輝達 (NVDA) 2020 Q4 法說會逐字稿

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

午安.我叫克莉絲蒂娜，今天我是你們的會議主持人。歡迎參加 NVIDIA 財務績效電話會議。（操作員指示）謝謝。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 Fourth 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 first quarter of fiscal 2021. 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財年第四季電話會議。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 2021 財年第一季的財務業績。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 forms 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, February 13, 2020, 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截至今天（2020 年 2 月 13 日）所掌握的資訊做出的。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. Q4 revenue was $3.11 billion, up 41% year-on-year and up 3% sequentially, well above our outlook, reflecting upside in our data center and gaming businesses. Full year revenue was $10.9 billion, down 7%. We recovered from the excess channel inventory in gaming and an earlier pause in hyperscale spending and exited the year with great momentum.

謝謝，西蒙娜。第四季營收為 31.1 億美元，年成長 41%，季增 3%，遠高於我們的預期，反映了我們的資料中心和遊戲業務的成長潛力。全年營收為109億美元，下降7%。我們從遊戲通路庫存過剩和超大規模支出早些時候的暫停中恢復過來，並以強勁的勢頭結束了這一年。

Starting with gaming. Revenue of $1.49 billion was up 56% year-on-year and down 10% sequentially. Full year gaming revenue was $5.52 billion, down 12% from our prior year.

從遊戲開始。營收為 14.9 億美元，年增 56%，季減 10%。全年博彩收入為 55.2 億美元，較上年下降 12%。

We enjoyed strong end demand for our desktop and notebook GPUs. Let me give you some more details. Our gaming lineup was exceptionally well positioned for the holidays with the unique ray tracing capabilities of our RTX GPUs and incredible performance at every price point. From the Singles Day shopping event in China through the Christmas season in the West, channel demand was strong for our entire stack. Fueling this were new blockbuster games like Call of Duty: Modern Warfare, continued eSports momentum and new RTX Super products. With RTX price points as low as $299, ray tracing is now the sweet spot for PC gamers.

我們的桌上型電腦和筆記型電腦 GPU 的終端需求強勁。讓我給你更多細節。我們的遊戲陣容非常適合假期，憑藉我們 RTX GPU 的獨特光線追蹤功能以及各個價位的出色性能。從中國的「光棍節」購物活動到西方的聖誕節期間，我們整個產品的通路需求都很強勁。推動這一成長的因素包括《決勝時刻：現代戰爭》等新大片遊戲、持續的電競勢頭以及新的 RTX Super 產品。RTX 的價格低至 299 美元，光線追蹤現在已成為 PC 遊戲玩家的最佳選擇。

Gaming is thriving and gamers prefer GeForce. The global phenomenon of eSports keeps gaming momentum with an audience now exceeding 440 million, up over 30% in just 2 years according to Newzoo. The League of Legends World Championship brought more than 100 million viewers, on par with this month's Super Bowl.

遊戲產業蓬勃發展，遊戲玩家更喜歡 GeForce。電子競技這一全球現象保持了遊戲業的蓬勃發展勢頭，根據 Newzoo 統計，電子競技觀眾數量現已超過 4.4 億，短短兩年內增長了 30% 以上。英雄聯盟全球總決賽吸引了超過 1 億觀眾，與本月的超級盃觀眾人數持平。

Ray tracing titles continue to come to market, and GeForce RTX GPUs are the only ones that support this important technology. This quarter, Wolfenstein: Young blood and Deliver Us The Moon were the latest titles to support ray tracing as well as NVIDIA's Deep Learning Super Sampling technique, which also uses AI to boost performance. With the proliferation of RTX-enabled games and our best ever top-to-bottom performance, we are solidly into the Turing architecture upgrade cycle. Gamers continue to move to higher-end GPUs, seeking better performance and support for ray tracing.

光線追蹤遊戲不斷湧入市場，而 GeForce RTX GPU 是唯一支援這項重要技術的 GPU。本季度，《德軍總部：新血脈》和《飛向月球》成為最新支援光線追蹤以及 NVIDIA 深度學習超級採樣技術的遊戲，該技術也使用 AI 來提升效能。隨著 RTX 遊戲的激增以及我們有史以來最好的自上而下的性能，我們已穩步進入圖靈架構升級週期。遊戲玩家繼續轉向更高階的 GPU，尋求更好的效能和對光線追蹤的支援。

Gaming laptops posted double-digit year-on-year growth for the eighth consecutive quarter. The category continues to expand, driven by appealing thin and light form factors with fantastic graphics performance. This holiday season, retailers stocked a record 125 gaming laptops based on NVIDIA GPUs, up from 94 last year, with our Max-Q designs up 2x. At CES, we launched the world's first 14-inch GeForce RTX laptop with ASUS. We also continue to expand our Studio lineup of laptops for the fast-growing population of freelance creators, designers and YouTubers with 13 new RTX Studio systems introduced at CES. Powered by Turing GPUs, these systems are optimized for over 55 creative and design applications with RTX accelerated ray tracing and/or AI.

遊戲筆記型電腦連續第八個季度達到兩位數年成長。在輕薄外形和出色圖形性能的推動下，該類別繼續擴大。今年假期季，零售商庫存了創紀錄的 125 台基於 NVIDIA GPU 的遊戲筆記型電腦，高於去年的 94 台，其中我們的 Max-Q 設計數量增加了 2 倍。在CES上，我們與華碩合作推出了全球首款14吋GeForce RTX筆記型電腦。我們也持續擴展 Studio 筆記型電腦產品線，以滿足快速成長的自由創作者、設計師和 YouTuber 群組的需求，並在 CES 上推出了 13 款全新 RTX Studio 系統。這些系統由 Turing GPU 提供支持，針對超過 55 種具有 RTX 加速光線追蹤和/或 AI 的創意和設計應用程式進行了最佳化。

Last week, we launched our GeForce NOW cloud gaming service. Powered by GeForce, GeForce NOW is the first cloud gaming service to deliver ray trace games. It's also the only open platform so gamers can enjoy the games they already have and use their existing store accounts without having to repurchase games. GeForce NOW enables PC games on Macs, Windows, PCs, TVs, Mobile devices and soon, Chromebooks. GFN has a freemium business model that includes 2 membership plans: a free membership with standard access; and a Founders tier with a starting price of $4.99 per month, which gives priority access and RTX ray tracing support.

上週，我們推出了 GeForce NOW 雲端遊戲服務。GeForce NOW 由 GeForce 提供支持，是首個提供光線追蹤遊戲的雲端遊戲服務。它也是唯一一個開放平台，因此遊戲玩家可以享受他們已有的遊戲並使用他們現有的商店帳戶，而無需重新購買遊戲。GeForce NOW 支援在 Mac、Windows、PC、電視、行動裝置以及即將推出的 Chromebook 上執行 PC 遊戲。GFN 採用免費增值商業模式，包括 2 種會員方案：具有標準存取權限的免費會員； Founders 等級的起價為每月 4.99 美元，可提供優先存取權和 RTX 光線追蹤支援。

Our goal with GeForce NOW is to expand GeForce gaming to more gamers. About 80% of GeForce NOW gamers are playing on underpowered PCs or devices with Mac OS or Android. With GeForce NOW, they are able to enjoy PC gaming on a GeForce GPU in the cloud. GeForce now can expand GeForce well beyond the roughly 200 million gamers we reach today.

我們對 GeForce NOW 的目標是將 GeForce 遊戲擴展到更多遊戲玩家。大約 80% 的 GeForce NOW 遊戲玩家都在效能不足的 PC 或搭載 Mac OS 或 Android 的裝置上玩遊戲。透過 GeForce NOW，他們可以在雲端的 GeForce GPU 上享受 PC 遊戲。GeForce 現在可以將 GeForce 擴展到目前約 2 億遊戲玩家之外。

Separately, we entered into a collaboration with Tencent, the world's largest gaming platform, to bring PC gaming in the cloud to China, the world's largest gaming market. NVIDIA GPU technology will power Tencent's Start cloud gaming service, which is in early testing stages.

另外，我們與全球最大的遊戲平台騰訊達成合作，將雲端PC遊戲引進全球最大的遊戲市場中國。NVIDIA GPU 技術將為騰訊的 Start 雲端遊戲服務提供支持，該服務目前處於早期測試階段。

Moving to data center. Revenue was a record $968 million, up 43% year-on-year and up 33% sequentially, our strongest ever sequential growth in dollar terms. Full year fiscal year '20 data center revenue was a record $2.98 billion, up 2% from the prior year. Strong growth was fueled by hyperscale and vertical industry end customers. Hyperscale demand was driven by purchases of both our training and inference products in support of key AI workloads, such as natural language understanding, conversational AI and deep recommendators. Hyperscale demand was also driven by cloud computing. AWS now makes the T4 available in every region. This underscores the versatility of the T4, which excels at a wide array of high-performance computing workloads, including AI inference, cloud gaming, rendering and virtual desktop.

移至資料中心。營收達到創紀錄的 9.68 億美元，年增 43%，環比成長 33%，以美元計算，這是我們有史以來最強勁的環比成長。20 財年全年資料中心營收創紀錄達到 29.8 億美元，比前一年成長 2%。超大規模和垂直產業終端客戶推動了強勁成長。超大規模需求是由我們的訓練和推理產品的購買所推動的，這些產品支援關鍵的人工智慧工作負載，例如自然語言理解、會話式人工智慧和深度推薦器。超大規模的需求也受到雲端運算的推動。AWS 現已在每個區域提供 T4。這凸顯了 T4 的多功能性，它在各種高效能運算工作負載方面表現出色，包括 AI 推理、雲端遊戲、渲染和虛擬桌面。

Vertical industry growth was driven primarily by consumer Internet companies. Other verticals such as retail, health care and logistics continue to grow from early-stage build-outs with a strong foundation of deep learning engagements, and we see an expanding set of opportunities across high-performance computing, data science and edge computing applications.

垂直產業的成長主要由消費互聯網公司推動。零售、醫療保健和物流等其他垂直行業繼續從早期建設階段發展，並擁有強大的深度學習基礎，我們看到高效能運算、數據科學和邊緣運算應用領域的機會不斷擴大。

T4, our inference platform, had another strong quarter, with shipments up 4x year-on-year, driven by public cloud deployments as well as edge AI video analytics applications. T4 and V100, reflecting strong demand for inference and training, respectfully, set records this quarter for both shipments and revenue.

我們的推理平台 T4 本季表現再創佳績，出貨量年增 4 倍，這主要得益於公有雲部署以及邊緣 AI 視訊分析應用。T4 和 V100 分別反映了對推理和訓練的強勁需求，本季出貨量和收入均創下了紀錄。

Even as NVIDIA remains the leading platform for AI model training, NVIDIA's inference platform is getting wide use by some of the world's leading enterprise and consumer Internet companies, including American Express, Microsoft, PayPal, Pinterest, Snap and Twitter.

儘管 NVIDIA 仍然是 AI 模式訓練的領先平台，但 NVIDIA 的推理平台正被一些全球領先的企業和消費者網路公司廣泛使用，包括美國運通、微軟、PayPal、Pinterest、Snap 和 Twitter。

The industry continues to do groundbreaking AI work for NVIDIA. For example, Microsoft's biggest quality improvements made over the past year in its Bing search engine stem from its use of NVIDIA GPUs and software for training and inference of its natural language understanding models. These DNN transformer models popularized by BERT have computational requirements for training that are in the order of magnitude higher than earlier image-based models. Conversational AI is a major new workload, requiring GPUs for inference to achieve high throughput within the desired low latency. Indeed, Microsoft cited an inference throughput increase of up to 800x on NVIDIA GPUs compared with CPUs, enabling it to serve over 1 million BERT inferences per second worldwide. And just this week, Microsoft researchers announced a new breakthrough in natural language processing with the largest ever publicized model trained on NVIDIA DGX-2. This advances the state of the art for AI assistance in tasks, such as answering questions, summarization and natural language generation.

該行業繼續為 NVIDIA 進行開創性的 AI 工作。例如，微軟在過去一年中對其必應搜尋引擎的最大品質改進源於其使用 NVIDIA GPU 和軟體來訓練和推理其自然語言理解模型。這些由 BERT 推廣的 DNN Transformer 模型對訓練的計算要求比早期基於圖像的模型高出一個數量級。對話式人工智慧是一項重要的新工作負載，需要 GPU 進行推理以在所需的低延遲內實現高吞吐量。事實上，微軟指出，與 CPU 相比，NVIDIA GPU 的推理吞吐量提高了 800 倍，使其能夠在全球範圍內每秒提供超過 100 萬次 BERT 推理。就在本週，微軟研究人員宣佈在自然語言處理方面取得新突破，發布了在 NVIDIA DGX-2 上訓練的迄今為止最大的模型。這推動了人工智慧輔助任務（例如回答問題、總結和自然語言生成）的發展。

Recommendators are also an important machine learning model for the Internet, powering billions of queries per second. The industry is moving to deep recommendators such as wide and deep model, which leverage deep learning to enable automatic feature learning and to support unstructured content. Running these models on GPUs can dramatically increase inference throughput and reduce latency compared with CPUs. For example, Alibaba's and Baidu's recommendation engines run on NVIDIA AI, boosting their inference throughput by orders of magnitudes beyond CPUs. Deep recommendators enabled Alibaba to achieve 10% increase in click-through rates.

推薦器也是網路的重要機器學習模型，每秒可處理數十億次查詢。業界正在轉向深度推薦器，例如廣泛和深度模型，它利用深度學習實現自動特徵學習並支援非結構化內容。與 CPU 相比，在 GPU 上運行這些模型可以顯著提高推理吞吐量並減少延遲。例如，阿里巴巴和百度的建議引擎在 NVIDIA AI 上運行，其推理吞吐量比 CPU 提高了幾個數量級。深度推薦使阿里巴巴的點擊率提高了 10%。

We also announced the availability of a new GPU-accelerated supercomputer on Microsoft Azure. It enables customers for the first time to rent an entire AI supercomputer on demand from their desk, matching the capabilities of large on-premise supercomputers that can take months to deploy. And in Europe, energy company Eni announced the world's fastest industrial supercomputer based on NVIDIA GPUs.

我們也宣佈在 Microsoft Azure 上推出一款新型 GPU 加速超級電腦。它首次使客戶能夠從辦公桌上按需租用整台人工智慧超級計算機，其功能可與需要數月才能部署的大型內部超級電腦相媲美。在歐洲，能源公司 Eni 宣布推出基於 NVIDIA GPU 的全球速度最快的工業超級電腦。

AI has even come to pizza delivery. At the National Retail Federation's Annual Conference last month, we announced Domino's as a customer deploying our platform for deep learning and data science applications, helping with customer engagement and order accuracy prediction. More broadly in retail, we have seen a significant increase in the adoption of NVIDIA's edge computing offerings by large retailers for powering AI applications that reduce shrinkage, optimize logistics and create operational efficiencies.

人工智慧甚至已經應用在披薩外送。在上個月舉行的全國零售聯合會年會上，我們宣布達美樂作為客戶部署了我們的深度學習和數據科學應用平台，以幫助提高客戶參與度和預測訂單準確性。更廣泛地講，在零售業中，我們看到大型零售商對 NVIDIA 邊緣運算產品的採用顯著增加，這些產品用於支援 AI 應用程序，從而減少損耗、優化物流並提高營運效率。

At the SC19 Supercomputing conference, we introduced a reference design platform for GPU-accelerated ARM-based servers, along with ecosystem partners, ARM, Ampere Computing, Fujitsu and Marvell. We made available our ARM-compatible software development kit consisting of NVIDIA CUDA-X libraries and development tools for accelerating computing. This opens the floodgates of innovation to support growing new applications from hyperscale cloud to Exascale supercomputing. We also introduced NVIDIA Magnum IO, a suite of software optimized to eliminate storage and input/output bottlenecks. Magnum IO delivers up to 20x faster data processing for multi-server, multi-GPU computing nodes when working with massive data sets to carry out complex financial analysis, climate modeling and other workloads for data scientists, high-performance computing and AI researchers.

在 SC19 超級運算大會上，我們與生態系統合作夥伴 ARM、Ampere Computing、富士通和 Marvell 一起推出了基於 GPU 加速 ARM 的伺服器參考設計平台。我們提供了與 ARM 相容的軟體開發套件，其中包括 NVIDIA CUDA-X 程式庫和用於加速運算的開發工具。這打開了創新的閘門，支援從超大規模雲端到百億億次超級運算不斷增長的新應用。我們還推出了 NVIDIA Magnum IO，這是一套經過最佳化的軟體，可消除儲存和輸入/輸出瓶頸。Magnum IO 在處理大量資料集時，可為多伺服器、多 GPU 運算節點提供高達 20 倍的資料處理速度，為資料科學家、高效能運算和人工智慧研究人員執行複雜的財務分析、氣候建模和其他工作負載。

Finally, we introduced TensorRT 7, the seventh generation of our inference software development kit, which speeds up components of conversational AI by 10x comparing to running on CPUs. This helps drive latency below the 300 millisecond threshold considered necessary for real-time interactions supporting our growth in conversational AI.

最後，我們推出了 TensorRT 7，這是我們的第七代推理軟體開發套件，與在 CPU 上運行相比，它將對話式 AI 組件的速度提高了 10 倍。這有助於將延遲降低到即時互動所需的 300 毫秒閾值以下，從而支持我們在對話式 AI 領域的發展。

Moving to ProVis. Revenue reached a record $331 million, up 13% year-on-year and up 2% sequentially. Full year revenue was a record $1.21 billion, an increase of 7% from the prior year. ProVis accelerated in Q4 as the rollout of more RTX-enabled applications is driving strong upgrade cycle for our Turing GPUs. RTX is also opening up new market segment opportunities, such as rendering and studio for freelance creatives.

轉向 ProVis。營收達到創紀錄的 3.31 億美元，年增 13%，季增 2%。全年營收達到創紀錄的 12.1 億美元，比上年成長 7%。隨著更多支援 RTX 的應用程式的推出推動了我們的 Turing GPU 的強勁升級週期，ProVis 在第四季度加速發展。RTX 也開闢了新的細分市場機會，例如為自由創意人員提供的渲染和工作室。

In November, V-ray, Arnold and Blender software renderers began shipping with RTX technology. These joined our leading creative and design applications, including Premier Pro, Dimension, SOLIDWORKS, CATIA and Maya. With RTX, these applications enable enhanced creativity and notable productivity gains. In Blender Cycles, for example, real-time rendering performance is boosted 4x versus a CPU. RTX is now supported by more than 40 leading creative and design applications, reaching a combined user base of over 40 million.

11 月，V-ray、Arnold 和 Blender 軟體渲染器開始搭載 RTX 技術。這些加入了我們領先的創意和設計應用程序，包括 Premier Pro、Dimension、SOLIDWORKS、CATIA 和 Maya。借助 RTX，這些應用程式可以增強創造力並顯著提高生產力。例如，在 Blender Cycles 中，即時渲染效能比 CPU 提高了 4 倍。目前，已有 40 多個領先的創意和設計應用程式支援 RTX，總用戶群已超過 4,000 萬。

Finally, turning to automotive. Revenue was $163 million, flat from a year ago and up 1% sequentially. Full year revenue reached a record $700 million, up 9% year-on-year. During the quarter, we announced DRIVE AGX Orin, the next-generation platform for autonomous vehicles and robots, powered by our new Orin SoC and delivering nearly 7x the performance of the previous generation Xavier SoC. The platform scales from level 2 plus AI-assisted driving up to level 5 fully driverless operation. Orin is software-defined and compatible with Xavier, allowing developers to leverage their investment across multiple product generations.

最後，轉向汽車。營收為 1.63 億美元，與去年同期持平，比上一季成長 1%。全年營收達到創紀錄的7億美元，年增9%。在本季度，我們宣布推出 DRIVE AGX Orin，這是用於自動駕駛汽車和機器人的下一代平台，由我們的新 Orin SoC 提供支持，其性能幾乎是上一代 Xavier SoC 的 7 倍。該平台的等級從 2 級加人工智慧輔助駕駛一直到 5 級完全無人駕駛。Orin 是軟體定義的並且與 Xavier 相容，允許開發人員在多代產品中利用他們的投資。

Moving to the rest of the P&L. Q4 GAAP gross margins was 64.9% and non-GAAP was 65.4%, up sequentially, largely reflecting a higher contribution of data center products. Q4 GAAP operating expenses were $1.02 billion and non-GAAP operating expenses were $810 million, up 12% and 7% year-on-year, respectively.

轉到損益表的其餘部分。第四季 GAAP 毛利率為 64.9%，非 GAAP 毛利率為 65.4%，環比成長，主要反映了資料中心產品的貢獻率上升。第四季 GAAP 營運費用為 10.2 億美元，非 GAAP 營運費用為 8.1 億美元，分別年增 12% 和 7%。

Q4 GAAP EPS was $1.53, up 66% from a year earlier. Non-GAAP EPS was $1.89, up 136% from a year ago. Q4 cash from operations was $1.46 billion. Fiscal year '20 cash flow from operations was a record $4.76 billion.

第四季 GAAP EPS 為 1.53 美元，年增 66%。非公認會計準則每股收益為 1.89 美元，較去年同期成長 136%。第四季經營活動現金流為 14.6 億美元。20 財年的經營現金流達到創紀錄的 47.6 億美元。

With that, let me turn the outlook for the first quarter of fiscal 2021. The outlook does not include any contribution from the pending acquisition of Mellanox. We are engaged and progressing with China on the regulatory approval and believe the acquisition will likely close in the first part of calendar 2020.

接下來，讓我來展望 2021 財年第一季。該展望不包括即將進行的 Mellanox 收購所帶來的任何貢獻。我們正與中國監管部門接洽並推動審批工作，相信此次收購很可能在 2020 年上半年完成。

Before we get to the new -- the numbers, let me comment on the impact of the coronavirus. While it is still early and the ultimate effect is difficult to estimate, we have reduced our Q1 revenue outlook by $100 million to account for the potential impact. We expect revenue to be $3 billion, plus or minus 2%. GAAP and non-GAAP gross margins are expected to be 65% and 65.4%, respectively, plus or minus 50 basis points. GAAP and non-GAAP operating expenses are expected to be approximately $1.05 billion and $835 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 $150 million to $170 million. Further financial details are included in the CFO commentary and other information available on the IR website.

在我們了解新的數字之前，讓我先評論一下冠狀病毒的影響。雖然現在還為時過早，最終影響難以估計，但為了考慮潛在的影響，我們已將第一季的營收預期下調了 1 億美元。我們預計營收為 30 億美元，上下浮動 2%。預計 GAAP 和非 GAAP 毛利率分別為 65% 和 65.4%，上下浮動 50 個基點。預計 GAAP 和非 GAAP 營運費用分別約為 10.5 億美元和 8.35 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1.5億至1.7億美元。進一步的財務細節包含在財務長評論和 IR 網站上的其他資訊中。

In closing, let me highlight an upcoming event for the financial community. We will be at the Morgan Stanley Technology, Media and Telecom Conference on March 2 in San Francisco.

最後，讓我重點介紹一下金融界即將舉行的一個活動。我們將於 3 月 2 日參加在舊金山舉行的摩根士丹利科技、媒體和電信會議。

With that, we will now open the call for questions. Operator, will you please poll for questions.

現在，我們將開始提問。接線員，請您調查問題。

(Operator Instructions)

（操作員指示）

And our first question comes from the line of Toshiya Hari with Goldman Sachs.

我們的第一個問題來自高盛的 Toshiya Hari。

I guess on data center, Colette or Jensen, can you speak to some of the areas that drove the upside in the quarter? You talked about inference and -- both the T4 and the V100 having record quarters but relative to your internal expectations, what were some of the businesses that drove the upside? And if you can also speak to the breadth of your customer profile today relative to a couple of years ago, how that's expanded, that would be helpful as well.

我想問一下資料中心，Colette 或 Jensen，您能談談推動本季成長的一些領域嗎？您談到了推論——T4 和 V100 都創下了季度記錄，但相對於您的內部預期，推動其上漲的一些業務是什麼？如果您能談談與幾年前相比，您今天的客戶資料的廣度是如何擴大的，這也會很有幫助。

Yes. Toshiya, thanks a lot for your question. The primary driver for our growth is AI. There are 4 fundamental dynamics. The first is that the AI models that are being created are achieving breakthroughs and quite amazing breakthroughs, in fact, in natural language understanding, in conversational AI, in recommendation systems. And you know this, but for the others in the audience, recommendation systems are essentially the engine of the Internet today. And the reason for that is because there are so many items in the world, whether it's a store or whether it's content or websites or information you are querying, there are hundreds of billions, trillions, and depending on how you count it, hundreds of trillions of items in the world. And there are billions of people, each with their own characteristics and their countless contexts. And between the items, the people, the users and the various contexts that we're in, location and what you're looking for and weather or what's happening in the environment, those kind of contexts affects the search query that -- the answer they provide you. The recommendation system is just foundational now to search. And some people have said this is the end of search and the beginning -- and the era of recommendation systems. Work is being done everywhere around the world in advancing recommendation systems. And very first time over the last year, it's been able to be done in deep learning.

是的。Toshiya，非常感謝您的提問。我們成長的主要動力是人工智慧。有 4 種基本動力。首先，正在創建的人工智慧模型正在取得突破，事實上，在自然語言理解、對話人工智慧和推薦系統方面取得了相當驚人的突破。你們知道這一點，但對於其他觀眾來說，推薦系統本質上是當今互聯網的引擎。原因在於，世界上有如此多的事物，無論是商店，還是內容、網站或您正在查詢的信息，世界上都有數千億、數萬億，甚至數百萬億的事物，這取決於您的計算方式。世界上有數十億人，每個人都有自己的特徵和無數的背景。在物品、人物、使用者和我們所處的各種環境、位置、您正在尋找的內容、天氣或環境中發生的事情之間，這些環境都會影響搜尋查詢——它們會為您提供答案。推薦系​​統現在只是搜尋的基礎。有些人說這是搜尋時代的終結，也是推薦系統時代的開始。世界各地都在努力推動推薦系統的發展。去年，我們首次在深度學習中實現了這個目標。

And so the first thing is just the breakthroughs in AI. The second is production AI, which means that whereas we had significant and we continue to have significant opportunities in training because the models are getting larger, and there are more of them, we're seeing a lot of these models going into production, and that business is called inference. Inference, as Colette mentioned, grew 4x year-over-year. It's a substantial part of our business now. But one of the interesting statistics is TensorRT 7, the entire TensorRT download this year was about 500,000, a doubling over a year ago. What most people don't understand about inference is it's an incredibly complex computational problem, but it's an enormously complex software problem. And so the second dynamic is moving from training or growing from training and models going into production called inference.

因此，第一件事就是人工智慧的突破。第二個是生產型人工智慧，這意味著，儘管我們在訓練方面擁有重大機遇，並且將繼續擁有重大機遇，因為模型越來越大，數量也越來越多，但我們看到很多這樣的模型投入生產，這種業務被稱為推理。正如 Colette 所提到的，推理量比去年同期增加了 4 倍。現在它是我們業務的重要組成部分。但其中一個有趣的統計數據是 TensorRT 7，今年整個 TensorRT 的下載量約為 50 萬次，比一年前增加了一倍。大多數人不理解推理是一個極其複雜的計算問題，但它是一個極其複雜的軟體問題。因此，第二個動力是從訓練轉變或從訓練中成長，然後模型進入生產階段，稱為推理。

The third is the growth, not just in hyperscale anymore, but in public cloud and in vertical industries. Public cloud because of thousands of AI start-ups that are now developing AI software in the cloud. And the OpEx model works much better for them as they're younger. When they become larger, they could decide to build their own data center infrastructure on-prem, but the thousands of start-ups start their lives in the cloud.

第三是成長，不再只限於超大規模，還包括公有雲和垂直產業。公有雲是因為現在有成千上萬的人工智慧新創公司在雲端開發人工智慧軟體。由於他們還比較年輕，因此 OpEx 模型對他們來說更為有效。當它們變得更大時，它們可能會決定在本地建立自己的資料中心基礎設施，但成千上萬的新創公司都是在雲端開始它們的生命。

We're also seeing really great success in verticals. One of the most exciting vertical is logistics. Logistics, retail, warehousing. We announced, I think, this quarter or last -- end of last quarter, USPS, American Express, Walmart, just large companies who have enormous amounts of data that they're trying to do data analytics on and do predictive analytics on. And so the third dynamic is the growth in -- beyond hyperscale and public cloud as well as vertical industries.

我們在垂直領域也取得了巨大的成功。最令人興奮的垂直行業之一是物流。物流、零售、倉儲。我認為，我們在本季或上個季度末宣布，美國郵政服務、美國運通、沃爾瑪等大公司都擁有大量數據，他們正在嘗試對其進行數據分析和預測分析。因此，第三個動力是超大規模和公共雲以及垂直行業的成長。

And then the last dynamic is being talked about a lot, and this is really, really exciting, and it's called edge AI. We used to call it industries and AI where the action is. But the industry now calls edge AI. We're seeing a lot of excitement there. And the reason for that is you need to have low latency inference. You might not be able to stream the data all the way to the cloud for cost reasons or data sovereignty reasons, and you need the response time. And so those 4 dynamics around AI really drove our growth.

最後一個動態被廣泛討論，這真的非常令人興奮，它被稱為邊緣人工智慧。我們過去稱之為行動所在的產業和人工智慧。但現在業界稱之為邊緣AI。我們看到那裡有很多令人興奮的事情。原因是您需要低延遲推理。由於成本原因或資料主權原因，您可能無法將資料一直傳輸到雲端，並且您需要回應時間。因此，圍繞人工智慧的這四種動力確實推動了我們的成長。

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。

Great. Just following up on that. As you look back at the last 12 months and the deceleration that you saw in your HPC cloud business, now that you have the perspective of seeing what's driving the rebound, any thoughts on what drove it to slow down in the first place? Was it just digestion? Was it sort of a handoff from image recognition to these newer applications that you just talked about? Just help us -- what happened there? And I guess as it pertains to the future, do we think of this as a business that will have that kind of lumpiness to it?

偉大的。只是跟進一下。回顧過去 12 個月以及 HPC 雲端業務的減速，現在您已經了解了推動反彈的因素，您認為最初導致其放緩的原因是什麼？只是消化嗎？這是否是從圖像識別到您剛才談到的這些較新的應用程式的一種轉變？幫幫我們吧──那裡發生了什麼事？我想，就未來而言，我們是否會認為這是一個將具有這種不穩定性的業務？

Yes. That's a really good question. In fact, if you look backwards, now we only have the benefit of history. The deep recommendation systems, the natural language understanding breakthroughs, the conversational AI breakthroughs, all happened in this last year. And the velocity by which the industry captured the benefits here and continue to evolve and advance from these what so-called transformer models was really quite incredible. And so all of a sudden, the number of breakthroughs in AI has just grown tremendously, and these models have grown tremendously. Just this last week, Microsoft announced that they've trained a neural net model in collaboration with work that we did, we call Megatron, increased the size of a model from 7.5 billion parameters to 17.5 billion parameters. And the accuracy of their natural language understanding has just -- has really been boosted.

是的。這真是一個好問題。事實上，如果你回顧過去，我們現在只受益於歷史。深度推薦系統、自然語言理解的突破、對話式人工智慧的突破，都是在去年發生的。而產業從這些所謂的轉型模型中獲取利益並不斷發展和進步的速度確實令人難以置信。因此，突然之間，人工智慧領域的突破數量急劇增加，這些模型也得到了極大的發展。就在上週，微軟宣布他們與我們合作訓練了一個神經網路模型，我們稱之為 Megatron，模型的規模從 75 億個參數增加到 175 億個參數。他們的自然語言理解的準確性確實得到了提升。

And so the models are -- AI is finding really fantastic breakthroughs, and models are getting bigger and there are more of them. And when you look back and look at when these breakthroughs happened, it essentially happened this last year.

因此，模型——人工智慧正在尋找真正了不起的突破，而且模型越來越大，數量也越來越多。當你回顧這些突破發生的時間時，你會發現它基本上是去年發生的。

The second, we've been working on inference for some time. And until this last year, very few of those inference models went into production. And now we have deep learning models across all of the hyperscalers in production. And this last year, we saw really great growth in inference.

第二，我們已經研究推理一段時間了。直到去年，這些推理模型中只有極少數投入生產。現在，我們在生產中的所有超大規模計算設備上都擁有深度學習模型。去年，我們看到推理方面確實取得了巨大的成長。

The third dynamic is public clouds. All these AI startups that are being started all over the world, there's about 6,000 of them, they're starting to develop and be able to put their models into production. And with the scale out of AWS, we now have T4s in every single geography. So the combination of the availability of our GPUs in the cloud, and the startups and vertical industries deploying their AI models into production, the combination of all that just kind of came together. And all of that happened this last year. And as a result, we had record sales of V100s and T4s. And so we're quite excited with the developments, and it's all really powered by AI.

第三個動力是公有雲。世界各地正在興起的人工智慧新創公司大約有 6,000 家，它們開始開發並能夠將其模型投入生產。隨著 AWS 的擴展，我們現在在每個地區都有 T4。因此，我們的 GPU 在雲端的可用性，以及新創公司和垂直產業將其 AI 模型部署到生產中，所有這些結合在一起。這一切都發生在去年。結果，我們的 V100 和 T4 銷售量創下了紀錄。因此，我們對這些發展感到非常興奮，這一切都是由人工智慧推動的。

Your next question comes from the line of Vivek Arya with Bank of America Securities.

您的下一個問題來自美國銀行證券公司的 Vivek Arya。

Congratulations on returning the business back to the strong growth. Jensen, I wanted to ask about how you are positioned from a supply perspective for this coming year? Your main foundry is running pretty tight. How will you be able to support the 20% or so growth here that many investors are looking for? If you could just give us some commentary on how you're positioned from a supply perspective, that will be very helpful.

恭喜您的業務恢復強勁成長。詹森，我想問一下，從供應角度來看，您對來年是如何定位的？您主要的鑄造廠運作非常緊張。您將如何支持許多投資者所期望的 20% 左右的成長？如果您能從供應角度就您的定位給我們一些評論，那將非常有幫助。

Well, I think we're in pretty good shape on supply. We surely won't have ample supply. It is true that the industry is tight and the combination of supporting multiple processes, multiple fabs across our partner, TSMC. We've got a lot of different factories and a lot of different -- several different nodes of process qualified. I think we're in good shape. And so we just have to watch it closely. And we're working very closely with all of our customers in forecasting. And of course, that gives us better visibility as well and -- but all of us have to do a better job forecasting, and we're working very closely between our customers and our foundry partners, TSMC.

嗯，我認為我們的供應狀況相當良好。我們的供應肯定不夠。確實，這個行業很緊張，需要我們的合作夥伴台積電來支持多種製程、多個晶圓廠的組合。我們有很多不同的工廠和許多不同的工廠——幾個不同的合格流程節點。我認為我們的狀況良好。因此我們必須密切關注。我們正在與所有客戶密切合作進行預測。當然，這也為我們提供了更好的可見性——但我們所有人都必須做得更好，並且我們正在與客戶和我們的代工合作夥伴台積電密切合作。

Your next question comes from the line of Timothy Arcuri with UBS.

您的下一個問題來自瑞銀的 Timothy Arcuri。

Colette, I'm wondering if you can give us -- in data center, if you can give us a little idea of what the mix was between industries and hyperscale. I think last quarter, hyperscale was a little bit less than 50%. Can you give us maybe the mix or how much it was up, something like that?

科萊特，我想知道您是否可以為我們介紹一下資料中心，以及產業和超大規模之間的混合情況。我認為上個季度超大規模的比例略低於 50%。您能否告訴我們混合物的成分或含量，諸如此類的資訊？

Yes. Tim, thanks for the question. Similar to what we had seen last quarter, with all things growing as we moved into this quarter, growth in terms of the hyperscales, continued expansion in terms of those vertical industries and even in the cloud instances. We're still looking at around the same split of 50-50 between our hyperscales and our vertical industries and maybe a little bit tad below 50 in terms of our total overall hyperscales.

是的。提姆，謝謝你的提問。與我們上個季度看到的情況類似，進入本季度，一切都在增長，超大規模的增長，垂直行業甚至雲端運算的持續擴張。我們仍然希望超大規模資料中心和垂直產業之間的比例保持在 50-50 左右，而就整體超大規模資料中心而言，這一比例可能略低於 50。

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。

Congratulations on the results. When I look at the numbers, the growth on an absolute basis sequentially in data center was almost 2x or north of 2x, what we've seen in the past as far as the absolute sequential change. Through the course of this quarter, you were pretty clear that you would expect to see an acceleration of growth in the December quarter. I'm just curious of how you think about that going into the April quarter? And how we should think about that growth rate through the course of this year? If you can give us any kind of framework.

恭喜你取得這樣的成績。當我查看這些數字時，我發現資料中心的絕對環比增長幾乎是 2 倍或 2 倍以上，這是我們過去看到的絕對環比變化。在整個本季度中，您非常清楚，您預計 12 月季度的成長將會加速。我只是好奇您對四月季度的情況有何看法？我們該如何看待今年的成長率？如果您能給我們任何類型的框架。

And Jensen, just curious, I mean, as you think about the bigger picture, where do you think we stand from an industry perspective today in terms of the amount or the attach rate of GPUs, is it for acceleration in the server market? And where do you think that might be looking out over the next 3 years or so?

詹森，我只是好奇，我的意思是，當你考慮更大範圍的情況時，您認為從行業角度來看，就 GPU 的數量或附加率而言，我們目前處於什麼位置，這是否是為了加速伺服器市場？您認為未來 3 年左右會出現什麼情況？

Thanks, Aaron. Colette, do you want to go first?

謝謝，亞倫。科萊特，你想先走嗎？

Sure. When we think about going into Q1 and our data center overall growth, we do expect to see continued growth, both going into Q1. We believe our visibility still remains positive quite well, and we're expecting that as we move into it and go forward.

當然。當我們考慮進入第一季以及我們的資料中心的整體成長時，我們確實預計第一季將繼續成長。我們相信，我們的知名度仍然保持相當積極的狀態，我們期待隨著我們進入這一領域並繼續前進，這一點也同樣重要。

Yes. Aaron, I believe that every query on the Internet will be accelerated someday. And at the very core of it, most -- almost all queries will have some natural language understanding component to it. Almost all queries will have to sort through and make a recommendation from the trillions of possibilities, filter it down and recommend a handful of recommended answers to your queries. Whether it's shopping or movies or just asking locations or even asking a question, the number of the possibilities of all the answers versus what is best answer is -- needs to be filtered down. And that filtering process is called recommendation. That recommendation system is really complex, and deep learning is going to be involved in all that. That's the first thing. I believe that every query will be accelerated.

是的。亞倫，我相信有一天網路上的每一個查詢都會被加速。從本質上講，大多數——幾乎所有查詢都會包含一些自然語言理解元件。幾乎所有查詢都必須從數萬億種可能性中進行篩選並提出建議，對其進行篩選並為您的查詢推薦少量的答案。無論是購物、看電影、詢問地點或提出問題，所有答案的可能性數量與最佳答案的數量都需要進行篩選。這個過濾過程就稱為推薦。推薦系​​統確實很複雜，其中涉及深度學習。這是第一件事。我相信每一個查詢都會加速。

The second is, as you know, CPU scaling has really slowed, and there's just no two ways about it. It's not a marketing thing. It's a physics thing. And the ability for CPUs to continue to scale without increasing cost or increasing power has ended. And it's called the end of Dennard scaling. And so there has to be another approach. The combination of the emergence of deep learning and the use of artificial intelligence and the amount of computation that's necessary to -- for every single query but the benefit that comes along with that, and the end of Dennard scaling, suggests that there needs to be another approach, and we believe that approach is acceleration.

第二，如你所知，CPU 擴展速度確實已經減慢，這是無法改變的。這不是行銷的事。這是物理學的事情。CPU 在不增加成本或功率的情況下繼續擴展的能力已經終結。這被稱為丹納德縮放定律的終結。因此必須採取另一種方法。深度學習的出現和人工智慧的使用以及每個查詢所需的計算量以及隨之而來的好處以及 Dennard 縮放定律的終結表明需要另一種方法，我們相信這種方法就是加速。

Now our approach for acceleration is fundamentally different than an accelerator. Notice, we never say accelerator, we say accelerated computing. And the reason for that is because we believe that a software-defined data center will have all kinds of different AIs. The AIs will continue to evolve, the models will continue to evolve and get larger, and a software-defined data center needs to be programmable. It is one of the reasons why we've been so successful. And if you go back and think about all the questions that have been asked of me over the last 3 or 4 years around this area, the consistency of the answer has to do with the programmability of architecture, the richness of the software, the difficulties of the compilers, the ever-growing size of the models, the diversity of the models and the advances that these models are creating. And so we're seeing the beginning of a new computing era.

現在，我們的加速方法與加速器有著根本的不同。請注意，我們從不說加速器，我們說的是加速運算。原因在於我們相信軟體定義的資料中心將擁有各種不同的人工智慧。人工智慧將繼續發展，模型將繼續發展並變得更大，軟體定義的資料中心需要可編程。這就是我們如此成功的原因之一。如果你回想一下過去三、四年來在這個領域向我提出的所有問題，答案的一致性與架構的可程式性、軟體的豐富性、編譯器的難度、不斷增長的模型規模、模型的多樣性以及這些模型所創造的進步有關。因此，我們正在見證一個新計算時代的開始。

And a fixed function accelerator is simply not the right answer. And so we believe that the future is going to be accelerated. It's going to require an accelerated computing platform, and software richness is really vital, so that these data centers could be software defined. And so I think that we're in the early innings, the early innings, very, very early innings of this new future. And I think that accelerated computing is going to become more and more important.

而固定功能加速器根本不是正確的答案。因此我們相信未來將會加速發展。它需要一個加速運算平台，軟體豐富性至關重要，以便這些資料中心能夠由軟體定義。所以我認為我們正處於這個新未來的早期階段，早期階段，非常非常早期。我認為加速運算將變得越來越重要。

Your next question comes from the line of Matt Ramsay with Cowen.

您的下一個問題來自 Cowen 的 Matt Ramsay。

Obviously, congratulations on the data center success. I wanted to ask a little bit, Colette, about the -- you took $100 million out for coronavirus, and I wanted to ask a little bit about how you got to that number. Really 2 pieces. One, if you could remind us maybe in terms of units or revenue, how -- what percentage of your gaming business is within China? And as you look at that $100 million that you pulled out of the guidance, are you thinking about that from a demand disruption perspective? Or are you thinking about it from something in the supply chain that might limit your sales?

顯然，祝賀資料中心的成功。科萊特，我想問一下——您為對抗冠狀病毒撥款 1 億美元，我想問一下您是如何得出這個數字的。真的是2件。首先，如果您能提醒我們一下，從單位數或收入方面來看，你們的遊戲業務在中國的佔比是多少？當您看到從指導中抽出的 1 億美元時，您是否從需求中斷的角度考慮過這個問題？或者您是否從供應鏈中某些可能限制您銷售的因素來考慮這個問題？

Sure. Thanks for the question, Matt. So it's really still quite early in terms of trying to figure out what the impact from the overall coronavirus may be. So we're not necessarily precise in terms of our estimate. Yes, our estimates are split between an impact possibly on gaming and data center and split pretty much equally. The $100 million also reflects what may be supply challenges or may be overall demand. But we're still looking at those to get a better understanding where we think that might be.

當然。謝謝你的提問，馬特。因此，現在試圖弄清楚冠狀病毒的整體影響還為時過早。因此我們的估計不一定準確。是的，我們的估計是將對遊戲和資料中心的影響分成了兩部分，而且分成的份額幾乎是均等的。1億美元也反映了可能的供應挑戰或整體需求。但我們仍在研究這些問題，以便更好地了解我們認為的可能情況。

In terms of our business and our business makeup, yes, our overall China business for gaming is an important piece. We have about 30% of our overall China gaming as a percentage of our overall gaming business. For data center, it's -- it moves quite a bit. They are a very important market for us, but it moves from quarter-to-quarter just based on the overall end customer mix as well as the system builds that they may choose. So it's a little harder to determine.

就我們的業務和業務組成而言，是的，我們整個中國的遊戲業務是其中重要的一部分。我們中國市場的遊戲業務占我們整體遊戲業務的 30% 左右。對於資料中心來說，它移動得相當多。對我們來說，它們是一個非常重要的市場，但它每個季度的變化都取決於整體最終客戶組合以及他們可能選擇的系統建構。所以這有點難確定。

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。

Congratulations on the strong results and guidance. On gaming -- yes, no problem. Good to see the recent launch of your GeForce NOW service. But on the partnership with Tencent on cloud gaming, seems like Tencent should have a smoother transition to the cloud model. They are the largest gaming company in the world, so they own many of the games. They also have their own data center infrastructure already in place. But how is the NVIDIA team going to be supporting this partnership? Is it going to be [deal your] GeForce NOW hardware framework? Or will you just be supporting them with your standalone GPU products? And when do you expect the service to go mainstream?

恭喜您取得的優異成績和指導。關於遊戲——是的，沒問題。很高興看到您最近推出了 GeForce NOW 服務。但在與騰訊在雲端遊戲方面的合作方面，騰訊似乎應該能夠更順利地過渡到雲端模式。他們是世界上最大的遊戲公司，因此他們擁有許多遊戲。他們也已經擁有自己的資料中心基礎設施。但是 NVIDIA 團隊將如何支持這項合作關係？它會是 GeForce NOW 硬體框架嗎？或者您只是透過獨立的 GPU 產品來支援它們？您預計該服務何時成為主流？

Let's see. Tencent is the world's largest publisher. China represents about 1/3 of the world's gaming, and transitioning to the cloud is going to be a long-term journey. And the reason for that is because Internet connection is not consistent throughout the entire market. And a lot of application still needs to be onboarded, and we're working very closely with them. We're super enthusiastic about it. If we're successful long term, and we're talking about an extra 1 billion gamers that we might be able to reach. And so I think that this is an exciting opportunity, just a long-term journey.

讓我們來看看。騰訊是全球最大的出版商。中國約佔全球遊戲市場的三分之一，轉型為雲端將是一個長期的過程。原因在於整個市場的網路連線並不一致。還有很多應用程式需要加入，我們正在與他們密切合作。我們對此非常熱衷。如果我們取得長期成功，我們可能會額外吸引 10 億遊戲玩家。所以我認為這是一個令人興奮的機會，也是一段長期的旅程。

Now here in the West, we've had a lot more opportunity to refine the connections around the world and working through the data centers, the local hubs as well as people's WiFi routers at home. And so we've been in beta for quite some time, as you know. And here in the West, our platform is open. And we have several hundred games now and we're in the process of onboarding another 1,500 games. We're the only cloud platform that's based on Windows and allows us to be able to bring PC games to the cloud. And so the reach is -- we've had more experience here in the West with reach, and we've had -- we obviously have a lot more games that we can onboard. But I'm super enthusiastic about the partnership we have with Tencent.

現在在西方，我們有更多的機會完善世界各地的連接，並透過資料中心、本地樞紐以及人們家中的 WiFi 路由器進行工作。如你所知，我們已經處於測試階段相當長一段時間了。在西方，我們的平台是開放的。我們現在有幾百款遊戲，並且正在準備增加另外 1,500 款遊戲。我們是唯一基於 Windows 並能夠將 PC 遊戲帶到雲端的雲端平台。因此，覆蓋範圍是——我們在西方擁有更多的覆蓋範圍經驗，而且我們顯然擁有更多可以支持的遊戲。但我對與騰訊的合作非常熱衷。

Overall, our GeForce NOW -- you guys saw the launch, it's -- the reception has been fantastic, the reviews have been fantastic. Our strategy has 3 components. There's the GeForce NOW service that we provide ourselves. We also have GeForce NOW alliances with telcos around the world to reach the regions around the world that we don't have a presence in. And that is going super well, and I'm excited about that. And then lastly, partnerships with large publishers, for example, like Tencent. And we offer them our platform, of course, and a great deal of software and just a lot of engineering that has to be done in collaboration to refine the service.

總的來說，我們的 GeForce NOW——你們都看到了它的發布，它的反應非常好，評論也非常好。我們的策略有三個組成部分。我們自己提供 GeForce NOW 服務。我們還與世界各地的電信公司建立了 GeForce NOW 聯盟，以覆蓋我們尚未涉足的世界地區。一切進展順利，我對此感到非常興奮。最後，與大型出版商建立合作關係，例如騰訊。當然，我們為他們提供我們的平台，以及大量的軟體和大量需要合作完成的工程，以完善服務。

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

您的下一個問題來自 Evercore 的 C.J. Muse。

I guess a question on the gaming side. If I look at your overall revenue guide, it would seem to suggest that you're looking for typically, I guess, better seasonal trends into April. And I guess can you speak to that? And then how are you seeing desktop gaming demand with ray tracing content becoming more available? How should we think about the growth trajectory through 2020? And then just really as a modeling question as part of gaming, with notebook now 1/3 of the revenues, how should we think about kind of the seasonality going into April and July for that part of your business?

我想這是一個關於遊戲方面的問題。如果我看一下您的整體收入指南，似乎表明您正在尋找四月更好的季節性趨勢。我想您能談談這個嗎？那麼，隨著光線追蹤內容變得越來越普及，您如何看待桌上遊戲需求？我們該如何看待2020年的成長軌跡？那麼，作為遊戲的一部分，一個建模問題實際上是筆記型電腦現在佔收入的 1/3，我們應該如何看待 4 月和 7 月這部分業務的季節性？

Yes. So C.J., I'm going to go first, and then Colette is going take it home here. So the first part of it is this, our gaming business has at the end -- I'm sorry. Okay. Our gaming business, the end market demand is really terrific. It's really healthy. It's been healthy throughout the whole year. And it's pretty clear that RTX is doing fantastic. And it's very -- it's super clear now that ray tracing is the most important new feature of next-generation graphics. We have 30 -- over 30 games that have been announced, 11 games or so that have been shipped. The pipeline of ray tracing games that are going to be coming out is just really, really exciting. The second factor -- and one more thing about RTX, we finally have taken RTX down to $299. So it's now at the sweet spot of gaming. And so RTX is doing fantastic. The sell-through is fantastic all over the world.

是的。所以 C.J.，我先進去，然後 Colette 再帶它回家。所以第一部分是這樣的，我們的遊戲業務已經結束了——我很抱歉。好的。我們的遊戲業務，終端市場需求確實非常棒。這真的很健康。全年都很健康。很明顯，RTX 表現非常出色。現在非常清楚的是，光線追蹤是下一代圖形最重要的新功能。我們已經發布了 30 多個遊戲，其中大約 11 個遊戲已經發行。即將推出的光線追蹤遊戲系列真的非常令人興奮。第二個因素——關於 RTX 的另一件事，我們最終將 RTX 降至 299 美元。因此，現在它正處於遊戲的最佳狀態。因此 RTX 的表現非常出色。全球的銷售情況都十分出色。

The second part of our business that is changing in gaming is this -- the amount of notebook sales and the success of Nintendo Switch has really changed the profile of our overall gaming business. Our notebook business, as Colette mentioned earlier, has seen double-digit growth for 8 consecutive quarters, and this is unquestionably a new gaming category. Like it's a new game console. This is going to be the largest game console in the world, I believe. And the reason for that is because there are more people with laptops than there are of any other device. And so the fact that we've been able to get RTX into a thin and light notebook, a thin and light notebook, is really a breakthrough. And it's one of the reasons why we're seeing such great success in notebook. Between the notebook business and our Nintendo Switch business, the profile of gaming overall has changed and has become more seasonal. It's more seasonal because devices, systems, like notebooks and Switch, are built largely in 2 quarters, Q2 and Q3. And they build it largely in Q2 and Q3 because it takes a while to build them and ship them and put them into the hubs around the world. And they tend to build it ahead of the holiday season. And so that's one of the reasons why Q3 will tend to be larger and Q4 will tend to be more seasonal and Q1 will tend to be more seasonal than the past. But the end demand is fantastic. RTX is doing great. And part of it is just a result of the success of our notebooks. I'm going to hand it over to Colette.

我們的遊戲業務變化的第二個部分是——筆記型電腦的銷售和任天堂 Switch 的成功確實改變了我們整體遊戲業務的狀況。我們的筆記型電腦業務，正如 Colette 之前提到的，已經連續 8 個季度實現兩位數增長，這毫無疑問是一個新的遊戲類別。就像是新的遊戲機。我相信這將成為世界上最大的遊戲機。原因在於擁有筆記型電腦的人比擁有其他任何設備的人都多。因此，我們能夠將 RTX 融入輕薄筆電，這確實是一個突破。這也是我們在筆記型電腦領域取得如此巨大成功的原因之一。在筆記型電腦業務和我們的 Nintendo Switch 業務之間，遊戲的整體狀況發生了變化，並且變得更具季節性。它更具季節性，因為筆記型電腦和 Switch 等設備、系統主要在第二季和第三季生產。他們主要在第二季和第三季進行生產，因為生產、運輸並運送到世界各地的樞紐需要一段時間。他們往往會在假期前建造它。這就是為什麼第三季規模趨於更大、第四季趨於更具季節性、第一季趨於比過去更具季節性的原因之一。但最終需求非常旺盛。RTX 表現非常出色。其中一部分只是我們筆記型電腦成功的結果。我要把它交給科萊特。

Yes. So with that from a background and you think about all those different components that are within gaming, the notebook, the overall Switch, and of course, all of the ray tracing that we have in terms of desktop, our normal seasonality, as we look at Q1 for gaming with all those 3 pieces, is usually sequentially down from Q4, sequentially down Q4 to Q1. This year, the outlook assumes it will probably be a little bit more pronounced due to the coronavirus. So in total, we're probably looking at Q1 to be in the low double-digit sequential decline in gaming.

是的。因此，從這個背景來看，你會考慮遊戲中的所有不同組件、筆記型電腦、整體 Switch，當然還有我們在桌面方面的所有光線追踪，當我們查看第一季度的所有這三個部分的遊戲時，我們的正常季節性通常從第四季度開始連續下降，從第四季度到第一季度連續下降。今年，展望認為，由於冠狀病毒的影響，這種現象可能會更加明顯。因此，總體而言，我們可能會預期第一季遊戲營收將出現兩位數的低點連續下滑。

Your next question comes from the line of Atif Malik with Citi.

您的下一個問題來自花旗銀行的阿蒂夫馬利克 (Atif Malik)。

Good job on results and guide. On the same topic, coronavirus. Colette, I'm a bit surprised that the guidance -- the range on the guidance is not wider versus historic. Can you just talk about why not widen the range? And what went into that $100 million hit from the coronavirus?

結果和指導都很好。關於同一話題，冠狀病毒。科萊特，我有點驚訝，指導範圍與歷史範圍相比並沒有擴大。能談談為什麼不擴大範圍嗎？那麼，冠狀病毒造成的 1 億美元損失是怎麼造成的呢？

So Atif, thanks for the question. Again, it's still very early regarding the coronavirus. Our thoughts are out with both the employees, the families and others that are in China. So our discussions, both with our supply chain that is very prominent in the overall Asia region as well as our overall AIC makers as well as our customers, is as about as timely as we can be. And that went into our discussion and our thoughts on the overall guidance that we gave into our $100 million. We'll just have to see how the quarter comes through, and we'll discuss more when we get to it. But at this time, that was our best estimate at this time.

所以阿蒂夫，謝謝你的提問。再次強調，關於冠狀病毒，現在還為時過早。我們向在中國的罹難者員工、家屬和其他所有人表示慰問。因此，我們與整個亞洲地區非常突出的供應鏈以及我們的整體 AIC 製造商以及我們的客戶進行的討論是盡可能及時的。這涉及到我們對 1 億美元整體指引的討論和思考。我們只需要看看本季度的進展情況，到時候我們會進行更多討論。但就目前而言，這是我們目前最好的估計。

Your next question comes from the line of William Stein with SunTrust.

您的下一個問題來自 SunTrust 的 William Stein。

Jensen, I'd love to hear your thoughts as to how you anticipate the inference market playing out. Historically, NVIDIA's had essentially all of the training market and little of the inference market in the last 1.5 years or so. I think that's changed where you've done much better in inference. Now you have the T4 in the cloud, you have EGX at the edge. And you have Jetson, I think, is what it's called at the sort of endpoint device. How do you anticipate that market for inference developing across those various positions? And how are you aligning your portfolio for that growth?

詹森，我很想聽聽你對推理市場未來發展的看法。從歷史上看，NVIDIA 在過去 1.5 年左右的時間裡基本上佔據了整個訓練市場，但在推理市場卻只佔據了很小一部分。我認為這已經發生了變化，你在推理方面做得更好了。現在，您在雲端擁有 T4，在邊緣擁有 EGX。我認為，您有 Jetson，這是一種端點設備。您如何預測推理市場在這些不同職位上的發展？您如何調整投資組合以實現這一成長？

Yes. Thanks a lot, Will. Let's see, I think the -- historically, inference has been a small part of our business because AI was still being developed. Deep learning, AI is not -- historical AI, classical machine learning weren't particularly suited for GPUs and weren't particularly suited for acceleration. It wasn't until deep learning came along that the amount of computation necessary is just extraordinary. And the second factor is the type of AI models that were developed. Eventually, if -- the type of models related to natural language understanding and conversational AI and recommendation systems, these require instantaneous response. The faster the answer, the more likely someone is going to click on the answer. And so you know that latency matters a great deal, and it's measurable. The effect on the business is directly measurable.

是的。非常感謝，威爾。讓我們看看，我認為——從歷史上看，推理只是我們業務的一小部分，因為人工智慧仍在開發中。深度學習，人工智慧不是——歷史上的人工智慧，經典的機器學習並不特別適合 GPU，也不特別適合加速。直到深度學習出現，所需的計算量才變得非常大。第二個因素是開發的人工智慧模型的類型。最終，如果——與自然語言理解、會話式人工智慧和推薦系統相關的模型類型，這些都需要即時回應。回答得越快，人們點擊答案的可能性就越大。所以你知道延遲非常重要，而且它是可測量的。對業務的影響是直接可衡量的。

And so for conversational AI, for example, we've been able to reduce the latency of the entire pipeline from speech recognition to the language processing to, for example, fix the errors and such, come up with a recommendation to text to speech to the voice synthesis. That entire pipeline could take several seconds. We run it so fast that it's possible now for us to process the entire pipeline within a couple of hundred, 200, 300 milliseconds. That is in the realm of interactive conversation.

例如，對於對話式人工智慧，我們已經能夠減少從語音識別到語言處理的整個流程的延遲，例如，修復錯誤等，提出從文字到語音到語音合成的建議。整個管道可能需要幾秒鐘。我們運行的速度非常快，現在我們可以在幾百、200、300 毫秒內處理整個管道。這屬於互動對話的範疇。

Beyond that, it's just simply too slow. And so the combination of AI models that are large and complex that are moving to inference, moving to production. And then secondarily, conversational AI and latency-sensitive models and applications where our GPUs are essential, now moving forward, I think you're going to see a lot more opportunities for us in inference.

除此之外，它實在太慢了。因此，大型和複雜的人工智慧模型的組合正在轉向推理，轉向生產。其次，對話式人工智慧和延遲敏感模型和應用程式對我們的 GPU 至關重要，現在繼續前進，我認為你會看到我們在推理方面有更多機會。

The way to think about that long-term is acceleration is essential because of end of Dennard scaling. Process technology is going to demand that we compute in a different way. And the way that AI has evolved and deep learning, it suggests that acceleration on GPUs is just a really phenomenal approach.

由於登納德縮放定律的結束，長期思考加速至關重要。製程技術將要求我們以不同的方式進行計算。人工智慧和深度學習的發展方式表明，GPU 加速是一種非常了不起的方法。

Data centers are going to have to be software-defined. And I think as I mentioned, I think I mentioned earlier to another question, I believe that in the future, the data center will all be accelerated. It will be all running AI models, and it will be software-defined and it will be programmable and having an accelerated computing platform is essential. As you move out to the edge, it really depends on whether your platform is software-defined, whether it has to be programmable or whether it's fix functioned. There are many, many devices where the inference work is very specific. It could be something as simple as detecting changes in temperature or changes in sound or detecting motion. Those type of inference models are -- could still be based on deep learning. It's function-specific. You don't have to change it very often and you're running 1 or 2 models at any given point in time. And so those devices are going to be incredibly cost-effective.

資料中心必須由軟體定義。我認為正如我之前提到的，我想我之前提到過另一個問題，我相信未來資料中心將會加速發展。它將運行所有 AI 模型，它將是軟體定義的，它將是可編程的，並且擁有加速運算平台至關重要。當你移到邊緣時，它實際上取決於你的平台是否是軟體定義的，是否必須是可編程的，或者是否是固定功能的。很多設備的推理工作都非常具體。它可以是一些簡單的事情，例如檢測溫度變化或聲音變化或檢測運動。這些類型的推理模型仍然可以基於深度學習。它是特定於功能的。您不必經常更改它，並且在任何給定時間點都可以運行 1 或 2 個模型。因此這些設備的成本效益將非常高。

I believe, those AI chips, you're going to have AI chips that are $0.50, $1, and you're just going to put it into something and it's going to be doing magical detections. The type of platforms that we're in, such as self-driving cars and robotics, the software is so complicated and there's so much evolution to come yet, and it's going to constantly get better. Those software-defined platforms are really the ideal targets for us. And so we call it AI at the edge, edge computing devices. One of the edge computing devices I'm very excited about is what people call mobile edge or basically 5G telco edge. That data center will be programmable. We recently announced that we partnered with Ericsson and we're going to be accelerating the 5G stack. And so that needs to be a software-defined data center. It runs all kinds of applications, including 5G. And those applications are going to be -- those opportunities are fantastic for us.

我相信，那些人工智慧晶片，你將會擁有價值 0.50 美元、1 美元的人工智慧晶片，你只需將它放入某個東西中，它就會進行神奇的檢測。我們所處的平台類型，例如自動駕駛汽車和機器人，軟體非常複雜，未來還有很大的發展空間，而且還會不斷改進。這些軟體定義的平台確實是我們的理想目標。因此我們稱之為邊緣人工智慧、邊緣運算設備。令我非常興奮的邊緣運算設備之一就是人們所說的行動邊緣或基本上是 5G 電信邊緣。該數據中心將是可編程的。我們最近宣布與愛立信合作，並將加速 5G 堆疊的開發。所以這需要是一個軟體定義的資料中心。它運行各種應用程序，包括5G。這些應用將會是──這些機會對我們來說是極好的。

Your next question comes from the line of Mark Lipacis with Jefferies.

您的下一個問題來自 Jefferies 的 Mark Lipacis。

Jensen, I guess I had a question about your -- how you think about the sustainability of your market position in the data center? And I guess in my simplistic view, about 12 years ago, you made out a consensus call to invest in CUDA software, distribute it to universities. Neural networking took off and you were the de facto standard, and here we are right now. And for me, what's interesting to hear is that the demand that you're seeing today for your products is from markets that's just developed within the last year. And my question is like, how do you think about your investment, your R&D investment strategy to make sure that you are staying way ahead of the market, of the competition and even your customers who are investing in these markets, too?

詹森，我想我有一個問題關於你——你如何看待你在資料中心的市場地位的可持續性？我想，以我的簡單觀點來看，大約 12 年前，大家一致呼籲投資 CUDA 軟體，並將其分發給大學。神經網路開始起飛，你成為了事實上的標準，我們現在就在這裡。對我來說，有趣的是，您今天看到的對您產品的需求來自去年剛發展起來的市場。我的問題是，您如何看待您的投資、您的研發投資策略，以確保您始終領先於市場、競爭對手，甚至也領先於在這些市場投資的客戶？

Yes. Thanks, Mark. Our company has to live 10 years ahead of the market. And so we have to imagine where the world is going to be in 10 years' time, in 5 years' time and work our way backwards. Now our company is focused on one singular thing. The simplicity of it is incredible. And that one singular thing is accelerated computing, accelerated computing. And accelerated computing is all about the architecture, of course. It's about the complicated systems that we're in because throughput is high. When our acceleration, we can -- when we can compute 10x, 20x, 50x, 100x faster than the CPU, all of a sudden, everything becomes a bottleneck. Memory's a bottleneck, networking's a bottleneck, storage is a bottleneck, everything is a bottleneck. And so we have to be -- NVIDIA has to be a supremely good system designer. But the complexity of our stack, which is the software stack above it, is really where the investments over the course of the last -- some 29 years now, has really paid off.

是的。謝謝，馬克。我們的公司必須領先市場10年。因此，我們必須想像 10 年後、5 年後世界會變成什麼樣子，然後再反過來想。現在我們公司專注於一件事。它的簡單性令人難以置信。而這唯一的一件事就是加速運算，加速運算。當然，加速運算完全取決於架構。這與我們身處的複雜系統有關，因為吞吐量很高。當我們的加速能夠——當我們能夠以比 CPU 快 10 倍、20 倍、50 倍、100 倍的速度進行計算時，突然間，一切都變成了瓶頸。記憶體是瓶頸，網路是瓶頸，儲存是瓶頸，一切都是瓶頸。所以我們必須－NVIDIA 必須成為極為優秀的系統設計師。但是我們的堆疊（即上方的軟體堆疊）的複雜性才是過去 29 年的投資真正獲得回報的地方。

NVIDIA, frankly, has been an accelerated computing company since the day it was born. And so we -- our company is constantly trying to expand the number of applications that we can accelerate. Of course, computer graphics was an original one, and we're reinventing it with real-time ray tracing. We have rendering, which is a brand-new application that we're making great progress in. We just talked -- I just mentioned 5G acceleration. Recently, we announced genomics computing. And so those are new applications that are really important to the future of computing.

坦白說，NVIDIA從誕生那天起就是一家加速運算公司。因此，我們公司一直在努力擴大我們可以加速的應用程式的數量。當然，電腦圖形學是原創的，我們正在利用即時光線追蹤技術對其進行重新發明。我們有渲染，這是一個全新的應用，我們正在取得巨大進展。我們剛才談到——我剛才提到了 5G 加速。最近，我們宣布了基因組計算。這些對於計算的未來來說都是非常重要的新應用。

In the area of artificial intelligence, from image recognition to natural language understanding, to conversation, to recommendation systems, to robotics and animation, the number of applications that we're going to accelerate in the field of AI is really, really broad. And each one of them are making tremendous progress and getting more and more complex. And so the question about the sustainability of our company really comes down to 2 dimensions. Let's assume for the fact -- let's assume for now that accelerated computing is the path forward, and we surely believe so. And there's a lot of evidence from the laws of physics to the laws of computer science that would suggest that accelerated computing is the right path forward. But this really basically comes down to 2 dimensions. One dimension is are we continuing to expand? Are we continuing to expand the number of applications that we can accelerate? Whether it's AI or computer graphics or genomics or 5G, for example.

在人工智慧領域，從圖像識別到自然語言理解、對話、推薦系統、機器人和動畫，我們將在人工智慧領域加速的應用數量非常非常廣泛。而且每一個都取得了巨大的進步並且變得越來越複雜。因此，我們公司可持續性的問題實際上可以歸結為兩個方面。讓我們假設這個事實——讓我們現在假設加速運算是前進的道路，我們當然相信這一點。從物理定律到電腦科學定律，有大量證據顯示加速計算是正確的前進道路。但這實際上基本上歸結為二維。一個方面是我們是否繼續擴張？我們是否會繼續擴大可以加速的應用程式數量？例如，無論是人工智慧、電腦圖形、基因組學或 5G。

And then the number -- and then the second is those applications, are they getting more impactful and adopted by the ecosystem, the industry? And are they continuing to be more complex? Those dimensions, the number of applications and the rich -- and the impact of those applications and the evolution, the growth of complexity of those applications, if those dynamics continue to grow, then I think we're going to do a good job. We're going to sustain. And so -- and I think when I spelled it out that way, it's basically the equation of growth of our company. I think it's fairly clear that the opportunities are fairly exciting ahead.

然後是數字——第二個是這些應用程序，它們是否變得更具影響力並被生態系統和行業所採用？它們還會變得越來越複雜嗎？這些維度、應用程式的數量和豐富性——以及這些應用程式的影響和演變、這些應用程式的複雜性的增長，如果這些動態繼續增長，那麼我認為我們會做得很好。我們會堅持下去。所以——我想當我這樣闡述的時候，它基本上就是我們公司的成長方程式。我認為很明顯，未來的機會相當令人興奮。

Your next question comes from the line of Blayne Curtis with Barclays.

您的下一個問題來自巴克萊銀行的布萊恩柯蒂斯。

Jensen, I just wanted to ask you on the auto side. I think at least one of your customers might have slowed out their program. Just kind of curious as you look out the next couple of years, the challenges, if the OEM is moving slower? And then just any perspective on the regulatory side, has anything changed there, would be helpful.

詹森，我只是想問你關於汽車方面的問題。我認為您的至少一位客戶可能已經放慢了他們的計劃。我只是有點好奇，展望未來幾年，如果 OEM 發展速度變慢，會面臨哪些挑戰？那麼，從監管方面來看，任何變化都會有所幫助。

I think that the automotive industry is struggling, but -- for all of the reasons that everybody knows. However, the enthusiasm to redefine and reinvent their business model has never been greater. Every single one of them, every single one of them would know now and they surely -- they've known for some time, and autonomous capabilities is really the vehicle to do that. They need to be tech companies. Every car company wants to be a tech company. They need to be a tech company. Every car company needs to be software-defined. And the platform by which to do so is an electric vehicle with autonomous autopilot capability. That car has to be software-defined. And this is their future and they're racing to get there.

我認為汽車產業正在苦苦掙扎，但原因大家都知道。然而，重新定義和重塑其商業模式的熱情從未如此強烈。他們每個人，他們每個人現在都知道了，而且他們肯定已經知道一段時間了，而自主能力確實是實現這一目標的工具。他們需要成為科技公司。每家汽車公司都想成為科技公司。他們需要成為一家科技公司。每家汽車公司都需要軟體定義。實現這一目標的平台是具有自動駕駛功能的電動車。那輛汽車必須由軟體定義。這就是他們的未來，他們正在努力實現它。

And so although the automotive industry is struggling in near term, their opportunity has never been better in my opinion. The future of AV is more important than ever. The opportunity is very real. The benefits of autonomous is for whether it's safety, whether it's utility, whether it's cost reduction and productivity, has never been more clear. And so I think that I'm as enthusiastic as ever about the autonomous vehicles and the projects that we're working on are moving ahead. And so the near-term challenges of the automotive industry or whatever sales slowdown in China that they're experiencing, I feel badly about that. But the industry is as clearheaded about the importance of AV as ever.

因此，儘管汽車產業短期內陷入困境，但我認為他們的機會從未如此好。AV 的未來比以往任何時候都更重要。這個機會是真實存在的。自動駕駛的好處無論是安全性、實用性、降低成本或提高生產力，都已經變得非常清晰。因此我認為我對自動駕駛汽車的熱情一如既往，而且我們正在進行的項目也在不斷推進。因此，對於汽車產業近期面臨的挑戰，或中國正在經歷的銷售放緩，我感到很難過。但業界對 AV 的重要性仍然一如既往的清醒。

I will 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。

We had an excellent quarter with strong demand for NVIDIA RTX graphics and NVIDIA AI platforms and record data center revenue. NVIDIA RTX is reinventing computer graphics, and the market's response is excellent, driving a powerful upgrade cycle in both gaming and professional graphics, while opening whole new opportunities for us to serve the huge community of independent creative workers and social content creators and new markets in rendering and cloud gaming. Our data center business is enjoying a new wave of growth, powered by 3 key trends in AI, natural language understanding, conversational AI, deep recommenders, are changing the way people interact with the Internet. The public cloud demand for AI is growing rapidly. And as AI shifts from development to production, our inference business is gaining momentum. We'll be talking a lot more about these key trends and much more at next month's GTC Conference in San Jose. Come join me. You won't be disappointed. Thanks, everyone.

我們度過了一個出色的季度，NVIDIA RTX 顯示卡和 NVIDIA AI 平台的需求強勁，資料中心收入創下了紀錄。NVIDIA RTX 正在重塑電腦圖形學，市場反應非常好，推動了遊戲和專業圖形學的強大升級週期，同時為我們服務於龐大的獨立創意工作者和社交內容創作者群體以及渲染和雲端遊戲的新市場開闢了全新的機會。我們的資料中心業務正在經歷新一輪的成長，這得益於人工智慧的三大關鍵趨勢：自然語言理解、對話式人工智慧和深度推薦，它們正在改變人們與網路互動的方式。AI公有雲需求快速成長。隨著人工智慧從開發轉向生產，我們的推理業務正在獲得發展動力。我們將在下個月於聖荷西舉行的 GTC 會議上進一步討論這些關鍵趨勢及其他內容。快來加入我吧。你不會失望的。謝謝大家。

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。