的人工智能初創公司提供了跳躍和創造東西的能力。如果你願意的話，深度學習的民主化——英偉達的 GPU 深度學習對於初創公司來說確實是非常有利的。

David Wong, Wells Fargo.

大衛王，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。您的遊戲收入增長了 60%，這確實令人印象深刻。那麼，這是否意味著 [在線] 零售商和零售店銷售的 [卡片] 增加了 60%？或者，增長是否反映了 NVIDIA 遊戲產品向客戶提供的新渠道？

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。我們的頻道已經相當穩定一段時間了。我們有一個龐大的網絡。我很欣賞你的問題。如果您願意，這是我們的一大優勢。二十多年來，我們建立了一個合作夥伴網絡，將 GeForce 平台推向世界。您可以訪問我們的 GPU。您可以從地球上幾乎任何地方訪問 GeForce 並成為 GeForce PC 遊戲平台的一部分。所以，這是一個真正的優勢，我們真的為他們感到驕傲。

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為這種增長做出了相當大的貢獻，而且在接下來的時間裡——如你所知，任天堂的架構和公司傾向於在很長一段時間內堅持一個架構，所以我們與他們合作現在快兩年了。這個令人難以置信的遊戲機的開發已經花費了數百年的工程時間。我真的相信當每個人看到它並享受它時，他們都會為它感到驚訝。這就像他們以前從未玩過的任何東西，當然還有品牌——他們的特許經營權和遊戲內容令人難以置信。我認為這種關係可能會持續二十年，我對此感到非常興奮。

We have no more time for questions.

我們沒有時間提問了。

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.

非常感謝您今天加入我們。我會給你留下幾個想法，首先，我們看到我們所有平台的增長，從遊戲到專業圖形，從汽車到數據中心。我們公司從芯片公司到計算平台公司的轉變真的很受關注，你可以看到你可以看到我們的工作成果，比如 GameWorks 和 GFE 和 Driveworks。在此之上的所有人工智能。我們的圖形虛擬化遠程處理平台（稱為 GRID 到 NVIDIA GPU 深度學習工具包）只是我們如何將公司從芯片公司轉變為計算平台公司的真實示例。

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.

在我們公司的歷史上，我們從未像今天這樣享受和應對如此令人興奮的大市場。無論是人工智能、自動駕駛汽車、不斷發展壯大的遊戲市場，還是虛擬現實。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波人工智能創新浪潮，而我們過去七年來一直在努力的戰略和事情是建立一個終結——端到端的人工智能計算平台。一個端到端的人工智能計算平台。從我們為深度學習優化、發展和增強的 GPU 到系統架構，再到深度學習算法，再到開發人員到框架所需的工具，以及我們與全球所有框架開發人員和 AI 研究人員開展的工作，從服務器到雲，從數據中心到生態系統，並與 ISV 和初創公司合作，一直到宣傳和教授人們如何使用深度學習來徹底改變他們構建的軟件。而且，我們將其稱為深度學習研究所，即 NVIDIA DLI。這些是我希望你得到的一些高水平的觀點，我期待下個季度再次與你交談。

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。您現在可以斷開連接。我們感謝您的參與。