輝達 (NVDA) 2020 Q2 法說會逐字稿

Good afternoon.

下午好。

My name is Christina, and I will be your conference operator today.

我的名字是克里斯蒂娜，今天我將成為你們的會議接線員。

Welcome to NVIDIA's financial results conference call.

歡迎參加 NVIDIA 的財務業績電話會議。

(Operator Instructions)

（操作員說明）

I'll now turn the call over to Simona Jankowski from Investor Relations to begin your conference.

我現在將電話轉給投資者關係部的 Simona Jankowski 開始您的會議。

Thank you.

謝謝你。

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

大家下午好，歡迎參加 NVIDIA 2020 財年第二季度電話會議。

With me on the call today from NVIDIA are Jen-Hsun 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 third quarter of fiscal 2020.

在電話會議討論我們 2020 財年第三季度的財務業績之前，該網絡廣播將可供重播。

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

今天通話的內容是 NVIDIA 的財產。

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

未經我們事先書面同意，不得複製或轉錄。

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

在本次電話會議期間，我們可能會根據當前預期做出前瞻性陳述。

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

這些受到許多重大風險和不確定性的影響，我們的實際結果可能存在重大差異。

For a discussion of factors that could affect our future financial results and business, please refer to the disclosure in today's earnings release, our most recent 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, August 15, 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，2019 年 8 月 15 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。

Q2 revenue was $2.58 billion, in line with our outlook, down 17% year-on-year and up 16% sequentially.

第二季度收入為 25.8 億美元，符合我們的預期，同比下降 17%，環比增長 16%。

Starting with our gaming business.

從我們的遊戲業務開始。

Revenue of $1.31 billion was down 27% year-on-year and up 24% sequentially.

收入為 13.1 億美元，同比下降 27%，環比增長 24%。

We are pleased with the strong sequential growth in the quarter when we launched our RTX SUPER lineup for desktop gamers, wrapped up our greatest ever number of gaming laptops and launched our new RTX studio laptops for creators.

我們對本季度強勁的連續增長感到高興，當時我們推出了面向桌面遊戲玩家的 RTX SUPER 系列，完成了有史以來數量最多的遊戲筆記本電腦，並推出了面向創作者的全新 RTX 工作室筆記本電腦。

In July, we unveiled 3 GeForce RTX SUPER GPUs, delivering the best-in-class gaming performance and power efficiency and real-time ray tracing for both current and next-generation games.

7 月，我們推出了 3 個 GeForce RTX SUPER GPU，為當前和下一代遊戲提供一流的遊戲性能和能效以及實時光線追踪。

These GPUs delivered a performance boost of up to 24% from our initial Turing GPUs launched a year earlier.

這些 GPU 的性能比我們一年前推出的最初的 Turing GPU 提升了高達 24%。

The SUPER lineup strengthens our leadership in the high end of the market, and the response has been great.

SUPER 陣容加強了我們在高端市場的領導地位，反響非常好。

We look forward to delighting gamers with the best performance in ray tracing as we get into the back to school and holiday shopping seasons.

當我們進入返校季和假日購物季時，我們期待以最佳的光線追踪性能取悅遊戲玩家。

Ray tracing is taking the gaming industry by storm and have quickly come to define the modern era of computer graphics.

光線追踪正在席捲遊戲行業，並迅速定義了現代計算機圖形學時代。

A growing number of blockbuster AAA titles have announced support for NVIDIA RTX ray tracing, including Call of Duty: Modern Warfare, Super Punk 2077 (sic) [Cyberpunk 2077], Watch Dogs: Legion and Wolfenstein: Youngblood.

越來越多的 AAA 大片宣布支持 NVIDIA RTX 光線追踪，包括《使命召喚：現代戰爭》、《超級朋克 2077》（原文如此）[賽博朋克 2077]、《看門狗：軍團》和《德軍總部：年輕血液》。

Excitement around these titles is tremendous.

圍繞這些標題的興奮是巨大的。

GameSpot called Cyberpunk one of the most anticipated games of the decade.

GameSpot 稱賽博朋克是十年來最受期待的遊戲之一。

NVIDIA GeForce RTX are the only graphic cards in the market with hardware support for ray tracing.

NVIDIA GeForce RTX 是市場上唯一支持光線追踪硬件的顯卡。

They deliver a 2 to 3x performance speed up over GPUs without a dedicated ray tracing core.

在沒有專用光線追踪核心的情況下，它們的性能比 GPU 提高了 2 到 3 倍。

The laptop business continues to be a standout growth driver as OEMs are ramping a record 100-plus gaming laptop models ahead of the back to school and holiday season.

筆記本電腦業務繼續成為突出的增長動力，因為 OEM 在返校和假期前推出了創紀錄的 100 多款遊戲筆記本電腦型號。

The combination of our energy-efficient Turing architecture and Max-Q technology enables beautifully crafted thin and light form factors that can deliver the performance of high-end gaming desktop or our next-generation console.

我們的節能 Turing 架構和 Max-Q 技術相結合，打造出精美的輕薄外形，可提供高端遊戲台式機或我們的下一代遊戲機的性能。

At Computex in May, we unveiled NVIDIA RTX Studio laptops, a new design artist platform that extends our reach to the large, underserved market of creators.

在 5 月的 Computex 上，我們推出了 NVIDIA RTX Studio 筆記本電腦，這是一個新的設計藝術家平台，將我們的影響力擴展到了服務不足的大型創作者市場。

In the age of YouTube, creators and freelancers are rapidly growing population, but they have traditionally not had access to professional-grade workstations through online and retail channels.

在 YouTube 時代，創作者和自由職業者的人口迅速增長，但他們傳統上無法通過在線和零售渠道獲得專業級工作站。

RTX Studio laptops are designed to meet their increasing complex workflows such as photorealistic ray tracing, AI image enhancement and ultra high-resolution video.

RTX Studio 筆記本電腦旨在滿足其日益複雜的工作流程，例如逼真的光線追踪、AI 圖像增強和超高分辨率視頻。

Powered by our RTX GPUs and optimized software, RTX Studio laptops deliver performance that's up to 7x faster than that of the MacBook Pro.

在我們的 RTX GPU 和優化軟件的支持下，RTX Studio 筆記本電腦的性能比 MacBook Pro 快 7 倍。

A total of 27 RTX Studio models have been announced by major OEMs.

主要 OEM 共發布了 27 款 RTX Studio 型號。

Sequential growth also benefited from the production ramp of the 2 new models of Nintendo Switch gaming console.

連續增長也受益於 2 款新型號 Nintendo Switch 遊戲機的產量增加。

We are expecting our console business to remain strong in Q3 before the seasonal production slowdown in Q4 when console-related revenue is expected to be fairly minimal, similar to last year.

在第四季度季節性生產放緩之前，我們預計我們的控制台業務將在第三季度保持強勁，屆時與控制台相關的收入預計將與去年相似。

Moving to data center.

搬到數據中心。

Revenue was $655 million, down 14% year-on-year and up 3% sequentially.

收入為 6.55 億美元，同比下降 14%，環比增長 3%。

In the vertical industries portion of the business, expanding AI workload drove sequential and year-over-year growth.

在業務的垂直行業部分，不斷擴大的 AI 工作負載推動了連續和同比增長。

In hyperscale portion, we continue to be impacted by relatively weak overall spending at a handful of CPU -- CSPs.

在超大規模部分，我們繼續受到少數 CPU（CSP）相對疲軟的總體支出的影響。

Sales of NVIDIA GPUs for use in the cloud were solid.

用於雲的 NVIDIA GPU 的銷售穩定。

While sales of internal hyperscale use were muted, the engineering focus on AI is growing.

雖然內部超大規模使用的銷售低迷，但對人工智能的工程關注正在增長。

Let me give some color on each of these areas.

讓我對這些區域中的每一個進行一些說明。

We are building a broad base of customers across multiple industries as they adopt NVIDIA's platforms to harness the power of AI.

我們正在多個行業建立廣泛的客戶群，因為他們採用 NVIDIA 的平台來利用 AI 的力量。

Public sectors, higher education and financial services were among the key verticals driving growth this quarter.

公共部門、高等教育和金融服務是本季度推動增長的主要垂直領域。

In addition, we won Lighthouse account deals in important industries that are on the cusp of being transformed by AI.

此外，我們還贏得了即將被人工智能改造的重要行業的燈塔客戶交易。

For example, in retail, Walmart is using NVIDIA GPUs to run some of its product demand forecasting models, slashing the time to do so in just 4 hours from several weeks on CPUs.

例如，在零售業，沃爾瑪正在使用 NVIDIA GPU 來運行其部分產品需求預測模型，從而將在 CPU 上運行幾週的時間縮短為 4 小時。

By accelerating its data science workflow, Walmart can improve its algorithms, reduce development cycles and test new features.

通過加速其數據科學工作流程，沃爾瑪可以改進其算法、縮短開發週期並測試新功能。

Earlier this week, we announced breakthroughs for the fastest training and inference of the state-of-the-art model for natural language process understanding called BERT, or Bidirectional Encoder Representations of -- from Transformers, a breakthrough AI language model that achieves a deeper sense of language, context and meaning.

本週早些時候，我們宣布了最先進的自然語言過程理解模型 BERT 或雙向編碼器表示的最快訓練和推理突破——來自 Transformers，這是一種突破性的 AI 語言模型，可實現更深層次的語言、語境和意義的感覺。

This can enable mere human comprehension in real-time by chat box, intelligent personal assistants and search engines.

這可以通過聊天框、智能個人助理和搜索引擎實現人類的實時理解。

We are working with Microsoft as an early adopter of these advances.

作為這些進步的早期採用者，我們正在與 Microsoft 合作。

AI computing leadership is a high priority for NVIDIA.

人工智能計算領導地位是 NVIDIA 的重中之重。

Last month, we set records for training deep learning neural network models on the latest MLPerf benchmarks, particularly in the most demanding areas.

上個月，我們創造了在最新的 MLPerf 基准上訓練深度學習神經網絡模型的記錄，特別是在最苛刻的領域。

In just 7 months, we have achieved up to 80% speed-ups enabled by new algorithms and software optimizations across the full stack while using the same hardware.

在短短 7 個月內，我們在使用相同硬件的情況下，通過整個堆棧的新算法和軟件優化實現了高達 80% 的加速。

This is a direct result of the productive programming environment and flexibility of CUDA.

這是 CUDA 高效的編程環境和靈活性的直接結果。

Delivering AI at scale isn't just about silicon.

大規模交付人工智能不僅僅是關於芯片。

It's about optimizing across the entire high-performance computing system.

它是關於優化整個高性能計算系統的。

In fact, the NVIDIA AI platform is getting progressively faster.

事實上，NVIDIA AI 平台正在變得越來越快。

Every month, we publish new optimization and performance improvements to CUDA-X AI libraries, supporting every AI framework and development environment.

每個月，我們都會發布對 CUDA-X AI 庫的新優化和性能改進，支持每個 AI 框架和開發環境。

All in, our ecosystem of developers is now 1.4 million strong.

總而言之，我們的開發者生態系統現在有 140 萬強大。

In setting these MLPerf records, we leveraged our new DGX SuperPOD AI supercomputer, demonstrating that leadership in AI research demands leadership in computing infrastructure.

在設置這些 MLPerf 記錄時，我們利用了新的 DGX SuperPOD AI 超級計算機，證明了 AI 研究的領導地位需要計算基礎設施的領導地位。

This system debuted in June at #22 on the TOP500 list of the world's fastest supercomputers at the annual International Supercomputing Conference.

該系統於 6 月首次亮相，在年度國際超級計算大會上世界上最快的超級計算機 TOP500 榜單中排名第 22 位。

Used to meet the massive demand for autonomous vehicle development program, it is powered by more than 1,500 NVIDIA V100 Tensor Core GPUs linked with Mellanox interconnects.

用於滿足自動駕駛汽車開發計劃的大量需求，它由 1,500 多個與 Mellanox 互連連接的 NVIDIA V100 Tensor Core GPU 提供支持。

We've made DGX SuperPOD available commercially to customers, essentially providing them with the turnkey supercomputer that they can assemble in weeks rather than months.

我們已將 DGX SuperPOD 商業化提供給客戶，本質上為他們提供了交鑰匙超級計算機，他們可以在數周而不是數月內完成組裝。

It is roughly 400x smaller in size than other similarly performing TOP500 systems, which are built from thousands of servers.

它的大小大約比其他類似性能的 TOP500 系統小 400 倍，這些系統由數千台服務器構建而成。

Also at the conference, we announced that by next year's end, we will make available to the ARM ecosystem NVIDIA's full stack of AI and HPC software, which accelerates more than 600 HPC applications and all AI frameworks.

同樣在會議上，我們宣布，到明年年底，我們將向 ARM 生態系統提供 NVIDIA 的全棧 AI 和 HPC 軟件，可加速 600 多個 HPC 應用程序和所有 AI 框架。

With this announcement, NVIDIA will accelerate all major CPU architectures, including x86, POWER and ARM.

通過此次發布，NVIDIA 將加速所有主要的 CPU 架構，包括 x86、POWER 和 ARM。

Lastly, regarding our pending acquisition of Mellanox, we have received regulatory approval in the U.S. and are engaged with regulators in Europe and China.

最後，關於我們即將收購的 Mellanox，我們已獲得美國監管機構的批准，並正在與歐洲和中國的監管機構進行接觸。

The approval process is progressing as expected, and we continue to work toward closing the deal by the end of this calendar year.

審批過程正按預期進行，我們將繼續努力在本日曆年年底前完成交易。

Moving to pro visualization.

轉向專業可視化。

Revenue reached $291 million, up 4% from our prior year and up 9% sequentially.

收入達到 2.91 億美元，比上一年增長 4%，環比增長 9%。

Year-on-year and sequential growth was led by record revenue for mobile workstations with strong demand for new thin and light form factors.

移動工作站創紀錄的收入帶動了對新的輕薄外形的強勁需求，帶動了同比和環比增長。

We had a great showing at SIGGRAPH, the computer graphics industry's biggest annual conference held in Los Angeles.

我們在 SIGGRAPH（計算機圖形行業最大的年度會議在洛杉磯舉行）上展示了出色的表現。

Our researchers won several Best in Show awards.

我們的研究人員獲得了多項最佳展示獎。

In just a year since the launch of RTX ray tracing, over 40 design and creative applications with RTX technology had been announced by leading software vendors, including Adobe, Autodesk and Dassault systems and many others.

自 RTX 光線追踪推出僅一年時間，領先的軟件供應商就發布了 40 多種採用 RTX 技術的設計和創意應用程序，包括 Adobe、Autodesk 和 Dassault 系統等。

NVIDIA RTX technology has reinvigorated the computer graphics industry by enabling researchers and developers to take a leap in photorealistic rendering, augmented reality and virtual reality.

NVIDIA RTX 技術使研究人員和開發人員能夠在照片級真實感渲染、增強現實和虛擬現實方面取得飛躍，從而重振了計算機圖形行業。

Finally, turning to automotive.

最後，轉向汽車。

Q2 revenue was $209 million, up 30% from a year ago and up 26% sequentially.

第二季度收入為 2.09 億美元，同比增長 30%，環比增長 26%。

This reflects growing adoption of next-generation AI cockpit solutions and autonomous vehicle development projects, including 1 particularly sizable development services transaction that was recognized in the quarter.

這反映了下一代人工智能駕駛艙解決方案和自動駕駛汽車開發項目越來越多的採用，包括本季度確認的一項特別大規模的開發服務交易。

In addition, in June, we announced a new partnership with the Volvo Group to develop AI and autonomous trucks utilizing NVIDIA's end-to-end AI platform for training, simulation and in-vehicle computing.

此外，6 月，我們宣布與沃爾沃集團建立新的合作夥伴關係，利用 NVIDIA 的端到端人工智能平台開發人工智能和自動駕駛卡車，用於訓練、模擬和車載計算。

The strategic partnership will enable Volvo Group to develop a wide range of autonomous driving solutions for freight transport, recycling collection, public transport, construction, mining, forestry and more.

戰略合作夥伴關係將使沃爾沃集團能夠為貨運、回收收集、公共交通、建築、採礦、林業等領域開發廣泛的自動駕駛解決方案。

This collaboration is a great validation of our long-held position that every vehicle, not just cars but also trucks, shuttles, business, taxis and many others, will have autonomous capability 1 day.

這次合作是對我們長期持有的立場的一個很好的驗證，即每輛車，不僅是汽車，還包括卡車、班車、商務車、出租車和許多其他車輛，都將在 1 天后具備自動駕駛能力。

Autonomous features can bring enormous value to the trucking industry, in particular as the demand of online shopping put ever greater stress on the world's transport systems.

自主功能可以為貨運行業帶來巨大的價值，尤其是隨著在線購物的需求給世界運輸系統帶來越來越大的壓力。

Expectations for overnight or same-day deliveries create challenges that can only be met by autonomous trucks, which can operate 24 hours a day.

對隔夜或當日交貨的期望帶來了挑戰，只有自動駕駛卡車才能應對這種挑戰，自動駕駛卡車可以每天 24 小時運行。

To help address these needs, NVIDIA has created an end-to-end platform for autonomous vehicles from AI computing infrastructure to large-scale simulation to in-car computing.

為了幫助滿足這些需求，NVIDIA 為自動駕駛汽車創建了一個端到端平台，從人工智能計算基礎設施到大規模模擬再到車載計算。

Multiple customers from OEMs like Mercedes-Benz, Toyota and Volvo to Tier 1s like Bosch, Continental and ZF are already onboard.

從梅賽德斯-奔馳、豐田和沃爾沃等原始設備製造商到博世、大陸和採埃孚等 1 級公司的多家客戶已經參與其中。

We see this as a $30 billion addressable market by 2025.

我們認為，到 2025 年，這是一個價值 300 億美元的潛在市場。

Moving to the rest of the P&L.

轉到損益表的其餘部分。

Q2 GAAP gross margins was 59.8% and non-GAAP was 60.1%, up sequentially, reflecting higher automotive development services, a favorable mix in gaming and lower component cost.

第二季度 GAAP 毛利率為 59.8%，非 GAAP 毛利率為 60.1%，環比上升，這反映了更高的汽車開發服務、有利的遊戲組合和更低的組件成本。

GAAP operating expenses were $970 million, and non-GAAP operating expenses were $749 million, up 19% and 8% year-on-year, respectively.

GAAP 運營費用為 9.7 億美元，非 GAAP 運營費用為 7.49 億美元，同比分別增長 19% 和 8%。

We remain on track for high single-digit OpEx growth in fiscal 2020 while continuing to invest in the key platforms driving our long-term growth, namely graphics, AI and self-driving cars.

我們將在 2020 財年保持高個位數的運營支出增長，同時繼續投資於推動我們長期增長的關鍵平台，即圖形、人工智能和自動駕駛汽車。

GAAP EPS was $0.90, down 49% from a year earlier.

GAAP每股收益為0.90美元，同比下降49%。

Non-GAAP EPS was $1.24, down 36% from a year ago.

非美國通用會計準則每股收益為 1.24 美元，比一年前下降 36%。

With that, let me turn to the outlook for the third quarter of fiscal 2020.

有了這個，讓我轉向 2020 財年第三季度的展望。

We expect revenue to be $2.9 billion, plus or minus 2%.

我們預計收入為 29 億美元，正負 2%。

GAAP and non-GAAP gross margins are expected to be 62% and 62.5%, respectively, plus or minus 50 basis points.

GAAP 和非 GAAP 毛利率預計分別為 62% 和 62.5%，上下浮動 50 個基點。

GAAP and non-GAAP operating expenses are expected to be approximately $980 million and $765 million, respectively.

GAAP 和非 GAAP 運營費用預計分別約為 9.8 億美元和 7.65 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 10%, plus or minus 1%, excluding discrete items.

GAAP 和非 GAAP 稅率預計均為 10%，正負 1%，不包括離散項目。

Capital expenditures are expected to be approximately $100 million to $120 million.

資本支出預計約為 1 億至 1.2 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 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 Jefferies conference, hardware and communications infrastructure summit, on August 27 and at the Citi Global Technology Conference on September 25.

我們將參加 8 月 27 日的 Jefferies 會議、硬件和通信基礎設施峰會以及 9 月 25 日的 Citi 全球技術會議。

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

有了這個，我們現在開始提問。

Operator, would you please poll for the questions?

接線員，請您投票詢問問題嗎？

(Operator Instructions) And your first question comes from the line of C.J. Muse with Evercore.

（操作員說明）您的第一個問題來自於 Evercore 的 C.J. Muse。

I guess first question on gaming, how should we think about your outlook into the October quarter vis-à-vis kind of normal seasonality?

我想關於遊戲的第一個問題，我們應該如何看待您對 10 月季度的展望與正常的季節性？

How are you thinking about Switch within that?

您如何看待其中的 Switch？

And considering now that you have full Turing lineup as well as content truly coming to the forefront here, how do you think about trends beyond the October quarter?

現在考慮到您擁有完整的圖靈陣容以及真正走在最前沿的內容，您如何看待 10 月季度之後的趨勢？

Sure.

當然。

Colette, why don't you take the Switch question?

Colette，你為什麼不回答 Switch 的問題？

And then I'll take the rest of the RTX questions.

然後我會回答剩下的 RTX 問題。

Sure.

當然。

From a gaming perspective, the overall Switch or the overall console business definitely is a seasonal business.

從遊戲的角度來看，整體 Switch 或整體主機業務絕對是季節性業務。

We usually expect to see production ramping in Q2 and in Q3, with it coming down likely in Q4.

我們通常預計第二季度和第三季度產量會增加，第四季度可能會下降。

So you should see Switch to be a portion definitely of our gaming business in Q3.

所以你應該看到 Switch 在第三季度肯定會成為我們遊戲業務的一部分。

Yes.

是的。

C.J., thanks for the question.

C.J.，謝謝你的提問。

RTX, as you know, is -- first of all, RTX is doing great.

如您所知，RTX 是——首先，RTX 做得很好。

I think we've put all the pieces in place to bring ray tracing into the future of games.

我認為我們已經準備好將光線追踪帶入遊戲的未來。

The number of games, the blockbuster games that adopted RTX is really snowballing.

遊戲數量，採用RTX的重磅遊戲真是滾雪球。

We announced several -- 6 games in the last couple of months.

在過去的幾個月裡，我們宣布了幾款——6 款遊戲。

There's going to be some exciting announcements next week at gamescom.

下週在gamescom 將會有一些激動人心的公告。

It's pretty clear now the future of gaming will include ray tracing.

現在很清楚，遊戲的未來將包括光線追踪。

The number of software developers that create -- with creative tools that adopted RTX is really quite spectacular.

使用採用 RTX 的創意工具進行創建的軟件開發人員的數量確實非常驚人。

We now have 40 -- over 40 ISV tools that was announced at SIGGRAPH that have accelerated ray tracing and video editing.

我們現在有 40 - 超過 40 種 ISV 工具在 SIGGRAPH 上宣布，這些工具加速了光線追踪和視頻編輯。

And some of the applications' amazing AI capabilities for image optimization enhancement support RTX.

並且一些應用程序用於圖像優化增強的驚人 AI 功能支持 RTX。

And so looking forward, this is what I expect.

所以期待，這就是我所期望的。

I expect that ray tracing is going to drive a reinvigoration of gaming graphics.

我預計光線追踪將推動遊戲圖形的複興。

I expect that the over 100 laptops that we have RTX designed -- RTX GPUs designed into is going to contribute our growth.

我預計我們為 RTX 設計的 100 多台筆記本電腦——設計的 RTX GPU 將有助於我們的增長。

Notebook gaming is one of the fastest-growing segments of the gaming platform world.

筆記本遊戲是遊戲平台世界中增長最快的部分之一。

The number of notebooks that are able to game is only a few percent, so it's extremely underexposed.

能玩遊戲的筆記本只有百分之幾，曝光度極低。

And yet, we know that gamers are -- like the rest of us, they like thin and light notebooks, but they like it to be able to run powerful games.

然而，我們知道遊戲玩家——和我們其他人一樣，他們喜歡輕薄的筆記本電腦，但他們喜歡它能夠運行強大的遊戲。

And so this is an area that has grown significantly for us year-over-year, and we're expecting it to grow through the end of the -- through the second half and through next year.

因此，這是一個對我們來說逐年顯著增長的領域，我們預計它會在下半年和明年年底之前增長。

And one of the things that's really exciting is our RTX Studio line that we introduced recently.

真正令人興奮的事情之一是我們最近推出的 RTX Studio 系列。

We observed, and through our discussions with the PC industry, that the creatives are really underexposed and underserved by the latest technologies.

通過與 PC 行業的討論，我們觀察到創意確實曝光不足，並且沒有得到最新技術的充分服務。

And they want notebooks and they want PCs that have powerful graphics.

他們想要筆記本電腦，他們想要擁有強大圖形功能的個人電腦。

They use it for 3D content creation and high definition video editing and image optimization and things like that.

他們將其用於 3D 內容創建和高清視頻編輯和圖像優化等。

And we introduced a brand-new line of computers that we call RTX Studio.

我們推出了全新的計算機系列，我們稱之為 RTX Studio。

Now the OEMs were so excited about it.

現在，原始設備製造商對此非常興奮。

And at SIGGRAPH, we now have 27 different laptops shipping and more coming.

在 SIGGRAPH，我們現在有 27 款不同的筆記本電腦在發貨，而且還會有更多。

And so I think RTX is really geared for growth.

所以我認為 RTX 真的適合增長。

We have great games coming.

我們即將迎來精彩的比賽。

We got the SUPER line of GPUs.

我們得到了 SUPER 系列的 GPU。

We have all of our notebooks that were designed into that we're ramping and, of course, the new RTX Studio line.

我們所有的筆記本電腦都設計成我們正在升級的產品，當然還有新的 RTX Studio 系列。

And so I expect this to be a growth market for us.

所以我希望這對我們來說是一個增長的市場。

Very helpful.

很有幫助。

If I could follow up on the data center side, perhaps you can speak directly just to the hyperscale side, both internal and cloud, and whether you're seeing any green shoots, any signs of life there and how you're thinking about what that rate of recovery could look like over time.

如果我可以跟進數據中心方面，也許您可以直接與超大規模方面交談，包括內部和雲，以及您是否看到任何綠芽、那裡的任何生命跡像以及您如何思考什麼這種恢復速度可能會隨著時間的推移而變化。

With the exception of a couple of hyperscalers, C.J., I would -- we're seeing broad-based growth in data centers.

除了幾個超大規模的 C.J. 之外，我認為我們正在看到數據中心的廣泛增長。

In the area of training, the thing that's really exciting everybody, and everybody is racing towards, is training these large gigantic natural language understanding models, language models.

在訓練領域，真正讓每個人都興奮並且每個人都在爭先恐後的事情是訓練這些巨大的自然語言理解模型，語言模型。

The transformer model that was introduced by Google, called BERT, has since been enhanced into XLned and RoBERTa and, gosh, so many different, GP2, and Microsoft's MASS.

谷歌推出的稱為 BERT 的轉換器模型已被增強為 XLned 和 RoBERTa，天哪，還有很多不同的 GP2 和微軟的 MASS。

And there's so many different versions of these language models.

這些語言模型有很多不同的版本。

And in the AI, NLU, natural language understanding, is one of the most important areas that everybody's racing to go to.

在 AI 中，NLU，自然語言理解，是每個人都在爭相進入的最重要的領域之一。

And so these models are really, really large.

所以這些模型非常非常大。

It's over 1,000x larger than image models that we're training just a few years ago, and they're just gigantic models.

它比我們幾年前訓練的圖像模型大 1,000 倍以上，而且它們只是巨大的模型。

It's one of the reasons why we built the DGX SuperPOD so that we could train these gigantic models in a reasonable amount of time.

這是我們構建 DGX SuperPOD 的原因之一，以便我們可以在合理的時間內訓練這些巨大的模型。

The second area -- so that's training in the hyperscalers.

第二個領域——那就是超大規模訓練。

The second area where we're seeing enormous amounts of activity has to do with trying to put these conversational AI models into services so that they could be interactive and in real time.

我們看到大量活動的第二個領域與嘗試將這些對話式 AI 模型放入服務中有關，以便它們可以實時交互。

Whereas photo tagging and photo enhancement is something that you could put off-line and you could do that while you have excess capacity when it's off of the most busy time of the day.

而照片標記和照片增強是您可以脫機的事情，當您在一天中最繁忙的時間關閉時，您可以在容量過剩的情況下執行此操作。

You can't do that with language and conversational AI.

你不能用語言和對話式人工智能來做到這一點。

You better to respond to the person in real time.

您最好實時回复此人。

And so the performance that's required is significant.

因此，所需的性能非常重要。

But more importantly, the number of models necessary for conversational AI from speech recognition to language understanding to recommendation systems to text-to-speech to wave synthesis, these 5, 6, 7 models have to be processed in real time -- in series and in real time so that you can have a reasonable conversation with the AI agent.

但更重要的是，對話式 AI 從語音識別到語言理解到推薦系統到文本到語音再到波形合成所需的模型數量，這 5、6、7 個模型必須實時處理——串聯和實時，以便您可以與 AI 代理進行合理的對話。

And so these type of activities is really driving interest and activity at all of the hyperscalers.

因此，這些類型的活動確實激發了所有超大規模企業的興趣和活動。

My expectation is that this is going to continue to be a big growth opportunity for us.

我的期望是，這將繼續為我們帶來巨大的增長機會。

But more importantly, in addition to that, we're seeing that AI is -- the wave of AI is going from the cloud to the enterprise to the edge and all the way out to the autonomous systems.

但更重要的是，除此之外，我們看到人工智能是——人工智能的浪潮正在從雲到企業再到邊緣，一直到自治系統。

The place where we're seeing a lot of excitement, and we talked about that in the past and we're seeing growth there, has to do with the vertical industry enterprises that are starting to adopt AI to create new products, whether it's a delivery robot or some kind of a chat bot or the ability to detect fraud in financial services, these applications in vertical industries are really spreading all over the place.

我們看到很多令人興奮的地方，我們過去談到過，我們看到那裡的增長，與開始採用人工智能來創造新產品的垂直行業企業有關，無論是送貨機器人或某種聊天機器人或金融服務中檢測欺詐的能力，這些垂直行業的應用確實遍布各地。

There's some over 4,000 AI start-ups around the world.

全球有超過 4,000 家 AI 初創公司。

And the way that we engage them is they use our platform to start developing AI in the cloud.

我們吸引他們的方式是他們使用我們的平台開始在雲中開發人工智能。

And as you know, we're the only AI platform that's available on-prem and in every single cloud.

如您所知，我們是唯一可在本地和每個雲中使用的 AI 平台。

And so they can use our AI platforms for -- in all the clouds, which is driving our cloud computing, external cloud computing growth.

因此，他們可以將我們的 AI 平台用於 - 在所有云中，這推動了我們的雲計算、外部雲計算的增長。

And then they can also use it on-prem if their usage really grows significantly.

如果他們的使用量真的顯著增長，他們也可以在本地使用它。

And that's one of the reasons why our Tesla for OEMs and DGX is growing.

這也是我們為 OEM 和 DGX 提供的 Tesla 不斷增長的原因之一。

And so we're seeing broad-based excitement around AI as they use it for their products and new services.

因此，當他們將人工智能用於他們的產品和新服務時，我們看到了廣泛的興奮。

And these 4,000, 4,500 start-ups around the world is really driving consumption of that.

全球這 4,000 到 4,500 家初創企業確實在推動消費。

And your next question comes from the line of Vivek Arya with Bank of America Merrill Lynch.

您的下一個問題來自美銀美林的 Vivek Arya。

I actually had 2 as well, one quick one for Colette and one for Jensen.

實際上我也有 2 個，一個給 Colette 一個快速的，一個給 Jensen。

Colette, good to see the gross margin recovery getting into October.

Colette，很高興看到毛利率恢復到 10 月份。

Is this 62% to 63% range a more sustainable level and perhaps a level you could grow off of as sales get more normalized levels?

這個 62% 到 63% 的範圍是否是一個更可持續的水平，或者隨著銷售達到更加正常化的水平，你可能會增長的水平？

And then a bigger question is for Jensen.

然後一個更大的問題是給 Jensen 的。

Again, on the data center side, Jensen, when I look back between -- 2015 to 2018, your data center business essentially grew 10x.

同樣，在數據中心方面，Jensen，當我回顧 2015 年到 2018 年之間時，您的數據中心業務基本上增長了 10 倍。

And then the last year has been a tough one with the slowdown in cloud CapEx and so forth.

去年是艱難的一年，雲資本支出放緩等等。

When do you think your data center starts to grow back on a year-to-year -- on a year-on-year basis?

您認為您的數據中心什麼時候開始逐年增長——逐年增長？

Can that happen sometime -- later this year?

這會在某個時候發生嗎——今年晚些時候？

And then just longer term, what is the right way to think about this business?

然後從長遠來看，思考這項業務的正確方法是什麼？

Does it go back to prior levels?

它會回到以前的水平嗎？

Does it go at a different phase?

它是否處於不同的階段？

This is the one part of the business that I think is toughest for us to model, so any color would be very helpful.

這是我認為對我們來說最難建模的業務部分，所以任何顏色都會非常有幫助。

Great.

偉大的。

So let me start first with your question, Vivek, regarding gross margins.

所以讓我首先從你的問題開始，Vivek，關於毛利率。

Yes, thanks for recognizing that we are moving towards our expectations that, over time, we'll continue to see our overall volumes improve.

是的，感謝您認識到我們正在朝著我們的期望前進，隨著時間的推移，我們將繼續看到我們的整體銷量有所改善。

Essentially, our business is normalized.

從本質上講，我們的業務是正常化的。

We've reached normalized levels through the last couple of quarters.

在過去的幾個季度中，我們已經達到了正常化水平。

And this quarter, just very similar to what we will see going forward, is mix is the largest driver, what drives our overall gross margins and our gross margin improvements.

本季度，與我們將看到的非常相似，混合是最大的驅動力，是推動我們整體毛利率和毛利率提高的因素。

Yes, Vivek, if you look at the last several years, there's no question our data center business has grown a lot.

是的，Vivek，如果您回顧過去幾年，毫無疑問我們的數據中心業務增長了很多。

And my expectation is that it's going to grow a lot more, and let me explain to you why.

我的期望是它會增長得更多，讓我向你解釋原因。

Aside from a couple -- a few of uncontrollable circumstances and the exception of a couple of large customers, the overall trend, the broad-based trend, of our data center business is upward, to the right.

除了一些不可控的情況和幾個大客戶的例外，我們數據中心業務的整體趨勢，廣泛的趨勢是向上的，向右的。

And it is growing very nicely.

而且它的增長非常好。

There's a couple of different dynamics that's causing that on first principles to grow.

有幾個不同的動力導致第一原則上的增長。

And of course, one of them is as AI is well known now to require accelerated computing, our computing architecture is really ideal for it.

當然，其中之一是眾所周知的人工智能需要加速計算，我們的計算架構非常適合它。

AI is not just one network.

人工智能不僅僅是一個網絡。

It's thousands of different types of networks, and these networks are getting more and more complex over time, the amount of data you have to process is enormous.

這是成千上萬種不同類型的網絡，隨著時間的推移，這些網絡變得越來越複雜，你必須處理的數據量是巨大的。

And so like all software programs, you can't predict exactly how the software is going to get programmed.

因此，就像所有軟件程序一樣，您無法準確預測軟件將如何編程。

And having a programmable architecture like CUDA and yet optimized for AI like Tensor Cores that we've created is really the ideal architecture.

擁有像 CUDA 這樣的可編程架構，並針對我們創建的 Tensor Cores 等 AI 進行了優化，這確實是理想的架構。

We know also that AI is the most powerful technology force of our time.

我們也知道，人工智能是我們這個時代最強大的技術力量。

The ability for machines to learn and write software by itself and write software that no humans can write is pretty extraordinary.

機器自己學習和編寫軟件並編寫人類無法編寫的軟件的能力非常非凡。

And the applications of AI, as you guys are watching yourself, are just spreading in every single industry.

正如你們所看到的那樣，人工智能的應用正在每個行業中蔓延。

And so the way we think about AI is in waves, if you will.

因此，如果你願意的話，我們對人工智能的看法是波浪式的。

The first wave of AI is developing the computer architecture, and that was the first part where -- that's when a lot of people discovered who we are, and we emerged into the world of high-performance computing in AI.

人工智能的第一波浪潮正在開發計算機架構，那是第一部分——那時很多人發現了我們是誰，我們進入了人工智能高性能計算的世界。

The second wave is applying the AI for cloud service providers or hyperscalers.

第二波是將人工智能應用於雲服務提供商或超大規模企業。

They have a large amount of data.

他們有大量的數據。

They have a lot of consumer applications.

他們有很多消費者應用程序。

Many of them are not life-critical and so, therefore, the application of an early technology -- early-adoption technology was really viable.

其中許多不是生命關鍵，因此，早期技術的應用——早期採用技術確實是可行的。

And so you saw hyperscalers adopt AI.

所以你看到超大規模企業採用人工智能。

And the thing that's really exciting for us is beyond recommendations, beyond image enhancement, the area where we believe the most important application for AI is likely conversational AI.

對我們來說真正令人興奮的事情不僅僅是推薦，也不僅僅是圖像增強，我們認為人工智能最重要的應用領域可能是對話式人工智能。

Most people talking and asking questions and talking to their mobile devices and looking for something or asking for directions.

大多數人說話、提問、與他們的移動設備交談、尋找東西或問路。

Instead of having a page of -- a list of options, it responds with an answer that is very likely a good one.

它沒有一頁 - 選項列表，而是以一個很可能是一個好答案的答案進行響應。

The next phase of AI is what we call vertical industry enterprise AI.

AI的下一階段就是我們所說的垂直行業企業AI。

And this is where companies are using it not just to accelerate the business process internally, but they're using AI to create new products and services.

這就是公司不僅使用它來加速內部業務流程的地方，而且還使用人工智能來創造新的產品和服務。

They could be new medical instruments to IoT-based medical instruments to monitor your health.

它們可能是基於物聯網的醫療器械的新醫療器械，用於監測您的健康。

It could be something related to an application that -- used for financial services for forecasting or for fraud detection.

它可能與應用程序相關——用於金融服務的預測或欺詐檢測。

It could be some kind of device that delivers pizza to you, delivery bots.

它可能是某種為你送披薩的設備，送餐機器人。

And the combination of IoT and artificial intelligence, for the very first time, you actually have the software capabilities to make use of all of these sensors that you're putting all over the world.

物聯網和人工智能的結合，第一次，你實際上擁有了利用你在世界各地放置的所有這些傳感器的軟件能力。

And that's the next phase of growth.

這就是下一階段的增長。

And it affects companies from large industrials, transportation companies, retailers, you name it.

它會影響大型工業、運輸公司、零售商等公司。

Health care companies, you name it.

醫療保健公司，你的名字。

And so that phase of growth of AI is the phase that we're about to enter into.

因此，人工智能的發展階段就是我們即將進入的階段。

And then the longer term is an industry that we all know to be extremely large, but it takes time because it's life-critical, and it has to do with transportation.

從長遠來看，我們都知道這個行業非常大，但這需要時間，因為它對生命至關重要，而且與運輸有關。

It's a $100 trillion industry.

這是一個價值 100 萬億美元的產業。

We know it's going to be automated.

我們知道這將是自動化的。

We know that everything that moves in the future will be autonomous or have autonomous capabilities.

我們知道，未來移動的一切都將是自主的或具有自主能力。

And that's just a matter of time before we realize its full potential.

我們實現其全部潛力只是時間問題。

And so the net of it all is that I believe that AI is the single most powerful technology force of our time, and that's why we're all in on it.

所以這一切的本質是，我相信人工智能是我們這個時代最強大的技術力量，這就是我們全力以赴的原因。

And we know that acceleration and accelerated computing is the perfect model for that.

我們知道加速和加速計算是實現這一目標的完美模型。

And it started in the cloud, but it's going to keep moving out into the edge and through data centers and enterprises and hopefully -- well, eventually, all the way out into autonomous devices and machines in the real world.

它始於雲，但將繼續向邊緣移動，並通過數據中心和企業，並希望 - 最終，一直延伸到現實世界中的自主設備和機器。

And so this is a big market, and I'm super enthusiastic about it.

所以這是一個很大的市場，我對此非常感興趣。

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 had 2 as well, one for Jensen and the other for Colette.

我也有 2 個，一個給 Jensen，另一個給 Colette。

Jensen, you guys called out inference as a significant contributor to growth in data center last quarter.

Jensen，你們稱推理是上個季度數據中心增長的重要貢獻者。

I think you guys talked about it being a double-digit percentage contributor, curious what you saw from inference in the quarter.

我認為你們談到它是一個兩位數的百分比貢獻者，好奇你從本季度的推斷中看到了什麼。

And more importantly, if you can talk about the outlook, both near term and long term, as it relates to inference, that'll be helpful.

更重要的是，如果您可以談論與推理相關的近期和長期前景，那將很有幫助。

And then secondly, for Colette, just want to double click on the gross margin question.

其次，對於 Colette，只想雙擊毛利率問題。

The sequential improvement that you're guiding to is a pretty significant number.

您正在指導的順序改進是一個非常重要的數字。

So I was just hoping if you can kind of break it down for us in terms of overall volume growth mix dynamics, both between segments and within segments and also to the extent DRAM pricing is impacting that, any color on that will be helpful as well.

因此，我只是希望您能在整體銷量增長組合動態方面為我們分解它，無論是在細分市場之間還是細分市場內，以及 DRAM 定價影響的程度，任何顏色都會有所幫助.

Yes, Toshiya, I got to tell you, I'm less good at normal pre -- near-term productions than I am good at thinking about long-term dynamics.

是的，Toshiya，我得告訴你，我不太擅長正常的前期 - 近期製作，而不是擅長思考長期動態。

But let me talk to you about inference.

但是讓我和你談談推理。

Our inference business is -- remains robust.

我們的推理業務——仍然強勁。

It's double digits.

是兩位數。

It's a large part of our business.

這是我們業務的很大一部分。

And -- but more importantly, the 2 dynamics that I think are near term and that's going to drive growth, number one is interactive conversational AI, interactive conversational AI inference.

而且——但更重要的是，我認為近期會推動增長的兩種動力，第一是交互式對話人工智能，交互式對話人工智能推理。

If you simply ask a chat bot a simple question, where is the closest pizza and you would -- pizza shop, and you would like to have a conversation with this bot, it would have to do speech recognition, it has to understand what it is that you asked about, it has to look it up in a recommender based on the locations you're at, maybe your preferences of styles of pizza and the price ranges that you're interested and how far you're willing to go, to go get it.

如果你只是問一個聊天機器人一個簡單的問題，最近的披薩在哪裡，你會——披薩店，你想和這個機器人對話，它必須做語音識別，它必須理解它是你問的，它必鬚根據你所在的位置在推薦器中查找，可能是你對披薩風格的偏好以及你感興趣的價格範圍以及你願意走多遠，去拿它。

It has to recommend a pizza shop for you to go to.

它必須為您推荐一家比薩店。

It has to then translate that from text-to-speech and then into human -- a human understand a voice.

然後它必須將其從文本翻譯成語音，然後再翻譯成人類——人類可以理解聲音。

And those models have to happen in just a few -- ideally, a few hundred milliseconds.

這些模型必須在幾秒鐘內完成——理想情況下，幾百毫秒。

Currently, it's not that.

目前，並非如此。

And it makes it really hard for these services to be deployed quite broadly and used for all kinds of different applications.

這使得這些服務很難被廣泛部署並用於各種不同的應用程序。

And so that's the near-term opportunity, it's interactive conversational AI inference.

這就是近期的機會，它是交互式對話式 AI 推理。

And you could just imagine every single hyperscaler racing to go make this possible because recently, we had some important breakthroughs in machine learning language models.

你可以想像每一個超大規模的競賽都讓這成為可能，因為最近，我們在機器學習語言模型方面取得了一些重要的突破。

The BERT model that I mentioned earlier is really, really an important development, and it's caused a large number of derivatives that has improved upon it.

我之前提到的 BERT 模型確實是一個非常重要的發展，它導致了大量衍生品對其進行了改進。

And so near-term conversational AI inference.

所以近期的對話式人工智能推理。

We're also seeing near term the inference at the edge.

我們也在近期看到了邊緣的推論。

There are many types of applications where because of the laws of physics reasons, the speed of light reasons or the economics reasons or data sovereignty reasons, it's not possible to stream the data to the cloud and have the inference done at the cloud.

有許多類型的應用程序由於物理定律原因、光速原因或經濟原因或數據主權原因，不可能將數據流式傳輸到雲端並在雲端進行推理。

You have to do that at the edge.

你必須在邊緣這樣做。

You need the latency to be low, the amount of data that you're streaming is continuous.

您需要低延遲，您正在流式傳輸的數據量是連續的。

And so you don't want to be paying for that line rate the whole time, and maybe the data is of great confidentiality or privacy.

因此，您不想一直為該線路速率付費，而且數據可能具有很高的機密性或隱私性。

And so we're seeing a lot of excitement and a lot of development for edge AI.

因此，我們看到了邊緣 AI 的很多興奮和大量發展。

Smart retail, smart warehouses, smart factories, smart cities, smart airports, you just make a list of those kind of things, basically locations where there is a lot of activity, where safety or cost or large amount of materials is passing through, you could just imagine the applications.

智能零售、智能倉庫、智能工廠、智能城市、智能機場，你只需列出這些東西，基本上是有很多活動的地方，安全或成本或大量材料通過的地方，你可以想像應用程序。

All of those really want to be edge computing systems and edge inference systems.

所有這些都真正想要成為邊緣計算系統和邊緣推理系統。

And so those are near term -- 2 near-term drivers, and I think it's fair to say that both of them are quite large opportunities.

所以這些都是近期的——兩個近期的驅動因素，我認為可以公平地說它們都是相當大的機會。

So to answer your question regarding gross margin in a little bit more detail, probably our largest area that we expect improvement in terms of our mix is our mix return regarding our overall gaming business.

因此，為了更詳細地回答您關於毛利率的問題，我們期望在組合方面改進的最大領域可能是我們整體遊戲業務的組合回報。

We expect to have a full quarter of our SUPER lineup within the next quarter, including our RTX as well as our notebook becoming a bigger mix as well as it grows.

我們預計在下個季度我們的 SUPER 陣容將有一個完整的季度，包括我們的 RTX 以及我們的筆記本電腦，隨著它的增長，我們將成為一個更大的組合。

These drivers are one of the largest reasons why we see that growth in our gross margin.

這些驅動因素是我們看到毛利率增長的最大原因之一。

We always think about our component cost, our overall cost of manufacturing, so this is always baked in over time, but we'll continue to see improvements on that as well.

我們總是考慮我們的組件成本，我們的整體製造成本，所以這總是隨著時間的推移而出現的，但我們也會繼續看到這方面的改進。

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。

Again, your data center business, many of your peers on the compute and storage side are seeing spending recovery by cloud and hyperscalers in the second half of this year after a similar weak first half of the year.

同樣，您的數據中心業務、計算和存儲方面的許多同行在經歷了類似的上半年疲軟之後，今年下半年雲和超大規模企業的支出正在復蘇。

You guys saw some growth in Q2 driven primarily by enterprise.

你們在第二季度看到了一些主要由企業推動的增長。

It seems like you had some broadening out of the customer spending this quarter.

本季度您的客戶支出似乎有所擴大。

Inferencing continues to see strong momentum.

推理繼續保持強勁勢頭。

Would you guys expect that this translates into a double-digit percentage sequential growth in data center in Q3 off of the low base in Q2?

你們是否期望這會轉化為第三季度數據中心在第二季度的低基數基礎上實現兩位數的連續增長？

Our hyperscale data center with a few customers don't give us very much -- we don't get very much visibility from a handful of customers in hyperscale.

我們擁有少數客戶的超大規模數據中心並沒有給我們帶來太多好處——我們沒有從超大規模的少數客戶那裡獲得太多可見性。

However, we're seeing broad-based growth and excitement in data centers.

然而，我們在數據中心看到了廣泛的增長和興奮。

And the way to think about data center, our data center business consists of hyperscale training, internal training, hyperscale inference, cloud computing -- and that's hyperscale, and that cloud is a public cloud.

考慮數據中心的方式，我們的數據中心業務包括超大規模培訓、內部培訓、超大規模推理、雲計算——這就是超大規模，而云就是公共雲。

And then we have vertical industry enterprise, what sometimes we call enterprise, vertical industry enterprise, it could be transportation companies, retailers, telcos, vertical industry adoption of AI either to accelerate their business or to develop new products and services.

然後我們有垂直行業企業，有時我們稱之為企業，垂直行業企業，可能是運輸公司、零售商、電信公司，垂直行業採用人工智能來加速他們的業務或開發新產品和服務。

And then the -- so when you look at our data center from that perspective and these pieces, although we don't see as much -- we don't get as much visibility as we like in a couple of the large customers, the rest of the hyperscalers, we're seeing broad-based growth.

然後 - 所以當你從這個角度和這些部分來看我們的數據中心時，雖然我們看不到那麼多 - 我們在幾個大客戶中沒有得到我們想要的那樣多的可見性，其餘的超大規模企業，我們看到了廣泛的增長。

And so we're experiencing the enthusiasm and the energy that maybe the others are.

因此，我們正在體驗可能與其他人一樣的熱情和能量。

And so we'll keep reporting -- updating you guys as we go.

因此，我們將繼續報告 - 隨時更新你們。

We'll see how it goes.

我們會看看情況如何。

And 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）。

I had 2. I guess first for Jensen, Volta's been around now for about 2 years.

我有 2 個。我想首先是 Jensen，Volta 已經存在了大約 2 年。

Do you see signs of demand maybe building up ahead of the new set of nanometer products, whenever that comes out?

您是否看到需求的跡象可能會在新的納米產品系列出現之前出現？

I guess I'm just wondering whether there's some element of this is more around product cadence that gets resolved as you do roll out the product.

我想我只是想知道這是否有一些元素更多地是圍繞產品節奏而在您推出產品時得到解決。

That's the first question.

這是第一個問題。

And then I guess, the second question, Colette, is of the $300 million growth into October, it sounds like Switch is pretty flat, but I'm wondering if you can give us maybe some qualitative sense of where the growth is coming from, is it maybe like 2/3 gaming and 1/3 data centers, something like that?

然後我猜，第二個問題，Colette，關於到 10 月份的 3 億美元增長，聽起來 Switch 相當平淡，但我想知道你能否給我們一些關於增長來自哪裡的定性認識，是不是像 2/3 遊戲和 1/3 數據中心之類的？

Well, Volta -- data center products can churn that fast.

好吧，Volta——數據中心產品可以這麼快地流失。

We -- gamers could churn products quickly because they're bought and sold one at a time.

我們——遊戲玩家可以快速生產產品，因為它們一次被買賣一個。

But data centers -- data center infrastructure really has to be planned properly, and the build-out takes time.

但是數據中心——數據中心基礎設施確實必須正確規劃，而且擴建需要時間。

And we expect Volta to be successful all the way through next year.

我們預計 Volta 將在明年一直取得成功。

And software still continues to be improved on it.

並且軟件仍在繼續改進。

We're still improving systems on it.

我們仍在改進它的系統。

And in fact, just 1 year -- in just 1 year, we improved our AI performance on Volta by almost 2x, 80%.

事實上，僅僅 1 年——僅僅 1 年，我們在 Volta 上的 AI 性能提高了近 2 倍，即 80%。

And so you could just imagine the amount of software that's built on top of Volta and all the Tensor Cores and all the GPUs connected with NVLink and the large number of nodes that are connected to build supercomputers.

因此，您可以想像構建在 Volta 之上的軟件數量、所有張量核心和所有與 NVLink 連接的 GPU 以及連接到構建超級計算機的大量節點。

The software of building these systems, large-scale systems, is really, really hard.

構建這些系統、大型系統的軟件真的非常非常難。

And that's one of the reasons why you hear people talk about chips, but they never show up because building the software is just an enormous undertaking.

這就是你聽到人們談論芯片的原因之一，但他們從未出現過，因為構建軟件只是一項艱鉅的任務。

The number of software engineers we have in the company is in the thousands, and we have the benefit of having built on top of this architecture for over 1.5 decades.

我們公司擁有成千上萬的軟件工程師，我們受益於在這個架構之上構建了超過 1.5 年的時間。

And so when we're able to deploy into data centers as quickly as we do, I think we kind of lose sight of how hard it is to do that in the first place.

因此，當我們能夠像我們一樣快地部署到數據中心時，我認為我們首先忽略了這樣做的難度。

The last time a new processor entered into a data center was an x86 Xeon, and you just don't bring processors in the data centers that frequently or that easily.

上一次新處理器進入數據中心是 x86 Xeon，你只是不會頻繁或那麼容易地將處理器帶入數據中心。

And so I think the way to think about Volta is that it's surely in its prime, and it's going to keep -- continue to do well all the way through next year.

所以我認為考慮 Volta 的方式是它肯定處於鼎盛時期，並且它將保持 - 一直到明年繼續表現良好。

In regard to our guidance on revenue, and we do guide in terms of the total.

關於我們對收入的指導，我們確實在總量方面進行指導。

You have seen, in this last quarter, we executed a sequential increase really focusing on moving to a normalization of our gaming business.

您已經看到，在上個季度，我們執行了連續增長，真正專注於使我們的遊戲業務正常化。

And we're now approaching the second half of the year getting ready for the back to school and the holidays.

我們現在正接近下半年，為返校和假期做準備。

So you should expect also our gaming business to continue to grow to reach that full normalization by the end of Q3.

因此，您還應該期望我們的遊戲業務將繼續增長，到第三季度末達到完全正常化。

We do expect the rest of our platforms to likely also grow.

我們確實希望我們的其他平台也可能增長。

We have a couple different models on how that will come out.

我們有幾個不同的模型來說明它是如何產生的。

But yes, we do expect our data center business to grow, and then we'll see on the rest of our businesses as well.

但是，是的，我們確實希望我們的數據中心業務能夠增長，然後我們也會看到我們的其他業務。

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。

A couple of questions.

幾個問題。

I guess the first one is, Jensen, if you have any, I guess, high-level qualitative commentary on how the new SUPER upgrades of your Turing platform have been received in the market and how you might think about them progressing through the year.

我想第一個是，Jensen，如果你有任何關於你的 Turing 平台的新 SUPER 升級如何在市場上受到歡迎以及你如何看待它們在這一年中取得進展的高級定性評論。

And then, I guess, the second question is a bigger one.

然後，我想，第二個問題是一個更大的問題。

Intel's talked quite openly about One API.

英特爾非常公開地談論 One API。

The software stack at Xilinx is progressing with Versal ACAP.

Xilinx 的軟件堆棧正在與 Versal ACAP 一起進步。

I mean you guys get a lot of credit for the decade of work that you've done on CUDA.

我的意思是你們在 CUDA 上所做的十年工作獲得了很多讚譽。

But I wonder if you might comment on if you've seen any movement in the competitive landscape on the software side for the data center space.

但我想知道您是否可以評論一下您是否看到數據中心空間軟件方面的競爭格局有任何變化。

SUPER is off to a great start.

SUPER 開了個好頭。

Goodness, SUPER is off to a super start.

天哪，SUPER 是一個超級開始。

And if you look at -- if you do channel checks all over, even though we've got a lot of products in the channel and we -- last quarter was a transitional quarter for us actually.

如果你看看 - 如果你全面檢查渠道，即使我們在渠道中有很多產品而且我們 - 上個季度實際上對我們來說是一個過渡季度。

And we didn't -- we shipped SUPER later in the quarter.

我們沒有——我們在本季度晚些時候發貨了 SUPER。

But because the entire ecosystem and all of our execution engines are so primed, we were able to ship a fair number through the channel.

但由於整個生態系統和我們所有的執行引擎都已準備就緒，我們能夠通過該渠道運送相當數量的產品。

And so -- and yet, if you do spot checks all around the world, they're sold out almost everywhere.

所以——然而，如果你在世界各地進行抽查，它們幾乎到處都賣光了。

And the pricing in the spot market is drifting higher than MSRP.

現貨市場的定價正在高於建議零售價。

That just tells you something about demand.

這只是告訴你一些關於需求的事情。

And so that's really exciting.

所以這真的很令人興奮。

SUPER is off to a super start for -- and at this point, it's a foregone conclusion that we're going to buy a new graphics card, and it's going to the last 2, 3, 4 years to not have ray tracing is just crazy.

SUPER 是一個超級開始——在這一點上，我們將購買新顯卡已成定局，而且在過去的 2、3、4 年內沒有光線追踪只是瘋狂的。

Ray tracing content just keeps coming out.

光線追踪內容不斷湧現。

And between the performance of SUPER and the fact that it has ray tracing hardware, it's going to be super well positioned for -- throughout all of next year.

在 SUPER 的性能和它擁有光線追踪硬件的事實之間，它將在明年全年都處於非常有利的位置。

Your question about APIs and software programmability.

您關於 API 和軟件可編程性的問題。

APIs is just one of the issues.

API 只是問題之一。

The large issue about processors is how do you program it.

關於處理器的大問題是如何對其進行編程。

The reason why x86s and CPUs are so popular is because they solve the great challenge of software developers: how to program a computer.

x86s 和 CPU 之所以如此受歡迎，是因為它們解決了軟件開發人員面臨的巨大挑戰：如何對計算機進行編程。

And how to program a computer and how to compile for that computer is a paramount concern to computer science, and it's an area of tremendous research.

如何對計算機進行編程以及如何為該計算機進行編譯是計算機科學最關心的問題，也是一個巨大的研究領域。

Going from single CPU to multi-core CPUs was a great challenge.

從單 CPU 到多核 CPU 是一個巨大的挑戰。

Going from multi-core CPUs to multi-node multi-core CPUs is an enormous challenge.

從多核 CPU 到多節點多核 CPU 是一個巨大的挑戰。

And yet, when we created CUDA in our GPUs, we went from 1 CPU core or one processor core to a few to now, in the case of large-scale systems, millions of processor cores.

然而，當我們在我們的 GPU 中創建 CUDA 時，我們從 1 個 CPU 內核或一個處理器內核到現在的幾個，在大規模系統的情況下，數百萬個處理器內核。

And how do you program such a computer across multi-GPU, multi-node?

您如何跨多 GPU、多節點對這樣的計算機進行編程？

It's a concept that's not easy to grasp.

這是一個不容易掌握的概念。

And so I don't really know how one programming approach or a simple API is going to make 7 different type of weird things work together.

所以我真的不知道一種編程方法或一個簡單的 API 是如何讓 7 種不同類型的奇怪事物一起工作的。

And I can't make it fit in my head.

我不能讓它適合我的腦袋。

But programming isn't as simple as a PowerPoint slide, I guess.

但我猜編程並不像 PowerPoint 幻燈片那麼簡單。

And I think it's just -- time will tell whether one programming approach could fit 7 different types of processors when no time in history has it ever happened.

而且我認為這只是 - 時間會證明一種編程方法是否可以適用於 7 種不同類型的處理器，而歷史上從未發生過這種情況。

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 about the strength in the automotive business.

我想知道您是否可以談談汽車業務的實力。

Looks like the services piece of that is getting to be bigger.

看起來服務部分變得越來越大。

What's the outlook for that part of the business?

這部分業務的前景如何？

And can you give us a sense of the mix between services and components at this point?

您能否在這一點上讓我們了解服務和組件之間的混合？

Sure.

當然。

Thanks, Joe.

謝謝，喬。

Our approach to autonomous vehicles comes in basically 2 parts.

我們對自動駕駛汽車的方法基本上分為兩部分。

The first part is a full stack, which is building the architected processor, the system, the system software and all of the driving applications on top, including the deep neural nets.

第一部分是一個完整的堆棧，它正在構建架構處理器、系統、系統軟件和所有驅動應用程序，包括深度神經網絡。

The second part of it, we call that a full stack self-driving car computer.

它的第二部分，我們稱之為全棧自動駕駛汽車計算機。

The second part of DRIVE includes an end-to-end AV development system.

DRIVE 的第二部分包括一個端到端的 AV 開發系統。

For those who would like to use our processors, use our system software but create their own applications, we created a system that allows -- basically shares with them our computing infrastructure that we built for ourselves that allows them to do end-to-end development from deep learning development to the application of AV to simulating that application to doing regression testing of that application before they deploy it into a car.

對於那些想使用我們的處理器，使用我們的系統軟件但創建自己的應用程序的人，我們創建了一個系統，允許 - 基本上與他們共享我們為自己構建的計算基礎設施，允許他們進行端到端從深度學習開發到 AV 應用程序，再到模擬該應用程序，再到對該應用程序進行回歸測試，再將其部署到汽車中。

And the 2 systems that we use there is called DGX for training and Constellation for simulation and what is called Replay.

我們在那裡使用的兩個系統稱為用於訓練的 DGX 和用於模擬的 Constellation 以及所謂的 Replay。

And then the third part of our business model is development agreements, otherwise known as NRE.

然後我們商業模式的第三部分是開發協議，也稱為 NRE。

These 3 elements, full stack computer, end-to-end development flow and NRE project development -- product development consists of the overall DRIVE business.

這 3 個要素，全棧計算機、端到端開發流程和 NRE 項目開發——產品開發構成了整個 DRIVE 業務。

And so although the cars will take several years to go into production, we're seeing a lot of interest in working with us to develop self-driving cars using our development systems and entering into development projects.

因此，儘管這些汽車需要數年時間才能投入生產，但我們看到很多人對與我們合作使用我們的開發系統開發自動駕駛汽車並進入開發項目很感興趣。

And so we're -- the number of autonomous vehicle projects is quite large around the world as you can imagine.

所以我們 - 正如你想像的那樣，全球自動駕駛汽車項目的數量非常多。

And so my sense is that we're going to continue to do well here.

所以我的感覺是，我們將繼續在這裡做得很好。

The additional part of autonomous vehicles and where the capability has been derived and is going to seal up more near-term opportunities has to do with things like delivery shuttles, self-driving shuttles and maybe cargo movers inside walled warehouses.

自動駕駛汽車的附加部分以及能力已經獲得併將鎖定更多近期機會的部分與交付班車、自動駕駛班車以及可能在圍牆倉庫內的貨物搬運工等事情有關。

Those kind of autonomous machines require basically the same technology, but it's sooner and easier to deploy.

這類自主機器需要基本相同的技術，但部署起來更快、更容易。

And so we are seeing a lot of excitement around that area.

因此，我們在該地區看到了很多興奮。

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。

Congratulations on the improved performance.

祝賀性能提高。

At your Analyst Day back a couple of months ago, you had highlighted the installed base opportunity for RTX.

在幾個月前的分析師日上，您強調了 RTX 的安裝基礎機會。

And I think at that point in time, you talked about 50% being Pascal base, 48% being pre-Pascal.

我認為在那個時候，你談到 50% 是基於帕斯卡的，48% 是前帕斯卡。

You also alluded to the fact that you were seeing a positive mix shift higher in terms of the price points of this RTX cycle.

您還提到了這樣一個事實，即您看到這個 RTX 週期的價格點出現了積極的混合變化。

So I'm curious, where do we stand on the current product cycle?

所以我很好奇，我們在當前的產品週期中處於什麼位置？

And what are you seeing currently as we go through this product cycle on the Turing platforms?

當我們在圖靈平台上經歷這個產品週期時，你目前看到了什麼？

We launched -- well, first of all, the answer is that RTX adoption is faster than Pascal's adoption if you normalize to time 0 of launch.

我們推出了——嗯，首先，答案是，如果你標準化到發佈時間 0，RTX 的採用比 Pascal 的採用更快。

The reason for that is Pascal launched top to bottom on the same day.

原因是 Pascal 在同一天從上到下推出。

And as you guys know, we weren't able to do that for Turing.

正如你們所知，我們無法為圖靈做到這一點。

But if we did that for Turing, the adoption rate is actually faster.

但如果我們為圖靈這樣做，採用率實際上會更快。

And to me, it's a rather sensible.

對我來說，這是相當明智的。

And the reason for that is because Pascal was basically DX12.

原因是因為 Pascal 基本上是 DX12。

And Maxwell was DX12.

麥克斯韋是 DX12。

And Turing is the world's first DXR, the first ray tracing GPU, brand-new functionality, brand-new API and a lot more performance.

Turing 是世界上第一個 DXR、第一個光線追踪 GPU、全新的功能、全新的 API 和更多的性能。

And so I think it's sensible that Turing's adoption is going to be rapid.

所以我認為圖靈的採用將很快被採用是明智的。

The second element of Turing is something that we've never talked about before.

圖靈的第二個元素是我們以前從未談論過的東西。

We're mentioning it more and more because it's such an exciting book market for us is notebooks.

我們越來越多地提到它，因為它對我們來說是一個如此令人興奮的圖書市場，那就是筆記本。

The install base of Pascal has a very, very little notebook in it.

Pascal 的安裝基礎中有一個非常非常小的筆記本。

And the reason for that is because, in the past, we were never able to put a high performance gaming GPU into a thin and light notebook until we invented Max-Q.

其原因是，在過去，直到我們發明 Max-Q，我們才能夠將高性能遊戲 GPU 放入輕薄的筆記本電腦中。

And in combination with our energy efficiency, we were able to -- we're now able to put a 2080 into a laptop, and it's still beautiful.

結合我們的能源效率，我們能夠 - 我們現在能夠將 2080 放入筆記本電腦，而且它仍然很漂亮。

And so this is effectively a brand-new growth market for us.

所以這對我們來說實際上是一個全新的增長市場。

And with so few people and so few gamers in the world that are able to game on a laptop, I think this is going to be a nice growth market for us.

世界上能夠在筆記本電腦上玩遊戲的人和遊戲玩家如此之少，我認為這對我們來說將是一個不錯的增長市場。

And then the new market that we introduced and launched this last quarter is called RTX Studio.

然後我們在上個季度推出並推出的新市場稱為 RTX Studio。

And this is an underserved segment of the market where consumers, enthusiasts, they could be artists that are working on small firms, they need powerful computers to do their work.

這是市場中服務不足的部分，消費者、愛好者，他們可能是在小公司工作的藝術家，他們需要強大的計算機來完成他們的工作。

They need powerful computers to do rendering and high-definition video editing.

他們需要功能強大的計算機來進行渲染和高清視頻編輯。

And yet it's underserved by workstations because workstations are really sold on a B2B basis into large enterprises.

然而，工作站的服務不足，因為工作站實際上是以 B2B 的形式出售給大型企業的。

And so we aligned all of the OEMs and created a whole new line of notebooks called RTX Studio.

因此，我們調整了所有 OEM，並創建了一個全新的筆記本電腦系列，稱為 RTX Studio。

And the enthusiasm has been great.

而且熱情一直很大。

We've launched 27 different laptops, and I'm looking forward to seeing the results of that.

我們推出了 27 款不同的筆記本電腦，我期待看到結果。

This is tens of millions of people who are creators.

這是數以千萬計的創造者。

Some of them professionals, some of them hobbyists.

他們有些是專業人士，有些是業餘愛好者。

And they use Adobe suites, they use Autodesk in their suites and some of them use SolidWorks and some of them use all kinds of renders, like blender.

他們使用 Adobe 套件，在他們的套件中使用 Autodesk，其中一些使用 SolidWorks，其中一些使用各種渲染器，例如攪拌機。

And these are 3D artists and video artists, and this digital content creation is the modern way of creativity.

這些是 3D 藝術家和視頻藝術家，這種數字內容創作是現代的創作方式。

And so this is an underserved market that we're excited to go serve with RTX Studio.

所以這是一個服務不足的市場，我們很高興能與 RTX Studio 一起服務。

And your last question comes from the line of Stacy Rasgon with Bernstein Research.

你的最後一個問題來自 Bernstein Research 的 Stacy Rasgon。

I have 2 for Colette.

我有 2 個給科萊特。

My first question is on data center.

我的第一個問題是關於數據中心的。

So I know you say that you have a broad-based growth except for a few hyperscalers.

所以我知道你說你有一個廣泛的增長，除了一些超大規模的。

But you only grew at 3% sequentially, about $20 million.

但您的環比增長率僅為 3%，約為 2000 萬美元。

That doesn't sound like broad-based growth to me unless like -- did the hyperscalers get worse?

對我來說，這聽起來不像是基礎廣泛的增長，除非——超大規模企業變得更糟了嗎？

Or are they just still so much bigger than like the rest of it?

還是它們仍然比其他部分大得多？

I guess, what's going on in data center?

我想，數據中心發生了什麼？

How do I wrap my head around like broad-based growth with relatively minimal growth observed?

如何在觀察到的增長相對較小的情況下像基礎廣泛的增長一樣圍繞我的腦袋？

So to answer your question here, Stacy, on what we refer to when we're discussing the broad-based growth is the substantial expansion that we have on the types of customers and the industries that we are now approaching.

因此，斯泰西，在這裡回答你的問題，當我們討論基礎廣泛的增長時，我們所指的是我們對客戶類型和我們現在正在接近的行業的大幅擴張。

As you know, even a year ago, we had a very, very small base in terms of industry-based hyper -- excuse me, industry-based AI workloads that they were using.

如您所知，甚至在一年前，我們在基於行業的超能力方面的基礎非常非常小——對不起，他們正在使用的基於行業的 AI 工作負載。

Over this last quarter, we're continuing to see strong growth as we roll out all different types of AI solutions, both across the U.S. and worldwide, to these overall customers.

在上個季度，隨著我們在美國和全球範圍內向這些整體客戶推出所有不同類型的人工智能解決方案，我們將繼續看到強勁的增長。

Our hyperscalers, again, a couple of them, not necessarily growing.

我們的超大規模企業，再次，其中幾個，不一定在增長。

Some of them are flat and some of them are growing depending on whether or not that's for cloud instances or whether or not they're using it for internal use.

其中一些是平坦的，其中一些正在增長，這取決於這是否適用於雲實例，或者他們是否將其用於內部使用。

So we believe that our continued growth with the industries is important for us for the long term to expand the use of AI, and we're just really pleased with what we're seeing in that growth this quarter.

因此，我們認為，從長遠來看，我們與行業的持續增長對我們擴大人工智能的使用很重要，我們對本季度的增長感到非常滿意。

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're happy with our results this quarter and our return to growth across our platforms.

我們對本季度的業績以及跨平台的恢復增長感到滿意。

Gaming is doing great.

遊戲做得很好。

It's great to see NVIDIA RTX reinvigorating the industry.

很高興看到 NVIDIA RTX 重振行業。

GeForce has several growth drivers.

GeForce 有幾個增長動力。

Ray traced games continue to gain momentum.

光線追踪遊戲繼續獲得動力。

A large number of gaming laptops are rolling out, and our new Studio platform is reaching the large underserved community of creators.

大量的遊戲筆記本電腦正在推出，我們新的 Studio 平台正在接觸到服務不足的大型創作者社區。

Outside a few hyperscalers, we're seeing broad-based growth in data centers.

除了少數超大規模企業之外，我們還看到數據中心的廣泛增長。

AI is the most powerful technology force of our time and a once-in-a-lifetime opportunity.

人工智能是我們這個時代最強大的技術力量，也是千載難逢的機會。

More and more enterprises are using AI to create new products and services while leveraging AI to drive ultra-efficiency and speed in their business.

越來越多的企業正在使用人工智能來創造新的產品和服務，同時利用人工智能來推動其業務的超效率和速度。

And with hyperscalers racing to harness recent breakthroughs in conversational AI, we see growing engagements in training as well as interactive conversational inference.

隨著超大規模企業競相利用會話 AI 的最新突破，我們看到越來越多的人參與到訓練和交互式會話推理中。

RTX, CUDA accelerated computing, AI, autonomous vehicles, the work we're doing is important, impactful and incredibly fun.

RTX、CUDA 加速計算、人工智能、自動駕駛汽車，我們正在做的工作很重要、有影響力並且非常有趣。

We're just grateful there is so much of it.

我們很感激有這麼多。

We look forward to updating you on our progress next quarter.

我們期待在下個季度向您通報我們的進展情況。

This concludes today's conference call.

今天的電話會議到此結束。

You may now disconnect.

您現在可以斷開連接。