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.

午安.我是Desiree，今天將擔任本次電話會議的接線生。歡迎各位參加英偉達財務業績電話會議。所有線路均已靜音。發言人發言結束後，將進行問答環節。

(Operator Instructions)

（操作說明）

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

現在我將把電話交給英偉達投資者關係副總裁阿納布·錢達。您可以開始您的會議了。

Thank you.

謝謝。

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.

各位下午好，歡迎參加英偉達2017財年第二季業績電話會議。今天與我一同出席會議的還有英偉達總裁兼執行長黃仁勳，以及執行副總裁兼財務長科萊特·克雷斯。提醒各位，本次電話會議將在英偉達投資者關係網站上進行網路直播。

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.

會議內容已錄音。您可以透過電話收聽重播，截止日期為2016年8月18日。網路直播的重播將持續到下一季財報電話會議召開為止，屆時將討論第三季財務業績。

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

本次電話會議的內容歸英偉達所有。未經我們事先書面同意，不得複製或轉錄。

During 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.

在本次電話會議中，我們可能會基於目前預期做出前瞻性陳述。這些前瞻性陳述受許多重大風險和不確定因素的影響，而我們的實際結果可能與預期有重大差異。有關可能影響我們未來財務表現和業務的因素的討論，請參閱今日發布的盈利報告、我們最新的10-K表和10-Q表，以及我們可能向美國證券交易委員會提交的8-K表報告。所有陳述均截至2016年8月11日，並基於我們目前掌握的資訊。除法律要求外，我們不承擔更新任何此類陳述的義務。

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

在本次電話會議中，我們將討論非公認會計準則（非GAAP）財務指標。您可以在我們網站上發布的財務長評論中找到這些非GAAP財務指標與GAAP財務指標的調節表。

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

那麼，現在讓我把電話交給科萊特。

Thanks, Arnab.

謝謝你，阿爾納布。

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 架構的全新 GPU 系列，這是我們迄今為止最成功的產品發布之一。同時，我們也受惠於深度學習的快速普及以及與全球超大規模資料中心日益緊密的合作，這些資料中心正將深度學習應用於其提供的各項服務。

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.

營收持續加速成長，年增24%，創下14.3億美元的歷史新高。我們四大平台——遊戲、專業視覺化、數據中心和汽車——均實現了強勁的環比和同比增長。我們以將GPU運算平台推向高度精準市場為基礎的商業模式顯然取得了成功。 GPU業務年增25%，達到12億美元。 Tegra處理器業務年增30%，達到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.

第二季度，我們的四大平台貢獻了近 89% 的收入，高於去年同期的 85% 和上一季的 87%。它們的總收入年增了 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.

我們先來看遊戲平台。遊戲營收年增18%，達到7.81億美元，這反映了我們最新整合的基於Pascal架構的GPU取得了成功。各地區的市場需求都很強勁。

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架構帶來了一系列令人驚嘆的技術進步。它能夠以前所未有的性能和效率運行複雜的AAA級遊戲大作，並帶來豐富、沉浸式的VR體驗。

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.

在我們迄今為止最成功的產品發表會上，我們推出了四款主要產品。它們分別是發燒友市場的 GeForce GTX 1080、1070 和 1060，以及 Titan X，它是世界上速度最快的消費級 GPU，專為深度學習開發、數位內容創作和極限遊戲而設計。

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是前所未有的電子精密產品，擁有超乎想像的運算能力。 《富比士》雜誌稱讚GTX 1060是一款出色的產品，它讓更多人能夠體驗高階VR。 《Hardware Canucks》則將Titan X描述為一項技術壯舉，其幀率令人嘆為觀止。 GTX 1080、1070、1060和Titan X現已全面投產，對全球消費者發售。

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.

我們本季發布的一款全新開源遊戲生動展現了VR的潛力。這款名為NVIDIA VR Funhouse的開源遊戲已在Steam平台發售，它使用我們的GameWorks SDK開發。遊戲將實體模擬融入VR，並擁有驚豔的畫面和精準的觸覺回饋，讓您彷彿置身於真實的嘉年華之中。

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.

在專業視覺化領域，Quadro 的營收成長至創紀錄的 2.14 億美元，年增 22%，季增 13%。成長主要來自即時渲染工具和行動工作站等高端市場。今年稍早發布的 24GB M6000 GPU 吸引了許多客戶的濃厚興趣。

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製作的效率，這些專案對工期要求尤其嚴格。工程巨頭 AECOM 和耶魯大學建築學院也正在使用 Quadro 來加速其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 大會上，我們推出了一系列新產品，將逼真的沉浸式體驗融入包含 Iray 和 VR 的工作流程中。我們發布了基於 Pascal 架構的 Quadro P6000，這是目前最先進的工作站 GPU，使設計師能夠以比以往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應用於工業設計領域，從而更快地創造更優質的產品。此外，我們還發布了八個全新且更新的軟體庫，例如 VRWorks 360 視訊 SDK，它將全景影片引入 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.

資料中心業務營收達到創紀錄的1.51億美元，較去年同期成長超過一倍，較上季成長6%。這一亮眼業績反映了超級運算、超大規模資料中心和網格虛擬化領域的強勁成長。隨著各行業日益尋求利用這項革命性技術，人們對深度學習的興趣也與日俱增。

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.

超大規模企業仍然是深度學習的快速採用者，無論是用於訓練還是即時推理，尤其是在自然語言處理、視訊和影像分析領域。其中包括 Facebook、微軟、亞馬遜、阿里巴巴和 Bidoo。主流雲端服務供應商也為其客戶提供 GPU 運算服務。微軟 Azure 目前就使用 NVIDIA 的 GPU 來提供運算和圖形虛擬化服務。

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.

本季度，我們開始出貨 Tesla P100，這是基於 Pascal 架構的全球最先進的 GPU 加速器。它專為加速深度學習訓練而設計，並利用我們的 NV Link 互連技術，可將應用程式效能擴展到多達 8 個 GPU。此外，我們還發布了基於 PCI Express 的 P100 版本，使其適用於各種加速伺服器。

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.

在四月的GPU技術大會上，我們推出了DGX1，這是全球首台深度學習超級電腦。它將八顆P100處理器整合在一個機箱內，可提供相當於250台傳統伺服器的深度學習效能。 DGX1預先安裝了NVIDIA軟體，可供人工智慧應用開發者使用。

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.

我們看到來自學術界、政府實驗室和大型企業的AR研究人員和開發人員對DGX1表現出濃厚的興趣。兩天前，Jen-Hsun親自將首台DGX1生產型模型交付給了開放人工智慧研究所。他們計劃利用該系統建構聊天機器人、汽車和機器人等自主代理。更廣泛的交付將於本季稍後開始。

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.

今年晚些時候，我們將在遍佈全球八個城市的GPU技術大會區域版上，更深入探討深度學習。這些城市包括北京、阿姆斯特丹、東京、首爾以及華盛頓特區。

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.

本季我們的網格圖形虛擬化業務成長超過一倍。該技術在各行各業的普及速度正在加快，尤其是在汽車和建築、工程及施工（AEC）行業。本季新增客戶包括挪威石油天然氣公司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，在汽車業務方面，營收成長至創紀錄的1.19億美元，年增68%，季增5%，主要得益於主流車型中高階資訊娛樂系統和數位座艙功能的普及。我們助力合作夥伴開發自動駕駛汽車的步伐正在不斷加快。我們已開始向80多家使用我們硬體和DRI工作軟體開發自動駕駛技術的公司交付Drive PX2汽車超級電腦。

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.

我們基於驅動平台的自動駕駛解決方案的交付工作仍在按計劃進行。除了我們四大平台之外，我們的OEM和[IPA]業務收入為1.63億美元，年減6%，與主流PC的需求一致。接下來來看損益表的其他部分，我們實現了創紀錄的57.9%的GAAP毛利率，非GAAP毛利率為58.1%。這反映了我們GeForce遊戲GPU的強勁表現、平台策略的成功以及市場對深度學習的強勁需求。 GAAP營運費用為5.09億美元，較去年同期下降9%。非GAAP營運費用為4.48億美元，年增6%。這反映了研發和行銷方面的人員增加，但部分被法律費用的減少所抵消。

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GAAP營業收入為3.17億美元，去年同期為7,600萬美元。非GAAP營業收入為3.82億美元，年增65%。非GAAP營業利益率較去年同期增加680個基點，達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，展望2017財年第三季度，我們預計營收為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.

預計GAAP營運費用約5.3億美元，非GAAP營運費用約4.65億美元。 2017財年第三季GAAP和非GAAP稅率預計均為21%，上下浮動1%。更多財務詳情請參閱財務長評論以及公司投資者關係網站上的其他資訊。

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

現在開始接受提問。接線生，請您詢問大家的問題好嗎？謝謝。

(Operator Instructions)

（操作說明）

Your first question comes from the line of Mark Lipacis.

你的第一個問題出自馬克·利帕西斯之口。

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?

您好。感謝您回答我的問題。第一個問題是關於資料中心業務的。能否請各位幫忙分析一下，深度學習應用和傳統的計算密集型設計應用分別在多大程度上推動了資料中心的需求？

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。資料中心－如你所提到的，我們的資料中心業務由三個基本市場所構成。其中之一是高效能運算，你可以把它看作是傳統的超級運算市場，運算量非常大。第二個市場是網格運算，也就是我們的資料中心虛擬化，本質上就是圖形應用虛擬化。你可以將資料中心的任何PC或任何PC應用程式串流並提供給任何客戶端設備。第三個應用是深度學習，這主要是超大規模資料中心應用深度學習來增強其應用程序，使其更加聰明、更加出色。迄今為止，絕大部分成長都來自深度學習，原因在於高效能運算是一個相對穩定的業務。它仍然是一個成長型業務，我預計高效能運算在未來幾年將發展良好。網格計算是一個快速成長的業務。我認為科萊特說過，它同比增長了 100%，但這是基於一個較小的基數，而且深度學習不僅規模龐大，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.

非常感謝，這很有幫助。最後一個問題。關於新的Pascal架構－你們現在才開始出貨。我的理解是，通常情況下，新產品出貨時良率會有提升空間，出貨量越大，良率曲線就越低。那麼，良率通常會如何改變呢？這會對未來三到四個季度的毛利率產生正面影響嗎？謝謝。

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，在過去幾年裡，我們一直在深入探討如何為新的製程節點做好準備。長期關注英偉達的讀者可能還記得，40奈米製程節點對我們來說是一個極具挑戰性的節點。而所有這些挑戰，也為我們提供了一個提升公司的機會。我們實施了一套非常嚴謹的製程節點準備方法，而這首先要歸功於我們擁有世界一流的製程工程師、電路設計工程師和製程準備團隊。我們有一個非常優秀的團隊，專門負責為我們做好流程準備。

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.

其次，關鍵在於如何將這個流程準備整合到整個公司，所以我對公司在Pascal架構上的執行方式感到非常自豪。 16奈米FinFET製程絕非易事，更不用說我們使用的記憶體速度了。這是全球首款G5x記憶體。我們也實現了全球首款HBM2記憶體和3D記憶體堆疊技術的量產。因此，我們在Pascal架構量產過程中克服的技術挑戰數量相當驚人。我為團隊感到無比驕傲。展望未來，我們將持續提升良率，這點毋庸置疑。然而，我們在進入16奈米製程階段時已經做好了充分的準備，因此現在預測良率和利潤率的長期走勢還為時過早。但我們會按季度逐步給予指引。

Your next question comes from the line of Toshi Otani.

你的下一個問題出自大谷俊之手。

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?

您好。感謝您回答我的問題，並祝賀貴公司取得了非常亮眼的季度業績。您的第三季營收預期表明，年成長將進一步加速。您預計在哪些一兩個終端市場會出現超常成長？或者，我們是否應該預期本季整體成長將較為普遍？

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，Toshi，非常感謝。我們所有業務都在成長。我們專注於深度學習、自動駕駛汽車、遊戲和虛擬實境的策略——在這些市場中，GPU 能夠發揮非常顯著的作用——正在取得顯著成效。本季是我們所有業務首次成長，我預計下個季度所有業務也將繼續成長。但這主要得益於我們對這些關鍵市場的關注，以及對傳統大宗商品組件業務的轉型。我認為有一個特別突出的動態，也是我們擁有獨特優勢的重要成長驅動力，那就是深度學習。您可能聽說過，深度學習是一種新的計算方法。它是一種新的運算模型，需要新的運算架構，而 GPU 的平行運算能力恰好完美契合這一需求。五年前，我們開始大力投資深度學習，並對我們的整個技術堆疊進行了根本性的變革和改進，從 GPU 架構到 GPU 設計，再到 GPU 連接的系統，都旨在提升深度學習的效能。例如，NVLink 可以連接到其他專為它設計的系統軟體，例如 [Kudi] 和 [N]，還可以連接到我們公司現有的所有深度學習專家。過去五年，我們一直在默默地投資深度學習，因為我們相信深度學習的未來對整個軟體產業乃至整個電腦產業都具有深遠的影響，所以我們全力投入其中。現在，我們發現自己正處於這重要動態的中心，而且──如果說有什麼成長因素意義重大，那麼很可能就是深度學習。

Your next question comes from the line of Vivek Arya.

你的下一個問題出自維韋克·阿利亞之口。

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?

感謝您回答我的問題，也祝賀貴公司取得良好的成長和執行力。 Jen-Hsun，第一個問題與PC遊戲相關，這方面趨勢非常強勁。我想請問您能否量化一下有多少用戶升級到了Pascal架構？另外，您是否注意到玩家在這次升級週期中的行為發生了任何變化？例如價格方面，或者他們現在購買的硬體配置有哪些變化？還有，與Kepler和Maxwell架構週期相比，他們的升級速度如何？

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，Vivek。嗯，關於PC遊戲，情況比較複雜。我們的GeForce顯示卡全球活躍用戶約有8000萬。事實上，只有大約三分之一的用戶升級到了Maxwell架構，而我們直到上個季度才開始出貨Pascal顯示卡。所以，這就能讓你明白Pascal的市場規模有多大──毫無疑問，Pascal是我們GPU發展史上最大的世代躍進。它不僅性能卓越，而且能效極高，還包含了一些令人興奮的全新圖形技術，可用於VR等領域。我認為Pascal將會取得巨大的成功，而且它推出的時機也與五年前的PC遊戲市場截然不同。

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.

如今電子遊戲的製作品質和內容水平比以往任何時候都高得多，這是一個非常強大的趨勢。原因在於——我在之前的電話會議中多次提到——擁有架構兼容且性能強大的遊戲平台的裝機量大幅增長，這意味著PlayStation 4、Xbox One和PC在架構上基本兼容。因此，開發者的開發空間比起以往幾代主機有了顯著擴大。這是一個相對較新的趨勢。因此，遊戲品質的提升意味著對GPU效能的需求也隨之成長。我認為我們確實看到了這種趨勢。我非常興奮的是，下一代遊戲機即將到來，效能將大幅提升，達到兩倍。這將使遊戲內容提供者和遊戲開發者能夠追求更高的目標，我認為這將有助於PC遊戲毛利率和平均售價的長期成長。

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.

我認為還有一些其他強大的因素，正如您所知，那就是電子競技不再只是一種興趣。電子競技已成為一股席捲全球的現象，在亞洲幾乎所有發展中國家都非常流行，當然，在西方世界也是如此。我認為除此之外，VR 不僅開局良好，我們現在也看到了許多優秀的內容。而且，我們最近在 Pascal 架構中推出的一些功能，正是利用了這種草根但又具有全球性的興趣，將電子遊戲作為一種藝術媒介。我們推出了 NVIDIA Ansel 項目，這是世界上第一個遊戲內攝影系統。它允許您創建虛擬現實照片，效果非常驚人。因此，您可以使用電子遊戲捕捉精彩瞬間，並以 VR 或高解析度與所有朋友分享。現在有很多不同的方式可以享受遊戲，而且製作水平也在不斷提高，這對我們的平台來說是件好事。所以，我想總結一下你最初的問題，已安裝的設備中有多少升級到了 Pascal 架構——當然，比例非常非常小，因為我們才剛開始量產。但即便如此，也只有三分之一的設備升級到了 Maxwell 架構，所以我們前方還有相當大的升級機會。

Your next question comes from the line of Steven Chin.

你的下一個問題來自 Steven Chin 的一句話。

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，感謝您回答我的問題。 Jen-Hsun，如果可以的話，我想先問一個關於資料中心競爭格局的問題。本週早些時候，我們看到您的一家資料中心競爭對手收購了一家規模較小的私人公司。我想請您詳細談談您如何看待自身在資料中心市場（尤其是機器學習人工智慧領域）的定位？另外，您的產品在高階和低階機器學習應用效能方面的定位又是什麼？

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。正如您所想，我們對行業現狀瞭如指掌，而且我們從一開始就身處其中。深度學習真正蓬勃發展，源自於世界各地的先驅研究人員發現了利用GPU加速深度學習的方法，並使之成為一種切實可行的軟體開發方法。 GPU堪稱完美之選，因為它本質上是由無數小型處理器組成的，而非單一的大型處理器。更重要的是，這些小型處理器透過連接組織相互連接。這種連接組織位於處理器內部，連接內存，連接網路。這使得所有處理器能夠同時相互通訊。這項架構創新正是我們十年前啟動GPU運算計畫的靈感來源。這項發明具有真正的突破性意義。因此，GPU與深度學習可謂天作之合，因為神經網路本質上是由相互溝通的神經元所組成──其靈感正是來自神經元之間的同步通訊。所以，GPU確實是天作之合。

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.

五年後的今天，如果你審視深度學習，我認為毋庸置疑，它已被融入幾乎所有互聯網服務，使其更加智能、更加便捷，並為用戶帶來更愉悅的體驗。因此，我們可以看到，深度學習的超大規模應用不僅覆蓋面廣，而且規模龐大，遍及全球。我們意識到這種新的計算方法具有非常重要的長期意義，因此五年前，我們開始在公司整個技術堆疊上進行大量投資。 GPU 運算不僅僅是 GPU 晶片，它還包括 GPU 架構、GPU 設計、GPU 系統，以及運行在其上的所有演算法、工具、框架——以及我們與世界各地研究人員的合作。正是這種合作和我們的投資，在過去兩代 GPU 架構中顯著提升了深度學習的效能。我們最初啟動這項工作時，使用的是開普勒架構；麥克斯韋架構的效能提升了約 10 倍；而帕斯卡架構的效能又比麥克斯韋架構提升了約 10 倍。所以，僅僅兩代技術，短短五年時間，我們就極大地改進了深度學習，如今的 GPU 與過去的 GPU 相比已經截然不同，這都歸功於我們所做的所有工作。

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－這正是我們與眾不同之處，不僅在於我們對GPU的專注和在平行運算方面的專業知識，更在於我們獨特的深度學習架構。我們基本上將所有投資都投入到這項架構中。我們讓這項架構能夠應用於從超大規模資料中心到工作站、筆記型電腦、個人電腦、車載系統、嵌入式計算機，甚至是我們稱為DGX的全新、完全整合的高性能一體機——NVIDIA DGX1。因此，使用者可以透過多種方式存取NVIDIA架構，即NVIDIA深度學習平台。

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.

它簡直無所不在，就在你身邊。無論是在零售店、電商平台、雲端，或是世界各地的大學，你都能透過嵌入式電腦套件輕鬆取得它。因此，我們的策略非常獨特且專注，我認為我們擁有相當大的領先優勢，市場地位非常穩固。但是，正如你所見，我們並沒有止步不前，在過去五年裡，我們一直在進行大量的投資。因此，我認為在未來幾年，隨著我們在這個領域的不斷推進，你會看到我們取得顯著的進步。

Your next question comes from the line of Romit Shah.

你的下一個問題來自羅米特·沙阿的詩句。

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。我有個關於汽車產業的問題。您提到drive PX現在已經向80家汽車公司供貨。 Jen-Hsun，我很好奇，這些訂單的規模是否與drive PX類似，並且更專注於原型開發？或者，這些訂單中是否存在最終能夠轉化為全面量產訂單，並對汽車業務產生不成比例的推動作用的機會？

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.

感謝您的提問。是的，我們本季剛開始交付Drive PX2。在回答您的問題之前，讓我先解釋一下Drive PX2是什麼。 Drive PX2當然是一款處理器。它是Drive PX2的單處理器版本，僅包含Parker和我們的Tegra處理器。此外，您也可以選擇配備獨立GPU，從而打造具備自動駕駛或AI副駕駛功能的汽車，直到完全自動駕駛。它能夠進行感測器融合，並能進行SLAM（即時定位與地圖建構）和環境偵測，利用深度神經網路來感知周圍環境。在環景系統中，車輛周圍的所有攝影機都會將資料傳輸到處理器。 Drive PX處理器會對環視攝影機的即時影像進行推理，最終完成車輛的實際規劃和駕駛——所有這些都由這台車載計算機，這台車載AI超級計算機完成。

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，本季我們開始向所有合作夥伴和開發者出貨，以便他們能夠基於我們的電腦和軟體堆疊開發軟體和系統。我們計劃批量生產其中許多產品，但很難確切知道每個人的進度安排。不過，時間跨度很大，從很快到未來幾年都有可能。開發自動駕駛汽車絕非易事——這是一項相當艱鉅的任務。當然，沒有人會把它當成兒戲。問題或許可以換個角度問：我期望人們生產OEM汽車嗎？或者，我們期望他們生產帶有地理圍欄的班車？我們期望他們生產卡車嗎？要知道，現在路上有多少卡車，世界經濟的很大一部分都圍繞著卡車運輸產品和各種服務（包括計程車服務）。答案是，我們正在與涵蓋整個領域的客戶和合作夥伴合作，從銷售的汽車到卡車、貨車、接駁車以及各種服務。

Your next question comes from the line of Craig Ellis.

你的下一個問題來自克雷格·艾利斯的詩句。

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，謝謝您回答這個問題。第一個問題是關於本季遊戲業務的強勁表現，該公司在本季推出了創始版遊戲產品。能談談這部分銷售狀況嗎？另外，這些產品的毛利率與僅銷售晶片（用於遊戲顯示卡OEM）的毛利率相比如何？

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].

首先，關於創始版—感謝您的提問。創始版由NVIDIA設計、完全由NVIDIA製造、直接銷售並提供技術支援。有些玩家和客戶更傾向於與我們公司直接溝通。創始版的供應量有限，並且我們嚴格按照最高品質標準進行設計。此外，我們也限制了其產量。原因在於，我們擁有一個強大的合作夥伴網絡，他們能夠迅速地將NVIDIA架構推廣到世界各地。據我們所知，我們的合作夥伴遍布全球各個國家，他們可以提供不同尺寸、形狀、款式、散熱方案、配置和價位的產品。我們認為，這種多樣性是NVIDIA GeForce平台如此受歡迎的原因之一，它激發了市場的活力，並催生了許多有趣且多樣化的設計。因此，我認為這兩種策略相輔相成。但關鍵在於，我們推出創始版的初衷是讓部分客戶能夠直接購買並與我們建立直接聯繫。但整體而言，我們的策略是透過合作夥伴網路開拓市場。至於毛利率，[利潤率不變]。

Your next question comes from the line of Matt Ramsay.

你的下一個問題來自馬特拉姆齊的一句話。

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.

下午好，謝謝。 Jen-Hsun，我想再問幾個關於資料中心業務的問題。首先，我們團隊做了一些工作，試圖估算加速器（包括GPU）的長期伺服器存取率。所以，很想聽聽您的看法。其次，資料中心市場對APU類型的產品有需求嗎？我知道你們有丹佛專案和其他一些CPU方面的項目，但貴公司是否正在考慮開發深度學習整合CPU/GPU方案，以期從長遠來看開拓更大的市場潛力？謝謝。

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。首先，資料中心的工作負載類型確實發生了巨大變化。以前，資料中心主要處理資料庫搜索，但現在情況已經大不相同了。它不再只是處理文本，也不再只是處理資料。如您所知，如今網路和資料中心傳輸的大部分內容都是圖像和語音。即時視訊正日益成為一種重要的新型資料格式。

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.

仔細想想，即時視訊就是即時視訊。它不會保存在伺服器上，也不會被錄製下來。這意味著，如果你想觀看即時視頻，資料中心需要具備相當的人工智慧能力，能夠即時處理即時視訊串流，以便讓可能對你正在播放的視訊串流感興趣的人知道應該通知誰，邀請誰來觀看。因此，展望未來資料中心的流量，我認為其工作負載將持續成長，吞吐量越來越高，多媒體內容也越來越豐富，當然，人工智慧和深度學習也將越來越多地發揮作用。所以，我認為這是第一點。

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.

第二點是，資料中心的架構正在發生顯而易見且可以理解的變化，因為工作負載也在改變。深度學習的工作負載與以往截然不同，因此其架構——它是一種全新的運算模型——也需要相應的架構。它意識到自身需要一種新的運算架構，而像GPU這樣的加速器恰好非常契合。所以，現在的另一個問題是，這種架構的規模究竟有多大？這很難說。很難預測具體規模。但我的感覺是，它將會蓬勃發展，而且無需任何預測，其規模將遠遠超過我們目前的交付量。我認為深度學習的成長機會非常巨大。我認為每個超大規模資料中心都將採用GPU加速。訓練和推理都將使用GPU加速。或許還會有其他方法，但我認為使用GPU將是其中非常重要的一環。

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.

最後，我們來談談APU。我認為對於資料中心來說，能源效率至關重要。雖然工作負載越來越多地轉向人工智慧、即時視訊和多媒體，GPU在這些領域可以發揮巨大作用，但仍有許多工作負載以CPU為中心，因此我們仍然需要一顆性能卓越的CPU。我認為沒有人會否認英特爾生產世界上最好的CPU。這點毋庸置疑，沒有之一。英特爾CPU的精湛工藝和卓越品質是毋庸置疑的。對於大多數資料中心而言，如果存在CPU工作負載，我認為英特爾的至強處理器很難被超越，這就是我的觀點。

Your next question comes from the line of Ian Ing.

你的下一個問題來自 Ian Ing 的詩句。

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。您之前提到過，目前已經停止生產所有 Pascal 產品。現在市面上已經有三款產品了，是不是代表你們要把 250 美元以下的顯示卡市場拱手讓給競爭對手了？或者，你們會用之前的 Maxwell 產品或即將推出的新產品來滿足這個市場需求？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。我們已經完成了流片，驗證完畢，所有帕斯卡架構的GPU都已量產。沒錯。但是，我們還沒有推出所有型號。

Your next question comes from the line of Steve Smigie.

你的下一個問題來自史蒂夫·斯米吉的詩句。

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，非常感謝您的提問。我只是想再跟進一下虛擬實境方面的問題。您之前提到過一些相關的投資。我很好奇目前市場反應如何，以及您認為推動虛擬實境發展的最大動力是什麼？是更多的頭顯設備？還是更多的開發者？謝謝。

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。我認為以上所有因素都很重要。我們必須持續推動VR技術的發展，讓頭戴式設備走向世界。我認為ACC Vive做得非常出色，Oculus也做得非常出色。我們密切注意所有上市的頭戴式設備，而且數量還在不斷增加。其次，VR內容非常精彩，人們也樂在其中。所以，我們需要提供更多內容，世界各地的開發者都在積極投入VR領域。這確實是一種很棒的全新體驗。但是，正如你所知，VR不僅限於遊戲。我們在企業和工業設計、醫療、醫學影像以及建築工程等領域取得了巨大成功，也看到了巨大的發展潛力。

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.

我們自己也在使用這項技術。我們正在對工作空間進行大量的設計，並使用我們名為 Iray 的照片級渲染器進行渲染，該渲染器完全由我們的 GPU 加速。然後，我們將渲染結果匯入 VR，並在 VR 中盡情享受。置身於一個照片級逼真的 VR 環境中，並完全沉浸其中，這是一種截然不同的體驗。因此，建築工程和施工行業將從中受益。所以，我們看到 VR 技術得到了廣泛的應用。

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.

我們所做的其中一項真正令人矚目的成就，就是Pascal架構的多解析度、多投影渲染功能。它是世界上首個能夠同時渲染到多個投影而非單一投影的GPU架構。原因在於，過去的GPU設計初衷是用於在單一顯示器上顯示影像。那時，你只有一個鍵盤，一個顯示器。但是，隨著我們邁入虛擬實境領域，以及各種各樣有趣的顯示配置和顯示類型的出現，電腦圖形的這種模式發生了翻天覆地的變化。因此，多投影是圖形領域的革命性技術，而Pascal架構正是這項技術的引進者。它在虛擬實境中尤其受益匪淺。

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——也就是現實世界的實體模擬——融入虛擬實境（VR）中。好處在於，如你所知，如果沒有物理定律，你就無法感受到任何東西。物體不會碰撞，不會彈跳。當你拿起東西時，你感受不到任何觸覺回饋。我們對整個環境進行了物理模擬，因此，你能夠感受到整個環境。當你打翻一瓶水時，它的行為就像一瓶被打翻的水。球的運動方式就像球的運動方式，物體之間不會互相融合。我們相信，這種與觸覺回饋的融合徹底革新了虛擬現實，而實體模擬則是另一項突破。因此，我認為我們在虛擬實境領域佔據著非常有利的地位，我對虛擬實境的發展充滿熱情。

Your next question comes from the line of Vijay Rakesh.

你的下一個問題來自 Vijay Rakesh 的名言。

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?

您好。我只是想問一下資料中心方面的問題。 Jen-Hsun，您提到了三個關鍵領域：HBC、網格運算和深度學習。這些領域在資料中心中所佔的比例是多少？

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.

我認為目前深度學習約佔50%，高效能運算佔35%左右，甚至更多。剩下的部分就是虛擬化。展望未來，這也是你問題的一部分，我認為深度學習將成為其中非常重要的一部分。另一點需要認識到的是，深度學習不僅適用於網路服務供應商的語音辨識、影像辨識和人臉辨識等應用。深度學習是一種利用數學和軟體從大量資料中挖掘洞見的方法。而我們產生海量資料的地方正是高效能運算。世界上每個超級運算中心都將轉向深度學習，原因在於它們會產生大量數據，而它們本身幾乎沒有能力進行梳理和篩選。現在，借助深度學習，它們可以從超維資料中發現極其細微的洞見。這就是理解深度學習的方式——它本質上是數學。這是一種非常強大的新型數學形式，也是一種全新的軟體開發方法。但是，不要把它看作是一個市場。我認為每個市場最終都會成為深度學習市場，每個應用最終都會成為深度學習應用，從長遠來看，每個軟體都將融入人工智慧。

Your next question comes from the line of Harlan Sur.

你的下一個問題來自哈蘭·蘇爾的詩句。

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.

午安.季度業績報告執行得非常出色。你們的專業視覺化業務成長強勁，營收也創下新高。我原本以為大部分成長會來自即將發布的基於 Pascal 架構的 P5000 和 P6000 系列。因此，我驚訝地發現大部分需求來自你們目前的 M6000 系列，這顯然意味著 Pascal 的需求週期尚未完全到來。首先，我的這種看法是否正確？其次，是什麼因素推動了 M6000 系列的強勁成長？如果你們還沒有發布基於 Pascal 架構的 5000 和 6000 系列，預計何時發布？謝謝。

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。我很感激你的提問。多年來，團隊一直致力於改變電腦輔助設計（CAD）的運作方式。你的觀察完全正確，而且這種改變源自於多個面向。首先，越來越多的設計工作其實是產品設計、工業設計，產品的觸感和美感與產品的機械設計同等重要。無論是建築、消費品或汽車，我們都需要能夠使用真實的材料模擬，以照片級的逼真方式模擬其美感。實現這一點所需的計算量確實非常巨大。我們現在看到，無論是DeSo的領先軟體包、SolidWorks的領先軟體包，或是AutoDesk、Adobe等，各種設計軟體對GPU的使用量都顯著增加，而且成長速度非常驚人。

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.

我認為部分原因在於，對於所有獨立軟體開發商（ISV）和開發者而言，市場對更早看到照片級真實感設計圖的需求不僅是重要的決策標準，而且他們還可以依賴幾乎每台電腦都配備的高效能GPU。因此，GPU的普及使他們能夠充分利用GPU的優勢，並真正相信他們軟體包中依賴GPU的軟體功能能夠獲得GPU帶來的優勢。

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在設計領域開始看到的虛擬循環。幾年前我們對照片級渲染的投入，對光學GPU加速的運用，以及幾乎所有軟體都在使用的路徑追蹤層，還有我們持續推廣GPU及其通用用途（從電腦圖形學到圖像處理）的努力，我認為已經開始顯現成效。這是第一點。

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 是有史以來能效最高的 GPU，直到 Pascal 的出現。 Maxwell 的能源效率是 Kepler 的兩倍，而更令人驚訝的是，Pascal 的能效又是 Maxwell 的兩倍。 Maxwell 使得筆記型電腦能夠擁有劇院等級的視覺效果，工作站也更加優雅，並且由於功耗的考慮，工作站可以擁有更強大的性能和更豐富的功能。 Maxwell 的出現使整個產業得以提升其所使用的 GPU 水準。

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.

我認為，關於您最後一個問題，展望未來，我們將如何看待 Pascal 架構？ Pascal 架構正在全球逐步應用於工作站。因此，我認為在接下來的幾個季度裡，我們將會看到 Pascal 的身影。我預計，我剛才提到的這些因素——軟體開發人員對照片級真實感功能的需求增加、我們發明的 GPU 加速照片渲染技術（包括 I-ray、光學和 NDL 材質描述語言），以及 GPU 的能效提升——這三者結合起來將對工作站大有裨益。最後，VR 也即將到來。為了真正享受這類設計應用帶來的樂趣，目前您需要一台效能非常強大的 GPU。

Your next question comes from the line of Ross Seymore.

你的下一個問題出自羅斯·西摩的詩句。

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.

大家好。感謝允許我提問。 Jen-Hsun，關於汽車方面，我想問你幾個問題。首先，我們看到最近幾個月你們與一些競爭對手和客戶建立了合作關係，但也看到其中一些合作關係最終破裂。那麼，NVIDIA 在這個整體生態系統中，在建立合作關係方面，扮演著怎樣的角色？其次，如果我們粗略估計一年時間，你認為你們汽車業務中自動駕駛部分的規模何時才能超過車載資訊娛樂系統？謝謝。

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。我們一直秉持著優雅、友善和開放的態度進行合作。我是認真的。我們堅信這一點。我們認為，打造一輛自動駕駛汽車需要大量的軟體，而且是非常複雜的軟體。它真的非常非常複雜，不可能由一家公司獨立完成。而且，對於那些在未來二十年不斷改進演算法和提升自動駕駛汽車性能的大型公司來說，將自動駕駛汽車的整個技術堆疊外包給其他公司也是不合邏輯的。我們始終認為，自動駕駛汽車是個軟體問題，大型公司需要能夠掌控自己的命運。這就是 PX2 是一個開放技術堆疊和開放平台的原因。這樣，每家汽車公司都可以在其基礎上建立自己的自動駕駛汽車。這是第一點。

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 架構具有可擴充性。原因在於，高速公路上的自動煞車和自動駕駛、虛擬副駕駛、完全自動駕駛汽車、自動駕駛卡車、地理圍欄自動駕駛班車、完全自動駕駛計程車等等，所有這些平台都無法僅靠一個晶片來實現。這根本不合邏輯。實現這些功能所需的計算量極為龐大。為了應對各種意外情況，您希望在安全方面達到更高的精度（或更高的準確度），也就是您希望的「9」越多，所需的計算量就越大。就像語音辨識一樣，過去四、五年間，語音辨識所需的計算量增加了100倍。但請注意，語音辨識的精確度和準確度已經達到了多麼高的水平。圖像識別、視訊環境識別、上下文識別等等，所有這些都需要大量的計算。因此，我們認為可擴展的平台是必要的，這是第二點。

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.

第三點，偵測，也就是電腦視覺和目標偵測，只是整個自動駕駛問題的一小部分。真的只是一小部分。我們一直都說，自動駕駛汽車本質上是一個人工智慧運算問題。之所以說是運算問題，是因為處理器不只需要偵測，還需要運算，CPU很重要，GPU很重要，整個系統架構都很重要，所以整個運算引擎都很重要。

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.

第二點是，計算不僅僅是晶片的問題，它很大程度上是一個軟體問題。而構成整個系統軟體堆疊（或自動駕駛即時電腦、即時超級電腦的作業系統）所需的軟體目前還不存在。大多數超級計算機都是盡力而為超級計算機，它們會盡可能快地運行任務，直到完成為止。但這對於自動駕駛汽車來說遠遠不夠。這台超級電腦必須即時運行，並且必須在發現危險的瞬間做出反應，而盡力而為超級電腦根本無法勝任。我們需要的是一台真正的即時超級電腦。而世界上從未製造過真正的即時超級計算機，這正是 DRIVE PX2 的目標：一台用於實現全面自主人工智慧的即時超級電腦。

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.

所以這就是我們關注的重點，也是我們採取的方向。我認為你們現在看到的是，市場開始對此做出反應。或許隨著自動駕駛技術的不斷深入，他們發現問題與我們所遇到的問題類型相似，而這正是 DRIVE PX 採用電腦而非智慧攝影機的原因。

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

你的下一個問題源自約瑟夫·摩爾的理論。

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?

太好了，非常感謝。您談到了超大規模環境下的深度學習，但似乎您在企業級環境中也取得了一些進展。我知道我們接觸過的至少有一個IT部門已經在進行一些部署。您能否談談您在企業級環境中的進展，以及您需要做些什麼才能在更傳統的企業中建立影響力？

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.

如您所知，深度學習不僅僅是一種網路服務方法。它實際上是機器學習的超級版本。深度學習的真正意義在於從大數據、大型非結構化資料和多維資料中挖掘洞見。這就是深度學習，我稱之為從天而降的雷神之鎚。這是研究人員發現的一項令人驚嘆的技術。而我們為此做好了極為充分的準備，因為GPU本身就具備並行處理能力。這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.

但如果你從軟體開發的角度來看待它，它是一種全新的軟體開發方法，也是一種從資料中挖掘洞見的新途徑，那麼哪家公司不需要它呢？所以，每家醫療公司、每家生命科學公司都需要它。每家醫療保健公司都需要它。每家能源勘探公司都需要它。每家電商零售公司都需要它。每個人都有大量數據，而這些數據都是他們自己擁有的。每家製造公司都需要它。每家重視安全、處理大量客戶資料的公司都能從深度學習中受益。因此，從這個角度來看，我認為深度學習在企業級市場的潛力甚至比在消費性網路服務領域更大。

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.

這正是我們打造 NVIDIA DTX1 的原因。因為大多數企業要不是缺乏專業知識，就是根本沒有意願去建構超級運算資料中心或高效能電腦。他們更願意購買一台預先安裝所有軟體、效能經過精心調校、開箱即用的一體機。 NVIDIA DGX1 也是如此。它就像一台裝在盒子裡的超級計算機，專為深度學習的高效能運算而設計和最佳化。

Your next question comes from the line of Ambrish Srivastava.

你的下一個問題出自安布里什·斯里瓦斯塔瓦之口。

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.

您好。非常感謝您抽出時間。我有一個關於毛利率的問題，Jen-Hsun。營收預期非常高，但毛利率預期卻維持不變。這是為什麼呢？我知道這兩者並不總是完全相關的，毛利率理應大幅成長，但究竟是定價問題還是良率問題呢？因為產品組合似乎也朝著正確的方向發展，增加了專業視訊設備（Pro Ves）和高效能運算（HPC）產品，減少了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.

嗯，我們的預測只是我們目前的最佳估計。下個季度我們再談的時候，就能知道最終結果了。但總的來說，我同意你的看法，隨著我們不斷深入，越來越依賴平台化業務模式，我們的平台專業化且軟體豐富，我們所提供產品的價值也越來越具有非凡的企業價值。使用該產品的好處不僅體現在幀速率上，還能真正降低企業的總擁有成本 (TCO)，並透過減少伺服器叢集數量來節省成本，並切實提高生產力。因此，我認為我們完全有理由相信，從長遠來看，這種平台化模式能夠創造更大的價值。至於下個季度，我想我們還是拭目以待吧。

Your next question comes from the line of Rajvindra Gill.

你的下一個問題出自拉傑文德拉·吉爾之口。

Thank you.

謝謝。

It's Vindra. How are you?

我是文德拉。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.

沒錯。很好。 Jen-Hsun，關於DRIVE PX2，我有個問題。正如您所描述的，我的理解是，它是一個可擴展的架構，涵蓋從駕駛艙到ADAS、地圖繪製再到自動駕駛的各個環節。但我很好奇，這與你們的一些競爭對手針對不同等級的ADAS系統（例如L1、L2、L3）提供不同解決方案的做法有何不同。特別是V2X通訊方面，對於L4自動駕駛，需要6到20個不同的雷達單元、3到6個不同的攝影機（包括雷射雷達）。我想了解你們在半導體領域的做法與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，問得好。這個問題無法簡單解決，也沒有理由簡單解決，你也找不到唯一的答案。之所以找不到唯一的答案，是因為沒有人確切知道該如何實現。我們都對最終如何解決長期完全自動駕駛汽車的問題抱有直覺和信念，希望無論我身在何處，我坐進的汽車，我們坐進的汽車，都能完全自動駕駛，內外都配備了人工智能，帶來令人難以置信的體驗。但我們距離目標還很遠。

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——並非所有公司——對未來的願景都略有不同。有些人認為，通往未來的道路是立即在預先繪製好地圖的地理圍欄區域內實現完全自動駕駛。有些人認為，可以先從高速公路自動駕駛開始，快速推進到完全自動駕駛。還有一些人認為，實現完全自動駕駛的最佳方法是透過接駁車和卡車。因此，你會看到各種各樣的願景。我認為所有這些願景都出自聰明人之手，他們正在做明智的事情，他們關注的是交通運輸的不同方面。

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.

我認為有一種謬誤，每個國家、每種形式的運輸都完全相同。事實並非如此。因此，我們需要了解技術，了解市場，也要考慮技術願景與切入點之間的關係。我認為，這就是所有難題無法解決的地方。你試圖解決一個根本不可能實現的簡單方程式。然而，有一件事我們堅信一定會實現。我們絕對確信人工智慧將參與其中，我們相信，憑藉深度學習和人工智慧，我們最終將掌握破解這些難題的秘訣，並在一段時間內解決這些難題。這是第一點。

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.

第二點。我們堅信，根據您要解決的問題，所需的運算能力也各不相同。我們明確認為這是一個軟體問題，對於規模最大的運輸公司而言，他們需要與您合作開發自己的軟體，但他們不會讓您獨自完成，也不會將其視為黑盒子。我們明確認為這是一個運算問題，一個即時超級運算問題，它不僅僅關乎一個特殊的元件，而是需要運算能力、處理器、電腦系統、系統軟體以及強大的作業系統能力才能建構這樣的系統。

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.

這是一個龐大的軟體難題。否則，我們早就解決了。所以我認為今年你會看到許多極具遠見且令人興奮的新功能的雛形。但在接下來的幾年裡，你會看到越來越多的新功能。我認為這只是一個開始，我們正在與一些非常非常優秀的人合作，共同完成這項工作。

Your next question comes from the line of Mitch Steves.

你的下一個問題來自米奇史蒂夫斯那一串話。

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?

謝謝各位回答我的問題。那麼，回到資料中心和深度學習方面，當你們進入這個市場時，是否觀察到平均售價（ASP）的變化？

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.

所以資料中心銷售的利潤率基本上不會有任何變化。我想汽車業也存在同樣的問題。

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，自動駕駛汽車的平均售價將遠高於車載資訊娛樂系統。這才是更棘手的問題。世界上每輛車都配備了車載資訊娛樂系統。除了少數開創性工作或早期最先進的車型外，幾乎沒有汽車是自動駕駛的。所以我認為，自動駕駛汽車所需的技術遠比車道維持、自適應巡航控製或第一代、第二代高級駕駛輔助系統（ADAS）複雜得多。問題要複雜得多。

Your next question comes from the line of Brian Alger.

你的下一個問題出自布萊恩·阿爾傑的詩句。

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?

大家好。感謝你們抽出時間。我想這應該是第一次，實際上，恭喜你們取得了非常出色的季度業績和令人矚目的業績指引。我想再談談Pascal架構與英特爾或AMD其他競爭對手之間的差異。已經有許多資料討論了Pascal和Polaris架構在功耗上的差異。雖然這在遊戲領域很重要，也引起了廣泛關注，但對於我們過去半小時到45分鐘一直在討論的深度學習應用來說，功耗實際上更為重要。你們能否談談這方面的設計，不是說它的性能，而是它的能效，以及這對於解決大型問題意味著什麼？

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.

布萊恩，非常感謝。首先，我很感激你的評論。團隊真的付出了很多努力。在過去的幾年，特別是五年裡，英偉達的所有員工一直在實踐一項策略，直到今天，我們才真正向世人證明，這項策略確實有效，而且它是一種非常獨特的商業模式，一種非常獨特的方法。我只想祝賀所有為我們取得今天的成就而辛勤付出的員工。

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.

我也很贊同關於能效的評論。事實上，能源效率是當今乃至未來處理器最重要的特性。原因在於，我們所處的每個環境都受到電力限制，每個環境都是如此。即使是你的個人電腦，功率750瓦或1000瓦，也受到電力限制。因為我們肯定可以在電腦裡安裝超過1000瓦的GPU。所以，這仍然是電力限制。我們身處的環境可能只有一兩瓦的電力。例如無人機，我們需要完全依賴電力，所以能源效率真的非常重要。我們可能在資料中心進行深度學習，訓練神經網路或進行神經網路推理。在這種情況下，儘管資料中心擁有大量的電力資源，但他們想要使用的GPU數量卻是成千上萬。因此，能效成為首要問題。從字面上講，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，從功能和架構特性來看，我們在 Pascal 中所做的架構變更具有開創性意義，這些變更使我們能夠在深度學習研究和發展方面保持領先地位。人們開始發現我們在 Pascal 中引入的架構變更，它將在未來幾年對深度學習產生巨大影響。因此，這既是一項特性，也是一項架構創新。

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，當然還有所有運作在處理器之上的軟體。我們稱之為GPU運算，而不是簡單的GPU，因為GPU運算關乎運算本身，關乎軟體，關乎系統，關乎GPU與記憶體以及系統周圍所有儲存裝置、網路、互連和儲存之間的內在聯繫，它是一個大規模的運算問題。它也是全球吞吐量最高的運算問題，正因如此，我們才被國家委派建造世界上速度最快的兩台超級電腦。高吞吐量計算是我們公司的專長。從基礎架構到晶片設計、系統設計、系統軟體、演算法、運算數學，以及所有各個科學領域的專家，高吞吐量運算是我們過去五年來投入的巨資。我認為成果已經開始顯現。

Your next question comes from the line of Blayne Curtis.

你的下一個問題來自布萊恩·柯蒂斯的詩句。

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。感謝你們抽空接待我，本季業績表現優異。我有兩個相關的問題。第一，Colette，我很好奇你對資金使用和股票回購的看法。顯然，回購速度加快了，上個季度只回購了900萬美元。你對未來的計畫有什麼看法？

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，關於資金的便利性，或許可以問一個更重要的問題。您說過CPU不是您想涉足的領域，但顯然GPU很有發展前景。所以我很好奇，如果您考慮其他領域，例如資料中心，您能否在那裡創造價值？

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理念的重要組成部分。但請記住，它仍然包含兩個面向：一部分是分紅，另一部分是我們回購股票。因此，展望未來，分紅無疑是一項長期策略，我們將密切關注股息收益率，以保持競爭力，同時也將關注我們的獲利能力。至於股票回購，我們會尋找合適的時機，並確保謹慎行事。

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.

關於資本的長期運用，我想說的是，英偉達真正擁有的，是遠見卓識、創造力和創新勇氣。也因為如此，我們幾乎每次討論任何事情，都會先召集公司裡的優秀人才，共同探討我們能為自己和世界創造怎樣的未來。所以，我認為我們對資本的運用，在於培養現有員工，為他們提供創新的平台，創造條件，讓他們能夠成功，並成就畢生事業。這就是我們理念的出發點。

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.

我們並不排斥收購，我們一直在尋找合適的收購目標，我們很榮幸與世界各地各種規模的公司合作，共同推動產業發展。所以，我們當然對收購持開放態度。但我們始終堅持的原則是投資員工，投資公司本身的創新能力，進而引領未來。

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

你的下一個問題出自 C.J. Muse 的詩句。

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.

午安.感謝您抽出時間。我想問兩個問題。第一個問題，感謝您將深度學習業務單獨列出，作為資料中心的百分比。您能否提供一下四月份季度的百分比是多少？第二個問題是，回顧過去四個季度，根據您給予的業績指引，您預期新增營業利潤率約為 50%。我想知道這個數字是否符合您的預期，或者我們是否應該考慮優化產品組合，以及代工廠合作夥伴的工藝和製造水平的提高，從而使未來的利潤率更高？謝謝。

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，深度學習是一種軟體方法，一種全新的運算架構，也是業界研究人員已經開發了20年的全新運算方法。直到五年前，深度學習和GPU領域的開創性工作才真正推動了它的發展，可以說，它為業界提供了一台時光機，將未來帶到了現在。深度學習的強大之處在於，它的能力正在不斷擴展，人們正在探索更多使用它的方法、更多應用以及新的深度學習架構，網路也變得越來越大、越來越深、越來越複雜。因此，我認為這個領域將會顯著成長。它目前佔據了我們資料中心收入的絕大部分，我認為它將繼續保持這一重要地位。

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?

第二個問題是什麼？我錯過了嗎？我覺得他的問題是關於資料中心和深度學習的，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、資料中心佔比以及這種趨勢的發展。

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.

你的下一個問題來自凱文·卡西迪的台詞。

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?

感謝您回答我的問題。或許我可以換個角度，談談能源效率。除了車載資訊娛樂系統之外，您是否發現了Tegra的其他應用前景？

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，感謝你的提問。 Tegra 是我們所有自動駕駛汽車專案的核心。如果沒有 Tegra，就不會有自動駕駛汽車。 Tegra 是我們自動駕駛汽車專案的核心，也是自動駕駛汽車的運算平台。 DRIVE PX2 不僅包含 Tegra，還包含 Pascal 架構的獨立 GPU，但其核心部分，絕大部分繁重的運算工作都由 Tegra 完成，我們預計未來也將如此。因此，Tegra 對我們至關重要。

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​​動智慧機器設計的平台。您可以想像，未來哪些智慧機器將受益於深度學習和人工智慧，例如機器人、無人機以及建築物和城市內部的嵌入式應用等等。應用領域非常廣泛。我對 Jetson 的未來非常樂觀，而 Tegra 正是其核心。您可以將 Tegra 看作是我們的晶片級計算機，它是我們的晶片級人工智慧計算機。

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，我非常感謝大家提出的所有問題。感謝各位今天參與我們的討論。我們的成長主要受以下幾個因素驅動。首先，我們專注於深度學習、自動駕駛汽車、遊戲和虛擬實境（這些市場對GPU至關重要），而這些努力正開始獲得回報。其次，Pascal是迄今為止最先進的GPU，我們對此感到無比興奮。上個季度，我們成功地實現了Pascal的量產。我為團隊上個季度所做的準備和執行工作感到無比自豪。第三，深度學習的超大規模應用如今已十分普及，並且在全球範圍內都得到了廣泛應用。以上就是我們未來成長的幾個主要驅動因素。

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.

展望未來，我們也期待與您分享我們在深度學習人工智慧領域的許多進展。隨著我們逐步拓展市場，深度學習的真正成長才剛開始。如今，深度學習的應用已十分廣泛，並在各個超大規模資料中心迅速普及。它是一種全新的運算模型，需要全新的運算架構，而GPU剛好完美契合這項需求。我非常欣慰的是，我們五年前啟動的策略，旨在從處理器層面、架構層面、晶片設計層面、系統、軟體和演算法層面，對GPU運算平台進行端到端的最佳化，使其更適合深度學習。公司內部匯集了許多深度學習專家，我們與世界各地的研究人員和開發人員合作，這些都使我們能夠持續推動這一領域和平台的發展。

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取得了遠超預期的進步。如果僅基於半導體物理學進行預測，其速度和性能的提升幅度將遠低於我們每一代產品所取得的飛躍式發展：從開普勒到麥克斯韋，我們實現了10倍的提升；從麥克斯韋到帕斯卡，我們又實現了10倍的提升。可以肯定的是，在未來幾年裡，我們將帶來更顯著的改善和提升。

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.

我們真正的優勢不僅在於它是一個卓越的深度學習和網路訓練平台，更在於它是一個出色的可擴展平台。無論是在雲端、資料中心、超級電腦、工作站、桌上型電腦、筆記型電腦，還是車載設備、嵌入式電腦（正如我剛才提到的Jetson），您都可以盡情享受我們的平台帶來的便利。我們採用的是單一架構，因此，我們始終將開發者、軟體程式設計師和研究人員的投入和投入放在首位。眾所周知，運算的核心在於架構、平台，而開發者則是運算的根本。我們深知這對開發者至關重要。因此，全球各行各業的開發者都可以使用這個單一架構，並在擴展和部署工作的過程中，充分發揮其科學研究成果和應用潛力。

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。我們度過了一個非常棒的季度，期待下個季度向大家報告進度。感謝大家的參與。

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

今天的電話會議到此結束。您可以掛斷電話了。