輝達 (NVDA) 2020 Q1 法說會逐字稿

Good afternoon, my name is Christina, and I will be your conference operator today. Welcome to NVIDIA's financial results conference call. (Operator Instructions)

下午好，我叫克里斯蒂娜，今天我將擔任您的會議主持人。歡迎參加 NVIDIA 財務績效電話會議。（操作員指示）

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

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

Thank you. Good afternoon, everyone, and welcome to NVIDIA's Conference Call for the First Quarter of Fiscal 2020. With me on the call today from NVIDIA are Jensen Huang, President and Chief Executive Officer; and Colette Kress, Executive Vice President and Chief Financial Officer. I'd like to remind you that our call is being webcast live on NVIDIA's Investor Relations website. The webcast will be available for replay until the conference call to discuss our financial results for the second quarter of fiscal 2020. The content of today's call is NVIDIA's property. It can't be reproduced or transcribed without our prior written consent.

謝謝。大家下午好，歡迎參加NVIDIA 2020財年第一季電話會議。今天與我一起參加電話會議的還有 NVIDIA 總裁兼執行長黃仁勳；以及執行副總裁兼財務長 Colette Kress。我想提醒您，我們的電話會議正在 NVIDIA 的投資者關係網站上進行網路直播。網路直播將可重播，直至電話會議討論我們 2020 財年第二季的財務業績。今天電話會議的內容屬於 NVIDIA 的財產。未經我們事先書面同意，不得複製或轉錄。

During this call, we may make forward-looking statements based on current expectations. These are subject to a number of significant risks and uncertainties, and our actual results may differ materially. For a discussion of factors that could affect our future financial results and business, please refer to the disclosure in today's earnings release, our most recent forms 10-K and 10-Q and the reports that we may file on Form 8-K with the Securities and Exchange Commission. All our statements are made as of today, May 16, 2019, based on information currently available to us. Except as required by law, we assume no obligation to update any such statements.

在本次電話會議中，我們可能會根據目前預期做出前瞻性陳述。這些都受到許多重大風險和不確定性的影響，我們的實際結果可能會有重大差異。有關可能影響我們未來財務表現和業務的因素的討論，請參閱今天的收益報告中的披露內容、我們最新的 10-K 和 10-Q 表格以及我們可能向美國證券交易委員會提交的 8-K 表格報告。我們所有的聲明都是根據我們目前掌握的資訊截至 2019 年 5 月 16 日做出的。除法律要求外，我們不承擔更新任何此類聲明的義務。

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

在本次電話會議中，我們將討論非公認會計準則財務指標。您可以在我們網站上發布的 CFO 評論中找到這些非 GAAP 財務指標與 GAAP 財務指標的對帳表。

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

說完這些，讓我把電話轉給科萊特。

Thanks, Simona. Q1 revenue was $2.2 billion, in line with our outlook and down 31% year-on-year and up 1% sequentially. Starting with our gaming business. Revenue of $1.05 billion was down 39% year-on-year and up 11% sequentially, consistent with our expectations. We are pleased with the initial ramp of Turing and the reduction of inventory in the channel. During the quarter, we filled out our Turing lineup with the launch of midrange GeForce products that enable us to delight gamers with the best performance at every price point starting at $149. New product launches this quarter included the GeForce GTX 1660 Ti, 1660 and 1650, which bring Turing to the high-volume PC gaming segment for both desktop and laptop. These GPUs deliver up to 50% performance improvement over their Pascal-based predecessors, leveraging new share innovations such as concurrent floating point and integer operations, a unified cache and Adaptive Shading, all with the incredibly power-efficient architecture.

謝謝，西蒙娜。第一季營收為 22 億美元，符合我們的預期，年減 31%，季增 1%。從我們的遊戲業務開始。營收為 10.5 億美元，年減 39%，季增 11%，符合我們的預期。我們對圖靈的初始產量提升和通路庫存的減少感到滿意。在本季度，我們推出了中階 GeForce 產品，進一步豐富了我們的 Turing 產品線，這些產品使我們能夠以最佳性能取悅遊戲玩家，價格從 149 美元起。本季推出的新產品包括 GeForce GTX 1660 Ti、1660 和 1650，將 Turing 帶入桌上型電腦和筆記型電腦的高容量 PC 遊戲領域。與基於 Pascal 的前代產品相比，這些 GPU 的效能提高了 50%，利用了新的共享創新，例如並發浮點和整數運算、統一快取和自適應著色，所有這些都具有極其節能的架構。

We expect continued growth in the gaming laptops this year. GeForce gaming laptops are one of the shining spots of the consumer PC market. This year, OEMs have built a record of nearly 100 GeForce gaming laptops. GeForce laptops start at $799 and all the way up to an amazing GeForce RTX 2080 4K laptops that are more powerful than even next-generation consoles.

我們預計今年遊戲筆記型電腦將繼續成長。GeForce 遊戲筆記型電腦是消費 PC 市場的亮點之一。今年，OEM廠商已創紀錄地生產了近100台GeForce遊戲筆記型電腦。GeForce 筆記型電腦的起價為 799 美元，最高可達令人驚嘆的 GeForce RTX 2080 4K 筆記型電腦，其性能甚至比下一代遊戲機還要強大。

The content ecosystem for ray-traced games is gaining significant momentum. At the March Game Developers Conference, ray-tracing sessions were packed. Support for ray tracing was announced by the industry's most important game engines, Microsoft DXR, Epic's Unreal Engine and Unity. Ray tracing will be the standard for next-generation games.

光線追蹤遊戲的內容生態系統正在獲得顯著的發展動能。在三月的遊戲開發者大會上，光線追蹤會議座無虛席。業界最重要的遊戲引擎 Microsoft DXR、Epic 的 Unreal Engine 和 Unity 都宣布支援光線追蹤。光線追蹤將成為下一代遊戲的標準。

In March, at our GPU Technology Conference, we also announced more details on our cloud gaming strategies through our GeForce NOW service and the newly announced GFN Alliance. GeForce NOW is a GeForce gaming PC in the cloud for the 1 billion PCs that are not game-ready, expanding our reach well beyond today's 200 million GeForce gamers. It's an open platform that allows gamers to play the games they own instantly in the cloud on any PC or Mac anywhere they like. The service currently has 300,000 monthly active users with 1 million more on the waitlist.

今年 3 月，在我們的 GPU 技術大會上，我們也透過 GeForce NOW 服務和新宣布的 GFN 聯盟宣布了有關雲端遊戲策略的更多細節。GeForce NOW 是一款雲端 GeForce 遊戲 PC，適用於 10 億台尚未準備好玩遊戲的 PC，將我們的覆蓋範圍擴大到當今 2 億名 GeForce 遊戲玩家之外。它是一個開放平台，允許遊戲玩家在任何他們喜歡的地方的任何 PC 或 Mac 上即時在雲端玩他們擁有的遊戲。該服務目前每月有 30 萬活躍用戶，還有 100 萬名等待用戶。

To scale out to millions of gamers worldwide, we announced the GeForce NOW Alliance, expanding GFN through partnerships with the global telecom providers. Softbank in Japan and LG Uplus in South Korea will be among the first to launch GFN later this year. NVIDIA will develop the software and manage the service and share the subscription revenue with alliance partners. GFN runs on NVIDIA's edge computing servers. As telcos race to offer the new services for their 5G network, GFN is an ideal new 5G application.

為了擴展到全球數百萬遊戲玩家，我們宣布成立 GeForce NOW 聯盟，透過與全球電信供應商合作擴大 GFN。日本軟銀和韓國 LG Uplus 將於今年稍後率先推出 GFN。NVIDIA 將開發軟體並管理服務，並與聯盟合作夥伴分享訂閱收入。GFN 在 NVIDIA 的邊緣運算伺服器上運作。隨著電信業者競相為其 5G 網路提供新服務，GFN 是理想的新型 5G 應用。

Moving to data center. Revenue was $634 million, down 10% year-on-year and down 7% sequentially, reflecting the pause in hyperscale spending. While demand from some hyperscale customers bounced back nicely, others paused or cut back. Despite the uneven demand drop -- backdrop, the quarter had significant positives consistent with the growth drivers we outlined on our previous earnings call. First, inference revenue was up sharply both year-on-year and sequentially with broad-based adoption across a number of hyperscale and consumer Internet companies. As announced at GTC, Amazon and Alibaba joined other hyperscalers such as Google, Baidu and Tencent in adopting the T4 in their data centers. A growing list of consumer Internet companies is also adapting our GPUs for inference, including LinkedIn, Expedia, Microsoft, PayPal, Pinterest, Snap and Twitter. The contribution of inference to our data center revenue is now well into the double-digit percent.

移至資料中心。營收為 6.34 億美元，年減 10%，季減 7%，反映出超大規模支出暫停。雖然一些超大規模客戶的需求強勁反彈，但其他客戶的需求卻暫停或減少。儘管需求下降幅度不均衡，但本季仍表現出顯著的積極因素，與我們在上次收益電話會議上概述的成長動力一致。首先，隨著許多超大規模和消費網路公司的廣泛採用，推理收入年比和季比均大幅成長。正如在 GTC 上宣布的那樣，亞馬遜和阿里巴巴與谷歌、百度和騰訊等其他超大規模企業一起在其數據中心採用了 T4。越來越多的消費網路公司也正在採用我們的 GPU 進行推理，其中包括 LinkedIn、Expedia、微軟、PayPal、Pinterest、Snap 和 Twitter。推理對我們資料中心收入的貢獻現已達到兩位數百分比。

Second, we expanded our reach in enterprise, teaming up with major OEMs to introduce the T4 enterprise and edge computing servers. These are optimized to run the NVIDIA CUDA-X AI acceleration libraries for AI and data analytics. With an easy-to-deploy software stack from NVIDIA and our ecosystem partners, this wave of NVIDIA edge AI computing systems enables companies in the world's largest industries, transportation, manufacturing, industrial, retail, health care and agricultural, to bring intelligence to the edge where the customers operate.

其次，我們擴大了企業範圍，與主要 OEM 合作推出了 T4 企業和邊緣運算伺服器。它們經過最佳化，可運行用於 AI 和數據分析的 NVIDIA CUDA-X AI 加速程式庫。借助 NVIDIA 和我們的生態系統合作夥伴提供的易於部署的軟體堆疊，這波 NVIDIA 邊緣 AI 運算系統使全球最大產業（運輸、製造、工業、零售、醫療保健和農業）的公司能夠將智慧帶到客戶營運的邊緣。

And third, we made significant progress in data center rendering and graphics. We unveiled a new RTX Server configuration packing 40 GPUs into an 8 use case and up to 32 servers in a pod, providing unparalleled density, efficiency and scalability. With a complete stack, this server design is optimized for 3 data center graphic workloads: rendering, remote workstations and cloud gaming. The rendering opportunity is starting to take shape with early RTX Server deployments at leading studios, including Disney, Pixar and Weta.

第三，我們在資料中心渲染和圖形方面取得了重大進展。我們推出了新的 RTX 伺服器配置，將 40 個 GPU 裝入 8 個用例中，並將最多 32 個伺服器裝入一個 pod 中，提供無與倫比的密度、效率和可擴充性。這台伺服器設計具有完整的堆疊，並針對 3 個資料中心圖形工作負載進行了最佳化：渲染、遠端工作站和雲端遊戲。隨著迪士尼、皮克斯和維塔等領先工作室早期部署 RTX 伺服器，渲染機會開始顯現。

In the quarter, we announced our pending acquisition of Mellanox for $125 per share in cash, representing a total enterprise value of approximately $6.9 billion, which we believe will strengthen our strategic position in data center. Once complete, the acquisition will unite 2 of the world's leading companies in high-performance computing. Together, NVIDIA's computing platform and Mellanox's interconnects power over 250 of the world's TOP500 supercomputer and have as customers every major cloud service provider and computer maker.

本季度，我們宣布以每股 125 美元的現金收購 Mellanox，企業總價值約為 69 億美元，我們相信此舉將加強我們在資料中心的戰略地位。一旦完成，此次收購將使全球兩家領先的高效能運算公司合併。NVIDIA 的運算平台和 Mellanox 的互連共同為全球 TOP500 超級電腦中的 250 多台提供支持，其客戶包括所有主要的雲端服務供應商和電腦製造商。

Data centers in the future will be architected as giant compute engines with tens of thousands of compute nodes designed holistically with their interconnects for optimal performance. With Mellanox, NVIDIA will optimize data center scale workloads across the entire computing, networking and storage stack to achieve higher performance, greater utilization and lower operating cost for customers. Together, we can create better AI computing systems for the cloud to enterprise to the edge. As stated at the time of the announcement, we look forward to closing the acquisition by the end of this calendar year.

未來的資料中心將被建構為巨型運算引擎，擁有數以萬計的運算節點，並透過整體設計相互連接以實現最佳效能。借助 Mellanox，NVIDIA 將優化整個運算、網路和儲存堆疊的資料中心規模工作負載，從而為客戶實現更高的效能、更高的利用率和更低的營運成本。我們可以共同為雲端、企業和邊緣創建更好的人工智慧運算系統。正如公告發佈時所述，我們期待在今年年底前完成收購。

Moving to pro visualization. Revenue reached $266 million, up 6% from the prior year and down 9% sequentially. Year-on-year growth was driven by both desktop and mobile workstations, while the sequential decline was largely seasonal. Areas of strength included the public sector, oil and gas and manufacturing. Emerging applications, such as AI, AR, VR, contributed an estimated 38% of pro visualization revenue.

轉向專業視覺化。營收達 2.66 億美元，比上年成長 6%，比上一季下降 9%。年比成長主要由桌上型電腦和行動工作站推動，而環比下降則主要是季節性因素。優勢領域包括公共部門、石油天然氣和製造業。人工智慧、擴增實境、虛擬實境等新興應用貢獻了專業視覺化收入的約 38%。

The real-time ray-tracing capabilities of RTX are a game changer for the visual effects industry, and we are seeing tremendous momentum in the ecosystem. At GTC, we announced that the world's top 3D application providers have adopted NVIDIA RTX in their product releases set for later this year, including Adobe, Autodesk, Chaos Group, Dassault and Pixar. With this rich software ecosystem, NVIDIA RTX is transforming the 3D market. For example, Pixar is using NVIDIA RTX ray tracing on its upcoming films. Weta Digital is using it for upcoming Disney projects and Siemens NX ray-traced studios users will be able to generate rendered images up to 4x faster in their product design workloads. We are excited to see the tremendous value that NVIDIA RTX is bringing to the millions of creators and designers served by ecosystem partners.

RTX 的即時光線追蹤功能改變了視覺效果產業的遊戲規則，我們看到生態系統中出現了巨大的發展勢頭。在 GTC 上，我們宣布全球頂級 3D 應用程式供應商已在其定於今年稍後發布的產品中採用了 NVIDIA RTX，其中包括 Adob​​e、Autodesk、Chaos Group、Dassault 和 Pixar。憑藉這個豐富的軟體生態系統，NVIDIA RTX 正在改變 3D 市場。例如，皮克斯正在其即將上映的電影中使用 NVIDIA RTX 光線追蹤。Weta Digital 正在將其用於即將推出的迪士尼項目，而西門子 NX 光線追蹤工作室用戶將能夠在其產品設計工作負載中以高達 4 倍的速度生成渲染圖像。我們很高興看到 NVIDIA RTX 為生態系統合作夥伴服務的數百萬創作者和設計師帶來的巨大價值。

Finally, turning to automotive. Q1 revenue was $166 million, up 14% from a year ago and up 2% sequentially. Year-on-year growth was driven by growing adoption of next-generation AI cockpit solutions and autonomous vehicle development deals. At GTC, we had major customer and product announcements. Toyota selected NVIDIA's end-to-end platform to develop, train and validate self-driving vehicles. This broad partnership includes advancements in AI computing, infrastructure using NVIDIA GPUs, simulation using NVIDIA DRIVE Constellation platform and in-car AV computers based on the DRIVE AGX Xavier or Pegasus. We also announced the public availability of DRIVE Constellation, which enables millions of miles to be driven in virtual worlds across the broad range of scenarios with greater efficiency, cost effectiveness and safety than what's possible to achieve in the real world. Constellation will be reported in our data center market platform.

最後，轉向汽車。第一季營收為 1.66 億美元，較去年同期成長 14%，較上一季成長 2%。年比成長的動力來自於下一代人工智慧駕駛艙解決方案和自動駕駛汽車開發交易的日益普及。在 GTC 上，我們發布了重要的客戶和產品公告。豐田選擇 NVIDIA 的端到端平台來開發、訓練和驗證自動駕駛汽車。這項廣泛的合作包括人工智慧運算的進步、使用 NVIDIA GPU 的基礎設施、使用 NVIDIA DRIVE Con​​stellation 平台的模擬以及基於 DRIVE AGX Xavier 或 Pegasus 的車載 AV 電腦。我們還宣布了 DRIVE Con​​stellation 的公開發布，它能夠在虛擬世界中的廣泛場景中實現數百萬英里的行駛，並且比現實世界中實現的效率、成本效益和安全性更高。Constellation 將在我們的資料中心市場平台上進行報告。

And we introduced NVIDIA Safety Force Field, a computational defensive driving framework that shields autonomous vehicles from collisions. Mathematically verified and validated in simulation, Safety Force Field will prevent a vehicle from creating, escalating or contributing to an unsafe driving situation. We continue to believe that every vehicle will have an autonomous capability one day, whether with a driver or driverless. To help make that vision a reality, NVIDIA has created an end-to-end platform for autonomous vehicles from AI computing infrastructure to simulation to in-car computing. And Toyota is our first major win that validates this strategy. We see this as a $30 billion addressable market by 2025.

我們還推出了 NVIDIA Safety Force Field，這是一種計算防禦性駕駛框架，可保護自動駕駛汽車免受碰撞。安全力場經過數學驗證和模擬確認，可防止車輛造成、加劇或加劇不安全的駕駛狀況。我們始終相信，每輛車終有一天都會具備自動駕駛能力，無論是有人駕駛還是無人駕駛。為了幫助實現這一願景，NVIDIA 為自動駕駛汽車創建了一個端到端平台，從 AI 運算基礎設施到類比再到車載運算。豐田是我們取得的第一個重大勝利，驗證了這個策略。我們認為到 2025 年，這個潛在市場規模將達到 300 億美元。

Moving to the rest of the P&L and balance sheet. Q1 GAAP gross margin was 58.4% and non-GAAP was 59%, down year-on-year to lower gaming margins and mix, up sequentially from Q4, which had $128 million charge from DRAM boards and other components. GAAP operating expenses were $938 million, and non-GAAP operating expenses were $753 million, up 21% and 16% year-on-year, respectively. 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.

轉到損益表和資產負債表的其餘部分。第一季 GAAP 毛利率為 58.4%，非 GAAP 毛利率為 59%，年減，原因是遊戲利潤率和產品組合較低，但環比第四季度有所上升，第四季度 DRAM 板和其他組件的費用為 1.28 億美元。GAAP營運費用為9.38億美元，非GAAP營運費用為7.53億美元，分別較去年同期成長21%和16%。我們仍有望在 2020 財年實現高個位數營運支出成長，同時繼續投資於推動我們長期成長的關鍵平台，即圖形、人工智慧和自動駕駛汽車。

GAAP EPS was $0.64, and non-GAAP EPS was $0.88. We did not make any stock repurchases in the quarter. Following the announcement of the pending Mellanox acquisition, we remain committed to returning $3 billion to shareholders through the end of fiscal 2020 in the form of dividends and repurchases. So far, we have returned $800 million through share repurchases and quarterly cash dividends.

GAAP EPS 為 0.64 美元，非 GAAP EPS 為 0.88 美元。本季我們沒有進行任何股票回購。在宣布即將收購 Mellanox 之後，我們仍致力於在 2020 財年結束前以股息和回購的形式向股東返還 30 億美元。到目前為止，我們已透過股票回購和季度現金分紅返還了 8 億美元。

With that, let me turn to the outlook for the second quarter of fiscal 2020. While we anticipate substantial quarter-over-quarter growth, our Q2 outlook is somewhat lower than our expectations earlier in the quarter where our outlook for fiscal 2020 revenue was flat to down slightly from fiscal 2019. The data center spending pause around the world will likely persist in the second quarter, and visibility remains low. In gaming, the CPU shortages while improving will affect the initial ramp of our laptop business.

接下來，讓我來談談 2020 財年第二季的展望。雖然我們預計季度環比將大幅增長，但我們對第二季度的預期略低於本季早些時候的預期，當時我們對 2020 財年收入的預期與 2019 財年持平或略有下降。全球資料中心支出暫停的局面很可能將持續到第二季​​度，可見度仍然很低。在遊戲領域，CPU 短缺雖然正在改善，但會影響我們筆記型電腦業務的初始成長。

For Q2, we expect revenue to be $2.55 billion, plus or minus 2%. We expect a stronger second half than the first half, and we are returning to our practice of providing revenue outlook one quarter at a time. Q2 GAAP and non-GAAP gross margins are expected to be 59.2% and 59.5%, respectively, plus or minus 50 basis points. GAAP and non-GAAP operating expenses are expected to be approximately $985 million, $765 million, respectively. GAAP and non-GAAP OI&E are both expected to be income of approximately $27 million. GAAP and non-GAAP tax rates are both expected to be 10%, plus or minus 1%, excluding discrete items. Capital expenditures are expected to be approximately $120 million to $140 million. Further financial details are included in the CFO commentary and other information available on our IR website.

對於第二季度，我們預計營收為 25.5 億美元，上下浮動 2%。我們預計下半年的營收將比上半年更加強勁，並且我們將恢復逐季提供收入預測的做法。預計第二季 GAAP 和非 GAAP 毛利率分別為 59.2% 和 59.5%，上下浮動 50 個基點。預計 GAAP 和非 GAAP 營運費用分別約為 9.85 億美元和 7.65 億美元。預計 GAAP 和非 GAAP OI&E 收入均為約 2,700 萬美元。預計 GAAP 和非 GAAP 稅率均為 10%，上下浮動 1%，不包括單一項目。預計資本支出約1.2億至1.4億美元。進一步的財務細節包含在財務長評論和我們 IR 網站上提供的其他資訊中。

In closing, let me highlight upcoming events for the financial community. We'll be presenting at the Bank of America Global Technology Conference on June 5, at the RBC Future of Mobility Conference on June 6 and at the NASDAQ investor conference on June 13. Our next earnings call to discuss financial results for the second quarter of fiscal 2020 will take place on August 15.

最後，讓我重點介紹一下金融界即將發生的事件。我們將於 6 月 5 日參加美國銀行全球技術會議、6 月 6 日參加加拿大皇家銀行未來移動會議以及 6 月 13 日參加納斯達克投資者會議。我們將於 8 月 15 日舉行下一次財報電話會議，討論 2020 財年第二季的財務表現。

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

我們現在開始提問。接線員，請問您可以調查問題嗎？謝謝。

(Operator Instructions) Your first question comes from the line of Aaron Rakers with Wells Fargo.

（操作員指示）您的第一個問題來自富國銀行的 Aaron Rakers。

Colette, I was wondering if you could give a little bit more color or discussion around what exactly you've seen in the data center segment and whether or not or what you're looking for in terms of signs that we can kind of return to growth or maybe this pause is behind us. I guess what I'm really asking is kind of what's changed over the last, let's call it, 3 months relative to your prior commentary from a visibility perspective and just demand perspective within that segment.

科萊特，我想知道您是否可以提供更多細節或討論一下您在資料中心領域到底看到了什麼，以及您是否在尋找什麼跡象表明我們可以恢復增長，或者也許這種暫停已經過去了。我想我真正想問的是，從可見性角度和該領域的需求角度來看，相對於您之前的評論，過去 3 個月發生了哪些變化。

Sure. Thanks for the question as we start out here. I think when we had discussed our overall data center business 3 months ago, we did indicate that our visibility as we turned into the new calendar year was low. We had a challenge in terms of completing some of the deals at the end of that quarter. As we moved into Q1, I think we felt solid in terms of how we completed. We saw probably a combination of those moving forward continuing with their CapEx expenditures and building out in terms of what they need for the data centers. Some others are still in terms of the pause.

當然。感謝您在我們剛開始討論這個問題時提出這個問題。我想，當我們三個月前討論我們的整體資料中心業務時，我們確實表示，進入新的一年，我們的可見度較低。我們在該季度末完成一些交易方面遇到了挑戰。當我們進入第一季時，我認為我們的完成情況很穩健。我們可能會看到，這些公司一方面持續增加資本支出，另一方面又根據資料中心的需求進行建置。另一些人仍處於暫停狀態。

So as we look in terms of with Q2, I think we see a continuation of what we have in terms of the visibility, not the best visibility going forward but still rock-solid to what we think are benefits of what we provide in terms of a platform. Our overall priorities are aligned to what we see with the hyperscalers as well as the enterprises as they think about using AI in so many of their different workloads. But we'll just have to see as we go forward how this turns out. But right now, visibility probably just remains the same about as where we were when we started 3 months ago.

因此，當我們展望第二季度時，我認為我們看到了可見性的延續，雖然不是未來最好的可見性，但對於我們認為我們在平台方面提供的好處，仍然堅如磐石。我們的整體優先事項與超大規模企業和企業在考慮在許多不同的工作負載中使用人工智慧時所看到的情況一致。但我們只能拭目以待，看看事情會如何發展。但目前，可見度可能與我們三個月前剛開始時的情況差不多。

Okay. And then as a quick follow-up on the gaming side, last quarter, you talked about that being down -- I think it was termed as being down slightly for the full year. Is that still the expectation? Or how has that changed?

好的。然後，作為上個季度遊戲的快速跟進，您談到了遊戲方面的下降——我認為可以說全年略有下降。這仍然是我們的期望嗎？或者說發生了怎樣的變化？

So at this time, we don't plan on giving a full year overall guidance. I think our look in terms of gaming, all of the drivers that we thought about earlier in the quarter and we talked about on our Investor Day and we have continued to talk about are still definitely in line. The drivers of our gaming business and Turing RTX for the future are still on track, but we're not providing guidance at this time for the full year.

因此目前我們不打算提供全年的整體指導。我認為，就遊戲而言，我們在本季度早些時候考慮過的所有驅動因素，以及我們在投資者日討論過的所有驅動因素，以及我們一直在討論的所有驅動因素，都仍然一致。我們遊戲業務和 Turing RTX 未來的驅動力仍在正軌上，但目前我們不會為全年提供指導。

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

您的下一個問題來自摩根大通的 Harlan Sur。

On the last earnings call, you'd mentioned China gaming demand as a headwind. At the Analyst Day in mid-March, I think Jensen had mentioned that the team was already starting to see better demand trends out of China maybe given the relaxed stance on gaming bans. Do you anticipate continued China gaming demand on a go-forward basis? And maybe talk about some of the dynamics driving that demand profile in the China geography.

在上次財報電話會議上，您提到中國的遊戲需求是一個阻力。在三月中旬的分析師日上，我認為詹森曾提到，或許是因為中國放鬆了遊戲禁令，團隊已經開始看到來自中國更好的需求趨勢。您是否預期中國的遊戲需求未來會持續成長？也許可以談談推動中國地區需求狀況的一些動力。

Sure. China looks fine. I think China has stabilized. The gaming market in China is really vibrant, and it continues to be vibrant. Tencent's releasing new games. I think you might have heard that Epic stores now are open in Asia, and games are available from the West. So there are all kinds of positive signs in China. There are some 300 million PC gamers in China, and people are expecting it to grow. We're expecting the total number of gamers to continue to grow from the 1-plus billion PC gamers around the world to something more than that. And so things look fine.

當然。中國看起來不錯。我認為中國已經穩定了。中國的遊戲市場確實充滿活力，並且將繼續保持活力。騰訊發布新遊戲。我想你可能聽說過 Epic 商店現在在亞洲開業了，並且可以購買來自西方的遊戲。所以中國出現了各種積極的跡象。中國有大約 3 億 PC 遊戲玩家，人們預計這一數字還會成長。我們預計全球 PC 遊戲玩家總數將持續成長，從目前的 10 億增加到更多。一切看起來都很好。

And then as a follow-up, a big part of the demand profile in the second half of the year for the gaming business is always the lineup of AAA-rated games. Obviously, you guys have a very close partnership with all of the game developers. How does the pipeline of new games look? Kind of they get launched October, November time frame, either total number of blockbuster games and also games supporting real-time ray tracing as well as some of your DLSS capabilities.

接下來，下半年遊戲業務需求的很大一部分始終是 AAA 級遊戲陣容。顯然，你們與所有遊戲開發商都保持著非常密切的合作關係。新遊戲的開發進度如何？它們會在 10 月或 11 月左右推出一系列大片遊戲以及支援即時光線追蹤和部分 DLSS 功能的遊戲。

Yes. Well, it's seasonal. Second half of the year, we expect to see some great games. We won't preannounce anybody else's games for them, but this is a great PC cycle because it's the end of the console cycle, and PCs is just where the action's at these days with battle royale and eSports and so much social going on, the PC gaming ecosystem is just really vibrant. Our strategy with RTX was to take a lead and move the world to ray tracing. And at this point, it's fairly, fairly safe to say that the leadership position that we've taken has turned into a movement that has turned next-generation gaming ray tracing into a standard. Almost every single game platform will have to have ray tracing, and some of them already announced it.

是的。嗯，這是季節性的。下半年，我們期待看到一些精彩的比賽。我們不會預先宣布任何其他人的遊戲，但這是一個偉大的 PC 週期，因為它是控制台週期的結束，而 PC 正是當今大逃殺和電子競技的活躍之地，並且有如此多的社交活動，PC 遊戲生態系統非常活躍。我們採用 RTX 的策略是發揮主導作用，推動世界走向光線追蹤。此時，可以相當肯定地說，我們所佔據的領導地位已經演變成一場運動，將下一代遊戲光線追蹤變成了一項標準。幾乎每個遊戲平台都必須具備光線追蹤功能，其中一些平台已經宣布了這一點。

And the partnerships that we've developed are fantastic. Microsoft DXR is supporting ray tracing. Unity is supporting ray tracing. Epic is supporting ray tracing. Leading publishers like EA has adopted RTX and supporting ray tracing. And movie studios, Pixar has adopted -- announced that they're using RTX and will use RTX to accelerate their rendering of films. And so Adobe and Autodesk jumped on top -- jumped onto RTX and will bring ray tracing to their content and their tools. And so I think at this point, it's fair to say that ray tracing is the next generation and is going to be adopted all over the world.

我們建立的合作關係非常棒。Microsoft DXR 支援光線追蹤。Unity 支援光線追蹤。Epic 支援光線追蹤。EA 等領先發行商已採用 RTX 並支援光線追蹤。電影工作室皮克斯已經宣布他們正在使用 RTX，並將使用 RTX 來加速他們的電影渲染。因此，Adobe 和 Autodesk 搶佔了先機，進入了 RTX 領域，並將光線追蹤引入他們的內容和工具中。因此我認為，從這一點來看，可以公平地說，光線追蹤是下一代技術，並將在世界各地被採用。

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

您的下一個問題來自瑞銀的 Timothy Arcuri。

I guess the first question is for Colette. So what went into the decision to pull full year guidance versus just cutting it? Is it really to see around how long it could take for data center to come back?

我想第一個問題是問科萊特的。那麼，為什麼決定取消全年業績指引而不是直接削減業績指引呢？真的是為了看看資料中心需要多長時間才能恢復嗎？

Yes. I'll start off here and kind of go back to where our thoughts were in Q1 and why we provided full year guidance when we were in Q1. When we looked at Q1 and what we were guiding, we understood that it was certainly an extraordinary quarter, something that we didn't feel was a true representative of our business, and we wanted to get a better view of our trajectory of our business in terms of going forward.

是的。我將從這裡開始，回顧我們在第一季的想法，以及為什麼我們在第一季提供全年指導。當我們回顧第一季以及我們的預期時，我們明白這無疑是一個非凡的季度，我們認為它並不能真正代表我們的業務，我們希望更好地了解我們未來的業務發展軌跡。

We are still experiencing, I think, the uncertainty as a result of the pause in terms of with the overall hyperscale data centers. And we do believe that's going to extend into Q2. However, we do know and expect that our Q2 -- or excuse me, our H2 will likely be sizably larger than our overall H1, and the core dynamics of our business at every level is exactly what we expected. Just that said though, we're going to return to just quarterly guidance at this time.

我認為，我們仍在經歷由於整體超大規模資料中心暫停而導致的不確定性。我們確實相信這種情況將延續到第二季​​。然而，我們確實知道並預期我們的第二季度——或者不好意思，我們的下半年——可能會比我們上半年的整體業績大得多，而且我們各個層面業務的核心動態都正是我們所預期的。儘管如此，我們這次將恢復季度指引。

Okay. And then just as a follow-up, can you give us some even qualitative, if not quantitative, sense of the $320 million incremental revenue for July, how that breaks out? Is the thinking sort of that data center is going to be flat to maybe up a little bit and pretty much the remainder of the growth comes from gaming?

好的。然後作為後續問題，您能否給我們一些定性（即使不是定量）的信息，說明 7 月份 3.2 億美元的增量收入，這是如何實現的？您是否認為資料中心的成長將保持平穩或略有上升，而其餘大部分成長將來自遊戲？

Yes. So when you think about our growth between Q1 and Q2, yes, we do expect in terms of our gaming to increase. We do expect our Nintendo Switch to start again in sizable amounts once we move into Q2, and we do at this time expect probably our data center business to grow.

是的。因此，當您考慮我們第一季和第二季之間的成長時，是的，我們確實預計遊戲方面會有所成長。我們確實預計，一旦進入第二季度，我們的 Nintendo Switch 銷量將再次大幅成長，而且我們目前預計我們的資料中心業務可能會成長。

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

您的下一個問題來自高盛的 Toshiya Hari。

Jensen, I had a follow-up on the data center business. I was hoping you could provide some color in terms of what you're seeing not only from your hyperscale customers, which you've talked extensively on, but more on the enterprise and HP side of your business and specifically on the hyperscale side. You guys talked about this pause that you're seeing from your customer base. When you're having conversations with your customers, do they give you a reason as to why they're pausing? Is it too much inventory of GPUs and CPUs and so on and so forth? Or is it optimization giving them extra capacity? Is it caution on their own business going forward? Or is it a combination of all the above? Any color on that would be helpful, too.

詹森，我對資料中心業務進行了跟進。我希望您能夠提供一些細節，不僅介紹一下您從超大規模客戶那裡看到的情況（您已經詳細討論過），還介紹一下企業和惠普方面的業務，特別是超大規模方面的情況。你們談到了從客戶群中看到的這種停頓。當您與客戶交談時，他們是否會告訴您他們暫停的原因？GPU、CPU 等庫存是否太多了？或者說是優化賦予了它們額外的容量？這是對他們未來自身業務的謹慎嗎？或是以上所有情況的綜合？任何顏色都會有幫助。

Hyperscalers are digesting the capacity they have. They -- at this point, I think it's fairly clear that in the second half of last year, they took on a little bit too much capacity. And so everybody has paused to give themselves a chance to digest. However, our business on inference is doing great, and we're working with CSPs all over the world to accelerate their inference models.

超大規模企業正在消化其擁有的產能。就這一點而言，我認為很明顯，去年下半年他們承擔了過多的產能。因此每個人都停下來，給自己一個消化的機會。然而，我們的推理業務做得很好，我們正在與世界各地的 CSP 合作，以加速他們的推理模型。

Now the reason why recently the inference activity has gotten just off the charts is because of breakthroughs in what we call conversational AI. In fact, today, I think that -- I just saw it today, but I've known about this work for some time, Harry Shum's group, Microsoft AI research group, today announced their multitask DNN general language understanding model. And it just -- it broke benchmark records all over the place. And basically, what this means is the 3 fundamental components of conversational AI, which is speech recognition; natural language understanding, which does multitask DNN is a breakthrough, and it's based on a piece of work that Google did recently called BERT; and then text-to-speech. All of the major pieces of a conversational AI are now put together.

推理活動最近之所以能有如此大的進展，是因為我們所謂的對話式人工智慧取得了突破。事實上，今天，我認為——我今天才看到，但我已經知道這項工作有一段時間了，Harry Shum 的團隊，微軟人工智慧研究團隊，今天公佈了他們的多任務 DNN 通用語言理解模型。它只是——它打破了所有方面的基準記錄。基本上，這意味著對話式人工智慧的三個基本組成部分，即語音識別；自然語言理解，多任務 DNN 是一項突破，它基於谷歌最近所做的一項名為 BERT 的工作；然後進行文字轉語音。對話式人工智慧的所有主要部分現已組合在一起。

Of course, it's going to continue to evolve, but these models are gigantic to train. And in the case of Microsoft's network, it was trained Volta GPUs, And these systems require large amounts of memory. The models are enormous. It takes an enormous amount of time to train these systems. And so we're seeing a breakthrough in conversational AI. And across the board, internet companies would like to make their AI much more conversational, so that you can access your phones and smart speakers and be able to engage AI practically everywhere.

當然，它會繼續發展，但訓練這些模型非常困難。而在微軟的網路案例中，它是用 Volta GPU 進行訓練的，而這些系統需要大量的記憶體。這些模型非常巨大。訓練這些系統需要花費大量的時間。因此，我們看到了對話式人工智慧的突破。總體而言，網路公司都希望他們的人工智慧更具對話性，以便人們可以存取手機和智慧揚聲器，並能夠在幾乎任何地方使用人工智慧。

The work that we're doing in the industries makes a ton of sense. We're seeing AI adoption in just about all the industries from transportation to health care to retail to logistics, industrials, agriculture. And the reason for that is because they have a vast amount of data that they're collecting. And I heard a statistics just the other day from a talk that Satya gave that some 90% of today's data was created just 2 years ago and is being created by and gathered by these industrial systems all over the world.

我們在業界所做的工作非常有意義。我們看到幾乎所有行業都採用了人工智慧，從交通運輸到醫療保健，從零售到物流、工業、農業。原因在於他們收集了大量的數據。前幾天，我從 Satya 的演講中聽到一個統計數據：當今大約 90% 的數據都是在 2 年前創建的，並且是由世界各地的工業系統創建和收集的。

And so if you want to put that data to work -- and you could create the models using our systems, our GPUs for training, and then you can extend that all the way out to the edge. This last quarter, we started to talk about our enterprise server based on T4. This inference engine that has been really successful for us at the CSPs is now going out into the edge, and we call them edge servers and enterprise servers. And these edge systems are going to do AI basically instantaneously. It's too much data to move all the way to the cloud. You might have data sovereignty concerns. You want to have very, very low latency. Maybe it needs to have multi-sensor fusion capabilities so it understands the context better. For example, what it sees and what it hears has to be harmonious. And so you need that kind of AI, those kind of sensor computing at the edge.

因此，如果您想讓這些資料發揮作用——您可以使用我們的系統、我們的 GPU 來建立模型進行訓練，然後您可以將其擴展到邊緣。上個季度，我們開始討論基於 T4 的企業伺服器。這種推理引擎在 CSP 中為我們帶來了真正的成功，現在它正在進入邊緣，我們稱之為邊緣伺服器和企業伺服器。這些邊緣系統基本上可以立即實現人工智慧。資料量太大，無法一路移動到雲端。您可能有數據主權方面的擔憂。您希望獲得非常非常低的延遲。也許它需要具有多感測器融合能​​力，以便更好地理解環境。例如，它所看到的和聽到的必須是和諧的。所以你需要那種人工智慧，那種邊緣的感測器運算。

And so we're seeing a ton of excitement around this area. Some people call it intelligent edge, some people call it edge computing. And now with 5G networks coming, we're seeing a lot of interest around the edge computing servers that we're making. And so those are the activities that we're seeing.

因此，我們看到這個領域充滿了興奮。有些人稱之為智慧邊緣，有些人稱之為邊緣運算。現在隨著 5G 網路的到來，我們看到人們對我們正在製造的邊緣運算伺服器產生了濃厚的興趣。這些就是我們所看到的活動。

As a quick follow-up on the gaming site, Colette, can you characterize product mix within gaming? You saw in the current quarter -- you cited mix is one of the key reasons why gross margins were down year-over-year, albeit off a high base. Going into Q2 in the back half, would you expect SKU mix within gaming to improve or stay the same? I asked because it's important for gross margins obviously.

作為對遊戲網站的快速跟進，Colette，您能描述一下遊戲中的產品組合嗎？您在本季看到—您指出，產品組合是毛利率年減的主要原因之一，儘管基數較高。進入下半年第二季度，您預計遊戲中的 SKU 組合會改善還是保持不變？我之所以問這個問題，是因為這對毛利率來說顯然很重要。

Yes, when you look at our sequential gross margin increase, that will be influenced by our larger revenue -- our larger revenue and better mix, which you are correct, is our largest driver of our overall gross margin. However, we will be beginning the Nintendo Switch backup, and that does have lower gross margins than the company average, influencing therefore Q2 gross margin guidance that we had provided. As we look forward towards the rest of the year, we think mix and the higher revenue again will influence and likely rise our overall gross margins for the full year.

是的，當您看到我們連續的毛利率成長時，這將受到我們更大收入的影響——我們更大的收入和更好的組合，您說得對，這是我們整體毛利率的最大驅動力。然而，我們將開始 Nintendo Switch 備份，其毛利率確實低於公司平均水平，因此會影響我們提供的第二季毛利率指引。展望今年剩餘時間，我們認為產品組合和更高的收入將再次影響並可能提高我們全年的整體毛利率。

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

您的下一個問題來自摩根士丹利的喬·摩爾。

You talked quite a bit about GeForce NOW in the prepared remarks and at the Analyst Day. It seems like cloud gaming is going to be a big topic at E3. Is that going to be your preferred way to go to market with cloud gaming? And do you expect to sell GPUs to sort of traditional cloud vendors in non-GeForce NOW fashion?

您在準備好的演講和分析師日上談了很多關於 GeForce NOW 的事情。看起來雲端遊戲將成為 E3 的熱門話題。這會是你們進入雲端遊戲市場的首選方式嗎？您是否希望以非 GeForce NOW 的方式向傳統雲端供應商銷售 GPU？

Yes. Our strategy for cloud gaming is to extend our PC position for GeForce gamers into the cloud, and our strategy for building out our network is partnerships with telcos around the world. And so we'll build out some of it. And on top of the service, we have our entire PC gaming stack. And when we host the service, we'll move to a subscription model. And with our telcos around the world who would like to provision the service at their edge servers, and many of them would like to do so in conjunction with our 5G telco services to offer cloud gaming as a differentiator. And all of these different countries where PC exposure has been relatively low, we have an opportunity to extend our platform out to that billion PC gamers. And so that's our basic strategy.

是的。我們的雲端遊戲策略是將 GeForce 遊戲玩家的 PC 地位擴展到雲端，而我們建立網路的策略是與世界各地的電信公司建立合作夥伴關係。因此我們將建構其中的一些內容。除了服務之外，我們還擁有整個 PC 遊戲堆疊。當我們提供該服務時，我們將轉向訂閱模式。我們遍布全球的電信業者都希望在其邊緣伺服器上提供該服務，其中許多營運商希望將此服務與我們的 5G 電信服務結合起來，以提供雲端遊戲作為差異化優勢。在所有這些 PC 遊戲普及率相對較低的國家，我們都有機會將我們的平台擴展到十億 PC 遊戲玩家。這就是我們的基本策略。

We also offer our edge server platform to all of the cloud service providers. Google has NVIDIA GPU graphics in the cloud, amazon has NVIDIA GPU graphics in the cloud and Microsoft has NVIDIA GPU graphics in the cloud. And these GPUs will be fantastic also for cloud gaming and workstation graphics and also ray tracing. And so the platform is capable of running all of the things that NVIDIA runs, and we try to put it in every data center, in every cloud from every region that's possible.

我們也向所有雲端服務提供者提供邊緣伺服器平台。谷歌在雲端有 NVIDIA GPU 圖形，亞馬遜在雲端有 NVIDIA GPU 圖形，微軟在雲端有 NVIDIA GPU 圖形。這些 GPU 對於雲端遊戲、工作站圖形和光線追蹤也非常有用。因此，該平台能夠運行 NVIDIA 運行的所有內容，並且我們嘗試將其放置在每個資料中心、每個可能區域的每個雲端中。

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 a clarification for Colette and a question for Jensen. Colette, are you now satisfied that the PC gaming business is operating at a normal level when you look at the Q2 guidance? Like are all the issues regarding inventory and other issues, are they over? Or do you think that the second half of the year is more the normalized run rate for your PC gaming business?

我實際上想向 Colette 澄清一下，並向 Jensen 提出一個問題。科萊特，從第二季的業績指引來看，您是否對 PC 遊戲業務的正常運作感到滿意？例如有關庫存和其他問題的所有問題都結束了嗎？或者您認為下半年的 PC 遊戲業務的運作率更為正常？

And then Jensen, on the data center, NVIDIA has dominated the training market. Inference sounds a lot more fragmented and competitive. There's a lot of talk of software being like more at the framework level. How should we get the confidence that your lead in training will help you maintain a good lead in inference also?

然後是 Jensen，在資料中心方面，NVIDIA 已經主導了訓練市場。推理聽起來更加分散且競爭更加激烈。有很多關於軟體在框架層面上更相似的討論。我們應該如何確信，您在訓練中的領先優勢也將幫助您在推理中保持良好的領先優勢？

Thanks for the question. So let's start with your first part of the question regarding how we reach overall normalized gaming levels. When we look at our overall inventory in the channel, we believe that this is relatively behind us and moving forward that it will not be an issue. Going forward, we will probably reach normalized level for gaming somewhere between Q2 and Q3 similar to our discussion that we had back at Analyst Day, at the beginning of the quarter.

謝謝你的提問。那麼，讓我們從問題的第一部分開始，關於我們如何達到整體正常的遊戲水平。當我們查看渠道中的整體庫存時，我們相信這已經相對落後了，並且在未來不會再成為問題。展望未來，我們可能會在第二季和第三季之間達到遊戲的正常水平，類似於我們在本季初分析師日上討論的那樣。

NVIDIA's strategy is accelerated computing. It is very different than an accelerator strategy. For example, if you were building a smart microphone, you need an accelerator for speech recognition, ASR. Our company is focused on accelerated computing, and the reason for that is because the world's body of software is really gigantic, and the world's body of software continues to evolve. And AI is nowhere near done. We're probably at the first or a couple of innings of AI at that. And so the amount of the software and the size of the models are going to have to continue to evolve.

NVIDIA的策略是加速運算。它與加速器策略非常不同。例如，如果您正在建立智慧麥克風，則需要一個語音辨識加速器 (ASR)。我們公司專注於加速運算，原因在於世界軟體體系非常龐大，並且世界軟體體係不斷發展。但人工智慧還遠未完成。我們可能正處於人工智慧的初始階段或幾個階段。因此，軟體的數量和模型的大小必須繼續發展。

Our accelerated computing platform is designed to enable the computer industry to bring forward into the future all the software that exists today, whether it's TensorFlow or Caffe or PyTorch or it could be classical machine learning algorithms like XGBoost, which is actually right now the most popular framework and machine learning overall. And there's so many different types of classical algorithms and not to mention all of the handwritten engineered algorithms by programmers. And those algorithms and those hand-engineered algorithms also would like to be mixed in with all of the deep learning or otherwise classical machine learning algorithms.

我們的加速運算平台旨在使電腦產業能夠將現有的所有軟體推向未來，無論是 TensorFlow、Caffe 還是 PyTorch，或者是像 XGBoost 這樣的經典機器學習演算法，它實際上是目前最受歡迎的框架和機器學習。而且有許多不同類型的經典演算法，更不用說程式設計師手寫的所有工程演算法了。這些演算法和手工設計的演算法也希望與所有深度學習或其他經典機器學習演算法混合在一起。

This whole body of software doesn't run on a single function accelerator. If you would like that body of software to run on something, it would have to be sufficiently general purpose. And so the balance that we made was we invented this thing called a Tensor Core that allows us to accelerate deep learning to the speed of light. Meanwhile, it has the flexibility of CUDA so that we can bring forward everything in classical machine learning as people have started to see with RAPIDS and is being announced, being integrated into machine learning pipelines, in the cloud and elsewhere, and then also all of the high-performance computing applications or computer vision algorithms, image processing algorithms that don't have deep learning or machine learning alternatives. And so our company is focused on accelerated computing.

整個軟體主體並不依賴單一功能加速器運作。如果您希望該軟體能夠在某些東西上運行，那麼它必須具有足夠的通用性。因此，我們實現的平衡是發明一種稱為 Tensor Core 的東西，它使我們能夠將深度學習加速到光速。同時，它具有 CUDA 的靈活性，因此我們可以將經典機器學習中的所有內容推進，正如人們已經開始看到的 RAPIDS 和即將宣布的那樣，將其整合到機器學習管道、雲端和其他地方，然後是所有高效能運算應用程式或電腦視覺演算法、沒有深度學習或機器學習替代方案的影像處理演算法。因此，我們公司專注於加速運算。

And speaking of inference, that's one of the reasons why we're so successful in inference right now. We're seeing really great pickup. And the reason for that is because the type of models that people want to run on one application, and let's just use one application, one very, very exciting one, conversational AI, you would have to do speech recognition. You would have to then do natural language understanding to understand what the speech is. You might have to convert -- you have to translate to another language. Then you have to do something related to maybe making a recommendation or making a search. And then after that, you have to convert that recommendation and search and the intent into speech. While some of it could be 8-bit integers, some of it really wants to be 16-bit floating point. And some of it because of development state of it may want to be in 32-bit floating point. And so the mixed precision nature and the computational algorithm nature or flexibility nature of our approach make it possible for cloud providers and people who are developing AI applications to not have to worry about exactly what model it runs or not. We run every single model. And if it doesn't currently run, we'll help you make it run.

說到推理，這就是我們現在在推理上如此成功的原因之一。我們看到了非常好的成長。原因在於，人們希望在一個應用程式上運行的模型類型，我們只使用一個應用程序，一個非常非常令人興奮的應用程序，對話式人工智慧，你必須進行語音識別。然後你必須進行自然語言理解才能理解語音的內容。您可能需要轉換—您必須翻譯成另一種語言。然後你必須做一些與提出建議或搜尋相關的事情。然後，您必須將該推薦、搜尋和意圖轉換為語音。雖然其中一些可能是 8 位元整數，但其中一些實際上希望是 16 位元浮點數。其中一些由於其開發狀態可能需要採用 32 位元浮點。因此，我們的方法的混合精度特性和計算演算法特性或靈活性特性使得雲端提供者和開發人工智慧應用程式的人員不必擔心它到底運行什麼模型。我們運行每一個模型。如果它目前無法運行，我們將幫助您使其運行。

And so the flexibility of our architecture and the incredible performance in deep learning is really a great balance and allows customers to deploy it easily. So our strategy is very different than an accelerator. I think the only accelerators that I really see successful at the moment are the ones that go into smart speakers. And surely, there are a whole bunch being talked about, but I think the real challenge is how to make it run real, real workloads. And we're going to keep cranking along in our current strategy and keep raising the bar as we have in the past.

因此，我們架構的靈活性和深度學習的出色性能確實達到了很好的平衡，讓客戶能夠輕鬆部署它。所以我們的策略與加速器非常不同。我認為目前我真正看到的唯一成功加速器是那些進入智慧揚聲器領域的加速器。當然，有很多問題正在討論，但我認為真正的挑戰是如何讓它運行真實的工作負載。我們將繼續推進當前的策略，並像過去一樣不斷提高標準。

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

您的下一個問題來自伯恩斯坦研究公司的 Stacy Rasgon。

This is a question for Colette. Colette, so you said inference and rendering within data center were both up very strongly. But I guess that has to imply that like the training/acceleration pieces is quite weak, even weaker than the overall. And given those should be adding to efficiency, I'm just surprised it's down that much. I mean is this truly just digestion? I mean is it share? I mean like your competitor is now shipping some parts here. I mean I guess how do we get confidence that just we haven't seen the ceiling on this? I mean do you think given the trajectory, you can exit the year above the prior peaks? I guess you kind of have to given at least the qualitative outlook in the second -- I guess maybe just any color you can give us on any of those trends would be super helpful.

這是問科萊特的問題。科萊特，您說資料中心內的推理和渲染都成長非常強勁。但我想這意味著訓練/加速部分相當弱，甚至比整體還要弱。考慮到這些應該會提高效率，我只是很驚訝它下降了這麼多。我的意思是這真的只是消化嗎？我的意思是它是共享的嗎？我的意思是你的競爭對手現在正在運送一些零件到這裡。我的意思是，我想我們如何確信我們還沒有看到這個上限？我的意思是，您是否認為，按照這樣的發展軌跡，今年的業績能夠超過之前的峰值？我想你至少應該在第二個方面給出定性的展望——我想也許你能給我們關於這些趨勢的任何資訊都會非常有幫助。

Sure. As we discussed, Stacy, we are seeing many of the hyperscalers definitely purchasing in terms of the inferencing into the installment that it continues, also in terms of the training instances that they will need for their cloud or for internal use absolutely. But we have some that have paused and are going through those periods. So that -- we do believe this will come back. We do believe as we look out into the future that they will need that overall deep learning for much of their research as well as many of their workloads. So no concern on that, but right now, we do see a pause. I'll turn it over to Jensen to see if he has any additional comments.

當然。正如我們所討論的，Stacy，我們看到許多超大規模企業肯定會購買其繼續進行的推斷分期付款，也會購買他們在雲端或內部使用所需的訓練實例。但我們有一些已經暫停並正在經歷這些時期。所以——我們確實相信它會回來。展望未來，我們確實相信，他們將需要全面的深度學習來完成大部分研究以及許多工作。所以對此不用擔心，但是現在，我們確實看到了暫停。我會把它交給 Jensen，看看他是否還有其他評論。

Let's see. I think that when it comes down to training, if your infrastructure team tells you not to buy anything, the thing that suffers is time to market and some amount of experimentation that allows you to get better comps and waiting longer. And I think that for computer vision type of algorithms and recommendation type of algorithms, those -- that posture may not be impossible. However, the type of work that everybody is now jumping on top of, which is natural language understanding and conversational AI and the breakthrough that Microsoft just announced, if you want to keep up with that, you're going to have to buy much, much larger machines. And I'm looking forward to that, and I expect that that's going to happen. But in the latter part of last year, Q4 and Q1 of this year, we did see pause from the hyperscalers and -- but I don't expect it to last.

讓我們來看看。我認為，當涉及培訓時，如果你的基礎設施團隊告訴你不要購買任何東西，那麼受到影響的就是上市時間和一定量的實驗，這些實驗可以讓你獲得更好的補償並等待更長的時間。我認為對於電腦視覺類型的演算法和推薦類型的演算法來說，這些姿勢可能並非不可能。然而，現在每個人都在努力的工作類型，即自然語言理解和會話人工智慧以及微軟剛剛宣布的突破，如果你想跟上這些，你將不得不購買更大的機器。我對此充滿期待，並且我期望它會發生。但在去年下半年、今年第四季和第一季度，我們確實看到超大規模企業暫停業務——但我預計這種情況不會持續下去。

Got it. Just as a quick follow-up, I just wanted to ask about the regulatory around Mellanox in the context of what we're seeing out of China now. How do we sort of gauge the risk of, I guess, potential for the deterioration in relationships sort of spilling over on the regulatory front around that deal? We've seen that obviously with some of the other large deals in the space. What are your thoughts on that?

知道了。作為一個快速的跟進，我只是想根據我們現在在中國看到的情況詢問有關 Mellanox 的監管情況。我想，我們如何衡量雙方關係惡化對這筆交易的監管方面所產生的潛在風險？我們已經在該領域的一些其他大型交易中明顯看到了這一點。您對此有何看法？

Well, on first principles, the acquisition is going to enable data centers around the world, whether it's U.S. or elsewhere in China, to be able to advance much, much more quickly. We're going to invest and building infrastructure technology. And as a combined company, we'll be able to do that much better. And so this is good for customers, and it's great for customers in China. The 2 matters, whether it's -- the 2 matters that we're talking about are just -- are different. And one is related to competition in a -- with respect to our acquisition, the competition in the market, and the other one is related to trade. And so the 2 matters are just different. And in our particular case, we bring so much value to the marketplace in China, and I'm confident that the market will see that.

從首要原則上來說，此次收購將使世界各地的資料中心（無論是美國還是中國其他地方）更快發展。我們將投資和建設基礎設施技術。作為一家合併後的公司，我們將能夠做得更好。這對客戶來說是好事，對中國客戶來說更是好事。我們正在討論的這兩件事是不同的。一個與​​我們的收購、市場競爭有關，另一個與貿易有關。所以這兩件事是不同的。就我們的具體情況而言，我們為中國市場帶來了巨大的價值，我相信市場將會看到這一點。

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

您的下一個問題來自 Evercore ISI 的 C.J. Muse。

I guess a question on the noncloud part of your data center business. So if you think about the trends you're seeing in enterprise virtualization, in HPC and all the work you're doing around RAPIDS, rendering, et cetera, can you kind of talk through the visibility you have today for that part of your business? I think that's roughly 50% of the mix. So is that a piece that you feel confident you can grow in 2019? And any color around that would be appreciated.

我猜這是一個關於您的資料中心業務的非雲端部分的問題。因此，如果您考慮企業虛擬化、HPC 以及您在 RAPIDS、渲染等方面所​​做的所有工作中看到的趨勢，您能否談談您目前對這部分業務的了解程度？我認為這大約佔了混合物的 50%。那麼，您是否有信心在 2019 年實現這一目標？並且任何周圍的顏色都會受到讚賞。

We expected to grow in 2019. The -- a lot of our T4 inference work is related to what people call edge computing. And it has to be done at the edge because of the amount of data that otherwise would be transferred to the cloud is just too much. It has to be done at the edge because of data sovereignty issues and data privacy issues. And it has to be done at the edge because the latency requirement is really, really high. It has to respond basically like a reflex and to make a prediction or make a suggestion or stop a piece of machinery instantaneously. And so a lot of that work, a lot of the work that we're doing in T4 inference is partly in the cloud, a lot of it is at the edge.

我們預計 2019 年將會成長。我們的許多 T4 推理工作與人們所說的邊緣計算有關。而且必須在邊緣完成，因為否則傳輸到雲端的資料量實在太大了。由於資料主權問題和資料隱私問題，這必須在邊緣完成。而且它必須在邊緣完成，因為延遲要求非常非常高。它必須像反射一樣做出反應，並立即做出預測、提出建議或停止機器運轉。因此，我們在 T4 推理中所做的許多工作，一部分是在雲端，一部分是在邊緣。

T4 servers for enterprise were announced, I guess, about halfway through the quarter. And the OEMs are super excited about that because the number of companies in the world who want to do data analytics, predictive analytics is quite large, and the size of the data is growing so significantly. And with Moore's Law ending, it's really hard to power through terabytes of data at a time. And so we've been working on building the software stack from the new memory architectures and storage architectures all the way to the computational middleware, and it's called RAPIDS. And I appreciate you saying that, and that's being put together. And the activity in GitHub is just fantastic. And so you can see all kinds of companies jumping in to make contributions because they would like to be able to take that open-source software and run it in their own data center on our GPUs. And so I expect the enterprise side of our business, both for enterprise big data analytics or for edge computing, to be a really good growth driver for us this year.

我猜想，企業級 T4 伺服器是在本季中期發布的。原始設備製造商對此感到非常興奮，因為世界上想要進行數據分析和預測分析的公司數量相當多，而且數據量正在顯著增長。隨著摩爾定律的終結，一次性處理數 TB 的資料將變得十分困難。因此，我們一直致力於建立從新記憶體架構和儲存架構一直到計算中間件的軟體堆疊，它被稱為 RAPIDS。我很感謝你這麼說，而這些正在整理中。GitHub 上的活動非常活躍。因此，你可以看到各種各樣的公司紛紛加入進來並做出貢獻，因為他們希望能夠採用開源軟體並在我們的 GPU 上在自己的資料中心中運行它。因此，我預計我們業務的企業方面，無論是企業大數據分析還是邊緣運算，都將成為我們今年真正良好的成長動力。

And a follow-up real quickly on auto. It's a business that you talked about, more R&D focused, but clearly I think has surprised positively. What's the visibility like there? And how should we think about growth trajectory into the second half of the year?

並快速自動跟進。這是您談到的一項業務，更注重研發，但顯然我認為它帶來了驚喜。那裡的能見度怎麼樣？我們該如何看待下半年的成長軌跡？

Our automotive strategy has several components. There's the engineering component of it where we -- our engineers and their engineers have to codevelop the autonomous vehicles. And then there's 3 other components. There's the component of AI computing infrastructure we call DGX and/or any of the OEM service that include our GPUs that are used for developing the AIs. The cars are collecting a couple of terabytes per day per test car, and all of that data has to be powered through and crunched through in the data center. And so we have an infrastructure of what we call DGX that people could use, and we're seeing a lot of success there.

我們的汽車策略有幾個組成部分。其中有一個工程部分，我們的工程師和他們的工程師必須共同開發自動駕駛汽車。然後還有其他 3 個組件。我們稱之為 DGX 的 AI 運算基礎設施元件和/或任何包含用於開發 AI 的 GPU 的 OEM 服務。每輛測試車每天會收集幾 TB 的數據，所有這些數據都必須經過資料中心的傳輸和處理。因此，我們擁有一個名為 DGX 的基礎設施，人們可以使用它，並且我們在那裡看到了許多成功。

We just announced this last quarter, a new infrastructure called Constellation that lets you essentially drive thousands and thousands of test cars in your data center, and they're all going through pseudo-directed random or directed scenarios that allows you to either test untestable scenarios or regress against previous scenarios, and we call that Constellation. And then lastly, after working on a car for several years, we would install the computer inside the car, and we call that DRIVE.

我們剛剛在上個季度宣布了一項名為 Constellation 的新基礎設施，它可以讓您在資料中心駕駛成千上萬輛測試汽車，它們都會經歷偽定向隨機或定向場景，讓您可以測試不可測試的場景或回歸以前的場景，我們稱之為 Constellation。最後，在汽車上工作了幾年後，我們會在汽車內安裝計算機，我們稱之為 DRIVE。

And so these are the 4 components of opportunities that we have in the automotive industry. We're doing great in China. There's a whole bunch of electric vehicles being created. The robotaxis' developments around the world largely use NVIDIA's technology. We recently announced the partnership with Toyota. There's a whole bunch of stuff that we're working on, and I'm anxious to announce them to you.

這就是我們在汽車產業面臨的四個機會。我們在中國做得很好。大量電動車正在被製造出來。全球各地的自動駕駛計程車開發大多採用了NVIDIA的技術。我們最近宣布與豐田建立合作關係。我們正在做很多事情，我迫不及待地想向你們宣布。

But this is an area that is the tip of the iceberg of a larger space we call robotics and computing at the edge. But if you think about the basic computational pipeline of a self-driving car, it's no different essentially than a smart retail or the future of computational medical instruments, agriculture, industrial inspection, delivery drones, all basically use essentially the same technique. And so this is the foundational work that we're going to do for a larger space that people call the intelligent edge or computing at the edge.

但這只是我們稱之為邊緣機器人和運算的更大領域的冰山一角。但如果你考慮一下自動駕駛汽車的基本計算流程，它本質上與智慧零售或未來的計算醫療儀器、農業、工業檢查、送貨無人機沒有什麼不同，它們基本上都使用相同的技術。因此，這是我們將要為更大的空間（人們稱之為智慧邊緣或邊緣運算）所做的基礎工作。

Your next question comes from the line of Chris Caso with Raymond James.

您的下一個問題來自 Raymond James 的 Chris Caso。

First question is on notebooks and just to clarify what's been different from your expectations this year. Is it simply that the OEMs didn't launch the new models you'd expected given the shortage? Or is it more just about unit volume? And then just following up on that, what's your level of confidence in that coming back to be a driver as you go into the second half of the year?

第一個問題是關於筆記本的，只是想澄清一下今年與您的預期有何不同。是因為原始設備製造商 (OEM) 因缺貨而沒有推出您所期望的新車型？還是它僅僅與單位體積有關？那麼接下來的問題是，進入下半年，您對重返車手生涯有多大的信心？

In Q2, we were -- we had to deal with some CPU shortage issues at the OEMs. It's improving, but the initial ramp will be affected. And so the CPU shortage situation has been described fairly broadly, and it affected our initial ramp. We don't expect it to affect our ramp going forward. And the new category of gaming notebooks that we created called Max-Q has made it possible for really amazing gaming performance to fit into a thin and light. And these new generations of notebooks with our Max-Q design and the Turing GPU, which is super energy efficient, in combination, made it possible for OEMs to create notebooks that are both affordable all the way down to $799, thin and really delightful, all the way up to something incredible with RTX 2080 and a 4K display. And these are thin notebooks that are really beautiful that people would love to use.

在第二季度，我們必須處理一些 OEM 廠商的 CPU 短缺問題。情況正在改善，但最初的成長將受到影響。因此，CPU 短缺的情況已經被相當廣泛地描述，並且它影響了我們最初的生產成長。我們預計它不會影響我們未來的發展。我們創造的全新遊戲筆記型電腦 Max-Q 讓真正令人驚嘆的遊戲效能融入輕薄的筆記型電腦中。這些新一代筆記型電腦採用了我們的 Max-Q 設計和超節能的 Turing GPU，使 OEM 能夠製造價格低至 799 美元、輕薄且賞心悅目的筆記型電腦，甚至能夠製造出配備 RTX 2080 和 4K 顯示器的令人難以置信的筆記型電腦。這些筆記本非常薄，非常漂亮，人們很喜歡使用。

And this -- the invention of the Max-Q design method and all the software that went into it that we announced last year, we had -- I think last year, we had some 40 notebooks or so, maybe a little bit less than that. And this year, we have some 100 notebooks that are being designed at different price segments by different OEMs across different regions. And so I think this year is going to be quite a successful year for notebooks. And it's also the most successful segment of consumer PCs. It's the fastest-growing segment. It is very largely underpenetrated because until Max-Q came along, it wasn't really possible to design a notebook that is both great in performance and experience and also something that a gamer would like to own. And so finally, we've been able to solve that difficult puzzle and created powerful gaming machines that are inside a notebook that's really wonderful to own and carry around.

我們去年宣布發明了 Max-Q 設計方法以及所有相關軟體，我想去年我們有大約 40 台筆記型電腦，可能比這少一點。今年，我們有大約 100 款筆記型電腦，由不同地區的不同 OEM 廠商設計，價格各有不同。所以我認為今年對於筆記型電腦來說將是相當成功的一年。這也是消費性個人電腦中最成功的領域。這是成長最快的部分。它之所以在很大程度上未被充分普及，是因為在 Max-Q 出現之前，設計出一款既具有出色性能和體驗，又能讓遊戲玩家願意擁有的筆記型電腦幾乎是不可能的。最後，我們終於解決了這個難題，並在筆記型電腦中創造了功能強大的遊戲機，擁有和攜帶它真的非常棒。

And so this is going to be a really -- this is a fast-growing segment, and all the OEMs know it. And that's why they put so much energy into creating all these different types of designs and styles and sizes and shapes. And we have 100 Turing GPU notebooks, gaming PCs ramping right now.

因此，這將是一個真正快速成長的領域，所有 OEM 都知道這一點。這就是為什麼他們投入如此多的精力來創造各種不同類型的設計、風格、尺寸和形狀。目前，我們擁有 100 台 Turing GPU 筆記型電腦和遊戲電腦。

That's very helpful. As a follow-up, I just want to follow up on some of the previous questions on the automotive market. And we've been talking about it for a while. Obviously, the design cycles are very long, so you do have some visibility, and I guess the question is when can we expect an acceleration of auto revenue. Is next year the year? And then what will be the driver of that in terms of dollar contribution? I presume some of the Level 2+ things you've been talking about would probably mean most likely they're given the amount of volume there. If you can confirm that and just give some color on expectations for drivers.

這非常有幫助。作為後續問題，我只想跟進一些關於汽車市場的先前問題。我們已經討論這個問題有一段時間了。顯然，設計週期很長，所以你確實有一定的可見性，我想問題是我們什麼時候可以預期汽車收入的成長。明年是那一年嗎？那麼，就美元貢獻而言，推動這項進程的因素是什麼？我認為您所談論的一些 2 級以上的事情很可能意味著它們在那裡具有一定的數量。如果您能確認這一點，並對駕駛員的期望給予一些說明。

Yes. Level 2+, call it 2020, late 2021 or like 2022-ish. So that's Level 2+. I would say 2019, very, very early for robotaxis; next year, substantially more volume for robotaxis; 2021, bigger volumes for robotaxis. The ASP differences, the amount of computation you put into a robotaxi because of sensor resolutions, sensor diversity and redundancy, the computational redundancy and the richness of the algorithm, all of it put together, it's probably an order of magnitude plus in computation. And so the economics would reflect that. And so that robotaxi is kind of like next year, the year after, ramp. And then think of Level 2+ as 2021, 2022.

是的。2 級以上，稱為 2020 年、2021 年末或 2022 年左右。這就是 2 級以上。我想說 2019 年對自動駕駛計程車來說還非常早；明年，自動駕駛計程車的銷售量將大幅增加； 2021 年，自動駕駛計程車的銷售量將會更大。ASP 的差異，由於感測器解析度、感測器多樣性和冗餘度、計算冗餘度和演算法的豐富性而投入到自動駕駛計程車的計算量，所有這些加在一起，計算量可能會增加一個數量級。因此，經濟狀況會反映這一點。因此，自動駕駛計程車就像是明年、後年才會出現。然後將 2 級以上視為 2021 年、2022 年。

Overall, remember that our economics come from 4 different parts. And so there's the NRE components of it, there's the AI development infrastructure, computing infrastructure part of it, the simulation part of it called Constellation and then the economics of the car. And so we just announced Constellation. The enthusiasm around it is really great. Nobody should ever ship anything they don't simulate. And my expectation is that billions of miles will get simulated inside a simulator long before they'll ship it. And so that's a great opportunity for Constellation.

總的來說，請記住我們的經濟來自四個不同的部分。因此，它有 NRE 組件、AI 開發基礎設施、計算基礎設施部分、稱為 Constellation 的模擬部分，然後是汽車的經濟性。因此我們剛剛宣告了 Constellation。周圍的熱情確實很高。任何人都不應該運送任何他們沒有模擬的東西。我的預期是，在發貨之前，數十億英里的距離將在模擬器內進行模擬。這對 Constellation 來說是一個絕佳的機會。

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

下一個問題來自 Cowen 的 Matt Ramsay。

I have a few questions, one for Jensen and one for Colette. I guess, Jensen, you've done -- you said in many forums that the move down to the new process node at 7-nanometer across the business was not really sufficient to have a platform approach, and I agree with that. But maybe you could talk a little bit about your product plans at least in general terms around a 7-nanometer franchise in the gaming business and also in your training accelerator program. And I wonder if that might be waiting for some of those products or at least anticipation of those might be the cause of a little bit of a pause here.

我有幾個問題，一個問 Jensen，一個問 Colette。我想，Jensen，你已經在許多論壇上說過，在整個業務範圍內轉向 7 奈米的新製程節點並不足以真正實現平台方法，我同意這一點。但也許您可以至少概括地談談您的產品計劃，圍繞遊戲業務中的 7 奈米特許經營權以及培訓加速器計劃。我想知道這是否可能是在等待其中一些產品，或者至少對這些產品的期望可能會導致這裡稍微停頓。

And secondly, Colette, maybe you could talk us through your expectations. I understand there's a lack of visibility in certain parts of the business on revenue, but maybe you could talk about OpEx trends through the rest of the year where you might have a little more visibility.

其次，科萊特，也許您可以和我們談談您的期望。我知道某些業務部分的收入缺乏可見性，但也許您可以談談今年剩餘時間的營運支出趨勢，這樣您可能會有更多了解。

The entire reason for Q4 and Q1 is attributed to oversupply in the channel as a result of cryptocurrency. It has nothing to do with Turing. In fact, Turing is off to a faster start than Pascal was, and it continues to be on a faster pace than Pascal was. And so the pause in gaming is now behind us. We're in a growth trajectory with gaming. RTX has -- took the lead on ray tracing and has now become the standard for next-generation gaming, support from basically every major platform and software provider on the planet. And our notebook growth is going to be really great because of the Max-Q design that we invented. And the last couple of quarters would also intersect it with -- overlap with the seasonal slowdown that -- not sell but build -- the seasonal builds of the Nintendo Switch, and we're going to go back to the normal build cycle.

Q4 和 Q1 的全部原因都是因為加密貨幣導致通路供應過剩。這與圖靈無關。事實上，圖靈的起步比帕斯卡更快，而且其發展速度仍然比帕斯卡更快。因此，遊戲產業的停頓已經過去了。我們的遊戲業務正處於成長軌道。RTX 在光線追蹤方面處於領先地位，現已成為下一代遊戲的標準，基本上得到了全球所有主要平台和軟體供應商的支持。由於我們發明了 Max-Q 設計，我們的筆記型電腦銷售將會大幅成長。最後幾季也會與季節性放緩相交叉——不是銷售而是製造——任天堂 Switch 的季節性構建，我們將回到正常的構建週期。

And as Colette said earlier, somewhere between Q2 and Q3, we'll get back to normal levels for gaming. And so we're off to a great start in Turing, and I'm super excited about that. And in the second half of the year, we would have fully ramped up from top to bottom our Turing architecture, spanning everything from 179 to as high performance as you like. And we have the best price, best performance and best GPU at every single price point. And so I think we're in a pretty good shape.

正如 Colette 之前所說，在第二季和第三季之間，我們的遊戲業務將恢復正常水準。因此，我們在圖靈方面取得了良好的開端，我對此感到非常興奮。到今年下半年，我們將從上到下全面提升我們的圖靈架構，涵蓋從 179 到您想要的高效能的所有內容。而且我們在每個價位上都擁有最優惠的價格、最佳的性能和最好的 GPU。所以我認為我們的狀況非常好。

In terms of process nodes, we tend to design our own process with TSMC. If you look at our process and you measure its energy efficiency, it's off the charts. And in fact, if you take our Turing and you compare it against a 7-nanometer GPU on energy efficiency, it's incomparable. In fact, the world's 7-nanometer GPU already exists. And it's easy to go and pull that and compare the performance and energy efficiency against one of our Turing GPUs. And so the real focus for our engineering team is to engineer a process that makes sense for us and to create an architecture that is energy efficient. And the combination of those 2 things allows us to sustain our leadership position. Otherwise, buying off the shelf process is something that we can surely do, but we want to do much more than that.

在製程節點方面，我們傾向於與台積電合作設計自己的製程。如果您觀察我們的流程並測量其能源效率，您會發現它是超乎尋常的。事實上，如果將我們的圖靈與 7 奈米 GPU 的能源效率進行比較，你會發現它們是無法比擬的。事實上，世界上7奈米的GPU已經存在。我們可以輕鬆地將其與我們的一款 Turing GPU 的效能和能源效率進行比較。因此，我們的工程團隊的真正重點是設計一個對我們有意義的流程並創建一個節能的架構。這兩件事的結合使我們能夠保持領導地位。否則，購買現成的工藝是我們當然可以做的事情，但我們想做的遠不止於此。

Okay. And to discuss your question regarding OpEx trajectory for the rest of the year, we're still on track to our thoughts on leaving the fiscal year with a year-over-year growth and overall OpEx on a non-GAAP basis in the high single digits. We'll see probably an increase sequentially quarter-to-quarter along there, but our year-over-year growth will start to decline as we will not be growing at the speed that we did in this last year. But I do believe we're on track to meet that goal.

好的。關於您提出的今年剩餘時間營運支出走勢的問題，我們仍然預計本財年將實現同比增長，並且非 GAAP 基礎上的整體營運支出將達到高個位數。我們可能會看到季度環比成長，但同比成長將開始下降，因為我們的成長速度不會像去年那麼快。但我確實相信我們正朝著實現這一目標的方向前進。

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

現在我將把時間交回給詹森，請他做最後發言。

Thanks, everyone. We're glad to be returning to growth. We are focused on driving 3 growth strategies. First, RTX ray tracing. It's now clear that ray tracing is the future of gaming and digital design, and video RTX is leading the way. With the support of Microsoft DXR, Epic, Unity, Adobe and Autodesk, game publishers like EA, movie studios like Pixar, industry support has been fantastic.

謝謝大家。我們很高興能夠恢復成長。我們專注於推動三大成長策略。首先，RTX光線追蹤。現在很明顯，光線追蹤是遊戲和數位設計的未來，而視訊 RTX 正在引領這一潮流。在 Microsoft DXR、Epic、Unity、Adobe 和 Autodesk、EA 等遊戲發行商、Pixar 等電影工作室的支援下，業界支援非常出色。

Second, accelerated computing and AI computing. The pause in hyperscale spending will pass. Accelerated computing in AI are the greatest forces in computing today, and NVIDIA is leading these movements. Whether cloud or enterprise or AI at the edge for 5G or industries, NVIDIA's one scalable architecture from cloud to edge is the focal point platform for the industry to build AI upon.

第二，加速計算和AI計算。超大規模支出的暫停期將會過去。人工智慧加速運算是當今運算領域最強大的力量，而 NVIDIA 正在引領這一發展趨勢。無論是雲端、企業或 5G 或產業的邊緣 AI，NVIDIA 從雲端到邊緣的可擴展架構都是業界建構 AI 的焦點平台。

Third, robotics. Some call it an embedded AI, some edge AI or autonomous machines. The same computing architecture is used for self-driving cars, pick-and-place robotics arms, delivery drones and smart retail stores. Everything machine -- every machine that moves or machines that watch other things that move, whether with driver or driverless, will have robotics and AI capabilities. Our strategy is to create an end-to-end platform that spans NVIDIA's DGX AI computing infrastructure to NVIDIA Constellation simulation to NVIDIA AGX embedded AI computing.

第三，機器人技術。有些人稱之為嵌入式人工智慧，有些人稱之為邊緣人工智慧或自主機器。相同的運算架構也用於自動駕駛汽車、拾放機器人手臂、送貨無人機和智慧零售店。一切機器－每一台移動的機器或觀察其他移動物體的機器，無論是有人駕駛或無人駕駛，都將具備機器人和人工智慧能力。我們的策略是建立一個端對端平台，涵蓋 NVIDIA DGX AI 運算基礎架構、NVIDIA Constellation 類比和 NVIDIA AGX 嵌入式 AI 運算。

And finally, we are super excited about the pending acquisition of Mellanox. Together, we can advance cloud and edge architectures for HPC and AI computing. See you next quarter.

最後，我們對即將進行的 Mellanox 收購感到非常興奮。我們可以共同推進 HPC 和 AI 運算的雲端和邊緣架構。下個季度見。

And this concludes today's conference call. You may now disconnect.

今天的電話會議到此結束。您現在可以斷開連線。