輝達 (NVDA) 2020 Q3 法說會逐字稿

Good afternoon. My name is Christina, and I'm your conference operator for today. Welcome to NVIDIA's Financial Results Conference Call. (Operator Instructions)

午安.我叫克里斯蒂娜，我是今天的會議主持人。歡迎參加 NVIDIA 財務績效電話會議。（操作員指示）

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

現在我將把電話轉給投資者關係副總裁 Simona Jankowski，開始您的會議。

Thank you. Good afternoon, everyone, and welcome to NVIDIA's conference call for the third quarter of fiscal 2020. 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. I'd like to remind you that our call is being webcast live on NVIDIA's Investor Relations website. The webcast will be available for replay until the conference call to discuss our financial results for the fourth quarter of fiscal 2020. The content of today's call is NVIDIA's property. It can't be reproduced or transcribed without our prior written consent.

謝謝。大家下午好，歡迎參加NVIDIA 2020財年第三季電話會議。今天與我一起參加電話會議的還有 NVIDIA 總裁兼執行長黃仁勳；以及執行副總裁兼財務長 Colette Kress。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路直播。網路直播將可重播，直至電話會議討論我們 2020 財年第四季的財務業績。今天電話會議的內容屬於 NVIDIA 的財產。未經我們事先書面同意，不得複製或轉錄。

During this call, we may make forward-looking statements based on current expectations. These 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 Form 10-K and 10-Q and the reports that we may file on Form 8-K with the Securities and Exchange Commission. All our statements are made as of today, November 14, 2019, based on information currently available to us. Except as required by law, we assume no obligation to update any such statements.

在本次電話會議中，我們可能會根據目前預期做出前瞻性陳述。這些都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會有重大差異。有關可能影響我們未來財務表現和業務的因素的討論，請參閱今天的收益報告中的披露內容、我們最新的 10-K 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。我們所有的聲明都是根據我們目前掌握的資訊截至 2019 年 11 月 14 日做出的。除法律要求外，我們不承擔更新任何此類聲明的義務。

During this call, we will discuss non-GAAP financial measures. You can find 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，我們將討論非公認會計準則財務指標。您可以在我們網站上發布的 CFO 評論中找到這些非 GAAP 財務指標與 GAAP 財務指標的對帳表。

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

說完這些，讓我把電話轉給科萊特。

Thanks, Simona. Q3 revenue was $3.01 billion, down 5% year-on-year and up 17% sequentially.

謝謝，西蒙娜。第三季營收為 30.1 億美元，年減 5%，季增 17%。

Starting with our gaming business. Revenue of $1.66 billion was down 6% year-on-year and up 26% sequentially. Results exceeded our expectations driven by strength in both desktop and notebook gaming. Our GeForce RTX lineup features the most advanced GPU for every price point and uniquely offers hardware-based ray tracing for cinematic graphics. While ray tracing launched a little more than a year ago, 2 dozen top titles have shipped with it or are on the way. Ray tracing is supported by all the major publishers, including all-star titles and franchise such as Minecraft, Call of Duty, Battlefield, Watch Dogs, Tomb Raider, Doom, Wolfenstein and Cyberpunk. Of note, Call of Duty: Modern Warfare, had a record-breaking launch in late October that came on heels of CONTROL, an action-adventure game with multiple ray trace features. Reviews have praised both for their ray tracing implementation and game-play performance. With last week's PC release of Red Dead Redemption 2 as a strong gaming lineup for the holiday season, our business reflects this growing excitement. RTX GPUs now drive more than 2/3 of our desktop gaming GPU revenue.

從我們的遊戲業務開始。營收為 16.6 億美元，年減 6%，季增 26%。由於桌上型電腦和筆記型電腦遊戲的強勁表現，業績超出了我們的預期。我們的 GeForce RTX 系列配備各個價位最先進的 GPU，並以獨特的方式提供基於硬體的電影圖形光線追蹤。雖然光線追蹤技術推出才一年多一點，但已有 20 多個頂級遊戲搭載該技術或即將搭載該技術。所有主要發行商均支援光線追踪，包括《我的世界》、《決勝時刻》、《戰地》、《看門狗》、《古墓奇兵》、《毀滅戰士》、《德軍總部》和《賽博龐克》等全明星遊戲和系列遊戲。值得注意的是，《決勝時刻：現代戰爭》於 10 月下旬推出，創下了紀錄，緊隨具有多種光線追蹤功能的動作冒險遊戲《CONTROL》之後。評論對它們的光線追蹤實現和遊戲性能都給予了高度評價。隨著上週《荒野大鏢客 2》在 PC 版上的發布，成為假期季節的強大遊戲陣容，我們的業務反映了這種日益增長的興奮感。RTX GPU 目前佔據我們桌上遊戲 GPU 收入的 2/3 以上。

Gaming laptops were a standout, driving strong sequential and year-on-year growth. This holiday season, our partners are addressing the growing demand for high-performance laptops for gamers, students and prosumers by bringing more than 130 NVIDIA-powered gaming and studio laptop models to market. This includes many thin and light form factors enabled by our Max-Q technology, triple the number of Max-Q laptops last year.

遊戲筆記型電腦表現突出，推動了強勁的環比和同比增長。今年假期，我們的合作夥伴將向市場推出 130 多款搭載 NVIDIA 晶片的遊戲和工作室筆記型電腦型號，以滿足遊戲玩家、學生和專業消費者對高效能筆記型電腦日益增長的需求。其中包括許多由我們的 Max-Q 技術支援的輕薄外形，是去年 Max-Q 筆記型電腦數量的三倍。

In late October, we announced the GeForce GTX 1660 Super and the 1650 Super, which refresh our mainstream desktop GPUs with more performance, faster memory and new features. The 1660 Super delivers 50% more performance than our prior-generation Pascal-based 1060, the best-selling gaming GPU of all time. It began shipping on October 29, priced at just $229. PC World called it the best GPU you can buy for 1080p gaming.

10 月下旬，我們發布了 GeForce GTX 1660 Super 和 1650 Super，它們以更高的效能、更快的記憶體和新功能刷新了我們主流的桌上型 GPU。1660 Super 的效能比我們上一代基於 Pascal 的 1060（有史以來最暢銷的遊戲 GPU）高出 50%。它於 10 月 29 日開始發貨，售價僅為 229 美元。PC World 稱它為您可以買到的 1080p 遊戲的最佳 GPU。

We also announced the next generation of our streaming media player with 2 new models, Shield TV and Shield TV Pro, which launched on October 28. These bring AI to the streaming market for the first time with the ability to upscale video real time from high definition to 4K using NVIDIA-trained deep neural networks. Shield TV has been widely recognized as the best streamer on the market.

我們也宣布了下一代串流媒體播放器，包括兩款新型號：Shield TV 和 Shield TV Pro，於 10 月 28 日上市。這些技術首次將 AI 引入串流媒體市場，能夠使用 NVIDIA 訓練的深度神經網路將視訊即時從高清升級到 4K。Shield TV 已被廣泛認為是市場上最好的串流媒體播放器。

Finally, we made progress in building out our cloud gaming business. Two global service providers, Taiwan Mobile and Russia's Rostelecom with GFN.ru joined SoftBank and Korea's LG as partners for our GeForce NOW game-streaming service. Additionally, Telefónica will kick off a cloud gaming proof-of-concept in Spain.

最後，我們在拓展雲端遊戲業務方面取得了進展。兩家全球服務供應商台灣大哥大和俄羅斯的 Rostelecom（擁有 GFN.ru）與軟銀和韓國 LG 一起成為我們的 GeForce NOW 遊戲串流服務的合作夥伴。此外，Telefónica 將在西班牙啟動雲端遊戲概念驗證。

Moving to data center. Revenue was $726 million, down 8% year-on-year and up 11% sequentially. Our hyperscale revenue grew both sequentially and year-on-year, and we believe our visibility is improving. Hyperscale activity is being driven by conversational AI, the ability for computers to engage in human-like dialogue, capturing context and providing intelligent responses. Google's breakthrough, introduction of the BERT model, with its superhuman levels of natural language understanding, is driving a way of neural networks for the language understanding. That, in turn, is driving demand for our GPUs on 2 fronts. First, these models are massive and highly complex. They have 10 to 20x, in some cases 100x, more parameters than image-based models. As a result, training these models requires V100-based compute infrastructure that, in orders of magnitude, beyond what is needed in the past. Model complexity is expected to grow significantly from here.

移至資料中心。營收為 7.26 億美元，年減 8%，季增 11%。我們的超大規模收入環比和同比增長，我們相信我們的知名度正在提高。超大規模活動由對話式人工智慧驅動，即電腦進行類似人類的對話、捕捉上下文並提供智慧回應的能力。谷歌的突破，推出了 BERT 模型，其具有超越人類的自然語言理解水平，正在推動神經網路在語言理解方面的發展。這反過來又從兩個方面推動了我們 GPU 的需求。首先，這些模型龐大且高度複雜。與基於影像的模型相比，它們的參數多 10 到 20 倍，在某些情況下甚至多 100 倍。因此，訓練這些模型需要基於 V100 的運算基礎設施，其數量級遠遠超出過去的需求。預計模型複雜性將從現在開始顯著增加。

Second, real-time conversational AI requires very low latency and multiple neural networks running in quick succession from de-noising to speech recognition, language understanding, text-to-speech and voice encoding. While conventional approaches fail at these tasks, NVIDIA's GPUs can handle the entire inference chain, in less than 30 milliseconds. This is the first AI application where inference requires acceleration. Conversational AI is a major driver for GPU-accelerated inference.

其次，即時對話式人工智慧需要非常低的延遲和多個神經網路的快速連續運行，從去噪到語音辨識、語言理解、文字轉語音和語音編碼。雖然傳統方法無法完成這些任務，但 NVIDIA 的 GPU 可以在不到 30 毫秒的時間內處理整個推理鏈。這是第一個推理需要加速的AI應用。對話式人工智慧是 GPU 加速推理的主要驅動力。

In addition to this type of internal hyperscale activity, our T4 GPU continue to gain adoption in public clouds. In September, Amazon AWS announced general availability of the T4 globally, following the T4 rollout on Google Cloud platform earlier in the year. We shipped a higher volume of T4 inference GPU this quarter with V100 training GPUs, and both were records. Inference revenue more than doubled from last year and continued a solid double-digit percentage of total data center revenue.

除了這種內部超大規模活動之外，我們的 T4 GPU 在公有雲中也持續被採用。繼今年稍早在Google雲端平台上推出 T4 之後，亞馬遜 AWS 於 9 月宣佈在全球全面推出 T4。本季度，我們出貨了更多數量的 T4 推理 GPU 和 V100 訓練 GPU，這兩項都創下了紀錄。推理收入比去年增長了一倍多，並繼續在數據中心總收入中保持兩位數的穩定百分比。

Last week, the results of the first industry benchmark for AI inference, MLPerf inference, were announced. We won. In addition to demonstrating the best performance among commercially available solutions for both data center and edge applications, NVIDIA accelerators were the only ones that completed in all 5 MLPerf benchmarks. This demonstrates the programmability and performance of our computing platform across diverse AI workloads, which is critical for wide-scale data center deployment and is a key differentiator for us.

上週，首個AI推理產業基準MLPerf推理結果公佈。我們贏了。除了展示出資料中心和邊緣應用商用解決方案中的最佳效能之外，NVIDIA 加速器也是唯一完成全部 5 個 MLPerf 基準測試的加速器。這證明了我們的運算平台在不同人工智慧工作負載下的可程式性和效能，這對於大規模資料中心部署至關重要，也是我們的關鍵差異化因素。

Several product announcements this quarter helped extend our AI computing platform into new markets, the enterprise edge. At Mobile World Congress, Los Angeles, we announced a software-defined 5G wireless RAN solution accelerated by GPUs in collaboration with Ericsson. This opens up the wireless brand market to NVIDIA GPUs. It enables new AI applications as well as AR, VR and gaming to be more accessible to the telco edge.

本季發布的幾款產品幫助我們的人工智慧運算平台擴展到新市場，即企業邊緣。在洛杉磯世界行動通訊大會上，我們與愛立信合作推出了由 GPU 加速的軟體定義 5G 無線 RAN 解決方案。這為 NVIDIA GPU 開啟了無線品牌市場。它使新的 AI 應用以及 AR、VR 和遊戲能夠更輕鬆地存取電信邊緣。

We announced the NVIDIA EGX Intelligent Edge Computing Platform. With an ecosystem of more than 100 technology companies worldwide, early adopters include Walmart, BMW, Procter & Gamble, Samsung Electronics, NTT East and the cities of San Francisco and Las Vegas. Additionally, we announced a collaboration with Microsoft on intelligent edge computing. This will help industries better manage and gain insights from the growing flood of data created by retail stores, warehouses, manufacturing facilities and urban infrastructure.

我們發布了 NVIDIA EGX 智慧邊緣運算平台。該生態系統由全球 100 多家科技公司組成，早期採用者包括沃爾瑪、寶馬、寶潔、三星電子、NTT East 以及舊金山和拉斯維加斯等城市。此外，我們也宣布與微軟在智慧邊緣運算方面展開合作。這將有助於各行各業更好地管理零售店、倉庫、製造工廠和城市基礎設施產生的大量數據，並從中獲得洞察力。

Finally, last week, we held our GPU Technology Conference in Washington, D.C., which was sold out with more than 3,500 registered developers, CIOs and federal employees. At the event, we announced that the U.S. Postal Service, the world's largest delivery service with almost 150 billion pieces of mail delivered annually, is adopting AI technology from NVIDIA, enabling 10x faster processing of package data and with higher accuracy.

最後，上週，我們在華盛頓特區舉辦了 GPU 技術大會，超過 3,500 名註冊開發人員、資訊長和聯邦員工參加了此次大會。在此次活動中，我們宣布，全球最大的快遞服務機構美國郵政局每年遞送近 1500 億件郵件，該機構正在採用 NVIDIA 的 AI 技術，使包裹數據的處理速度提高 10 倍，準確性更高。

Moving to ProVis. Revenue reached a record $324 million, up 6% from the prior year and up 11% sequentially driven primarily by mobile workstations. NVIDIA RTX graphic and Max-Q technology have enabled a new wave of mobile workstations that are powerful enough for design applications yet thin and light enough to carry. We expect this to become a major new category with exciting growth opportunities.

轉向 ProVis。營收達到創紀錄的 3.24 億美元，比上年增長 6%，比上一季增長 11%，這主要得益於行動工作站的推動。NVIDIA RTX 圖形和 Max-Q 技術帶來了新一波行動工作站，其功能強大，足以支援設計應用程序，同時又足夠輕薄，方便攜帶。我們預計這將成為一個具有令人興奮的成長機會的重要新類別。

Over 40 top creative design applications are being accelerated with RTX GPUs. Just last week, at the Adobe Max Conference, RTX accelerated capabilities were added to 3 Adobe Creative apps. RTX-accelerated apps are now available to tens of millions of artists and designers, driving demand for our RTX GPUs. We also continue to see growing customer deployment of data science, AI and VR applications. Strong demand this quarter came from manufacturing, public sector, higher education and health care customers.

超過 40 個頂級創意設計應用程式正在使用 RTX GPU 加速。就在上週，在 Adob​​e Max 大會上，RTX 加速功能被添加到 3 款 Adob​​e Creative 應用程式中。數千萬藝術家和設計師現在都可以使用 RTX 加速應用程序，從而推動了對我們 RTX GPU 的需求。我們也看到客戶對資料科學、人工智慧和虛擬實境應用的部署不斷增長。本季強勁的需求來自製造業、公共部門、高等教育和醫療保健客戶。

Finally, turning to automotive. Revenue was $162 million, down 6% from a year ago and down 22% sequentially. The sequential decline was driven by a onetime nonreoccurring development services contract recognized in Q2. Additionally, we saw a roll-off of legacy infotainment revenue and general industry weakness. Our AI cockpit business grew driven by the continued ramp of the Daimler as they deploy their AI-based infotainment systems across their fleet of Mercedes-Benz vehicles.

最後，轉向汽車。營收為 1.62 億美元，較去年同期下降 6%，較上一季下降 22%。連續下降的原因是第二季確認的一次性非重複性開發服務合約。此外，我們也看到傳統資訊娛樂收入的下滑和整個產業的疲軟。隨著戴姆勒在其梅賽德斯-奔馳車隊中部署基於人工智慧的資訊娛樂系統，我們的人工智慧駕駛艙業務持續成長。

In August, Optimus Ride launched New York City's first autonomous driving pilot program powered by NVIDIA DRIVE. Urban settings pose unique challenges for autonomous vehicles given the number of density of objects that need to be perceived and comprehended in real time. Our DRIVE computer and software stack allows these shuttles to safely and effectively provide first- and last-mile transit services. We remain excited about the long-term opportunity in auto. Our offering is -- consists of in-car AV computing platforms as well as GPU servers for all AI development and simulation. We believe we are well positioned in the industry with leading end-to-end platform that enables customers to develop, test and safely operate autonomous vehicles, ranging from cars and trucks to shuttles and robo-taxis.

8 月，Optimus Ride 啟動了紐約市第一個由 NVIDIA DRIVE 提供支援的自動駕駛試點計畫。由於需要即時感知和理解的物體密度較高，城市環境對自動駕駛汽車提出了獨特的挑戰。我們的 DRIVE 電腦和軟體堆疊使這些班車能夠安全有效地提供首英里和最後一英里的交通服務。我們仍然對汽車產業的長期機會感到興奮。我們提供的產品包括車載 AV 運算平台以及用於所有 AI 開發和模擬的 GPU 伺服器。我們相信，我們在行業中佔據領先地位，擁有領先的端到端平台，使客戶能夠開發、測試和安全操作自動駕駛汽車，從汽車和卡車到班車和機器人出租車。

Moving to the rest of the P&L. Q3 GAAP gross margins was 63.6%, and non-GAAP was 64.1%, up sequentially, reflecting a benefit from sales of previously written-off inventory, higher GeForce GPUs average selling prices and lower component costs. GAAP operating expenses were $989 million, and non-GAAP operating expenses were $774 million, up 15% and 6% year-on-year, respectively. GAAP EPS was $1.45, down 26% from a year earlier. Non-GAAP EPS was $1.78, down 3% from a year ago. Cash flow from operations was a record $1.6 billion.

轉到損益表的其餘部分。第三季 GAAP 毛利率為 63.6%，非 GAAP 毛利率為 64.1%，均比上一季上升，這反映了先前註銷庫存的銷售收益、GeForce GPU 平均售價上漲以及組件成本下降。GAAP 營運費用為 9.89 億美元，非 GAAP 營運費用為 7.74 億美元，分別年增 15% 和 6%。GAAP EPS 為 1.45 美元，較去年同期下降 26%。非公認會計準則每股收益為 1.78 美元，較去年同期下降 3%。經營活動現金流達到創紀錄的 16 億美元。

With that, let me turn to the outlook for the fourth quarter of fiscal 2020, which does not include any contribution from the pending acquisition of Mellanox.

接下來，讓我來談談 2020 財年第四季的展望，其中不包括即將進行的 Mellanox 收購的任何貢獻。

We expect revenue to be $2.95 billion, plus or minus 2%. This reflects expectations for strong sequential growth in data center, offset by a seasonal decline in notebook GPUs for gaming and Switch-related revenue. GAAP and non-GAAP gross margins are expected to be 64.1% and 64.5%, respectively, plus or minus 50 basis points. GAAP and non-GAAP operating expenses are expected to be approximately $1.02 billion and $805 million, respectively. GAAP and non-GAAP OI&E are both expected to be income of approximately $25 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 $130 million to $150 million. Further financial details are included in the CFO commentary and other information available on our IR website.

我們預計營收為 29.5 億美元，上下浮動 2%。這反映了對資料中心強勁連續成長的預期，但被遊戲用筆記型電腦 GPU 和 Switch 相關收入的季節性下降所抵消。預計 GAAP 和非 GAAP 毛利率分別為 64.1% 和 64.5%，上下浮動 50 個基點。預計 GAAP 和非 GAAP 營運費用分別約為 10.2 億美元和 8.05 億美元。預計 GAAP 和非 GAAP OI&E 收入均為約 2,500 萬美元。預計 GAAP 和非 GAAP 稅率均為 9%，上下浮動 1%，不包括單一項目。預計資本支出約1.3億至1.5億美元。進一步的財務細節包含在財務長評論和我們 IR 網站上提供的其他資訊中。

In closing, let me highlight the upcoming events for the financial community. We will be at the Crédit Suisse Annual Technology Conference on December 3, Deutsche Bank's Auto Tech Conference on December 10 and Barclays Global Technology, Media and Telecommunications Conference on December 11.

最後，讓我重點介紹一下金融界即將發生的事件。我們將於 12 月 3 日參加瑞士信貸年度技術會議、12 月 10 日參加德意志銀行汽車技術會議以及 12 月 11 日參加巴克萊全球技術、媒體和電信會議。

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

我們現在開始提問。接線員，請問可以投票詢問嗎？

(Operator Instructions) And your first question comes from the line of Vivek Arya with Bank of America Merrill Lynch.

（操作員指示）您的第一個問題來自美銀美林的 Vivek Arya。

For my first one, you mentioned that you were seeing strong sequential growth in the data center going into Q4. Jensen, I was wondering if you could give us some color on what's driving that, and just how you think about the sustainability of data center growth going into next year and what markets do you think will drive that. Is it more enterprise, more hyperscale, more HPC? Just some color on near and longer term on data center. And then I have a follow-up for Colette.

對於我的第一個問題，您提到您看到資料中心在進入第四季度時出現了強勁的連續成長。詹森，我想知道您是否可以向我們解釋一下推動這一趨勢的因素，以及您如何看待明年資料中心成長的可持續性，以及您認為哪些市場將推動這一趨勢。它是否更具企業性、更具超大規模、更具 HPC 性？這只是關於數據中心近期和長期的一些資訊。然後我對 Colette 進行了跟進。

Yes. Thanks a lot, Vivek. We had a strong Q3 in hyperscale data centers. As Colette mentioned earlier, we shipped a record number of V100s and T4s. And for the very first time, we shipped more T4s than V100. And most of the T4s are driven by inference. In fact, our inference business is now a solid double-digit, and it doubled year-over-year. And all -- most -- that is really driven by several factors. The -- as you know, we've been working on deep learning for some time, and people have been developing deep learning models. It started with computer vision. But image recognition doesn't really take that much of the data center capacity.

是的。非常感謝，Vivek。我們在超大規模資料中心的第三季表現強勁。正如 Colette 之前提到的，我們出貨的 V100 和 T4 數量創下了紀錄。而且，我們的 T4 出貨量首次超過了 V100。而大多數 T4 都是由推理驅動的。事實上，我們的推理業務現在已經穩定地實現了兩位數成長，並且比去年同期成長了一倍。而這一切——大多數——實際上是由幾個因素驅動的。如你所知，我們研究深度學習已經有一段時間了，人們也一直在發展深度學習模型。它始於電腦視覺。但影像辨識其實並不會佔用那麼多資料中心的容量。

Over the last couple of years, a couple of very important developments have happened. One development is a breakthrough in using deep learning for recommendation systems. As you know, recommendation systems is the backbone of the Internet. Whenever you do shopping, whenever you're watching movies, looking at news, doing search, all of the personalized web pages, all of just about your entire experience on the Internet is made possible by recommendation systems because there is just so much data out there putting the right data in front of you based on your social profile or your personal use patterns or your interest or your connections. All of that is vitally important. For the very first time, we're seeing recommendation system based on deep learning throughout the world. And so increasingly, you're going to see people roll this out. And the backbone of the Internet is now going to be based on deep learning.

在過去的幾年裡，發生了一些非常重要的發展。其中一個發展是將深度學習應用於推薦系統的突破。眾所周知，推薦系統是網路的支柱。無論何時購物、看電影、看新聞、搜索，所有個人化網頁，幾乎所有網路體驗都是透過推薦系統實現的，因為有太多數據可以根據您的社交資料、個人使用模式、興趣或人脈將正確的數據呈現在您面前。所有這些都至關重要。我們第一次在世界各地看到基於深度學習的推薦系統。因此，你會看到越來越多的人開始推廣這種技術。現在網路的骨幹將基於深度學習。

The second part is conversational AI. Conversational AI has been coming together in pieces; at first, speech recognition, which requires some amount of noise processing or beam forming. Then you go into speech recognition. Then it goes to natural language understanding, which then gets connected to a recommendation system, which then gets connected to text-to-speech and a speech encoder. And then that has to be done very, very quickly. Whereas images could be done off-line, conversation has to be done in real time. And without acceleration and without NVIDIA's accelerators, it's really not possible to do it in real time. It takes seconds to process all of the handful of deep learning models, and now we're able to do that all on an accelerator and do it in real time.

第二部分是對話式AI。對話式人工智慧正在逐漸成型；首先是語音識別，這需要一定程度的噪音處理或波束形成。然後進入語音辨識。然後它進入自然語言理解，然後連接到推薦系統，然後連接到文字到語音和語音編碼器。而且這必須非常非常快地完成。影像可以離線完成，而對話必須即時完成。如果沒有加速，沒有 NVIDIA 的加速器，即時做到這一點是不可能的。處理所有深度學習模型只需幾秒鐘，現在我們可以在加速器上即時完成所有這些操作。

And so the combination of these various breakthroughs from deep learning-based recommenders, the speech stack as well as natural language understanding breakthrough in what is called a bidirectional encoded transformer, that breakthrough is really quite significant. And since then, derivative works have come from that approach. And natural language understanding is really, really working incredibly well. And so what we're seeing people do is -- the hyperscalers across the world, we work with just about everybody, this area of work is really complicated. The models are very, very large. There's a whole bunch of models that has to work together, and they're getting larger. And so that's one large category, which is the hyperscalers.

因此，基於深度學習的推薦器、語音堆疊以及自然語言理解（即所謂的雙向編碼轉換器）的各種突破相結合，這一突破確實意義重大。從那時起，衍生作品就以這種方式誕生。自然語言理解確實非常有效。所以我們看到人們所做的是——世界各地的超大規模企業，我們幾乎與所有人合作，這個工作領域非常複雜。這些模型非常非常大。有大量的模型需要協同工作，而且它們變得越來越大。這是一個大類別，即超大規模企業。

The second, which we introduced this quarter, is really about taking AI out to the edge. And the reason for that is because there are many applications, whether it's based on video or other types of sensors of all kinds where there's a vibration sensor, temperature sensors, barometric sensor. There's all kinds of sensors that are used in industries to monitor the health of equipment, monitor the conditions of various situations. And you want to do the processing at the point of action. This way, you don't have to screen the data, which is continuous back into the cloud, which costs a lot of money. You want to take the action at the point of action because latency matters. Maybe you're controlling gates or vehicles or robots or drones or whatnot.

第二個是我們在本季推出的，實際上是將人工智慧推向邊緣。原因在於有許多應用，無論是基於視訊還是其他類型的感測器，例如振動感測器、溫度感測器、氣壓感測器。工業領域使用各種感測器來監測設備的健康狀況、監測各種情況的狀況。並且您希望在行動點進行處理。這樣，你就不必篩選數據，數據會連續地回到雲端，這會花費很多錢。您希望在行動點採取行動，因為延遲很重要。也許您正在控制大門、車輛、機器人、無人機等等。

And then lastly, one major issue is data sovereignty. Maybe your company doesn't own all of the data that you are processing and, therefore, you have to do that processing at the edge, and you can't afford to put that into the cloud. And so these various industries: retail, warehouse, logistics, smart cities, we're just seeing so much enthusiasm there around that. And this -- so we built a platform called the EGX, which basically is a cloud native, completely secure, takes advantage of NVIDIA's full stack of every single model. And it's managed with Kubernetes remotely, and you could deploy these services at the edge in faraway places because IT departments can't afford to go out there to manage them. And we've seen some really great adoption. We announced this last quarter. Walmart is using our platform. BMW is using it for logistics, Procter & Gamble for manufacturing, Samsung Electronics for manufacturing, visual inspection. And then last week, we announced probably the largest logistics operation in the world, the United States Postal Service.

最後，一個主要問題是資料主權。也許您的公司並不擁有您正在處理的所有數據，因此您必須在邊緣進行處理，而無法將其放入雲端。我們看到零售、倉儲、物流、智慧城市等各行業都對它們充滿熱情。因此，我們建立了一個名為 EGX 的平台，它基本上是雲端原生的、完全安全的，利用了 NVIDIA 的每個模型的完整堆疊。它是透過 Kubernetes 進行遠端管理的，您可以在遙遠的地方的邊緣部署這些服務，因為 IT 部門無法負擔到那裡管理它們的費用。我們已經看到了一些非常好的採用。我們在上個季度宣布了這一消息。沃爾瑪正在使用我們的平台。寶馬將其用於物流，寶潔將其用於製造，三星電子將其用於製造和視覺檢查。上週，我們宣布成立可能是世界上最大的物流公司—美國郵政服務。

And so those are -- I would say that intelligent edge will likely be the largest AI industry in the world for rather clear reasons. If you just kind of estimated the size of retail, it's nearly $30 trillion. And if retail stores could be made a little bit more convenient, it could save the industry a lot of money: warehouses, logistics, transportation, farming. I think there's like 0.5 million farms in the world, covers 1/3 of the world's land mass. And so there's a lot of places where AI could be put at the edge and could make a big difference. And I think this is going to be the grand adventure that we started this last quarter with the announcement of NVIDIA EGX.

所以——我想說智慧邊緣可能會成為世界上最大的人工智慧產業，原因相當明確。如果你估算一下零售業的規模，它接近 30 兆美元。如果零售店能夠變得更加便捷，那麼就可以為產業節省大量資金：倉庫、物流、運輸、農業。我認為世界上大約有 50 萬個農場，佔世界陸地面積的 1/3。因此，人工智慧可以在許多地方發揮作用並發​​揮巨大作用。我認為這將是我們上個季度隨著 NVIDIA EGX 的發布而開始的偉大冒險。

Right. And Jensen, as quick follow-up, on PC gaming, how are you looking at growth going forward in that you had a very good quarter in October? I think in January, you're probably guiding to some seasonal declines, but I imagine a lot more of that is due to console decline. Just how are you looking at PC gaming growth going into October -- into January and then next year as you get competition from 2 new consoles that are also supposed to come out?

正確的。詹森，關於 PC 遊戲，請問您接下來會如何看待未來的成長？ 10 月季度表現非常好。我認為，一月份可能會出現一些季節性下滑，但我想其中很大一部分是由於遊戲機銷量下滑造成的。您如何看待 10 月、1 月以及明年的 PC 遊戲成長情況，因為您面臨來自 2 款即將推出的新遊戲機的競爭？

Yes. The -- during Q3 -- during Q4 and Q1, we see normal seasonal declines of console builds, and we also see a normal seasonal decline of notebook builds. And the reason for that is because the notebook vendors have to line up all their manufacturing in Q3 so that they could meet the hot selling season in Q4. And so we're seeing -- what we see in the Q4 and Q1 time frame are just normal seasonal declines of these systems. Overall, for PC gaming -- and RTX is doing fantastic. Let me tell you why it's so important. I would say that at this point, I think it's fairly clear that ray tracing is the future and that RTX is a home run. Just about every major game developer has signed on to ray tracing. Even the next-generation consoles had to stutter step and include ray tracing in their next-generation consoles. The effects -- the photorealistic look is just so compelling, it's not possible to really go back anymore. And so I think that it's fairly clear now that RTX ray tracing is the future. And there are several hundred million PC gamers in the world that don't have the benefits of it, and I'm looking forward to upgrading them.

是的。在第三季、第四季和第一季度，我們看到遊戲機銷量出現正常的季節性下降，筆記型電腦銷量也出現正常的季節性下降。原因是筆記型電腦供應商必須在第三季安排所有生產，以便能夠迎接第四季的熱銷季節。因此我們看到——我們在第四季度和第一季看到的只是這些系統的正常季節性下降。總體而言，對於 PC 遊戲而言，RTX 的表現非常出色。讓我告訴你為什麼它如此重要。我想說，就目前的情況來看，我認為光線追蹤代表著未來，而 RTX 則將大獲成功，這一點已經相當清楚。幾乎每個主要遊戲開發商都已簽署了光線追蹤協議。甚至下一代遊戲機也必須步履蹣跚地在其下一代遊戲機中加入光線追蹤功能。其效果－照片般逼真的外觀是如此引人注目，讓人不可能再回頭了。所以我認為現在很明顯 RTX 光線追蹤代表著未來。世界上有幾億 PC 遊戲玩家還沒有享受到它的好處，我期待為他們升級。

Second, and this is a combination of RTX and Max-Q, we really created a brand-new game platform, notebook PC gaming. Notebook PC gaming really didn't exist until Max-Q came along. And our second-generation Max-Q, this last season, really turbocharged this segment. Over 100 laptops now are available for PC gaming. And my sense is that this is likely going to be the largest gaming platform, new gaming platform that emerges. And we're just in the beginning innings of that. And so the combination of upgrading the entire installed base of PC gamers to RTX and ray tracing and this new gaming segment called notebook PC gaming is really quite exciting, and it's going to drive our continued growth for some time. And so I'm excited about that.

第二，這是RTX和Max-Q的結合，我們真正創造了一個全新的遊戲平台，筆記型電腦遊戲。直到 Max-Q 出現後，筆記型電腦遊戲才真正出現。上個季度，我們的第二代 Max-Q 確實為這個細分市場注入了新的活力。目前有超過 100 款筆記型電腦可用於 PC 遊戲。我的感覺是，這很可能成為最大的遊戲平台，新興的遊戲平台。而我們才剛開始這一階段。因此，將整個 PC 遊戲玩家安裝基礎升級到 RTX 和光線追踪，以及這個稱為筆記型電腦遊戲的新遊戲領域的結合確實非常令人興奮，並且它將在一段時間內推動我們的持續增長。所以我對此感到很興奮。

Your next question comes from the line of Aaron Rakers with Wells Fargo.

您的下一個問題來自富國銀行的 Aaron Rakers。

I have a follow-up if I can as well. Just thinking about the trajectory of gross margin here, solid gross margin upside in the quarter, you also noted that you had the benefit of selling through some written-off components. So I guess first question is what was that impact in this most recent reported quarter. And how do we think about the trajectory of gross margin here even beyond the January quarter? What should we be thinking about in terms of that gross margin trend? And again, I have a quick follow-up.

如果可以的話我也會跟進。只需考慮這裡的毛利率走勢，本季毛利率穩步上升，您還注意到，您透過銷售一些註銷組件獲得了收益。所以我想第一個問題是，這對最近一個報告季度有何影響。我們如何看待一月份季度之後的毛利率走勢？就毛利率趨勢而言，我們應該考慮什麼？我再次快速跟進。

Sure. Thanks for the question. In the current quarter, the net benefit, as we refer to as the net release of our inventory provisions primarily associated with our components, was about 1 percentage point to our overall gross margin. As you know, going forward, mix is still the largest driver of our gross margin over time. Over the long term, we do expect gross margins to improve, and we'll continue to see, outside of the benefit that we received, gross margin improvement for the long term.

當然。謝謝你的提問。本季度，淨收益（我們稱之為主要與我們的零件相關的庫存準備金淨釋放）約占我們整體毛利率的 1 個百分點。如您所知，展望未來，產品組合仍然是我們毛利率的最大驅動力。從長遠來看，我們確實預期毛利率會提高，除了我們所獲得的利益之外，我們還將繼續看到長期毛利率的提高。

Yes. As you know, just to add to that, as you know, NVIDIA's really become a software company. If you take a look at almost all of our products, the GPU -- having the world's best GPU, of course, is the starting point. But almost everything that we do, whether it's in artificial intelligence or data analytics or health care or robotics or self-driving cars, almost all of these platforms: gaming, rendering, cloud graphics, all of these platforms start from a really rich stack of software. And you can't just put a chip in these scenarios and they work. And so most of our businesses are now highly software-rich, and they address verticals that we focus on. And then secondarily, we're a platform company. And so our platform is available from all the OEMs and cloud providers. And as a platform company that has a great deal of software intensity, it's natural that the margins would be higher over time.

是的。如您所知，補充一點，NVIDIA 確實已成為一家軟體公司。如果你看我們幾乎所有的產品，GPU——擁有世界上最好的 GPU，當然，這是起點。但我們所做的幾乎所有事情，無論是人工智慧、數據分析、醫療保健、機器人或自動駕駛汽車，幾乎所有這些平台：遊戲、渲染、雲端圖形，所有這些平台都是從非常豐富的軟體堆疊開始的。你不能只在這些場景中放置一個晶片，它們就能發揮作用。因此，我們的大多數業務現在都高度依賴軟體，並且它們涉及我們關注的垂直領域。其次，我們是一家平台公司。因此，所有 OEM 和雲端提供者都可以使用我們的平台。作為一家軟體密集度很高的平台公司，利潤率隨著時間的推移自然會更高。

Yes. Very helpful. And then you mentioned in your prepared remarks that you've seen hyperscale -- your hyperscale business within data center grow both on a quarter-over-quarter as well as year-over-year basis in this last print. You also mentioned that your visibility is improving. Can you just help us understand what exactly you're seeing in the hyperscale guys because it feels like there's some mixed data points out there? What underpins your improved visibility? Or what are you seeing in that piece of your business?

是的。非常有幫助。然後您在準備好的演講中提到，您已經看到超大規模——在最近的印刷中，您的資料中心內的超大規模業務按季度和按年均實現了增長。您還提到您的知名度正在提高。您能否幫助我們了解您在超大規模中到底看到了什麼，因為感覺那裡有一些混合數據點？是什麼支撐了您提高知名度？或者說，您在業務的這一部分看到了什麼？

Yes. We had a strong Q3. We're going to see a much stronger Q4. And the foundation of that is AI, it's deep learning inference. That is -- this deep learning inference is understandably going to be one of the largest computer industry opportunities. And the reason for that is because the computation intensity is so high. And for the very first time, aside from computer graphics, this mode of software is not really practical without accelerators. And so I mentioned earlier about the large-scale movement to deep learning recommendation systems. Those models are really, really hard to train.

是的。我們的第三季表現強勁。我們將看到更強勁的第四季。其基礎就是人工智慧，即深度學習推理。也就是說——深度學習推理無疑將成為電腦產業最大的機會之一。原因在於計算強度太高。而這是第一次，除了電腦圖形學之外，這種軟體模式如果沒有加速器就無法真正實用。我之前提到大規模向深度學習推薦系統的轉變。這些模型真的非常難訓練。

I mentioned earlier about conversational AI. Because conversation requires real-time processing, several seconds is really not practical. And so you have to do it in milliseconds, tens of milliseconds. And our accelerator makes that possible. What makes it really complicated and the reason why -- although so many people talk about it, only we demonstrated -- we submitted all 5 results -- all 5 tests for the MLPerf inference benchmark, and we won them. And the reason for that is because it's far more than just a chip. The software stack that sits on top of the chip and the compilers that sits on top of the chip are so complicated. And it's understandably complicated because a supercomputer wrote the software, and this body of software is really, really large. And if you have to make it both accurate as well as performant, it's really quite a great challenge. And it's one of the great computer science challenges. This is one of those problems that hasn't been solved, and we've been working hard at it for the last 6, 7 years now.

我之前提到過對話式人工智慧。因為對話需要即時處理，所以幾秒鐘的時間確實不切實際。所以你必須在幾毫秒甚至幾十毫秒內完成它。我們的加速器使這成為可能。是什麼讓它變得真正複雜，以及為什麼——儘管很多人都在談論它，但只有我們證明了——我們提交了所有 5 個結果——MLPerf 推理基準的所有 5 個測試，並且我們贏得了它們。原因在於它不只是一個晶片。位於晶片頂部的軟體堆疊和位於晶片頂部的編譯器非常複雜。它的複雜性是可以理解的，因為該軟體是由超級電腦編寫的，而且軟體體非常非常龐大。如果你必須使其既準確又高效，那真是一個巨大的挑戰。這是計算機科學的重大挑戰之一。這是尚未解決的問題之一，過去 6、7 年我們一直在努力解決這個問題。

And so this is really the great opportunity. We've been talking about inference for some time now. Finally, the workloads and a very large diverse set of workloads are now moving into production. And so I'm hoping -- I'm enthusiastic about the progress and seeing the trends and the visibility that inference should be a large market opportunity for us.

所以這確實是一個絕佳的機會。我們已經討論推理有一段時間了。最後，工作負載以及大量不同的工作負載現在正在投入生產。所以我希望——我對進展充滿熱情，看到趨勢和可見性，推理對我們來說應該是一個巨大的市場機會。

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

您的下一個問題來自 Evercore ISI 的 C.J. Muse。

I guess I'd love to follow on, on that last question. So clearly, your commentary, Jensen, here is much more bullish than I've heard you, I think, before on inference, particularly as it relates to this first benchmark. And so I guess can you talk a bit about how you see mix within data center looking out over the next 12, 24 months as you see kind of training versus inference as well as cloud versus enterprise, considering, I would think, inference over time could be -- could grow into a large opportunity there as well?

我想我很想繼續回答最後一個問題。因此，很明顯，詹森，你的評論比我以前聽到的要樂觀得多，特別是與第一個基準相關的評論。所以我想您能否談談您如何看待未來 12 到 24 個月資料中心的混合情況，您會看到訓練與推理以及雲端與企業的結合，考慮到我認為推理隨著時間的推移可能會發展成為一個巨大的機會？

Yes. C.J., that's really good. Let me break it down. So when we think about hyperscale, there are 3 parts: training, inference and public cloud.

是的。C.J.，那真的很好。讓我來分解一下。因此，當我們考慮超大規模時，有 3 個部分：訓練、推理和公有雲。

Training, you might have seen the work that was done at open AI recently where they've been measuring and monitoring the amount of computation necessary to train these large models. These large models are now only getting larger. The amount of data necessary, therefore, has to scale as well. The computation is now growing and doubling every 3 months. And the reason for that is because of recent breakthroughs in natural language understanding. And all of a sudden, a whole wave of problems are now able to be solved. And just as AlexNet 7 years ago kind of was the watershed event for a lot of computer vision-oriented AI work, now the transformer-based natural language understanding model and the work that Google did with BERT really is a watershed event also for natural language understanding. This is, of course, a much, much harder problem. And so the scale of the training has grown tremendously. I think what we're going to see this year is a fair number of very sizable installations of GPU systems to do this very thing, training.

訓練，您可能已經看到了最近在開放人工智慧上所做的工作，他們一直在測量和監控訓練這些大型模型所需的計算量。這些大型模型現在只會變得更大。因此，必要的資料量也必須相應擴大。現在計算量正在成長，每三個月翻一番。原因在於自然語言理解領域最近取得了突破。突然之間，一大堆問題都能夠解決。正如 7 年前的 AlexNet 成為許多以電腦視覺為導向的人工智慧工作的分水嶺一樣，現在基於 Transformer 的自然語言理解模型和谷歌在 BERT 上所做的工作實際上也是自然語言理解的分水嶺。當然，這是一個更困難的問題。因此，培訓規模大為擴大。我認為今年我們將會看到相當數量的、規模相當大的 GPU 系統安裝，用於進行這項任務，即訓練。

The second part is an untapped market for us, and this untapped market is really inference. The reason why I haven't really spoken about it until now is because we've never really been able to validate our intuition that inference is going to be a large market opportunity for us, that it's going to be very complicated. The models are very large. They're very diverse. They require large amount of computation, large amount of memory bandwidth and large amounts of memory and large and significant capabilities of programmability. And so I've talked about this before, but I've never been able to validate it. And of course, with MLPerf and sweeping the benchmarks and, frankly, only -- the only one although so many have attempted, they submitted results and some of them resented it, that this benchmark is just really, really hard. Inference is hard. And then finally, our business results also validated the -- our intuition. And so our engagement now with CSPs are now global. We're working across natural language understanding, recommendation systems, conversational AI, just a whole bunch of really, really interesting problems.

第二部分對我們來說是一個尚未開發的市場，這個尚未開發的市場其實是推論。我之所以直到現在才真正談論它，是因為我們從來沒有真正能夠驗證我們的直覺，即推理對我們來說將是一個巨大的市場機會，它將非常複雜。模型非常大。它們非常多樣化。它們需要大量的計算、大量的記憶體頻寬和大量的記憶體以及強大的可編程能力。我之前談過這個問題，但我從未能夠證實它。當然，MLPerf 橫掃了基準測試，坦白說，這是唯一的一個，儘管很多人嘗試過，他們提交了結果，但有些人對此表示不滿，認為這個基準測試真的非常非常難。推理很難。最後，我們的業務成果也驗證了我們的直覺。因此，我們現在與 CSP 的合作是全球性的。我們正在研究自然語言理解、推薦系統、會話人工智慧等一大堆非常非常有趣的問題。

Now the cloud is the third piece. And the reason why cloud is growing so well and represents about half almost of many of our CSPs, particularly the ones with the public cloud, the reason for that is because the number of AI start-ups in the world is still growing so incredibly. I think we're tracking something close to 10,000 and more AI start-ups around the world. In health care, in transportation, in retail, in consumer Internet, in Fintech, the number of AI companies out there is just extraordinary. I think over the last 3 or 4, 5 years, some $20 billion, $30 billion have been invested into start-ups. And these start-ups, of course, use cloud service providers so that they don't have to invest in their own infrastructure because it's fairly complicated. And so we're seeing a lot of growth there.

現在雲是第三部分。雲端運算之所以發展得如此良好，佔據了我們許多 CSP 的近一半，特別是那些擁有公有雲的 CSP，原因在於全球人工智慧新創公司的數量仍在以驚人的速度成長。我認為我們正在追蹤全球近 10,000 多家人工智慧新創公司。在醫療保健、交通運輸、零售、消費網路、金融科技等領域，人工智慧公司的數量非常多。我認為在過去的 3、4、5 年裡，大約有 200 億、300 億美元被投資於新創公司。當然，這些新創公司使用雲端服務供應商，這樣他們就不必投資自己的基礎設施，因為這相當複雜。因此我們看到那裡出現了很大的成長。

And so that's just the hyperscalers. The hyperscalers give us 3 points of growth -- 3 areas of growth: training, inference and public cloud. And the public cloud is primarily AI start-ups. Then there's the intelligent edge, which we recently ventured into, and we've been building this platform called EGX for some time. And it's cloud native. It's incredibly secure. You can manage it from afar. It's -- the stack is complicated. It's performant. And we saw some -- we've been working with some early adopters. And this last quarter, we announced some of them: Walmart and BMW and Procter & Gamble and the largest logistics company in the world, USPS. And so this new platform, I think, long term, will likely be the largest opportunity. And the reason for that is because of the industries that it serves.

這就是超大規模企業。超大規模企業為我們帶來了三個成長點——三個成長領域：訓練、推理和公有雲。而公有雲主要都是AI新創企業。然後是智慧邊緣，我們最近開始涉足這個領域，我們已經建立這個名為 EGX 的平台已經有一段時間了。而且它是雲端原生的。它非常安全。您可以從遠端管理它。堆疊很複雜。它的性能很好。我們看到了一些——我們一直在與一些早期採用者合作。上個季度，我們宣布了其中一些合作：沃爾瑪、寶馬、寶潔以及全球最大的物流公司美國郵政服務。因此，我認為，從長遠來看，這個新平台可能將帶來最大的機會。原因在於它所服務的產業。

And your next question comes from the line of Harlan Sur with JPMorgan.

您的下一個問題來自摩根大通的 Harlan Sur。

There are a lot of concerns around China trade tensions, economic slowdown. But history has shown that gamers tend to be less sensitive to these macro trends and, in fact, also somewhat insensitive to price changes, at least at the enthusiast level. So given that China is such a big part of the gaming segment, can you just discuss the gaming demand trends out of this geography?

人們對中國貿易緊張局勢和經濟放緩有很多擔憂。但歷史表明，遊戲玩家往往對這些宏觀趨勢不太敏感，事實上，至少在發燒友層面，他們對價格變化也不太敏感。鑑於中國在遊戲領域佔有如此重要的地位，您能否討論一下該地區的遊戲需求趨勢？

Gaming is solid in China, and it is also the fastest adopter of our gaming notebooks. This gaming RTX notebooks or GeForce notebooks is really a brand new category. This category never existed before because we couldn't get the technology in there so that it's both delightful to own as well as powerful to enjoy. And so we saw really great success with RTX notebooks and GeForce notebooks in China, and RTX adoption has been fast.

中國的遊戲市場表現強勁，中國也是我們遊戲筆記本採用速度最快的國家。這款遊戲 RTX 筆電或 GeForce 筆電確實是一個全新的類別。這個類別以前從未存在過，因為我們無法將技術融入其中，使其既令人愉悅又能享受強大的功能。因此，我們看到 RTX 筆記本和 GeForce 筆記本在中國取得了巨大成功，而且 RTX 的採用速度很快。

Your comments make sense because most of the games are free-to-play these days. The primary games that people play are esports, which you want the best gear, but you could -- after you buy the gear, you pretty much enjoy it forever; and mobile, which is largely free-to-play. You invest in some of your own personal outfits. And after that, I think you can enjoy it for quite a long time. And so the gear is really important. One of the areas where we've done really great work, particularly in China, has to do with social. We have this platform called GeForCe Experience. And as an extension of that, there's a new feature called RTX Broadcast Engine. And it basically applies AI to broadcasting your content to share it. You could make movies. You could capture your favorite scenes and turn it into art, applying AI. And one of the coolest features is that you could overlay yourself on top of the game and share it with all the social networks without a green screen behind you. We use AI to stitch you out, basically, to cut you out of the background and irrespective of what noisy background you've got.

您的評論很有道理，因為現在大多數遊戲都是免費的。人們玩的主要遊戲是電子競技，你想要最好的裝備，但你可以－在你購買裝備後，你幾乎可以永遠享受它；而行動端基本上是免費的。您投資購買一些自己的個人服裝。此後，我想您可以享受很長一段時間。所以裝備真的很重要。我們做得非常出色的一個領域，特別是在中國，與社交有關。我們有一個叫做 GeForCe Experience 的平台。作為其擴展，有一項名為 RTX Broadcast Engine 的新功能。它基本上應用人工智慧來廣播您的內容並進行分享。你可以拍電影。您可以捕捉自己喜歡的場景，並利用人工智慧將其轉化為藝術品。最酷的功能之一是，您可以將自己疊加在遊戲之上，並與所有社交網路分享，而無需身後有綠幕。我們使用人工智慧來拼接您，基本上就是將您從背景中剪掉，而不管您的背景有多麼吵雜。

And so as you know, China has really a super hyper social community -- communities back there and they have all kinds of really cool social platforms to share games and user-generated content and short videos and all kinds of things like that. And so GeForce has that one additional feature that really makes it successful.

如你所知，中國確實有一個超級社交社群——那裡的社群有各種非常酷的社交平台來分享遊戲、用戶生成的內容、短片以及諸如此類的東西。因此，GeForce 擁有一個真正使其成功的附加功能。

And your next question comes from the line of Toshiya Hari with Goldman Sachs.

您的下一個問題來自高盛的 Toshiya Hari。

I wanted to ask on automotive. Colette, in your prepared remarks, you talked about your legacy infotainment business being down in the quarter. Just curious, what percentage of automotive revenue at this point is legacy infotainment versus the newer AI/ADAS solutions? And more importantly, Jensen, if you can speak to the growth trajectory in automotive over the next 1.5 years, maybe 2, that would be appreciated. And I do ask the question because it feels like we've heard many, many announcements, customer announcements, collaborative work that you're doing with your customers, yet we haven't quite seen sort of a hockey-stick inflection that some of us were expecting a couple of years ago. So just kind of curious when we should -- how we should set our expectations going forward.

我想問一下關於汽車的問題。科萊特，在您準備好的發言中，您談到了本季度您的傳統資訊娛樂業務出現下滑。只是好奇，目前汽車收入中有多少比例來自傳統的資訊娛樂系統，有多少來自較新的 AI/ADAS 解決方案？更重要的是，Jensen，如果您能談談未來 1.5 年甚至 2 年汽車產業的成長軌跡，我們將不勝感激。我確實問了這個問題，因為感覺我們已經聽到了很多公告、客戶公告、您與客戶進行的合作工作，但我們還沒有看到幾年前我們中的一些人所期待的那種曲棍球棒式變化。所以我只是有點好奇我們應該什麼時候——我們該如何設定我們對未來的期望。

Yes. Toshiya, let me address the first question regarding our legacy infotainment systems for our automotive business. It is still representing maybe about half or more of our overall revenue in the automotive business. We have our AI cockpit continuing to grow and grow quite well, both sequentially as well as year-over-year, as well as our autonomous vehicle solutions that we may be doing, including development services.

是的。Toshiya，讓我來回答第一個問題，關於我們汽車業務的傳統資訊娛樂系統。它仍然占我們汽車業務總收入的一半或更多。我們的人工智慧駕駛艙持續成長，並且成長相當良好，無論是環比增長還是同比增長，以及我們可能正在做的自動駕駛汽車解決方案，包括開發服務。

Let's see. The -- we're the first -- probably the first AV car that's going to be passenger-owned on the road, and I think we've talked about it before, is Volvo. And we're expecting them to be in the late 2020, early 2021 time frame. And I'm still expecting so. And then there's the 2022, 2023 generations. Most -- I would say most of the passenger-owned vehicle developments are going quite well. The industry, as you know, is under some amount of pressure, and so a lot of them have slipped it out a couple of years or so. And this is something that I think we've already spoken about in the past.

讓我們來看看。我們是第一家——可能是第一家在路上行駛的乘客專用自動駕駛汽車，我想我們之前已經討論過這個，我們就是沃爾沃。我們預計它們將在 2020 年底或 2021 年初推出。我仍然期待著如此。然後是 2022、2023 代。大多數——我想說大多數乘用車的發展都進展得相當順利。如你所知，這個行業面臨著一定的壓力，因此許多公司在幾年左右的時間裡就退出了市場。我認為我們過去已經談論過這個話題。

Our focus, our strategy consists of several areas. One area, of course, is passenger-owned vehicles. The second part is robot taxis. We have developments going with just about every major robot taxi company that we know of. And they're here in the states. They're in Europe. They're in China. And when you hear news of them, we're delighted to see their progress. And then the third part has to do with trucks, shuttles and increasingly a large number of vehicles that don't carry people, they carry goods. And so we have a major development with Volvo. That was Volvo Trucks. Volvo Cars and Volvo Trucks, as you know, are 2 different companies. One of them belongs to Geely, Volvo Cars. Volvo Trucks is the heritage Volvo. And we have a major program going with them to automate the delivery of goods.

我們的重點、我們的策略包括幾個領域。當然，其中一個領域是乘用車。第二部分是機器人計程車。我們與幾乎所有我們所知的大型機器人計程車公司都有合作開發。他們現在在美國。他們在歐洲。他們在中國。當您聽到他們的消息時，我們很高興看到他們的進步。第三部分與卡車、接駁車以及越來越多的不載人、載貨的車輛有關。因此，我們與沃爾沃取得了重大進展。那是沃爾沃卡車。如您所知，沃爾沃汽車和沃爾沃卡車是兩家不同的公司。其中一家屬於吉利、沃爾沃汽車。沃爾沃卡車是沃爾沃的傳統。我們與他們正在實施一項重要計劃，以實現貨物運輸的自動化。

You also see us during various GTCs, I'll mention companies that we're working with on grocery delivery or goods delivery or within a warehouse product delivery. You're going to see a whole bunch of things like that because the technology is very similar, and it's starting to -- the development -- the technology we develop for passenger-owned vehicles has started to propagate down into logistics vehicles. I continue to believe that everything that moves eventually will have autonomous capability or be fully autonomous. And that, I think, is, at this point, fairly certain.

您也可以在各種 GTC 期間看到我們，我會提到我們正在與之合作進行雜貨配送或貨物配送或倉庫產品配送的公司。你會看到很多這樣的事情，因為技術非常相似，而且它開始——發展——我們為乘用車開發的技術已經開始傳播到物流車輛中。我始終相信，一切運動的事物最終都將具備自主能力或完全自主。我認為，就目前而言，這一點是相當肯定的。

Now our strategy is both in developing the in-car AV computing system, and it's software-defined, it's scalable, as well as the AI development and simulation systems. And so when somebody's working on AV and they're using AI, and most of them are, there's a great opportunity for us. And when they start ramping up and they're collecting miles of data, it becomes a very large market opportunity for us. And so I'm anxious to see every single car company be as progressive and aggressive in developing AV. And they will be. They will be. This is a foregone conclusion.

現在我們的策略是開發車載 AV 運算系統，它是軟體定義的、可擴展的，以及人工智慧開發和模擬系統。因此，當有人從事 AV 工作並使用 AI 時（而且大多數人都是這麼做的），這對我們來說是一個巨大的機會。當他們開始加強並收集大量數據時，這對我們來說就是一個非常大的市場機會。因此，我渴望看到每一家汽車公司在開發 AV 方面都同樣進步和積極。他們會的。他們會的。這是已成定局。

Your next question comes from the line of Stacy Rasgon with Bernstein.

您的下一個問題來自伯恩斯坦的 Stacy Rasgon。

I have 2 data center questions for Colette. The first question, I want to return to your kind of outlook for strong sequential data center growth in Q4. Now this business grew 11% sequentially in Q3. And you didn't actually call out strong growth as we were going into the quarter. You are calling it out for Q4. Does that suggest to me that you expect sequential growth in Q4 to be stronger than Q3 given you're calling it out in Q4 and you didn't call it out in Q3? Or would you define like what you saw in Q3 as well as already being strong sequential growth? Like how do we think about the wording of that in relation to what we've seen in Q3 and what you expect for Q4?

我有 2 個關於資料中心的問題想問 Colette。第一個問題，我想回到您對第四季度資料中心強勁連續成長的展望。目前，該業務第三季環比成長了 11%。當我們進入本季度時，您實際上並沒有提到強勁的成長。您是在為第四季呼籲。鑑於您在第四季度預測了這一點，而在第三季度沒有預測，這是否意味著您預計第四季度的連續成長將強於第三季？或者您將其定義為像您在第三季度看到的那樣以及已經強勁的連續增長？例如，我們如何看待與我們在第三季看到的情況以及您對第四季的預期相關的措辭？

Sure, Stacy. When we had provided guidance in Q3 and how we finished the quarter in Q3, we had indicated that our growth would stem from both gaming and data center. We completed that. And we also had stronger than expected from guidance from both gaming and data center in our Q3 results. Moving to Q4, Q4 is a sequential decrease in totality versus Q3. We have reminded the teams about our overall seasonality that we sometimes have in gaming associated with our consoles as well as also with our notebooks that seem to be primarily in Q2 and Q3 being our strongest quarters and likely, therefore, a seasonal downtick as it move to Q4. What we wanted to do was, if we have in totality overall decline associated with that, we did want to emphasize what we are expecting in terms of data center with the overall strong growth sequentially.

當然，史黛西。當我們在第三季提供指導以及我們如何結束第三季時，我們表示我們的成長將源自於遊戲和資料中心。我們完成了。我們在第三季的業績中對遊戲和資料中心的預測也比預期要好。進入第四季度，與第三季度相比，第四季的總量呈現連續下降趨勢。我們已經提醒團隊注意我們的整體季節性，我們的遊戲機和筆記型電腦有時會出現遊戲相關的季節性，這似乎主要在第二季度和第三季度，這是我們最強勁的季度，因此，隨著進入第四季度，可能會出現季節性下滑。我們想要做的是，如果整體上與此相關的整體下滑，我們確實想強調我們對資料中心的預期，即整體上連續強勁成長。

So I guess to ask the question again, would you define what you saw in Q3 as being strong growth as well?

所以我想再問一次這個問題，您是否也將第三季所看到的情況定義為強勁成長？

I would believe our growth of 17% was higher than we expected to Q3. Again, when we get into Q4, we'll see how the quarter ends in terms of data center, but we are expecting strong growth. Thanks, Stacy.

我相信我們的 17% 的成長率高於我們對第三季的預期。同樣，當我們進入第四季度時，我們將看到資料中心的季度結束情況，但我們預計會有強勁的成長。謝謝，史黛西。

Okay. And for my second question, hyperscale you said was up year-over-year. Now -- and that's after, off of last year, where it was the peak. Inference doubled year-over-year. And this suggests to me -- I know you said enterprise was down year-over-year. But this suggests to me that it wasn't just down year-over-year, it was down a lot year-over-year. How do we think about that in the context of like the growth that we've seen very strongly over the last few quarters in enterprise. And going back to your commentary at the Analyst Day, which was almost entirely about the opportunity coming from enterprise growth, what's going on there? What drove that? And what should we expect going forward?

好的。關於我的第二個問題，您說超大規模是年成長的。現在——這是在去年達到頂峰之後。推理量去年翻了一番。這對我來說意味著——我知道您說過企業業績年減。但這對我來說意味著它不僅僅是同比下降，而是同比下降了很多。在過去幾季我們看到企業強勁成長的背景下，我們如何看待這個問題。回到您在分析師日的評論，幾乎完全是關於企業成長帶來的機遇，那裡發生了什麼？是什麼導致了這現象？那麼，我們對未來該抱持怎樣的期待呢？

Sure. Our enterprise business has been beginning to ramp from over a year ago at a very, very, very small base. We've continued to see great traction in there with a lot of the things that we've announced throughout. But keep in mind in our year ago quarter, we also had very strong systems and a very large deal associated with our DGX. So when we look from a quarter-over-quarter period or just looking at 1 quarter, we can have a little bit of lumpiness. So that year-over-year impact is really just due to an extremely large deal in the prior year Q3.

當然。我們的企業業務從一年前開始以非常非常小的規模快速成長。我們宣布的很多事情都持續獲得了巨大的關注。但請記住，在去年同期，我們也擁有非常強大的系統和與 DGX 相關的非常大的交易。因此，當我們從一個季度到另一個季度或僅僅看一個季度時，我們可能會發現一些不均勻的情況。因此，同比影響實際上只是由於去年第三季的一筆巨額交易。

Your next question comes from the line of Mitch Steves with RBC.

您的下一個問題來自 RBC 的 Mitch Steves。

I apologize for any background noise, but I just have one question, just for Jensen. So in 2018, can you give us a rough update on what the GPU utilization was for deep learning application? What it is today? I'm just wondering how the -- how that's advanced over the last couple of year or 2.

對於背景噪音我深表歉意，但我只有一個問題，只針對 Jensen。那麼，您能否粗略地介紹一下 2018 年深度學習應用的 GPU 利用率？今天是什麼？我只是想知道在過去的一兩年裡它取得了怎樣的進展。

Let's see. I would say 2018, it was nearly all related to training. And this year, we started to see the growth of inference to the point where we now -- we have now sold more -- this last quarter, we sold more T4 GPUs for inference than we sold V100s that's used for training, and both of them were record highs. And so the comment that Colette just made, comparing to year-over-year, we had a large DGX system sale a year ago that we didn't have this year. But if you excluded that, the V100 and the T4 is doing great. They're at record levels. And T4 didn't hardly existed a year ago, now it's selling more than V100s, and both of them are record highs. And so that kind of gives you a feeling for it. I think that's really the major difference that inference is really kicking into gear, and my sense is that it's going to continue to grow quite nicely.

讓我們來看看。我想說 2018 年幾乎全部與訓練有關。今年，我們開始看到推理的增長，現在我們的銷量已經超過了上個季度，我們銷售的用於推理的 T4 GPU 比用於訓練的 V100 還要多，這兩個數字都創下了歷史新高。正如科萊特剛才所說，與去年同期相比，我們去年的 DGX 系統銷量很大，但今年卻沒有。但如果排除這一點，V100 和 T4 的表現就很棒了。它們達到了創紀錄的水平。而T4在一年前幾乎不存在，現在銷量已經超過V100，而且都創下了歷史新高。這會讓你對此有某種感覺。我認為這確實是推理真正開始發揮作用的主要區別，我的感覺是它會繼續很好地發展。

And your next question comes from the line of Joe Moore with Morgan Stanley.

您的下一個問題來自摩根士丹利的喬·摩爾。

I wonder if you could talk a little bit more about the 5G opportunity that you announced at Mobile World. And I guess you talked a lot about AI and IoT services in a C-RAN environment. But is there -- how big is that opportunity? And can you address kind of the core compute aspect to C-RAN with the GPU?

我想知道您是否可以進一步談談您在世界行動通訊大會上宣布的 5G 機會。我想您已經談了很多關於 C-RAN 環境中的 AI 和 IoT 服務。但存在嗎──這個機會有多大？您能否解決使用 GPU 的 C-RAN 核心運算方面的問題？

Yes. If you look at the world of mobile today, there are players that are building DRAMs and their radio heads in the BBU, basically the baseband units. In the data center where people would like to move the software for radio networks, it's really an untapped market. And the reason for that is because the CPU is just not able to support the level of performance that's necessary for 5G. And ASICs are too rigid to be able to put into a data center. And so the data center needs a programmable solution that is data center-ready that can support all of the software richness that goes along with the data center, whether it's a VM environment like VMware. And we -- recently, during the quarter, we announced another partnership with VMware. They recognize that increasingly, our GPUs are becoming a core part of data centers and cloud. We had a partner -- we announced a partnership with Red Hat. They realize the momentum that they're seeing us in, in telcos, and they would like to adapt their entire stack from open stack to OpenShift on top of our GPUs. And so now with VMware, with Red Hat, we're going to have a world-class telco enterprise stack that ranges all the way from hypervisors and virtual machines all the way to Kubernetes.

是的。如果你看看今天的行動世界，你會發現有些廠商正在 BBU（基本上是基頻單元）中建造 DRAM 和無線電頭。在資料中心，人們希望行動無線電網路軟體，這確實是一個尚未開發的市場。原因在於 CPU 無法支援 5G 所需的效能等級。而且 ASIC 太過僵化，無法放入資料中心。因此，資料中心需要一個可編程的解決方案，該解決方案是資料中心就緒的，可以支援與資料中心相關的所有軟體豐富性，無論它是否是像 VMware 這樣的 VM 環境。而且我們—最近，在本季度，我們宣布與 VMware 建立另一項合作夥伴關係。他們認識到，我們的 GPU 正日益成為資料中心和雲端的核心部分。我們有一個合作夥伴——我們宣布與 Red Hat 建立合作關係。他們意識到我們在電信領域的發展勢頭，他們希望在我們的 GPU 之上將整個堆疊從開放堆疊調整為 OpenShift。現在有了 VMware 和 Red Hat，我們將擁有一個世界一流的電信企業堆疊，涵蓋從虛擬機器管理程式和虛擬機器一直到 Kubernetes。

And so our strategy is to -- our goal is to really create this new world of C-RAN, vRAN centralized data centers and software-defined networking. And the software-defined networking will, of course, include things like in the data center networking as well as firewalls. But the computationally-intensive stuff is really the 5G radio. And so we're going to create a software stack for 5G and basically exactly the same way that we've done for creating a -- excuse me, a software stack for deep learning. And we call it Aerial. Aerial is to 5G essentially what Cuda-NN is for deep learning and essentially what optics is for ray tracing. And this software stack is going to allow us to run the whole software -- run the whole 5G stack in software and deliver the highest performance, the incredible flexibility and scale to as many layers of MIMO as customers need and to be able to put all of it in the data center.

因此，我們的策略是——我們的目標是真正創造 C-RAN、vRAN 集中式資料中心和軟體定義網路的新世界。當然，軟體定義網路將包括資料中心網路以及防火牆等內容。但真正計算密集型的東西是 5G 無線電。因此，我們將為 5G 創建一個軟體堆疊，基本上與我們為深度學習創建的軟體堆疊完全相同。我們稱之為「空中」。天線對於 5G 的作用本質上就像 Cuda-NN 對於深度學習的作用以及光學對於光線追蹤的作用。這個軟體堆疊將允許我們運行整個軟體——在軟體中運行整個 5G 堆疊，並提供最高的效能、令人難以置信的靈活性和可擴展性，以滿足客戶對 MIMO 多層的需求，並能夠將所有這些放在資料中心。

The power of putting it into data center, as you know, is flexibility and fungibility. With the low latency capability of 5G, you could put a data center somewhere in the regional hub. And depending on where the traffic is going, you could shift the traffic computation from 1 data center to another data center, something that you can't do in basebands, in baseband units in the cell towers, but you can do that in the data center. And that helps them reduce the cost. The second benefit is that the telcos would love to be a service provider for a data center's computation at the edge. And the edge applications are things like smart cities and whether it's warehouses or retail stores or whatever it is because they're geographically located and is distributed all over the world. And so to be able to use their data center to also be able to use AI in combination with IoT is really exciting to them. And so I think that that's really -- this is really the future that we're going to see a lot more service providers at the edge. And these edge data centers will have to run the data center, the networking, including the mobile network and software as well as run 5G and IoT -- AI and IoT applications

如您所知，將其放入資料中心的力量在於靈活性和可替代性。利用 5G 的低延遲能力，您可以在區域樞紐的某個地方設立資料中心。根據流量去向，您可以將流量計算從一個資料中心轉移到另一個資料中心，這是在基頻、在蜂巢塔的基頻單元中無法做到的，但您可以在資料中心做到。這有助於他們降低成本。第二個好處是電信公司願意成為邊緣資料中心運算的服務供應商。邊緣應用包括智慧城市等，無論是倉庫還是零售店，或其他什麼，因為它們的地理位置分佈在世界各地。因此，能夠利用他們的資料中心將人工智慧與物聯網結合起來，這對他們來說真的令人興奮。所以我認為這確實是——這確實是未來，我們將會看到更多的邊緣服務提供者。這些邊緣資料中心必須運行資料中心、網絡，包括行動網路和軟體，以及運行 5G 和物聯網——人工智慧和物聯網應用程式

And your last question comes from the line of Harsh Kumar with Piper Jaffray.

您的最後一個問題來自 Piper Jaffray 的 Harsh Kumar。

I apologize for the background noise. But Colette, maybe you could give us an idea of gaming. In the guidance, it's down. And I was wondering, could you maybe give us the impact of the console business versus the laptop and give us an idea of what might be the bigger driver there?

我對於背景噪音表示歉意。但是科萊特，也許你可以跟我們講講遊戲的概念。在指導中，它是下降的。我想知道，您能否為我們講講遊戲機業務與筆記型電腦業務的影響，並告訴我們什麼可能是更大的驅動力？

I would say, for our Q4, both of them are expected to be seasonally down. In the case of the consoles, we do wait for Nintendo to assist in terms of what they need. So we will have to see how the quarter ends on that. But in both cases, in totality, these businesses have ranged, maybe in totality of the 2, of about $500 million a quarter. And we'll see both of them sequentially decline. Thank you.

我想說，對於我們的第四季來說，預計它們都會出現季節性下滑。就遊戲機而言，我們確實在等待任天堂提供他們所需的幫助。因此，我們必須看看本季的結局如何。但在這兩種情況下，這些業務的總額約為每季 5 億美元。我們將看到它們兩個都逐漸衰落。謝謝。

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

現在我將把電話轉回給詹森，請他做最後發言。

Thanks, everyone. We had a good quarter driven by strong gaming growth and hyperscale demand. We're making great strides in 3 big impact initiatives. The world of computer graphics is moving to ray tracing, and our business reflects that. Some of the biggest blockbuster games this holiday season and beyond are RTX-enabled, including Call of Duty: Modern Warfare; and the best-selling game of all-time, Minecraft. Design applications used by millions of artists and creators are rapidly adopting RTX ray tracing. We're reinventing computer graphics and look forward to upgrading the hundreds of millions of PC gamers to RTX.

謝謝大家。在強勁的遊戲成長和超大規模需求的推動下，我們本季表現良好。我們在三項具有重大影響力的舉措上取得了長足進步。電腦圖形學的世界正在轉向光線追踪，我們的業務也反映了這一點。今年假期季節及以後的一些最賣座遊戲大片都支援 RTX，包括《決勝時刻：現代戰爭》；以及有史以來最暢銷的遊戲《我的世界》。數百萬藝術家和創作者使用的設計應用程式正在迅速採用 RTX 光線追蹤。我們正在重塑電腦圖形學，並期待將數億 PC 遊戲玩家升級到 RTX。

Hyperscale demand was strong this quarter, and our visibility continues to improve. The race is on for conversational AI, which will be a powerful catalyst for us in both training and inference. And lastly, we have extended our computing platform beyond the cloud to the edge, where GPU-accelerated 5G, AI and IoT, will revolutionize the world's largest industries. We look forward to updating you on our progress in February.

本季超大規模需求強勁，我們的知名度持續提高。對話式人工智慧的競賽已經拉開序幕，它將成為我們在訓練和推理方面的強大催化劑。最後，我們將運算平台從雲端擴展到邊緣，GPU 加速的 5G、AI 和 IoT 將徹底改變世界上最大的產業。我們期待在二月向您報告我們的進展。

Ladies and gentlemen, this concludes today's conference call. Thank you for participating. You may now disconnect.

女士們、先生們，今天的電話會議到此結束。感謝您的參與。您現在可以斷開連線。