輝達 (NVDA) 2019 Q2 法說會逐字稿

Good afternoon. My name is Kelsey, and I'm your conference operator for today. Welcome to NVIDIA's financial results conference call. (Operator Instructions) I'll now turn the call over to Simona Jankowski, Vice President of Investor Relations, to begin your conference.

午安.我叫凱爾西，我是今天的會議接線生。歡迎參加英偉達財務業績電話會議。（操作員指示）現在我將把電話交給投資者關係副總裁西蒙娜·揚科夫斯基，由她開始您的會議。

Thank you. Good afternoon, everyone, and welcome to NVIDIA's Conference Call for the Second Quarter of Fiscal 2019. With me on the call today from NVIDIA are Jensen Huang, President and Chief Executive Officer; and Colette Kress, Executive Vice President and Chief Financial Officer.

謝謝。各位下午好，歡迎參加英偉達2019財年第二季業績電話會議。今天與我一起參加電話會議的英偉達總裁兼執行長黃仁勳，以及執行副總裁兼財務長科萊特·克雷斯。

I'd like to remind you that our call is being webcast live on NVIDIA's Investor Relations website. It's also being recorded. You can hear a replay by telephone until August 23, 2018. The webcast will be available for replay until the conference call to discuss our financial results for the third quarter of fiscal 2019.

我想提醒各位，我們的電話會議正在英偉達投資者關係網站上進行網路直播。整個過程都被錄影了。您可以在 2018 年 8 月 23 日之前透過電話收聽重播。網路直播將提供回放，直至召開電話會議討論我們 2019 財年第三季的財務業績。

The content of today's call is NVIDIA's property. It can't be reproduced or transcribed without our prior written consent. During this call, we may make forward-looking statements based on current expectations. These are subject to a number of significant risks and uncertainties, and our actual results may differ materially. For a discussion of factors that could affect our future financial results and business, please refer to the disclosure in today's earnings release, our most recent Forms 10-K and 10-Q and the reports that we may file on Form 8-K with the Securities and Exchange Commission. Forward-looking statements are made as of today, August 16, 2018, based on information currently available to us. Except as required by law, we assume no obligation to update any such statements.

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。在本次電話會議中，我們可能會根據目前的預期發表一些前瞻性聲明。這些都存在著許多重大風險和不確定性，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，請參閱今天發布的收益報告中的披露資訊、我們最新的 10-K 表格和 10-Q 表格以及我們可能向美國證券交易委員會提交的 8-K 表格報告。截至2018年8月16日，我們根據目前可取得的資訊作出了前瞻性陳述。除法律另有規定外，我們不承擔更新任何此類聲明的義務。

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

在本次電話會議中，我們將討論非GAAP財務指標。您可以在我們的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，該評論已發佈在我們的網站上。

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, Simona. This is a big week for NVIDIA. We just announced the biggest leap in GPU architecture in over a decade. We can't wait to tell you more about it, but first, let's talk about the quarter.

謝謝你，西蒙娜。本週對英偉達來說意義重大。我們剛剛發布了十多年來GPU架構的最大飛躍。我們迫不及待地想告訴大家更多詳情，但首先，讓我們來談談本季的情況。

We had another strong quarter, led by data center and gaming. Q2 revenue reached $3.12 billion, up 40% from a year earlier. Each market platform -- gaming, data center, pro visualization and automotive hit record levels -- with strong growth both sequentially and year-on-year. These platforms collectively grew more than 50% year-on-year. Our revenue outlook had anticipated cryptocurrency-specific products declining to approximately $100 million while actual crypto-specific product revenue was $18 million, and we now expect a negligible contribution going forward.

在資料中心和遊戲產業的帶動下，我們又迎來了一個強勁的季度。第二季營收達 31.2 億美元，比上年同期成長 40%。遊戲、資料中心、專業視覺化和汽車等各市場平台均創下歷史新高，實現了強勁的環比和同比增長。這些平台的總成長率超過 50%。我們先前的營收預期是加密貨幣相關產品的營收將下降到約 1 億美元，而實際的加密貨幣相關產品收入為 1,800 萬美元，我們現在預計未來其貢獻將微乎其微。

Gross margins grew nearly 500 basis points year-on-year while both GAAP and non-GAAP net income exceeded $1 billion for the third consecutive quarter. Profit nearly doubled. From a reporting segment perspective, GPU revenue grew 40% from last year to $2.66 billion. Tegra processor revenue grew 40% to $467 million.

毛利率年增近 500 個基點，GAAP 和非 GAAP 淨收入連續第三個季度超過 10 億美元。利潤幾乎翻了一番。從報告分部來看，GPU 營收比去年成長了 40%，達到 26.6 億美元。Tegra處理器營收成長40%，達到4.67億美元。

Let's start with our gaming business. Revenue of $1.8 billion was up 52% year-on-year and up 5% sequentially. Growth was driven by all segments of the business with desktop, notebook and gaming consoles up all strong double-digit percentages year-on-year.

讓我們先從遊戲業務說起。營收達 18 億美元，年增 52%，季增 5%。業務成長由所有部門共同推動，其中桌上型電腦、筆記型電腦和遊戲機年成長均達到兩位數百分比。

Notebooks were a standout this quarter, with strong demands for thin and light form factors based on our Max-Q technology. Max-Q enables gaming PC OEMs to pack a high-performance GPU into a slim notebook that is just 20 millimeters thick or less. All major notebook OEMs and ODMs have adopted Max-Q for their top-of-the-line gaming notebooks, just in time for back-to-school. And we expect to see 26 models based on Max-Q in stores for the holidays.

本季筆記型電腦表現突出，基於我們 Max-Q 技術的輕薄筆電需求強勁。Max-Q 技術使遊戲 PC OEM 廠商能夠將高效能 GPU 整合到厚度僅為 20 毫米或更薄的超薄筆記型電腦中。各大筆記型電腦廠商（OEM 和 ODM）都已在其高階遊戲筆記型電腦中採用了 Max-Q 技術，正好趕上開學季。我們預計假期期間將在商店裡看到 26 款基於 Max-Q 的車型。

The gaming industry remains vibrant. The eSports audience now approaches 400 million, up 18% over the past year. The unprecedented success of Fortnite and PUBG has popularized this new battle royale genre and expanded the gaming market. In fact, the battle royale mode is coming to games like the much-anticipated Battlefield V. We are thrilled to partner with EA to make GeForce the best PC-gaming platform for the release of Battlefield V in October.

遊戲產業依然充滿活力。電子競技觀眾人數現已接近 4 億，比去年增長了 18%。《要塞英雄》和《絕地求生》的空前成功使這種新的大逃殺遊戲類型流行起來，並擴大了遊戲市場。事實上，大逃殺模式即將登陸包括備受期待的《戰地5》在內的多款遊戲。我們很高興能與EA合作，讓GeForce成為10月《戰地5》發售時最佳的PC遊戲平台。

We have also partnered with Square Enix to make GeForce the best platform for its upcoming Shadow of the Tomb Raider. Monster Hunter: World arrived on PCs earlier this month and it was an instant hit. And many more titles are lined up for what promises to be a big holiday season.

我們也與 Square Enix 合作，讓 GeForce 成為即將推出的《古墓奇兵：暗影》的最佳平台。《魔物獵人：世界》本月初登陸PC平台，並立即大受歡迎。還有更多影片正在籌備中，預計將迎來一個盛大的假日檔期。

It's not just new titles that are building anticipation. The gaming community is excited over the Turing architecture announced earlier this week at SIGGRAPH. Turing is our most important innovation since the invention of the CUDA GPU over a decade ago. The architecture includes new dedicated ray-tracing processors or RT cores and new Tensor Cores for AI inferencing, which together will make real-time ray-tracing possible for the first time. We will enable the cinematic quality gaming, amazing new effects powered by neural networks and fluid interactivity on highly complex models.

不只是新遊戲能引發人們的期待。遊戲界對本週稍早在 SIGGRAPH 大會上發布的 Turing 架構感到興奮不已。圖靈架構是我們自十多年前發明 CUDA GPU 以來最重要的創新。該架構包括新的專用光線追蹤處理器或 RT 核心以及用於 AI 推理的新 Tensor 核心，它們將首次共同實現即時光線追蹤。我們將實現電影級的遊戲體驗，利用神經網路驅動的驚人新特效，以及在高度複雜的模型上實現流暢的互動。

Turing will reset the look of video games and open up the 250 billion visual effects industries to GPUs. Turing is the result of more than 10,000 engineering years to -- of effort. It's -- delivers up to 6x performance increase over Pascal for ray-traced graphics and up to 10x boost for peak inference flops. This new architecture will be the foundation of a new portfolio of products across our platforms going forward.

圖靈將重塑電子遊戲的視覺效果，並為價值 2,500 億美元的視覺特效產業打開 GPU 的大門。圖靈是超過 10,000 年工程努力的成果。它——在光線追蹤圖形方面比 Pascal 效能提升高達 6 倍，在峰值推理浮點運算方面提升高達 10 倍。這項新架構將成為我們未來在各個平台上推出全新產品組合的基礎。

Moving to data center. We had another strong quarter with revenue of $760 million, accelerating to 83% year-on-year growth and up 8% sequentially. This performance was driven by hyperscale demand as Internet services used daily by billions of people increasingly leverage AI. Our GPUs power real-time services such as search, voice recognition, voice synthesis, translation, recommender engines, fraud detection and retail applications.

遷移至資料中心。我們又迎來了一個強勁的季度，營收達到 7.6 億美元，年增 83%，季增 8%。這一業績成長是由超大規模需求驅動的，因為數十億人每天使用的網路服務越來越多地利用人工智慧。我們的 GPU 為即時服務提供支持，例如搜尋、語音識別、語音合成、翻譯、推薦引擎、詐欺檢測和零售應用。

We also saw a growing adoption of our AI and high-performance computing solutions by vertical industries, representing one of the most fastest areas of growth in our business. Companies in sectors ranging from oil and gas to financial services through transportation are harnessing the power of AI and our accelerating computing platform to turn data into actionable insights.

我們也看到，各垂直產業對我們的人工智慧和高效能運算解決方案的採用率不斷提高，這代表了我們業務成長最快的領域之一。從石油天然氣到金融服務，再到交通運輸等各行業的公司都在利用人工智慧和我們不斷加速的運算平台的力量，將數據轉化為可操作的見解。

Our flagship Tensor Core GPU, the Tesla V100, based on Volta architecture, continue to ramp for both AI and high-performance computing applications. Volta has been adopted by every major cloud provider and hyperscale data center operator around the world. Customers have quickly moved to qualify the new version of V100, which doubled the on-chip DRAM to 32 gig to support much larger data sets and neural networks. Major server OEMs, HP Enterprise, IBM, Lenovo, Cray and Supermicro also brought the V100 32-gig to market in the quarter.

我們基於 Volta 架構的旗艦 Tensor Core GPU Tesla V100，將繼續擴大其在人工智慧和高效能運算應用領域的應用。Volta 已被全球所有主要雲端服務供應商和超大規模資料中心營運商採用。客戶們迅速行動起來，對新版 V100 進行認證，該版本將片上 DRAM 容量翻倍至 32 GB，以支援更大的資料集和神經網路。主要伺服器 OEM 廠商，如 HP Enterprise、IBM、聯想、Cray 和 Supermicro，也在本季推出了 V100 32-GB 版本。

We continue to gain traction with AI inference solution, which helped expand our addressable market in the data center. During the quarter, we released our TensorRT 4 AI inference accelerator software for general availability. While prior versions of the TensorRT optimized image and video-related workloads, TensorRT 4 expands the aperture to include more use cases, such as speech recognition, speech synthesis, translation and recommendation systems. This means we can now address a much larger portion of deep learning inference workloads, delivering up to 190x performance speed-up, relative to CPUs.

我們的人工智慧推理解決方案持續獲得市場認可，這有助於擴大我們在資料中心的潛在市場。本季度，我們正式發布了 TensorRT 4 AI 推理加速器軟體。雖然先前的 TensorRT 版本優化了影像和視訊相關的工作負載，但 TensorRT 4 擴大了應用範圍，涵蓋了更多用例，例如語音辨識、語音合成、翻譯和推薦系統。這意味著我們現在可以處理更大比例的深度學習推理工作負載，與 CPU 相比，效能提升高達 190 倍。

NVIDIA and Google engineers have integrated TensorRT into the TensorFlow deep learning framework, making it easier to run AI inference on our GPUs. And Google Cloud announced that NVIDIA Tesla P4 GPU, our small form factor GPU for AI inference and graphic virtualization, is available on Google Cloud Platform.

NVIDIA 和 Google 的工程師已將 TensorRT 整合到 TensorFlow 深度學習框架中，從而更容易在我們的 GPU 上運行 AI 推理。谷歌雲端宣布，NVIDIA Tesla P4 GPU（我們用於人工智慧推理和圖形虛擬化的小型 GPU）已在Google雲端平台上推出。

Data center growth was also driven by DGX, our fully optimized AI server, which incorporates V100 GPUs, our proprietary high-speed interconnect and our fully optimized software stack. The annual run rate for DGX is in the hundreds of millions of dollars. DGX-2, announced in March at our GPU Technology Conference, is being qualified by customers and is on track to ramp in the third quarter.

資料中心的成長也得益於我們完全優化的 AI 伺服器 DGX，它整合了 V100 GPU、我們專有的高速互連和我們完全優化的軟體堆疊。DGX的年運行率高達數億美元。DGX-2 於 3 月在我們的 GPU 技術大會上發布，目前正在接受客戶的認證，並預計在第三季實現量產。

At GTC Taiwan in June, we announced that we are bringing DGX-2 technology to our HGX-2 server platform. We make HGX-2 available to OEM and ODM partners so they can quickly deploy our newest innovations in their own server designs. In recent weeks, we announced partnerships with NetApp and Pure Storage to help customers speed AI deployment from months to days or even hours, with highly integrated optimized solutions that combine DGX with the company's all-flash storage offerings and third-party networking.

在六月的台灣GTC大會上，我們宣布將DGX-2技術引入我們的HGX-2伺服器平台。我們向 OEM 和 ODM 合作夥伴提供 HGX-2，以便他們能夠快速地將我們最新的創新成果部署到自己的伺服器設計中。最近幾週，我們宣布與 NetApp 和 Pure Storage 建立合作夥伴關係，透過高度整合的最佳化解決方案，將 DGX 與公司的全快閃儲存產品和第三方網路結合，幫助客戶將 AI 部署時間從數月縮短到數天甚至數小時。

At GTC Taiwan, we also revealed that we are -- set 5 speed records for AI training and inference. Key to our strategy is our software stack. From CUDA to our training and inference SDKs as well as our work with our developers to accelerate their applications, it is the reason we can achieve such dramatic performance gains in such a short period of time.

在 GTC 台灣大會上，我們也宣布我們創造了 5 項 AI 訓練和推理速度記錄。我們的策略關鍵在於我們的軟體堆疊。從 CUDA 到我們的訓練和推理 SDK，以及我們與開發人員合作加速他們的應用程序，這就是我們能夠在如此短的時間內取得如此顯著的效能提升的原因。

And our developer ecosystem is getting stronger. In fact, we just passed 1 million members in our developer program, up 70% from 1 year ago. One of our proudest moments this quarter was the launch of the Summit AI supercomputer in Oak Ridge National Laboratory. Summit is powered by over 27,000 Volta Tensor Core GPUs and helped the U.S. reclaim the #1 spot on the TOP500 Supercomputer list for the first time in 5 years.

我們的開發者生態系統正在變得越來越強大。事實上，我們的開發者計畫成員人數剛剛突破 100 萬，比 1 年前增加了 70%。本季我們最引以為傲的時刻之一是橡樹嶺國家實驗室啟動了 Summit AI 超級電腦。Summit 超級電腦由超過 27,000 個 Volta Tensor Core GPU 提供支持，幫助美國時隔 5 年首次重奪 TOP500 超級電腦排行榜第一名。

Other NVIDIA power systems joined the TOP500 list were Sierra at Lawrence Livermore National Laboratory in the third spot, and the ABCI, Japan's fastest supercomputer, in the fifth spot. NVIDIA now powers 5 of the world's 7 fastest supercomputers, reflecting the broad shift in supercomputing to GPUs. Indeed, the majority of the computing performance added to the latest TOP500 list comes from NVIDIA GPUs, and more than 550 HPC applications are now GPU accelerated. With our Tensor Core GPUs, supercomputers can now combine simulation with the power of AI to advance many scientific applications from molecular dynamics to seismic processing to genomics and materials science.

其他進入 TOP500 榜單的 NVIDIA 超級電腦系統包括位列第三的勞倫斯利弗莫爾國家實驗室的 Sierra 超級計算機，以及位列第五的日本最快超級計算機 ABCI。NVIDIA 目前為全球 7 台最快的超級電腦中的 5 台提供技術支持，這反映了超級運算領域向 GPU 的廣泛轉變。事實上，最新 TOP500 名單中新增的大部分運算效能都來自 NVIDIA GPU，目前已有超過 550 個 HPC 應用程式採用 GPU 加速。借助我們的 Tensor Core GPU，超級電腦現在可以將模擬與人工智慧的強大功能相結合，從而推進從分子動力學到地震處理，再到基因組學和材料科學等眾多科學應用的發展。

Moving to pro visualization. Revenue grew to $281 million, up 20% year-on-year and 12% sequentially, driven by demand for real-time rendering and mobile workstations as well as emerging applications like AI and VR. These emerging applications now represent approximately 35% of pro visualization sales. Strength extended across several key industries, including health care, oil and gas and media and entertainment. Key wins in the quarter include Raytheon, Lockheed, GE, Siemens and Philips Healthcare.

邁向專業可視化。營收成長至 2.81 億美元，年增 20%，環比成長 12%，這主要得益於對即時渲染和行動工作站的需求，以及人工智慧和虛擬實境等新興應用的發展。這些新興應用目前約佔專業視覺化銷售額的 35%。強勁勢頭遍及多個關鍵產業，包括醫療保健、石油天然氣以及媒體娛樂。本季的主要客戶包括雷神公司、洛克希德馬丁公司、通用電氣公司、西門子公司和飛利浦醫療保健公司。

In announcing the Turing architecture at SIGGRAPH, we also introduced the first Turing-based processors, the Quadro RTX 8000, 6000 and 5000 GPUs, bringing interactive ray tracing to the world years before it has been predicted. We also announced that the NVIDIA RTX server, a full ray-tracing global illumination rendering server that will give a giant boost to the world's render farms as Moore's Law ends.

在 SIGGRAPH 大會上發布 Turing 架構的同時，我們也推出了首批基於 Turing 架構的處理器、Quadro RTX 8000、6000 和 5000 GPU，將互動式光線追蹤技術帶到了世界，比人們預期的要早了好幾年。我們也宣布推出 NVIDIA RTX 伺服器，這是一款全光線追蹤全域光照渲染伺服器，隨著摩爾定律的終結，它將極大地推動全球渲染農場的發展。

Turing is set to revolutionize the work of 5 -- 50 million designers and artists, enabling them to render photorealistic scenes in real time and add new AI-based capabilities to their work flows. Quadro GPUs based on the Turing will be available in the fourth quarter. Dozens of leading software providers, developers and OEMs have already expressed support for Turing. Our pro viz partners view it as a game-changer for professionals in the media and entertainment, architecture and manufacturing industries.

圖靈有望徹底改變 500 萬至 5000 萬設計師和藝術家的工作方式，使他們能夠即時渲染逼真的場景，並在工作流程中添加基於人工智慧的新功能。基於圖靈架構的 Quadro GPU 將於第四季上市。數十家領先的軟體供應商、開發商和原始設備製造商已經表示支持圖靈。我們的專業視覺化合作夥伴認為，它對於媒體和娛樂、建築和製造業的專業人士來說，是一項顛覆性的技術。

Finally, turning to automotive. Revenue was a record $161 million, up 13% year-on-year and up 11% sequentially. This reflects growth in our autonomous vehicle production and development engagements around the globe as well as the ramp of next-generation AI-based smart cockpit infotainment solutions.

最後，我們來談談汽車產業。營收達到創紀錄的 1.61 億美元，年增 13%，季增 11%。這反映了我們在全球範圍內自動駕駛汽車生產和開發業務的成長，以及下一代基於人工智慧的智慧座艙資訊娛樂解決方案的快速發展。

We continue to make progress on our autonomous vehicle platform with key milestones and partnerships announced this quarter. In July, Daimler and Bosch selected DRIVE Pegasus as the AI brain for their Level 4 and Level 5 autonomous fleets. Pilot testing will begin next year in Silicon Valley. This collaboration brings together NVIDIA's leadership in AI and self-driving platforms, Bosch's hardware and systems expertise as the world's largest Tier 1 automotive supplier and Daimler's vehicle expertise and global brand synonymous with safety and quality.

本季度，我們在自動駕駛汽車平台方面持續取得進展，並宣布了多項重要里程碑和合作夥伴關係。7 月，戴姆勒和博世選擇 DRIVE Pegasus 作為其 L4 級和 L5 級自動駕駛車隊的 AI 大腦。試點測試將於明年在矽谷啟動。此次合作匯集了英偉達在人工智慧和自動駕駛平台領域的領先地位、博世作為全球最大的汽車一級供應商在硬體和系統方面的專業知識，以及戴姆勒在車輛領域的專業知識和全球知名品牌（該品牌是安全和品質的代名詞）。

This quarter, we started shipping development systems for DRIVE Pegasus, an AI supercomputer designed specifically for autonomous vehicles. Pegasus delivers 320 trillion operations per second to handle diverse and redundant algorithms and is architected for safety as well as performance. This automotive-grade functionally safe production solution uses 2 NVIDIA Xavier SoCs and 2 next-generation GPUs designed for AI and visual processing, delivering more than 10x greater performance and 10x higher data bandwidth compared to the previous generation. With co-designed hardware and software, the platform is created to achieve ASIL D ISO 26262, the industry's highest level of automotive functional safety.

本季度，我們開始交付 DRIVE Pegasus 的開發系統，這是一款專為自動駕駛汽車設計的 AI 超級電腦。Pegasus 每秒可執行 320 兆次運算，以處理各種冗餘演算法，其架構兼顧安全性和效能。這款汽車級功能安全生產解決方案採用 2 個 NVIDIA Xavier SoC 和 2 個專為 AI 和視覺處理而設計的下一代 GPU，與上一代產品相比，性能提高了 10 倍以上，數據頻寬提高了 10 倍以上。該平台採用協同設計的硬體和軟體，旨在達到 ASIL D ISO 26262 標準，這是汽車功能安全產業的最高等級。

We have created a scalable AI car platform that spans the entire range of automated and autonomous driving from traffic jam pilots to Level 5 robo-taxis. More than 370 companies and research institutions are using NVIDIA's automotive platform. With this growing momentum and accelerating revenue growth, we remain excited about the intermediate and long-term opportunities for autonomous driving business.

我們創建了一個可擴展的 AI 汽車平台，涵蓋了從交通擁堵引導到 5 級無人駕駛計程車的整個自動化和無人駕駛範圍。超過 370 家公司和研究機構正在使用英偉達的汽車平台。隨著這一發展勢頭不斷增強，收入成長加速，我們對自動駕駛業務的中長期機會仍然充滿信心。

This quarter, we also introduced our Xavier platform for Jetson for the autonomous machine market. With more than 9 billion transistors, it delivers over 30 trillion operations per second, more processing capability than a powerful workstation while using 1/3 the energy of a lightbulb. Jetson Xavier establishes customers to deliver AI computing at the edge, powering autonomous machines like robots or drones with applications in manufacturing, logistics, retail, agricultural, health care and more.

本季度，我們也針對自主機器市場推出了適用於 Jetson 的 Xavier 平台。它擁有超過 90 億個晶體管，每秒鐘可進行超過 30 兆次運算，處理能力比強大的工作站還要強，但能耗卻只有燈泡的三分之一。Jetson Xavier 協助客戶在邊緣提供 AI 運算，為機器人或無人機等自主機器提供動力，這些機器可應用於製造業、物流業、零售業、農業、醫療保健等領域。

Lastly, in our OEM segment, revenue declined by 54% year-on-year and 70% sequentially. This was primarily driven by the sharp decline of cryptocurrency revenues to fairly minimal levels.

最後，在我們的OEM業務板塊，營收年減54%，較上季下降70%。這主要是由於加密貨幣收入急劇下降至相當低的水平所致。

Moving to the rest of the P&L. Q2 GAAP gross margin was 63.3% and non-GAAP was 63.5%, in line with our outlook. GAAP operating expenses were $818 million. Non-GAAP operating expenses were $692 million, up 30% year-on-year. We can continue to invest in the key platforms driving our long-term growth, including gaming, AI and automotive.

接下來來看損益表的其餘部分。第二季 GAAP 毛利率為 63.3%，非 GAAP 毛利率為 63.5%，與我們的預期一致。GAAP營運費用為8.18億美元。非GAAP營運費用為6.92億美元，年增30%。我們可以繼續投資於推動我們長期成長的關鍵平台，包括遊戲、人工智慧和汽車。

GAAP net income was $1.1 billion and EPS was $1.76, up 89% and 91%, respectively, from a year earlier. Some of the upside was driven by a tax rate near 7% compared to our outlook of 11%. Non-GAAP net income was $1.21 billion and EPS was $1.94, up 90% and 92%, respectively, from a year ago, reflecting revenue strength as well as gross and operating margin expansion and lower taxes. Quarterly cash flow from operations was $913 million. Capital expenditures were $128 million.

GAAP淨利為11億美元，每股盈餘為1.76美元，分別較上年同期成長89%及91%。部分上漲行情是由接近 7% 的稅率推動的，而我們先前的預期是 11%。非GAAP淨利潤為12.1億美元，每股收益為1.94美元，分別比上年同期增長90%和92%，反映出收入強勁、毛利率和營業利潤率擴張以及稅收減少。本季經營活動產生的現金流量為9.13億美元。資本支出為1.28億美元。

With that, let me turn to the outlook for the third quarter of fiscal 2019. We are including no contribution from crypto in our outlook. We expect revenue to be $3.25 billion, plus or minus 2%. GAAP and non-GAAP gross margins are expected to be 62.6% and 62.8%, respectively, plus or minus 50 basis points. GAAP and non-GAAP operating expenses are expected to be approximately $870 million and $730 million, respectively. GAAP and non-GAAP OI&E are both expected to be income of $20 million. GAAP and non-GAAP tax rates are both expected to be 9%, plus or minus 1%, excluding discrete items. Capital expenditures are expected to be approximately $125 million to $150 million.

接下來，我將展望一下 2019 財年第三季的情況。我們的展望中不包含加密貨幣的任何貢獻。我們預計營收為 32.5 億美元，上下浮動 2%。GAAP 和非 GAAP 毛利率預計分別為 62.6% 和 62.8%，上下浮動 50 個基點。GAAP 和非 GAAP 營運費用預計分別約為 8.7 億美元和 7.3 億美元。GAAP 與非 GAAP 下的營業損益預計均為 2,000 萬美元。GAAP 和非 GAAP 稅率預計均為 9%，上下浮動 1%，不包括特殊項目。預計資本支出約 1.25 億美元至 1.5 億美元。

Further financial details are included in the CFO commentary and other information available on our IR website. In closing, I'd like to highlight some of the upcoming events for the financial community. We'll be presenting at the Citi Global Technology Conference on September 6 and meeting with the financial community at our GPU Technology Conferences in Tokyo on September 13 and Munich on October 10. And our next earnings calls to discuss our financial results is in the third quarter of 2019 will take place on November 15.

更多財務細節請參閱財務長評論以及我們投資者關係網站上的其他資訊。最後，我想重點介紹一下金融界即將發生的一些事件。我們將於 9 月 6 日在花旗全球技術大會上演講，並於 9 月 13 日在東京和 10 月 10 日在慕尼黑舉行的 GPU 技術大會上與金融界人士會面。我們將於 2019 年第三季舉行下一次財報電話會議，討論我們的財務業績，會議將於 11 月 15 日舉行。

We will now open the call for questions. (Operator Instructions) And operator, would you please poll for questions?

現在開始接受提問。（操作員指示）操作員，請問大家有問題嗎？

(Operator Instructions) Your first question comes from Mark Lipacis with Jefferies.

（操作說明）您的第一個問題來自 Jefferies 的 Mark Lipacis。

The question's on ray tracing, to what extent is this creating new markets versus enabling greater capabilities in your existing markets?

問題在於光線追蹤技術，它在多大程度上創造了新的市場，又在多大程度上增強了現有市場的能力？

Yes, Mark. So first of all, Turing, as you know, is the world's first ray-tracing GPU. And it completes our new computer graphics platform, which is going to reinvent computer graphics altogether. It unites 4 different computing modes: rasterization, accelerated ray tracing, computing with CUDA and artificial intelligence. It uses these 4 basic methods to create imagery for the future. There's 2 different -- 2 major ways that we'll experience the benefits right away. The first is for the markets of visualization today, they require photorealistic images. Whether it's an IKEA catalog or a movie or architectural engineering or product design, car design, all of these types of markets require photorealistic images. And the only way to really achieve that is to use ray tracing with physically based materials and lighting. The technology is rather complicated. It's been computing-intensive for a very long time. And it wasn't until now that we've been able to achieve it in a productive way. And so Turing has the ability to do ray tracing, accelerated ray tracing, and it also has the ability to combine very large frame buffers because these data sets are extremely large. And so that marketplace is quite large and it's never been served by GPUs before until now. All of that has been run on CPU render farms, gigantic render farms in all these movie studios and service centers and so on and so forth. The second area where you're going to see the benefits of ray tracing, we haven't announced.

是的，馬克。首先，如你所知，圖靈是世界上第一款光線追蹤GPU。至此，我們的全新電腦圖形平台就全部完成了，它將徹底革新電腦圖形學。它融合了 4 種不同的運算模式：光柵化、加速光線追蹤、CUDA 運算和人工智慧。它運用這 4 種基本方法來創造面向未來的影像。有兩種不同的—兩種主要方式，我們可以立即體驗到這些好處。首先，就當今的視覺化市場而言，他們需要逼真的圖像。無論是IKEA產品目錄、電影、建築工程、產品設計、汽車設計，所有這些類型的市場都需要逼真的影像。而真正實現這一點的唯一方法是使用基於物理的材質和光照進行光線追蹤。這項技術相當複雜。長期以來，它一直都是計算密集型的。直到現在，我們才能夠以富有成效的方式實現這一目標。因此，圖靈架構具備光線追蹤、加速光線追蹤的能力，也能夠合併非常大的幀緩衝區，因為這些資料集非常大。因此，這個市場規模相當大，在此之前從未有GPU為其服務。所有這些都是在 CPU 渲染農場上運行的，這些巨型渲染農場遍布各個電影製片廠和服務中心等等。光線追蹤技術帶來的第二個好處領域，我們尚未公佈。

If I could have a follow-up on the gaming side. Where do you think the industry is on creating content that leverages that kind of capability?

如果可以的話，我想就遊戲方面再補充一些資訊。您認為目前業界在創作能夠充分利用這種能力的內容方面處於什麼階段？

Yes, Mark, the -- at GTC this last year in March, GDC and GTC, we announced a brand-new platform called NVIDIA RTX. And this platform has those 4 computation methods that I described for generating images. We put that platform out with the support of Microsoft. They call it the Microsoft DirectX Raytracing and the major game engine companies. Epic has implemented real-time raytracing and the RTX into the Epic engine, the Unreal Engine. And at GDC and GTC, we demonstrated, for the very first time, on 4 Volta GPUs, on 4 Volta GPUs, the ability to do that. And it was the intention of -- to get this platform out to all of the game developers. And we've been working with game developers throughout this time.

是的，馬克，在去年三月的 GTC 大會上，我們發布了一個名為 NVIDIA RTX 的全新平台。這個平台具備我之前描述的 4 種用於生成影像的計算方法。我們在微軟的支持下推出了這個平台。他們稱之為微軟 DirectX 光線追蹤技術，以及各大遊戲引擎公司所採用的技術。Epic Games 已將即時光線追蹤和 RTX 技術整合到其引擎中——虛幻引擎中。在 GDC 和 GTC 上，我們首次在 4 個 Volta GPU 上展示了實現這一目標的能力。而其目的就是為了讓所有遊戲開發者都能使用這個平台。在此期間，我們一直與遊戲開發商保持合作。

Thus, this week at SIGGRAPH, we announced Quadro, which is the first -- the Quadro RTX 8000, 6000 and 5000 -- the world's first accelerated ray-tracing GPUs. And I demonstrated one Quadro running the same application that we demonstrated on 4 Volta GPUs running in March. And the performance is really spectacular. And so I think the answer to your question is developers all have access to RTX. It's in Microsoft's DirectX. It's in the most popular game engine in the world, and you're going to start to see developers use it. On the workstation side, on the professional visualization side, all of the major ISPs have jumped on to adopt it. And at SIGGRAPH this year, there you could see a whole bunch of developers demonstrating the NVIDIA RTX with accelerated ray tracing, generating photorealistic images. And so I would say that in no platform in our history has, on day 1 of announcement, had so many developers jump onto it. And stay tuned, we've got a lot more stories to tell you about RTX.

因此，在本週的 SIGGRAPH 大會上，我們發布了 Quadro，它是世界上首款加速光線追蹤 GPU——Quadro RTX 8000、6000 和 5000。我示範了一台 Quadro 顯示卡運行與我們 3 月在 4 台 Volta GPU 上演示的相同的應用程式。演出真是精彩絕倫。所以我認為你問題的答案是，所有開發者都可以使用 RTX。它存在於微軟的DirectX中。它採用的是世界上最受歡迎的遊戲引擎，你很快就會看到開發者開始使用它。在工作站方面，在專業視覺化方面，所有主要的網路服務供應商都已紛紛採用它。在今年的 SIGGRAPH 大會上，你可以看到許多開發者展示了 NVIDIA RTX 的加速光線追蹤功能，產生了逼真的影像。因此，我認為，在我們歷史上沒有任何一個平台，在發布首日就吸引瞭如此多的開發者加入。敬請期待，我們還有很多RTX的故事要跟大家分享。

Your next question is from Matt Ramsay with Cowen.

下一個問題來自考恩公司的馬特拉姆齊。

Colette, I had a couple of questions about inventory, the first of which is, I understand you've launched a new product set in pro viz, and the data center business is obviously ramping really strongly. But if you look at the balance sheet, I think the inventory level is up by around mid-30s percent sequentially and you're guiding revenue up 3% or so. Maybe you could help us sort of walk through the contribution to that inventory and what it might mean for future products. And secondly, if you could talk a little bit about the gaming channel in terms of inventory, how things are looking in the channel as you guys see it during this period of product transition.

科萊特，我有一些關於庫存的問題，第一個問題是，我了解到你們在專業視覺化領域推出了一套新的產品，而且資料中心業務顯然發展勢頭非常強勁。但如果你看一下資產負債表，我認為庫存水準環比增長了約 30% 左右，而你們預計收入增長約 3%。或許您可以幫我們梳理一下庫存狀況，以及這對未來產品可能意味著什麼。其次，能否請您談談遊戲通路的庫存狀況，以及在產品過渡期間，您如何看待該通路的現況？

Sure. Thanks for your question. So when you look at our inventory on the balance sheet, I think it's generally consistent with what you have seen over the last several months in terms of what we will be bringing to market. Turing is an extremely important piece of architecture, and as you know, it will be with us for some time. So I think the inventory balance is getting ready for that. And don't forget our work in terms of data center and what we have for Volta is also a very, very complex computer, in some cases, in terms of what we have also in terms of there. So just those things together, plus our Pascal architecture is still here, makes up almost all of what we have there in terms of inventory.

當然。謝謝你的提問。所以，當你查看資產負債表上的庫存時，我認為它與過去幾個月我們看到的我們將推向市場的產品基本上一致。圖靈架構是一件極為重要的作品，而且如你所知，它將伴隨我們很長一段時間。所以我認為庫存餘額已經為此做好了準備。別忘了我們在資料中心方面的工作，我們為 Volta 開發的電腦也非常非常複雜，在某些情況下，就我們在那裡所擁有的電腦而言。所以，這些加起來，再加上我們仍然保留的 Pascal 架構，幾乎構成了我們所有的庫存。

Matt, on the channel inventory side, we see inventory in the lower ends of our stack. And that inventory is well positioned for back-to-school and the building season that's coming up on Q3. And so I feel pretty good about that. The rest of our product launches and the ramp-up of Turing is going really well. And so I think the rest of the announcements we haven't made, but stay tuned. The RTX family is going to be a real game-changer for us and the reinvention of computer graphics altogether has been embraced by so many developers. We're going to see some really exciting stuff this year.

Matt，就通路庫存而言，我們看到庫存處於我們產品組合的低端。而這些庫存已經為即將到來的返校季和第三季的建築旺季做好了充分準備。所以我覺得這方面還不錯。我們其他產品的發布以及 Turing 的產能提升都進展得非常順利。所以，我想剩下的消息我們還沒公佈，敬請期待。RTX 系列顯示卡對我們來說將帶來真正的變革，它徹底革新了電腦圖形技術，並受到了許多開發者的歡迎。今年我們將看到一些非常精彩的內容。

Next question is from Vivek Arya with Bank of America.

下一個問題來自美國銀行的維韋克·阿亞。

Actually, just a clarification and then a question. On the clarification, Colette, if you could also help us understand the gross margin sequencing from Q2 to Q3. And then, Jensen, how would you contrast the Pascal cycle, the Turing cycle? Because I think in your remarks, you mentioned Turing is a very strong advancement over what you had before. But when you launched Pascal, you had guided to very strong Q3s and then Q4s. This time, the Q3 outlook, even though it's good on an absolute basis, on a sequential and a relative basis, it's perhaps not as strong. So if you could just help us contrast the Pascal cycle with what we should expect with the Turing cycle.

其實，我只是想澄清一下，然後問一個問題。科萊特，關於澄清問題，如果您也能幫助我們了解從第二季到第三季的毛利率排序，那就太好了。那麼，Jensen，你會如何比較帕斯卡循環和圖靈循環呢？因為我認為你在發言中提到，圖靈系統相比之前的系統有了非常大的進步。但是，在 Pascal 發布時，你們曾預測第三季和第四季業績會非常強勁。這一次，儘管第三季的前景從絕對值、環比值和相對值來看都不錯，但或許沒有之前那麼強勁。所以，如果您能幫我們對比帕斯卡循環和圖靈循環的預期結果，那就太好了。

Sure, thanks for that -- for the question. Let me start first with your question regarding gross margins. We have essentially reached, as we move into Q3, a normalization of our gross margins. I believe, over the last several quarters, we have seen the impacts of crypto and what that can do to elevate our overall gross margins. We believe we have reached a normal period as we're looking forward to essentially no cryptocurrency as we go forward.

當然，謝謝你的提問。首先，讓我回答您關於毛利率的問題。隨著我們進入第三季度，我們的毛利率基本上已經恢復正常。我認為，在過去的幾個季度裡，我們已經看到了加密貨幣的影響，以及它如何提升我們的整體毛利率。我們認為我們已經進入了一個正常時期，因為我們期待未來基本上不會再有加密貨幣。

Let's see, Pascal was really successful. Pascal, relative to Maxwell, was a leap in fact. And it was a really significant upgrade. The architectures were largely the same. They were both programmable shading, they were both at the same generation programmable shading. But Pascal was much, much more energy efficient. I think it was something like 30%, 40% more energy efficient than Maxwell, and that translated to performance benefits to customers. The success of Pascal was fantastic. There's just simply no comparison to Turing. Turing is a reinvention of computer graphics. It is the first ray-tracing GPU in the world. It's the first GPU that will be able to ray trace light in an environment and create photorealistic shadows and reflections and be able to model things like area lights and global illumination and indirect lighting. But the images are going to be so subtle and so beautiful, it -- when you look at it, it just looks like a movie. And yet it's backwards compatible with everything that we've done.

讓我們來看看，帕斯卡確實很成功。相對於麥克斯韋而言，帕斯卡實際上是一次飛躍。這是一次意義重大的升級。它們的架構大致相同。它們都是可編程遮陽系統，它們都是同一代可編程遮陽系統。但帕斯卡電路的能源效率要高得多。我認為它的能效比 Maxwell 高出 30% 到 40%，這為客戶帶來了性能上的好處。Pascal 的成功令人驚嘆。圖靈根本無法與之相提並論。圖靈是對電腦圖形學的重新發明。它是世界上第一款光線追蹤GPU。它是首款能夠在環境中進行光線追蹤、創造逼真陰影和反射效果，並且能夠模擬區域光、全局光照和間接光照等效果的GPU。但這些影像會非常細膩、非常美麗，當你觀看時，就像在看電影一樣。然而，它與我們之前所做的一切都向後相容。

This new hybrid rendering model, which extends what we've built before but added to it 2 new capabilities, artificial intelligence and accelerated ray tracing, is just fantastic. So everything of the past will be brought along and benefits, and it's going to create new visuals that were impossible before. We also did a good job on laying the foundations of the development platform for the developers. We've partnered with Microsoft to create DXR. Vulkan RT is also coming, and we have optics that are used by pro viz renderers and developers all over the world. And so we have the benefit of laying the foundation stack by stack by stack over the years. And as a result, on the day that Turing comes out, we're going to have a richness of applications that gamers will be able to enjoy.

這個全新的混合渲染模型，在我們之前建立的基礎上，增加了人工智慧和加速光線追蹤這兩項新功能，簡直太棒了。因此，過去的一切都將被保留下來，並帶來益處，同時也將創造出以前不可能出現的全新視覺效果。我們也為開發者搭建了良好的開發平台基礎。我們與微軟合作開發了 DXR。Vulkan RT 也即將推出，我們擁有被世界各地的專業視覺化渲染器和開發人員使用的光學元件。因此，我們有幸多年來一疊一疊地打好基礎。因此，在圖靈架構發布之日，我們將擁有豐富的應用程式供遊戲玩家享用。

You mentioned guidance. I actually think that on a year-over-year performance, we're doing terrific. And I'm super excited about the ramp of Turing. It is the case that we benefited in the last several quarters from an unusual lift from crypto. In the beginning of the year, we thought and we projected that crypto would be a larger contribution through the rest of the year, but at this time, we consider it to be immaterial for the second half. And so that makes comparisons on a sequential basis on, I guess, a quarterly sequential basis harder. But on a year-to-year basis, I think we're doing terrific. Every single one of our platforms are growing. High-performance computing, of course, data centers is growing. AI, the adoption continues to sweep from one industry to another industry. The automation that's going to be brought about by AI is going to bring productivity gains to industries like nobody's ever seen before.

你提到了指導。我認為，從年比來看，我們做得非常出色。我對圖靈的崛起感到無比興奮。過去幾個季度，我們確實受益於加密貨幣帶來的異常上漲。年初的時候，我們認為加密貨幣在今年餘下的時間裡會做出更大的貢獻，但現在，我們認為它在下半年的貢獻微乎其微。因此，按季度進行連續比較就變得更加困難。但從年度數據來看，我認為我們做得非常出色。我們的每個平台都在發展壯大。當然，高效能運算和資料中心正在不斷發展壯大。人工智慧的應用正持續從一個產業擴展到另一個產業。人工智慧帶來的自動化將為各行各業帶來前所未有的生產力提升。

And now with Turing, we're going to be able to reignite the professional visualization business, open us up to photorealistic rendering for the very first time, render farms and everybody who's designing products that has to visualize it photo realistically, to reinventing and resetting graphics for video games. And so I think we're in a great position, and I'm looking forward to reporting Q3 when the time comes.

現在有了圖靈架構，我們將能夠重振專業視覺化業務，首次實現照片級真實感渲染，讓渲染農場以及所有需要以照片級真實感進行可視化的產品設計者，重新發明和重塑視頻遊戲的圖形技術。所以我認為我們目前處境很好，我很期待在適當的時候向大家報告第三季業績。

Your next question is from Atif Malik with Citi.

下一個問題來自花旗銀行的阿提夫‧馬利克。

Colette, I have a question on data center. In your prepared remarks, you talked about AI and high-performance computing driving new verticals and some of these verticals are fastest growing. Some of your peers have talked about enterprise spending slowing down in the back half of this year and so unit demand, and you guys are not a unit play but more of an AI adoption. Just curious in terms of your thinking about second half data center growth.

科萊特，我有一個關於資料中心的問題。在您事先準備好的演講稿中，您談到了人工智慧和高效能運算如何推動新興垂直產業的發展，而其中一些垂直產業正以最快的速度成長。你們的一些同行談到，今年下半年企業支出將會放緩，因此單位需求也會下降，而你們的業務重點不是單位產品，而是人工智慧的應用。我很好奇您對下半年資料中心成長的看法。

So as you know, we generally give our view on guidance for 1 quarter out. You are correct that our data center results that we see is always a tremendous unique mix every single quarter in terms of what we're seeing. But there's still some underlying points of that, that will likely continue. The growth in terms of use by the hyperscales, continued industry by industry coming on board, essentially just because the needs of accelerated computing for the workloads and for the data that they have is so essential. So we still expect, as we go into Q3, for data center to grow both sequentially and year-over-year. And we'll see probably a mix of both selling our Tesla V100 platforms but also a good contribution from our DGX.

如您所知，我們通常會給予未來一個季度的業績指引。您說得對，我們資料中心的業績每季都呈現出極為獨特的組合。但其中一些基本觀點仍然存在，而且很可能會繼續存在。超大規模資料中心的使用量不斷成長，各行各業也陸續加入進來，這主要是因為他們的工作負載和資料對加速運算的需求非常迫切。因此，我們仍然預計，進入第三季度，資料中心業務將環比和同比增長。我們可能會看到特斯拉 V100 平台的銷售以及 DGX 平台的良好貢獻相結合。

Yes, that's right. Atif, let me just add a little bit more to that. I think the one simple way to think about that is this. In the transportation industry, let's take one particular vertical. There are 2 dynamics that are happening that are very abundantly clear and that will transform that industry. The first, of course, is ride hailing and ride sharing. Those platforms, in order to make the recommendation of which taxi to bring to which passenger, to which customer, is a really large computing problem. It's a machine-learning problem. It's an optimization problem of very, very large scale. And in every -- in each and every one of those instances, you need high-performance computers to use machine learning to figure out how to make that perfect match or the most optimal match.

是的，沒錯。阿提夫，我再補充一點。我認為最簡單的思考方式是這樣的。在交通運輸業中，我們以一個特定的垂直領域為例。有兩種非常明顯的動態正在發生，它們將改變這個產業。首先，當然是叫車和共乘。這些平台要向每位乘客、每位顧客推薦哪一輛計程車，這本身就是一個龐大的運算問題。這是一個機器學習問題。這是一個規模非常非常大的最佳化問題。在每種情況下，都需要高效能電腦使用機器學習來找出如何做出完美匹配或最佳匹配。

On the second -- the second is self-driving cars. Every single car company that's working on robot taxis or self-driving cars needs to collect data, label data, train a neural network or train a whole bunch of neural networks and to run those neural networks in cars. And so you just make your list of how many people are actually building self-driving cars. And every single one of them will need even more GPU-accelerated servers. And that's just for developing the model. Then the next stage is to simulate the entire software. Because we know that the industry or the world travels 10 trillion miles per year, and the best we could possibly do is to drive several million normal miles. And what we really want to do is to be able to simulate and stress -- stress-test our software stack and the only way to do that is doing virtual reality. And so that's another supercomputer that you have to build for simulating all your software costs, those billions and billions of virtually created, challenging miles.

第二個面向－第二個面向是自動駕駛汽車。每一家致力於研發機器人計程車或自動駕駛汽車的汽車公司都需要收集資料、標註資料、訓練一個或多個神經網絡，並在汽車上運行這些神經網路。所以你只需要列出究竟有多少人在製造自動駕駛汽車。而且它們中的每一個都需要更多的GPU加速伺服器。這僅僅是開發模型所需的時間。接下來，下一步是對整個軟體進行模擬。因為我們知道，整個產業或世界每年行駛里程達 10 兆英里，而我們所能做的最好的事情也只是行駛幾百萬英里的正常里程。我們真正想要做的是能夠模擬和測試我們的軟體堆疊，而實現這一目標的唯一方法就是進行虛擬實境測試。所以，你還得建造另一台超級電腦來模擬你所有的軟體成本，那些虛擬創建的數十億英里的挑戰性里程。

And then lastly, before you OTA the software, you're going to have to re-sim and replay against all of the miles that you've collected over the years to make sure that you have no regressions before you OTA the new models into a fleet of cars. And so transportation is going to be a very large industry. Health care is the same way, from medical imaging that is now using AI just about everywhere to genomics that has discovered deep learning and the benefits of artificial intelligence; and in the future, pathology. The list goes on. And so industry after industry after industry, we're discovering the benefits of deep learning and the industries could be really, really revolutionized by it.

最後，在透過 OTA 更新軟體之前，您需要重新模擬並回放多年來累積的所有里程，以確保在將新模型通過 OTA 更新到車隊之前不會出現任何倒退。因此，交通運輸業將成為一個非常龐大的產業。醫療保健領域也是如此，從現在幾乎無所不在的醫學影像，到已經發現深度學習和人工智慧優勢的基因組學；未來，病理學也將如此。這樣的例子不勝枚舉。因此，各行業都在不斷發現深度學習的好處，而這些產業可能會因此而發生真正的改變。

Your next question is from C.J. Muse with Evercore.

您的下一個問題來自 Evercore 公司的 C.J. Muse。

I guess, short term and the long term. So for short term, as you think about your gaming guide, are you embedding any drawdown of channel inventory there? And then longer term, as you think about Turing Tensor Cores, can you talk a bit about differentiation versus Volta V100, particularly as you think about 8-bit integer and the opportunities there for inferencing?

我想，既包括短期因素，也包括長期因素。那麼短期來看，在製定遊戲指南時，你是否考慮過減少通路庫存？那麼從長遠來看，當您考慮 Turing Tensor Core 時，您能否談談它與 Volta V100 的區別，特別是當您考慮 8 位整數以及它在推理方面的機會時？

We're expecting the channel inventory to work itself out. We are masters at managing our channel, and we understand the channel very well. As you know, the way that we go to market is through the channels around the world. We're not concerned about the channel inventory. As we ramp Turing, any -- whenever we ramp a new architecture, we ramp it from the top down. And so we have plenty of opportunities as the -- as we go back to the back-to-school and the gaming cycle to manage the inventory, so we feel pretty good about that. As a result, comparing Volta and Turing, CUDA's compatible. That's one of the benefits of CUDA. CUDA -- all of the applications that take advantage of CUDA that are written on top of cuDNN, which is our deep neural network platform, to TensorRT that takes advantage -- that takes the output of the frameworks and optimize it for run time. All of those tools and libraries run on top of Volta and run on top of Turing and run on top of Pascal.

我們預期渠道庫存問題會自行解決。我們非常擅長管理自己的頻道，並且對頻道有著深刻的了解。如您所知，我們進入市場的方式是透過世界各地的管道。我們並不擔心渠道庫存。隨著 Turing 架構的逐步部署，任何架構——無論何時我們部署新的架構，我們都是由上而下部署。因此，隨著開學季和遊戲週期的到來，我們有很多機會來管理庫存，所以我們對此感覺相當不錯。因此，比較 Volta 和 Turing，CUDA 是相容的。這就是 CUDA 的優點之一。CUDA——所有利用 CUDA 編寫的應用程序，這些應用程式都基於我們的深度神經網路平台 cuDNN，以及利用 TensorRT 的應用程式——TensorRT 會獲取框架的輸出並針對運行時進行最佳化。所有這些工具和函式庫都可以在 Volta 之上運行，也可以在 Turing 之上運行，也可以在 Pascal 之上運行。

What Turing adds over Pascal is the same Tensor Core that is inside Volta. Of course, Volta is designed for large-scale training. 8 GPUs could be connected together. They have the fastest HBM2 memories, and it's designed for data center applications, has 64-bit double-precision ECC, high-resilience computing and all of the software and system software capability and tools that make Volta the perfect high-performance computing accelerator. In the case of Turing, it's really designed for 3 major applications. The first application is to open up pro visualization, which is a really large market that has historically used render farms and were really unable to use GPUs until we now have the ability to do full path trace, global illumination with very, very large data sets. So that's one market that's brand new as a result of Turing.

圖靈架構比較帕斯卡架構增加的，是與伏特架構內部相同的張量核心。當然，Volta 是為大規模訓練而設計的。可以將 8 個 GPU 連接在一起。它們擁有最快的 HBM2 內存，專為資料中心應用而設計，具有 64 位元雙精度 ECC、高彈性運算以及所有軟體和系統軟體功能和工具，使 Volta 成為完美的高效能運算加速器。圖靈系統其實是為三大主要應用而設計的。第一個應用是開拓專業視覺化領域，這是一個非常大的市場，歷史上一直使用渲染農場，直到現在我們才能夠利用 GPU 進行全路徑追蹤、全局光照以及處理非常非常大的資料集。所以，這是圖靈帶來的一個全新市場。

The second market is to reinvent computer graphics, real-time computer graphics, for video games and other real-time visualization applications. When you see the images created by Turing, you're going to have a really hard time wanting to see the images of the past. It just looks amazing. And then the third, Turing has a really supercharged Tensor Core. And this Tensor Core is used for image generation. It's also used for high throughput deep learning inferencing for data centers. And so these applications for Turing, which suggests that there are multiple SKUs of Turing, which is one of the reasons why we have such a great engineering team, we could scale one architecture across a whole lot of platforms at one time. And so I hope that answers your question. The Tensor Core inference capability of Turing is going to be off the charts.

第二個市場是重新發明電腦圖形學、即時電腦圖形學，用於視訊遊戲和其他即時視覺化應用。當你看到圖靈創作的圖像時，你很難再想去看過去的圖像了。看起來太棒了。第三，圖靈架構擁有一個非常強大的張量核心。這個 Tensor Core 用於圖像生成。它也用於資料中心的高吞吐量深度學習推理。因此，這些針對 Turing 的應用程式顯示 Turing 有多個 SKU，這也是我們擁有如此優秀的工程團隊的原因之一，我們可以同時將一個架構擴展到許多平台。希望這能解答你的疑問。Turing的Tensor Core推理能力將會非常強大。

Next question is from Joe Moore with Morgan Stanley.

下一個問題來自摩根士丹利的喬·摩爾。

I wonder if you could talk about cryptocurrency now that the dust has settled. You guys have done a good job of kind of laying out exactly how much of the OEM business has been driven by that. But there's also been, I think, some sense of -- some of the GeForce business was being driven by crypto. Can you -- looking backwards, can you size that for us? And I guess, I'm trying to understand the impact that crypto would have on the guidance for October, given that it seems like it was very small in the July quarter.

現在塵埃落定，我想請您談談加密貨幣。你們做得很好，詳細闡述了OEM業務有多少是由這種情況驅動的。但我認為，GeForce 的部分業務也受到了加密貨幣的推動。你能－回顧一下，你能幫我們估算一下它的大小嗎？我想了解加密貨幣會對10月份的業績指引產生怎樣的影響，因為在7月份的季度中，加密貨幣的影響似乎非常小。

Well, I think -- I mean, the second question is easier to answer, and the reason -- the first one is just -- it's ambiguous and hard to predict anyway. It's hard to estimate no matter what. But the second question, the answer is we're expecting -- we're projecting 0 basically. And for the first question, how much of GeForce could have been used for crypto, a lot of gamers at night, they could -- while they're sleeping, they could do some mining. And so did they buy it for mining or did they buy it for gaming, it's kind of hard to say. And some miners were unable to buy our OEM products, and so they jumped onto the market to buy it from retail. And that probably happened a great deal as well. And that all happened in the last -- the previous several quarters, probably starting from Q -- late Q3, Q4, Q1 and very little last quarter, and we're projecting no crypto mining going forward.

嗯，我覺得──我的意思是，第二個問題比較容易回答，而第一個問題的原因──它本身就很模糊，很難預測。無論如何，都很難估算。但對於第二個問題，答案是我們預期——我們基本上預測為 0。至於第一個問題，即有多少 GeForce 顯示卡可能被用於加密貨幣挖礦，許多遊戲玩家在晚上睡覺的時候可以進行挖礦。所以他們買它是為了挖礦還是為了玩遊戲呢？這很難說。有些礦工買不起我們的 OEM 產品，於是他們轉而從零售市場購買。這種情況可能也經常發生。這一切都發生在過去幾個季度，可能從第三季末、第四季、第一季開始，上個季度幾乎沒有發生，我們預計未來不會再有加密貨幣挖礦活動。

Your next question is from Toshiya Hari with Goldman Sachs.

下一個問題來自高盛的 Toshiya Hari。

I had one for Jensen and one for Colette. Jensen, I was hoping you could remind us how meaningful your inference business is today within data center and how you would expect growth to come about over the next 2 years as you -- as your success at accounts like Google proliferate across a broader set of customers. And then for Colette, if you can give directional guidance for each of your platforms. I know you talked about data center a little bit, but if you can talk about the other segments. And on gaming specifically, if you can talk about whether or not new products are embedded in that guide.

我為詹森準備了一份，為科萊特準備了一份。Jensen，我希望你能提醒我們，你的推理業務如今在資料中心領域有多麼重要，以及隨著你在谷歌等客戶的成功經驗擴展到更廣泛的客戶群體，你預計未來兩年你的業務將如何增長。然後，對於 Colette，如果您能為您的每個平台提供方向性指導就太好了。我知道您剛才談到了資料中心，但如果您能談談其他領域就更好了。具體到遊戲領域，能否談談新產品是否包含在該指南中？

Thanks, Toshiya. Inference is going to be a very large market for us. It is surely material now in our data center business. It's not the largest segment, but I believe it's going to be a very large segment of our data center business. There are 30 million servers around the world, let's kind of estimate, in the cloud, and there are a whole lot more in enterprises. I believe that almost every server in the future will be accelerated. And the reason for that is because artificial intelligence and deep learning software and neural net models are going to -- prediction models, are going to be infused into software everywhere. And acceleration has proven to be the best approach going forward. We've been laying the foundations for inferencing for a couple 2, 3 years. And as we've described at GTCs, inference is really, really complicated. And the reason for that is you have to take the output of these massive, massive networks that are output of the training frameworks and optimize it. This is the -- probably the largest computational graph optimization problem the world's ever seen. And this is brand-new invention territory.

謝謝你，Toshiya。推理技術將成為我們一個非常大的市場。這無疑是我們資料中心業務中非常重要的一部分。雖然它不是最大的細分市場，但我相信它將是我們資料中心業務中非常重要的組成部分。我們粗略估計一下，全球雲端有 3,000 萬台伺服器，而企業中的伺服器數量則更多。我相信未來幾乎所有伺服器都會加速運作。原因在於人工智慧、深度學習軟體和神經網路模型——預測模型——將會融入各種軟體中。事實證明，加速發展是未來最好的發展方向。我們已經為推理奠定了基礎兩、三年了。正如我們在 GTC 上所描述的那樣，推理真的非常非常複雜。原因在於，你必須取得這些龐大網路（即訓練框架的輸出）的輸出並對其進行最佳化。這可能是世界上迄今為止規模最大的計算圖優化問題。這是全新的發明領域。

There are so many different network architectures from CNNs to RCNNs to autoencoders to RNNs and LSTMs, there is just so many different species of neural networks these days, and it's continuing to grow. And so the compiler technology is really, really complicated. And this year, we announced 2 things. Earlier this year, we announced that we've been successful in taking the Tesla P4 low-profile, high-energy efficiency inference accelerator into hyperscale data centers. And we announced our fourth generation TensorRT optimizing compiler -- neural network optimizing compiler. And TRT 4 goes well beyond CNNs and image recognition in the beginning. It now allows us to support and optimize for voice recognition or speech recognition, natural language understanding, recommendation systems, translation. And all of these applications are really pervasive from Internet services all over the world. And so now from images to video to voice to recommendation systems, we now have a compiler that can address it.

從 CNN 到 RCNN，再到自編碼器、RNN 和 LSTM，如今的神經網路架構種類繁多，而且還在不斷增長。因此，編譯器技術真的非常複雜。今年，我們宣布了兩件事。今年早些時候，我們宣布已成功將特斯拉 P4 低調、高能源效率推理加速器引入超大規模資料中心。我們也發布了第四代 TensorRT 最佳化編譯器—神經網路最佳化編譯器。而且，TRT 4 的初始功能遠遠超出了 CNN 和影像辨識。現在，我們可以支援和優化語音辨識、自然語言理解、推薦系統和翻譯。而且所有這些應用都透過網路服務在全球廣泛普及。因此，現在從圖像到視訊到語音再到推薦系統，我們已經有了一個可以處理這些問題的編譯器。

We are actively working with just about every single Internet service provider in the world to incorporate inference acceleration into their stack. And the reason for that is because they need high throughput and, very importantly, they need low latency. Voice recognition is only useful if it responds in a relatively short period of time. And our platform is just really, really excellent for that. And then this last week -- this week, we announced Turing. And I announced that the inference performance of Turing is 10x the inference performance of Pascal, which is already a couple of hundred times the inference performance of CPUs. And so you take a look at the rate at which we're moving, both in the support of new neural networks, the ever-increasing optimization and performance output of the compilers and the rate at which we're advancing our processors, I think we're raising the bar pretty high, okay? So with that, Colette?

我們正在積極與世界上幾乎所有網路服務供應商合作，將推理加速技術融入他們的技術堆疊中。原因在於，它們需要高吞吐量，而且非常重要的是，它們需要低延遲。語音辨識只有在相對較短的時間內做出反應才有用。而我們的平台在這方面真的非常出色。然後就在上週──這週，我們宣告了圖靈。我宣布，圖靈架構的推理效能是帕斯卡架構的 10 倍，而帕斯卡架構的推理效能已經是 CPU 的幾百倍。所以，看看我們發展的速度，無論是在支援新型神經網路方面，還是在編譯器不斷提高的最佳化和效能輸出方面，以及在處理器發展方面，我認為我們把標準提高得相當高了，好嗎？所以，科萊特，就這麼回事嗎？

Yes. So when you look at our overall segments, as you will have seen our results in terms of this last Q2, there was growth across every single one of our platforms from a year-over-year standpoint. We probably possibly see that again in our Q3 guidance, the year-over-year growth across each and every one of those platforms. Of course, our OEM business will be down likely year-over-year, again just due to the absence of cryptocurrency in our forecast. When we think about sequentially, our hopes is absolutely, our data center will grow and we'll likely see the growth of our gaming business as well. It's still early, still we've got many different scenarios and -- on our pro viz and auto. But definitely, our gaming and our data center are expected to grow sequentially.

是的。因此，從整體來看，正如您在上個季度第二季度的業績中所看到的，我們所有平台都實現了同比增長。我們可能會在第三季業績預期中再次看到這種情況，即每個平台都將實現同比增長。當然，由於我們的預測中沒有包含加密貨幣，我們的 OEM 業務可能會比去年同期下降。從長遠來看，我們當然希望我們的資料中心能夠發展壯大，而且我們的遊戲業務也可能會隨之成長。現在還為時過早，我們還有很多不同的場景，而且——在我們的專業視覺化和自動化方面。但可以肯定的是，我們的遊戲業務和資料中心業務預計會相繼成長。

Your next question is from Blayne Curtis with Barclays.

下一個問題來自巴克萊銀行的布萊恩‧柯蒂斯。

Two on gross margin. Colette, I just want to make sure I understood, July to October gross margins down. I know you've been getting a benefit from crypto, but it's pretty de minimis in July. Just is there any other moving pieces? And then kind of longer picture here, how do you think about the ramp of Turing affecting gross margins? You're obviously enabling a lot of capabilities. You get paid for it, 12-nanometers, fairly stable. Just kind of curious how to think about over the next couple of quarters gross margin with that ramp.

毛利率兩項。科萊特，我只是想確認我的理解是否正確，7 月至 10 月的毛利率下降。我知道你從加密貨幣中獲益，但七月的收益微乎其微。還有其他需要調整的因素嗎？那麼從更長遠的角度來看，您認為圖靈的崛起會對毛利率產生什麼影響？你顯然啟用了很多功能。你會因此獲得報酬，12奈米，相當穩定。我只是有點好奇，在接下來的幾季裡，隨著產能爬坡的推進，毛利率會如何改變。

Yes. So let me take your first part of the question regarding our gross margins and what we had seen from crypto. Although crypto revenue may not be large, it still has a derivative impact on our stock in terms of what we are selling in to both replenish the overall channel and such. So over the last several quarters that we had stabilizing that overall channel, we did get the great effect of selling just about everything, and our margin's really been able to benefit from that. Again, when we look at the overall growth year-over-year for Q2, you have 500 basis points in terms of growth. We're excited about what we have now here for Q3 as well, which is also significant growth year-over-year. Of course, we have our high value-added platforms as we move forward, both those in data center, those in terms of what we expect the effects of Turing in terms of -- on our Quadro piece as well. But that will take some time for that all to partake. So we'll see how that goes. We haven't announced anything further at this time. But yes, we'll see probably over the longer term, the effects of what Turing can do.

是的。那麼，讓我來回答您關於毛利率以及我們從加密貨幣領域所看到的情況的第一部分問題。雖然加密貨幣收入可能不大，但它仍然會對我們的股票產生衍生影響，因為我們出售的股票既可以補充整個通路的庫存等等。因此，在過去幾個季度裡，隨著我們穩定了整個管道，我們幾乎賣出了所有產品，這帶來了巨大的好處，我們的利潤率也因此受益匪淺。再看第二季年比整體成長率，成長了 500 個基點。我們對第三季的業績也感到非常興奮，年成長幅度也相當可觀。當然，隨著我們不斷前進，我們擁有高附加價值的平台，包括資料中心平台，以及我們預期圖靈架構對我們的 Quadro 顯示卡的影響。但要讓所有人參與進來，還需要一些時間。我們拭目以待。目前我們暫無其他消息公佈。但是，從長遠來看，我們或許會看到圖靈所能帶來的影響。

Next question is from Aaron Rakers with Wells Fargo.

下一個問題來自富國銀行的亞倫·雷克斯。

I'm curious as we look at the data center business, if you can help us understand the breakdown of demand between hyperscale, the supercomputing piece of the business and the AIPs. And I guess, on top of that, I'm just curious, one of the metrics that's pretty remarkable over the last couple of quarters is you've seen significant growth in China. I'm curious if that's related to the data center business or what's really driving that as kind of a follow-up question.

我很好奇，在研究資料中心業務時，您能否幫助我們了解超大規模資料中心、超級運算業務和AIP之間的需求細分情況？除此之外，我還挺好奇的，過去幾季裡一個非常顯著的指標是，中國市場實現了顯著成長。我很好奇這是否與資料中心業務有關，或者說，真正驅動這一趨勢的是什麼，這算是一個後續問題。

Yes, Aaron, I think that if you look at the -- if you start from first principles, here's the simple way to look at it. Demand is continuing to grow at historical levels of 10x computing demand. Computing demand is increasing at historical levels of 10x every 5 years. 10x every 5 years is approximately Moore's Law. And computing demand continues to grow at 10x every 5 years, however, Moore's Law stopped. And so that gap in the world in high-performance computing, in medical imaging, in life sciences computing, in artificial intelligence, that gap -- because those applications demand more computing capability, that gap can only be served in another way. And NVIDIA's GPU accelerated computing that we pioneered really stands to benefit from that. And so at the highest level, whether it's supercomputing -- and this year, you heard Colette say earlier that NVIDIA GPUs represented 56% of all the new performance that came into the world's TOP500. The TOP500 is called the TOP500 because it reflects the future computing. And my expectation is that more and more, from one vertical industry after another -- and I mentioned transportation, I mentioned health care, the vertical industries go on and on -- that as computing demand continues at a factor of 10x every 5 years, developers are rational and logical to have jumped on NVIDIA's GPU computing to boost their demand. I think that's probably the best way to answer it.

是的，亞倫，我認為如果你從基本原理出發，這裡有一個簡單的看待問題的方法。需求持續成長，達到歷史最高水準的10倍。運算需求正以每 5 年 10 倍的歷史新高成長。每五年成長十倍，大致符合摩爾定律。儘管計算需求每 5 年仍以 10 倍的速度成長，但摩爾定律已經停止了。因此，在高效能運算、醫學影像、生命科學運算、人工智慧等領域，世界存在著這樣的差距——因為這些應用需要更強大的運算能力，所以只能透過其他方式來彌補這一差距。而我們率先開發的英偉達GPU加速運算技術將從中受益匪淺。因此，在最高層面上，無論是超級運算——今年早些時候，你也聽到科萊特說，NVIDIA GPU 佔全球 TOP500 新增效能的 56%。TOP500之所以被稱為TOP500，是因為它反映了未來計算的發展方向。我的預期是，越來越多的垂直行業——我提到了交通運輸，我提到了醫療保健，垂直行業不勝枚舉——隨著計算需求每 5 年增長 10 倍，開發人員理所當然地會選擇 NVIDIA 的 GPU 計算來滿足他們的需求。我覺得這可能是最好的回答方式。

Your next question is from Harlan Sur with JPMorgan.

下一個問題來自摩根大通的哈蘭‧蘇爾。

When we think about cloud and hyperscale, we tend to think about the top guys, right? They're designing their own platform using your Tesla-based products or sometimes even designing their own chips for AI and deep learning. But there's a larger base of medium to smaller cloud and hyperscale customers out there who don't have the R&D scale. And I think that's where your HGX platform seems to be focused on. So Jensen, can you just give us an update on the uptake of your first-generation HGX-1 reference platform and the initial interest on the HGX-2?

當我們想到雲端運算和超大規模時，我們往往會想到那些頂尖的公司，對吧？他們正在利用特斯拉的產品設計自己的平台，有時甚至設計自己的人工智慧和深度學習晶片。但還有大量中小型雲端和超大規模客戶，他們沒有研發規模。我認為這正是你們的HGX平台所關注的重點。Jensen，您能否向我們介紹第一代 HGX-1 參考平台的市場接受度以及 HGX-2 的初步反響？

HGX-1 was, I guess, kind of the prototype of HGX-2. HGX-2 is doing incredibly well for all the reasons that you mentioned. It is -- and even the largest hyperscale data centers can't afford to create these really complicated motherboards at the scale that we're talking about. And so we created HGX-2 and it was immediately adopted by several most important hyperscalers in the world. And we were at GTC Taiwan, and we announced basically all of the leading server OEMs and ODMs supporting HGX-2 and are ready to take it to market. So we're in the process of finishing the -- finishing HGX-2 and ramping them into production. And so I think HGX-2 is a huge success for exactly the reasons that you mentioned. We could use it for essentially a standard motherboard like the ATX motherboard for PCs that could be used for hyperscalers, it could be used for HPC, it could be used for data centers. And it's really -- it's a really fantastic design. It just allows people to adopt this really complicated and high performance and really high-speed interconnect motherboard in a really easy way.

我猜想，HGX-1 是 HGX-2 的原型機。HGX-2 的表現非常出色，原因如你所說。確實如此——即使是最大的超大規模資料中心也無法負擔以我們所說的規模製造這些非常複雜的主機板。於是我們開發了 HGX-2，它立即被世界上幾個最重要的超大規模資料中心採用。我們在台灣GTC大會上宣布，幾乎所有領先的伺服器OEM和ODM廠商都支援HGX-2，並準備將其推向市場。所以我們正在完成 HGX-2 的製造，並計劃將其投入生產。所以我認為 HGX-2 之所以取得巨大成功，正是因為你提到的原因。我們可以將其用於類似 ATX 主機板的標準主機板，可用於超大規模資料中心、高效能運算 (HPC) 和資料中心。這真是一個非常棒的設計。它讓人們能夠以非常簡單的方式採用這種非常複雜、高效能、高速互連的主機板。

Your next question is from Tim Arcuri with UBS.

下一個問題來自瑞銀集團的提姆·阿庫裡。

Actually, I had 2 questions, Jensen, both for you. First, now that crypto has fallen off, I'm curious what you think the potential is that maybe we see a slug of cards that get resold on eBay or some other channel and that could cannibalize new Pascal sales. Is that something that keeps you up at night? Number one. Number two, obviously, the stories about gaming and data center, and I know that you don't typically talk about customers, but since Tesla did talk about you on their call, I'm curious what your comments are about the development for Hardware 3 and their own efforts to move away from your drive platform.

實際上，詹森，我有兩個問題想問你。首先，既然加密貨幣已經下跌，我很好奇你認為這會帶來什麼影響，例如我們可能會看到大量顯示卡在 eBay 或其他管道被轉售，從而蠶食新的 Pascal 顯示卡的銷售。這是讓你夜不能寐的事嗎？第一。第二點，顯然是關於遊戲和資料中心的故事，我知道你通常不談論客戶，但既然特斯拉在電話會議上談到了你，我很好奇你對硬體 3 的發展以及他們自身擺脫你的驅動平台的努力有何評論。

Sure. Well, the crypto mining market is very different today than it was 3 years ago. And even though new cards -- at the current prices, it doesn't make much sense for new cards to be sold into the mining market. The existing capacity is still being used, and you could see that the hash rates continue. And so my sense is that the installed base of miners will continue to use their cards. And then probably the more important factor though is that we're in the process of announcing a brand-new way of doing computer graphics. And with the -- with Turing and the RTX platform, computer graphics will never be the same. And so I think this -- our new generation of new GPUs is really going to do great.

當然。如今的加密貨幣挖礦市場與三年前相比已經大不相同了。即使新顯示卡——以目前的價格來看，將新顯示卡出售到挖礦市場也沒有什麼意義。現有容量仍在被使用，你可以看到哈希率仍在持續增長。因此，我的感覺是，現有礦工用戶群將繼續使用他們的礦機。不過，更重要的因素可能是，我們正在宣布一種全新的電腦圖形學方法。有了圖靈架構和RTX平台，電腦圖形學將徹底改變。所以我認為，我們新一代的GPU一定會表現出色。

I also think that -- I appreciate Elon's comments about our company, and I also think Tesla makes great cars, and I drive them very happily. And with respect to the next generation, it is the case that when we first started working on autonomous vehicles, they needed our help. And we used the 3-year-old Pascal GPU for the current generation of autopilot computers. And it is very clear now that in order to have a safe autopilot system, we need a lot more computing horsepower. In order to have safe computing -- in order to have safe driving, the algorithms have to be rich, it has to be able to handle corner conditions in a lot of diverse situations. And every time that there's more and more corner conditions or more subtle things that you have to do or you have to drive more smoothly or be able to take turns more quickly, all of those requirements require greater computing capability. And that's exactly the reason why we built Xavier. Xavier is in production now. We're seeing great success, and customers are super excited about Xavier. And that's exactly the reason why we built it. And I think it's super hard to build Xavier and all the software stack on top of it. And if it doesn't turn out -- for whatever reason, it doesn't turn out for them, he can give me a call, and I'd be more than happy to help.

我也認為——我很欣賞伊隆對我們公司的評價，我也認為特斯拉製造的汽車很棒，我開著它們非常開心。至於下一代汽車，事實是，當我們最初開始研發自動駕駛汽車時，它們確實需要我們的幫助。我們為當前一代自動駕駛電腦使用了 3 年前的 Pascal GPU。現在很明顯，為了擁有安全的自動駕駛系統，我們需要更強大的運算能力。為了實現安全的計算——為了實現安全的駕駛，演算法必須足夠豐富，必須能夠處理各種不同情況下的複雜情況。每次彎道條件越來越複雜，或者需要處理更微妙的事情，或者需要更平穩地駕駛，或者需要更快地轉彎時，所有這些要求都需要更強大的計算能力。而這正是我們創建 Xavier 的原因。Xavier目前正在製作中。我們取得了巨大的成功，客戶們對 Xavier 也感到非常興奮。而這正是我們建造它的原因。我認為建立 Xavier 以及在其之上的所有軟體堆疊都非常困難。如果事情沒有成功——無論出於什麼原因，如果他們的事情沒有成功，他可以給我打電話，我非常樂意幫忙。

Unfortunately, we have run out of time. I will now turn the call back over to Jensen for any closing remarks.

很遺憾，我們時間不夠了。現在我將把電話轉回給詹森，請他作總結發言。

We had a great quarter. Our core platforms exceeded expectations even as crypto largely disappeared. Each of our platforms -- AI, gaming, pro viz and self-driving cars -- continue to enjoy great adoption. These markets are -- we are enabling are some of the most impactful to the world today. We launched Turing this week. It was 10 years in the making and completes the NVIDIA RTX platform. And NVIDIA RTX with Turing is the greatest advance since CUDA nearly a decade ago. I'm incredibly proud of our company for tackling this incredible challenge, reinventing the entire graphic stack and giving the industry a surge of excitement as we reinvent computer graphics. Stay tuned as we unfold the exciting RTX story. See you guys next time.

我們這個季度表現出色。即使加密貨幣基本上消失，我們的核心平台也超出了預期。我們的每個平台——人工智慧、遊戲、專業視覺化和自動駕駛汽車——都持續廣泛採用。我們正在扶持的這些市場，是當今世界最具影響力的市場之一。我們本週發布了圖靈系統。經過十年研發，NVIDIA RTX 平台終於問世。NVIDIA RTX 圖靈架構是自近十年前的 CUDA 以來最大的進步。我為我們公司能夠應對這一巨大的挑戰而感到無比自豪，我們重新發明了整個圖形堆棧，並在重新發明電腦圖形的過程中，為整個行業帶來了巨大的興奮。請關注，我們將為您揭開激動人心的 RTX 故事。下次再見。

Thank you for joining. You may now disconnect.

感謝您的參與。您現在可以斷開連線了。