輝達 (NVDA) 2017 Q2 法說會逐字稿

Good afternoon. My name is Desiree, and I will be your conference Operator today. I would like to welcome you to the NVIDIA financial results Conference Call. All lines have been placed on mute. After the speakers' remarks, there will be a question-and-answer period.

下午好。我的名字是 Desiree，今天我將成為您的會議接線員。歡迎您參加 NVIDIA 財務業績電話會議。所有線路均已靜音。演講者發言後，將進入問答環節。

(Operator Instructions)

（操作員說明）

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

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

Thank you.

謝謝你。

Good afternoon, everyone, and welcome to NVIDIA's Conference Call for the Second Quarter of FY17. With me on the call today from NVIDIA are Jen-Hsun Huang, President and Chief Executive Officer, and Colette Kress, Executive Vice President and Chief Financial Officer. I'd like to remind you that today's call is being Webcast live on NVIDIA's Investor Relations website.

大家下午好，歡迎參加 NVIDIA 2017 財年第二季度電話會議。 NVIDIA 總裁兼首席執行官黃仁勳和執行副總裁兼首席財務官 Colette Kress 今天與我通話。我想提醒您，今天的電話會議正在 NVIDIA 的投資者關係網站上進行網絡直播。

It is also being recorded. You can hear a replay by telephone until the 18th of August, 2016. The Webcast will be available for replay up until next quarter's Conference Call to discuss Q3 financial results.

它也在被記錄。您可以在 2016 年 8 月 18 日之前通過電話收聽重播。該網絡廣播將在下一季度的電話會議之前進行重播，以討論第三季度的財務業績。

The content of today's call is NVIDIA's property. It cannot be reproduced or transcribed without our prior written consent.

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 the course of 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 11th of August 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的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基於我們目前可獲得的信息，截至今天，即 2016 年 8 月 11 日。除法律要求外，我們不承擔更新任何此類聲明的義務。

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

在本次電話會議中，我們將討論非 GAAP 財務指標。您可以在我們網站上發布的 CFO 評論中找到這些非 GAAP 財務指標與 GAAP 財務指標的對賬。

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

有了這個，讓我把電話轉給科萊特。

Thanks, Arnab.

謝謝，阿納布。

This quarter, we introduced our new family of Pascal-based GPUs, one of our most successful launches ever. We also benefited from both the winding adoption of deep learning and our expanding engagement with hyperscale data centers around the world as they apply deep learning to all the services they provide.

本季度，我們推出了基於 Pascal 的 GPU 新系列，這是我們有史以來最成功的發布之一。我們還受益於深度學習的廣泛採用以及我們與世界各地超大規模數據中心的不斷擴大的合作，因為它們將深度學習應用於他們提供的所有服務。

Revenue continued to accelerate, rising 24% to a record $1.43 billion. We saw strong sequential and year-on-year growth across our four platforms. Gaming, professional visualization, data center, and automotive. Our business model based on driving GPU compute platforms into highly targeted markets is clearly succeeding. The GPU business was up 25% to $1.2 billion from a year ago. The Tegra processor business increased 30% to $166 million.

收入繼續加速增長，增長 24%，達到創紀錄的 14.3 億美元。我們在四個平台上看到了強勁的連續和同比增長。遊戲、專業可視化、數據中心和汽車。我們基於將 GPU 計算平台推向高度目標市場的商業模式顯然是成功的。 GPU 業務同比增長 25% 至 12 億美元。 Tegra 處理器業務增長 30% 至 1.66 億美元。

In Q2, our four platforms contributed nearly 89% of revenue, up from 85% a year earlier and 87% in the preceding quarter. They collectively increased 29% year-over-year.

在第二季度，我們的四個平台貢獻了近 89% 的收入，高於去年同期的 85% 和上一季度的 87%。它們總共同比增長了 29%。

Let's begin with our gaming platform. Gaming revenue increased 18% year on year to $781 million reflecting the success of our latest integration of Pascal-based GPUs. Demand was strong in every geographic region.

讓我們從我們的遊戲平台開始。遊戲收入同比增長 18% 至 7.81 億美元，這反映了我們最新集成基於 Pascal 的 GPU 的成功。每個地理區域的需求都很強勁。

The Pascal architecture offers a number of amazing technological advances. It enables unprecedented performance and efficiencies for playing sophisticated AAA gaming titles and driving rich, immersive VR experiences.

Pascal 架構提供了許多驚人的技術進步。它為玩複雜的 AAA 遊戲和驅動豐富的沉浸式 VR 體驗提供了前所未有的性能和效率。

In our most successful launch ever, we introduced four major products. They are GeForce GTX1080, 1070, and 1060 for the enthusiast market and the Titan X, the world's fastest consumer GPU for deep learning development, digital content creation, and extreme gaming.

在我們有史以來最成功的發布中，我們推出了四種主要產品。它們是面向發燒友市場的 GeForce GTX1080、1070 和 1060，以及用於深度學習開發、數字內容創建和極限遊戲的世界上最快的消費級 GPU。

Wired magazine called the GTX 1080 an unprecedented piece of electronic precision, one that performs Herculean feats of computational strength. Forbes called GTX1060, which brings a premium VR experience within reach of many, a fantastic product, and Hardware Canucks describes Titan X as a technological tour de force with frame rates that are simply mind-boggling. The GTX 1080, 1070, 1060, and Titan X are now in full production and available to consumers worldwide.

《連線》雜誌稱 GTX 1080 是一款前所未有的電子精度產品，它具有強大的計算能力。福布斯稱 GTX1060 為許多人帶來了優質的 VR 體驗，這是一款出色的產品，而 Hardware Canucks 則將 Titan X 描述為幀速率令人難以置信的技術傑作。 GTX 1080、1070、1060 和 Titan X 現已全面投產，可供全球消費者使用。

VR's potential is on vivid display in a new, open source game that we released during the quarter. Available on Steam, NVIDIA VR Funhouse is an open source title created with our GameWorks SDK. It integrates physical simulation into VR along with amazing graphics and precise haptics that you feel like you're actually at a carnival.

VR 的潛力在我們在本季度發布的一款新的開源遊戲中得到了生動的展示。 Steam 上提供的 NVIDIA VR Funhouse 是一款使用我們的 GameWorks SDK 創建的開源遊戲。它將物理模擬與令人驚嘆的圖形和精確的觸覺一起集成到 VR 中，讓您感覺自己彷彿置身於狂歡節中。

Moving to professional visualization, Quadro revenue grew to a record $214 million, up 22% year on year and up 13% sequentially. Growth came from the high end of the market for realtime rendering tools and mobile workstations. The M6000 GPU 24-gig launched earlier this year is drawing strong interest from a broad range of customers.

轉向專業可視化，Quadro 收入增長至創紀錄的 2.14 億美元，同比增長 22%，環比增長 13%。增長來自實時渲染工具和移動工作站的高端市場。今年早些時候推出的 M6000 GPU 24-gig 引起了廣大客戶的濃厚興趣。

Digital Domain, a leading special effects studio, is using Quadro to accelerate productivity for its work on films and commercials, which requires especially tight turnaround times. Engineering giant AECOM and the Yale school of architecture are using Quadro to accelerate their design and engineering workflows.

領先的特效工作室 Digital Domain 正在使用 Quadro 來提高其電影和廣告工作的生產力，這需要特別緊迫的周轉時間。工程巨頭 AECOM 和耶魯大學建築學院正在使用 Quadro 來加速他們的設計和工程工作流程。

Last month at SIGGRAPH conference, we introduced a series of new products that embed photo-realistic and immersive experience into workflows incorporating Iray a and VR. We launched the Pascal-based Quadro P6000, the most advanced workstation GPU, enabling designers to manipulate complex designs up to twice as fast as before. We demonstrated how deep learning is being brought to the realm of the industrial design to create better products faster. And, we launched eight new and updated software libraries such as VRWorks 360 video SDK which brings panoramic video to VR.

上個月在 SIGGRAPH 會議上，我們推出了一系列新產品，將照片般逼真的沉浸式體驗嵌入到 Iray a 和 VR 的工作流程中。我們推出了基於 Pascal 的 Quadro P6000，它是最先進的工作站 GPU，使設計人員能夠以比以前快兩倍的速度處理複雜的設計。我們展示瞭如何將深度學習帶入工業設計領域，以更快地創造出更好的產品。並且，我們推出了 8 個新的和更新的軟件庫，例如 VRWorks 360 視頻 SDK，將全景視頻帶入 VR。

Moving to data center, revenue reached a record $151 million, more than doubling year on year and up 6% sequentially. This impressive performance reflects strong growth in supercomputing, hyperscale data centers, and grid virtualization. Interest in deep learning is surging as industries increasingly seek to harness this revolutionary technology.

轉向數據中心，收入達到創紀錄的 1.51 億美元，同比增長一倍多，環比增長 6%。這一令人印象深刻的性能反映了超級計算、超大規模數據中心和網格虛擬化的強勁增長。隨著行業越來越多地尋求利用這種革命性的技術，人們對深度學習的興趣正在飆升。

Hyperscale companies remain fast adopters of deep learning. Both for training and realtime inference, particularly for natural lingual processing, video, and image analysis. Among them are Facebook, Microsoft, Amazon, Ali Baba, and Bidoo. Major cloud providers are also offering GPU computing for their customers. Microsoft Azure is now using NVIDIA's GPUs to provide computing and graphics virtualizations.

超大規模公司仍然是深度學習的快速採用者。既適用於訓練也適用於實時推理，尤其適用於自然語言處理、視頻和圖像分析。其中包括 Facebook、微軟、亞馬遜、阿里巴巴和 Bidoo。主要的雲提供商也為其客戶提供 GPU 計算。 Microsoft Azure 現在使用 NVIDIA 的 GPU 來提供計算和圖形虛擬化。

During the quarter, we began shipping Tesla P100, the world's most advanced GPU accelerator based on the Pascal architecture. Designed to accelerate deep learning training, it allows application performance to scale up to 8 GPUs using our NV link interconnect. We also announced a variant of P100 based on PCI express that makes it suitable for a wide range of accelerated servers.

在本季度，我們開始出貨 Tesla P100，這是世界上最先進的基於 Pascal 架構的 GPU 加速器。它旨在加速深度學習訓練，使用我們的 NV 鏈路互連允許應用程序性能擴展到多達 8 個 GPU。我們還宣布了一種基於 PCI express 的 P100 變體，使其適用於各種加速服務器。

At our GPU Technology Conference in April, we introduced DGX1, the world's first deep learning super computer. Equipped with eight P100s in a single box, it provides deep learning performance that is equivalent to 250 traditional servers. It comes loaded with NVIDIA software and AI application developers.

在我們四月份的 GPU 技術大會上，我們介紹了世界上第一台深度學習超級計算機 DGX1。單盒8個P100，提供相當於250台傳統服務器的深度學習性能。它配備了 NVIDIA 軟件和 AI 應用程序開發人員。

We are seeing strong interest in DGX1 from AR researchers and developers across academia, government labs, and large enterprises. Two days ago, Jen-Hsun hand-delivered the very first DGX1 production model to the open AI institute. They plan to use the system in part to build autonomous agents like chat box, cars, and robots. Broader deliveries, will commence later this quarter.

我們看到學術界、政府實驗室和大型企業的 AR 研究人員和開發人員對 DGX1 產生了濃厚的興趣。兩天前，仁勳親手將第一台 DGX1 量產模型交付給開放的 AI 研究所。他們計劃部分使用該系統來構建自主代理，如聊天框、汽車和機器人。更廣泛的交付將在本季度晚些時候開始。

We will be talking more about deep learning later this year at regional versions of our GPU technology conference set for eight cities around the world. Among them Beijing, Amsterdam, Tokyo, and Seoul, as well as Washington DC.

我們將在今年晚些時候為全球八個城市舉辦的 GPU 技術會議的區域版本中更多地討論深度學習。其中北京、阿姆斯特丹、東京、首爾，還有華盛頓特區。

Our grid graphics virtualization business more than doubled in the quarter. Adoption is accelerating across a variety of industries, particularly automotive and AEC. Among customers added this quarter was StatOil, a Norwegian Oil & Gas Company.

我們的網格圖形虛擬化業務在本季度翻了一番多。各種行業的採用正在加速，特別是汽車和 AEC。本季度新增的客戶包括挪威石油和天然氣公司 StatOil。

Finally, in automotive, revenue increased to a record $119 million, up 68% year-over-year and up 5% sequentially, driven by premium infotainment and digital cockpit features in mainstream cars. Our effort to help partners develop self-driving cars continues to accelerate. We have started to ship our drive PX2 automotive super computer to the 80-plus companies using both our hardware and DRI work software to develop autonomous driving technologies.

最後，在汽車領域，在主流汽車的高級信息娛樂和數字駕駛艙功能的推動下，收入增長至創紀錄的 1.19 億美元，同比增長 68%，環比增長 5%。我們幫助合作夥伴開發自動駕駛汽車的努力繼續加快。我們已經開始使用我們的硬件和 DRI 工作軟件向 80 多家公司交付我們的 drive PX2 汽車超級計算機，以開發自動駕駛技術。

We remain on track to ship our autopilot solution based on the drive platform. Beyond our four platforms, our OEM and [IPA] business was $163 million, down 6% year on year in line with mainstream PC demands. Now, turning to the rest of the Income Statement, we had a record GAAP gross margins of 57.9% while non-GAAP gross margin was 58.1%. 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 $509 million, down 9% from a year earlier. Non-GAAP operating expenses were $448 million, up 6% from a year earlier. This reflects increased hiring in R&D and Marketing expenses, partially offset by lower legal fees.

我們仍在按計劃推出基於驅動平台的自動駕駛解決方案。除了我們的四個平台，我們的 OEM 和 [IPA] 業務為 1.63 億美元，同比下降 6%，符合主流 PC 需求。現在，轉向損益表的其餘部分，我們的 GAAP 毛利率達到創紀錄的 57.9%，而非 GAAP 毛利率為 58.1%。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 5.09 億美元，同比下降 9%。非美國通用會計準則運營費用為 4.48 億美元，同比增長 6%。這反映了研發和營銷費用的招聘增加，部分被較低的法律費用所抵消。

GAAP operating income for the Second Quarter was $317 million compared to $76 million a year earlier. Non-GAAP operating income was $382 million, up 65%. Non-GAAP operating margins improved 680 basis points from a year ago to 26.8%.

第二季度的 GAAP 營業收入為 3.17 億美元，而去年同期為 7600 萬美元。非美國通用會計準則營業收入為 3.82 億美元，增長 65%。非美國通用會計準則營業利潤率比一年前提高了 680 個基點，達到 26.8%。

Now, turning to the outlook for the third quarter of FY17,we expect revenue to be $1.68 billion, plus or minus 2%. Our GAAP and non-GAAP gross margins are expected to be 57.8% and 58%, respectively, plus or minus 50 basis points.

現在，轉向 2017 財年第三季度的前景，我們預計收入為 16.8 億美元，上下浮動 2%。我們的 GAAP 和非 GAAP 毛利率預計分別為 57.8% 和 58%，上下浮動 50 個基點。

GAAP operating expenses are expected to be approximately $530 million. Non-GAAP operating expenses are expected to be approximately $465 million. GAAP and non-GAAP tax rates for the third quarter of FY17 are both expected to be 21%, plus or minus 1%. Further financial details are included in the CFO commentary and other information available on our IR website.

GAAP 運營費用預計約為 5.3 億美元。非公認會計原則的運營費用預計約為 4.65 億美元。 2017 財年第三季度的 GAAP 和非 GAAP 稅率預計均為 21%，正負 1%。更多財務細節包含在 CFO 評論和我們投資者關係網站上提供的其他信息中。

We will now open the call for questions. Operator, could you please poll for questions? Thank you.

我們現在將打開問題的電話。接線員，請您投票提問嗎？謝謝你。

(Operator Instructions)

（操作員說明）

Your first question comes from the line of Mark Lipacis.

您的第一個問題來自 Mark Lipacis。

Hi. Thanks for taking my questions. First question on the data center business. Can you all help us understand to what extent is the demand being driven by the deep learning applications versus the classic computationally intense design applications?

你好。感謝您提出我的問題。關於數據中心業務的第一個問題。你們能否幫助我們了解深度學習應用程序與經典計算密集型設計應用程序所驅動的需求在多大程度上？

Sure, Mark. Data center -- our data center business is comprised of three basic markets as you're alluding to. One of them is high-performance computing, and one could say that or characterize it as a traditional supercomputing market, very computationally intensive. A second market is grid which is our data center virtualization, basically graphics application virtualization. You could stream and serve any PC or any PC application from data center to any client device. And, the third application is deep learning, and this is largely hyperscale data centers applying deep learning to enhance their applications to make them much smarter and much more delightful. The vast majority of the growth comes from deep learning by far, and the reason for that is because high performance computing is a relatively stable business. It's still a growing business, and I expect the high-performance computing to do quite well over the coming years. Grid is a fast-growing business. I think Colette said that it was growing 100% year-over-year, but it's from a much smaller base, and deep learning is not only significant in size, it's also growing quite substantially.

當然，馬克。數據中心——我們的數據中心業務由您提到的三個基本市場組成。其中之一是高性能計算，可以說或將其描述為傳統的超級計算市場，計算量非常大。第二個市場是網格，這是我們的數據中心虛擬化，基本上是圖形應用程序虛擬化。您可以從數據中心向任何客戶端設備傳輸和服務任何 PC 或任何 PC 應用程序。第三個應用是深度學習，這主要是超大規模數據中心應用深度學習來增強其應用程序，使其更智能、更令人愉悅。到目前為止，絕大多數增長來自深度學習，原因是高性能計算是一項相對穩定的業務。它仍然是一個不斷增長的業務，我預計高性能計算在未來幾年會做得很好。網格是一個快速增長的業務。我認為 Colette 說它同比增長了 100%，但它的基礎要小得多，而且深度學習不僅在規模上很重要，而且還在大幅增長。

That's very helpful, thank you. And then, last question. On the new -- so, you're just starting to ship Pascal now. I guess my understanding is that historically as you're shipping a new product, the yields have opportunity for improvement, and the more volume you ship the more you climb down the yield curve. What classically happens to here on the yield? And, does that positively impact gross margins over the next three or four quarters? Thank you.

這很有幫助，謝謝。然後，最後一個問題。在新的——所以，你現在才剛剛開始發布 Pascal。我想我的理解是，從歷史上看，當您交付新產品時，收益率有機會提高，並且您交付的數量越多，您在收益率曲線上爬下的次數就越多。典型情況下，收益率會發生什麼？而且，這會對未來三四個季度的毛利率產生積極影響嗎？謝謝你。

Yes, so we've talked extensively about the way we prepare for new process nodes over the last several years. For long-term NVIDIA followers, you might have recalled that 40-nanometer was a very challenging node for us. And, with all of these challenges, it's an opportunity for us to improve our Company. We've implemented a very rigorous process node preparation methodology, and it starts, of course, with some of the world's best process engineers, circuit design engineers, and process readiness teams. We have a fantastic group dedicated to just getting process ready for us.

是的，所以我們在過去幾年中廣泛討論了我們為新流程節點做準備的方式。對於 NVIDIA 的長期追隨者，您可能還記得 40 納米對我們來說是一個非常具有挑戰性的節點。而且，面對所有這些挑戰，這對我們來說是一個改進公司的機會。我們已經實施了非常嚴格的工藝節點準備方法，當然，它始於一些世界上最好的工藝工程師、電路設計工程師和工藝準備團隊。我們有一個很棒的團隊致力於為我們準備好流程。

And, the second part of it is how that process readiness is integrated throughout the entire Company so I'm really proud of the way that the Company executed on Pascal. 16-nanometer finfet is no trivial task, not to mention the speed of the memories that we use. It's the world's first G5x. We also ramped the world's first HBM2 memory and 3D memory stacking. So, the number of technological challenges that we overcame in the ramp of Pascal is quite extraordinary. I'm super-proud of the team. Going forward, we are going to continue to refine yields, and that is absolutely the case. However, we came into 16-nanometer with a great deal of preparedness, and so it's too early to guess what's going to happen to yields and margins long term. But, we'll guide one quarter at a time.

而且，它的第二部分是該流程準備如何在整個公司中集成，因此我為公司在 Pascal 上執行的方式感到非常自豪。 16 納米 finfet 絕非易事，更不用說我們使用的內存的速度了。這是世界上第一台 G5x。我們還增加了世界上第一個 HBM2 內存和 3D 內存堆疊。因此，我們在 Pascal 的斜坡上克服的技術挑戰數量非常多。我為這個團隊感到非常自豪。展望未來，我們將繼續提高產量，絕對是這樣。然而，我們在進入 16 納米時已經做好了充分的準備，因此現在猜測長期收益和利潤率會發生什麼還為時過早。但是，我們將一次指導四分之一。

Your next question comes from the line of Toshi Otani.

您的下一個問題來自 Toshi Otani。

Hi. Thank you for taking my questions and congrats on a very strong quarter. Your Q3 revenue guide implies further acceleration on a year-over-year basis. Are there one or two end markets where you expect outsized growth? Or, should we expect growth in the quarter to be broad-based?

你好。感謝您提出我的問題，並祝賀一個非常強勁的季度。您的第三季度收入指南意味著同比進一步加速。是否存在一兩個終端市場，您預計會出現大幅增長？或者，我們是否應該預期本季度的增長將是廣泛的？

Yes, Toshi. I appreciate it. We are experiencing growth in all of our businesses. Our strategy of focusing on deep learning, self-driving cars, gaming, and virtual reality where markets -- these are markets where GPU makes a very significant difference is really paying off. And, this quarter is really the first quarter where we saw growth across every single one of our businesses, and my expectation is that we're going to see growth across all of our businesses next quarter as well. But, it's driven by the focus on these key markets and away from traditional commodity components businesses. I think one particular dynamic sticks out, and it's a very significant growth driver where we have an extraordinary position in. It's deep learning. Deep learning, you may have heard is a new computing approach. It's a new computing model and requires a new computing architecture, and this is where the parallel approach of GPUs is perfectly suited. And, five years ago we started to invest in deep learning quite substantially, and we made fundamental changes and enhancements for deep learning across our entire stack of technology from the GPU architecture to the GPU design to the systems that GPUs connect into. For example, NVLink to other systems software that has been designed for it like [Kudi] and [N] and digits to all of the deep learning experts that we have now in our Company. The last five years, we've quietly invested in deep learning because we believe that the future of deep learning is so impactful to the entire software industry, the entire computer industry that we, if you will, pushed it all-in. Now, we find ourselves at the epicenter of this very important dynamic, and it is probably -- if there is one particular growth factor that is of great significance that would be deep learning.

是的，東芝。我很感激。我們的所有業務都在增長。我們專注於深度學習、自動駕駛汽車、遊戲和虛擬現實市場的戰略——這些是 GPU 產生非常顯著差異的市場，真正得到了回報。而且，本季度實際上是我們看到我們每一項業務都增長的第一季度，我的期望是我們將在下個季度看到我們所有業務的增長。但是，它是由對這些關鍵市場的關注而不是傳統的商品組件業務驅動的。我認為一個特殊的動態很突出，它是一個非常重要的增長驅動力，我們在其中擁有非凡的地位。它是深度學習。您可能聽說過深度學習是一種新的計算方法。這是一種新的計算模型，需要新的計算架構，而這正是 GPU 的並行方法非常適合的地方。而且，五年前，我們開始對深度學習進行大量投資，我們在整個技術堆棧中對深度學習進行了根本性的改變和增強，從 GPU 架構到 GPU 設計再到 GPU 連接的系統。例如，將 NVLink 連接到為它設計的其他系統軟件，如 [Kudi] 和 [N]，以及我們公司現在擁有的所有深度學習專家的數字。在過去的五年裡，我們悄悄地投資於深度學習，因為我們相信深度學習的未來對整個軟件行業、整個計算機行業的影響是如此之大，以至於我們，如果你願意的話，會全力以赴。現在，我們發現自己處於這一非常重要的動態的中心，如果有一個具有重要意義的特定增長因素，那就是深度學習。

Your next question comes from the line of Vivek Arya.

您的下一個問題來自 Vivek Arya。

Thank you for taking my question, and congratulations on good growth and the execution. Jen-Hsun, the first question is tied to PC gaming, very strong trends. I was curious if you could quantify how much of your base has upgraded to Pascal? And, have you noticed any change in the behavior of gamers in this upgrade cycle? Whether it's the price or what part of the stack they are buying now? And, how quickly they are refreshing versus what you might have seen in the Kepler and the Maxwell cycles?

感謝您提出我的問題，並祝賀良好的成長和執行。 Jen-Hsun，第一個問題與 PC 遊戲相關，趨勢非常強勁。我很好奇您是否可以量化您的基礎升級到 Pascal 的程度？而且，您是否注意到在此升級週期中游戲玩家的行為有任何變化？無論是價格還是他們現在購買的堆棧的哪一部分？而且，與您在開普勒和麥克斯韋循環中看到的相比，它們的刷新速度有多快？

Sure. Thanks a lot, Vivek. Let's see, on PC gaming, there's a few dynamics. Our installed base represents somewhere around 80 million active GeForce users around the world. And, in fact, only about one-third has even upgraded to Maxwell, and we only started shipping Pascal half of this last quarter. And so, that gives you a sense of how much -- and Pascal is unquestionably the biggest leap we've ever made generationally in GPUs ever. It is not only high performance, it is also energy efficient, and it includes some really exciting new graphics technologies for VR and others. I think Pascal is going to be enormously successful for us, and it comes at a time when the PC gaming marketplace is also quite different than the PC gaming market five years ago.

當然。非常感謝，維維克。讓我們看看，在 PC 遊戲中，有一些動態。我們的安裝基礎代表了全球大約 8000 萬活躍的 GeForce 用戶。而且，事實上，只有大約三分之一甚至升級到 Maxwell，而我們僅在上個季度的一半時間裡開始交付 Pascal。因此，這讓您了解了多少——Pascal 毫無疑問是我們在 GPU 方面所取得的最大飛躍。它不僅具有高性能，而且節能，並且包含一些非常令人興奮的 VR 和其他新圖形技術。我認為 Pascal 對我們來說將取得巨大成功，而此時 PC 遊戲市場也與五年前的 PC 遊戲市場大不相同。

One dynamic that's really quite powerful is that the production quality, the production content is much, much higher in video games today than ever. And, the reason for that -- I've mentioned several times in previous calls -- is that the installed base of capable game platforms that are architecturally compatible, meaning that PlayStation 4 and Xbox 1 and PCs are essentially architecturally compatible. And so, the footprint for developers has grown tremendously over previous generations. This is a dynamic that's relatively new. And so, as a result, the quality of games go up which means that the consumption of GPU capability goes up with it. I think we're absolutely seeing that dynamic. I'm super-excited about the fact that the Next-Generation game console, the big boost, the 2x boost is coming just around the corner. That's going to allow game content providers, game developers to aim even higher, and I think that that's going to support long-term expansion of our gross margins and ASPs of PC gaming.

一個非常強大的動態是，今天的視頻遊戲的製作質量和製作內容比以往任何時候都高得多。而且，其原因——我在之前的電話會議中多次提到——是在架構上兼容的強大遊戲平台的安裝基礎，這意味著 PlayStation 4 和 Xbox 1 以及 PC 在架構上基本上是兼容的。因此，與前幾代相比，開發人員的足跡已大大增加。這是一個相對較新的動態。因此，遊戲的質量會上升，這意味著 GPU 能力的消耗也會隨之上升。我認為我們絕對看到了這種動態。我對下一代遊戲機、大幅提升、2 倍提升即將到來的事實感到非常興奮。這將使遊戲內容提供商、遊戲開發商的目標更高，我認為這將支持我們的 PC 遊戲毛利率和 ASP 的長期擴張。

I would say that there's some other dynamics that are quite powerful as well as you know very well which is eSports is no longer just an interest. E-Sports is a full-force global phenomenon and very powerful in Asia in just about every developing country, and of course, the western world as well. I think that on top of that, not only is VR off to a great start. We're seeing some great content now. But, some of the things that we introduced recently with Pascal, tapping into this grassroots but rather global interest in using video game as an art medium. We introduced project NVIDIA Ansel which is the world's first in-game photography system. It allows you to create virtual reality photographs, and it's just really, really amazing. And so, you could use your video game, capture your amazing moments, share it in VR, or in high resolution with all of your friends. There's a lot of different ways to enjoy games now, and the production value just continues to go up which is great for our platforms. And so, I think just to summarize your initial question, how much of the installed base has upgraded to Pascal -- very, very small, of course, because we just started production ramp. But, even then, only one-third has upgraded to Maxwell, and so there's a pretty large, pretty significant upgrade opportunity ahead of us.

我想說，還有一些其他的動力非常強大，正如你所知道的那樣，電子競技不再只是一種興趣。電子競技是一種全面的全球現象，在亞洲幾乎每個發展中國家都非常強大，當然在西方世界也是如此。我認為最重要的是，VR 不僅是一個良好的開端。我們現在看到了一些很棒的內容。但是，我們最近與 Pascal 一起介紹了一些東西，利用了這種草根但全球對使用視頻遊戲作為藝術媒介的興趣。我們介紹了世界上第一個遊戲內攝影系統項目 NVIDIA Ansel。它允許您創建虛擬現實照片，這真的非常非常棒。因此，您可以使用視頻遊戲，捕捉精彩瞬間，在 VR 中分享，或以高分辨率與所有朋友分享。現在有很多不同的方式來享受遊戲，而且產值還在不斷上升，這對我們的平台來說非常棒。所以，我想總結一下你最初的問題，有多少安裝基礎升級到 Pascal - 當然非常非常小，因為我們剛剛開始生產斜坡。但是，即便如此，也只有三分之一的人升級到 Maxwell，因此我們面前有一個相當大、相當重要的升級機會。

Your next question comes from the line of Steven Chin.

您的下一個問題來自 Steven Chin。

Hi, thanks for taking my questions. Jen-Hsun, the first one if I could on the data center competitive landscape. Earlier this week, we saw one of your data center competitors make an acquisition of a smaller private Company, and I was wondering if you could talk a little bit more about how you view your position in the data center market with respect to machine learning AI? And also, how your products are positioned from the high end or low end-type of machine learning application performance?

您好，感謝您提出我的問題。 Jen-Hsun，如果可以的話，我是數據中心競爭格局中的第一個。本週早些時候，我們看到您的一個數據中心競爭對手收購了一家較小的私人公司，我想知道您是否可以多談談您如何看待您在數據中心市場中在機器學習 AI 方面的地位?還有，你們的產品是如何從高端或低端類型的機器學習應用性能定位的？

Sure, thanks. Well, as you can imagine, we have a good pulse on the state of the industry, and we've been in this industry since the very beginning. Deep learning was really ignited when pioneering researchers around the world discovered the use of GPUs to accelerate deep learning and made a practical -- made it even practical to use deep learning as an approach for developing software. The GPU was a perfect match because the nature of the GPU is a sea of small processors, not one big processor, but a whole bunch of small processors. And, vitally, they're connected by this connecting tissue. This connecting tissue inside our processor, connecting memory, connecting fabric. That makes it possible for the processors to communicate with each other all simultaneously. That architectural innovation has been the source of our GPU computing initiative some 10 years ago. That invention has really been groundbreaking. And so, the GPU was really quite a perfect match for deep learning where neural nets are communicating neurons essentially -- inspired by neurons communicating with each other all simultaneously. And so, the GPU was really quite a perfect match.

當然，謝謝。好吧，您可以想像，我們對行業狀況有很好的把握，而且我們從一開始就涉足這個行業。當世界各地的先驅研究人員發現使用 GPU 來加速深度學習並將其付諸實踐時，深度學習真正被點燃了——使使用深度學習作為開發軟件的方法變得更加實用。 GPU 是一個完美的匹配，因為 GPU 的本質是小處理器的海洋，不是一個大處理器，而是一大堆小處理器。而且，至關重要的是，它們是通過這種連接組織連接起來的。我們的處理器內部的這種連接組織，連接內存，連接織物。這使得處理器可以同時相互通信。大約 10 年前，這種架構創新一直是我們 GPU 計算計劃的源泉。這項發明確實具有開創性。因此，GPU 確實非常適合深度學習，其中神經網絡本質上是在與神經元通信——受神經元同時相互通信的啟發。因此，GPU 真的是非常完美的匹配。

If you look at deep learning today, five years later, I think that it's a foregone conclusion that deep learning is being infused into just about every single Internet service to make them smarter, more intelligent, more delightful to consumers. And so, you could see that the hyperscale adoption of deep learning is not only broad, it's large scale and is completely global. This new computing approach, we realized was going to be quite significant long term and so five years ago we started making quite significant investments across the entire stack of our Company. GPU computing is not just the GPU chip. It's GPU architecture, it's the GPU design. It's the GPU system. All of the algorithms that run on top of it. All of the tools that run on top of it. The frameworks -- our collaborations with researchers all over the world. And so, that collaboration and our investment has improved deep learning on GPUs dramatically in the last two generations when we started this, we were in Kepler. Maxwell was some 10 times better, and Pascal is some 10 times better than Maxwell. So, in just two generations, just five years time, we've improved deep learning by an enormous amount, and the GPU today is very unlike a GPU back in the good old days because of all of the work that we've done to it.

如果你看五年後的今天的深度學習，我認為深度學習正在被注入幾乎每一項互聯網服務中，以使它們更智能、更智能、更讓消費者滿意，這已成定局。因此，您可以看到深度學習的超大規模採用不僅是廣泛的，而且是大規模的並且完全是全球性的。我們意識到這種新的計算方法從長遠來看將非常重要，因此五年前我們開始對公司的整個堆棧進行相當大的投資。 GPU計算不僅僅是GPU芯片。它是 GPU 架構，它是 GPU 設計。這是GPU系統。在它之上運行的所有算法。在它之上運行的所有工具。框架——我們與世界各地研究人員的合作。因此，這種合作和我們的投資在過去兩代人中顯著改善了 GPU 上的深度學習，當我們開始這項工作時，我們在 Kepler。麥克斯韋比麥克斯韋好 10 倍，而帕斯卡比麥克斯韋好 10 倍。因此，在短短的兩代人，短短五年的時間裡，我們已經極大地改進了深度學習，而今天的 GPU 與過去的 GPU 非常不同，因為我們所做的所有工作都是為了它。

Our strategy -- and this is where we are different not only the focus on the GPU and the expertise in parallel computing, but where we are really different, I would say, is our singular architecture approach to deep learning. We've essentially put all of our investment behind one architecture. We make this architecture available from hyperscale to data centers to workstations to notebooks to PCs, to cars, to embedded computers, to even a brand new, fully integrated, high performance computer in a box we call DGX, the NVIDIA DGX1. So, there's so many different ways to gain access to the NVIDIA architecture, the NVIDIA platform for deep learning.

我們的策略——這就是我們不同的地方，不僅關注 GPU 和並行計算的專業知識，而且我們真正不同的地方，我想說的是，我們獨特的深度學習架構方法。我們基本上將所有投資都放在了一個架構上。我們將這種架構從超大規模到數據中心、工作站、筆記本電腦、個人電腦、汽車、嵌入式計算機，甚至是全新的、完全集成的、高性能計算機，我們稱之為 DGX，NVIDIA DGX1。因此，有很多不同的方法可以訪問 NVIDIA 架構，即用於深度學習的 NVIDIA 平台。

It's just literally all over the place, all around you. It's available to you in retail stores and E-tail stores from OEMs in the cloud or even universities all over the world just in embedded computer kits. So, our approach is quite singular and quite focused, and my sense is that our lead is quite substantial, and our position is very good. But, we are not sitting on our laurels as you can tell, and for the last five years, we've been investing quite significantly. And so, over the next several years, I think you're going to continue to see quite significant jumps from us as we continue to advance in this area.

它只是字面上到處都是，在你周圍。您可以在雲中的 OEM 甚至世界各地的大學的零售店和電子零售店中使用嵌入式計算機套件。所以，我們的方法非常單一，非常專注，我的感覺是我們的領先優勢相當大，我們的位置非常好。但是，我們並沒有固步自封，而且在過去五年中，我們一直在進行大量投資。因此，在接下來的幾年中，我認為隨著我們在這一領域的不斷發展，您將繼續看到我們的巨大飛躍。

Your next question comes from the line of Romit Shah.

您的下一個問題來自 Romit Shah。

Yes, thank you. I had a question on automotive. You mentioned that drive PX is now shipping to 80 car companies. Jen-Hsun, I'm curious, are the wins here similar in size and focused more on prototyping? Or, are there opportunities here that could ultimately translate into full production wins and drive the automotive business disproportionately?

是的，謝謝。我有一個關於汽車的問題。您提到 drive PX 現在正在向 80 家汽車公司發貨。 Jen-Hsun，我很好奇，這裡的勝利規模是否相似，並且更側重於原型設計？或者，這裡是否有機會最終轉化為全面的生產勝利並不成比例地推動汽車業務？

Well, I appreciate the question. Yes, we've just started this quarter shipping drive PX2. Before I answer your question, let me tell you what drive PX2 is. Drive PX2, of course, is a processor. It's the drive PX2 version with just one single processor with just Parker and our Tegra processor. And, optionally, with discrete GPUs, you could build a car with auto-pilot capability or an AI co-pilot capability all the way to self-driving car capability. And, it was able to do sensor fusion. It was able to do SLAM, which is localization and mapping. Detection, using deep neural nets of the environment. In a surround matter, all of the cameras around the car, all feeding into the processor. And, the drive PX processor doing realtime inferencing of surround cameras. All the way to the actual planning and driving of the car -- all done in this one car computer, this one car AI super computer.

好吧，我很欣賞這個問題。是的，我們剛剛在本季度開始運送驅動器 PX2。在我回答你的問題之前，讓我告訴你什麼是驅動器 PX2。 Drive PX2 當然是一個處理器。它是驅動器 PX2 版本，只有一個處理器，只有 Parker 和我們的 Tegra 處理器。並且，您還可以選擇使用離散 GPU 構建具有自動駕駛功能或 AI 副駕駛功能的汽車，直至實現自動駕駛汽車功能。而且，它能夠進行傳感器融合。它能夠做 SLAM，即本地化和映射。檢測，使用環境的深度神經網絡。在一個環繞問題中，汽車周圍的所有攝像頭都輸入處理器。並且，驅動 PX 處理器對環繞攝像機進行實時推理。一直到汽車的實際規劃和駕駛——都在這台車載計算機，這台車載人工智能超級計算機中完成。

And so, this quarter, we started them shipping to all of our partners and developers so that they can start developing their software and their systems around our computer and on top of our software stack. We have the intentions of shipping in volume production many of these, and it's hard to know exactly what everybody's schedule is. But, it ranges everything from very soon to the next couple of years. And, developing a self-driving car is no -- it's a fairly significant undertaking. Nobody does it for fun surely, and the question is maybe if I could frame the question just slightly differently, do I expect people to be building OEM cars? Or, do we expect them to be building shuttles that are maybe geofenced? Do we expect them to be building trucks? And, you know how many trucks are on the road and how much of the world's economy is built around trucking products all over the world to services of basically taxi as a service. The answer is that we're working with customers and partners across that entire range from cars that are sold to trucks to vans to shuttles to services.

因此，本季度，我們開始向我們所有的合作夥伴和開發人員發貨，以便他們可以開始圍繞我們的計算機和我們的軟件堆棧開發他們的軟件和系統。我們打算批量生產其中的許多產品，而且很難確切地知道每個人的日程安排是什麼。但是，它的範圍從很快到未來幾年。而且，開發自動駕駛汽車不是——這是一項相當重要的任務。肯定沒有人這樣做是為了好玩，問題是如果我可以稍微不同地提出問題，我是否希望人們製造 OEM 汽車？或者，我們是否希望他們建造可能有地理圍欄的航天飛機？我們期望他們製造卡車嗎？而且，你知道有多少卡車在路上行駛，世界經濟有多少是圍繞著世界各地的卡車運輸產品建立起來的，基本上是出租車即服務。答案是，我們正在與整個範圍內的客戶和合作夥伴合作，從汽車銷售到卡車，再到貨車，再到穿梭巴士再到服務。

Your next question comes from the line of Craig Ellis.

您的下一個問題來自 Craig Ellis。

Yes, thanks for taking the question. The first is just a follow-up on some of the gaming strength in the quarter with the Company launching the Founders addition availability of gaming products in the quarter. Can you talk about how that went? And, for those products, how gross margins compare to just chip bait sales that would go into a gaming card OEM?

是的，感謝您提出問題。第一個只是對本季度一些遊戲實力的跟進，公司在本季度推出了 Founders 額外的遊戲產品可用性。你能談談這是怎麼回事嗎？而且，對於這些產品，毛利率與進入遊戲卡 OEM 的芯片誘餌銷售相比如何？

Well, first of all Founders edition -- I appreciate you asking that. Founders edition is engineered by NVIDIA, completely built by NVIDIA, and sold directly by NVIDIA and supported by NVIDIA. There's some people that -- some gamers and customers who would prefer to have a direct relationship with our Company. It's availability is limited, and it's engineered just at the highest possible level of quality. And, we limit the production of it. The reason for that is because we have a network of partners who are much, much more able to take the NVIDIA architecture to every corner of the world literally overnight. We have a fair number of partners who blanket every single country on the planet as we know, and they can provide them in different sizes and shapes and styles and different thermal solutions and different configurations and different price points. I think we believe that that diversity is one of the reasons why the NVIDIA GeForce platform is so popular, and it creates a lot of excitement in the marketplace and a lot of interesting and different diversified designs. So, I think those two strategies are harmonious with each other. But, the key point is we built the Founders edition really as a way for some customers to be able to purchase directly and have a relationship directly with us. But, largely, our strategy is to go to the market with a network of partners. As for gross margins, [the margins are the same].

好吧，首先是創始人版——感謝您提出這個問題。 Founders edition 由 NVIDIA 設計，完全由 NVIDIA 構建，由 NVIDIA 直接銷售並由 NVIDIA 提供支持。有些人——一些遊戲玩家和客戶更願意與我們公司建立直接關係。它的可用性是有限的，而且它的設計只是在盡可能高的質量水平上。而且，我們限制它的生產。原因是我們擁有一個合作夥伴網絡，他們能夠將 NVIDIA 架構一夜之間帶到世界的每一個角落。據我們所知，我們有相當多的合作夥伴覆蓋地球上的每個國家，他們可以提供不同尺寸、形狀和样式的合作夥伴，以及不同的熱解決方案、不同的配置和不同的價格點。我認為我們相信多樣性是 NVIDIA GeForce 平台如此受歡迎的原因之一，它在市場上創造了很多興奮和許多有趣和不同的多樣化設計。所以，我認為這兩種策略是相互協調的。但是，關鍵是我們構建 Founders 版本確實是為了讓一些客戶能夠直接購買並直接與我們建立關係。但是，在很大程度上，我們的策略是與合作夥伴網絡一起進入市場。至於毛利率，[毛利率相同]。

Your next question comes from the line of Matt Ramsay.

您的下一個問題來自 Matt Ramsay。

Yes, good afternoon. Thank you. Jen-Hsun, I wanted to ask a couple questions again on the data center business. The first being, we've done a little bit of work trying to estimate in our team what the long-term server attach rate for accelerators in general could be and for GPUs within that. So, it would be really interesting to hear your perspectives on that? And then, secondly, is there a market there for an APU-type product in the data center? I know you have project Denver and some other things going on from the CPU perspective, but is there a deep learning integrated CPU/GPU play that might open up more TAM long term for your Company that you are considering pursuing? Thank you.

是的，下午好。謝謝你。 Jen-Hsun，我想再問幾個關於數據中心業務的問題。首先，我們已經做了一些工作，試圖在我們的團隊中估計一般加速器的長期服務器連接率以及其中的 GPU 可能是多少。那麼，聽聽您對此的看法真的很有趣嗎？其次，APU類產品在數據中心有市場嗎？我知道從 CPU 的角度來看，您有 Denver 項目和其他一些事情，但是是否有深度學習集成 CPU/GPU 可能會為您正在考慮追求的公司開闢更多 TAM 長期？謝謝你。

Sure. Yes, first of all, the type of work loads in the data center has really changed. Back in the good old days, it largely ran database searches, but that has changed so much. It's no longer just about text. It's no longer just about data. The vast majority of what's going through the internet and what's going through data centers today as you know very well are images. They are voice. They are increasingly and probably one of the most important new data formats is live video.

當然。是的，首先，數據中心的工作負載類型確實發生了變化。回到過去的美好時光，它主要運行數據庫搜索，但情況發生了很大變化。它不再只是關於文本。它不再只是關於數據。眾所周知，當今互聯網和數據中心中的絕大多數內容都是圖像。他們是聲音。它們越來越多，並且可能最重要的新數據格式之一是實時視頻。

Live video, if you think about it for just a moment, it's live video. So, it doesn't stay in the server, and it doesn't get recorded which means that if you want to enjoy that live video there needs to be a fair amount of artificial intelligence capability in the data center that's running realtime on their live video so that the person that might be interested the video stream that you're streaming knows who to alert and who to invite to come and watch the live video. And so, if you think about data center traffic going forward, my sense is that the workload is going to continue to be increasingly high throughput, increasingly high multimedia content, and increasingly, of course, powered by AI and powered by deep learning. And so, I think that's number one.

實時視頻，如果您考慮一下，它就是實時視頻。因此，它不會留在服務器中，也不會被記錄下來，這意味著如果您想欣賞直播視頻，數據中心需要有相當數量的人工智能功能，可以實時運行視頻，以便可能對您正在流式傳輸的視頻流感興趣的人知道要提醒誰以及邀請誰來觀看實時視頻。因此，如果您考慮未來的數據中心流量，我的感覺是工作負載將繼續變得越來越高吞吐量，越來越多的多媒體內容，當然，越來越多地由人工智能和深度學習提供支持。所以，我認為這是第一名。

The second is that the architecture of the data center is recognizably, understandably changing because the workload is changing. Deep learning is a very different workload than the workload of the past, and so the architecture -- it's a new computing model. It recognized it needs a new computing architecture, and accelerators like GPUs are really well -- a good fit. So, now the other question is how much. It's hard to say. It's hard to say how much. But, my sense is that it going to be alive, and without any predictions, it's going to be a lot more than we currently ship. I think the growth opportunity for deep learning is quite significant. I think every hyperscale data center will be GPU-accelerated. It will be GPU-accelerated for training and GPU-accelerated for inferencing. There may be other approaches, but I think using GPUs is going to be a very large part of that.

第二個是數據中心的架構發生了可識別的、可以理解的變化，因為工作負載正在發生變化。深度學習是一種與過去的工作負載截然不同的工作負載，因此架構——它是一種新的計算模型。它認識到它需要一種新的計算架構，而像 GPU 這樣的加速器非常適合——非常合適。所以，現在另一個問題是多少錢。很難說。很難說有多少。但是，我的感覺是它會活著，而且沒有任何預測，它會比我們目前發布的要多得多。我認為深度學習的增長機會非常重要。我認為每個超大規模數據中心都將採用 GPU 加速。它將被 GPU 加速用於訓練和 GPU 加速用於推理。可能還有其他方法，但我認為使用 GPU 將是其中很大一部分。

And then, lastly, APUs. I guess my sense is for data centers, energy efficiency is such a vital part and although the work load is increasingly AI and increasingly live video and multimedia where GPUs can add a lot of value. There's still a lot of workload that is CPU-centric, and you still want to have an extraordinary CPU. I don't think anybody would argue that Intel makes the world's best CPUs. It's not even close. There's not even a close second, and so I think the artfulness of and the craftsman ship of Intel CPUs is pretty hard to deny. For most data centers, if you have CPU workloads anyway, I think Intel's Xeons are hard to beat, and so that's my opinion anyway.

然後，最後是 APU。我想我的感覺是數據中心，能源效率是如此重要的一部分，儘管工作負載越來越多的是人工智能和越來越多的實時視頻和多媒體，GPU 可以增加很多價值。仍然有很多以 CPU 為中心的工作負載，您仍然希望擁有一個非凡的 CPU。我認為沒有人會爭辯說英特爾製造了世界上最好的 CPU。它甚至不接近。甚至沒有緊隨其後，所以我認為英特爾 CPU 的藝術性和工藝是很難否認的。對於大多數數據中心，如果你有 CPU 工作負載，我認為英特爾的 Xeons 很難被擊敗，所以這就是我的看法。

Your next question comes from the line of Ian Ing.

您的下一個問題來自 Ian Ing。

Yes, thank you. Earlier you talked about taping out all of the Pascal products at this point. Are you with three products in the market, are you ceding the sub-$250 price point for cards to competition? Or, is this something you can serve with older Maxwell product or some upcoming product? Thanks.

是的，謝謝。之前您談到過此時將所有 Pascal 產品流片。您是否在市場上擁有三種產品，您是否將低於 250 美元的卡片價格讓給了競爭對手？或者，這是否可以與舊的 Maxwell 產品或即將推出的產品一起使用？謝謝。

Thanks a lot, Ian. We have taped out. We have verified. We have ramped every Pascal GPU. That's right. However, we have not introduced every one.

非常感謝，伊恩。我們已經錄音了。我們已經核實。我們已經升級了每個 Pascal GPU。這是正確的。但是，我們並沒有介紹每一個。

Your next question comes from the line of Steve Smigie.

您的下一個問題來自 Steve Smigie。

Great, thanks a lot for the question. I just wanted to follow up a little bit on virtual reality. You had talked a little bit about investments there. I was just curious what reception you're getting at this point, and what's going to be in your mind the biggest driver getting that going? Is it more headsets? Or, more developers working on that? Thank you.

太好了，非常感謝這個問題。我只是想跟進一點虛擬現實。你談了一些關於那裡的投資。我只是想知道你在這一點上得到了什麼樣的接待，你認為最大的推動力是什麼？是不是多了個耳機？或者，更多的開發人員正在為此工作？謝謝你。

Yes, Steve. I think it's all of that. We have to keep pushing VR and get the head mounts out to the world. I think ACC Vive -- they're doing a great job. Oculus is doing a great job. We track very carefully all of the head mounts that are going out there, and it's growing all time. Second, the content is really cool and people are really enjoying it. And so, we've just got to get more content, and developers all over the world are jumping on to VR. It really is a great new experience. But, it's not just games as you know. One of the areas where we have a lot of success, and we see a lot of excitement is in enterprise and industrial design, in medicine, medical imaging, and architectural engineering.

是的，史蒂夫。我想就是這樣。我們必須繼續推動 VR 並將頭戴設備推向世界。我認為 ACC Vive——他們做得很好。 Oculus 做得很好。我們非常仔細地跟踪所有正在使用的頭戴式耳機，並且它一直在增長。其次，內容真的很酷，人們真的很喜歡它。因此，我們只需要獲得更多內容，全世界的開發者都在跳入 VR 領域。這真的是一次很棒的新體驗。但是，這不僅僅是你所知道的遊戲。在企業和工業設計、醫學、醫學成像和建築工程方面，我們取得了很大的成功，我們看到了很多令人興奮的領域。

We use it ourselves. We are doing a fair amount of design of our workspace, and we render everything using our photo realistic renderer called Iray, fully accelerated by our GPUs. And then, we render it into VR, and we enjoy it, enjoy it completely in VR. And, it's something else to be inside an environment that's photo-realistic to be rendered and completely enjoying in VR. So, architectural engineering and construction is going to benefit from that. So, we see a lot of broad-based adoption of VR.

我們自己使用它。我們正在對我們的工作空間進行大量設計，我們使用名為 Iray 的逼真渲染器渲染所有內容，並由我們的 GPU 完全加速。然後，我們將它渲染到 VR 中，我們享受它，在 VR 中完全享受它。而且，在一個逼真的環境中渲染並在 VR 中完全享受是另一回事。因此，建築工程和建築將從中受益。因此，我們看到了很多廣泛的 VR 應用。

Now, one of the things that we did which was really spectacular is the multi-resolution, multi-projection renderings of Pascal. It's the world's first GPU architecture that has the ability to render into multiple projections simultaneously instead of just one, and the reason for that is because the GPU back in the good old days was designed for displaying into one display. You have one keyboard. You have one display. But, that mode of computer graphics has really changed as we moved into the world of virtual reality and all kinds of interesting different display configurations and display types. And so, multi-projection was a revolutionary approach to graphics, and Pascal introduced it. You really benefit in VR.

現在，我們所做的一件非常壯觀的事情是 Pascal 的多分辨率、多投影渲染。它是世界上第一個能夠同時渲染到多個投影而不是一個投影的 GPU 架構，其原因是因為過去的 GPU 是為顯示在一個顯示器上而設計的。你有一個鍵盤。你有一個顯示器。但是，隨著我們進入虛擬現實世界以及各種有趣的不同顯示配置和顯示類型，這種計算機圖形模式確實發生了變化。因此，多投影是一種革命性的圖形方法，Pascal 介紹了它。你真的在 VR 中受益。

The second thing that we did was we integrated physics -- real world physics simulation into VR. The benefit is that without the laws of physics, as you know, you can't feel anything. Things don't collide. Things don't bounce. When you pick up something, you don't feel the haptics of it. We made the entire environment physically simulated, and so as a result, you feel the entire environment. When you tip a bottle of water over, it behaves like a bottle of water tipped over. Balls behave the way balls behave, and things don't merge into each other. That integration with haptics has completely revolutionized VR, we believe, and that's physics simulation is another thing. And so, I think our position in VR is really quite great, and I'm super-enthusiastic about the development of VR.

我們做的第二件事是將物理——現實世界的物理模擬集成到 VR 中。好處是沒有物理定律，如你所知，你什麼都感覺不到。事情不會發生衝突。事情不反彈。當您拿起某物時，您不會感覺到它的觸感。我們對整個環境進行了物理模擬，因此，您可以感受到整個環境。當您將一瓶水翻倒時，它的行為就像一瓶水翻倒了一樣。球的行為方式與球的行為方式相同，事物不會相互融合。我們相信，與觸覺的集成徹底改變了 VR，而物理模擬是另一回事。所以，我認為我們在 VR 中的地位非常好，我對 VR 的發展非常感興趣。

Your next question comes from the line of Vijay Rakesh.

您的下一個問題來自 Vijay Rakesh。

Hi. Just on the data center side. Jen-Hsun, you mentioned three key segments, HBC, grid, and deep learning. What percent of mix are those for the data center?

你好。就在數據中心方面。 Jen-Hsun，您提到了三個關鍵部分，HBC、網格和深度學習。數據中心的混合百分比是多少？

I would say it's about 50% deep learning at the moment, and probably, call it, 35% -- one third is high performance computing. Maybe more than that. And, the rest of it is virtualization. Going forward, which is part of your question, my sense is that deep learning would become a very significant part of that. The other thing to realize is that deep learning is not just for internet Service Providers for voice recognition and image recognition and face recognition and such. Deep learning is a way of using mathematics, using software to discover insight in a huge amount of data. And, the one place where we generate a huge amount of data is high-performance computing. Every single super computing center in the world is going to be moving towards deep learning, and the reason for that is because they generate a huge amount of data that they really have very little ability to comb through -- to sort through. And now, with deep learning, they could discover really, really subtle insights in data that is hyper-dimensional. That's the way to think about deep learning is it's really mathematics. It's a new form of mathematics that is very, very powerful. It's a new approach to software. But, don't think of it as a market. I think every market is going to be a deep learning market. Every application is going to be a deep learning application, and every piece of software will be infused by AI long term.

我會說目前大約 50% 是深度學習，並且可能，稱之為 35%——三分之一是高性能計算。也許不止於此。而且，剩下的就是虛擬化。展望未來，這是您問題的一部分，我的感覺是深度學習將成為其中非常重要的一部分。另一件需要意識到的事情是，深度學習不僅適用於語音識別、圖像識別和人臉識別等互聯網服務提供商。深度學習是一種使用數學的方式，使用軟件從大量數據中發現洞察力。而且，我們產生大量數據的地方是高性能計算。世界上每一個超級計算中心都將朝著深度學習方向發展，原因是它們產生了大量數據，而它們實際上幾乎沒有能力梳理——整理。現在，通過深度學習，他們可以在超維數據中發現非常非常微妙的見解。這就是思考深度學習的方式，它真的是數學。這是一種新的數學形式，非常非常強大。這是一種新的軟件方法。但是，不要將其視為市場。我認為每個市場都將成為深度學習市場。每個應用程序都將成為深度學習應用程序，並且每個軟件都將長期注入人工智能。

Your next question comes from the line of Harlan Sur.

您的下一個問題來自 Harlan Sur。

Good afternoon. Solid job on the quarterly execution. You guys had really good growth in professional visualization, record revenues. I would have thought that most of the growth was being driven by the upcoming release of the Pascal based P5000 and P6000 family. So I was sort pleasantly surprised that most of the demand was driven by your current generation M6000 family, which means obviously that the Pascal demand cycle is still ahead of you. Number one, is that a fair view? And then what's driving the strong adoption of M6000, and if you haven't already released it, when do you expect to launch the Pascal-based 5000 and 6000 family? Thank you.

下午好。季度執行的紮實工作。你們在專業可視化方面的增長非常好，創紀錄的收入。我原以為大部分增長是由即將發布的基於 Pascal 的 P5000 和 P6000 系列推動的。所以我有點驚喜的是，大部分需求是由你們當前這一代 M6000 系列驅動的，這顯然意味著帕斯卡需求週期仍然領先於你們。第一，這是一個公平的觀點嗎？那麼是什麼推動了 M6000 的廣泛採用，如果您尚未發布它，您預計何時推出基於 Pascal 的 5000 和 6000 系列？謝謝你。

Yes. Thanks, Harlan. I appreciate the question. The team's been working really hard over the years to really change the way that computer aided design is done. Your observation is absolutely right, and it's coming from several different places. First of all, more and more of design is really about product design, industrial design, where the feeling of the product, the aesthetics of the product is just as important as the mechanical design of the product. And whether you're talking about a building or just a consumer product or a car, we need to be able to simulate the aesthetics of it in a photo realistic way, using real material simulations. The computational load necessary to do that is just really quite extraordinary. And we're now seeing one design package after another, whether it's [DeSo's] leading packages, Solid Work's leading packages, AutoDesk, Adobe the amount of GPU use has really, really increased and it's increasing quite dramatically.

是的。謝謝，哈蘭。我很欣賞這個問題。多年來，該團隊一直在努力工作，以真正改變計算機輔助設計的完成方式。你的觀察是絕對正確的，它來自幾個不同的地方。首先，越來越多的設計真的是產品設計，工業設計，產品的感覺，產品的美感和產品的機械設計一樣重要。無論您談論的是建築物還是消費品或汽車，我們都需要能夠使用真實的材料模擬，以照片逼真的方式模擬它的美學。執行此操作所需的計算負載確實非常驚人。我們現在看到一個接一個的設計包，無論是 [DeSo 的] 領先包、Solid Work 的領先包、AutoDesk 還是 Adobe，GPU 的使用量真的、真的增加了，而且增加得相當驚人。

I think partly because, finally for all of the ISVs, of all the developers, not only is the market demand for earlier views of photo realistic designs an important decision criteria. They can also rely on the fact that great GPUs are available in just about every computer. And so the pervasiveness of GPUs allows them to take advantage of the GPUs and to really trust that the software capabilities that they put into their packages, if they rely on GPUs, will have the benefits of GPUs there.

我認為部分原因是，最終對於所有 ISV 和所有開發人員而言，市場對照片逼真設計早期視圖的需求不僅是一個重要的決策標準。他們還可以依靠幾乎每台計算機都提供出色的 GPU 的事實。因此，GPU 的普及使他們能夠利用 GPU 並真正相信他們放入軟件包中的軟件功能，如果他們依賴 GPU，將在那裡獲得 GPU 的好處。

And so I think that's the virtual cycle you're starting to see in design. And so the investment that we made in the photo realistic renderings several years ago, the GPU acceleration of optics, this layer for path tracing that is used by just about every software package in the world, our continued evangelism of GPUs and its general purpose use, from computer graphics all the way to imaging, is something that I think is starting to see benefits. That's number one.

所以我認為這就是你開始在設計中看到的虛擬循環。因此，我們幾年前在照片逼真的渲染中進行的投資，光學的 GPU 加速，世界上幾乎所有軟件包都使用的路徑跟踪層，我們對 GPU 的持續宣傳及其通用用途，從計算機圖形一直到成像，我認為我開始看到好處。那是第一名。

Number two, Maxwell was the most energy efficient GPU ever made, until {Pascal. Maxwell was twice the energy efficiency of Kepler, and the amazing thing is that Pascal is twice the energy efficiency of Maxwell. But Maxwell made it possible for cinema-like designs in laptops and more elegant workstations and the ability to put more horsepower, more capability into any workstation because of power concerns. Maxwell made it possible for the entire industry to uplift the level of GPU that it uses.

第二，Maxwell 是有史以來最節能的 GPU，直到 {Pascal. Maxwell 的能效是 Kepler 的兩倍，而令人驚奇的是 Pascal 的能效是 Maxwell 的兩倍。但是 Maxwell 使筆記本電腦和更優雅的工作站中的類似電影的設計成為可能，並且由於功率問題，能夠為任何工作站提供更多馬力和更多功能。 Maxwell 使整個行業能夠提升其使用的 GPU 水平。

And I think that going forward, your last question is going forward, do we see -- how do we see Pascal? Pascal is in the process of ramping into workstations all over the world. And so I think in the coming quarters, we're going to expect to see Pascal out there. And my expectation is that the dynamics that I just described, which is software developers using more photo realistic capabilities, our inventioning of GPU accelerated photo rendering, I-ray and optics and NDL material description language, and then lastly, the energy efficiency of GPUs, those three factors combined is going to be really helpful for workstations, and then last, VR. VR is coming, and in order to really enjoy these type of applications for design, you're going to need a pretty powerful GPU at this point.

而且我認為接下來，您的最後一個問題是繼續前進，我們是否看到 - 我們如何看待 Pascal？ Pascal 正在進入世界各地的工作站。所以我認為在接下來的幾個季度裡，我們會期待看到帕斯卡在那裡。我的期望是我剛才描述的動態，即軟件開發人員使用更多照片逼真的功能，我們發明的 GPU 加速照片渲染、I-ray 和光學以及 NDL 材料描述語言，最後是 GPU 的能效，這三個因素結合起來將對工作站非常有幫助，最後是 VR。 VR 即將到來，為了真正享受這些類型的設計應用程序，此時您將需要一個非常強大的 GPU。

Your next question comes from the line of Ross Seymore.

您的下一個問題來自 Ross Seymore。

Hi, guys. Thanks for letting me ask a question. A couple for you, Jen-Hsun, on the automotive side. I guess the first part would be, we've seen in the recent months some partnerships being formed with some of your competitors and some of your customers, and we've seen some of those partnerships actually dissolve. So how does NVIDIA play in this general ecosystem in forming partnerships or not? And then the second part, if we put even just a rough year on it, when would you think the autonomous driving part of your automotive business would actually exceed the infotainment size of your automotive business? Thank you.

嗨，大家好。謝謝你讓我問一個問題。一對夫婦，仁勳，在汽車方面。我想第一部分是，最近幾個月我們已經看到一些與您的一些競爭對手和一些客戶建立了合作夥伴關係，我們已經看到其中一些合作夥伴關係實際上已經解散。那麼 NVIDIA 在這個總體生態系統中如何建立合作夥伴關係呢？然後第二部分，如果我們把它放在艱難的一年裡，你認為你的汽車業務的自動駕駛部分何時會真正超過你汽車業務的信息娛樂規模？謝謝你。

Yes, thanks a lot, Ross. Well, we play in a graceful, friendly and open way. And I mean that rather seriously. We believe this. We believe that building an autonomous self-driving car is a pile of software and it's really complicated software. It's really, really complicated software and it's not like one company is going to do it. And it's also not logical that large, great companies who are refining their algorithms and the capabilities of their self-driving cars over the course of the next two decades can outsource to someone the self-driving car stack. We've always felt that self-driving cars is a software problem and that large companies need to be able to own their own destiny. And that's the reason why PX2 is an open stack. And it's an open platform. So that every car company can build their self-driving car on top of it. Number one.

是的，非常感謝，羅斯。好吧，我們以優雅、友好和開放的方式玩耍。我的意思是相當嚴肅的。我們相信這一點。我們認為，建造一輛自動駕駛汽車是一堆軟件，而且是非常複雜的軟件。這是一個非常非常複雜的軟件，而且不像一家公司會這樣做。而且，在接下來的二十年裡不斷完善其算法和自動駕駛汽車功能的大型偉大公司將自動駕駛汽車堆棧外包給某人也是不合邏輯的。我們一直認為自動駕駛汽車是一個軟件問題，大公司需要能夠掌握自己的命運。這就是為什麼 PX2 是一個開放堆棧的原因。它是一個開放的平台。這樣每家汽車公司都可以在此基礎上建造他們的自動駕駛汽車。第一。

Number two, the DRIVE PX2 architecture is scalable. And the reason for that is because automatic braking and auto pilot on a highway and a virtual co-pilot and a completely autonomous self-driving car, a self-driving truck, a geofenced autonomous shuttle, all of these, a completely autonomous taxi, all of these platforms cannot be solved by one chip. It's just not even logical. The computation necessary to do it is so diverse. The more digits of accuracy or the more digits of precision towards safety that you would like to have in dealing with all of the unexpected circumstances, the more nines you would like to have, if you will, the more computation you have to do. Just as voice recognition, the amount of computation necessary for voice recognition over the last just four or five years has increased by 100 times. But notice how precise and how accurate voice recognition has become. And image recognition, video circumstance recognition, context recognition, all of that is going to require just an enormous amount of computation. So we believe that scalable platforms is necessary, number two.

第二，DRIVE PX2 架構是可擴展的。原因是高速公路上的自動制動和自動駕駛，虛擬副駕駛和完全自動駕駛汽車，自動駕駛卡車，地理圍欄自動穿梭車，所有這些，一輛完全自動駕駛的出租車，所有這些平台都不是一個芯片就能解決的。這甚至不合邏輯。這樣做所需的計算是如此多樣化。在處理所有意外情況時，您希望擁有的準確度數越多或對安全性的精確度數越多，您希望擁有的 9 越多，如果您願意的話，您需要做的計算就越多。就像語音識別一樣，語音識別所需的計算量在過去短短四五年內增加了 100 倍。但請注意語音識別已經變得多麼精確和準確。圖像識別、視頻環境識別、上下文識別，所有這些都需要大量的計算。因此，我們認為可擴展平台是必要的，第二。

And then number three. Detection, computer vision and detection, object detection, is just one tiny sliver of the entire autonomous driving problem. It's just one tiny sliver. And we've always said that autonomous vehicles, self-driving cars, is really a AI computing problem. It's a computing problem because the processors needs not just detection, but also computation, the CPU matters, the GPU matters, the entire system architecture matters, and so the entire computation engine matters.

然後是第三個。檢測，計算機視覺和檢測，物體檢測，只是整個自動駕駛問題的一小部分。它只是一小條。我們一直說自動駕駛汽車，自動駕駛汽車，真的是一個人工智能計算問題。這是一個計算問題，因為處理器不僅需要檢測，還需要計算，CPU 很重要，GPU 很重要，整個系統架構很重要，所以整個計算引擎都很重要。

Number two computation is -- computing is not just a chip problem. It's largely a software problem. And the body of software necessary for the entire system software stack, if you would, the operating system of a self-driving realtime computer, realtime super computer doesn't exist. Most super computers are best effort super computers. They run a job as fast as they can until they're done. But that's not good enough for self-driving cars. This super computer has to run in real time and it has to react at the moment that it sees that there's danger in the way, and best effort doesn't cut it. You need it to be a real time super computer. And the world has never built a real time super computer before and that's what DRIVE PX2 is all about, a real time super computer for some round, autonomous AI.

第二個計算是——計算不僅僅是一個芯片問題。這主要是軟件問題。而整個系統軟件堆棧所需的軟件主體，如果你願意的話，自動駕駛實時計算機的操作系統，實時超級計算機是不存在的。大多數超級計算機都是盡力而為的超級計算機。他們盡可能快地完成工作，直到完成。但這對於自動駕駛汽車來說還不夠好。這台超級計算機必須實時運行，它必須在看到危險的那一刻做出反應，並且盡力而為。你需要它是一台實時超級計算機。世界以前從未建造過實時超級計算機，這就是 DRIVE PX2 的全部意義所在，一種用於某種圓形自主人工智能的實時超級計算機。

And so that's the focus that we have. That's the direction that we've taken. And I think what you're seeing is that the market is starting to react to that. That maybe as they go further and further into autonomous driving, that they're discovering that the problems are related to the type of problems that we're seeing and that's the reason why DRIVE PX is a computer, not a smart camera.

這就是我們的重點。這就是我們所採取的方向。我認為你所看到的是市場開始對此做出反應。也許隨著他們在自動駕駛領域的深入研究，他們發現問題與我們所看到的問題類型有關，這就是為什麼 DRIVE PX 是一台計算機而不是智能相機的原因。

And your next question comes from the line of Joseph Moore.

你的下一個問題來自約瑟夫摩爾。

Great. Thank you so much. You talk about deep learning in the hyperscale environment, but it seems like you're getting some traction as well in the enterprise environment. I know at least one IT department that we've talked to has been doing some implementation. Can you talk about your progress there and what does it take for you to build that presence within more traditional enterprises?

偉大的。太感謝了。您在超大規模環境中談論深度學習，但似乎您在企業環境中也獲得了一些牽引力。我知道至少有一個與我們交談過的 IT 部門一直在進行一些實施。您能否談談您在這方面取得的進展，以及您如何在更傳統的企業中建立這種影響力？

Well, as you know, deep learning is not just a internet service approach. Deep learning is really machine learning super charged. And deep learning is really about discovering insight in big data, in big unstructured data, in multi-dimensional data. And that's what deep learning, that's what I've called it Thor's hammer that fell from the sky. And it's amazing technology that these researchers discovered. And we were incredibly, incredibly well prepared, because GPUs is naturally parallel. And we put us in a position to really be able to contribute to this new computing revolution.

好吧，如您所知，深度學習不僅僅是一種互聯網服務方法。深度學習真的是機器學習超級充電。深度學習實際上是在大數據、非結構化大數據和多維數據中發現洞察力。這就是深度學習，這就是我所說的從天而降的雷神之鎚。這些研究人員發現了驚人的技術。而且我們準備得非常充分，因為 GPU 天生是並行的。我們使我們能夠真正為這場新的計算革命做出貢獻。

But when you think about it in the context that it's just, it's software development, it's a new method of doing software and it's a new way of discovering insight from data, what company wouldn't need it? So every medical, every life sciences company needs it. Every health care company needs it. Every energy discovery company needs it. Every E-tail, retail company needs it. Everybody has lots of data. Everybody has lots and lots of data that they own themselves. Every manufacturing company needs it. Every company that cares about security, every company that deals with a massive amount of customer data has the benefit, can benefit from deep learning. And so when you frame it in that context, I think I would say that deep learning's market opportunity is even greater in enterprises than it is in consumer internet services.

但是當你在它只是軟件開發的背景下考慮它時，它是一種新的軟件開發方法，它是一種從數據中發現洞察力的新方法，哪家公司不需要它？因此，每家醫療、每家生命科學公司都需要它。每個醫療保健公司都需要它。每個能源發現公司都需要它。每個 E-tail、零售公司都需要它。每個人都有很多數據。每個人都有自己擁有的大量數據。每個製造公司都需要它。每家關心安全的公司，每家處理大量客戶數據的公司都可以從深度學習中受益。因此，當你在這種情況下構建它時，我想我會說深度學習在企業中的市場機會比在消費者互聯網服務中更大。

And that's exactly the reason why we built the NVIDIA DTX1. Because most of these Enterprises don't have the expertise or simply don't have the willpower to want to build a super computing data center or a high performance computer. They would rather buy an appliance, if you will, with all of the software integrated and the performance incredibly well tuned, and it comes out of a box. And that's essentially what NVIDIA DGX1 is. It's a super computer in a box and it's designed and tuned for high performance computing for deep learning.

這正是我們構建 NVIDIA DTX1 的原因。因為這些企業大多不具備專業知識或根本沒有意志力來構建超級計算數據中心或高性能計算機。如果你願意的話，他們寧願購買一個設備，它集成了所有軟件，性能調整得非常好，而且它是開箱即用的。這就是 NVIDIA DGX1 的本質。它是一台裝在盒子裡的超級計算機，專為深度學習的高性能計算而設計和調整。

Your next question comes from the line of Ambrish Srivastava.

您的下一個問題來自 Ambrish Srivastava。

Hello. Thank you very much for squeezing me in. I had one question on gross margin, Jen-Hsun. Very big top line guidance, but yet gross margin is guided to flat. What is the reason? And I understand it's not always perfectly correlated, margins should be going up that much, but is it pricing, is it yield? Because the mix also seems to be moving in the right direction, more [Pro Ves], more HPC and less of the OEM business.

你好。非常感謝你擠我進來。我有一個關於毛利率的問題，仁勳。非常大的頂線指導，但毛利率被引導至持平。是什麼原因？而且我知道它並不總是完全相關，利潤率應該會上升那麼多，但它是定價嗎，是收益嗎？因為這種組合似乎也在朝著正確的方向發展，更多的 [Pro Ves]、更多的 HPC 和更少的 OEM 業務。

Well, our guidance is our best estimate. And we'll know how everything turns out next quarter when we talk again. But at some high level, I would agree with you that as we move further and further and more and more into our platform approach of business, where our platform is specialized and rich with software, that increasingly the value of the product that we bring has extraordinary enterprise value, that the benefits of using it is not just measured in frames per second but real TCO for companies and real cost savings as they reduce the number of server clusters, and real increases and real boosts in their productivity. And so I think there's every reason to believe that long term, this platform approach can drive greater value. But as for the next quarter, I think let's just wait and see how it goes.

好吧，我們的指導是我們最好的估計。當我們再次交談時，我們將知道下個季度的結果如何。但在某種程度上，我同意你的觀點，隨著我們越來越多地進入我們的平台業務方法，我們的平台是專業的並且軟件豐富，我們帶來的產品的價值越來越非凡的企業價值，使用它的好處不僅以每秒幀數衡量，而且以公司的實際總擁有成本和實際成本節約為衡量標準，因為它們減少了服務器集群的數量，並真正提高和提高了他們的生產力。所以我認為有充分的理由相信，從長遠來看，這種平台方法可以帶來更大的價值。但至於下一季度，我想讓我們拭目以待吧。

Your next question comes from the line of Rajvindra Gill.

您的下一個問題來自 Rajvindra Gill。

Thank you.

謝謝你。

It's Vindra. How are you?

是文德拉。你好嗎？

Exactly. Good. A question, Jen-Hsun, on the DRIVE PX2. So my understanding, as you described it, it's one scalable architecture from the cockpit to ADAS to mapping to autonomous driving. But I'm curious to see how that compares to the approach that some of your competitors are taking with respect to providing different solutions for different levels of the ADAS systems, whether it's level one, level two, level three, specifically with the V2x communication where, for level four autonomous driving, where you're going to need 6 to 20 different radar units, 3 to 6 different cameras, [lidar]. I'm trying to square how your approach is different from some of your competitors in the semiconductor space.

確切地。好的。 Jen-Hsun 關於 DRIVE PX2 的一個問題。所以我的理解，正如你所描述的，它是一種從駕駛艙到 ADAS 再到映射到自動駕駛的可擴展架構。但我很想知道這與您的一些競爭對手在為不同級別的 ADAS 系統（無論是第一級、第二級、第三級，特別是 V2x 通信）提供不同解決方案方面所採取的方法相比如何其中，對於四級自動駕駛，您將需要 6 到 20 個不同的雷達單元，3 到 6 個不同的攝像頭，[激光雷達]。我試圖弄清楚您的方法與半導體領域的一些競爭對手有何不同。

Yes, good question. There's no way to square and there's no reason to square, and you aren't going to find one answer. And the reason why you aren't going to find one answer is because nobody knows exactly how to get it done. We all have intuitions and we all have beliefs about how we're going to be able to ultimately solve the long-term fully autonomous vehicle, that wherever I am, the car I step into, the automotive automobile we step into, is completely autonomous, and that it has AI inside and out, and it's just an incredible experience. But we aren't there yet.

是的，好問題。沒有辦法解決，也沒有理由解決，而且你不會找到一個答案。你找不到一個答案的原因是因為沒有人確切知道如何完成它。我們都有直覺，我們都有關於我們將如何最終解決長期全自動駕駛汽車的信念，無論我在哪裡，我踏入的汽車，我們踏入的汽車，都是完全自動駕駛的，而且它內外都有人工智能，這只是一次令人難以置信的體驗。但我們還沒有。

And all of these companies have slightly -- not all -- but many companies have slightly different visions of the future. Some people believe that the path to the future is fully autonomous right away in a geofenced area that has been fully mapped in advance. Some people believe that you can use it just for highway auto pilot as a first starting point and work quickly towards fully autonomy, and some people believe the best way to do that is through shuttles and trucks. So you see a lot of different visions out there. And I think all of those visions are coming from smart people doing smart things and they're targeting different aspects of transportation.

所有這些公司都略有不同——不是全部——但許多公司對未來的看法略有不同。一些人認為，未來的道路在預先完全繪製好的地理圍欄區域內是完全自主的。有些人認為您可以將其僅用於高速公路自動駕駛作為第一個起點，並迅速朝著完全自主的方向努力，而有些人認為最好的方法是通過穿梭巴士和卡車。所以你會看到很多不同的願景。而且我認為所有這些願景都來自做聰明事的聰明人，他們針對的是交通的不同方面。

I think there's a fallacy that transportation in every single country in every single form is exactly the same. It just doesn't work that way. And so there's technology insight and then there's market insight, and there's a technology vision versus your entry point. And I think that's where all of the squaring doesn't happen. So you're solving for a simple equation that won't happen. However there's one thing that we believe absolutely will happen. We are absolutely certain that AI will be involved in this endeavor, that finally with deep learning and finally with AI that we believe we have the secret sauce necessary to break these puzzles and to solve these puzzles over a period of time. Number one.

我認為存在一種謬論，即每個國家/地區的每種形式的交通方式都完全相同。它只是不那樣工作。所以有技術洞察力，然後是市場洞察力，還有一個技術願景與你的切入點。我認為這就是所有平方都不會發生的地方。所以你正在求解一個不會發生的簡單方程。然而，有一件事我們相信絕對會發生。我們絕對肯定人工智能將參與這項努力，最終通過深度學習，最後通過人工智能，我們相信我們擁有打破這些難題並在一段時間內解決這些難題所必需的秘訣。第一。

Number two. We believe unquestionably that depending on the problem you want to solve, you need a different amount of computational capability. We believe unambiguously this is a software problem, and that for the largest of transportation companies, they need to own their own software in collaboration with you, but they aren't going to let you do it and keep it as a black box. We believe unambiguously that this is a computing problem, that this is a real time super computing problem., that it's not just about a special widget, but computation is necessary, processors, a computer system, systems software, and an enormous amount of operating system capability is necessary to build something like this.

第二。我們毫無疑問地相信，根據您要解決的問題，您需要不同數量的計算能力。我們毫不含糊地認為這是一個軟件問題，對於最大的運輸公司來說，他們需要與您合作擁有自己的軟件，但他們不會讓您這樣做並將其保留為黑匣子。我們毫不含糊地認為，這是一個計算問題，這是一個實時超級計算問題。它不僅僅是一個特殊的小部件，而是需要計算，處理器、計算機系統、系統軟件和大量的操作系統能力是構建這樣的東西所必需的。

It is a massive software problem. Otherwise, we would have done it already. And so I think you'll see this year the beginnings of a lot of some very visionary and really quite exciting introductions. But in the next year and the year after that, I think you'll see more and more and more. I think this is the beginning and we're working with some really, really amazing people to get this done.

這是一個巨大的軟件問題。否則，我們早就做了。所以我認為今年你會看到很多非常有遠見且非常令人興奮的介紹的開始。但在接下來的一年和之後的一年裡，我想你會看到越來越多。我認為這是一個開始，我們正在與一些非常非常了不起的人合作來完成這項工作。

Your next question comes from the line of Mitch Steves.

您的下一個問題來自 Mitch Steves。

Thanks for taking my question, guys. So just circling back to the data center piece and the deep learning aspect, is there a change in ASPs you guys are seeing when you enter that market?

謝謝你提出我的問題，伙計們。回到數據中心和深度學習方面，你們進入那個市場時看到的 ASP 有變化嗎？

No.

不。

So essentially there's going to be no margin change from the data center sales. And I guess the same question in automotive, as well.

因此，數據中心銷售基本上不會有任何利潤變化。我猜汽車也有同樣的問題。

Oh, automotive ASPs for self-driving cars will be much higher than infotainment. It's a much tougher problem. Every car in the world has infotainment. With the exception of some pioneering work or early, the best, the most leading edge cars today, almost no cars are self-driving. So I think that the technology necessary for self-driving cars is much, much more complicated than lane keeping or adaptive cruise control or first generation, first and second generation ADAS. The problem is much, much more complicated.

哦，自動駕駛汽車的汽車 ASP 將遠高於信息娛樂系統。這是一個更棘手的問題。世界上每輛車都有信息娛樂系統。除了一些開創性的工作或早期，當今最好、最前沿的汽車，幾乎沒有汽車是自動駕駛的。因此，我認為自動駕駛汽車所需的技術比車道保持或自適應巡航控製或第一代、第一代和第二代 ADAS 複雜得多。問題要復雜得多。

Your next question comes from the line of Brian Alger.

您的下一個問題來自 Brian Alger。

Hi, guys. Thanks for squeezing me in. I think this will be the first, congrats, actually, on a pretty darn good quarter and amazing guidance. I want to come back to the difference of Pascal versus what would be otherwise competition from either Intel or AMD. There's been a fair amount of documentation talking about the power requirements or the power draw differences between Pascal versus Polaris. And one would think that while that's important in gaming and it's gotten a lot of notice, it would actually be more important for these deep learning applications that we've been talking so much about over the past half hour, 45 minutes. Can you maybe talk to that side of the design, not so much the horsepower, but maybe the power efficiency of it and what that means for when you scale it up into really big problems?

嗨，大家好。謝謝你把我擠進去。我認為這將是第一個，實際上，恭喜，這是一個非常好的季度和驚人的指導。我想回到 Pascal 與英特爾或 AMD 競爭的區別。有相當多的文檔討論了 Pascal 與 Polaris 之間的功率要求或功耗差異。有人會認為，雖然這在遊戲中很重要並且引起了很多關注，但實際上對於我們在過去半小時 45 分鐘內一直在談論的這些深度學習應用程序來說更重要。您能否談談設計的那一面，不是馬力，而是它的功率效率，以及當您將其擴展到真正的大問題時這意味著什麼？

Brian, thank you very much. First of all, I appreciate your comment. The team worked really, really hard. And over the last several years, the last five years, all of the employees of NVIDIA have been pursuing a strategy that took until today, really, to show people that it really pays off and it's a very unique business model. It's a very unique approach. But I just want to congratulate all of the employees that have worked so hard to get us here.

布賴恩，非常感謝。首先，我很欣賞你的評論。團隊真的非常非常努力。在過去的幾年裡，過去的五年裡，NVIDIA 的所有員工一直在追求一種直到今天才真正向人們展示它確實有回報的戰略，這是一種非常獨特的商業模式。這是一種非常獨特的方法。但我只想祝賀所有辛勤工作讓我們來到這裡的員工。

I appreciate the comment also about energy efficiency. In fact, energy efficiency is the single most important feature of processors today and going forward. And the reason for that is because every single environment that we're in is power constrained, every single environment. Even your PC, with 750 watts or 1,000 watts, is power constrained. Because we could surely put more GPUs in there than 1,000 watts. And so that's power constrained. We're in environments where we only have one or two watts. It might be a drone and we need to be, we're completely power constrained, so energy efficiency is really, really important. We might be in a data center where we're doing deep learning and training we're training neuronets or we're inferencing neuronets. And in this particular case, although the data center has a lot of power to provision, the number of GPUs that they want to use in it is measured in tens, tens and tens of thousands. And so energy efficiency becomes the predominant issue. Energy efficiency, literally, is the most important feature of the processor.

我也很欣賞關於能源效率的評論。事實上，能效是當今和未來處理器最重要的一個特性。這樣做的原因是因為我們所處的每一個環境都是功率受限的，每一個環境。即使是 750 瓦或 1,000 瓦的 PC，也受到功率限制。因為我們肯定可以在其中放置超過 1,000 瓦的 GPU。所以這是權力受限的。我們所處的環境只有一兩瓦。它可能是一架無人機，我們需要，我們完全受到功率限制，所以能源效率非常非常重要。我們可能在一個數據中心裡進行深度學習和訓練，我們正在訓練神經網絡，或者我們正在推理神經網絡。在這種特殊情況下，儘管數據中心有很多可供配置的電源，但他們想要在其中使用的 GPU 數量以數万、數万和數万計。因此，能源效率成為主要問題。從字面上看，能效是處理器最重要的特性。

Now from there, from there, there are functionality and architectural features, that the architectural changes that we made in Pascal so that we could stay ahead of the deep learning research work and the deep learning progress, was groundbreaking and people are starting to discover the architectural changes that we put into Pascal and it's going to make a huge difference in the next several years of deep learning. And so that's a feature and an architectural innovation.

現在從那裡，從那裡開始，有功能和架構特性，我們在 Pascal 中所做的架構更改，以便我們能夠領先於深度學習研究工作和深度學習進展，這是開創性的，人們開始發現我們對 Pascal 進行的架構更改，它將在未來幾年的深度學習中產生巨大的變化。所以這是一個功能和架構創新。

And then lastly, of course, there's all of the software that goes on top of the processor base. We call it GPU computing instead of just GPUs, because GPU computing is about computing, it's about software, it's systems, it's about the inner relationship of our GPU with the memories and all of the memories around the system and the networking and the inter connect and storage, and it's a large scale computing problem. It is also the highest throughput computing problem on the planet, which is the reason why we've been called upon by our nation to build the world's next two fastest super computers. High throughput computing is our company's expertise. High throughput computing from fundamental architecture to chip design to system design to system software to algorithms to computational mathematics, and all of the experts in all the various fields of science, that is the great investment that we made in the last five years. And I think the results are really starting to show.

最後，當然，所有的軟件都在處理器基礎之上。我們稱它為 GPU 計算而不僅僅是 GPU，因為 GPU 計算是關於計算的，它是關於軟件的，它是系統的，它是關於我們的 GPU 與內存和系統周圍的所有內存以及網絡和互連的內在關係和存儲，這是一個大規模的計算問題。這也是地球上吞吐量最高的計算問題，這就是為什麼我們被國家要求建造世界上接下來兩台最快的超級計算機的原因。高吞吐量計算是我們公司的專長。從基礎架構到芯片設計到系統設計到系統軟件到算法再到計算數學的高吞吐量計算，以及所有科學領域的所有專家，這是我們在過去五年中所做的巨大投資。我認為結果真的開始顯現了。

Your next question comes from the line of Blayne Curtis.

您的下一個問題來自 Blayne Curtis。

Hey, guys. Thanks for squeezing me in here and great execution on the quarter. Two related questions. One, Colette, just curious your view on the use of capital and buybacks. Obviously, an accelerated one, only $9 million in the last quarter. What's your view going forward?

大家好。感謝您在這裡擠我，並在本季度表現出色。兩個相關的問題。一，科萊特，只是好奇你對資本使用和回購的看法。顯然，這是一個加速的，上一季度只有 900 萬美元。你對未來有什麼看法？

And then Jen-Hsun, maybe a bigger question in terms of ease of capital, whether you could talk about, you said CPU is not an area that you would want to go into, but obviously GPUs have legs. So just curious, if you look around other areas, maybe in the data center, where you could also add value?

然後Jen-Hsun，就資金的易用性而言，也許是一個更大的問題，你是否可以談論，你說CPU不是你想要進入的領域，但顯然GPU有腿。所以只是好奇，如果你環顧其他領域，也許在數據中心，你也可以在哪裡增加價值？

Yes, thanks, Blayne. The return of capital continues to be an important part of our shareholder value message But remember, it is still two parts of it. Part of it is still dividends and part of it has been our purchasing of stock. So as we continue to go forward, the dividend is definitely a long term perspective and we'll make sure that we can watch the dividend yield there to stay competitive and also looking at our profitability. Our share repurchase, we'll look at the opportunistic time for those repurchases and making sure that we're also doing that carefully, as well.

是的，謝謝，布萊恩。資本回報仍然是我們股東價值信息的重要組成部分，但請記住，它仍然是其中的兩個部分。一部分仍然是股息，一部分是我們購買股票。因此，隨著我們繼續前進，股息絕對是一個長期的觀點，我們將確保我們可以觀察那裡的股息收益率以保持競爭力並著眼於我們的盈利能力。我們的股票回購，我們將研究這些回購的機會主義時間，並確保我們也在謹慎行事。

And long-term use of capital, I would say this, that what NVIDIA is really rich with is we're rich with vision and creativity and the courage to innovate. And that's one of the reasons why we start almost every conversation with anything by gathering our great people around the Company and seeing what kind of future we can invent for ourselves and for the world. And so I think our use of capital is nurturing the employees that we have and providing them the platform to innovate and create the conditions by which they can be successful and do their live's work. And so that's philosophically where we start.

而長期使用資本，我想說的是，英偉達真正富有的是我們富有遠見、創造力和創新的勇氣。這就是為什麼我們通過召集公司周圍的優秀員工並看看我們可以為自己和世界創造什麼樣的未來來開始幾乎每一次對話的原因之一。所以我認為我們對資本的使用是在培養我們擁有的員工，為他們提供創新的平台，創造條件，讓他們能夠成功並完成他們的生活。所以這就是我們從哲學上開始的地方。

We aren't allergic to acquisitions and purchases, and we look all the time and we have the benefit of working with and partnering with companies large and small all over the world as we move the industry forward. So we're surely open to that. But our natural posture is always to invest in our people and invest in our own company's ability to invent the future.

我們對收購和購買並不過敏，我們一直在關注，隨著我們推動行業向前發展，我們受益於與世界各地大大小小的公司合作和合作。所以我們肯定對此持開放態度。但我們的自然姿態始終是投資於我們的員工，投資於我們自己公司創造未來的能力。

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

您的下一個問題來自 C.J. Muse。

Good afternoon. Thank you for squeezing me in. I guess, two quick questions. The first one, thank you for breaking out deep learning as a percentage of the data center. Can you provide what that percentage was for the April quarter? And then the follow-up question is if I look back over the last four quarters and I look at your implied guide, you're looking at roughly 50% incremental operating margin. And curious if that's the right number you would underwrite here, or should we be thinking about improving mix, as well as maturing process and manufacturing at your foundry partners such that that could actually be higher as we look ahead? Thank you.

下午好。謝謝你把我擠進去。我想，兩個快速的問題。第一個，感謝您將深度學習劃分為數據中心的百分比。你能提供四月季度的百分比嗎？然後接下來的問題是，如果我回顧過去四個季度並查看您的隱含指南，您會看到大約 50% 的增量營業利潤率。並且好奇這是否是您在這裡承保的正確數字，或者我們是否應該考慮改進組合，以及在您的代工合作夥伴處成熟工藝和製造，以便我們展望未來實際上可能更高？謝謝你。

Yes, deep learning is a software approach, a new computing architecture, a new computing approach that the industry, that researchers have been developing for 20 years. And it was only until five years ago when pioneer work was done on deep learning and on GPUs that really turbo charged it and gave the industry, if you will, a time machine that brought the future to the present. And the power of deep learning is so great that this capability is expanding and people are discovering more ways to use it and more applications and new deep learning architectures, and the networks are getting bigger and deeper and more complicated. And so I think that this area, this area is going to grow quite significantly. It represents a vast majority of our data center revenues recently, and my sense is that it's going to continue to be a significant part of it.

是的，深度學習是一種軟件方法，一種新的計算架構，一種行業的新計算方法，研究人員已經開發了 20 年。直到五年前，在深度學習和 GPU 方面的開創性工作才真正推動了它的發展，並為行業提供了一台時間機器，將未來帶到了現在。深度學習的力量是如此之大，以至於這種能力正在擴展，人們正在發現更多使用它的方法、更多應用程序和新的深度學習架構，網絡變得越來越大、越來越深、越來越複雜。所以我認為這個領域，這個領域將會非常顯著地增長。它代表了我們最近數據中心收入的絕大部分，我的感覺是它將繼續成為其中的重要組成部分。

So what was the second question? Did I miss it? I think his question was really about data centers and deep learning, right? What's that?

那麼第二個問題是什麼？我錯過了嗎？我認為他的問題實際上是關於數據中心和深度學習的，對吧？那是什麼？

I think your question was regarding deep learning and the percentage of data center and how that has moved.

我認為你的問題是關於深度學習和數據中心的百分比以及它是如何移動的。

Yes, and it's the vast majority.

是的，而且是絕大多數。

And your next question comes from the line of Kevin Cassidy.

您的下一個問題來自 Kevin Cassidy。

Thanks for taking my question. Maybe I'll go to the other end of the spectrum and speaking of energy efficiency. Are you finding new opportunities for Tegra, aside from the infotainment in automotive?

感謝您提出我的問題。也許我會走到光譜的另一端，談談能源效率。除了汽車信息娛樂，您是否正在為 Tegra 尋找新的機會？

Kevin, I appreciate the question. Tegra is at the core of all of our self-driving car initiatives. And so without Tegra, there would be no self-driving cars. So Tegra is the core of our self-driving car initiative, the computing platform for self-driving cars. And DRIVE PX2 includes Tegra, as well as discrete GPUs of Pascal, but the core of it, the vast majority of the heavy lifting is done by Tegra, and we expect that going forward. And so Tegra is incredibly important to us.

凱文，我很欣賞這個問題。 Tegra 是我們所有自動駕駛汽車計劃的核心。因此，沒有 Tegra，就沒有自動駕駛汽車。因此，Tegra 是我們自動駕駛汽車計劃的核心，即自動駕駛汽車的計算平台。 DRIVE PX2 包括 Tegra，以及 Pascal 的離散 GPU，但它的核心，絕大多數繁重的工作都由 Tegra 完成，我們預計未來會如此。所以 Tegra 對我們來說非常重要。

Tegra is also the core of the processor of Jetson. Jetson is a platform that is designed for other embedded autonomous and intelligent machines. And so you could imagine what kind of intelligent machines in the future will benefit from deep learning and AI, but robotics and drones and embedded applications inside buildings and cities. There are all kinds of applications. I'm very, very optimistic about the future of Jetson, but at the core of that is also Tegra. So think of Tegra as our computer on a chip, and it's our AI computer on a chip.

Tegra 也是 Jetson 處理器的核心。 Jetson 是一個專為其他嵌入式自主和智能機器設計的平台。所以你可以想像未來什麼樣的智能機器會從深度學習和人工智能中受益，但機器人和無人機以及建築物和城市中的嵌入式應用程序會受益。有各種各樣的應用程序。我對 Jetson 的未來非常非常樂觀，但其核心也是 Tegra。所以把 Tegra 想像成我們的芯片上的計算機，它是我們的人工智能芯片上的計算機。

Okay, may I -- I appreciate all the questions. Thank you all for joining us today. Our growth is really driven by several factors. Our focus on deep learning, self-driving cars, gaming, and VR, markets where GPU has been vital, is really starting to pay off. The second factor is that Pascal is the most advanced GPU ever created and we're incredibly excited about it. And we, this last quarter, we ramped it with enormous success. And I'm so proud of the team for all of the preparation and the executions last quarter. And the third is hyper scale adoption of deep learning is now widespread, is large scale and we're seeing it globally. Those are the several growth drivers ahead of us.

好的，我可以 - 我感謝所有的問題。感謝大家今天加入我們。我們的增長實際上是由幾個因素驅動的。我們專注於深度學習、自動駕駛汽車、遊戲和 VR，GPU 至關重要的市場，真正開始得到回報。第二個因素是 Pascal 是有史以來最先進的 GPU，我們對此感到非常興奮。而我們，在最後一個季度，我們取得了巨大的成功。我為團隊上個季度的所有準備和執行感到非常自豪。第三是深度學習的超大規模採用現在很普遍，規模很大，我們正在全球範圍內看到它。這些是擺在我們面前的幾個增長動力。

As we go forward, we're also looking to sharing our many developments in deep learning AI with you. We're really just in the beginning of seeing the actual growth of deep learning as we scale out into the market. Deep learning adoption is now widespread and is ramping at every hyper scale data center. It's a new computing model that requires a new computing architecture, one that GPU is perfectly suited for. And the thing that we've done that I'm really delighted with is the strategy that started five years ago to optimize our GPU computing platform from end-to-end and optimize it for deep learning, at the processor level, at the architecture level, at the chip design level, systems and software and algorithms and a richness of deep learning experts at the Company, and the collaboration we have all over the world with researchers and developers has made it possible for us to continue to advance this field and this platform.

在我們前進的過程中，我們還希望與您分享我們在深度學習 AI 方面的許多發展。隨著我們向市場擴展，我們真的才剛剛開始看到深度學習的實際增長。深度學習的採用現在很普遍，並且在每個超大規模數據中心都在增加。這是一種新的計算模型，需要一種新的計算架構，而 GPU 非常適合這種架構。我們所做的讓我非常高興的事情是五年前開始的戰略，旨在從端到端優化我們的 GPU 計算平台，並在處理器級別和架構上針對深度學習進行優化層面，在芯片設計層面，系統和軟件和算法以及公司豐富的深度學習專家，我們與世界各地的研究人員和開發人員的合作使我們有可能繼續推進這個領域和這個平台。

And as a result of that our deep learning platform improved far more than anybody would have expected. If you just projected it based on semiconductor physics, it would be nowhere near the level of speed up and step up that we got from generation to generation, from Kepler to Maxwell, we got 10x, from Maxwell to Pascal, we got another 10x. And you can surely expect pretty substantial improvements and increases from us over the next several years.

因此，我們的深度學習平台的改進遠遠超出了任何人的預期。如果你只是根據半導體物理學來預測它，它遠不及我們一代又一代的加速和提高水平，從開普勒到麥克斯韋，我們得到了 10 倍，從麥克斯韋到帕斯卡，我們又得到了 10 倍。您肯定可以期待我們在接下來的幾年中做出相當大的改進和增加。

Where we really shine is not only as a fantastic platform for deep learning and the training of the networks, but it's also a fantastic platform to scale out. You can enjoy our platform, whether it's in cloud or in data center or in super computers and workstations and desk side PCs and notebook computers to cars to embedded computers, as I mentioned just now with Jetson. This is a one singular architecture approach, so the thoughtfulness and the care of the investment of the developers and the software programmers and researchers is really our preeminent concern. And as we know, computing is about architecture and computing is about platform, and mostly, computing is about developers. And we've been quite thoughtful about the importance it is to developers. And as a result, developers all over the world, all over the industry, can use this singular architecture and get the benefits of their science and their applications as they scale and deploy their work.

我們真正閃耀的地方不僅是作為深度學習和網絡訓練的絕佳平台，而且還是橫向擴展的絕佳平台。您可以享受我們的平台，無論是在雲中還是在數據中心，或者在超級計算機和工作站、台式 PC 和筆記本電腦、汽車到嵌入式計算機，正如我剛才提到的 Jetson。這是一種單一的架構方法，因此開發人員、軟件程序員和研究人員的周到和投資關懷確實是我們最關心的問題。正如我們所知，計算與架構有關，計算與平台有關，而且大多數情況下，計算與開發人員有關。我們一直在深思熟慮它對開發人員的重要性。因此，世界各地、整個行業的開發人員都可以使用這種單一架構，並在擴展和部署工作時從他們的科學和應用程序中受益。

So that's it. We had a great quarter and I look forward to reporting our progress next quarter. Thank you all for joining us.

就是這樣了。我們有一個很棒的季度，我期待在下個季度報告我們的進展。感謝大家加入我們。

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

今天的電話會議到此結束。您現在可以斷開連接。