輝達 (NVDA) 2019 Q2 法說會逐字稿

Good afternoon.

下午好。

My name is Kelsey, and I'm your conference operator for today.

我的名字是凱爾西，我是你今天的會議接線員。

Welcome to NVIDIA's financial results conference call.

歡迎參加 NVIDIA 的財務業績電話會議。

(Operator Instructions) I'll now turn the call over to Simona Jankowski, Vice President of Investor Relations, to begin your conference.

（操作員說明）我現在將電話轉給投資者關係副總裁 Simona Jankowski，開始您的會議。

Thank you.

謝謝你。

Good afternoon, everyone, and welcome to NVIDIA's Conference Call for the Second Quarter of Fiscal 2019.

大家下午好，歡迎參加 NVIDIA 2019 財年第二季度電話會議。

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.

NVIDIA 總裁兼首席執行官黃仁勳今天與我通話；和 Colette Kress，執行副總裁兼首席財務官。

I'd like to remind you that our call is being webcast live on NVIDIA's Investor Relations website.

我想提醒您，我們的電話會議正在 NVIDIA 的投資者關係網站上進行網絡直播。

It's also being recorded.

它也在被記錄。

You can hear a replay by telephone until August 23, 2018.

您可以在 2018 年 8 月 23 日之前通過電話收聽重播。

The webcast will be available for replay until the conference call to discuss our financial results for the third quarter of fiscal 2019.

在電話會議討論我們 2019 財年第三季度的財務業績之前，該網絡廣播將可供重播。

The content of today's call is NVIDIA's property.

今天通話的內容是 NVIDIA 的財產。

It can't be reproduced or transcribed without our prior written consent.

未經我們事先書面同意，不得複製或轉錄。

During this call, we may make forward-looking statements based on current expectations.

在本次電話會議期間，我們可能會根據當前預期做出前瞻性陳述。

These are subject to a number of significant risks and uncertainties, and our actual results may differ materially.

這些受到許多重大風險和不確定性的影響，我們的實際結果可能存在重大差異。

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.

有關可能影響我們未來財務業績和業務的因素的討論，請參閱今天的收益發布中的披露、我們最近的 10-K 和 10-Q 表格以及我們可能在 8-K 表格中與證券交易委員會。

Forward-looking statements are made as of today, August 16, 2018, based on information currently available to us.

根據我們目前可獲得的信息，截至今天（2018 年 8 月 16 日）作出前瞻性陳述。

Except as required by law, we assume no obligation to update any such statements.

除法律要求外，我們不承擔更新任何此類聲明的義務。

During this call, we will discuss non-GAAP financial measures.

在本次電話會議中，我們將討論非 GAAP 財務指標。

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.

您可以在我們的 CFO 評論中找到這些非 GAAP 財務指標與 GAAP 財務指標的對賬，該評論發佈在我們的網站上。

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

有了這個，讓我把電話轉給科萊特。

Thanks, Simona.

謝謝，西蒙娜。

This is a big week for NVIDIA.

對於 NVIDIA 來說，這是重要的一周。

We just announced the biggest leap in GPU architecture in over a decade.

我們剛剛宣布了十多年來 GPU 架構的最大飛躍。

We can't wait to tell you more about it, but first, let's talk about the quarter.

我們迫不及待地想告訴你更多關於它的信息，但首先，讓我們談談這個季度。

We had another strong quarter, led by data center and gaming.

在數據中心和遊戲行業的帶動下，我們又迎來了一個強勁的季度。

Q2 revenue reached $3.12 billion, up 40% from a year earlier.

第二季度收入達到 31.2 億美元，同比增長 40%。

Each market platform -- gaming, data center, pro visualization and automotive hit record levels -- with strong growth both sequentially and year-on-year.

每個市場平台——遊戲、數據中心、專業可視化和汽車都達到創紀錄水平——環比和同比增長強勁。

These platforms collectively grew more than 50% year-on-year.

這些平台合計同比增長超過 50%。

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.

我們的收入展望預計特定於加密貨幣的產品將下降至約 1 億美元，而實際特定於加密貨幣的產品收入為 1800 萬美元，我們現在預計未來的貢獻可以忽略不計。

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.

毛利率同比增長近 500 個基點，而 GAAP 和非 GAAP 淨利潤連續第三個季度超過 10 億美元。

Profit nearly doubled.

利潤幾乎翻了一番。

From a reporting segment perspective, GPU revenue grew 40% from last year to $2.66 billion.

從報告部分的角度來看，GPU 收入比去年增長了 40%，達到 26.6 億美元。

Tegra processor revenue grew 40% to $467 million.

Tegra 處理器收入增長 40% 至 4.67 億美元。

Let's start with our gaming business.

讓我們從我們的遊戲業務開始。

Revenue of $1.8 billion was up 52% year-on-year and up 5% sequentially.

收入為 18 億美元，同比增長 52%，環比增長 5%。

Growth was driven by all segments of the business with desktop, notebook and gaming consoles up all strong double-digit percentages year-on-year.

增長受到所有業務部門的推動，台式機、筆記本電腦和遊戲機同比均實現兩位數的強勁增長。

Notebooks were a standout this quarter, with strong demands for thin and light form factors based on our Max-Q technology.

筆記本電腦在本季度表現出色，對基於我們 Max-Q 技術的輕薄外形有著強烈的需求。

Max-Q enables gaming PC OEMs to pack a high-performance GPU into a slim notebook that is just 20 millimeters thick or less.

Max-Q 使遊戲 PC OEM 能夠將高性能 GPU 裝入厚度僅為 20 毫米或更小的超薄筆記本電腦中。

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.

所有主要的筆記本 OEM 和 ODM 都在他們的頂級遊戲筆記本電腦中採用 Max-Q，正好趕上返校。

And we expect to see 26 models based on Max-Q in stores for the holidays.

我們預計假期期間將在商店中看到 26 款基於 Max-Q 的型號。

The gaming industry remains vibrant.

遊戲行業依然充滿活力。

The eSports audience now approaches 400 million, up 18% over the past year.

電子競技觀眾現在接近 4 億，比過去一年增長 18%。

The unprecedented success of Fortnite and PUBG has popularized this new battle royale genre and expanded the gaming market.

Fortnite 和 PUBG 的空前成功推廣了這種新的大逃殺類型並擴大了遊戲市場。

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.

事實上，大逃殺模式即將出現在備受期待的戰地 V 等遊戲中。我們很高興與 EA 合作，讓 GeForce 成為 10 月份發布的戰地 V 的最佳 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.

我們還與 Square Enix 合作，讓 GeForce 成為其即將推出的《古墓麗影》的最佳平台。

Monster Hunter: World arrived on PCs earlier this month and it was an instant hit.

《怪物獵人：世界》本月早些時候登陸 PC，一炮而紅。

And many more titles are lined up for what promises to be a big holiday season.

還有更多的頭銜被排成一列，有望成為一個重要的假期。

It's not just new titles that are building anticipation.

建立預期的不僅僅是新遊戲。

The gaming community is excited over the Turing architecture announced earlier this week at SIGGRAPH.

遊戲社區對本週早些時候在 SIGGRAPH 上宣布的圖靈架構感到興奮。

Turing is our most important innovation since the invention of the CUDA GPU over a decade ago.

Turing 是我們自十多年前發明 CUDA GPU 以來最重要的創新。

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.

該架構包括新的專用光線追踪處理器或 RT 核心和用於 AI 推理的新張量核心，它們共同將使實時光線追踪首次成為可能。

We will enable the cinematic quality gaming, amazing new effects powered by neural networks and fluid interactivity on highly complex models.

我們將在高度複雜的模型上實現電影質量的遊戲、由神經網絡驅動的驚人新效果和流暢的交互性。

Turing will reset the look of video games and open up the 250 billion visual effects industries to GPUs.

圖靈將重置視頻遊戲的外觀，並將 2500 億個視覺效果行業開放給 GPU。

Turing is the result of more than 10,000 engineering years to -- of effort.

圖靈是超過 10,000 工程年努力的結果。

It's -- delivers up to 6x performance increase over Pascal for ray-traced graphics and up to 10x boost for peak inference flops.

與 Pascal 相比，它的光線追踪圖形性能提升高達 6 倍，峰值推理觸發器性能提升高達 10 倍。

This new architecture will be the foundation of a new portfolio of products across our platforms going forward.

這種新架構將成為我們未來平台上新產品組合的基礎。

Moving to data center.

搬到數據中心。

We had another strong quarter with revenue of $760 million, accelerating to 83% year-on-year growth and up 8% sequentially.

我們又迎來了一個強勁的季度，收入為 7.6 億美元，同比增長 83%，環比增長 8%。

This performance was driven by hyperscale demand as Internet services used daily by billions of people increasingly leverage AI.

這種表現是由超大規模需求推動的，因為數十億人每天使用的互聯網服務越來越多地利用人工智能。

Our GPUs power real-time services such as search, voice recognition, voice synthesis, translation, recommender engines, fraud detection and retail applications.

我們的 GPU 支持實時服務，例如搜索、語音識別、語音合成、翻譯、推薦引擎、欺詐檢測和零售應用程序。

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.

我們還看到垂直行業越來越多地採用我們的人工智能和高性能計算解決方案，這是我們業務增長最快的領域之一。

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.

從石油和天然氣到金融服務到交通運輸等行業的公司正在利用人工智能的力量和我們加速的計算平台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.

我們的旗艦 Tensor Core GPU，Tesla V100，基於 Volta 架構，繼續為人工智能和高性能計算應用提供支持。

Volta has been adopted by every major cloud provider and hyperscale data center operator around the world.

Volta 已被全球所有主要雲提供商和超大規模數據中心運營商採用。

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.

客戶已迅速採取行動來驗證新版本的 V100，它將片上 DRAM 翻倍至 32 gig，以支持更大的數據集和神經網絡。

Major server OEMs, HP Enterprise, IBM, Lenovo, Cray and Supermicro also brought the V100 32-gig to market in the quarter.

主要服務器 OEM、HP Enterprise、IBM、Lenovo、Cray 和 Supermicro 在本季度也將 V100 32-gig 推向市場。

We continue to gain traction with AI inference solution, which helped expand our addressable market in the data center.

我們繼續通過人工智能推理解決方案獲得牽引力，這有助於擴大我們在數據中心的潛在市場。

During the quarter, we released our TensorRT 4 AI inference accelerator software for general availability.

在本季度，我們發布了 TensorRT 4 AI 推理加速器軟件，以實現全面可用性。

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.

雖然先前版本的 TensorRT 優化了圖像和視頻相關工作負載，但 TensorRT 4 擴展了範圍以包含更多用例，例如語音識別、語音合成、翻譯和推薦系統。

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.

這意味著我們現在可以處理更大部分的深度學習推理工作負載，相對於 CPU 提供高達 190 倍的性能加速。

NVIDIA and Google engineers have integrated TensorRT into the TensorFlow deep learning framework, making it easier to run AI inference on our GPUs.

NVIDIA 和 Google 工程師已將 TensorRT 集成到 TensorFlow 深度學習框架中，從而更輕鬆地在我們的 GPU 上運行 AI 推理。

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.

Google Cloud 宣布，我們用於 AI 推理和圖形虛擬化的小型 GPU NVIDIA Tesla P4 GPU 可在 Google Cloud Platform 上使用。

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.

數據中心的增長也受到 DGX 的推動，DGX 是我們完全優化的 AI 服務器，它包含 V100 GPU、我們專有的高速互連和我們完全優化的軟件堆棧。

The annual run rate for DGX is in the hundreds of millions of dollars.

DGX 的年運行率為數億美元。

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.

DGX-2 於 3 月在我們的 GPU 技術大會上宣布，正在獲得客戶的認可，並有望在第三季度實現量產。

At GTC Taiwan in June, we announced that we are bringing DGX-2 technology to our HGX-2 server platform.

在 6 月的 GTC 台灣，我們宣布將 DGX-2 技術引入我們的 HGX-2 服務器平台。

We make HGX-2 available to OEM and ODM partners so they can quickly deploy our newest innovations in their own server designs.

我們向 OEM 和 ODM 合作夥伴提供 HGX-2，以便他們可以在自己的服務器設計中快速部署我們的最新創新。

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.

最近幾週，我們宣布與 NetApp 和 Pure Storage 建立合作夥伴關係，通過將 DGX 與公司的全閃存存儲產品和第三方網絡相結合的高度集成優化解決方案，幫助客戶將 AI 部署速度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.

在 GTC 台灣，我們還透露，我們在 AI 訓練和推理方面創造了 5 項速度記錄。

Key to our strategy is our software stack.

我們戰略的關鍵是我們的軟件堆棧。

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.

從 CUDA 到我們的訓練和推理 SDK，以及我們與開發人員合作以加速他們的應用程序，這就是我們能夠在如此短的時間內實現如此顯著的性能提升的原因。

And our developer ecosystem is getting stronger.

我們的開發者生態系統正在變得越來越強大。

In fact, we just passed 1 million members in our developer program, up 70% from 1 year ago.

事實上，我們的開發者計劃成員剛剛超過 100 萬，比一年前增長了 70%。

One of our proudest moments this quarter was the launch of the Summit AI supercomputer in Oak Ridge National Laboratory.

本季度我們最自豪的時刻之一是在橡樹嶺國家實驗室推出了 Summit AI 超級計算機。

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.

Summit 由超過 27,000 個 Volta Tensor Core GPU 提供支持，並幫助美國 5 年來首次在 TOP500 超級計算機榜單上奪回第一名。

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.

其他進入 TOP500 榜單的 NVIDIA 電源系統包括位於勞倫斯利弗莫爾國家實驗室的 Sierra 排名第三，日本最快的超級計算機 ABCI 排名第五。

NVIDIA now powers 5 of the world's 7 fastest supercomputers, reflecting the broad shift in supercomputing to GPUs.

NVIDIA 現在為全球 7 台最快的超級計算機中的 5 台提供支持，這反映了超級計算向 GPU 的廣泛轉變。

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.

事實上，最新 TOP500 榜單中的大部分計算性能都來自 NVIDIA GPU，現在超過 550 個 HPC 應用程序都經過 GPU 加速。

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.

借助我們的 Tensor Core GPU，超級計算機現在可以將模擬與 AI 的強大功能相結合，推動從分子動力學到地震處理再到基因組學和材料科學的許多科學應用。

Moving to pro visualization.

轉向專業可視化。

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.

收入增長至 2.81 億美元，同比增長 20%，環比增長 12%，這得益於對實時渲染和移動工作站以及 AI 和 VR 等新興應用的需求。

These emerging applications now represent approximately 35% of pro visualization sales.

這些新興應用程序現在約佔專業可視化銷售額的 35%。

Strength extended across several key industries, including health care, oil and gas and media and entertainment.

實力延伸到幾個關鍵行業，包括醫療保健、石油和天然氣以及媒體和娛樂。

Key wins in the quarter include Raytheon, Lockheed, GE, Siemens and Philips Healthcare.

本季度的主要贏家包括雷神、洛克希德、通用電氣、西門子和飛利浦醫療保健。

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.

在 SIGGRAPH 上宣布 Turing 架構時，我們還推出了第一款基於 Turing 的處理器，Quadro RTX 8000、6000 和 5000 GPU，在預測之前數年將交互式光線追踪帶入了世界。

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.

我們還宣布推出 NVIDIA RTX 服務器，這是一款全光線追踪全局照明渲染服務器，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.

Turing 將徹底改變 5 到 5000 萬設計師和藝術家的工作，使他們能夠實時渲染逼真的場景，並為他們的工作流程添加新的基於 AI 的功能。

Quadro GPUs based on the Turing will be available in the fourth quarter.

基於 Turing 的 Quadro GPU 將於第四季度上市。

Dozens of leading software providers, developers and OEMs have already expressed support for Turing.

數十家領先的軟件供應商、開發商和原始設備製造商已經表達了對圖靈的支持。

Our pro viz partners view it as a game-changer for professionals in the media and entertainment, architecture and manufacturing industries.

我們的專業可視化合作夥伴將其視為媒體和娛樂、建築和製造業專業人士的遊戲規則改變者。

Finally, turning to automotive.

最後，轉向汽車。

Revenue was a record $161 million, up 13% year-on-year and up 11% sequentially.

收入達到創紀錄的 1.61 億美元，同比增長 13%，環比增長 11%。

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.

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增長，以及下一代基於人工智能的智能駕駛艙信息娛樂解決方案的興起。

We continue to make progress on our autonomous vehicle platform with key milestones and partnerships announced this quarter.

我們通過本季度宣布的關鍵里程碑和合作夥伴關係繼續在我們的自動駕駛汽車平台上取得進展。

In July, Daimler and Bosch selected DRIVE Pegasus as the AI brain for their Level 4 and Level 5 autonomous fleets.

7 月，戴姆勒和博世選擇 DRIVE Pegasus 作為其 4 級和 5 級自動駕駛車隊的人工智能大腦。

Pilot testing will begin next year in Silicon Valley.

試點測試將於明年在矽谷開始。

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.

此次合作將 NVIDIA 在人工智能和自動駕駛平台方面的領先地位、博世作為全球最大的一級汽車供應商的硬件和系統專業知識以及戴姆勒的汽車專業知識和安全和質量代名詞的全球品牌結合在一起。

This quarter, we started shipping development systems for DRIVE Pegasus, an AI supercomputer designed specifically for autonomous vehicles.

本季度，我們開始為 DRIVE Pegasus 交付開發系統，這是一款專為自動駕駛汽車設計的人工智能超級計算機。

Pegasus delivers 320 trillion operations per second to handle diverse and redundant algorithms and is architected for safety as well as performance.

Pegasus 每秒可提供 320 萬億次操作來處理各種冗餘算法，並在架構上兼顧安全性和性能。

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.

這款汽車級功能安全生產解決方案使用 2 個 NVIDIA Xavier SoC 和 2 個專為 AI 和視覺處理而設計的下一代 GPU，與上一代相比，性能提高 10 倍以上，數據帶寬提高 10 倍以上。

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.

借助共同設計的硬件和軟件，該平台旨在實現 ASIL D ISO 26262，即業界最高級別的汽車功能安全。

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.

我們創建了一個可擴展的人工智能汽車平台，涵蓋從交通擁堵飛行員到 5 級自動駕駛出租車的整個範圍的自動駕駛和自動駕駛。

More than 370 companies and research institutions are using NVIDIA's automotive platform.

超過 370 家公司和研究機構正在使用 NVIDIA 的汽車平台。

With this growing momentum and accelerating revenue growth, we remain excited about the intermediate and long-term opportunities for autonomous driving business.

憑藉這種不斷增長的勢頭和加速的收入增長，我們對自動駕駛業務的中長期機遇仍然感到興奮。

This quarter, we also introduced our Xavier platform for Jetson for the autonomous machine market.

本季度，我們還為自動駕駛機器市場推出了適用於 Jetson 的 Xavier 平台。

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.

它擁有超過 90 億個晶體管，每秒可提供超過 30 萬億次操作，比強大的工作站具有更多的處理能力，同時使用燈泡的 1/3 能量。

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.

Jetson Xavier 建立客戶在邊緣提供人工智能計算，為機器人或無人機等自主機器提供動力，並在製造、物流、零售、農業、醫療保健等領域得到應用。

Lastly, in our OEM segment, revenue declined by 54% year-on-year and 70% sequentially.

最後，在我們的 OEM 部門，收入同比下降 54%，環比下降 70%。

This was primarily driven by the sharp decline of cryptocurrency revenues to fairly minimal levels.

這主要是由於加密貨幣收入急劇下降至相當低的水平。

Moving to the rest of the P&L.

轉到損益表的其餘部分。

Q2 GAAP gross margin was 63.3% and non-GAAP was 63.5%, in line with our outlook.

第二季度 GAAP 毛利率為 63.3%，非 GAAP 毛利率為 63.5%，與我們的展望一致。

GAAP operating expenses were $818 million.

GAAP 運營費用為 8.18 億美元。

Non-GAAP operating expenses were $692 million, up 30% year-on-year.

非美國通用會計準則運營費用為 6.92 億美元，同比增長 30%。

We can continue to invest in the key platforms driving our long-term growth, including gaming, AI and automotive.

我們可以繼續投資於推動我們長期增長的關鍵平台，包括遊戲、人工智能和汽車。

GAAP net income was $1.1 billion and EPS was $1.76, up 89% and 91%, respectively, from a year earlier.

GAAP 淨收入為 11 億美元，每股收益為 1.76 美元，同比分別增長 89% 和 91%。

Some of the upside was driven by a tax rate near 7% compared to our outlook of 11%.

與我們預期的 11% 相比，接近 7% 的稅率推動了部分上漲。

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.

非美國通用會計準則淨收入為 12.1 億美元，每股收益為 1.94 美元，分別比一年前增長 90% 和 92%，這反映了收入實力以及毛利率和營業利潤率的增長以及稅收的降低。

Quarterly cash flow from operations was $913 million.

季度運營現金流為 9.13 億美元。

Capital expenditures were $128 million.

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.

有了這個，讓我轉向 2019 財年第三季度的展望。

We are including no contribution from crypto in our outlook.

我們的展望中不包括加密貨幣的貢獻。

We expect revenue to be $3.25 billion, plus or minus 2%.

我們預計收入為 32.5 億美元，上下浮動 2%。

GAAP and non-GAAP gross margins are expected to be 62.6% and 62.8%, respectively, plus or minus 50 basis points.

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 and non-GAAP operating expenses are expected to be approximately $870 million and $730 million, respectively.

GAAP 和非 GAAP 運營費用預計分別約為 8.7 億美元和 7.3 億美元。

GAAP and non-GAAP OI&E are both expected to be income of $20 million.

GAAP 和非 GAAP OI&E 預計收入均為 2000 萬美元。

GAAP and non-GAAP tax rates are both expected to be 9%, plus or minus 1%, excluding discrete items.

GAAP 和非 GAAP 稅率預計均為 9%，正負 1%，不包括離散項目。

Capital expenditures are expected to be approximately $125 million to $150 million.

資本支出預計約為 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.

更多財務細節包含在 CFO 評論和我們投資者關係網站上提供的其他信息中。

In closing, I'd like to highlight some of the upcoming events for the financial community.

最後，我想強調一下金融界即將舉行的一些活動。

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.

我們將在 9 月 6 日的花旗全球技術會議上發表演講，並在 9 月 13 日在東京和 10 月 10 日在慕尼黑舉行的 GPU 技術會議上與金融界會面。

And our next earnings calls to discuss our financial results is in the third quarter of 2019 will take place on November 15.

我們下一次討論財務業績的財報電話會議是在 2019 年第三季度，將於 11 月 15 日舉行。

We will now open the call for questions.

我們現在將打開問題的電話。

(Operator Instructions) And operator, would you please poll for questions?

（操作員說明）和操作員，請您投票提問嗎？

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

（操作員說明）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?

問題在於光線追踪，這在多大程度上創造了新市場，而不是在現有市場中實現了更大的功能？

Yes, Mark.

是的，馬克。

So first of all, Turing, as you know, is the world's first ray-tracing GPU.

所以首先，眾所周知，Turing 是世界上第一個光線追踪 GPU。

And it completes our new computer graphics platform, which is going to reinvent computer graphics altogether.

它完善了我們新的計算機圖形平台，它將徹底改造計算機圖形。

It unites 4 different computing modes: rasterization, accelerated ray tracing, computing with CUDA and artificial intelligence.

它結合了 4 種不同的計算模式：光柵化、加速光線追踪、CUDA 計算和人工智能。

It uses these 4 basic methods to create imagery for the future.

它使用這 4 種基本方法為未來創建圖像。

There's 2 different -- 2 major ways that we'll experience the benefits right away.

有 2 種不同的 - 2 種主要方式，我們將立即體驗到好處。

The first is for the markets of visualization today, they require photorealistic images.

首先是針對當今的可視化市場，它們需要逼真的圖像。

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.

無論是宜家目錄、電影、建築工程或產品設計、汽車設計，所有這些類型的市場都需要逼真的圖像。

And the only way to really achieve that is to use ray tracing with physically based materials and lighting.

真正實現這一目標的唯一方法是將光線追踪與基於物理的材質和照明結合使用。

The technology is rather complicated.

技術比較複雜。

It's been computing-intensive for a very long time.

很長一段時間以來，它一直是計算密集型的。

And it wasn't until now that we've been able to achieve it in a productive way.

直到現在，我們才能夠以富有成效的方式實現它。

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.

所以圖靈有能力做光線追踪，加速光線追踪，它也有能力組合非常大的幀緩衝區，因為這些數據集非常大。

And so that marketplace is quite large and it's never been served by GPUs before until now.

所以這個市場非常大，直到現在它還沒有被 GPU 服務過。

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.

所有這些都在 CPU 渲染農場、所有這些電影製片廠和服務中心的巨大渲染農場等上運行。

The second area where you're going to see the benefits of ray tracing, we haven't announced.

您將看到光線追踪優勢的第二個領域，我們尚未公佈。

If I could have a follow-up on the gaming side.

如果我可以在遊戲方面進行跟進。

Where do you think the industry is on creating content that leverages that kind of capability?

您認為該行業在創建利用這種能力的內容方面處於什麼位置？

Yes, Mark, the -- at GTC this last year in March, GDC and GTC, we announced a brand-new platform called NVIDIA RTX.

是的，馬克，在去年 3 月的 GTC、GDC 和 GTC 上，我們宣布了一個名為 NVIDIA RTX 的全新平台。

And this platform has those 4 computation methods that I described for generating images.

這個平台有我描述的用於生成圖像的 4 種計算方法。

We put that platform out with the support of Microsoft.

我們在 Microsoft 的支持下推出了該平台。

They call it the Microsoft DirectX Raytracing and the major game engine companies.

他們稱其為 Microsoft DirectX Raytracing 和主要的遊戲引擎公司。

Epic has implemented real-time raytracing and the RTX into the Epic engine, the Unreal Engine.

Epic 在 Epic 引擎 Unreal Engine 中實現了實時光線追踪和 RTX。

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.

在 GDC 和 GTC 上，我們首次展示了在 4 Volta GPU 上，在 4 Volta GPU 上實現這一點的能力。

And it was the intention of -- to get this platform out to all of the game developers.

其目的是讓所有遊戲開發者都能使用這個平台。

And we've been working with game developers throughout this time.

我們一直在與遊戲開發者合作。

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.

因此，本週在 SIGGRAPH 上，我們發布了 Quadro，它是第一款——Quadro RTX 8000、6000 和 5000——世界上第一款加速光線追踪 GPU。

And I demonstrated one Quadro running the same application that we demonstrated on 4 Volta GPUs running in March.

我演示了一個 Quadro 運行與我們在 3 月份運行的 4 個 Volta GPU 上演示的相同應用程序。

And the performance is really spectacular.

而且表演真的很精彩。

And so I think the answer to your question is developers all have access to RTX.

所以我認為你的問題的答案是開發人員都可以訪問 RTX。

It's in Microsoft's DirectX.

它在微軟的 DirectX 中。

It's in the most popular game engine in the world, and you're going to start to see developers use it.

它位於世界上最流行的遊戲引擎中，您將開始看到開發人員使用它。

On the workstation side, on the professional visualization side, all of the major ISPs have jumped on to adopt it.

在工作站方面，在專業可視化方面，所有主要的 ISP 都開始採用它。

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.

在今年的 SIGGRAPH 上，您可以看到一大群開發人員展示 NVIDIA RTX 加速光線追踪，生成逼真的圖像。

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.

所以我想說，在我們歷史上沒有一個平台，在公告的第一天，有這麼多的開發者跳上它。

And stay tuned, we've got a lot more stories to tell you about RTX.

請繼續關注，我們還有更多關於 RTX 的故事要告訴您。

Your next question is from Matt Ramsay with Cowen.

您的下一個問題來自 Matt Ramsay 和 Cowen。

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.

Colette，我有幾個關於庫存的問題，第一個是，我知道你已經推出了一套新產品，數據中心業務顯然非常強勁。

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.

但如果你看一下資產負債表，我認為庫存水平環比增長了 30% 左右，而你的指導收入增長了 3% 左右。

Maybe you could help us sort of walk through the contribution to that inventory and what it might mean for future products.

也許您可以幫助我們了解對庫存的貢獻以及它對未來產品的意義。

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.

其次，如果您可以從庫存方面談談遊戲渠道，那麼你們在產品過渡期間看到的渠道情況如何。

Sure.

當然。

Thanks for your question.

謝謝你的問題。

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.

因此，當您查看資產負債表上的庫存時，我認為就我們將推向市場的產品而言，這與您在過去幾個月中所看到的基本一致。

Turing is an extremely important piece of architecture, and as you know, it will be with us for some time.

圖靈是一個極其重要的架構，如您所知，它將伴隨我們一段時間。

So I think the inventory balance is getting ready for that.

所以我認為庫存餘額正在為此做好準備。

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.

不要忘記我們在數據中心方面的工作，我們為 Volta 所擁有的也是一台非常、非常複雜的計算機，在某些情況下，就我們在那裡所擁有的而言。

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.

因此，這些東西加在一起，加上我們的 Pascal 架構仍然存在，幾乎構成了我們在庫存方面的所有內容。

Matt, on the channel inventory side, we see inventory in the lower ends of our stack.

Matt，在渠道庫存方面，我們看到庫存處於堆棧的低端。

And that inventory is well positioned for back-to-school and the building season that's coming up on Q3.

而且該庫存非常適合返校和第三季度即將到來的建築季節。

And so I feel pretty good about that.

所以我對此感覺很好。

The rest of our product launches and the ramp-up of Turing is going really well.

我們其他產品的發布和圖靈的升級進展非常順利。

And so I think the rest of the announcements we haven't made, but stay tuned.

所以我認為我們還沒有發布其他公告，但請繼續關注。

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.

RTX 系列將成為我們真正的遊戲規則改變者，計算機圖形的徹底改造已被眾多開發人員所接受。

We're going to see some really exciting stuff this year.

今年我們將看到一些非常令人興奮的東西。

Next question is from Vivek Arya with Bank of America.

下一個問題來自美國銀行的 Vivek Arya。

Actually, just a clarification and then a question.

實際上，只是一個澄清，然後是一個問題。

On the clarification, Colette, if you could also help us understand the gross margin sequencing from Q2 to Q3.

關於澄清，科萊特，如果你也可以幫助我們了解從第二季度到第三季度的毛利率排序。

And then, Jensen, how would you contrast the Pascal cycle, the Turing cycle?

然後，Jensen，你會如何對比帕斯卡循環和圖靈循環？

Because I think in your remarks, you mentioned Turing is a very strong advancement over what you had before.

因為我認為在你的發言中，你提到圖靈比你以前的東西有了很大的進步。

But when you launched Pascal, you had guided to very strong Q3s and then Q4s.

但是當你推出 Pascal 時，你已經引導到了非常強勁的 Q3 和 Q4。

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.

這一次，第三季度的前景，儘管它在絕對基礎上是好的，在連續和相對基礎上，它可能並不那麼強勁。

So if you could just help us contrast the Pascal cycle with what we should expect with the Turing cycle.

因此，如果您能幫助我們將帕斯卡循環與我們應該期望的圖靈循環進行對比。

Sure, thanks for that -- for the question.

當然，感謝您提出的問題。

Let me start first with your question regarding gross margins.

讓我首先從你關於毛利率的問題開始。

We have essentially reached, as we move into Q3, a normalization of our gross margins.

隨著我們進入第三季度，我們基本上已經實現了毛利率的正常化。

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.

我相信，在過去的幾個季度中，我們已經看到了加密的影響以及它可以做些什麼來提高我們的整體毛利率。

We believe we have reached a normal period as we're looking forward to essentially no cryptocurrency as we go forward.

我們相信我們已經達到了一個正常的時期，因為我們期待在我們前進的過程中基本上沒有加密貨幣。

Let's see, Pascal was really successful.

讓我們看看，帕斯卡真的很成功。

Pascal, relative to Maxwell, was a leap in fact.

相對於麥克斯韋，帕斯卡實際上是一個飛躍。

And it was a really significant upgrade.

這是一個非常重要的升級。

The architectures were largely the same.

架構基本相同。

They were both programmable shading, they were both at the same generation programmable shading.

它們都是可編程著色，它們都是同一代可編程著色。

But Pascal was much, much more energy efficient.

但是帕斯卡的能源效率要高得多。

I think it was something like 30%, 40% more energy efficient than Maxwell, and that translated to performance benefits to customers.

我認為它的能效比 Maxwell 高 30%、40%，這轉化為客戶的性能優勢。

The success of Pascal was fantastic.

帕斯卡的成功非常棒。

There's just simply no comparison to Turing.

根本無法與圖靈相提並論。

Turing is a reinvention of computer graphics.

圖靈是計算機圖形學的再發明。

It is the first ray-tracing GPU in the world.

它是世界上第一款光線追踪 GPU。

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.

它是第一款能夠在環境中追踪光線並創建逼真的陰影和反射並能夠對區域光、全局照明和間接照明等事物進行建模的 GPU。

But the images are going to be so subtle and so beautiful, it -- when you look at it, it just looks like a movie.

但這些圖像會如此微妙和美麗，它——當你看它時，它就像一部電影。

And yet it's backwards compatible with everything that we've done.

然而，它向後兼容我們所做的一切。

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.

這種新的混合渲染模型擴展了我們之前構建的模型，但添加了 2 項新功能，即人工智能和加速光線追踪，真是太棒了。

So everything of the past will be brought along and benefits, and it's going to create new visuals that were impossible before.

所以過去的一切都會被帶走並帶來好處，它將創造出以前不可能的新視覺效果。

We also did a good job on laying the foundations of the development platform for the developers.

我們在為開發者打好開發平台的基礎上也做得很好。

We've partnered with Microsoft to create DXR.

我們已與 Microsoft 合作創建 DXR。

Vulkan RT is also coming, and we have optics that are used by pro viz renderers and developers all over the world.

Vulkan RT 也即將推出，我們擁有可供全球專業渲染器和開發人員使用的光學器件。

And so we have the benefit of laying the foundation stack by stack by stack over the years.

因此，多年來，我們受益於逐層堆疊奠定基礎。

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.

因此，在圖靈問世之日，我們將擁有豐富的應用程序，讓遊戲玩家能夠享受。

You mentioned guidance.

你提到了指導。

I actually think that on a year-over-year performance, we're doing terrific.

實際上，我認為就同比表現而言，我們做得非常好。

And I'm super excited about the ramp of Turing.

我對圖靈的斜坡感到非常興奮。

It is the case that we benefited in the last several quarters from an unusual lift from crypto.

在過去的幾個季度中，我們從加密貨幣的異常提升中受益就是這種情況。

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.

在今年年初，我們認為並預計加密貨幣將在今年餘下的時間裡做出更大的貢獻，但目前，我們認為它在下半年並不重要。

And so that makes comparisons on a sequential basis on, I guess, a quarterly sequential basis harder.

這樣一來，我想，按季度進行比較就更難了。

But on a year-to-year basis, I think we're doing terrific.

但在逐年的基礎上，我認為我們做得非常好。

Every single one of our platforms are growing.

我們的每一個平台都在增長。

High-performance computing, of course, data centers is growing.

高性能計算，當然，數據中心正在增長。

AI, the adoption continues to sweep from one industry to another industry.

人工智能的採用繼續從一個行業席捲到另一個行業。

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.

人工智能將帶來的自動化將為行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.

現在有了圖靈，我們將能夠重新點燃專業的可視化業務，第一次向照片級渲染敞開大門，渲染農場和所有設計產品的人，這些產品必須將照片逼真地可視化，重新發明和重置視頻遊戲的圖形。

And so I think we're in a great position, and I'm looking forward to reporting Q3 when the time comes.

所以我認為我們處於一個很好的位置，我期待在時機成熟時報告第三季度。

Your next question is from Atif Malik with Citi.

您的下一個問題來自花旗的 Atif Malik。

Colette, I have a question on data center.

Colette，我有一個關於數據中心的問題。

In your prepared remarks, you talked about AI and high-performance computing driving new verticals and some of these verticals are fastest growing.

在你準備好的評論中，你談到了人工智能和高性能計算推動了新的垂直領域，其中一些垂直領域增長最快。

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.

你們的一些同行談到了今年下半年企業支出放緩以及單位需求，你們不是單位遊戲，而是更多的人工智能採用。

Just curious in terms of your thinking about second half data center growth.

只是好奇你對下半年數據中心增長的看法。

So as you know, we generally give our view on guidance for 1 quarter out.

如您所知，我們通常對 1 個季度的指導意見發表看法。

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.

您說得對，就我們所看到的而言，我們看到的每個季度的數據中心結果總是一個巨大的獨特組合。

But there's still some underlying points of that, that will likely continue.

但仍有一些潛在的觀點，這種情況可能會繼續下去。

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.

超大規模使用量的增長，各行業的持續加入，主要是因為加速計算對工作負載和他們擁有的數據的需求是如此重要。

So we still expect, as we go into Q3, for data center to grow both sequentially and year-over-year.

因此，當我們進入第三季度時，我們仍然預計數據中心將連續增長和同比增長。

And we'll see probably a mix of both selling our Tesla V100 platforms but also a good contribution from our DGX.

我們可能會看到銷售我們的 Tesla V100 平台以及我們的 DGX 的良好貢獻。

Yes, that's right.

是的，這是正確的。

Atif, let me just add a little bit more to that.

Atif，讓我再補充一點。

I think the one simple way to think about that is this.

我認為考慮這一點的一種簡單方法是這樣。

In the transportation industry, let's take one particular vertical.

在運輸行業，讓我們以一個特定的垂直行業為例。

There are 2 dynamics that are happening that are very abundantly clear and that will transform that industry.

有兩種動態正在發生，非常明顯，它們將改變該行業。

The first, of course, is ride hailing and ride sharing.

首先，當然是叫車服務和拼車服務。

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.

那些平台，為了推薦哪輛出租車給哪個乘客，給哪個客戶，是一個非常大的計算問題。

It's a machine-learning problem.

這是一個機器學習問題。

It's an optimization problem of very, very large scale.

這是一個非常非常大規模的優化問題。

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.

在每一種情況下，您都需要高性能計算機來使用機器學習來確定如何進行完美匹配或最佳匹配。

On the second -- the second is self-driving cars.

第二個——第二個是自動駕駛汽車。

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.

每家致力於機器人出租車或自動駕駛汽車的汽車公司都需要收集數據、標記數據、訓練神經網絡或訓練一大堆神經網絡，並在汽車中運行這些神經網絡。

And so you just make your list of how many people are actually building self-driving cars.

因此，您只需列出實際製造自動駕駛汽車的人數。

And every single one of them will need even more GPU-accelerated servers.

每一個都需要更多的 GPU 加速服務器。

And that's just for developing the model.

這只是為了開發模型。

Then the next stage is to simulate the entire software.

然後下一階段是模擬整個軟件。

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.

因為我們知道這個行業或世界每年行駛 10 萬億英里，而我們能做的最好的事情就是行駛幾百萬英里。

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.

而我們真正想做的是能夠模擬和強調——對我們的軟件堆棧進行壓力測試，而做到這一點的唯一方法就是做虛擬現實。

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.

因此，這是您必須構建的另一台超級計算機，用於模擬您的所有軟件成本，即數十億和數十億虛擬創建的具有挑戰性的里程。

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.

最後，在您 OTA 軟件之前，您將不得不重新模擬並重播您多年來收集的所有里程，以確保在您 OTA 新模型之前沒有回歸車隊。

And so transportation is going to be a very large industry.

所以運輸將是一個非常大的行業。

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.

醫療保健也是如此，從現在幾乎無處不在使用人工智能的醫學成像到發現深度學習和人工智能優勢的基因組學；未來，病理學。

The list goes on.

名單還在繼續。

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.

因此，一個又一個行業，一個又一個行業，我們正在發現深度學習的好處，而這些行業可能會因此而真正地、真正地發生革命性的變化。

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

您的下一個問題來自 C.J. Muse 和 Evercore。

I guess, short term and the long term.

我想，短期和長期。

So for short term, as you think about your gaming guide, are you embedding any drawdown of channel inventory there?

因此，就短期而言，當您考慮您的遊戲指南時，您是否在其中嵌入了任何渠道庫存的縮減？

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?

然後從長遠來看，當您考慮圖靈張量核心時，您能否談談與 Volta V100 的差異化，特別是當您考慮 8 位整數和推理機會時？

We're expecting the channel inventory to work itself out.

我們希望渠道庫存能夠自行解決。

We are masters at managing our channel, and we understand the channel very well.

我們是管理渠道的大師，我們非常了解渠道。

As you know, the way that we go to market is through the channels around the world.

如您所知，我們進入市場的方式是通過世界各地的渠道。

We're not concerned about the channel inventory.

我們不關心渠道庫存。

As we ramp Turing, any -- whenever we ramp a new architecture, we ramp it from the top down.

當我們升級圖靈時，任何時候——每當我們升級一個新架構時，我們都是從上到下升級它。

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.

所以我們有很多機會——當我們回到學校和遊戲週期來管理庫存時，我們對此感覺很好。

As a result, comparing Volta and Turing, CUDA's compatible.

因此，比較 Volta 和 Turing，CUDA 是兼容的。

That's one of the benefits of CUDA.

這是 CUDA 的優勢之一。

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.

CUDA——所有利用 CUDA 的應用程序都編寫在我們的深度神經網絡平台 cuDNN 之上，到利用框架的輸出並針對運行時進行優化的 TensorRT。

All of those tools and libraries run on top of Volta and run on top of Turing and run on top of Pascal.

所有這些工具和庫都在 Volta 之上運行，在 Turing 之上運行，在 Pascal 之上運行。

What Turing adds over Pascal is the same Tensor Core that is inside Volta.

圖靈在 Pascal 上添加的是與 Volta 內部相同的 Tensor Core。

Of course, Volta is designed for large-scale training.

當然，Volta 是為大規模訓練而設計的。

8 GPUs could be connected together.

8個GPU可以連接在一起。

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.

它們擁有最快的 HBM2 內存，專為數據中心應用而設計，具有 64 位雙精度 ECC、高彈性計算以及使 Volta 成為完美的高性能計算加速器的所有軟件和系統軟件功能和工具。

In the case of Turing, it's really designed for 3 major applications.

就圖靈而言，它實際上是為 3 個主要應用程序設計的。

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.

第一個應用程序是打開專業可視化，這是一個非常大的市場，歷史上一直使用渲染農場，並且在我們現在能夠使用非常非常大的數據集進行全路徑跟踪和全局照明之前真的無法使用 GPU .

So that's one market that's brand new as a result of Turing.

因此，這是圖靈帶來的全新市場。

The second market is to reinvent computer graphics, real-time computer graphics, for video games and other real-time visualization applications.

第二個市場是重新發明計算機圖形，實時計算機圖形，用於視頻遊戲和其他實時可視化應用。

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.

當您看到圖靈創建的圖像時，您將很難看到過去的圖像。

It just looks amazing.

它看起來很神奇。

And then the third, Turing has a really supercharged Tensor Core.

然後第三個，圖靈有一個真正增壓的張量核心。

And this Tensor Core is used for image generation.

而這個 Tensor Core 用於圖像生成。

It's also used for high throughput deep learning inferencing for data centers.

它還用於數據中心的高吞吐量深度學習推理。

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.

因此，這些圖靈應用程序表明圖靈存在多個 SKU，這也是我們擁有如此出色的工程團隊的原因之一，我們可以一次將一個架構擴展到許多平台。

And so I hope that answers your question.

所以我希望這能回答你的問題。

The Tensor Core inference capability of Turing is going to be off the charts.

Turing 的 Tensor Core 推理能力將超出預期。

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.

我想知道既然塵埃落定，你是否可以談論加密貨幣。

You guys have done a good job of kind of laying out exactly how much of the OEM business has been driven by that.

你們做得很好，準確地說明了 OEM 業務有多少是受此驅動的。

But there's also been, I think, some sense of -- some of the GeForce business was being driven by crypto.

但我認為，也有一些感覺——一些 GeForce 業務是由加密驅動的。

Can you -- looking backwards, can you size that for us?

你能 - 向後看，你能幫我們調整一下嗎？

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.

而且我想，我正試圖了解加密貨幣對 10 月份指引的影響，因為它在 7 月季度似乎非常小。

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.

嗯，我認為——我的意思是，第二個問題更容易回答，而原因——第一個問題只是——它是模棱兩可的，無論如何都難以預測。

It's hard to estimate no matter what.

無論如何都很難估計。

But the second question, the answer is we're expecting -- we're projecting 0 basically.

但是第二個問題，答案是我們期待的——我們基本上預測為 0。

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.

對於第一個問題，有多少 GeForce 可以用於加密，很多遊戲玩家在晚上可以——在他們睡覺的時候，他們可以進行一些挖礦。

And so did they buy it for mining or did they buy it for gaming, it's kind of hard to say.

他們是為了挖礦還是為了遊戲而購買它，這很難說。

And some miners were unable to buy our OEM products, and so they jumped onto the market to buy it from retail.

一些礦工無法購買我們的 OEM 產品，因此他們跳入市場從零售店購買。

And that probably happened a great deal as well.

這可能也發生了很多。

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.

而這一切都發生在最後一個季度——前幾個季度，可能從 Q 開始——第三季度末、第四季度、第一季度末，上個季度很少，我們預計未來不會有加密貨幣挖礦。

Your next question is from Toshiya Hari with Goldman Sachs.

您的下一個問題來自高盛的 Toshiya Hari。

I had one for Jensen and one for Colette.

我為 Jensen 準備了一份，為 Colette 準備了一份。

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.

Jensen，我希望您能提醒我們，您的推理業務在今天在數據中心內的意義何在，以及您預計未來 2 年的增長將如何——隨著您在 Google 等賬戶上的成功在更廣泛的領域中激增顧客。

And then for Colette, if you can give directional guidance for each of your platforms.

然後對於 Colette，如果您可以為您的每個平台提供方向指導。

I know you talked about data center a little bit, but if you can talk about the other segments.

我知道你談到了一點數據中心，但如果你能談談其他領域的話。

And on gaming specifically, if you can talk about whether or not new products are embedded in that guide.

特別是在遊戲方面，如果您可以談論該指南中是否嵌入了新產品。

Thanks, Toshiya.

謝謝，東也。

Inference is going to be a very large market for us.

推理對我們來說將是一個非常大的市場。

It is surely material now in our data center business.

它現在在我們的數據中心業務中肯定很重要。

It's not the largest segment, but I believe it's going to be a very large segment of our data center business.

這不是最大的部分，但我相信它將成為我們數據中心業務的一個非常大的部分。

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.

全世界有 3000 萬台服務器，讓我們估計一下，在雲中，而在企業中還有更多。

I believe that almost every server in the future will be accelerated.

相信未來幾乎每台服務器都會被加速。

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.

其原因是因為人工智能和深度學習軟件以及神經網絡模型將——預測模型，將被注入到任何地方的軟件中。

And acceleration has proven to be the best approach going forward.

事實證明，加速是最好的方法。

We've been laying the foundations for inferencing for a couple 2, 3 years.

我們已經為推理奠定了 2、3 年的基礎。

And as we've described at GTCs, inference is really, really complicated.

正如我們在 GTC 上所描述的，推理非常非常複雜。

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.

原因是你必須獲取這些作為訓練框架輸出的龐大網絡的輸出並對其進行優化。

This is the -- probably the largest computational graph optimization problem the world's ever seen.

這可能是世界上最大的計算圖優化問題。

And this is brand-new invention territory.

而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.

從 CNN 到 RCNN，從自動編碼器到 RNN 和 LSTM，有許多不同的網絡架構，如今有許多不同種類的神經網絡，而且還在繼續增長。

And so the compiler technology is really, really complicated.

所以編譯器技術非常非常複雜。

And this year, we announced 2 things.

今年，我們宣布了兩件事。

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.

今年早些時候，我們宣布我們已經成功地將 Tesla P4 低調、高能效推理加速器引入超大規模數據中心。

And we announced our fourth generation TensorRT optimizing compiler -- neural network optimizing compiler.

我們還發布了我們的第四代 TensorRT 優化編譯器——神經網絡優化編譯器。

And TRT 4 goes well beyond CNNs and image recognition in the beginning.

TRT 4 一開始就遠遠超出了 CNN 和圖像識別。

It now allows us to support and optimize for voice recognition or speech recognition, natural language understanding, recommendation systems, translation.

它現在允許我們支持和優化語音識別或語音識別、自然語言理解、推薦系統、翻譯。

And all of these applications are really pervasive from Internet services all over the world.

所有這些應用程序在世界各地的互聯網服務中都非常普遍。

And so now from images to video to voice to recommendation systems, we now have a compiler that can address it.

所以現在從圖像到視頻到語音再到推薦系統，我們現在有了一個可以解決它的編譯器。

We are actively working with just about every single Internet service provider in the world to incorporate inference acceleration into their stack.

我們正在與世界上幾乎每一個互聯網服務提供商積極合作，將推理加速整合到他們的堆棧中。

And the reason for that is because they need high throughput and, very importantly, they need low latency.

原因是它們需要高吞吐量，而且非常重要的是，它們需要低延遲。

Voice recognition is only useful if it responds in a relatively short period of time.

語音識別只有在相對較短的時間內響應時才有用。

And our platform is just really, really excellent for that.

我們的平台非常非常適合這一點。

And then this last week -- this week, we announced Turing.

然後是上週——本週，我們宣布了圖靈。

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.

我宣布圖靈的推理性能是 Pascal 的推理性能的 10 倍，這已經是 CPU 的推理性能的幾百倍。

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?

所以你看看我們的發展速度，包括對新神經網絡的支持、編譯器不斷增加的優化和性能輸出以及我們推進處理器的速度，我認為我們把標準提高得很高，好嗎？

So with that, Colette?

那麼，科萊特？

Yes.

是的。

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.

因此，當您查看我們的整體細分市場時，您將看到我們在上個第二季度的業績，從同比的角度來看，我們的每個平台都有增長。

We probably possibly see that again in our Q3 guidance, the year-over-year growth across each and every one of those platforms.

我們可能會在我們的第三季度指南中再次看到這一點，即每個平台的同比增長。

Of course, our OEM business will be down likely year-over-year, again just due to the absence of cryptocurrency in our forecast.

當然，我們的 OEM 業務可能會同比下降，這也是因為我們的預測中沒有加密貨幣。

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.

當我們按順序考慮時，我們的希望絕對是，我們的數據中心將會增長，我們也可能會看到我們的遊戲業務的增長。

It's still early, still we've got many different scenarios and -- on our pro viz and auto.

現在還為時過早，我們仍然有許多不同的場景，而且——在我們的專業和汽車上。

But definitely, our gaming and our data center are expected to grow sequentially.

但可以肯定的是，我們的遊戲和數據中心預計將連續增長。

Your next question is from Blayne Curtis with Barclays.

您的下一個問題來自巴克萊銀行的 Blayne Curtis。

Two on gross margin.

二是毛利率。

Colette, I just want to make sure I understood, July to October gross margins down.

Colette，我只是想確保我理解，7 月到 10 月的毛利率下降。

I know you've been getting a benefit from crypto, but it's pretty de minimis in July.

我知道你已經從加密貨幣中受益，但在 7 月，它是微不足道的。

Just is there any other moving pieces?

只是還有其他可動的部分嗎？

And then kind of longer picture here, how do you think about the ramp of Turing affecting gross margins?

然後是更長的畫面，你如何看待圖靈的斜坡影響毛利率？

You're obviously enabling a lot of capabilities.

您顯然啟用了很多功能。

You get paid for it, 12-nanometers, fairly stable.

你得到報酬，12納米，相當穩定。

Just kind of curious how to think about over the next couple of quarters gross margin with that ramp.

只是有點好奇如何考慮接下來幾個季度的毛利率。

Yes.

是的。

So let me take your first part of the question regarding our gross margins and what we had seen from crypto.

因此，讓我回答您關於我們的毛利率以及我們從加密貨幣中看到的問題的第一部分。

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.

儘管加密貨幣收入可能並不大，但它仍然對我們的股票產生衍生影響，就我們銷售的東西而言，既能補充整個渠道等等。

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.

因此，在我們穩定整個渠道的過去幾個季度中，我們確實獲得了銷售幾乎所有東西的巨大效果，我們的利潤確實能夠從中受益。

Again, when we look at the overall growth year-over-year for Q2, you have 500 basis points in terms of growth.

同樣，當我們查看第二季度的整體同比增長時，您有 500 個基點的增長。

We're excited about what we have now here for Q3 as well, which is also significant growth year-over-year.

我們對我們現在在第三季度所擁有的一切感到興奮，這也是同比顯著增長。

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.

當然，隨著我們的發展，我們擁有高附加值的平台，無論是數據中心的平台，還是我們期望圖靈對我們的 Quadro 產生影響的平台。

But that will take some time for that all to partake.

但這需要一些時間才能讓所有人參與進來。

So we'll see how that goes.

所以我們會看看情況如何。

We haven't announced anything further at this time.

我們目前還沒有宣布任何進一步的消息。

But yes, we'll see probably over the longer term, the effects of what Turing can do.

但是，是的，從長遠來看，我們可能會看到圖靈能做的事情的影響。

Next question is from Aaron Rakers with Wells Fargo.

下一個問題來自富國銀行的 Aaron Rakers。

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.

當我們查看數據中心業務時，我很好奇，如果您能幫助我們了解超大規模、業務的超級計算部分和 AIP 之間的需求細分。

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.

而且我想，除此之外，我只是好奇，在過去幾個季度中非常顯著的指標之一是你已經看到中國的顯著增長。

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.

我很好奇這是否與數據中心業務有關，或者是什麼真正推動了這種後續問題。

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.

是的，Aaron，我認為如果你看一下——如果你從第一原則開始，這是看它的簡單方法。

Demand is continuing to grow at historical levels of 10x computing demand.

需求繼續以 10 倍計算需求的歷史水平增長。

Computing demand is increasing at historical levels of 10x every 5 years.

計算需求以每 5 年 10 倍的歷史水平增長。

10x every 5 years is approximately Moore's Law.

每 5 年 10 倍大約是摩爾定律。

And computing demand continues to grow at 10x every 5 years, however, Moore's Law stopped.

計算需求繼續以每 5 年 10 倍的速度增長，但是，摩爾定律停止了。

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.

因此，世界上在高性能計算、醫學成像、生命科學計算、人工智能方面的差距——因為這些應用需要更多的計算能力，這個差距只能通過另一種方式來彌補。

And NVIDIA's GPU accelerated computing that we pioneered really stands to benefit from that.

我們開創的 NVIDIA GPU 加速計算確實會從中受益。

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.

所以在最高級別，無論是超級計算——今年，你聽到 Colette 早些時候說過，NVIDIA GPU 代表了進入世界 TOP500 的所有新性能的 56%。

The TOP500 is called the TOP500 because it reflects the future computing.

TOP500之所以稱為TOP500，是因為它反映了未來的計算。

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.

我的期望是，越來越多，從一個又一個垂直行業——我提到運輸，我提到醫療保健，垂直行業繼續下去——隨著計算需求以每 5 年 10 倍的速度持續增長，開發人員使用 NVIDIA 的 GPU 計算來提高他們的需求是理性和合乎邏輯的。

I think that's probably the best way to answer it.

我認為這可能是最好的回答方式。

Your next question is from Harlan Sur with JPMorgan.

您的下一個問題來自摩根大通的 Harlan Sur。

When we think about cloud and hyperscale, we tend to think about the top guys, right?

當我們想到雲計算和超大規模時，我們往往會想到頂尖人物，對吧？

They're designing their own platform using your Tesla-based products or sometimes even designing their own chips for AI and deep learning.

他們正在使用基於 Tesla 的產品設計自己的平台，有時甚至為 AI 和深度學習設計自己的芯片。

But there's a larger base of medium to smaller cloud and hyperscale customers out there who don't have the R&D scale.

但是那裡有更大的中小型雲和超大規模客戶群，他們沒有研發規模。

And I think that's where your HGX platform seems to be focused on.

我認為這就是您的 HGX 平台似乎關注的地方。

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?

那麼 Jensen，您能否向我們介紹一下您對第一代 HGX-1 參考平台的採用情況以及對 HGX-2 的最初興趣？

HGX-1 was, I guess, kind of the prototype of HGX-2.

我猜 HGX-1 是 HGX-2 的原型。

HGX-2 is doing incredibly well for all the reasons that you mentioned.

由於您提到的所有原因，HGX-2 的表現令人難以置信。

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.

它是——即使是最大的超大規模數據中心也無法以我們正在談論的規模製造這些非常複雜的主板。

And so we created HGX-2 and it was immediately adopted by several most important hyperscalers in the world.

因此，我們創建了 HGX-2，並立即被世界上幾家最重要的超大規模企業採用。

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.

我們在台灣 GTC，我們宣布了所有支持 HGX-2 的領先服務器 OEM 和 ODM，並準備將其推向市場。

So we're in the process of finishing the -- finishing HGX-2 and ramping them into production.

所以我們正在完成——完成 HGX-2 並將它們投入生產。

And so I think HGX-2 is a huge success for exactly the reasons that you mentioned.

所以我認為 HGX-2 是一個巨大的成功，正是因為你提到的原因。

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.

我們基本上可以將它用於標準主板，例如用於超大規模計算機的 ATX 主板，它可以用於 HPC，它可以用於數據中心。

And it's really -- it's a really fantastic design.

它真的——這是一個非常棒的設計。

It just allows people to adopt this really complicated and high performance and really high-speed interconnect motherboard in a really easy way.

它只是讓人們以一種非常簡單的方式採用這種非常複雜、高性能和非常高速的互連主板。

Your next question is from Tim Arcuri with UBS.

您的下一個問題來自瑞銀的 Tim Arcuri。

Actually, I had 2 questions, Jensen, both for you.

實際上，Jensen，我有兩個問題要問你。

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.

首先，現在加密貨幣已經下降，我很好奇你認為潛力是什麼，也許我們會看到大量在 eBay 或其他渠道上轉售的卡片，這可能會蠶食新的 Pascal 銷售。

Is that something that keeps you up at night?

這是讓你徹夜難眠的事情嗎？

Number one.

第一。

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.

第二，顯然是關於遊戲和數據中心的故事，我知道你通常不會談論客戶，但由於特斯拉確實在電話中談到了你，我很好奇你對硬件開發的評論3和自己努力遠離你的驅動平台。

Sure.

當然。

Well, the crypto mining market is very different today than it was 3 years ago.

好吧，今天的加密採礦市場與 3 年前大不相同。

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.

即使新卡——以目前的價格，將新卡出售到採礦市場也沒有多大意義。

The existing capacity is still being used, and you could see that the hash rates continue.

現有容量仍在使用中，您可以看到哈希率仍在繼續。

And so my sense is that the installed base of miners will continue to use their cards.

所以我的感覺是礦工的安裝基礎將繼續使用他們的卡。

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.

然後可能更重要的因素是我們正在宣布一種全新的計算機圖形處理方式。

And with the -- with Turing and the RTX platform, computer graphics will never be the same.

有了 Turing 和 RTX 平台，計算機圖形將永遠不一樣。

And so I think this -- our new generation of new GPUs is really going to do great.

所以我認為——我們新一代的新 GPU 真的會做得很好。

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.

我也認為——我很欣賞埃隆對我們公司的評論，我也認為特斯拉製造了很棒的汽車，我開得很開心。

And with respect to the next generation, it is the case that when we first started working on autonomous vehicles, they needed our help.

至於下一代，當我們剛開始研究自動駕駛汽車時，他們需要我們的幫助。

And we used the 3-year-old Pascal GPU for the current generation of autopilot computers.

我們將使用了 3 年的 Pascal GPU 用於當前一代的自動駕駛計算機。

And it is very clear now that in order to have a safe autopilot system, we need a lot more computing horsepower.

現在很清楚，為了擁有一個安全的自動駕駛系統，我們需要更多的計算能力。

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.

為了安全計算——為了安全駕駛，算法必須豐富，它必須能夠在很多不同的情況下處理拐角條件。

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.

每當有越來越多的彎道條件或您必須做的更微妙的事情，或者您必須更平穩地駕駛或能夠更快地轉彎時，所有這些要求都需要更高的計算能力。

And that's exactly the reason why we built Xavier.

這正是我們構建 Xavier 的原因。

Xavier is in production now.

Xavier 現在正在生產中。

We're seeing great success, and customers are super excited about Xavier.

我們看到了巨大的成功，客戶對 Xavier 感到非常興奮。

And that's exactly the reason why we built it.

這正是我們建造它的原因。

And I think it's super hard to build Xavier and all the software stack on top of it.

而且我認為在它之上構建 Xavier 和所有軟件堆棧非常困難。

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.

如果沒有結果——無論出於何種原因，對他們來說都沒有結果，他可以給我打電話，我很樂意提供幫助。

Unfortunately, we have run out of time.

不幸的是，我們已經沒有時間了。

I will now turn the call back over to Jensen for any closing remarks.

我現在將把電話轉回給 Jensen 進行結束髮言。

We had a great quarter.

我們有一個很棒的季度。

Our core platforms exceeded expectations even as crypto largely disappeared.

即使加密貨幣在很大程度上消失了，我們的核心平台也超出了預期。

Each of our platforms -- AI, gaming, pro viz and self-driving cars -- continue to enjoy great adoption.

我們的每個平台——人工智能、遊戲、專業可視化和自動駕駛汽車——都繼續得到廣泛採用。

These markets are -- we are enabling are some of the most impactful to the world today.

這些市場是——我們正在推動的市場是當今對世界影響最大的市場。

We launched Turing this week.

我們本週推出了圖靈。

It was 10 years in the making and completes the NVIDIA RTX platform.

歷時 10 年的製作完成了 NVIDIA RTX 平台。

And NVIDIA RTX with Turing is the greatest advance since CUDA nearly a decade ago.

NVIDIA RTX with Turing 是近十年前 CUDA 以來最大的進步。

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.

我為我們公司應對這一令人難以置信的挑戰、重新發明整個圖形堆棧並在我們重新發明計算機圖形時給整個行業帶來一股興奮之情而感到無比自豪。

Stay tuned as we unfold the exciting RTX story.

請繼續關注我們展開激動人心的 RTX 故事。

See you guys next time.

各位下次見。

Thank you for joining.

感謝您的加入。

You may now disconnect.

您現在可以斷開連接。