輝達 (NVDA) 2020 Q3 法說會逐字稿

Good afternoon.

下午好。

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

我的名字是克里斯蒂娜，我是今天的會議接線員。

Welcome to NVIDIA's Financial Results Conference Call.

歡迎參加 NVIDIA 財務業績電話會議。

(Operator Instructions)

（操作員說明）

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 third quarter of fiscal 2020.

大家下午好，歡迎參加 NVIDIA 2020 財年第三季度電話會議。

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 fourth quarter of fiscal 2020.

在電話會議討論我們 2020 財年第四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 Form 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, November 14, 2019, 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 2019 年 11 月 14 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。

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

第三季度收入為 30.1 億美元，同比下降 5%，環比增長 17%。

Starting with our gaming business.

從我們的遊戲業務開始。

Revenue of $1.66 billion was down 6% year-on-year and up 26% sequentially.

收入為 16.6 億美元，同比下降 6%，環比增長 26%。

Results exceeded our expectations driven by strength in both desktop and notebook gaming.

在台式機和筆記本電腦遊戲的強勁推動下，結果超出了我們的預期。

Our GeForce RTX lineup features the most advanced GPU for every price point and uniquely offers hardware-based ray tracing for cinematic graphics.

我們的 GeForce RTX 產品系列具有適合每個價位的最先進的 GPU，並獨特地為電影圖形提供基於硬件的光線追踪。

While ray tracing launched a little more than a year ago, 2 dozen top titles have shipped with it or are on the way.

雖然光線追踪是在一年多前推出的，但已有 2 打頂級遊戲附帶或正在開發中。

Ray tracing is supported by all the major publishers, including all-star titles and franchise such as Minecraft, Call of Duty, Battlefield, Watch Dogs, Tomb Raider, Doom, Wolfenstein and Cyberpunk.

光線追踪得到所有主要發行商的支持，包括全明星作品和特許經營權，例如 Minecraft、使命召喚、戰地、看門狗、古墓麗影、毀滅戰士、德軍總部和賽博朋克。

Of note, Call of Duty: Modern Warfare, had a record-breaking launch in late October that came on heels of CONTROL, an action-adventure game with multiple ray trace features.

值得注意的是，《使命召喚：現代戰爭》於 10 月下旬推出了創紀錄的產品，緊隨 CONTROL 是一款具有多種光線追踪功能的動作冒險遊戲。

Reviews have praised both for their ray tracing implementation and game-play performance.

評論對他們的光線追踪實施和遊戲性能都表示讚賞。

With last week's PC release of Red Dead Redemption 2 as a strong gaming lineup for the holiday season, our business reflects this growing excitement.

隨著上週 PC 版 Red Dead Redemption 2 成為假日季節的強大遊戲陣容，我們的業務反映了這種日益增長的興奮。

RTX GPUs now drive more than 2/3 of our desktop gaming GPU revenue.

RTX GPU 現在占我們桌面遊戲 GPU 收入的 2/3 以上。

Gaming laptops were a standout, driving strong sequential and year-on-year growth.

遊戲筆記本電腦表現出色，推動了強勁的連續和同比增長。

This holiday season, our partners are addressing the growing demand for high-performance laptops for gamers, students and prosumers by bringing more than 130 NVIDIA-powered gaming and studio laptop models to market.

在這個假日季，我們的合作夥伴將 130 多款採用 NVIDIA 技術的遊戲和工作室筆記本電腦型號推向市場，從而滿足遊戲玩家、學生和專業消費者對高性能筆記本電腦日益增長的需求。

This includes many thin and light form factors enabled by our Max-Q technology, triple the number of Max-Q laptops last year.

這包括我們的 Max-Q 技術支持的許多輕薄外形，是去年 Max-Q 筆記本電腦數量的三倍。

In late October, we announced the GeForce GTX 1660 Super and the 1650 Super, which refresh our mainstream desktop GPUs with more performance, faster memory and new features.

10 月下旬，我們發布了 GeForce GTX 1660 Super 和 1650 Super，它們以更高的性能、更快的內存和新功能更新了我們的主流台式機 GPU。

The 1660 Super delivers 50% more performance than our prior-generation Pascal-based 1060, the best-selling gaming GPU of all time.

1660 Super 的性能比我們上一代基於 Pascal 的 1060（有史以來最暢銷的遊戲 GPU）高 50%。

It began shipping on October 29, priced at just $229.

它於 10 月 29 日開始發貨，售價僅為 229 美元。

PC World called it the best GPU you can buy for 1080p gaming.

PC World 稱其為您可以為 1080p 遊戲購買的最佳 GPU。

We also announced the next generation of our streaming media player with 2 new models, Shield TV and Shield TV Pro, which launched on October 28.

我們還宣布了我們的下一代流媒體播放器，包括 2 款新型號，Shield TV 和 Shield TV Pro，於 10 月 28 日推出。

These bring AI to the streaming market for the first time with the ability to upscale video real time from high definition to 4K using NVIDIA-trained deep neural networks.

這些將人工智能首次帶入流媒體市場，能夠使用 NVIDIA 訓練的深度神經網絡將視頻實時從高清升級到 4K。

Shield TV has been widely recognized as the best streamer on the market.

Shield TV 已被廣泛認為是市場上最好的流媒體。

Finally, we made progress in building out our cloud gaming business.

最後，我們在構建雲遊戲業務方面取得了進展。

Two global service providers, Taiwan Mobile and Russia's Rostelecom with GFN.ru joined SoftBank and Korea's LG as partners for our GeForce NOW game-streaming service.

兩家全球服務提供商，台灣移動和俄羅斯的 Rostelecom 與 GFN.ru 加入了軟銀和韓國的 LG，成為我們 GeForce NOW 遊戲流媒體服務的合作夥伴。

Additionally, Telefónica will kick off a cloud gaming proof-of-concept in Spain.

此外，西班牙電信將在西班牙啟動雲遊戲概念驗證。

Moving to data center.

搬到數據中心。

Revenue was $726 million, down 8% year-on-year and up 11% sequentially.

收入為 7.26 億美元，同比下降 8%，環比增長 11%。

Our hyperscale revenue grew both sequentially and year-on-year, and we believe our visibility is improving.

我們的超大規模收入環比和同比增長，我們相信我們的知名度正在提高。

Hyperscale activity is being driven by conversational AI, the ability for computers to engage in human-like dialogue, capturing context and providing intelligent responses.

對話式人工智能正在推動超大規模活動，這是計算機進行類人對話、捕捉上下文並提供智能響應的能力。

Google's breakthrough, introduction of the BERT model, with its superhuman levels of natural language understanding, is driving a way of neural networks for the language understanding.

谷歌的突破，引入了 BERT 模型，它具有超人的自然語言理解水平，正在推動一種用於語言理解的神經網絡方式。

That, in turn, is driving demand for our GPUs on 2 fronts.

這反過來又在兩個方面推動了對我們 GPU 的需求。

First, these models are massive and highly complex.

首先，這些模型龐大且高度複雜。

They have 10 to 20x, in some cases 100x, more parameters than image-based models.

它們的參數比基於圖像的模型多 10 到 20 倍，在某些情況下是 100 倍。

As a result, training these models requires V100-based compute infrastructure that, in orders of magnitude, beyond what is needed in the past.

因此，訓練這些模型需要基於 V100 的計算基礎架構，其數量級遠遠超出了過去的需求。

Model complexity is expected to grow significantly from here.

預計模型複雜性將從這裡顯著增長。

Second, real-time conversational AI requires very low latency and multiple neural networks running in quick succession from de-noising to speech recognition, language understanding, text-to-speech and voice encoding.

其次，實時對話式 AI 需要非常低的延遲和快速連續運行的多個神經網絡，從去噪到語音識別、語言理解、文本到語音和語音編碼。

While conventional approaches fail at these tasks, NVIDIA's GPUs can handle the entire inference chain, in less than 30 milliseconds.

雖然傳統方法無法完成這些任務，但 NVIDIA 的 GPU 可以在不到 30 毫秒的時間內處理整個推理鏈。

This is the first AI application where inference requires acceleration.

這是第一個需要加速推理的 AI 應用程序。

Conversational AI is a major driver for GPU-accelerated inference.

對話式 AI 是 GPU 加速推理的主要驅動力。

In addition to this type of internal hyperscale activity, our T4 GPU continue to gain adoption in public clouds.

除了這種類型的內部超大規模活動，我們的 T4 GPU 繼續在公共雲中獲得採用。

In September, Amazon AWS announced general availability of the T4 globally, following the T4 rollout on Google Cloud platform earlier in the year.

繼今年早些時候在谷歌云平台上推出 T4 之後，亞馬遜 AWS 於 9 月宣佈在全球範圍內全面推出 T4。

We shipped a higher volume of T4 inference GPU this quarter with V100 training GPUs, and both were records.

我們在本季度發布了更多帶有 V100 訓練 GPU 的 T4 推理 GPU，兩者都創下了記錄。

Inference revenue more than doubled from last year and continued a solid double-digit percentage of total data center revenue.

推理收入比去年增長了一倍多，並在數據中心總收入中繼續保持兩位數的穩定百分比。

Last week, the results of the first industry benchmark for AI inference, MLPerf inference, were announced.

上週，AI 推理的第一個行業基準 MLPerf 推理的結果公佈了。

We won.

我們贏了。

In addition to demonstrating the best performance among commercially available solutions for both data center and edge applications, NVIDIA accelerators were the only ones that completed in all 5 MLPerf benchmarks.

除了在數據中心和邊緣應用程序的商用解決方案中展示了最佳性能之外，NVIDIA 加速器是唯一在所有 5 個 MLPerf 基準測試中完成的加速器。

This demonstrates the programmability and performance of our computing platform across diverse AI workloads, which is critical for wide-scale data center deployment and is a key differentiator for us.

這證明了我們的計算平台在各種 AI 工作負載中的可編程性和性能，這對於大規模數據中心部署至關重要，也是我們的關鍵差異化因素。

Several product announcements this quarter helped extend our AI computing platform into new markets, the enterprise edge.

本季度發布的幾項產品有助於將我們的 AI 計算平台擴展到新市場，即企業邊緣。

At Mobile World Congress, Los Angeles, we announced a software-defined 5G wireless RAN solution accelerated by GPUs in collaboration with Ericsson.

在洛杉磯舉行的世界移動通信大會上，我們與愛立信合作推出了由 GPU 加速的軟件定義 5G 無線 RAN 解決方案。

This opens up the wireless brand market to NVIDIA GPUs.

這為 NVIDIA GPU 開闢了無線品牌市場。

It enables new AI applications as well as AR, VR and gaming to be more accessible to the telco edge.

它使新的人工智能應用程序以及增強現實、虛擬現實和遊戲更容易被電信邊緣訪問。

We announced the NVIDIA EGX Intelligent Edge Computing Platform.

我們宣布推出 NVIDIA EGX 智能邊緣計算平台。

With an ecosystem of more than 100 technology companies worldwide, early adopters include Walmart, BMW, Procter & Gamble, Samsung Electronics, NTT East and the cities of San Francisco and Las Vegas.

憑藉全球 100 多家科技公司的生態系統，早期採用者包括沃爾瑪、寶馬、寶潔、三星電子、NTT East 以及舊金山和拉斯維加斯等城市。

Additionally, we announced a collaboration with Microsoft on intelligent edge computing.

此外，我們宣布與微軟就智能邊緣計算展開合作。

This will help industries better manage and gain insights from the growing flood of data created by retail stores, warehouses, manufacturing facilities and urban infrastructure.

這將幫助行業更好地管理零售商店、倉庫、製造設施和城市基礎設施所產生的不斷增長的數據洪流並從中獲得洞察力。

Finally, last week, we held our GPU Technology Conference in Washington, D.C., which was sold out with more than 3,500 registered developers, CIOs and federal employees.

最後，上週，我們在華盛頓特區舉行了 GPU 技術大會，超過 3,500 名註冊開發人員、CIO 和聯邦僱員都被搶購一空。

At the event, we announced that the U.S. Postal Service, the world's largest delivery service with almost 150 billion pieces of mail delivered annually, is adopting AI technology from NVIDIA, enabling 10x faster processing of package data and with higher accuracy.

在此次活動中，我們宣布美國郵政服務是全球最大的遞送服務機構，每年遞送近 1500 億件郵件，它正在採用 NVIDIA 的 AI 技術，使包裹數據的處理速度提高 10 倍，並且準確性更高。

Moving to ProVis.

轉移到 ProVis。

Revenue reached a record $324 million, up 6% from the prior year and up 11% sequentially driven primarily by mobile workstations.

收入達到創紀錄的 3.24 億美元，同比增長 6%，環比增長 11%，主要受移動工作站的推動。

NVIDIA RTX graphic and Max-Q technology have enabled a new wave of mobile workstations that are powerful enough for design applications yet thin and light enough to carry.

NVIDIA RTX 圖形和 Max-Q 技術催生了新一波移動工作站，這些工作站功能強大，足以用於設計應用程序，但又輕薄又便於攜帶。

We expect this to become a major new category with exciting growth opportunities.

我們預計這將成為一個具有令人興奮的增長機會的主要新類別。

Over 40 top creative design applications are being accelerated with RTX GPUs.

超過 40 種頂級創意設計應用程序正在使用 RTX GPU 進行加速。

Just last week, at the Adobe Max Conference, RTX accelerated capabilities were added to 3 Adobe Creative apps.

就在上週，在 Adobe Max 大會上，RTX 加速功能被添加到了 3 個 Adobe Creative 應用程序中。

RTX-accelerated apps are now available to tens of millions of artists and designers, driving demand for our RTX GPUs.

數以千萬計的藝術家和設計師現在可以使用 RTX 加速的應用程序，從而推動對我們 RTX GPU 的需求。

We also continue to see growing customer deployment of data science, AI and VR applications.

我們還繼續看到越來越多的客戶部署數據科學、人工智能和虛擬現實應用程序。

Strong demand this quarter came from manufacturing, public sector, higher education and health care customers.

本季度的強勁需求來自製造業、公共部門、高等教育和醫療保健客戶。

Finally, turning to automotive.

最後，轉向汽車。

Revenue was $162 million, down 6% from a year ago and down 22% sequentially.

收入為 1.62 億美元，同比下降 6%，環比下降 22%。

The sequential decline was driven by a onetime nonreoccurring development services contract recognized in Q2.

連續下降是由第二季度確認的一次性非重複開發服務合同推動的。

Additionally, we saw a roll-off of legacy infotainment revenue and general industry weakness.

此外，我們看到傳統信息娛樂收入下降和行業普遍疲軟。

Our AI cockpit business grew driven by the continued ramp of the Daimler as they deploy their AI-based infotainment systems across their fleet of Mercedes-Benz vehicles.

隨著戴姆勒在梅賽德斯-奔馳車隊中部署基於人工智能的信息娛樂系統，我們的人工智能駕駛艙業務得到了增長。

In August, Optimus Ride launched New York City's first autonomous driving pilot program powered by NVIDIA DRIVE.

8 月，Optimus Ride 啟動了紐約市首個由 NVIDIA DRIVE 提供支持的自動駕駛試點計劃。

Urban settings pose unique challenges for autonomous vehicles given the number of density of objects that need to be perceived and comprehended in real time.

考慮到需要實時感知和理解的物體密度的數量，城市環境給自動駕駛汽車帶來了獨特的挑戰。

Our DRIVE computer and software stack allows these shuttles to safely and effectively provide first- and last-mile transit services.

我們的 DRIVE 計算機和軟件堆棧使這些班車能夠安全有效地提供第一英里和最後一英里的運輸服務。

We remain excited about the long-term opportunity in auto.

我們對汽車領域的長期機遇仍然感到興奮。

Our offering is -- consists of in-car AV computing platforms as well as GPU servers for all AI development and simulation.

我們的產品包括車載 AV 計算平台以及用於所有 AI 開發和模擬的 GPU 服務器。

We believe we are well positioned in the industry with leading end-to-end platform that enables customers to develop, test and safely operate autonomous vehicles, ranging from cars and trucks to shuttles and robo-taxis.

我們相信，我們憑藉領先的端到端平台在行業中處於有利地位，使客戶能夠開發、測試和安全操作自動駕駛汽車，從汽車和卡車到穿梭巴士和機器人出租車。

Moving to the rest of the P&L.

轉到損益表的其餘部分。

Q3 GAAP gross margins was 63.6%, and non-GAAP was 64.1%, up sequentially, reflecting a benefit from sales of previously written-off inventory, higher GeForce GPUs average selling prices and lower component costs.

第三季度 GAAP 毛利率為 63.6%，非 GAAP 毛利率為 64.1%，環比上升，這反映了先前註銷庫存的銷售、較高的 GeForce GPU 平均售價和較低的組件成本帶來的好處。

GAAP operating expenses were $989 million, and non-GAAP operating expenses were $774 million, up 15% and 6% year-on-year, respectively.

GAAP 運營費用為 9.89 億美元，非 GAAP 運營費用為 7.74 億美元，同比分別增長 15% 和 6%。

GAAP EPS was $1.45, down 26% from a year earlier.

GAAP每股收益為1.45美元，同比下降26%。

Non-GAAP EPS was $1.78, down 3% from a year ago.

非美國通用會計準則每股收益為 1.78 美元，比一年前下降 3%。

Cash flow from operations was a record $1.6 billion.

運營現金流達到創紀錄的 16 億美元。

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

有了這個，讓我轉向 2020 財年第四季度的前景，其中不包括即將收購的 Mellanox 的任何貢獻。

We expect revenue to be $2.95 billion, plus or minus 2%.

我們預計收入為 29.5 億美元，正負 2%。

This reflects expectations for strong sequential growth in data center, offset by a seasonal decline in notebook GPUs for gaming and Switch-related revenue.

這反映了對數據中心強勁連續增長的預期，但被用於遊戲和 Switch 相關收入的筆記本 GPU 的季節性下降所抵消。

GAAP and non-GAAP gross margins are expected to be 64.1% and 64.5%, respectively, plus or minus 50 basis points.

GAAP 和非 GAAP 毛利率預計分別為 64.1% 和 64.5%，上下浮動 50 個基點。

GAAP and non-GAAP operating expenses are expected to be approximately $1.02 billion and $805 million, respectively.

GAAP 和非 GAAP 運營費用預計分別約為 10.2 億美元和 8.05 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 $130 million to $150 million.

資本支出預計約為 1.3 億至 1.5 億美元。

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

更多財務細節包含在 CFO 評論和我們投資者關係網站上提供的其他信息中。

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

最後，讓我強調一下金融界即將發生的事件。

We will be at the Crédit Suisse Annual Technology Conference on December 3, Deutsche Bank's Auto Tech Conference on December 10 and Barclays Global Technology, Media and Telecommunications Conference on December 11.

我們將參加 12 月 3 日的瑞士信貸年度技術會議、12 月 10 日的德意志銀行汽車技術會議以及 12 月 11 日的巴克萊全球技術、媒體和電信會議。

We will now open the call for questions.

我們現在將打開問題的電話。

Operator, would you please poll for questions?

接線員，請您投票提問嗎？

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

（操作員說明）您的第一個問題來自美國銀行美林的 Vivek Arya。

For my first one, you mentioned that you were seeing strong sequential growth in the data center going into Q4.

對於我的第一個問題，您提到您看到數據中心進入第四季度的強勁連續增長。

Jensen, I was wondering if you could give us some color on what's driving that, and just how you think about the sustainability of data center growth going into next year and what markets do you think will drive that.

Jensen，我想知道您能否就推動這一趨勢的因素告訴我們一些看法，以及您如何看待明年數據中心增長的可持續性以及您認為哪些市場將推動這一趨勢。

Is it more enterprise, more hyperscale, more HPC?

是不是更企業、更超大規模、更多 HPC？

Just some color on near and longer term on data center.

只是數據中心近期和長期的一些顏色。

And then I have a follow-up for Colette.

然後我對 Colette 進行了跟進。

Yes.

是的。

Thanks a lot, Vivek.

非常感謝，維維克。

We had a strong Q3 in hyperscale data centers.

我們在超大規模數據中心的第三季度表現強勁。

As Colette mentioned earlier, we shipped a record number of V100s and T4s.

正如 Colette 之前提到的，我們交付了創紀錄數量的 V100 和 T4。

And for the very first time, we shipped more T4s than V100.

第一次，我們的 T4 出貨量超過了 V100。

And most of the T4s are driven by inference.

大多數 T4 都是由推理驅動的。

In fact, our inference business is now a solid double-digit, and it doubled year-over-year.

事實上，我們的推理業務現在是穩定的兩位數，並且同比翻了一番。

And all -- most -- that is really driven by several factors.

而所有 - 大多數 - 這實際上是由幾個因素驅動的。

The -- as you know, we've been working on deep learning for some time, and people have been developing deep learning models.

- 如您所知，我們一直在研究深度學習，人們一直在開發深度學習模型。

It started with computer vision.

它始於計算機視覺。

But image recognition doesn't really take that much of the data center capacity.

但圖像識別並沒有真正佔用數據中心的那麼多容量。

Over the last couple of years, a couple of very important developments have happened.

在過去的幾年裡，發生了一些非常重要的事態發展。

One development is a breakthrough in using deep learning for recommendation systems.

一項發展是將深度學習用於推薦系統的突破。

As you know, recommendation systems is the backbone of the Internet.

如您所知，推薦系統是互聯網的支柱。

Whenever you do shopping, whenever you're watching movies, looking at news, doing search, all of the personalized web pages, all of just about your entire experience on the Internet is made possible by recommendation systems because there is just so much data out there putting the right data in front of you based on your social profile or your personal use patterns or your interest or your connections.

每當你購物、看電影、看新聞、搜索，所有的個性化網頁，所有關於你在互聯網上的整個體驗都是通過推薦系統實現的，因為有太多的數據輸出根據您的社交資料或您的個人使用模式或您的興趣或您的關係，將正確的數據放在您面前。

All of that is vitally important.

所有這些都至關重要。

For the very first time, we're seeing recommendation system based on deep learning throughout the world.

我們第一次在全世界看到基於深度學習的推薦系統。

And so increasingly, you're going to see people roll this out.

因此，您會越來越多地看到人們推出它。

And the backbone of the Internet is now going to be based on deep learning.

互聯網的支柱現在將基於深度學習。

The second part is conversational AI.

第二部分是對話式人工智能。

Conversational AI has been coming together in pieces; at first, speech recognition, which requires some amount of noise processing or beam forming.

對話式 AI 已經分崩離析。首先是語音識別，這需要一些噪聲處理或波束形成。

Then you go into speech recognition.

然後你進入語音識別。

Then it goes to natural language understanding, which then gets connected to a recommendation system, which then gets connected to text-to-speech and a speech encoder.

然後是自然語言理解，然後連接到推薦系統，然後連接到文本到語音和語音編碼器。

And then that has to be done very, very quickly.

然後必須非常非常快地完成。

Whereas images could be done off-line, conversation has to be done in real time.

雖然圖像可以離線完成，但對話必須實時完成。

And without acceleration and without NVIDIA's accelerators, it's really not possible to do it in real time.

而且沒有加速，沒有 NVIDIA 的加速器，真的不可能做到實時。

It takes seconds to process all of the handful of deep learning models, and now we're able to do that all on an accelerator and do it in real time.

處理所有少數深度學習模型需要幾秒鐘的時間，現在我們可以在加速器上實時完成這一切。

And so the combination of these various breakthroughs from deep learning-based recommenders, the speech stack as well as natural language understanding breakthrough in what is called a bidirectional encoded transformer, that breakthrough is really quite significant.

因此，結合基於深度學習的推薦系統的各種突破、語音堆棧以及所謂的雙向編碼轉換器中的自然語言理解突破，這一突破確實非常重要。

And since then, derivative works have come from that approach.

從那時起，衍生作品就來自這種方法。

And natural language understanding is really, really working incredibly well.

自然語言理解真的非常非常好。

And so what we're seeing people do is -- the hyperscalers across the world, we work with just about everybody, this area of work is really complicated.

所以我們看到人們所做的是——世界各地的超大規模企業，我們幾乎與每個人合作，這個工作領域真的很複雜。

The models are very, very large.

模型非常非常大。

There's a whole bunch of models that has to work together, and they're getting larger.

有一大堆模型必須一起工作，而且它們變得越來越大。

And so that's one large category, which is the hyperscalers.

這是一大類，即超大規模。

The second, which we introduced this quarter, is really about taking AI out to the edge.

第二個，我們在本季度推出的，真正是關於將人工智能帶到邊緣。

And the reason for that is because there are many applications, whether it's based on video or other types of sensors of all kinds where there's a vibration sensor, temperature sensors, barometric sensor.

原因是因為有很多應用，無論是基於視頻還是其他類型的傳感器，其中有振動傳感器、溫度傳感器、氣壓傳感器。

There's all kinds of sensors that are used in industries to monitor the health of equipment, monitor the conditions of various situations.

工業中使用各種傳感器來監控設備的健康狀況，監控各種情況的狀況。

And you want to do the processing at the point of action.

你想在行動點進行處理。

This way, you don't have to screen the data, which is continuous back into the cloud, which costs a lot of money.

這樣，您就不必篩選數據，這些數據是連續返回到雲端的，這需要花費大量資金。

You want to take the action at the point of action because latency matters.

您想在行動點採取行動，因為延遲很重要。

Maybe you're controlling gates or vehicles or robots or drones or whatnot.

也許你正在控制大門、車輛、機器人、無人機或諸如此類。

And then lastly, one major issue is data sovereignty.

最後，一個主要問題是數據主權。

Maybe your company doesn't own all of the data that you are processing and, therefore, you have to do that processing at the edge, and you can't afford to put that into the cloud.

也許您的公司並不擁有您正在處理的所有數據，因此，您必須在邊緣進行處理，而您無法將其放入雲中。

And so these various industries: retail, warehouse, logistics, smart cities, we're just seeing so much enthusiasm there around that.

所以這些不同的行業：零售、倉庫、物流、智慧城市，我們只是看到了圍繞這些的如此多的熱情。

And this -- so we built a platform called the EGX, which basically is a cloud native, completely secure, takes advantage of NVIDIA's full stack of every single model.

這——所以我們建立了一個叫做 EGX 的平台，它基本上是一個雲原生的，完全安全的，利用了 NVIDIA 的每一個模型的完整堆棧。

And it's managed with Kubernetes remotely, and you could deploy these services at the edge in faraway places because IT departments can't afford to go out there to manage them.

而且它是通過 Kubernetes 遠程管理的，你可以將這些服務部署在遙遠的地方，因為 IT 部門負擔不起去那裡管理它們的費用。

And we've seen some really great adoption.

我們已經看到了一些非常好的採用。

We announced this last quarter.

我們在上個季度宣布了這一點。

Walmart is using our platform.

沃爾瑪正在使用我們的平台。

BMW is using it for logistics, Procter & Gamble for manufacturing, Samsung Electronics for manufacturing, visual inspection.

寶馬將其用於物流，寶潔用於製造，三星電子用於製造，視覺檢查。

And then last week, we announced probably the largest logistics operation in the world, the United States Postal Service.

然後上週，我們宣布了可能是世界上最大的物流業務，美國郵政服務。

And so those are -- I would say that intelligent edge will likely be the largest AI industry in the world for rather clear reasons.

所以這些是——我會說智能邊緣很可能是世界上最大的人工智能產業，原因很明確。

If you just kind of estimated the size of retail, it's nearly $30 trillion.

如果你只是估計一下零售的規模，它接近 30 萬億美元。

And if retail stores could be made a little bit more convenient, it could save the industry a lot of money: warehouses, logistics, transportation, farming.

如果零售店可以變得更方便一點，它可以為該行業節省大量資金：倉庫、物流、運輸、農業。

I think there's like 0.5 million farms in the world, covers 1/3 of the world's land mass.

我認為世界上有大約 50 萬個農場，佔世界陸地面積的 1/3。

And so there's a lot of places where AI could be put at the edge and could make a big difference.

因此，有很多地方可以將 AI 置於邊緣並產生重大影響。

And I think this is going to be the grand adventure that we started this last quarter with the announcement of NVIDIA EGX.

我認為這將是我們在上個季度發布 NVIDIA EGX 後開始的一次盛大冒險。

Right.

對。

And Jensen, as quick follow-up, on PC gaming, how are you looking at growth going forward in that you had a very good quarter in October?

Jensen，作為 PC 遊戲的快速跟進者，您如何看待未來的增長，因為您在 10 月份有一個非常好的季度？

I think in January, you're probably guiding to some seasonal declines, but I imagine a lot more of that is due to console decline.

我認為在一月份，您可能會引導一些季節性下降，但我想更多的是由於控制台下降。

Just how are you looking at PC gaming growth going into October -- into January and then next year as you get competition from 2 new consoles that are also supposed to come out?

你如何看待 PC 遊戲在 10 月份的增長——進入 1 月份，然後是明年，因為你會受到來自 2 款也應該問世的新遊戲機的競爭？

Yes.

是的。

The -- during Q3 -- during Q4 and Q1, we see normal seasonal declines of console builds, and we also see a normal seasonal decline of notebook builds.

- 在第三季度 - 在第四季度和第一季度，我們看到控制台構建的正常季節性下降，我們也看到筆記本構建的正常季節性下降。

And the reason for that is because the notebook vendors have to line up all their manufacturing in Q3 so that they could meet the hot selling season in Q4.

究其原因，是因為筆記本廠商不得不在第三季度把所有的生產線都排好，以便迎接第四季度的熱銷季。

And so we're seeing -- what we see in the Q4 and Q1 time frame are just normal seasonal declines of these systems.

所以我們看到——我們在第四季度和第一季度看到的只是這些系統的正常季節性下降。

Overall, for PC gaming -- and RTX is doing fantastic.

總的來說，對於 PC 遊戲來說，RTX 的表現非常出色。

Let me tell you why it's so important.

讓我告訴你為什麼它如此重要。

I would say that at this point, I think it's fairly clear that ray tracing is the future and that RTX is a home run.

我想說的是，在這一點上，我認為很明顯光線追踪是未來，RTX 是本壘打。

Just about every major game developer has signed on to ray tracing.

幾乎每個主要的遊戲開發商都簽署了光線追踪。

Even the next-generation consoles had to stutter step and include ray tracing in their next-generation consoles.

即使是下一代遊戲機也不得不步履蹣跚，並在其下一代遊戲機中包含光線追踪。

The effects -- the photorealistic look is just so compelling, it's not possible to really go back anymore.

效果 - 逼真的外觀是如此引人注目，再也不可能真正回到過去。

And so I think that it's fairly clear now that RTX ray tracing is the future.

所以我認為現在很清楚 RTX 光線追踪是未來。

And there are several hundred million PC gamers in the world that don't have the benefits of it, and I'm looking forward to upgrading them.

世界上有幾億 PC 遊戲玩家沒有享受到它的好處，我期待著升級他們。

Second, and this is a combination of RTX and Max-Q, we really created a brand-new game platform, notebook PC gaming.

其次，這是RTX和Max-Q的結合，我們真正創造了一個全新的遊戲平台，筆記本電腦遊戲。

Notebook PC gaming really didn't exist until Max-Q came along.

直到 Max-Q 出現之前，筆記本電腦遊戲才真正存在。

And our second-generation Max-Q, this last season, really turbocharged this segment.

而我們上一季的第二代 Max-Q 確實為這一細分市場提供了強勁動力。

Over 100 laptops now are available for PC gaming.

現在有超過 100 台筆記本電腦可用於 PC 遊戲。

And my sense is that this is likely going to be the largest gaming platform, new gaming platform that emerges.

我的感覺是，這很可能會成為最大的遊戲平台，出現的新遊戲平台。

And we're just in the beginning innings of that.

而我們只是處於起步階段。

And so the combination of upgrading the entire installed base of PC gamers to RTX and ray tracing and this new gaming segment called notebook PC gaming is really quite exciting, and it's going to drive our continued growth for some time.

因此，將整個 PC 遊戲玩家的安裝基礎升級到 RTX 和光線追踪以及這個稱為筆記本電腦遊戲的新遊戲領域的組合確實非常令人興奮，它將在一段時間內推動我們的持續增長。

And so I'm excited about that.

所以我對此很興奮。

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

您的下一個問題來自富國銀行的 Aaron Rakers。

I have a follow-up if I can as well.

如果可以的話，我也會跟進。

Just thinking about the trajectory of gross margin here, solid gross margin upside in the quarter, you also noted that you had the benefit of selling through some written-off components.

僅考慮這裡的毛利率軌跡，本季度毛利率穩健上升，您還注意到您通過一些註銷的組件進行了銷售。

So I guess first question is what was that impact in this most recent reported quarter.

所以我想第一個問題是在最近報告的這個季度中，這種影響是什麼。

And how do we think about the trajectory of gross margin here even beyond the January quarter?

我們如何看待甚至超過 1 月季度的毛利率軌跡？

What should we be thinking about in terms of that gross margin trend?

就毛利率趨勢而言，我們應該考慮什麼？

And again, I have a quick follow-up.

再一次，我有一個快速的跟進。

Sure.

當然。

Thanks for the question.

謝謝你的問題。

In the current quarter, the net benefit, as we refer to as the net release of our inventory provisions primarily associated with our components, was about 1 percentage point to our overall gross margin.

在本季度，淨收益（我們稱之為主要與我們的組件相關的庫存準備金的淨釋放）占我們整體毛利率的 1 個百分點左右。

As you know, going forward, mix is still the largest driver of our gross margin over time.

如您所知，展望未來，隨著時間的推移，混合仍然是我們毛利率的最大驅動力。

Over the long term, we do expect gross margins to improve, and we'll continue to see, outside of the benefit that we received, gross margin improvement for the long term.

從長遠來看，我們確實預計毛利率會提高，而且我們將繼續看到，除了我們獲得的利益之外，長期毛利率也會提高。

Yes.

是的。

As you know, just to add to that, as you know, NVIDIA's really become a software company.

如你所知，再補充一點，如你所知，NVIDIA 真的變成了一家軟件公司。

If you take a look at almost all of our products, the GPU -- having the world's best GPU, of course, is the starting point.

如果你看看我們幾乎所有的產品，GPU——擁有世界上最好的 GPU，當然是起點。

But almost everything that we do, whether it's in artificial intelligence or data analytics or health care or robotics or self-driving cars, almost all of these platforms: gaming, rendering, cloud graphics, all of these platforms start from a really rich stack of software.

但我們所做的幾乎所有事情，無論是人工智能、數據分析、醫療保健、機器人還是自動駕駛汽車，幾乎所有這些平台：遊戲、渲染、雲圖形，所有這些平台都從非常豐富的堆棧開始軟件。

And you can't just put a chip in these scenarios and they work.

而且你不能只在這些場景中放置一個芯片，它們就可以工作。

And so most of our businesses are now highly software-rich, and they address verticals that we focus on.

因此，我們的大多數業務現在都高度軟件化，它們針對的是我們關注的垂直領域。

And then secondarily, we're a platform company.

其次，我們是一家平台公司。

And so our platform is available from all the OEMs and cloud providers.

因此，我們的平台可從所有 OEM 和雲提供商處獲得。

And as a platform company that has a great deal of software intensity, it's natural that the margins would be higher over time.

作為一家擁有大量軟件強度的平台公司，隨著時間的推移，利潤自然會更高。

Yes.

是的。

Very helpful.

很有幫助。

And then you mentioned in your prepared remarks that you've seen hyperscale -- your hyperscale business within data center grow both on a quarter-over-quarter as well as year-over-year basis in this last print.

然後你在準備好的評論中提到你已經看到了超大規模——在上一次印刷中，你在數據中心內的超大規模業務環比和同比增長。

You also mentioned that your visibility is improving.

您還提到您的知名度正在提高。

Can you just help us understand what exactly you're seeing in the hyperscale guys because it feels like there's some mixed data points out there?

您能否幫助我們了解您在超大規模人員中究竟看到了什麼，因為感覺那裡有一些混合數據點？

What underpins your improved visibility?

是什麼支撐了您提高知名度？

Or what are you seeing in that piece of your business?

或者你在你的業務中看到了什麼？

Yes.

是的。

We had a strong Q3.

我們有一個強勁的第三季度。

We're going to see a much stronger Q4.

我們將看到更強勁的第四季度。

And the foundation of that is AI, it's deep learning inference.

其基礎是人工智能，它是深度學習推理。

That is -- this deep learning inference is understandably going to be one of the largest computer industry opportunities.

也就是說——這種深度學習推理將成為計算機行業最大的機會之一，這是可以理解的。

And the reason for that is because the computation intensity is so high.

原因是計算強度太高了。

And for the very first time, aside from computer graphics, this mode of software is not really practical without accelerators.

並且第一次，除了計算機圖形之外，這種軟件模式如果沒有加速器就不太實用。

And so I mentioned earlier about the large-scale movement to deep learning recommendation systems.

所以我之前提到了深度學習推薦系統的大規模遷移。

Those models are really, really hard to train.

這些模型真的很難訓練。

I mentioned earlier about conversational AI.

我之前提到過對話式人工智能。

Because conversation requires real-time processing, several seconds is really not practical.

因為對話需要實時處理，幾秒真的不實用。

And so you have to do it in milliseconds, tens of milliseconds.

所以你必須在幾毫秒、幾十毫秒內完成。

And our accelerator makes that possible.

我們的加速器使這成為可能。

What makes it really complicated and the reason why -- although so many people talk about it, only we demonstrated -- we submitted all 5 results -- all 5 tests for the MLPerf inference benchmark, and we won them.

是什麼讓它變得如此復雜，以及為什麼——儘管有很多人談論它，但只有我們演示——我們提交了所有 5 個結果——所有 5 個 MLPerf 推理基準測試的測試，我們贏得了它們。

And the reason for that is because it's far more than just a chip.

這樣做的原因是因為它不僅僅是一個芯片。

The software stack that sits on top of the chip and the compilers that sits on top of the chip are so complicated.

位於芯片頂部的軟件堆棧和位於芯片頂部的編譯器非常複雜。

And it's understandably complicated because a supercomputer wrote the software, and this body of software is really, really large.

它的複雜性是可以理解的，因為軟件是由一台超級計算機編寫的，而這個軟件體非常非常大。

And if you have to make it both accurate as well as performant, it's really quite a great challenge.

如果你必須讓它既準確又高性能，這真的是一個很大的挑戰。

And it's one of the great computer science challenges.

這是計算機科學面臨的巨大挑戰之一。

This is one of those problems that hasn't been solved, and we've been working hard at it for the last 6, 7 years now.

這是尚未解決的問題之一，過去 6、7 年來我們一直在努力解決這個問題。

And so this is really the great opportunity.

所以這真的是一個很好的機會。

We've been talking about inference for some time now.

我們談論推理已經有一段時間了。

Finally, the workloads and a very large diverse set of workloads are now moving into production.

最後，工作負載和大量不同的工作負載現在正在投入生產。

And so I'm hoping -- I'm enthusiastic about the progress and seeing the trends and the visibility that inference should be a large market opportunity for us.

所以我希望 - 我對進展充滿熱情，看到趨勢和可見性，推理應該是我們的巨大市場機會。

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

您的下一個問題來自於 Evercore ISI 的 C.J. Muse。

I guess I'd love to follow on, on that last question.

我想我很想繼續討論最後一個問題。

So clearly, your commentary, Jensen, here is much more bullish than I've heard you, I think, before on inference, particularly as it relates to this first benchmark.

很明顯，Jensen，你的評論比我之前聽到的關於推論的評論要樂觀得多，尤其是與第一個基準相關時。

And so I guess can you talk a bit about how you see mix within data center looking out over the next 12, 24 months as you see kind of training versus inference as well as cloud versus enterprise, considering, I would think, inference over time could be -- could grow into a large opportunity there as well?

所以我想你能談談你如何看待未來 12 個月、24 個月內數據中心內的混合，因為你會看到訓練與推理以及雲與企業的對比，我認為，隨著時間的推移，推理可能——也可能成為一個巨大的機會？

Yes.

是的。

C.J., that's really good.

C.J.，真的很好。

Let me break it down.

讓我分解一下。

So when we think about hyperscale, there are 3 parts: training, inference and public cloud.

因此，當我們考慮超大規模時，有 3 個部分：訓練、推理和公共雲。

Training, you might have seen the work that was done at open AI recently where they've been measuring and monitoring the amount of computation necessary to train these large models.

訓練，你可能已經看到最近在 Open AI 上所做的工作，他們一直在測量和監控訓練這些大型模型所需的計算量。

These large models are now only getting larger.

這些大型模型現在只會變得更大。

The amount of data necessary, therefore, has to scale as well.

因此，必要的數據量也必須擴展。

The computation is now growing and doubling every 3 months.

現在，計算量正在增長，每 3 個月翻一番。

And the reason for that is because of recent breakthroughs in natural language understanding.

其原因是由於最近在自然語言理解方面的突破。

And all of a sudden, a whole wave of problems are now able to be solved.

突然之間，一整波問題現在都可以解決了。

And just as AlexNet 7 years ago kind of was the watershed event for a lot of computer vision-oriented AI work, now the transformer-based natural language understanding model and the work that Google did with BERT really is a watershed event also for natural language understanding.

就像 7 年前 AlexNet 是許多面向計算機視覺的 AI 工作的分水嶺一樣，現在基於轉換器的自然語言理解模型以及 Google 使用 BERT 所做的工作確實是自然語言的分水嶺事件理解。

This is, of course, a much, much harder problem.

當然，這是一個非常非常困難的問題。

And so the scale of the training has grown tremendously.

因此，培訓的規模已經大大增加。

I think what we're going to see this year is a fair number of very sizable installations of GPU systems to do this very thing, training.

我認為今年我們將看到大量非常大規模的 GPU 系統安裝來完成這項工作，即培訓。

The second part is an untapped market for us, and this untapped market is really inference.

第二部分對我們來說是一個未開發的市場，而這個未開發的市場是真正的推理。

The reason why I haven't really spoken about it until now is because we've never really been able to validate our intuition that inference is going to be a large market opportunity for us, that it's going to be very complicated.

直到現在我還沒有真正談論它的原因是因為我們從來沒有真正能夠驗證我們的直覺，即推理對我們來說將是一個巨大的市場機會，它將非常複雜。

The models are very large.

模型非常大。

They're very diverse.

他們非常多樣化。

They require large amount of computation, large amount of memory bandwidth and large amounts of memory and large and significant capabilities of programmability.

它們需要大量的計算、大量的內存帶寬和大量的內存以及大量和重要的可編程能力。

And so I've talked about this before, but I've never been able to validate it.

所以我之前已經討論過這個，但我從來沒有能夠驗證它。

And of course, with MLPerf and sweeping the benchmarks and, frankly, only -- the only one although so many have attempted, they submitted results and some of them resented it, that this benchmark is just really, really hard.

當然，隨著 MLPerf 和廣泛的基準測試，坦率地說，只有 - 儘管有很多人嘗試過，但他們提交了結果，其中一些人對此表示不滿，因為這個基準測試真的非常非常難。

Inference is hard.

推理是困難的。

And then finally, our business results also validated the -- our intuition.

最後，我們的業務結果也驗證了我們的直覺。

And so our engagement now with CSPs are now global.

因此，我們現在與 CSP 的合作現在是全球性的。

We're working across natural language understanding, recommendation systems, conversational AI, just a whole bunch of really, really interesting problems.

我們正在研究自然語言理解、推薦系統、對話式人工智能，以及一大堆非常非常有趣的問題。

Now the cloud is the third piece.

現在雲是第三塊。

And the reason why cloud is growing so well and represents about half almost of many of our CSPs, particularly the ones with the public cloud, the reason for that is because the number of AI start-ups in the world is still growing so incredibly.

雲之所以發展得如此之好，幾乎佔了我們許多 CSP 的一半左右，尤其是那些擁有公共雲的 CSP，其原因是因為世界上人工智能初創企業的數量仍在以驚人的速度增長。

I think we're tracking something close to 10,000 and more AI start-ups around the world.

我認為我們正在追踪全球近 10,000 家甚至更多的 AI 初創企業。

In health care, in transportation, in retail, in consumer Internet, in Fintech, the number of AI companies out there is just extraordinary.

在醫療保健、交通運輸、零售、消費互聯網、金融科技領域，人工智能公司的數量非常驚人。

I think over the last 3 or 4, 5 years, some $20 billion, $30 billion have been invested into start-ups.

我認為在過去的 3、4、5 年中，約有 200 億、300 億美元被投資於初創企業。

And these start-ups, of course, use cloud service providers so that they don't have to invest in their own infrastructure because it's fairly complicated.

當然，這些初創企業使用雲服務提供商，這樣他們就不必投資自己的基礎設施，因為它相當複雜。

And so we're seeing a lot of growth there.

所以我們在那裡看到了很多增長。

And so that's just the hyperscalers.

所以這只是超大規模的。

The hyperscalers give us 3 points of growth -- 3 areas of growth: training, inference and public cloud.

超大規模器為我們提供了 3 個增長點—— 3 個增長領域：訓練、推理和公共雲。

And the public cloud is primarily AI start-ups.

而公有云主要是人工智能初創企業。

Then there's the intelligent edge, which we recently ventured into, and we've been building this platform called EGX for some time.

然後是智能邊緣，我們最近涉足了這個領域，我們已經構建了這個名為 EGX 的平台已有一段時間了。

And it's cloud native.

它是雲原生的。

It's incredibly secure.

它非常安全。

You can manage it from afar.

您可以從遠處管理它。

It's -- the stack is complicated.

這是——堆棧很複雜。

It's performant.

它是高性能的。

And we saw some -- we've been working with some early adopters.

我們看到了一些——我們一直在與一些早期採用者合作。

And this last quarter, we announced some of them: Walmart and BMW and Procter & Gamble and the largest logistics company in the world, USPS.

在上個季度，我們宣布了其中一些：沃爾瑪、寶馬、寶潔和世界上最大的物流公司 USPS。

And so this new platform, I think, long term, will likely be the largest opportunity.

因此，我認為，從長遠來看，這個新平台可能是最大的機會。

And the reason for that is because of the industries that it serves.

原因在於它所服務的行業。

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

您的下一個問題來自摩根大通的 Harlan Sur。

There are a lot of concerns around China trade tensions, economic slowdown.

圍繞中國貿易緊張局勢、經濟放緩有很多擔憂。

But history has shown that gamers tend to be less sensitive to these macro trends and, in fact, also somewhat insensitive to price changes, at least at the enthusiast level.

但歷史表明，遊戲玩家往往對這些宏觀趨勢不太敏感，事實上，對價格變化也有些不敏感，至少在愛好者層面是這樣。

So given that China is such a big part of the gaming segment, can you just discuss the gaming demand trends out of this geography?

因此，鑑於中國是遊戲領域的重要組成部分，您能否討論一下該地區以外的遊戲需求趨勢？

Gaming is solid in China, and it is also the fastest adopter of our gaming notebooks.

遊戲在中國很穩固，它也是我們遊戲筆記本的最快採用者。

This gaming RTX notebooks or GeForce notebooks is really a brand new category.

這款遊戲 RTX 筆記本或 GeForce 筆記本確實是一個全新的品類。

This category never existed before because we couldn't get the technology in there so that it's both delightful to own as well as powerful to enjoy.

這個類別以前從未存在過，因為我們無法在其中獲得技術，因此擁有它既令人愉悅又強大享受。

And so we saw really great success with RTX notebooks and GeForce notebooks in China, and RTX adoption has been fast.

因此，我們看到 RTX 筆記本電腦和 GeForce 筆記本電腦在中國取得了巨大成功，而且 RTX 的採用速度很快。

Your comments make sense because most of the games are free-to-play these days.

您的評論是有道理的，因為如今大多數遊戲都是免費的。

The primary games that people play are esports, which you want the best gear, but you could -- after you buy the gear, you pretty much enjoy it forever; and mobile, which is largely free-to-play.

人們玩的主要遊戲是電子競技，你想要最好的裝備，但你可以——購買裝備後，你幾乎可以永遠享受它；和移動，這在很大程度上是免費的。

You invest in some of your own personal outfits.

你投資了一些你自己的個人服裝。

And after that, I think you can enjoy it for quite a long time.

在那之後，我想你可以享受很長一段時間。

And so the gear is really important.

所以裝備真的很重要。

One of the areas where we've done really great work, particularly in China, has to do with social.

我們所做的工作非常出色的領域之一，尤其是在中國，與社交有關。

We have this platform called GeForCe Experience.

我們有一個叫做 GeForCe Experience 的平台。

And as an extension of that, there's a new feature called RTX Broadcast Engine.

作為其擴展，還有一個新功能，稱為 RTX 廣播引擎。

And it basically applies AI to broadcasting your content to share it.

它基本上將 AI 應用於廣播您的內容以共享它。

You could make movies.

你可以拍電影。

You could capture your favorite scenes and turn it into art, applying AI.

你可以捕捉你最喜歡的場景並將其轉化為藝術，應用人工智能。

And one of the coolest features is that you could overlay yourself on top of the game and share it with all the social networks without a green screen behind you.

最酷的功能之一是您可以將自己疊加在遊戲之上，並與所有社交網絡分享，而您身後不會出現綠屏。

We use AI to stitch you out, basically, to cut you out of the background and irrespective of what noisy background you've got.

基本上，我們使用人工智能將你從背景中剔除，而不管你有什麼嘈雜的背景。

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

如你所知，中國真的有一個超級超級社交社區——那裡的社區，他們有各種非常酷的社交平台來分享遊戲、用戶生成的內容、短視頻和各種類似的東西。

And so GeForce has that one additional feature that really makes it successful.

因此，GeForce 擁有一項真正使其成功的附加功能。

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

你的下一個問題來自高盛的 Toshiya Hari。

I wanted to ask on automotive.

我想問汽車。

Colette, in your prepared remarks, you talked about your legacy infotainment business being down in the quarter.

Colette，在您準備好的講話中，您談到了您的傳統信息娛樂業務在本季度下滑。

Just curious, what percentage of automotive revenue at this point is legacy infotainment versus the newer AI/ADAS solutions?

只是好奇，與較新的 AI/ADAS 解決方案相比，傳統信息娛樂系統佔汽車收入的百分比是多少？

And more importantly, Jensen, if you can speak to the growth trajectory in automotive over the next 1.5 years, maybe 2, that would be appreciated.

更重要的是，Jensen，如果你能談談汽車在未來 1.5 年，也許是 2 年的增長軌跡，那將不勝感激。

And I do ask the question because it feels like we've heard many, many announcements, customer announcements, collaborative work that you're doing with your customers, yet we haven't quite seen sort of a hockey-stick inflection that some of us were expecting a couple of years ago.

我確實問了這個問題，因為感覺我們已經聽到了很多很多公告，客戶公告，您與客戶進行的協作工作，但我們還沒有完全看到曲棍球棒的一些變化幾年前我們就期待了。

So just kind of curious when we should -- how we should set our expectations going forward.

所以只是有點好奇我們什麼時候應該 - 我們應該如何設定我們的期望。

Yes.

是的。

Toshiya, let me address the first question regarding our legacy infotainment systems for our automotive business.

Toshiya，讓我來回答第一個關於我們汽車業務的傳統信息娛樂系統的問題。

It is still representing maybe about half or more of our overall revenue in the automotive business.

它仍然占我們汽車業務總收入的一半或更多。

We have our AI cockpit continuing to grow and grow quite well, both sequentially as well as year-over-year, as well as our autonomous vehicle solutions that we may be doing, including development services.

我們的 AI 駕駛艙繼續增長並且增長非常好，無論是按順序還是同比，以及我們可能正在做的自動駕駛汽車解決方案，包括開發服務。

Let's see.

讓我們來看看。

The -- we're the first -- probably the first AV car that's going to be passenger-owned on the road, and I think we've talked about it before, is Volvo.

- 我們是第一輛 - 可能是第一輛將在道路上由乘客擁有的 AV 汽車，我想我們之前已經討論過它，是沃爾沃。

And we're expecting them to be in the late 2020, early 2021 time frame.

我們預計它們將在 2020 年末、2021 年初的時間範圍內。

And I'm still expecting so.

我仍然期待如此。

And then there's the 2022, 2023 generations.

然後是 2022、2023 代。

Most -- I would say most of the passenger-owned vehicle developments are going quite well.

大多數——我會說大多數乘用車開發進展順利。

The industry, as you know, is under some amount of pressure, and so a lot of them have slipped it out a couple of years or so.

如你所知，這個行業正承受著一定的壓力，所以他們中的很多人在幾年左右的時間裡就溜走了。

And this is something that I think we've already spoken about in the past.

這是我認為我們過去已經談過的事情。

Our focus, our strategy consists of several areas.

我們的重點，我們的戰略包括幾個領域。

One area, of course, is passenger-owned vehicles.

當然，其中一個領域是乘用車。

The second part is robot taxis.

第二部分是機器人出租車。

We have developments going with just about every major robot taxi company that we know of.

我們與我們所知道的幾乎所有主要的機器人出租車公司都有發展。

And they're here in the states.

他們在美國。

They're in Europe.

他們在歐洲。

They're in China.

他們在中國。

And when you hear news of them, we're delighted to see their progress.

當你聽到他們的消息時，我們很高興看到他們的進步。

And then the third part has to do with trucks, shuttles and increasingly a large number of vehicles that don't carry people, they carry goods.

然後第三部分與卡車、穿梭巴士以及越來越多的不載人、載貨的車輛有關。

And so we have a major development with Volvo.

因此，我們與沃爾沃取得了重大進展。

That was Volvo Trucks.

那是沃爾沃卡車。

Volvo Cars and Volvo Trucks, as you know, are 2 different companies.

如您所知，沃爾沃汽車和沃爾沃卡車是兩家不同的公司。

One of them belongs to Geely, Volvo Cars.

其中之一屬於吉利，沃爾沃汽車。

Volvo Trucks is the heritage Volvo.

沃爾沃卡車是沃爾沃的遺產。

And we have a major program going with them to automate the delivery of goods.

我們有一個與他們合作的主要計劃來自動化貨物交付。

You also see us during various GTCs, I'll mention companies that we're working with on grocery delivery or goods delivery or within a warehouse product delivery.

您還會在各種 GTC 期間看到我們，我會提到我們正在與我們合作的雜貨配送或貨物配送或倉庫產品配送的公司。

You're going to see a whole bunch of things like that because the technology is very similar, and it's starting to -- the development -- the technology we develop for passenger-owned vehicles has started to propagate down into logistics vehicles.

你會看到一大堆這樣的事情，因為技術非常相似，而且它開始——發展——我們為乘用車開發的技術已經開始向下傳播到物流車輛。

I continue to believe that everything that moves eventually will have autonomous capability or be fully autonomous.

我仍然相信，最終移動的一切都將具有自主能力或完全自主。

And that, I think, is, at this point, fairly certain.

我認為，在這一點上，這是相當確定的。

Now our strategy is both in developing the in-car AV computing system, and it's software-defined, it's scalable, as well as the AI development and simulation systems.

現在我們的戰略是開發車載 AV 計算系統，它是軟件定義的、可擴展的，以及人工智能開發和模擬系統。

And so when somebody's working on AV and they're using AI, and most of them are, there's a great opportunity for us.

因此，當有人在研究 AV 並且他們正在使用 AI 時，而且他們中的大多數人都在使用，這對我們來說是一個很好的機會。

And when they start ramping up and they're collecting miles of data, it becomes a very large market opportunity for us.

當他們開始加速並收集數英里的數據時，這對我們來說是一個非常大的市場機會。

And so I'm anxious to see every single car company be as progressive and aggressive in developing AV.

因此，我渴望看到每家汽車公司在開發 AV 方面都同樣進步和積極。

And they will be.

他們會的。

They will be.

他們會。

This is a foregone conclusion.

這是已成定局的結論。

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

您的下一個問題來自 Stacy Rasgon 與 Bernstein 的對話。

I have 2 data center questions for Colette.

我有 2 個關於 Colette 的數據中心問題。

The first question, I want to return to your kind of outlook for strong sequential data center growth in Q4.

第一個問題，我想回到你對第四季度數據中心強勁連續增長的看法。

Now this business grew 11% sequentially in Q3.

現在，該業務在第三季度環比增長了 11%。

And you didn't actually call out strong growth as we were going into the quarter.

當我們進入本季度時，您實際上並沒有提到強勁的增長。

You are calling it out for Q4.

您正在為第四季度大聲疾呼。

Does that suggest to me that you expect sequential growth in Q4 to be stronger than Q3 given you're calling it out in Q4 and you didn't call it out in Q3?

這是否表明您預計第四季度的連續增長將強於第三季度，因為您在第四季度提出了要求，而您在第三季度沒有提出要求？

Or would you define like what you saw in Q3 as well as already being strong sequential growth?

或者你會定義像你在第三季度看到的那樣以及已經強勁的連續增長嗎？

Like how do we think about the wording of that in relation to what we've seen in Q3 and what you expect for Q4?

就像我們如何看待與我們在第三季度看到的內容以及您對第四季度的預期相關的措辭？

Sure, Stacy.

當然，斯泰西。

When we had provided guidance in Q3 and how we finished the quarter in Q3, we had indicated that our growth would stem from both gaming and data center.

當我們在第三季度提供指導以及我們如何在第三季度完成該季度時，我們曾表示我們的增長將來自遊戲和數據中心。

We completed that.

我們完成了。

And we also had stronger than expected from guidance from both gaming and data center in our Q3 results.

而且我們在第三季度的業績中，遊戲和數據中心的指導也比預期的要強。

Moving to Q4, Q4 is a sequential decrease in totality versus Q3.

移至第四季度，第四季度的總量與第三季度相比連續下降。

We have reminded the teams about our overall seasonality that we sometimes have in gaming associated with our consoles as well as also with our notebooks that seem to be primarily in Q2 and Q3 being our strongest quarters and likely, therefore, a seasonal downtick as it move to Q4.

我們已經提醒團隊我們的整體季節性，我們有時會在與我們的遊戲機以及我們的筆記本電腦相關的遊戲中遇到這種情況到第四季度。

What we wanted to do was, if we have in totality overall decline associated with that, we did want to emphasize what we are expecting in terms of data center with the overall strong growth sequentially.

我們想要做的是，如果我們總體上出現了與此相關的整體下降，我們確實想強調我們對數據中心的預期以及整體強勁增長的順序。

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

所以我想再次問這個問題，你是否會將你在第三季度看到的定義為強勁增長？

I would believe our growth of 17% was higher than we expected to Q3.

我相信我們 17% 的增長高於我們對第三季度的預期。

Again, when we get into Q4, we'll see how the quarter ends in terms of data center, but we are expecting strong growth.

同樣，當我們進入第四季度時，我們將看到該季度在數據中心方面的結束情況，但我們預計會有強勁的增長。

Thanks, Stacy.

謝謝，斯泰西。

Okay.

好的。

And for my second question, hyperscale you said was up year-over-year.

對於我的第二個問題，你說的超大規模同比增長。

Now -- and that's after, off of last year, where it was the peak.

現在 - 那是在去年之後的高峰期。

Inference doubled year-over-year.

推理量同比翻了一番。

And this suggests to me -- I know you said enterprise was down year-over-year.

這向我表明——我知道你說企業同比下降。

But this suggests to me that it wasn't just down year-over-year, it was down a lot year-over-year.

但這向我表明，它不僅同比下降，而且同比下降很多。

How do we think about that in the context of like the growth that we've seen very strongly over the last few quarters in enterprise.

在過去幾個季度我們在企業中看到的非常強勁的增長的背景下，我們如何看待這一點。

And going back to your commentary at the Analyst Day, which was almost entirely about the opportunity coming from enterprise growth, what's going on there?

回到你在分析師日的評論，幾乎完全是關於來自企業增長的機會，那裡發生了什麼？

What drove that?

是什麼推動了它？

And what should we expect going forward?

我們應該期待什麼？

Sure.

當然。

Our enterprise business has been beginning to ramp from over a year ago at a very, very, very small base.

我們的企業業務從一年多前開始在一個非常、非常、非常小的基礎上增長。

We've continued to see great traction in there with a lot of the things that we've announced throughout.

通過我們自始至終宣布的許多事情，我們繼續看到了巨大的吸引力。

But keep in mind in our year ago quarter, we also had very strong systems and a very large deal associated with our DGX.

但請記住，在我們一年前的季度，我們也有非常強大的系統和與我們的 DGX 相關的大量交易。

So when we look from a quarter-over-quarter period or just looking at 1 quarter, we can have a little bit of lumpiness.

因此，當我們查看季度環比或僅查看 1 個季度時，我們可能會有些不穩。

So that year-over-year impact is really just due to an extremely large deal in the prior year Q3.

因此，與去年同期相比的影響實際上只是由於去年第三季度的一筆非常大的交易。

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

您的下一個問題來自 RBC 的 Mitch Steves。

I apologize for any background noise, but I just have one question, just for Jensen.

對於任何背景噪音，我深表歉意，但我只有一個問題，僅針對 Jensen。

So in 2018, can you give us a rough update on what the GPU utilization was for deep learning application?

那麼在 2018 年，您能否大致介紹一下深度學習應用程序的 GPU 利用率是多少？

What it is today?

今天是什麼日子？

I'm just wondering how the -- how that's advanced over the last couple of year or 2.

我只是想知道在過去的一兩年裡這是如何進步的。

Let's see.

讓我們來看看。

I would say 2018, it was nearly all related to training.

我會說 2018 年，這幾乎都與培訓有關。

And this year, we started to see the growth of inference to the point where we now -- we have now sold more -- this last quarter, we sold more T4 GPUs for inference than we sold V100s that's used for training, and both of them were record highs.

今年，我們開始看到推理的增長到現在——我們現在銷售了更多——上個季度，我們銷售的推理用 T4 GPU 比我們銷售的用於訓練的 V100 還要多，而且兩者他們創下歷史新高。

And so the comment that Colette just made, comparing to year-over-year, we had a large DGX system sale a year ago that we didn't have this year.

因此，Colette 剛剛發表的評論與去年同期相比，我們一年前進行了大規模的 DGX 系統銷售，而今年沒有。

But if you excluded that, the V100 and the T4 is doing great.

但如果你排除了這一點，V100 和 T4 就做得很好。

They're at record levels.

他們處於創紀錄的水平。

And T4 didn't hardly existed a year ago, now it's selling more than V100s, and both of them are record highs.

而T4一年前還不存在，現在它的銷量超過了V100，而且都創下了歷史新高。

And so that kind of gives you a feeling for it.

所以這種感覺給你一種感覺。

I think that's really the major difference that inference is really kicking into gear, and my sense is that it's going to continue to grow quite nicely.

我認為這確實是推理真正開始發揮作用的主要區別，而且我的感覺是它將繼續很好地增長。

And 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。

I wonder if you could talk a little bit more about the 5G opportunity that you announced at Mobile World.

我想知道您能否多談談您在 Mobile World 上宣布的 5G 機會。

And I guess you talked a lot about AI and IoT services in a C-RAN environment.

我猜你在 C-RAN 環境中談了很多關於人工智能和物聯網服務的話題。

But is there -- how big is that opportunity?

但是有沒有——這個機會有多大？

And can you address kind of the core compute aspect to C-RAN with the GPU?

您能否使用 GPU 解決 C-RAN 的核心計算方面的問題？

Yes.

是的。

If you look at the world of mobile today, there are players that are building DRAMs and their radio heads in the BBU, basically the baseband units.

如果你看看今天的移動世界，有些玩家正在 BBU 中構建 DRAM 和無線電頭，基本上是基帶單元。

In the data center where people would like to move the software for radio networks, it's really an untapped market.

在人們希望移動無線電網絡軟件的數據中心，這確實是一個尚未開發的市場。

And the reason for that is because the CPU is just not able to support the level of performance that's necessary for 5G.

原因是 CPU 無法支持 5G 所需的性能水平。

And ASICs are too rigid to be able to put into a data center.

ASIC 過於僵化，無法放入數據中心。

And so the data center needs a programmable solution that is data center-ready that can support all of the software richness that goes along with the data center, whether it's a VM environment like VMware.

因此，數據中心需要一個可編程的解決方案，該解決方案是數據中心就緒的，可以支持與數據中心相關的所有軟件豐富性，無論是像 VMware 這樣的 VM 環境。

And we -- recently, during the quarter, we announced another partnership with VMware.

我們 - 最近，在本季度，我們宣布與 VMware 建立另一項合作夥伴關係。

They recognize that increasingly, our GPUs are becoming a core part of data centers and cloud.

他們認識到，我們的 GPU 正日益成為數據中心和雲的核心部分。

We had a partner -- we announced a partnership with Red Hat.

我們有一個合作夥伴——我們宣布與紅帽合作。

They realize the momentum that they're seeing us in, in telcos, and they would like to adapt their entire stack from open stack to OpenShift on top of our GPUs.

他們意識到他們在電信公司中看到我們的勢頭，他們希望在我們的 GPU 之上調整他們的整個堆棧，從開放堆棧到 OpenShift。

And so now with VMware, with Red Hat, we're going to have a world-class telco enterprise stack that ranges all the way from hypervisors and virtual machines all the way to Kubernetes.

所以現在有了 VMware，有了 Red Hat，我們將擁有一個世界級的電信企業堆棧，範圍從管理程序和虛擬機一直到 Kubernetes。

And so our strategy is to -- our goal is to really create this new world of C-RAN, vRAN centralized data centers and software-defined networking.

因此，我們的戰略是——我們的目標是真正創建這個 C-RAN、vRAN 集中式數據中心和軟件定義網絡的新世界。

And the software-defined networking will, of course, include things like in the data center networking as well as firewalls.

當然，軟件定義的網絡將包括數據中心網絡和防火牆之類的東西。

But the computationally-intensive stuff is really the 5G radio.

但計算密集型的東西實際上是 5G 無線電。

And so we're going to create a software stack for 5G and basically exactly the same way that we've done for creating a -- excuse me, a software stack for deep learning.

因此，我們將為 5G 創建一個軟件堆棧，基本上與我們創建一個用於深度學習的軟件堆棧的方式完全相同。

And we call it Aerial.

我們稱之為空中。

Aerial is to 5G essentially what Cuda-NN is for deep learning and essentially what optics is for ray tracing.

Aerial 之於 5G，本質上是 Cuda-NN 之於深度學習，本質上是光學之於光線追踪。

And this software stack is going to allow us to run the whole software -- run the whole 5G stack in software and deliver the highest performance, the incredible flexibility and scale to as many layers of MIMO as customers need and to be able to put all of it in the data center.

這個軟件堆棧將使我們能夠運行整個軟件——在軟件中運行整個 5G 堆棧，並提供最高的性能、令人難以置信的靈活性和擴展到客戶需要的盡可能多的 MIMO 層，並且能夠將所有它在數據中心。

The power of putting it into data center, as you know, is flexibility and fungibility.

如您所知，將其放入數據中心的強大之處在於靈活性和可替代性。

With the low latency capability of 5G, you could put a data center somewhere in the regional hub.

借助 5G 的低延遲能力，您可以將數據中心放置在區域中心的某個位置。

And depending on where the traffic is going, you could shift the traffic computation from 1 data center to another data center, something that you can't do in basebands, in baseband units in the cell towers, but you can do that in the data center.

並且根據流量的去向，您可以將流量計算從一個數據中心轉移到另一個數據中心，這是您在基帶中無法做到的，在基站的基帶單元中，但您可以在數據中做到這一點中央。

And that helps them reduce the cost.

這有助於他們降低成本。

The second benefit is that the telcos would love to be a service provider for a data center's computation at the edge.

第二個好處是電信公司願意成為數據中心邊緣計算的服務提供商。

And the edge applications are things like smart cities and whether it's warehouses or retail stores or whatever it is because they're geographically located and is distributed all over the world.

邊緣應用程序是諸如智能城市之類的東西，無論是倉庫還是零售店或其他任何東西，因為它們位於地理上並且分佈在世界各地。

And so to be able to use their data center to also be able to use AI in combination with IoT is really exciting to them.

因此，能夠使用他們的數據中心，也能夠將人工智能與物聯網結合使用，這對他們來說真的很令人興奮。

And so I think that that's really -- this is really the future that we're going to see a lot more service providers at the edge.

所以我認為這真的是——這真的是我們將在邊緣看到更多服務提供商的未來。

And these edge data centers will have to run the data center, the networking, including the mobile network and software as well as run 5G and IoT -- AI and IoT applications

這些邊緣數據中心必須運行數據中心、網絡，包括移動網絡和軟件，以及運行 5G 和物聯網——人工智能和物聯網應用

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

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

I apologize for the background noise.

我為背景噪音道歉。

But Colette, maybe you could give us an idea of gaming.

但是科萊特，也許你可以給我們一個遊戲的想法。

In the guidance, it's down.

在指導中，它下降了。

And I was wondering, could you maybe give us the impact of the console business versus the laptop and give us an idea of what might be the bigger driver there?

我想知道，您能否給我們介紹一下游戲機業務與筆記本電腦業務的影響，並讓我們了解可能更大的驅動力是什麼？

I would say, for our Q4, both of them are expected to be seasonally down.

我想說，對於我們的第四季度，預計它們都會出現季節性下降。

In the case of the consoles, we do wait for Nintendo to assist in terms of what they need.

就遊戲機而言，我們確實在等待任天堂根據他們的需求提供幫助。

So we will have to see how the quarter ends on that.

所以我們必須看看這個季度是如何結束的。

But in both cases, in totality, these businesses have ranged, maybe in totality of the 2, of about $500 million a quarter.

但在這兩種情況下，總體而言，這些業務的範圍（可能是這兩種業務的總和）約為每季度 5 億美元。

And we'll see both of them sequentially decline.

我們將看到它們都依次下降。

Thank you.

謝謝你。

I'll 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。

Thanks, everyone.

謝謝大家。

We had a good quarter driven by strong gaming growth and hyperscale demand.

在強勁的遊戲增長和超大規模需求的推動下，我們的季度表現不錯。

We're making great strides in 3 big impact initiatives.

我們在 3 項重大影響計劃方面取得了長足的進步。

The world of computer graphics is moving to ray tracing, and our business reflects that.

計算機圖形世界正在轉向光線追踪，我們的業務也反映了這一點。

Some of the biggest blockbuster games this holiday season and beyond are RTX-enabled, including Call of Duty: Modern Warfare; and the best-selling game of all-time, Minecraft.

這個假期及以後的一些最熱門的遊戲都支持 RTX，包括使命召喚：現代戰爭；以及有史以來最暢銷的遊戲《我的世界》。

Design applications used by millions of artists and creators are rapidly adopting RTX ray tracing.

數百萬藝術家和創作者使用的設計應用程序正在迅速採用 RTX 光線追踪。

We're reinventing computer graphics and look forward to upgrading the hundreds of millions of PC gamers to RTX.

我們正在重塑計算機圖形，並期待將數億 PC 遊戲玩家升級到 RTX。

Hyperscale demand was strong this quarter, and our visibility continues to improve.

本季度超大規模需求強勁，我們的知名度不斷提高。

The race is on for conversational AI, which will be a powerful catalyst for us in both training and inference.

對話式人工智能的競賽正在進行中，這將成為我們在訓練和推理方面的強大催化劑。

And lastly, we have extended our computing platform beyond the cloud to the edge, where GPU-accelerated 5G, AI and IoT, will revolutionize the world's largest industries.

最後，我們將計算平台從雲端擴展到邊緣，GPU 加速的 5G、人工智能和物聯網將徹底改變世界上最大的行業。

We look forward to updating you on our progress in February.

我們期待在 2 月份向您通報我們的進展情況。

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。