輝達 (NVDA) 2020 Q4 法說會逐字稿

Good afternoon.

下午好。

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

我叫克里斯蒂娜，今天我是你們的會議接線員。

Welcome to NVIDIA's financial results conference call.

歡迎參加 NVIDIA 的財務業績電話會議。

(Operator Instructions) Thank you.

（操作員說明）謝謝。

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.

謝謝你。

Good afternoon, everyone, and welcome to NVIDIA's Conference Call for the Fourth Quarter of Fiscal 2020.

大家下午好，歡迎參加 NVIDIA 2020 財年第四季度電話會議。

With me on the call today from NVIDIA are Jensen Huang, President and Chief Executive Officer; and Colette Kress, Executive Vice President and Chief Financial Officer.

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

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

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

The webcast will be available for replay until the conference call to discuss our financial results for the first quarter of fiscal 2021.

在電話會議討論我們 2021 財年第一季度的財務業績之前，該網絡廣播將可供重播。

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

今天通話的內容是 NVIDIA 的財產。

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

未經我們事先書面同意，不得複製或轉錄。

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

在本次電話會議期間，我們可能會根據當前預期做出前瞻性陳述。

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

這些受到許多重大風險和不確定性的影響，我們的實際結果可能存在重大差異。

For a discussion of factors that could affect our future financial results and business, please refer to the disclosure in today's earnings release, our most recent forms 10-K and 10-Q and the reports that we may file on Form 8-K with the Securities and Exchange Commission.

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

All our statements are made as of today, February 13, 2020, based on information currently available to us.

我們所有的聲明都是基於我們目前可獲得的信息，截至今天，即 2020 年 2 月 13 日。

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

除法律要求外，我們不承擔更新任何此類聲明的義務。

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

在本次電話會議中，我們將討論非 GAAP 財務指標。

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

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

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

有了這個，讓我把電話轉給科萊特。

Thanks, Simona.

謝謝，西蒙娜。

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.

第四季度收入為 31.1 億美元，同比增長 41%，環比增長 3%，遠高於我們的預期，反映了我們的數據中心和遊戲業務的上行空間。

Full year revenue was $10.9 billion, down 7%.

全年收入為 109 億美元，下降 7%。

We recovered from the excess channel inventory in gaming and an earlier pause in hyperscale spending and exited the year with great momentum.

我們從遊戲中過多的渠道庫存和超大規模支出的早期暫停中恢復過來，並以強勁的勢頭結束了這一年。

Starting with gaming.

從遊戲開始。

Revenue of $1.49 billion was up 56% year-on-year and down 10% sequentially.

收入為 14.9 億美元，同比增長 56%，環比下降 10%。

Full year gaming revenue was $5.52 billion, down 12% from our prior year.

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.

我們享受到對台式機和筆記本 GPU 的強勁終端需求。

Let me give you some more details.

讓我給你一些更多的細節。

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.

我們的遊戲陣容憑藉我們的 RTX GPU 獨特的光線追踪功能以及在每個價位上都令人難以置信的性能，為假期做好了準備。

From the Singles Day shopping event in China through the Christmas season in the West, channel demand was strong for our entire stack.

從中國的光棍節購物活動到西方的聖誕節期間，我們整個堆棧的渠道需求都很強勁。

Fueling this were new blockbuster games like Call of Duty: Modern Warfare, continued eSports momentum and new RTX Super products.

推動這一趨勢的是新的重磅遊戲，如使命召喚：現代戰爭、持續的電子競技勢頭和新的 RTX Super 產品。

With RTX price points as low as $299, ray tracing is now the sweet spot for PC gamers.

RTX 價格低至 299 美元，光線追踪現在是 PC 遊戲玩家的最佳選擇。

Gaming is thriving and gamers prefer GeForce.

遊戲正在蓬勃發展，遊戲玩家更喜歡 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.

根據 Newzoo 的數據，全球電子競技現象保持了遊戲的發展勢頭，觀眾人數現已超過 4.4 億，在短短 2 年內增長了 30% 以上。

The League of Legends World Championship brought more than 100 million viewers, on par with this month's Super Bowl.

英雄聯盟全球總決賽吸引了超過 1 億觀眾，與本月的超級碗相當。

Ray tracing titles continue to come to market, and GeForce RTX GPUs are the only ones that support this important technology.

光線追踪遊戲繼續上市，而 GeForce RTX GPU 是唯一支持這項重要技術的遊戲。

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.

本季度，Wolfenstein: Young blood 和 Deliver Us The Moon 是支持光線追踪以及 NVIDIA 深度學習超級採樣技術的最新作品，該技術也使用 AI 來提升性能。

With the proliferation of RTX-enabled games and our best ever top-to-bottom performance, we are solidly into the Turing architecture upgrade cycle.

隨著支持 RTX 的遊戲的激增和我們有史以來最好的自上而下的性能，我們正穩步進入圖靈架構升級週期。

Gamers continue to move to higher-end GPUs, seeking better performance and support for ray tracing.

遊戲玩家繼續轉向更高端的 GPU，尋求更好的性能和對光線追踪的支持。

Gaming laptops posted double-digit year-on-year growth for the eighth consecutive quarter.

遊戲筆記本電腦連續第八個季度實現兩位數的同比增長。

The category continues to expand, driven by appealing thin and light form factors with fantastic graphics performance.

在吸引人的輕薄外形和出色圖形性能的推動下，該類別繼續擴大。

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.

在這個假日季，零售商庫存了創紀錄的 125 台基於 NVIDIA GPU 的遊戲筆記本電腦，高於去年的 94 台，我們的 Max-Q 設計增加了 2 倍。

At CES, we launched the world's first 14-inch GeForce RTX laptop with ASUS.

在 CES 上，我們推出了全球首款搭載華碩的 14 英寸 GeForce RTX 筆記本電腦。

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.

我們還在 CES 上推出了 13 款新的 RTX Studio 系統，繼續為快速增長的自由創作者、設計師和 YouTube 用戶擴展我們的 Studio 筆記本電腦系列。

Powered by Turing GPUs, these systems are optimized for over 55 creative and design applications with RTX accelerated ray tracing and/or AI.

這些系統由 Turing GPU 提供支持，針對超過 55 種具有 RTX 加速光線追踪和/或 AI 的創意和設計應用程序進行了優化。

Last week, we launched our GeForce NOW cloud gaming service.

上週，我們推出了 GeForce NOW 雲遊戲服務。

Powered by GeForce, GeForce NOW is the first cloud gaming service to deliver ray trace games.

GeForce NOW 由 GeForce 提供支持，是第一個提供光線追踪遊戲的雲遊戲服務。

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.

它也是唯一一個開放平台，讓玩家可以享受他們已經擁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 enables PC games on Macs, Windows, PCs, TVs, Mobile devices and soon, Chromebooks.

GeForce NOW 支持 Mac、Windows、PC、電視、移動設備以及即將推出的 Chromebook 上的 PC 遊戲。

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.

GFN 有一個免費增值業務模式，其中包括 2 個會員計劃：具有標準訪問權限的免費會員； Founders 層的起價為每月 4.99 美元，提供優先訪問和 RTX 光線追踪支持。

Our goal with GeForce NOW is to expand GeForce gaming to more gamers.

我們與 GeForce NOW 的目標是將 GeForce 遊戲擴展到更多遊戲玩家。

About 80% of GeForce NOW gamers are playing on underpowered PCs or devices with Mac OS or Android.

大約 80% 的 GeForce NOW 遊戲玩家在性能不佳的 PC 或 Mac OS 或 Android 設備上玩遊戲。

With GeForce NOW, they are able to enjoy PC gaming on a GeForce GPU in the cloud.

借助 GeForce NOW，他們能夠在雲端的 GeForce GPU 上享受 PC 遊戲。

GeForce now can expand GeForce well beyond the roughly 200 million gamers we reach today.

GeForce 現在可以將 GeForce 擴展到我們今天所接觸到的大約 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.

另外，我們與全球最大的遊戲平台騰訊達成合作，將雲端PC遊戲帶到全球最大的遊戲市場中國。

NVIDIA GPU technology will power Tencent's Start cloud gaming service, which is in early testing stages.

NVIDIA GPU 技術將為騰訊的 Start 雲遊戲服務提供支持，該服務處於早期測試階段。

Moving to data center.

搬到數據中心。

Revenue was a record $968 million, up 43% year-on-year and up 33% sequentially, our strongest ever sequential growth in dollar terms.

收入達到創紀錄的 9.68 億美元，同比增長 43%，環比增長 33%，以美元計算，這是我們有史以來最強勁的環比增長。

Full year fiscal year '20 data center revenue was a record $2.98 billion, up 2% from the prior year.

20 財年全年數據中心收入達到創紀錄的 29.8 億美元，比上年增長 2%。

Strong growth was fueled by hyperscale and vertical industry end customers.

超大規模和垂直行業終端客戶推動了強勁增長。

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.

購買我們的訓練和推理產品以支持關鍵 AI 工作負載（例如自然語言理解、對話式 AI 和深度推薦器）推動了超大規模需求。

Hyperscale demand was also driven by cloud computing.

雲計算也推動了超大規模需求。

AWS now makes the T4 available in every region.

AWS 現在在每個區域都提供 T4。

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.

這凸顯了 T4 的多功能性，它在各種高性能計算工作負載方面表現出色，包括 AI 推理、雲遊戲、渲染和虛擬桌面。

Vertical industry growth was driven primarily by consumer Internet companies.

垂直行業增長主要由消費互聯網公司推動。

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.

零售、醫療保健和物流等其他垂直行業在深度學習參與的堅實基礎下繼續從早期擴建中發展，我們看到高性能計算、數據科學和邊緣計算應用程序的機會不斷擴大。

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.

我們的推理平台 T4 在公共雲部署和邊緣 AI 視頻分析應用程序的推動下，在另一個季度表現強勁，出貨量同比增長 4 倍。

T4 and V100, reflecting strong demand for inference and training, respectfully, set records this quarter for both shipments and revenue.

T4 和 V100 反映了對推理和訓練的強烈需求，恭敬地在本季度創下了出貨量和收入的記錄。

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 模型訓練的領先平台，但 NVIDIA 的推理平台正被一些世界領先的企業和消費者互聯網公司廣泛使用，包括美國運通、微軟、PayPal、Pinterest、Snap 和 Twitter。

The industry continues to do groundbreaking AI work for NVIDIA.

該行業繼續為 NVIDIA 開展開創性的 AI 工作。

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.

例如，微軟過去一年在其 Bing 搜索引擎中取得的最大質量改進源於其使用 NVIDIA GPU 和軟件來訓練和推理其自然語言理解模型。

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.

BERT 推廣的這些 DNN 變換器模型對訓練的計算要求比早期的基於圖像的模型高出一個數量級。

Conversational AI is a major new workload, requiring GPUs for inference to achieve high throughput within the desired low latency.

對話式 AI 是一種主要的新工作負載，需要 GPU 進行推理以在所需的低延遲內實現高吞吐量。

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.

事實上，微軟表示，與 CPU 相比，NVIDIA GPU 的推理吞吐量提高了高達 800 倍，使其能夠在全球範圍內每秒處理超過 100 萬次 BERT 推理。

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.

就在本週，微軟研究人員宣布了自然語言處理方面的新突破，這是有史以來在 NVIDIA DGX-2 上訓練的最大公開模型。

This advances the state of the art for AI assistance in tasks, such as answering questions, summarization and natural language generation.

這推進了 AI 輔助任務的最新技術，例如回答問題、總結和自然語言生成。

Recommendators are also an important machine learning model for the Internet, powering billions of queries per second.

推薦器也是互聯網的重要機器學習模型，每秒支持數十億次查詢。

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.

該行業正在轉向深度推薦器，例如廣泛和深度模型，它們利用深度學習來實現自動特徵學習並支持非結構化內容。

Running these models on GPUs can dramatically increase inference throughput and reduce latency compared with CPUs.

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。

For example, Alibaba's and Baidu's recommendation engines run on NVIDIA AI, boosting their inference throughput by orders of magnitudes beyond CPUs.

例如，阿里巴巴和百度的推薦引擎在 NVIDIA AI 上運行，將推理吞吐量提高了幾個數量級，超過了 CPU。

Deep recommendators enabled Alibaba to achieve 10% increase in click-through rates.

深度推薦器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.

我們還宣佈在 Microsoft Azure 上推出新的 GPU 加速超級計算機。

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.

它首次使客戶能夠從他們的辦公桌上按需租用整個 AI 超級計算機，與可能需要數月才能部署的大型本地超級計算機的功能相匹配。

And in Europe, energy company Eni announced the world's fastest industrial supercomputer based on NVIDIA GPUs.

在歐洲，能源公司 Eni 宣布推出世界上最快的基於 NVIDIA GPU 的工業超級計算機。

AI has even come to pizza delivery.

人工智能甚至已經來到了披薩外賣領域。

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.

在上個月的全國零售聯合會年會上，我們宣布多米諾作為客戶部署我們的深度學習和數據科學應用平台，幫助客戶參與和訂單準確性預測。

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.

在更廣泛的零售領域，我們看到大型零售商採用 NVIDIA 的邊緣計算產品來支持人工智能應用程序，從而減少收縮、優化物流和提高運營效率的情況顯著增加。

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.

在 SC19 超級計算大會上，我們與生態系統合作夥伴 ARM、Ampere Computing、富士通和 Marvell 一起介紹了基於 GPU 加速的基於 ARM 的服務器的參考設計平台。

We made available our ARM-compatible software development kit consisting of NVIDIA CUDA-X libraries and development tools for accelerating computing.

我們提供了與 ARM 兼容的軟件開發套件，其中包括用於加速計算的 NVIDIA CUDA-X 庫和開發工具。

This opens the floodgates of innovation to support growing new applications from hyperscale cloud to Exascale supercomputing.

這打開了創新的閘門，以支持從超大規模雲到 Exascale 超級計算的不斷增長的新應用程序。

We also introduced NVIDIA Magnum IO, a suite of software optimized to eliminate storage and input/output bottlenecks.

我們還推出了 NVIDIA Magnum IO，這是一套經過優化以消除存儲和輸入/輸出瓶頸的軟件。

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.

在處理海量數據集為數據科學家、高性能計算和人工智能研究人員執行複雜的財務分析、氣候建模和其他工作負載時，Magnum IO 可為多服務器、多 GPU 計算節點提供高達 20 倍的數據處理速度。

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.

最後，我們推出了第七代推理軟件開發套件 TensorRT 7，與在 CPU 上運行相比，它可以將對話式 AI 組件的速度提高 10 倍。

This helps drive latency below the 300 millisecond threshold considered necessary for real-time interactions supporting our growth in conversational AI.

這有助於將延遲降低到 300 毫秒閾值以下，這些閾值被認為是支持我們在會話 AI 中增長的實時交互所必需的。

Moving to ProVis.

轉移到 ProVis。

Revenue reached a record $331 million, up 13% year-on-year and up 2% sequentially.

收入達到創紀錄的 3.31 億美元，同比增長 13%，環比增長 2%。

Full year revenue was a record $1.21 billion, an increase of 7% from the prior year.

全年收入達到創紀錄的 12.1 億美元，比上年增長 7%。

ProVis accelerated in Q4 as the rollout of more RTX-enabled applications is driving strong upgrade cycle for our Turing GPUs.

隨著更多支持 RTX 的應用程序的推出正在推動我們的 Turing GPU 的強勁升級週期，ProVis 在第四季度加速。

RTX is also opening up new market segment opportunities, such as rendering and studio for freelance creatives.

RTX 還開闢了新的細分市場機會，例如為自由創作者提供渲染和工作室。

In November, V-ray, Arnold and Blender software renderers began shipping with RTX technology.

11 月，V-ray、Arnold 和 Blender 軟件渲染器開始使用 RTX 技術。

These joined our leading creative and design applications, including Premier Pro, Dimension, SOLIDWORKS, CATIA and Maya.

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。

With RTX, these applications enable enhanced creativity and notable productivity gains.

借助 RTX，這些應用程序可以增強創造力並顯著提高生產力。

In Blender Cycles, for example, real-time rendering performance is boosted 4x versus a CPU.

例如，在 Blender Cycles 中，實時渲染性能比 CPU 提高了 4 倍。

RTX is now supported by more than 40 leading creative and design applications, reaching a combined user base of over 40 million.

RTX 現在得到 40 多個領先的創意和設計應用程序的支持，總用戶群超過 4000 萬。

Finally, turning to automotive.

最後，轉向汽車。

Revenue was $163 million, flat from a year ago and up 1% sequentially.

收入為 1.63 億美元，與一年前持平，環比增長 1%。

Full year revenue reached a record $700 million, up 9% year-on-year.

全年收入達到創紀錄的 7 億美元，同比增長 9%。

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.

在本季度，我們發布了新一代自動駕駛汽車和機器人平台 DRIVE AGX Orin，由我們的新 Orin SoC 提供支持，性能是上一代 Xavier SoC 的近 7 倍。

The platform scales from level 2 plus AI-assisted driving up to level 5 fully driverless operation.

該平台從 2 級加 AI 輔助駕駛擴展到 5 級完全無人駕駛操作。

Orin is software-defined and compatible with Xavier, allowing developers to leverage their investment across multiple product generations.

Orin 是軟件定義的並且與 Xavier 兼容，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.

轉到損益表的其餘部分。

Q4 GAAP gross margins was 64.9% and non-GAAP was 65.4%, up sequentially, largely reflecting a higher contribution of data center products.

第四季度 GAAP 毛利率為 64.9%，非 GAAP 毛利率為 65.4%，環比上升，很大程度上反映了數據中心產品的更高貢獻。

Q4 GAAP operating expenses were $1.02 billion and non-GAAP operating expenses were $810 million, up 12% and 7% year-on-year, respectively.

第四季度 GAAP 運營費用為 10.2 億美元，非 GAAP 運營費用為 8.1 億美元，同比分別增長 12% 和 7%。

Q4 GAAP EPS was $1.53, up 66% from a year earlier.

第四季度 GAAP 每股收益為 1.53 美元，同比增長 66%。

Non-GAAP EPS was $1.89, up 136% from a year ago.

非公認會計原則每股收益為 1.89 美元，比一年前增長 136%。

Q4 cash from operations was $1.46 billion.

第四季度運營現金為 14.6 億美元。

Fiscal year '20 cash flow from operations was a record $4.76 billion.

20 財年的運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.

有了這個，讓我改變 2021 財年第一季度的前景。

The outlook does not include any contribution from the pending acquisition of Mellanox.

展望不包括即將收購的 Mellanox 的任何貢獻。

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.

我們正在與中國就監管批准進行接觸並取得進展，並相信此次收購可能會在 2020 年上半年完成。

Before we get to the new -- the numbers, let me comment on the impact of the coronavirus.

在我們了解新的數字之前，讓我評論一下冠狀病毒的影響。

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.

雖然現在還為時過早，最終影響難以估計，但我們已將第一季度的收入前景下調了 1 億美元，以應對潛在影響。

We expect revenue to be $3 billion, plus or minus 2%.

我們預計收入為 30 億美元，上下浮動 2%。

GAAP and non-GAAP gross margins are expected to be 65% and 65.4%, respectively, plus or minus 50 basis points.

GAAP 和非 GAAP 毛利率預計分別為 65% 和 65.4%，上下浮動 50 個基點。

GAAP and non-GAAP operating expenses are expected to be approximately $1.05 billion and $835 million, respectively.

GAAP 和非 GAAP 運營費用預計分別約為 10.5 億美元和 8.35 億美元。

GAAP and non-GAAP OI&E are both expected to be income of approximately $25 million.

GAAP 和非 GAAP OI&E 的收入均預計約為 2500 萬美元。

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

GAAP 和非 GAAP 稅率預計均為 9%，正負 1%，不包括離散項目。

Capital expenditures are expected to be approximately $150 million to $170 million.

資本支出預計約為 1.5 億至 1.7 億美元。

Further financial details are included in the CFO commentary and other information available on the IR website.

更多財務細節包含在首席財務官評論和投資者關係網站上提供的其他信息中。

In closing, let me highlight an upcoming event for the financial community.

最後，讓我強調一下即將為金融界舉辦的活動。

We will be at the Morgan Stanley Technology, Media and Telecom Conference on March 2 in San Francisco.

我們將於 3 月 2 日在舊金山參加摩根士丹利技術、媒體和電信會議。

With that, we will now open the call for questions.

有了這個，我們現在開始提問。

Operator, will you please poll for questions.

接線員，請您投票提問。

(Operator Instructions)

（操作員說明）

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?

我想關於數據中心，Colette 或 Jensen，你能談談推動本季度上漲的一些領域嗎？

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?

您談到了推論，以及 T4 和 V100 都有創紀錄的季度，但相對於您的內部預期，哪些業務推動了上漲？

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.

而且，如果您還可以談論您今天相對於幾年前的客戶檔案的廣度，以及如何擴展，那也會有所幫助。

Yes.

是的。

Toshiya, thanks a lot for your question.

Toshiya，非常感謝你的提問。

The primary driver for our growth is AI.

我們增長的主要驅動力是人工智能。

There are 4 fundamental dynamics.

有4個基本動態。

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.

首先是正在創建的 AI 模型正在取得突破，並且取得了相當驚人的突破，事實上，在自然語言理解、對話式 AI 和推薦系統方面。

And you know this, but for the others in the audience, recommendation systems are essentially the engine of the Internet today.

你知道這一點，但對於觀眾中的其他人來說，推薦系統本質上是當今互聯網的引擎。

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.

而之所以會這樣，是因為世界上的東西太多了，不管是商店，還是內容，網站，或者你查詢的信息，有上千億，上萬億，看你怎麼算，上百個世界上數以萬億計的物品。

And there are billions of people, each with their own characteristics and their countless contexts.

有數十億人，每個人都有自己的特點和無數的背景。

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.

在項目、人、用戶和我們所處的各種上下文、位置和您正在尋找的內容以及天氣或環境中發生的事情之間，這些上下文會影響搜索查詢——答案他們為您提供。

The recommendation system is just foundational now to search.

推薦系統現在只是搜索的基礎。

And some people have said this is the end of search and the beginning -- and the era of recommendation systems.

有人說這是搜索的終結和開始——推薦系統的時代。

Work is being done everywhere around the world in advancing recommendation systems.

世界各地都在推進推薦系統。

And very first time over the last year, it's been able to be done in deep learning.

去年第一次，它能夠在深度學習中完成。

And so the first thing is just the breakthroughs in AI.

所以第一件事就是人工智能的突破。

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.

第二個是生產人工智能，這意味著雖然我們有很大的機會，而且我們繼續有很大的培訓機會，因為模型越來越大，而且數量更多，但我們看到很多這些模型投入生產，並且該業務稱為推理。

Inference, as Colette mentioned, grew 4x year-over-year.

正如 Colette 所說，Inference 同比增長了 4 倍。

It's a substantial part of our business now.

它現在是我們業務的重要組成部分。

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.

但其中一個有趣的統計數據是 TensorRT 7，今年整個 TensorRT 下載量約為 500,000，比一年前翻了一番。

What most people don't understand about inference is it's an incredibly complex computational problem, but it's an enormously complex software problem.

大多數人不了解推理是一個非常複雜的計算問題，但它是一個非常複雜的軟件問題。

And so the second dynamic is moving from training or growing from training and models going into production called inference.

因此，第二個動力是從訓練轉向或從訓練和模型發展到生產，稱為推理。

The third is the growth, not just in hyperscale anymore, but in public cloud and in vertical industries.

第三個是增長，不僅在超大規模領域，而且在公共雲和垂直行業。

Public cloud because of thousands of AI start-ups that are now developing AI software in the cloud.

公共雲是因為成千上萬的 AI 初創公司現在正在雲中開發 AI 軟件。

And the OpEx model works much better for them as they're younger.

OpEx 模式對他們更有效，因為他們更年輕。

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.

當他們變得更大時，他們可以決定在本地構建自己的數據中心基礎設施，但成千上萬的初創企業開始在雲中生活。

We're also seeing really great success in verticals.

我們也在垂直領域看到了巨大的成功。

One of the most exciting vertical is logistics.

最令人興奮的垂直領域之一是物流。

Logistics, retail, warehousing.

物流、零售、倉儲。

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.

我認為，我們在本季度或上一季度末宣布，USPS、美國運通、沃爾瑪，只是擁有大量數據的大公司，他們正試圖對其進行數據分析和預測分析。

And so the third dynamic is the growth in -- beyond hyperscale and public cloud as well as vertical industries.

因此，第三個動力是超越超大規模和公共雲以及垂直行業的增長。

And then the last dynamic is being talked about a lot, and this is really, really exciting, and it's called edge AI.

最後一個動態被談論了很多，這真的非常令人興奮，它被稱為邊緣人工智能。

We used to call it industries and AI where the action is.

我們曾經把它稱為工業和人工智能。

But the industry now calls edge AI.

但業界現在稱之為邊緣人工智能。

We're seeing a lot of excitement there.

我們在那裡看到了很多興奮。

And the reason for that is you need to have low latency inference.

原因是您需要進行低延遲推理。

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.

出於成本原因或數據主權原因，您可能無法將數據一直流式傳輸到雲端，並且您需要響應時間。

And so those 4 dynamics around AI really drove our growth.

因此，圍繞人工智能的這 4 種動力真正推動了我們的增長。

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

你的下一個問題來自摩根士丹利的喬摩爾。

Great.

偉大的。

Just following up on that.

只是跟進。

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?

當您回顧過去 12 個月以及您在 HPC 雲業務中看到的減速時，既然您已經看到了推動反彈的原因，那麼您對最初是什麼導致它放緩的想法有什麼想法嗎？

Was it just digestion?

只是消化嗎？

Was it sort of a handoff from image recognition to these newer applications that you just talked about?

是不是從圖像識別到你剛才談到的這些新應用程序的切換？

Just help us -- what happened there?

只要幫助我們——那裡發生了什麼？

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?

而且我想，因為它與未來有關，我們是否認為這是一個會有這種塊狀的業務？

Yes.

是的。

That's a really good question.

這是一個非常好的問題。

In fact, if you look backwards, now we only have the benefit of history.

事實上，如果你向後看，現在我們只有歷史的好處。

The deep recommendation systems, the natural language understanding breakthroughs, the conversational AI breakthroughs, all happened in this last year.

深度推薦系統、自然語言理解的突破、對話式人工智能的突破，都發生在去年。

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.

行業從這些所謂的變壓器模型中獲得收益並繼續發展和進步的速度確實令人難以置信。

And so all of a sudden, the number of breakthroughs in AI has just grown tremendously, and these models have grown tremendously.

就這樣突然之間，人工智能的突破數量急劇增加，這些模型也急劇增長。

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.

就在上週，微軟宣布他們已經與我們所做的工作合作訓練了一個神經網絡模型，我們稱之為 Megatron，將模型的大小從 75 億個參數增加到 175 億個參數。

And the accuracy of their natural language understanding has just -- has really been boosted.

他們對自然語言理解的準確性剛剛——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.

模型就是這樣——人工智能正在尋找真正奇妙的突破，模型變得越來越大，數量也越來越多。

And when you look back and look at when these breakthroughs happened, it essentially happened this last year.

當你回顧這些突破發生的時間時，它基本上發生在去年。

The second, we've been working on inference for some time.

第二，我們一直在研究推理。

And until this last year, very few of those inference models went into production.

直到去年，這些推理模型中很少有投入生產。

And now we have deep learning models across all of the hyperscalers in production.

現在，我們在生產中的所有超大規模計算機上都有深度學習模型。

And this last year, we saw really great growth in inference.

去年，我們在推理方面看到了巨大的增長。

The third dynamic is public clouds.

第三個動力是公共雲。

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.

所有這些 AI 初創公司都在世界各地興起，大約有 6,000 家，他們開始開發並能夠將他們的模型投入生產。

And with the scale out of AWS, we now have T4s in every single geography.

隨著 AWS 的擴展，我們現在在每個地理區域都有 T4。

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.

因此，我們在雲中的 GPU 的可用性以及初創公司和垂直行業將其 AI 模型部署到生產中的組合，所有這些結合在一起。

And all of that happened this last year.

而這一切都發生在去年。

And as a result, we had record sales of V100s and T4s.

結果，我們創下了 V100 和 T4 的銷售記錄。

And so we're quite excited with the developments, and it's all really powered by AI.

所以我們對這些發展感到非常興奮，這一切都是由人工智能驅動的。

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.

祝賀業務恢復強勁增長。

Jensen, I wanted to ask about how you are positioned from a supply perspective for this coming year?

Jensen，我想問一下，從供應的角度來看，您對來年的定位如何？

Your main foundry is running pretty tight.

你的主要代工廠運行非常緊張。

How will you be able to support the 20% or so growth here that many investors are looking for?

您將如何支持許多投資者正在尋找的 20% 左右的增長？

If you could just give us some commentary on how you're positioned from a supply perspective, that will be very helpful.

如果您可以從供應的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.

好吧，我認為我們的供應狀況非常好。

We surely won't have ample supply.

我們肯定不會有充足的供應。

It is true that the industry is tight and the combination of supporting multiple processes, multiple fabs across our partner, TSMC.

確實，這個行業很緊張，而且我們的合作夥伴台積電支持多個工藝、多個晶圓廠的組合。

We've got a lot of different factories and a lot of different -- several different nodes of process qualified.

我們有很多不同的工廠和很多不同的——幾個不同的工藝節點合格。

I think we're in good shape.

我認為我們的狀態很好。

And so we just have to watch it closely.

所以我們只需要密切關注它。

And we're working very closely with all of our customers in forecasting.

我們正在與所有客戶密切合作進行預測。

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.

當然，這也為我們提供了更好的可見性，而且——但我們所有人都必須做更好的預測工作，我們的客戶和我們的代工合作夥伴台積電之間的合作非常密切。

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

您的下一個問題來自瑞銀集團的蒂莫西·阿庫裡（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.

Colette，我想知道你是否可以給我們 - 在數據中心，如果你能給我們一些關於行業和超大規模之間的混合的想法。

I think last quarter, hyperscale was a little bit less than 50%.

我認為上個季度，超大規模的比例略低於 50%。

Can you give us maybe the mix or how much it was up, something like that?

你能不能給我們可能的組合或增加了多少，諸如此類？

Yes.

是的。

Tim, thanks for the question.

蒂姆，謝謝你的問題。

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.

與我們上個季度看到的情況類似，隨著我們進入本季度，所有事物都在增長，超大規模的增長，這些垂直行業甚至雲實例的持續擴張。

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.

我們仍然在考慮我們的超大規模和垂直行業之間大約 50-50 的相同比例，就我們的整體超大規模而言，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.

祝賀結果。

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.

當我查看這些數字時，數據中心的絕對順序增長幾乎是 2 倍或 2 倍以北，這是我們過去看到的絕對順序變化。

Through the course of this quarter, you were pretty clear that you would expect to see an acceleration of growth in the December quarter.

在本季度的整個過程中，您非常清楚，您希望在 12 月季度看到增長的加速。

I'm just curious of how you think about that going into the April quarter?

我只是好奇你如何看待進入 4 月季度的情況？

And how we should think about that growth rate through the course of this year?

我們應該如何看待今年的增長率？

If you can give us any kind of framework.

如果你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?

Jensen，只是好奇，我的意思是，當您考慮更大的圖景時，您認為從行業的角度來看，我們今天在 GPU 的數量或附加率方面處於什麼位置，是為了加速服務器市場嗎？

And where do you think that might be looking out over the next 3 years or so?

您認為在未來 3 年左右的時間裡，這可能會發生什麼？

Thanks, Aaron.

謝謝，亞倫。

Colette, do you want to go first?

科萊特，你想先走嗎？

Sure.

當然。

When we think about going into Q1 and our data center overall growth, we do expect to see continued growth, both going into Q1.

當我們考慮進入第一季度和我們的數據中心整體增長時，我們確實希望看到持續增長，兩者都進入第一季度。

We believe our visibility still remains positive quite well, and we're expecting that as we move into it and go forward.

我們相信我們的能見度仍然很好，我們期待著我們進入它並繼續前進。

Yes.

是的。

Aaron, I believe that every query on the Internet will be accelerated someday.

Aaron，我相信互聯網上的每一個查詢總有一天會被加速。

And at the very core of it, most -- almost all queries will have some natural language understanding component to it.

在它的核心，大多數——幾乎所有的查詢都會有一些自然語言理解組件。

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.

幾乎所有的查詢都必須從數以萬億計的可能性中進行排序並提出建議，將其過濾並為您的查詢推荐一些推薦的答案。

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.

無論是購物還是看電影，或者只是詢問地點，甚至是問一個問題，所有答案的可能性數量與最佳答案之間的關係 - 都需要被過濾掉。

And that filtering process is called recommendation.

這個過濾過程稱為推薦。

That recommendation system is really complex, and deep learning is going to be involved in all that.

推薦系統真的很複雜，深度學習將涉及所有這些。

That's the first thing.

這是第一件事。

I believe that every query will be accelerated.

相信每次查詢都會加速。

The second is, as you know, CPU scaling has really slowed, and there's just no two ways about it.

第二個是，如您所知，CPU 擴展速度確實變慢了，而且沒有兩種方法可以解決。

It's not a marketing thing.

這不是營銷的事情。

It's a physics thing.

這是一個物理問題。

And the ability for CPUs to continue to scale without increasing cost or increasing power has ended.

CPU 在不增加成本或增加功率的情況下繼續擴展的能力已經結束。

And it's called the end of Dennard scaling.

這被稱為登納德縮放的終結。

And so there has to be another approach.

所以必須有另一種方法。

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.

深度學習的出現和人工智能的使用以及計算量的結合——對於每一個查詢，但隨之而來的好處，以及登納德縮放的結束，表明需要另一種方法，我們認為這種方法是加速。

Now our approach for acceleration is fundamentally different than an accelerator.

現在我們的加速方法與加速器根本不同。

Notice, we never say accelerator, we say accelerated computing.

注意，我們從來不說加速器，我們說的是加速計算。

And the reason for that is because we believe that a software-defined data center will have all kinds of different AIs.

其原因是因為我們相信軟件定義的數據中心將擁有各種不同的人工智能。

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.

人工智能將繼續發展，模型將繼續發展並變得更大，軟件定義的數據中心需要可編程。

It is one of the reasons why we've been so successful.

這也是我們如此成功的原因之一。

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.

如果你回過頭來想想過去 3 或 4 年來我圍繞這個領域提出的所有問題，答案的一致性與架構的可編程性、軟件的豐富性、困難度有關編譯器的數量、模型的不斷增長的規模、模型的多樣性以及這些模型正在創造的進步。

And so we're seeing the beginning of a new computing era.

因此，我們看到了一個新的計算時代的開始。

And a fixed function accelerator is simply not the right answer.

固定功能的加速器根本不是正確的答案。

And so we believe that the future is going to be accelerated.

所以我們相信未來將會加速。

It's going to require an accelerated computing platform, and software richness is really vital, so that these data centers could be software defined.

這將需要一個加速的計算平台，而軟件的豐富性真的很重要，這樣這些數據中心就可以由軟件定義。

And so I think that we're in the early innings, the early innings, very, very early innings of this new future.

所以我認為我們正處於這個新未來的早期階段，早期階段，非常非常早期階段。

And I think that accelerated computing is going to become more and more important.

而且我認為加速計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 Matt Ramsay 與 Cowen 的對話。

Obviously, congratulations on the data center success.

顯然，祝賀數據中心成功。

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.

我想問一點，科萊特，關於你為冠狀病毒花費了 1 億美元，我想問一點你是如何達到這個數字的。

Really 2 pieces.

真的是2塊。

One, if you could remind us maybe in terms of units or revenue, how -- what percentage of your gaming business is within China?

第一，如果您可以從單位或收入方面提醒我們，您的遊戲業務在中國的比例是多少？

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?

當您查看您從指導中撤出的 1 億美元時，您是否從需求中斷的角度考慮這一點？

Or are you thinking about it from something in the supply chain that might limit your sales?

或者您是從供應鏈中可能會限制您的銷售的某些方面考慮它嗎？

Sure.

當然。

Thanks for the question, Matt.

謝謝你的問題，馬特。

So it's really still quite early in terms of trying to figure out what the impact from the overall coronavirus may be.

因此，就試圖弄清楚整個冠狀病毒可能產生的影響而言，現在還為時過早。

So we're not necessarily precise in terms of our estimate.

所以我們的估計不一定準確。

Yes, our estimates are split between an impact possibly on gaming and data center and split pretty much equally.

是的，我們的估計分為可能對遊戲和數據中心的影響和幾乎均等的影響。

The $100 million also reflects what may be supply challenges or may be overall demand.

1 億美元還反映了可能是供應挑戰或整體需求。

But we're still looking at those to get a better understanding where we think that might be.

但我們仍在研究這些以更好地了解我們認為可能的位置。

In terms of our business and our business makeup, yes, our overall China business for gaming is an important piece.

就我們的業務和業務構成而言，是的，我們在中國的整體遊戲業務是一個重要組成部分。

We have about 30% of our overall China gaming as a percentage of our overall gaming business.

我們在整個中國遊戲業務中的佔比約為 30%。

For data center, it's -- it moves quite a bit.

對於數據中心，它是——它移動了很多。

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.

它們對我們來說是一個非常重要的市場，但它會隨著最終客戶的整體組合以及他們可能選擇的系統構建而逐季變化。

So it's a little harder to determine.

所以判斷起來有點困難。

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.

祝賀取得了強有力的成果和指導。

On gaming -- yes, no problem.

在遊戲方面——是的，沒問題。

Good to see the recent launch of your GeForce NOW service.

很高興看到您最近推出的 GeForce NOW 服務。

But on the partnership with Tencent on cloud gaming, seems like Tencent should have a smoother transition to the cloud model.

但在與騰訊在雲遊戲方面的合作上，騰訊似乎應該更平穩地過渡到雲模式。

They are the largest gaming company in the world, so they own many of the games.

他們是世界上最大的遊戲公司，所以他們擁有許多遊戲。

They also have their own data center infrastructure already in place.

他們還擁有自己的數據中心基礎設施。

But how is the NVIDIA team going to be supporting this partnership?

但是 NVIDIA 團隊將如何支持這種合作關係呢？

Is it going to be [deal your] GeForce NOW hardware framework?

是否會[交易您的] GeForce NOW 硬件框架？

Or will you just be supporting them with your standalone GPU products?

或者您只是用您的獨立 GPU 產品來支持它們？

And when do you expect the service to go mainstream?

您預計該服務何時會成為主流？

Let's see.

讓我們來看看。

Tencent is the world's largest publisher.

騰訊是全球最大的發行商。

China represents about 1/3 of the world's gaming, and transitioning to the cloud is going to be a long-term journey.

中國約佔全球遊戲市場的 1/3，向雲過渡將是一個長期的旅程。

And the reason for that is because Internet connection is not consistent throughout the entire market.

原因是整個市場的互聯網連接並不一致。

And a lot of application still needs to be onboarded, and we're working very closely with them.

許多應用程序仍然需要加入，我們正在與他們密切合作。

We're super enthusiastic about it.

我們對此非常熱情。

If we're successful long term, and we're talking about an extra 1 billion gamers that we might be able to reach.

如果我們長期取得成功，並且我們正在談論我們可能能夠接觸到的額外 10 億遊戲玩家。

And so I think that this is an exciting opportunity, just a long-term journey.

所以我認為這是一個令人興奮的機會，只是一個長期的旅程。

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.

現在在西方，我們有更多的機會來完善世界各地的連接，並通過數據中心、本地集線器以及人們在家中的 WiFi 路由器工作。

And so we've been in beta for quite some time, as you know.

如您所知，我們已經進行了相當長一段時間的測試。

And here in the West, our platform is open.

在西方，我們的平台是開放的。

And we have several hundred games now and we're in the process of onboarding another 1,500 games.

我們現在有數百款遊戲，我們正在加入另外 1,500 款遊戲。

We're the only cloud platform that's based on Windows and allows us to be able to bring PC games to the cloud.

我們是唯一基於 Windows 的雲平台，讓我們能夠將 PC 遊戲帶到雲端。

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.

所以範圍是——我們在西部有更多的經驗，我們已經擁有——我們顯然有更多的遊戲可以加入。

But I'm super enthusiastic about the partnership we have with Tencent.

但我對我們與騰訊的合作非常感興趣。

Overall, our GeForce NOW -- you guys saw the launch, it's -- the reception has been fantastic, the reviews have been fantastic.

總體而言，我們的 GeForce NOW ——你們看到了發布，它是——接收非常好，評論非常好。

Our strategy has 3 components.

我們的策略有 3 個組成部分。

There's the GeForce NOW service that we provide ourselves.

有我們自己提供的 GeForce NOW 服務。

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.

我們還與世界各地的電信公司建立了 GeForce NOW 聯盟，以覆蓋我們未涉足的世界各地。

And that is going super well, and I'm excited about that.

而且進展非常順利，我對此感到很興奮。

And then lastly, partnerships with large publishers, for example, like Tencent.

最後，與大型發行商合作，例如騰訊。

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.

當然，我們為他們提供我們的平台，以及大量軟件和大量工程，必須協作完成以改進服務。

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

您的下一個問題來自 C.J. Muse with Evercore 的台詞。

I guess a question on the gaming side.

我猜是遊戲方面的一個問題。

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.

如果我查看您的整體收入指南，我猜您似乎通常在尋找到 4 月份更好的季節性趨勢。

And I guess can you speak to that?

我想你能談談嗎？

And then how are you seeing desktop gaming demand with ray tracing content becoming more available?

那麼，隨著光線追踪內容變得越來越可用，您如何看待桌面遊戲需求？

How should we think about the growth trajectory through 2020?

我們應該如何看待到 2020 年的增長軌跡？

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?

然後只是作為遊戲的一部分的建模問題，筆記本現在佔收入的 1/3，我們應該如何考慮進入 4 月和 7 月這部分業務的季節性？

Yes.

是的。

So C.J., I'm going to go first, and then Colette is going take it home here.

所以 C.J.，我要先走，然後 Colette 會把它帶回家。

So the first part of it is this, our gaming business has at the end -- I'm sorry.

所以它的第一部分是，我們的遊戲業務已經結束了——我很抱歉。

Okay.

好的。

Our gaming business, the end market demand is really terrific.

我們的遊戲業務，終端市場的需求真的很棒。

It's really healthy.

它真的很健康。

It's been healthy throughout the whole year.

一整年都很健康。

And it's pretty clear that RTX is doing fantastic.

很明顯，RTX 的表現非常出色。

And it's very -- it's super clear now that ray tracing is the most important new feature of next-generation graphics.

非常 - 現在非常清楚，光線追踪是下一代圖形最重要的新功能。

We have 30 -- over 30 games that have been announced, 11 games or so that have been shipped.

我們有 30 款——超過 30 款遊戲已經發布，大約 11 款遊戲已經發貨。

The pipeline of ray tracing games that are going to be coming out is just really, really exciting.

即將推出的光線追踪遊戲管道真的非常非常令人興奮。

The second factor -- and one more thing about RTX, we finally have taken RTX down to $299.

第二個因素——以及關於 RTX 的另一件事，我們最終將 RTX 降至 299 美元。

So it's now at the sweet spot of gaming.

所以它現在處於遊戲的最佳位置。

And so RTX is doing fantastic.

因此，RTX 表現出色。

The sell-through is fantastic all over the world.

全世界的銷售都很棒。

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.

我們在遊戲業務中發生變化的第二部分是——筆記本電腦的銷量和 Nintendo Switch 的成功確實改變了我們整體遊戲業務的形象。

Our notebook business, as Colette mentioned earlier, has seen double-digit growth for 8 consecutive quarters, and this is unquestionably a new gaming category.

我們的筆記本業務，正如 Colette 之前提到的，連續 8 個季度實現兩位數的增長，這無疑是一個新的遊戲品類。

Like it's a new game console.

就像它是一個新的遊戲機。

This is going to be the largest game console in the world, I believe.

我相信這將是世界上最大的遊戲機。

And the reason for that is because there are more people with laptops than there are of any other device.

原因是使用筆記本電腦的人比使用任何其他設備的人都多。

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.

因此，我們能夠將 RTX 融入輕薄筆記本電腦，輕薄筆記本電腦這一事實確實是一個突破。

And it's one of the reasons why we're seeing such great success in notebook.

這也是我們在筆記本電腦中看到如此巨大成功的原因之一。

Between the notebook business and our Nintendo Switch business, the profile of gaming overall has changed and has become more seasonal.

在筆記本業務和我們的 Nintendo Switch 業務之間，遊戲的整體形象發生了變化，並且變得更具季節性。

It's more seasonal because devices, systems, like notebooks and Switch, are built largely in 2 quarters, Q2 and Q3.

它更具季節性，因為筆記本電腦和 Switch 等設備和系統主要在第二季度和第三季度兩個季度構建。

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.

他們主要在第二季度和第三季度建造它，因為建造它們並運送它們並將它們放入世界各地的樞紐需要一段時間。

And they tend to build it ahead of the holiday season.

他們傾向於在假期之前建造它。

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.

這就是為什麼第三季度往往會更大，第四季度往往更具季節性，而第一季度往往比過去更具季節性的原因之一。

But the end demand is fantastic.

但最終需求是驚人的。

RTX is doing great.

RTX 做得很好。

And part of it is just a result of the success of our notebooks.

其中一部分只是我們筆記本電腦成功的結果。

I'm going to hand it over to Colette.

我要把它交給科萊特。

Yes.

是的。

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.

因此，從背景出發，您會考慮遊戲、筆記本電腦、整個 Switch 中的所有不同組件，當然還有我們在台式機方面擁有的所有光線追踪，我們的正常季節性，正如我們所看到的在第一季度，對於所有這 3 件的遊戲，通常從第四季度依次下降，從第四季度到第一季度依次下降。

This year, the outlook assumes it will probably be a little bit more pronounced due to the coronavirus.

今年，前景假設由于冠狀病毒，它可能會更加明顯。

So in total, we're probably looking at Q1 to be in the low double-digit sequential decline in gaming.

因此，總的來說，我們可能認為第一季度遊戲行業將處於兩位數的低位連續下降中。

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 Atif Malik。

Good job on results and guide.

在結果和指導方面做得很好。

On the same topic, coronavirus.

在同一主題上，冠狀病毒。

Colette, I'm a bit surprised that the guidance -- the range on the guidance is not wider versus historic.

科萊特，我對這個指導感到有點驚訝——指導的範圍沒有比歷史更寬。

Can you just talk about why not widen the range?

你能談談為什麼不擴大範圍嗎？

And what went into that $100 million hit from the coronavirus?

冠狀病毒造成的 1 億美元損失是什麼？

So Atif, thanks for the question.

所以阿蒂夫，謝謝你的問題。

Again, it's still very early regarding the coronavirus.

同樣，關于冠狀病毒還為時過早。

Our thoughts are out with both the employees, the families and others that are in China.

我們的想法與在中國的員工、家庭和其他人一樣。

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.

因此，我們與我們在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的討論都盡可能及時。

And that went into our discussion and our thoughts on the overall guidance that we gave into our $100 million.

這進入了我們的討論和我們對我們投入 1 億美元的總體指導的想法。

We'll just have to see how the quarter comes through, and we'll discuss more when we get to it.

我們只需要看看這個季度是如何度過的，當我們到達時我們會討論更多。

But at this time, that was our best estimate at this time.

但此時，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.

Jensen，我很想听聽您對推理市場如何發展的看法。

Historically, NVIDIA's had essentially all of the training market and little of the inference market in the last 1.5 years or so.

從歷史上看，在過去 1.5 年左右的時間裡，NVIDIA 基本上佔據了所有的訓練市場，而幾乎沒有推理市場。

I think that's changed where you've done much better in inference.

我認為你在推理方面做得更好的地方已經改變了。

Now you have the T4 in the cloud, you have EGX at the edge.

現在您在雲端擁有 T4，在邊緣擁有 EGX。

And you have Jetson, I think, is what it's called at the sort of endpoint device.

我認為，您擁有 Jetson，這就是它在端點設備上的名稱。

How do you anticipate that market for inference developing across those various positions?

您如何預測推理市場在這些不同職位上的發展？

And how are you aligning your portfolio for that growth?

您如何調整您的投資組合以適應這種增長？

Yes.

是的。

Thanks a lot, Will.

非常感謝，威爾。

Let's see, I think the -- historically, inference has been a small part of our business because AI was still being developed.

讓我們看看，我認為——從歷史上看，推理一直是我們業務的一小部分，因為人工智能仍在開發中。

Deep learning, AI is not -- historical AI, classical machine learning weren't particularly suited for GPUs and weren't particularly suited for acceleration.

深度學習，人工智能不是——歷史人工智能，經典機器學習並不特別適合 GPU，也不特別適合加速。

It wasn't until deep learning came along that the amount of computation necessary is just extraordinary.

直到深度學習出現時，所需的計算量才非常驚人。

And the second factor is the type of AI models that were developed.

第二個因素是開發的人工智能模型的類型。

Eventually, if -- the type of models related to natural language understanding and conversational AI and recommendation systems, these require instantaneous response.

最終，如果——與自然語言理解、對話式人工智能和推薦系統相關的模型類型，這些都需要即時響應。

The faster the answer, the more likely someone is going to click on the answer.

答案越快，有人點擊答案的可能性就越大。

And so you know that latency matters a great deal, and it's measurable.

所以你知道延遲很重要，而且它是可以測量的。

The effect on the business is directly measurable.

對業務的影響是可以直接衡量的。

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.

例如，對於對話式人工智能，我們已經能夠減少從語音識別到語言處理的整個管道的延遲，例如，修復錯誤等，提出從文本到語音的建議語音合成。

That entire pipeline could take several seconds.

整個管道可能需要幾秒鐘。

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.

我們運行得如此之快，以至於我們現在可以在幾百、200、300 毫秒內處理整個管道。

That is in the realm of interactive conversation.

那是在交互式對話領域。

Beyond that, it's just simply too slow.

除此之外，它只是太慢了。

And so the combination of AI models that are large and complex that are moving to inference, moving to production.

因此，大而復雜的 AI 模型的組合正在轉向推理，轉向生產。

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.

其次，我們的 GPU 必不可少的對話式 AI 和延遲敏感模型和應用程序，現在向前發展，我認為您將在推理方面為我們看到更多機會。

The way to think about that long-term is acceleration is essential because of end of Dennard scaling.

由於 Dennard 縮放的結束，考慮長期的方式是加速是必不可少的。

Process technology is going to demand that we compute in a different way.

過程技術將要求我們以不同的方式進行計算。

And the way that AI has evolved and deep learning, it suggests that acceleration on GPUs is just a really phenomenal approach.

人工智能的發展和深度學習的方式表明，GPU 上的加速只是一種非常了不起的方法。

Data centers are going to have to be software-defined.

數據中心必須是軟件定義的。

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.

而且我認為正如我所提到的，我想我之前提到過另一個問題，我相信在未來，數據中心將全部加速。

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.

它將全部運行人工智能模型，它將是軟件定義的，並且是可編程的，並且擁有一個加速的計算平台是必不可少的。

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.

當您移動到邊緣時，這實際上取決於您的平台是否是軟件定義的，它是否必須是可編程的，或者它是否具有修復功能。

There are many, many devices where the inference work is very specific.

有很多很多設備的推理工作非常具體。

It could be something as simple as detecting changes in temperature or changes in sound or detecting motion.

它可以像檢測溫度變化或聲音變化或檢測運動一樣簡單。

Those type of inference models are -- could still be based on deep learning.

那些類型的推理模型——仍然可以基於深度學習。

It's function-specific.

它是特定於功能的。

You don't have to change it very often and you're running 1 or 2 models at any given point in time.

您不必經常更改它，並且您可以在任何給定時間點運行 1 或 2 個模型。

And so those devices are going to be incredibly cost-effective.

因此，這些設備將具有難以置信的成本效益。

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.

我相信，那些 AI 芯片，你將擁有 0.50 美元、1 美元的 AI 芯片，你只需將其放入某些東西中，它就會進行神奇的檢測。

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.

我們所處的平台類型，例如自動駕駛汽車和機器人技術，軟件非常複雜，未來還有很多發展，而且會不斷變得更好。

Those software-defined platforms are really the ideal targets for us.

那些軟件定義的平台確實是我們的理想目標。

And so we call it AI at the edge, edge computing devices.

所以我們稱之為邊緣人工智能，邊緣計算設備。

One of the edge computing devices I'm very excited about is what people call mobile edge or basically 5G telco edge.

我非常興奮的邊緣計算設備之一是人們所說的移動邊緣或基本上是 5G 電信邊緣。

That data center will be programmable.

該數據中心將是可編程的。

We recently announced that we partnered with Ericsson and we're going to be accelerating the 5G stack.

我們最近宣布與愛立信合作，我們將加速 5G 堆棧。

And so that needs to be a software-defined data center.

所以這需要一個軟件定義的數據中心。

It runs all kinds of applications, including 5G.

它運行各種應用程序，包括 5G。

And those applications are going to be -- those opportunities are fantastic for us.

這些應用程序將是 - 這些機會對我們來說非常棒。

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?

Jensen，我想我有一個關於您的問題——您如何看待您在數據中心市場地位的可持續性？

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.

而且我想以我的簡單觀點，大約 12 年前，你提出了一個共識呼籲投資 CUDA 軟件，將其分發給大學。

Neural networking took off and you were the de facto standard, and here we are right now.

神經網絡起飛，你是事實上的標準，我們現在就在這裡。

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.

對我來說，有趣的是，您今天看到的對您的產品的需求來自去年剛剛開發的市場。

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?

我的問題是，你如何看待你的投資、你的研發投資策略，以確保你在市場、競爭對手甚至投資這些市場的客戶中保持領先？

Yes.

是的。

Thanks, Mark.

謝謝，馬克。

Our company has to live 10 years ahead of the market.

我們公司必須領先市場10年。

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.

所以我們必須想像 10 年後、5 年後的世界會變成什麼樣子，然後往回走。

Now our company is focused on one singular thing.

現在我們公司專注於一件獨特的事情。

The simplicity of it is incredible.

它的簡單性令人難以置信。

And that one singular thing is accelerated computing, accelerated computing.

一個獨特的事情是加速計算，加速計算。

And accelerated computing is all about the architecture, of course.

當然，加速計算與架構有關。

It's about the complicated systems that we're in because throughput is high.

這是關於我們所處的複雜系統，因為吞吐量很高。

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.

當我們加速時，我們可以——當我們的計算速度比 CPU 快 10 倍、20 倍、50 倍、100 倍時，突然之間，一切都變成了瓶頸。

Memory's a bottleneck, networking's a bottleneck, storage is a bottleneck, everything is a bottleneck.

內存是瓶頸，網絡是瓶頸，存儲是瓶頸，一切都是瓶頸。

And so we have to be -- NVIDIA has to be a supremely good system designer.

所以我們必須——英偉達必須是一個非常優秀的系統設計師。

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.

但是我們堆棧的複雜性，也就是它上面的軟件堆棧，實際上是過去 29 年的投資真正得到回報的地方。

NVIDIA, frankly, has been an accelerated computing company since the day it was born.

坦率地說，NVIDIA 從誕生之日起就是一家加速計算公司。

And so we -- our company is constantly trying to expand the number of applications that we can accelerate.

所以我們——我們的公司一直在努力擴大我們可以加速的應用程序的數量。

Of course, computer graphics was an original one, and we're reinventing it with real-time ray tracing.

當然，計算機圖形學是原創的，我們正在通過實時光線追踪對其進行改造。

We have rendering, which is a brand-new application that we're making great progress in.

我們有渲染，這是一個我們正在取得很大進展的全新應用程序。

We just talked -- I just mentioned 5G acceleration.

我們剛剛談過——我剛剛提到了 5G 加速。

Recently, we announced genomics computing.

最近，我們宣布了基因組學計算。

And so those are new applications that are really important to the future of computing.

因此，這些都是對計算的未來非常重要的新應用程序。

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.

在人工智能領域，從圖像識別到自然語言理解、對話、推薦系統、機器人和動畫，我們將在人工智能領域加速的應用數量非常非常廣泛。

And each one of them are making tremendous progress and getting more and more complex.

他們每個人都在取得巨大的進步，並且變得越來越複雜。

And so the question about the sustainability of our company really comes down to 2 dimensions.

因此，關於我們公司可持續性的問題實際上歸結為兩個方面。

Let's assume for the fact -- let's assume for now that accelerated computing is the path forward, and we surely believe so.

讓我們假設事實——讓我們現在假設加速計算是前進的道路，我們當然相信這一點。

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.

從物理定律到計算機科學定律，有大量證據表明加速計算是正確的前進道路。

But this really basically comes down to 2 dimensions.

但這實際上基本上歸結為二維。

One dimension is are we continuing to expand?

一個維度是我們是否在繼續擴張？

Are we continuing to expand the number of applications that we can accelerate?

我們是否繼續擴大我們可以加速的應用程序的數量？

Whether it's AI or computer graphics or genomics or 5G, for example.

例如，無論是人工智能、計算機圖形學、基因組學還是 5G。

And then the number -- and then the second is those applications, are they getting more impactful and adopted by the ecosystem, the industry?

然後是數字——第二個是那些應用程序，它們是否變得更具影響力並被生態系統、行業所採用？

And are they continuing to be more complex?

它們是否繼續變得更加複雜？

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.

那些維度，應用程序的數量和豐富——以及這些應用程序的影響和演變，這些應用程序的複雜性的增長，如果這些動態繼續增長，那麼我認為我們會做得很好。

We're going to sustain.

我們要堅持下去。

And so -- and I think when I spelled it out that way, it's basically the equation of growth of our company.

所以 - 我認為當我這樣拼寫時，它基本上是我們公司的增長方程。

I think it's fairly clear that the opportunities are fairly exciting ahead.

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 Blayne Curtis。

Jensen, I just wanted to ask you on the auto side.

Jensen，我只是想在汽車方面問你。

I think at least one of your customers might have slowed out their program.

我認為至少您的一位客戶可能已經放慢了他們的計劃。

Just kind of curious as you look out the next couple of years, the challenges, if the OEM is moving slower?

當您展望未來幾年的挑戰時，有點好奇，如果原始設備製造商發展緩慢？

And then just any perspective on the regulatory side, has anything changed there, would be helpful.

然後，監管方面的任何觀點，如果那裡發生了任何變化，都會有所幫助。

I think that the automotive industry is struggling, but -- for all of the reasons that everybody knows.

我認為汽車行業正在苦苦掙扎，但是——出於所有人都知道的所有原因。

However, the enthusiasm to redefine and reinvent their business model has never been greater.

然而，重新定義和重塑其商業模式的熱情從未如此強烈。

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.

他們中的每一個人，他們中的每一個人現在都會知道而且他們肯定——他們已經知道一段時間了，而自主能力確實是做到這一點的工具。

They need to be tech companies.

他們需要成為科技公司。

Every car company wants to be a tech company.

每家汽車公司都想成為一家科技公司。

They need to be a tech company.

他們需要成為一家科技公司。

Every car company needs to be software-defined.

每家汽車公司都需要軟件定義。

And the platform by which to do so is an electric vehicle with autonomous autopilot capability.

實現這一目標的平台是具有自主自動駕駛功能的電動汽車。

That car has to be software-defined.

那輛車必須是軟件定義的。

And this is their future and they're racing to get there.

這就是他們的未來，他們正在競相到達那裡。

And so although the automotive industry is struggling in near term, their opportunity has never been better in my opinion.

因此，儘管汽車行業在短期內陷入困境，但在我看來，他們的機會從未像現在這樣好。

The future of AV is more important than ever.

AV 的未來比以往任何時候都更加重要。

The opportunity is very real.

機會是非常真實的。

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.

自動駕駛的好處在於它的安全性、實用性、成本降低和生產力，從未如此清晰。

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.

因此，我認為我對自動駕駛汽車和我們正在推進的項目一如既往地充滿熱情。

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.

因此，汽車行業的近期挑戰或他們正在經歷的中國銷售放緩，我對此感到非常難過。

But the industry is as clearheaded about the importance of AV as ever.

但業界對 AV 的重要性一如既往地清醒。

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

我現在將把電話轉回給 Jensen 進行結束髮言。

We had an excellent quarter with strong demand for NVIDIA RTX graphics and NVIDIA AI platforms and record data center revenue.

我們有一個出色的季度，對 NVIDIA RTX 顯卡和 NVIDIA AI 平台的需求強勁，數據中心收入創紀錄。

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.

NVIDIA RTX 正在重塑計算機圖形，市場反應極佳，推動了遊戲和專業圖形的強大升級週期，同時為我們提供了全新的機會，為獨立創意工作者和社交內容創作者的龐大社區和新市場提供服務渲染和雲遊戲。

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.

我們的數據中心業務正在迎來新一輪的增長浪潮，這得益於人工智能的三大關鍵趨勢：自然語言理解、對話式人工智能、深度推薦，正在改變人們與互聯網互動的方式。

The public cloud demand for AI is growing rapidly.

公有云對人工智能的需求正在迅速增長。

And as AI shifts from development to production, our inference business is gaining momentum.

隨著人工智能從開發轉向生產，我們的推理業務正在獲得動力。

We'll be talking a lot more about these key trends and much more at next month's GTC Conference in San Jose.

我們將在下個月在聖何塞舉行的 GTC 會議上更多地討論這些關鍵趨勢以及更多內容。

Come join me.

快來加入我吧。

You won't be disappointed.

你不會失望的。

Thanks, everyone.

謝謝大家。

Ladies and gentlemen, this concludes today's conference call.

女士們，先生們，今天的電話會議到此結束。

Thank you for participating.

感謝您的參與。

You may now disconnect.

您現在可以斷開連接。