輝達 (NVDA) 2018 Q4 法說會逐字稿

Good afternoon.

下午好。

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

我的名字是 Victoria，今天我將擔任您的會議接線員。

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, Vice President of Investor Relations, to begin your conference.

現在，我將把電話轉給投資者關係副總裁 Simona Jankowski，開始您的會議。

Thank you.

謝謝你。

Good afternoon, everyone, and welcome to NVIDIA's Conference Call for the Fourth Quarter of Fiscal 2018.

大家下午好，歡迎參加 NVIDIA 2018 財年第四季度電話會議。

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

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

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

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

It's also being recorded.

它也在被記錄。

You can hear a replay by telephone until February 16, 2018.

您可以在 2018 年 2 月 16 日之前通過電話收聽重播。

The webcast will be available for replay up until next quarter's conference call to discuss our fiscal first quarter financial results.

該網絡廣播將一直播放到下個季度的電話會議，以討論我們的第一財季財務業績。

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

今天通話的內容是 NVIDIA 的財產。

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

未經我們事先書面同意，不得複製或轉錄。

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

在本次電話會議期間，我們可能會根據當前預期做出前瞻性陳述。

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

這些受到許多重大風險和不確定性的影響，我們的實際結果可能存在重大差異。

For a discussion of factors that could affect our future financial results and business, please refer to the disclosure in today's earnings release, our most recent Forms 10-K and 10-Q, and the report 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 表格中提交的報告證券交易委員會。

All our statements are made as of today, February 8, 2018, 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，2018 年 2 月 8 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 and our CFO commentary, which is posted on our website.

您可以在我們的網站上找到這些非 GAAP 財務指標與 GAAP 財務指標和我們的 CFO 評論的對賬。

With that, I will turn the call over to Colette.

有了這個，我會把電話轉給 Colette。

Thanks, Simona.

謝謝，西蒙娜。

We had an outstanding quarter and fiscal 2018 led by strong growth in our gaming and data center businesses.

在我們的遊戲和數據中心業務強勁增長的帶動下，我們在 2018 財年和 2018 財年表現出色。

Q4 revenue reached $2.91 billion, up 34% year-on-year, up 10% sequentially, and above our outlook of $2.65 billion.

第四季度收入達到 29.1 億美元，同比增長 34%，環比增長 10%，高於我們預期的 26.5 億美元。

All measures of profitability set records.

所有盈利能力指標均創下紀錄。

They also hit important milestones.

他們還達到了重要的里程碑。

For the first time, gross margins strongly exceeded 60%, non-GAAP operating margins exceeded 40% and net income exceeded $1 billion.

毛利率首次大幅超過 60%，非美國通用會計準則營業利潤率超過 40%，淨利潤超過 10 億美元。

Fiscal 2018 revenue was $9.71 billion, up 41% or $2.8 billion above the previous year.

2018 財年收入為 97.1 億美元，比上年增長 41% 或 28 億美元。

Each of our platforms posted record full year revenue, with data center growing triple digits.

我們的每個平台都公佈了創紀錄的全年收入，數據中心增長了三位數。

From a reporting segment perspective, Q4 GPU revenue grew 33% from last year to $2.46 billion.

從報告部門的角度來看，第四季度 GPU 收入比去年增長了 33%，達到 24.6 億美元。

Tegra Processor revenue rose 75% to $450 million.

Tegra 處理器收入增長 75% 至 4.5 億美元。

Let's start with our gaming business.

讓我們從我們的遊戲業務開始。

Q4 revenue was $1.74 billion, up 29% year-on-year and up 11% sequentially with growth across all regions.

第四季度收入為 17.4 億美元，同比增長 29%，環比增長 11%，所有地區均實現增長。

Driving GPU demand were a number of great titles during the holiday season, including Player's Battleground (sic) [PlayerUnknown's Battlegrounds], PUBG, Destiny 2, Call of Duty: WWII, Star Wars Battlefront II.

在假期期間，推動 GPU 需求的是許多出色的遊戲，包括 Player's Battleground (sic) [PlayerUnknown's Battlegrounds]、PUBG、Destiny 2、Call of Duty: WWII、Star Wars Battlefront II。

PUBG continued its remarkable run, reaching almost 30 million players and recording more than 3 million concurrent players.

PUBG 繼續其非凡的業績，達到近 3000 萬玩家並記錄了超過 300 萬的並發玩家。

These games deliver stunning visual effects that require strong graphics performance which has driven a shift toward the higher end of our gaming portfolio and adoption of our Pascal architecture.

這些遊戲提供令人驚嘆的視覺效果，需要強大的圖形性能，這推動了我們向高端遊戲產品組合的轉變，並採用了我們的 Pascal 架構。

eSports continues to grow, expanding the overall industry and our business.

電子競技繼續發展，擴大了整個行業和我們的業務。

In one sign of their popularity, Activision's Overwatch League launched in January and reached 10 million viewers globally in its first week.

Activision 的《守望先鋒聯賽》於 1 月推出，第一周全球觀眾達到 1000 萬，這是他們受歡迎的一個標誌。

We had a busy start to the year with a number of announcements at the annual Consumer Electronics Show in Las Vegas.

我們在一年一度的拉斯維加斯消費電子展上發布了許多公告，這一年的開始很忙。

We introduced NVIDIA BFGDs, big-format gaming displays, in a partnership with Acer, ASUS and HP.

我們與宏碁、華碩和惠普合作推出了 NVIDIA BFGD，即大幅面遊戲顯示器。

These high-end 65-inch 4K displays enable ultralow latency gaming and integrate our SHIELD streaming device, offering popular apps such as Netflix, gaming video (sic) [Amazon Video], YouTube and Hulu.

這些高端 65 英寸 4K 顯示器可實現超低延遲遊戲並集成我們的 SHIELD 流媒體設備，提供流行的應用程序，如 Netflix、遊戲視頻 (原文如此) [亞馬遜視頻]、YouTube 和 Hulu。

The BFGD won 9 Best of Show awards for various publications.

BFGD 為各種出版物贏得了 9 項最佳表演獎。

We expanded the free beta of GeForce NOW beyond Macs to Window-based PCs, and we enhanced GeForce Experience with new features, including NVIDIA freestyle for customizing gameplay with various filters.

我們將 GeForce NOW 的免費測試版從 Mac 擴展到基於 Window 的 PC，並通過新功能增強了 GeForce Experience，包括使用各種過濾器自定義遊戲玩法的 NVIDIA freestyle。

And updated NVIDIA Ansel's photo mode and support for new titles with ShadowPlay highlights for capturing gaming achievements.

並更新了 NVIDIA Ansel 的照片模式，並支持帶有 ShadowPlay 亮點的新遊戲，以捕捉遊戲成就。

Additionally, the Nintendo Switch gaming console contributed to our growth as it became the fastest-selling console of all time in the U.S.

此外，Nintendo Switch 遊戲機為我們的增長做出了貢獻，因為它成為美國有史以來最暢銷的遊戲機。

Strong demand in the cryptocurrency market exceeded our expectations.

加密貨幣市場的強勁需求超出了我們的預期。

We met some of this demand with a dedicated board in our OEM business, and some was met with our gaming GPUs.

我們通過 OEM 業務中的專用板滿足了其中一些需求，而我們的遊戲 GPU 滿足了一些需求。

This contributed to lower than historical channel inventory levels of our gaming GPUs throughout the quarter.

這導致整個季度我們的遊戲 GPU 的渠道庫存水平低於歷史水平。

While the overall contribution of cryptocurrency to our business remains difficult to quantify, we believe it was a higher percentage of revenue than the prior quarter.

雖然加密貨幣對我們業務的整體貢獻仍然難以量化，但我們認為它在收入中的百分比高於上一季度。

That said, our main focus remains on our core gaming market as cryptocurrency trends will likely remain volatile.

也就是說，我們的主要關注點仍然是我們的核心遊戲市場，因為加密貨幣趨勢可能會保持波動。

Moving to data center.

搬到數據中心。

Revenue of $606 million was up 105% year-on-year and up 20% sequentially.

收入為 6.06 億美元，同比增長 105%，環比增長 20%。

This excellent performance reflected strong adoption of Tesla V100 GPUs based on our Volta architecture which began shipping in Q2 and continued to ramp in Q3 and Q4.

這種出色的性能反映了基於我們 Volta 架構的 Tesla V100 GPU 的廣泛採用，該架構於第二季度開始出貨，並在第三季度和第四季度繼續增長。

V100s are available through every major computer maker and have been chosen by every major cloud provider to deliver AI and high-performance computing.

V100 可通過每個主要計算機製造商獲得，並且已被每個主要雲提供商選擇用於提供 AI 和高性能計算。

Hyperscale and cloud customers adopting the V100 include Alibaba, Amazon Web Services, Baidu, Google, IBM, Microsoft Azure, Oracle and Tencent.

採用 V100 的超大規模和雲客戶包括阿里巴巴、亞馬遜網絡服務、百度、谷歌、IBM、微軟 Azure、甲骨文和騰訊。

We continued our leadership in AI-trending markets where our GPUs remain the platform of choice for training deep learning networks.

我們繼續在 AI 趨勢市場保持領先地位，我們的 GPU 仍然是訓練深度學習網絡的首選平台。

During the quarter, Japan's Preferred Networks trained the ResNet-50 neural network for image classification in a record 15 minutes by using 1,024 Tesla P100 GPUs.

在本季度，日本的 Preferred Networks 使用 1,024 個 Tesla P100 GPU 在創紀錄的 15 分鐘內訓練了 ResNet-50 神經網絡進行圖像分類。

Our newer-generation V100s delivered even higher performance, with the Volta architecture offering 10x the deep learning performance of Pascal.

我們的新一代 V100 提供了更高的性能，Volta 架構提供了 10 倍於 Pascal 的深度學習性能。

We also saw a growing traction in the AI inference market where NVIDIA's platform can improve performance and efficiency by orders of magnitude over CPUs.

我們還看到 AI 推理市場的吸引力越來越大，NVIDIA 的平台可以將性能和效率提高幾個數量級，超過 CPU。

We continue to view AI inference as a significant new opportunity for our data center GPUs.

我們繼續將 AI 推理視為我們數據中心 GPU 的重要新機遇。

Hyperscale inference applications that run on GPUs include speech recognition, image and video analytics, recommender systems, translation, search and natural language processing.

在 GPU 上運行的超大規模推理應用程序包括語音識別、圖像和視頻分析、推薦系統、翻譯、搜索和自然語言處理。

The data center business also benefited from strong growth in high-performance computing.

數據中心業務也受益於高性能計算的強勁增長。

The HPC community has increasingly moved to accelerated computing in recent years as Moore's Law has begun to level off.

近年來，隨著摩爾定律開始趨於平穩，HPC 社區越來越多地轉向加速計算。

Indeed, more than 500 HPC applications are now GPU-accelerated, including all of the top 15.

事實上，現在有超過 500 個 HPC 應用程序是 GPU 加速的，包括所有前 15 名的應用程序。

NVIDIA added a record 34 new GPU-accelerated systems to the latest TOP500 supercomputer list, bringing our total to 87 systems.

NVIDIA 在最新的 TOP500 超級計算機列表中增加了創紀錄的 34 個新的 GPU 加速系統，使我們的系統總數達到 87 個。

We increased our total petaflops of list by 28%, and we captured 14 of the top 20 spots on the Green500 list of the world's most energy-efficient supercomputers.

我們將我們的總 petaflops 列表增加了 28%，我們在 Green500 世界上最節能的超級計算機列表的前 20 名中佔據了 14 個位置。

During the quarter, we continued to support the buildout of major next-generation supercomputers.

在本季度，我們繼續支持構建主要的下一代超級計算機。

Among them is the U.S. Department of Energy's Summit system, expected to be the world's most powerful supercomputer when it comes online later this year.

其中包括美國能源部的峰會系統，預計將在今年晚些時候上線時成為世界上最強大的超級計算機。

We also announced new wins such as Japan's fastest AI supercomputer, the ABCI system, which leverages more than 4,000 Tesla V100 GPUs.

我們還宣布了新的勝利，例如日本最快的 AI 超級計算機 ABCI 系統，該系統利用了 4,000 多個 Tesla V100 GPU。

Importantly, we are starting to see the convergence of HPC and AI as scientists embrace AI to solve problems faster.

重要的是，隨著科學家們採用 AI 來更快地解決問題，我們開始看到 HPC 和 AI 的融合。

Modern supercomputers will need to support multi-precision computation for applying deep learning together with simulation and testing.

現代超級計算機將需要支持多精度計算，以便將深度學習與模擬和測試結合使用。

By combining AI with HPC, supercomputers can deliver increased performance that is orders of magnitudes greater in computations ranging from particle physics to drug discovery to astrophysics.

通過將 AI 與 HPC 相結合，超級計算機可以在從粒子物理學到藥物發現再到天體物理學的計算中提供更高數量級的性能。

We are also seeing traction for AI in a growing number of vertical industries, such as transportation, energy, manufacturing, smart cities and health care.

我們也看到了人工智能在越來越多的垂直行業中的吸引力，例如交通、能源、製造、智慧城市和醫療保健。

We announced engagements with GE Health and Nuance in medical imaging; Baker Hughes, a GE company, in oil and gas; and Japan's Komatsu in construction and mining.

我們宣布與 GE Health 和 Nuance 在醫學成像領域進行合作；貝克休斯，一家通用電氣公司，從事石油和天然氣業務；和日本的小鬆在建築和採礦方面。

Moving to professional visualization.

轉向專業可視化。

Fourth quarter revenue grew a record -- to a record $254 million, up 13% from a year ago, up 6% sequentially, driven by demand for real-time rendering as well as emerging applications like AI and VR.

第四季度收入創歷史新高——達到創紀錄的 2.54 億美元，同比增長 13%，環比增長 6%，這得益於對實時渲染以及 AI 和 VR 等新興應用的需求。

These emerging applications now represent approximately 30% of pro visualization sales.

這些新興應用程序現在約佔專業可視化銷售額的 30%。

We saw strength across several key industries including defense, manufacturing, energy, health care and Internet service providers.

我們看到了包括國防、製造、能源、醫療保健和互聯網服務提供商在內的幾個關鍵行業的實力。

Among key customers, high-end Quadro products are being used by GlaxoSmithKline for AI and by Pemex oil and gas for seismic processing and visualization.

在主要客戶中，GlaxoSmithKline 將高端 Quadro 產品用於人工智能，Pemex 石油和天然氣用於地震處理和可視化。

Turning to automotive.

轉向汽車。

In automotive, for the fourth quarter, revenue grew 3% year-on-year to $132 million and was down 8% sequentially.

在汽車領域，第四季度收入同比增長 3% 至 1.32 億美元，環比下降 8%。

The sequential decline reflects our transition from infotainment, which is becoming commoditized, to next-generation AI cockpit systems and complete top-to-bottom self-driving vehicle platforms built on NVIDIA hardware and software.

連續下降反映了我們從正在商品化的信息娛樂系統過渡到下一代人工智能駕駛艙系統和基於 NVIDIA 硬件和軟件的完整自上而下的自動駕駛汽車平台。

At CES, we demonstrated our leadership position on autonomous vehicles with several key milestones and new partnerships that point to AI self-driving cars moving from deployment to production.

在 CES 上，我們展示了我們在自動駕駛汽車領域的領導地位，並通過幾個關鍵里程碑和新的合作夥伴關係表明人工智能自動駕駛汽車從部署到生產。

In a standing-room only keynote that drew nearly 8,000 attendees, Jensen announced that DRIVE Xavier, the world's first autonomous machine processor, will be available to customers this quarter.

Jensen 在一場吸引近 8,000 名與會者的僅限站立會議室的主題演講中宣布，世界上第一個自主機器處理器 DRIVE Xavier 將於本季度向客戶提供。

With more than 9 billion transistors, DRIVE Xavier is the most complex system on a chip ever created.

DRIVE Xavier 擁有超過 90 億個晶體管，是有史以來最複雜的片上系統。

We also announced that NVIDIA DRIVE is the world's first functionally safe AI self-driving platform, enabling automakers to create autonomous vehicles that can operate safely, a necessary ingredient for going to market.

我們還宣布，NVIDIA DRIVE 是世界上第一個功能安全的 AI 自動駕駛平台，使汽車製造商能夠創造出可以安全運行的自動駕駛汽車，這是進入市場的必要條件。

Additionally, we announced a number of collaborations at CES, including with Uber, which has been using NVIDIA technology for the AI computing system in its fleets of self-driving cars and freight trucks.

此外，我們在 CES 上宣布了多項合作，包括與 Uber 的合作，後者一直在其自動駕駛汽車和貨運卡車車隊中將 NVIDIA 技術用於人工智能計算系統。

We announced that ZF and Baidu are using NVIDIA DRIVE self-driving technologies to create a production-ready AI autonomous vehicle platform for China, the world's largest automotive market.

我們宣布採埃孚和百度正在使用 NVIDIA DRIVE 自動駕駛技術，為中國這個全球最大的汽車市場打造一個可投入生產的 AI 自動駕駛汽車平台。

Production vehicles utilizing this technology, including those from Chery, are expected on the road by 2020.

預計到 2020 年，採用該技術的量產車（包括奇瑞的車）將上路。

We also announced a partnership with Aurora, which is working to create a modular, scalable, Level 4 and Level 5 self-driving hardware platform, incorporating the NVIDIA DRIVE Xavier processor.

我們還宣布與 Aurora 建立合作夥伴關係，該公司正致力於創建一個模塊化、可擴展的 4 級和 5 級自動駕駛硬件平台，其中包含 NVIDIA DRIVE Xavier 處理器。

Jensen was joined on stage by Volkswagen CEO, Herbert Diess.

大眾汽車首席執行官赫伯特·迪斯 (Herbert Diess) 與 Jensen 一同上台。

They announced the new generation of intelligent VW vehicles will use the NVIDIA DRIVE intelligent experience, or DRIVE IX, platform to create the new AI-infused cockpit experiences and improved safety.

他們宣布新一代智能大眾汽車將使用 NVIDIA DRIVE 智能體驗或 DRIVE IX 平台來打造全新的人工智能駕駛艙體驗並提高安全性。

Later at CES, Mercedes Benz announced that MBUX, its new AI-based smart cockpit uses NVIDIA's graphics and AI technologies.

隨後在 CES 上，梅賽德斯奔馳宣布其基於 AI 的新型智能駕駛艙 MBUX 採用了 NVIDIA 的圖形和 AI 技術。

The MBUX user experience, which includes beautiful touchscreen displays and a new voice-activated assistant, debuted last week at Mercedes-Benz A-Class compact car and will ship this spring.

MBUX 用戶體驗包括漂亮的觸摸屏顯示器和新的語音激活助手，上週在梅賽德斯-奔馳 A 級緊湊型車上首次亮相，並將於今年春季發貨。

And earlier this week, we announced a partnership with Continental to build AI self-driving vehicle systems from enhanced Level 2 to Level 5 for production in 2021.

本週早些時候，我們宣布與大陸集團建立合作夥伴關係，以構建從增強 2 級到 5 級的人工智能自動駕駛汽車系統，並於 2021 年投入生產。

There are now more than 320 companies and research institutions using the NVIDIA DRIVE platform.

現在有超過 320 家公司和研究機構在使用 NVIDIA DRIVE 平台。

That's up 50% from a year ago and encompasses virtually every carmaker, truck maker, robotaxi company, mapping company, sensor manufacturer and software startup in the autonomous vehicle ecosystem.

這比一年前增長了 50%，幾乎涵蓋了自動駕駛汽車生態系統中的所有汽車製造商、卡車製造商、自動駕駛出租車公司、測繪公司、傳感器製造商和軟件初創公司。

With this growing momentum, we remain excited about the intermediate to long-term opportunities for autonomous driving.

隨著這種不斷增長的勢頭，我們仍然對自動駕駛的中長期機會感到興奮。

Now turning to the rest of the P&L.

現在轉向損益表的其餘部分。

Q4 GAAP gross margins was 61.9%, and non-GAAP was 62.1%, records that reflect continued growth in our value-added platforms.

第四季度 GAAP 毛利率為 61.9%，非 GAAP 毛利率為 62.1%，這些記錄反映了我們增值平台的持續增長。

GAAP operating expenses were $728 million, and non-GAAP operating expenses were $607 million, up 28% and 22% year-on-year, respectively.

GAAP 運營費用為 7.28 億美元，非 GAAP 運營費用為 6.07 億美元，同比分別增長 28% 和 22%。

We continue to invest in the key platforms driving our long-term growth, including gaming, AI and automotive.

我們繼續投資於推動我們長期增長的關鍵平台，包括遊戲、人工智能和汽車。

GAAP EPS was $1.78, up 80% from a year earlier.

GAAP每股收益為1.78美元，同比增長80%。

Some of the upside was driven by a lower-than-expected tax rate as a result of U.S. tax reform and excess tax benefits related to stock-based compensation.

由於美國稅制改革和與股票薪酬相關的超額稅收優惠導致稅率低於預期，推動了部分上漲。

Our fourth quarter GAAP effective tax rate was a benefit of 3.7% compared with our expectation of a tax rate of 17.5%.

與我們預期的 17.5% 的稅率相比，我們第四季度的 GAAP 有效稅率為 3.7%。

Non-GAAP EPS was $1.72, up 52% from a year ago, reflecting a quarterly tax rate of 10.5% compared with our expectation of 17.5%.

非美國通用會計準則每股收益為 1.72 美元，比一年前增長 52%，反映出季度稅率為 10.5%，而我們的預期為 17.5%。

We returned $1.25 billion to shareholders in the fiscal year through a combination of quarterly dividends and share repurchases.

我們在本財年通過季度股息和股票回購相結合的方式向股東返還了 12.5 億美元。

Our quarterly cash flow from operations reached record levels at $1.36 billion, bringing our fiscal year total to a record $3.5 billion.

我們的季度運營現金流達到創紀錄的 13.6 億美元，使我們的財政年度總額達到創紀錄的 35 億美元。

Capital expenditures were $416 million for the fourth quarter, inclusive of $335 million associated with the purchase of our previously financed Santa Clara campus building.

第四季度的資本支出為 4.16 億美元，其中包括與購買我們之前資助的聖克拉拉校園大樓相關的 3.35 億美元。

Let me take a moment to provide a bit more detail on the impact of U.S. corporate tax reform on the quarter and our go-forward financials.

讓我花點時間詳細介紹一下美國公司稅改革對本季度和我們未來財務狀況的影響。

In Q4, we recorded a GAAP-only one-time net tax benefit of $133 million or $0.21 per diluted share.

在第四季度，我們記錄了僅 GAAP 的一次性淨稅收收益 1.33 億美元或每股攤薄收益 0.21 美元。

This is primarily related to provisional tax amounts for the transition tax on accumulated foreign earnings and remeasurement of certain deferred tax assets and liabilities associated with the Tax Cuts and Jobs Act.

這主要與累積外國收入的過渡稅以及與《減稅和就業法》相關的某些遞延稅資產和負債的重新計量有關。

We previously accrued for taxes on a portion of forward earnings in excess of the provisional tax amount recorded for the transition tax, hence, the one-time benefit.

我們之前對超出過渡稅記錄的暫定稅額的部分遠期收益應計稅款，因此，一次性收益。

For fiscal 2019, we expect our GAAP and non-GAAP tax rates to be around 12%, which is down from approximately 17% previously.

對於 2019 財年，我們預計我們的 GAAP 和非 GAAP 稅率將在 12% 左右，低於之前的約 17%。

This does not take into effect the excess tax benefit from stock-based compensation which, depending on stock price and vesting schedule, could increase or decrease our tax rate and GAAP in a given quarter.

這不會影響股票薪酬帶來的超額稅收優惠，這取決於股票價格和歸屬時間表，可能會在給定季度增加或減少我們的稅率和 GAAP。

In terms of our capital allocation priorities, we continue to focus first and foremost on investing in our business as we see significant opportunities ahead.

就我們的資本配置優先事項而言，我們繼續將重點放在投資我們的業務上，因為我們看到了未來的重大機遇。

Our lower tax rate strengthens our ability to invest in both OpEx, such as adding engineering talent; as well as CapEx, such as investing in supercomputers for internal AI development.

我們較低的稅率增強了我們投資運營支出的能力，例如增加工程人才；以及資本支出，例如投資用於內部人工智能開發的超級計算機。

In addition, we remain committed to returning cash to shareholders, with our plan remaining at $1.25 billion for fiscal 2019.

此外，我們仍致力於向股東返還現金，我們的 2019 財年計劃仍為 12.5 億美元。

With that, let me turn to the outlook for the first quarter of fiscal 2019.

有了這個，讓我轉向 2019 財年第一季度的前景。

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 62.7% and 63%, respectively, plus or minus 50 basis points.

GAAP 和非 GAAP 毛利率預計分別為 62.7% 和 63%，上下浮動 50 個基點。

GAAP and non-GAAP operating expenses are expected to be approximately $770 million and $645 million, respectively.

GAAP 和非 GAAP 運營費用預計分別約為 7.7 億美元和 6.45 億美元。

GAAP and non-GAAP OI&E are both expected to be nominal.

GAAP 和非 GAAP OI&E 預計都是名義上的。

GAAP and non-GAAP tax rates are both expected to be 12%, plus or minus 1%, excluding discrete items.

GAAP 和非 GAAP 稅率預計均為 12%，正負 1%，不包括離散項目。

For the full fiscal year 2019, we expect our operating expenses to grow at a similar pace as in Q1.

對於整個 2019 財年，我們預計我們的運營費用將以與第一季度相似的速度增長。

Further financial details are included in the CFO commentary and other information available in 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, I'd like to highlight a few upcoming events for the financial community.

最後，我想強調一下金融界即將舉行的一些活動。

We'll be presenting at the Goldman Sachs Technology & Internet Conference on February 13 and at the Morgan Stanley Technology, Media & Telecom Conference on February 26.

我們將在 2 月 13 日的高盛技術和互聯網會議以及 2 月 26 日的摩根士丹利技術、媒體和電信會議上發表演講。

We will also be hosting our annual Investor Day on March 27 in San Jose, on the sidelines of our annual GPU Technology Conference, which we are very excited about.

我們還將於 3 月 27 日在聖何塞舉辦一年一度的投資者日，屆時我們將參加我們的年度 GPU 技術大會，對此我們感到非常興奮。

We will now open the call for questions.

我們現在將打開問題的電話。

Operator, will you poll for questions, please?

接線員，請您投票詢問問題嗎？

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

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

I guess first question, when I think about normal seasonality for gaming, that would imply data center potentially north of $700 million-plus into the coming quarter.

我想第一個問題，當我考慮遊戲的正常季節性時，這意味著數據中心可能在下一季度超過 7 億美元。

And so curious if I'm thinking about that right or whether crypto is being modeled more conservatively by you guys, and so would love to hear your thoughts there.

很好奇我是否在考慮這個權利，或者你們是否正在對加密進行更保守的建模，所以很想听聽你的想法。

Which way is more conservatively, C.J.?

哪種方式更保守，C.J.？

Yes?

是的？

Sorry.

對不起。

When you say conservatively, which direction were you saying it was.

當你保守地說，你說的是哪個方向。

Are you implying up or down?

你是在暗示向上還是向下？

Well, just curious to hear your thoughts there.

好吧，只是想听聽你的想法。

We model crypto approximately flat.

我們對加密進行近似平坦的建模。

Okay.

好的。

And then I guess as part of a larger question, how are you thinking about seasonality for gaming into the ensuing quarter?

然後我想作為一個更大問題的一部分，您如何看待下一季度的遊戲季節性？

Well, there's a lot of dynamics going on in gaming.

好吧，遊戲中有很多動態。

One dynamic, of course, is that there's a fairly sizable pent-up demand going into this quarter.

當然，一個動態是本季度有相當大的被壓抑的需求。

But I think the larger dynamics that are happening relate to just the really amazing games that are out right now.

但我認為正在發生的更大動態與現在推出的真正令人驚嘆的遊戲有關。

PUBG is just -- is doing incredibly well, as you might have known, and it's become a global phenomenon.

正如您可能已經知道的那樣，PUBG 的表現令人難以置信，而且它已經成為一種全球現象。

And whether it's here in the United States or in Europe, or in China, in Asia, PUBG is just doing incredibly well.

無論是在美國還是在歐洲，還是在中國，在亞洲，PUBG 都做得非常好。

And we expect other developers to come up with similar genre, like PUBG, that are going to be coming out in the near future.

我們預計其他開發者會想出類似的類型，比如 PUBG，這將在不久的將來出現。

And I'm super excited about these games.

我對這些遊戲感到非常興奮。

And then, of course, there's Call of Duty, there's Star Wars.

然後，當然，還有使命召喚，還有星球大戰。

There's just so many great games that are out in the marketplace today, Overwatch and League of Legends, still doing well.

今天市場上有很多很棒的遊戲，《守望先鋒》和《英雄聯盟》仍然表現良好。

There's just a countless number of great franchises that are out in the marketplace.

市場上有無數偉大的特許經營權。

And the gaming market is growing, and production value is going up.

遊戲市場正在增長，產值也在上升。

And that's driving increased unit sales of GPUs as well as ASPs of GPUs.

這推動了 GPU 的單位銷售額以及 GPU 的 ASP 的增加。

And so I think those are -- that's probably the larger dynamic of gaming.

所以我認為這些是 - 這可能是遊戲的更大動態。

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

您的下一個問題來自 Jefferies 的 Mark Lipacis。

The first question, the checks we've done indicate that the Tensor Cores you put into Volta give it a huge advantage in neural network applications in the data center.

第一個問題，我們所做的檢查表明，您放入 Volta 的張量核心使其在數據中心的神經網絡應用程序中具有巨大優勢。

And I'm wondering whether the Tensor Cores might also have a similar kind of utility in the gaming market.

我想知道張量核心是否在遊戲市場上也有類似的效用。

Yes, first of all, I appreciate you asking a Tensor Core question.

是的，首先，感謝您提出 Tensor Core 問題。

It is probably the single biggest innovation we had last year in data centers.

這可能是我們去年在數據中心中最大的創新。

Our GPUs, the equivalent performance to one of our GPUs -- one of our Volta GPUs would take something along the lines of 20-plus CPUs or 10-plus nodes.

我們的 GPU 與我們的其中一款 GPU 具有同等性能——我們的一款 Volta GPU 將採用 20 多個 CPU 或 10 多個節點。

And so 1 GPU alone would do deep learning so fast that it would take 10-plus CPU-powered server nodes to keep up with.

因此，僅 1 個 GPU 就能快速完成深度學習，以至於需要 10 多個 CPU 驅動的服務器節點才能跟上。

And then Tensor Core comes along last year, and we increased the throughput of deep learning, increased the computational throughput of deep learning by another factor of 8. And so Tensor Core really illustrates the power of GPUs.

然後去年出現了 Tensor Core，我們提高了深度學習的吞吐量，將深度學習的計算吞吐量提高了 8 倍。因此，Tensor Core 真正體現了 GPU 的強大功能。

It's very unlike a CPU where the instruction set remains locked for a long time, and it's hard -- it's difficult to advance.

它與指令集長時間保持鎖定的 CPU 非常不同，而且這很難——很難推進。

In the case of our GPUs and with CUDA, that's one of its fundamental advantages, we can continue to -- year in and year out, continue to add new capabilities to it.

對於我們的 GPU 和 CUDA，這是它的基本優勢之一，我們可以繼續——年復一年地繼續為其添加新功能。

And so Tensor Core's boost of the original great performance of our GPU has really raised the bar last year.

因此，去年，Tensor Core 對我們 GPU 原始出色性能的提升確實提高了標準。

And as Colette said earlier, our Volta GPU has now been adopted all over the world, whether it's in China with Alibaba, Tencent and Baidu, iFLYTEK, to here in the United States, Amazon and Facebook and Google and Microsoft and IBM and Oracle in Europe, in Japan.

而且正如 Colette 之前所說，我們的 Volta GPU 現在已經被全世界採用，無論是在中國與阿里巴巴、騰訊和百度、科大訊飛，還是在美國，亞馬遜、Facebook、谷歌、微軟、IBM 和甲骨文。歐洲，日本。

The number of cloud service providers that have adopted Volta has been terrific, and I think everybody really appreciates the work that we did with Tensor Core.

採用 Volta 的雲服務提供商數量非常多，我認為每個人都非常欣賞我們使用 Tensor Core 所做的工作。

And all of the updates that are now coming out from the frameworks, Tensor Core is a new instruction set, it's a new architecture.

現在所有來自框架的更新，Tensor Core 是一個新的指令集，它是一個新的架構。

And the deep learning developers have really jumped on it.

深度學習開發人員真的很喜歡它。

And almost every deep learning framework is being optimized to take advantage of Tensor Core.

幾乎每個深度學習框架都在優化以利用 Tensor Core。

And on the inference side, and that's where it would play a role in video games, you could use deep learning now to synthesize and to generate new art.

在推理方面，這就是它將在視頻遊戲中發揮作用的地方，你現在可以使用深度學習來合成和產生新的藝術。

And we've been demonstrating some of that at GTC, if you've seen some of that.

我們已經在 GTC 上展示了其中的一些，如果你已經看過其中的一些的話。

Whether it's improve the quality of textures, generating artificial characters, animating characters, whether it's facial animation with -- for speech or body animation, the type of work that you can do with deep learning for video games is growing.

無論是提高紋理質量、生成人造角色、動畫角色，還是面部動畫——對於語音或身體動畫，你可以用深度學習為視頻遊戲做的工作類型正在增長。

And that's where Tensor Core could be a real advantage.

這就是 Tensor Core 可能成為真正優勢的地方。

If you take a look at the computational capability that we have in Tensor Core, compare that to a nonoptimized GPU or even a CPU, it's now 2-plus orders of magnitude greater computational throughput.

如果您看一下我們在 Tensor Core 中的計算能力，將其與未優化的 GPU 甚至 CPU 進行比較，它現在的計算吞吐量要高出 2 個數量級以上。

And that allows us to do things like synthesize images in real time, synthesize virtual worlds, animate characters, animate faces, bring a new level of virtual reality and artificial intelligence to these video games.

這使我們能夠實時合成圖像、合成虛擬世界、動畫角色、動畫面孔，為這些視頻遊戲帶來新水平的虛擬現實和人工智能。

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

您的下一個問題來自美國銀行的 Vivek Arya。

Congratulations on the strong growth and the consistent execution.

祝賀強勁的增長和始終如一的執行。

Jensen, just a near- and longer-term question on the data center.

Jensen，只是關於數據中心的近期和長期問題。

Near term, you had, had a number of strong quarters in data center.

近期，您曾在數據中心擁有多個強勁的季度。

How is the utilization of these GPUs?

這些 GPU 的利用率如何？

And how do you measure whether you're over or under from a supply perspective?

從供應的角度來看，你如何衡量你是過剩還是不足？

And then longer term, there seems to be a lot of money going into startups developing silicon for deep learning.

從長遠來看，似乎有很多資金投入到為深度學習開發芯片的初創公司。

Is there any advantage they have in taking a clean-sheet approach?

他們在採取乾淨的方式方面有什麼優勢嗎？

Or is GPU the most optimal answer?

還是 GPU 是最佳答案？

Like if you were starting a new company looking at AI today, would you make another GPU?

就像你今天要創辦一家專注於 AI 的新公司，你會製造另一個 GPU 嗎？

Or would you make another ASIC or some other format?

或者你會製作另一個 ASIC 或其他格式嗎？

Just any color would be helpful.

任何顏色都會有幫助。

Sure.

當然。

In the near term, the best way to measure customers that are already using our GPUs for deep learning is repeat customers.

在短期內，衡量已經在使用我們的 GPU 進行深度學習的客戶的最佳方式是回頭客。

When they come back another quarter, another quarter, and they continue to buy GPUs, that would suggest that their workload has continued to increase.

當他們又一個季度，又一個季度回來時，他們繼續購買 GPU，這表明他們的工作量繼續增加。

The -- with existing customers that already have a very deep penetration, another opportunity for us would be using our GPUs for inference, and that's an untapped growth opportunity for our company that's really, really exciting, and we're seeing traction there.

- 對於已經擁有非常深入滲透的現有客戶，我們的另一個機會是使用我們的 GPU 進行推理，這對我們公司來說是一個尚未開發的增長機會，這真的非常令人興奮，而且我們正在那裡看到牽引力。

For companies that are not at the forefront, the absolute forefront, of deep learning, which -- with the exception of 1 or 2 or 3 hyperscalers, almost everybody else I would put in this category, and their deployment, their adoption of deep learning applying deep learning to all of their applications is still ongoing.

對於那些不在深度學習前沿、絕對前沿的公司，除了 1 或 2 或 3 個超大規模企業之外，幾乎所有其他人我都會歸入這一類別，以及他們的部署，他們對深度學習的採用將深度學習應用於他們的所有應用程序仍在進行中。

And so I think the second wave of customers is just showing up.

所以我認為第二波客戶剛剛出現。

And then there's the third wave of customers which is -- they're not hyperscalers, they -- they're Internet service applications, Internet applications for consumers.

然後是第三波客戶，他們不是超大規模的，他們是互聯網服務應用程序，面向消費者的互聯網應用程序。

They have enormous customer bases and -- that they could apply artificial intelligence to.

他們擁有龐大的客戶群，並且可以將人工智能應用於這些客戶群。

But they run their application in hyperscale clouds.

但他們在超大規模雲中運行他們的應用程序。

That third phase of growth is now really spiking, and I'm excited about that.

第三階段的增長現在真的很猛，我對此感到很興奮。

And so that's kind of the way to think about it.

所以這是一種思考方式。

There's the pioneers, the first phase, are the returning customers.

有先行者，第一階段，是回頭客。

Then there's the second phase that's now ramping.

然後是第二階段，現在正在加速。

The third phase that's now ramping.

現在正在加速的第三階段。

And then for everybody, we have an opportunity to apply our GPUs for inference.

然後對於每個人，我們都有機會應用我們的 GPU 進行推理。

If I had all the money in the world and I had, for example, billions and billions of dollars of R&D, I would give it to NVIDIA's GPU team, which is exactly what I do.

如果我擁有世界上所有的錢，並且擁有數十億美元的研發資金，我會把它交給 NVIDIA 的 GPU 團隊，這正是我所做的。

And the reason for that is because the GPU was already inherently the world's best high-throughput computational processor.

其原因是因為 GPU 本來就已經是世界上最好的高吞吐量計算處理器。

A high-throughput processor is a lot more complicated than linear algebra done that you instantiate from a synopsis tool, it's not quite that easy.

高吞吐量處理器比您從概要工具中實例化的線性代數複雜得多，它並不那麼容易。

The computation throughput, keeping everything moving through your chip with supreme levels of energy efficiency with all of the software that's needed to keep the data flowing, with all of the optimizations that you do with each and every one of the frameworks, the amount of complexity there is just really enormous.

計算吞吐量，通過您的芯片保持最高水平的能源效率以及保持數據流動所需的所有軟件，以及您對每個框架所做的所有優化，複雜性真的是巨大的。

The networks are changing all the time.

網絡一直在變化。

It started out with just basically CNNs, and then all kinds of versions of CNNs now.

一開始基本上只是 CNN，然後是各種版本的 CNN。

It started out with RNNs and simple RNNs, and now there's all kinds of LSTMs and gated RNNs, and all kinds of interesting networks that are growing.

它從 RNN 和簡單的 RNN 開始，現在有各種 LSTM 和門控 RNN，以及各種有趣的網絡正在增長。

It started out with just 8 layers, and now it's 152 layers going to 1,000 layers.

它開始時只有 8 層，現在從 152 層增加到 1,000 層。

It started with mostly recognition, and now it's moving to synthesis with GANs.

它開始主要是識別，現在它正在轉向與 GAN 的合成。

And there's so many versions of GANs.

並且有很多版本的 GAN。

And so all of these different types of networks are really, really hard to nail down.

所以所有這些不同類型的網絡真的很難確定。

And we're still at the beginning of AI.

我們仍處於人工智能的起步階段。

So the ability for our GPUs to be programmable to all of these different architectures and networks is just an enormous advantage.

因此，我們的 GPU 能夠針對所有這些不同的架構和網絡進行編程是一個巨大的優勢。

You don't ever have to guess whether NVIDIA GPUs could be used for one particular network or another.

您無需猜測 NVIDIA GPU 是否可用於一個特定網絡或另一個。

And so you could buy our GPUs at will and know that every single GPU that you buy gives you an opportunity to reduce the number of servers in your data center by 22 nodes, by 10 nodes, 22 CPUs.

因此，您可以隨意購買我們的 GPU，並且知道您購買的每一個 GPU 都讓您有機會將數據中心的服務器數量減少 22 個節點、10 個節點、22 個 CPU。

And so the more GPUs you buy, the more money you save.

因此，您購買的 GPU 越多，您節省的錢就越多。

And so I think that capability is really quite unique.

所以我認為這種能力真的很獨特。

And then if I could just give you one example from last year or from previous year, we introduced 16-bit mix precision, we introduced 8-bit integer, we introduced NVLink the year before this last year.

然後，如果我可以舉一個去年或前一年的例子，我們引入了 16 位混合精度，我們引入了 8 位整數，我們在去年的前一年引入了 NVLink。

This year -- this last year, we introduced Tensor Core, which increased it by another factor of nearly 10.

今年——去年，我們引入了 Tensor Core，它又增加了近 10 倍。

Meanwhile, our GPUs get more complex, energy-efficient.

與此同時，我們的 GPU 變得更複雜、更節能。

Efficiency gets better and better every single year, and the software richness gets more amazing.

效率一年比一年好，軟件豐富度也越來越驚人。

And so it's a much harder problem than just a multiply accumulator.

所以這是一個比乘法累加器更難的問題。

Artificial intelligence is the single most complex mode of software that the world has ever known.

人工智能是世界上已知的最複雜的軟件模式。

That's the reason why it's taken us so long to get here.

這就是為什麼我們花了這麼長時間才到達這裡的原因。

And these high-performance supercomputers is an essential ingredient and an essential instrument in advancing AI.

這些高性能超級計算機是推進人工智能的重要組成部分和重要工具。

And so I don't think it's nearly as simple as linear algebra.

所以我認為它不像線性代數那麼簡單。

But if I had all the money in the world, I would invest it in the team that we have.

但如果我擁有世界上所有的錢，我會把它投資到我們擁有的團隊中。

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

您的下一個問題來自 Bernstein Research 的 Stacy Rasgon。

I have a question for Colette.

我有一個問題要問科萊特。

So if I correct for the Switch revenue growth in the quarter, it means the gaming business [x], which was up, I don't know maybe $140 million, $150 million.

因此，如果我修正了本季度 Switch 的收入增長，這意味著遊戲業務 [x] 上升了，我不知道可能是 1.4 億美元，1.5 億美元。

In your Q3 commentary, you did not call out crypto as a driver, you are calling it out in Q4.

在您的 Q3 評論中，您沒有將加密稱為驅動程序，而是在 Q4 中將其稱為。

Is it fair to say that like that incremental growth is all crypto?

可以公平地說，增量增長都是加密貨幣嗎？

And I guess going forward, you mentioned pent-up demand.

我猜想，你提到了被壓抑的需求。

Normally, your seasonality for gaming will be down probably double digits.

通常，您的遊戲季節性可能會下降兩位數。

Do you think that pent-up demand is enough to reverse that normal seasonal pattern -- or normally down?

你認為被壓抑的需求足以扭轉正常的季節性模式——還是正常下降？

And frankly, do you think gamers can even find GPUs at retail at this point to buy in order to satisfy that pent-up demand?

坦率地說，你認為遊戲玩家現在甚至可以在零售店找到 GPU 來購買以滿足被壓抑的需求嗎？

So let me comment on the first one.

所以讓我評論第一個。

We did talk about our overall crypto business last quarter as well.

我們也確實在上個季度談到了我們的整體加密業務。

We indicated how much we had in OEM boards, and we also indicated that there was definitely some also in our GTX business.

我們指出了我們在 OEM 板上有多少，我們還指出我們的 GTX 業務中肯定也有一些。

Keep in mind, that's very difficult for us to quantify down to the end customer.

請記住，這對我們來說很難量化到最終客戶。

It is.

這是。

But yes, there is also some in our Q3, and we did comment on it.

但是，是的，我們的第三季度也有一些，我們確實對此發表了評論。

So here we are commenting in terms of what we saw in terms of Q4.

因此，我們在這裡根據我們在第四季度看到的情況發表評論。

It's up a bit from what we saw in Q3, and we do again expect probably going forward.

這比我們在第三季度看到的有所上升，我們再次預計可能會向前發展。

I'll let Jensen answer regarding the demand for gamers as we move forward.

在我們前進的過程中，我會讓 Jensen 回答遊戲玩家的需求。

Yes.

是的。

So if you -- one way to think about the pent-up demand is we typically have somewhere between 6 to 8 weeks of inventory in the channel.

因此，如果您考慮被壓抑的需求的一種方式是，我們通常在渠道中有 6 到 8 週的庫存。

And I think you would ascertain that globally right now the channel is relatively lean.

而且我認為您會確定，目前全球範圍內的渠道相對精簡。

We're working really hard to get GPUs down to the marketplace for the gamers, and we're doing everything we can to advise Etailers and system builders to serve the gamers.

我們非常努力地將 GPU 推向遊戲玩家的市場，並且我們正在盡我們所能建議 Etailers 和系統製造商為遊戲玩家服務。

And so we're doing everything we can.

所以我們正在盡我們所能。

But I think the most important thing is we just got to catch up with supply.

但我認為最重要的是我們必須趕上供應。

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

您的下一個問題來自 RBC 的 Mitch Steves。

I actually want to circle back on the autos, since I was at CES.

我實際上想回到汽車上，因為我在 CES 上。

So it's still kind of on track for calendar -- towards calendar year '19, at the end of that, where we see the autonomous kind of ASP uplift.

所以它仍然在日曆上——在 19 日曆年結束時，我們看到自主式的 ASP 提升。

And just to clarify, the expected ASP uplift is somewhere around $1,000.

澄清一下，預期的 ASP 漲幅約為 1,000 美元。

Is that about right?

這對嗎？

Yes, it just depends on mix.

是的，它只取決於混合。

I think the -- for autonomous vehicles that still have drivers, passenger cars, branded cars, ASPs anywhere from $500 to $1,000 make sense.

我認為——對於仍然有司機、乘用車、品牌汽車、平均售價從 500 美元到 1000 美元不等的自動駕駛汽車來說是有意義的。

For robot taxis, where they're driverless, they're not autonomous vehicles, they're actually driverless vehicles, the ASP will be several thousand dollars.

對於無人駕駛的機器人出租車，它們不是自動駕駛汽車，它們實際上是無人駕駛汽車，平均售價將達到數千美元。

And in terms of timing, I think that you're going to see larger and larger deployments starting this year and going through next year for sure, especially with robot taxis.

在時間方面，我認為從今年開始到明年肯定會看到越來越大的部署，尤其是機器人出租車。

And then with autonomous vehicles, cars that have autonomous driving capability, automatic driving capability starts late 2019.

然後是自動駕駛汽車，具有自動駕駛能力的汽車，自動駕駛能力將於 2019 年底開始。

You could see a lot more in 2020.

你可以在 2020 年看到更多。

And just almost every premium car by 2022 will have autonomous automatic driving capabilities.

到 2022 年，幾乎所有高檔汽車都將具備自動駕駛功能。

Your next question come from the line of Toshiya Hari with Goldman Sachs.

你的下一個問題來自高盛的 Toshiya Hari。

Great.

偉大的。

Jensen, I was hoping to ask a little bit about inferencing.

Jensen，我希望能問一些關於推理的問題。

How big was inferencing within data center in Q4 or fiscal '18?

第四季度或 18 財年數據中心內的推理有多大？

And more importantly, how do you expect it to trend over the next 12 to 18 months?

更重要的是，您預計未來 12 到 18 個月的趨勢如何？

Yes, thanks a lot, Toshi.

是的，非常感謝，Toshi。

First of all, just a comment about inference.

首先，只是關於推理的評論。

The way that it works is you take the output of these frameworks.

它的工作方式是獲取這些框架的輸出。

And the output of these frameworks is a really complex, large computational graph.

這些框架的輸出是一個非常複雜的大型計算圖。

When you think about these neural networks, and they have millions of parameters, millions of anything is very complex.

當你考慮這些神經網絡時，它們有數百萬個參數，數以百萬計的任何東西都非常複雜。

And these parameters are waves and activation layers and -- activation functions, and there are millions of them.

這些參數是波和激活層以及——激活函數，它們有數百萬個。

And it's millions of them that composes -- consists of this computational graph.

它是由數百萬個組成的——由這個計算圖組成。

And this computational graph has all kinds of interesting and complicated layers.

而這個計算圖有各種有趣和復雜的層。

And so you take this computational graph that comes out of each one of these frameworks, and they're all different.

因此，您可以使用來自這些框架中的每一個的計算圖，它們都是不同的。

They're in different formats, they're in different styles, they have different architectures.

它們有不同的格式，有不同的風格，有不同的架構。

They're all different.

他們都是不同的。

And you take these computational graphs, and you have to find a way to compile it, to optimize this graph, to rationalize all of the things that you could combine and fold, reduce the amount of conflict across all of the resources that are in your GPUs -- or in your processor.

你使用這些計算圖，你必須找到一種方法來編譯它，優化這個圖，合理化你可以組合和折疊的所有東西，減少你所有資源中的衝突量GPU - 或在您的處理器中。

And these conflicts could be on-chip memory and register files and data paths, and it could be the fabric, it could be the frame buffer interface, it could be the amount of memory.

這些衝突可能是片上內存、寄存器文件和數據路徑，可能是結構，可能是幀緩衝區接口，也可能是內存量。

I mean you got -- this computer is really complicated across all these different processors and the interconnect between GPUs, the network that connects multiple nodes.

我的意思是你得到了——這台計算機在所有這些不同的處理器和 GPU 之間的互連以及連接多個節點的網絡上都非常複雜。

And so you've got to figure out what all these different conflicts are, resources are, and compile and optimize to take advantage of it to keep it moving all the time.

所以你必須弄清楚所有這些不同的衝突是什麼，資源是什麼，並編譯和優化以利用它來保持它一直在移動。

And so TensorRT is basically a very sophisticated optimizing graph compilation -- graph compiler.

所以 TensorRT 基本上是一個非常複雜的優化圖編譯——圖編譯器。

And it targets each one of our processors.

它針對我們的每一個處理器。

The way it targets Xavier is different to the way it targets Volta, the way it targets our inference, the way it targets for low energy, for different precisions.

它針對 Xavier 的方式不同於針對 Volta 的方式，針對我們的推理的方式，針對低能量的方式，針對不同的精度。

All of that targeting is different.

所有這些目標都是不同的。

And so first of all, TensorRT, the software of inference, that's really where the magic is.

首先，TensorRT，推理軟件，這才是真正的魔力所在。

Then the second thing that we do, we optimize our GPUs for extremely high throughput and to support different precisions because some networks could afford to have 8-bit integer or even less, some really could barely get by with a 16-bit floating point and some, you really would like to keep it at 32-bit floating point so that you don't have to second-guess about any precision that you lost along the way.

然後我們要做的第二件事，我們優化我們的 GPU 以獲得極高的吞吐量並支持不同的精度，因為有些網絡可以承受 8 位整數甚至更少，有些網絡真的只能勉強用 16 位浮點數，並且一些，你真的想把它保持在 32 位浮點，這樣你就不必再猜測你在此過程中丟失的任何精度。

And so we created an architecture that consists of this optimizing graph, computational graph compiler, to processors that are very high throughput, that are mix precision.

因此，我們創建了一個架構，該架構由這個優化圖、計算圖編譯器和高吞吐量、混合精度的處理器組成。

Okay, so that's kind of the background.

好的，這就是背景。

We start -- we've been sampling our Tesla P4, which is our data center inference processor, and I -- we're seeing just really exciting response.

我們開始 - 我們一直在對我們的數據中心推理處理器 Tesla P4 進行採樣，而我 - 我們看到了非常令人興奮的反應。

And this quarter, we started shipping.

本季度，我們開始發貨。

We -- looking outwards, my sense is that the inference market is probably about as large in the data centers as training.

我們——向外看，我的感覺是數據中心的推理市場可能與訓練一樣大。

And the wonderful thing is everything that you train on our processor will inference wonderfully on our processors as well.

奇妙的是，您在我們的處理器上訓練的所有內容也會在我們的處理器上進行出色的推斷。

And the data centers are really awakening to the observation that the more GPUs they buy for offloading inference and training, the more money they save.

數據中心真正意識到他們購買的用於卸載推理和訓練的 GPU 越多，他們節省的錢就越多。

And the amount of money they save is not 20% or 50%, it's factors of 10.

他們節省的錢不是 20% 或 50%，而是 10 倍。

The money savings for all of these data centers that are becoming increasingly capital constrained is really quite dramatic.

對於所有這些資本日益受限的數據中心而言，節省的資金確實非常可觀。

And then the other inference opportunity for us is autonomous machines, which is self-driving cars.

然後我們的另一個推理機會是自動機器，即自動駕駛汽車。

TensorRT also targets Xavier.

TensorRT 還針對 Xavier。

TensorRT targets our Pegasus robot taxi computer.

TensorRT 以我們的 Pegasus 機器人出租車計算機為目標。

And they all have to inference incredibly efficiently so that we can sustain real time, keep the energy level low and keep the cost low for car companies, okay?

他們都必須非常有效地推斷，這樣我們才能維持實時，保持低能量水平並保持汽車公司的低成本，好嗎？

So I think inference is a very important work for us.

所以我認為推理對我們來說是一項非常重要的工作。

It is very complicated work, and we're making great progress.

這是一項非常複雜的工作，我們正在取得很大進展。

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

您的下一個問題來自巴克萊銀行的 Blayne Curtis。

Just kind of curious, as you look at the Gaming business -- I've kind of lost track of what seasonality is.

只是有點好奇，當您查看遊戲業務時-我有點忘記了季節性是什麼。

You clearly have a big ramp ahead of you.

你前面顯然有一個大坡道。

I'm kind of curious, as you think about Pascal versus seasonality ahead of Volta, if you can just kind of extrapolate as you look out into April and maybe July.

我有點好奇，當你考慮到 Pascal 與 Volta 之前的季節性時，如果你可以在展望 4 月甚至 7 月時進行推斷。

I -- well, we haven't announced anything for April or July.

我——嗯，我們還沒有宣布 4 月或 7 月的任何消息。

And so the best way to think about that is Pascal is the best gaming platform on the planet.

因此，考慮這一點的最佳方式是 Pascal 是這個星球上最好的遊戲平台。

It is the most feature-rich, the best software, the most energy-efficient.

它是功能最豐富、軟件最好、最節能的。

And from $99 to $1,000, you could buy the world's best GPUs, the most advanced GPUs.

從 99 美元到 1,000 美元，你可以買到世界上最好的 GPU，最先進的 GPU。

And if you buy Pascal, you know you've got the best.

如果您購買 Pascal，您就知道自己擁有最好的。

Seasonality is a good question and increasingly because gaming is a global market and because people play games every day.

季節性是一個很好的問題，而且越來越多地因為遊戲是一個全球市場，而且人們每天都在玩遊戲。

It's just part of their life.

這只是他們生活的一部分。

There's no -- I don't think there's much seasonality in TV or books or music.

沒有——我認為電視、書籍或音樂沒有太多的季節性。

People just -- whenever new titles come out, that's when a new season starts.

人們只是 - 每當新遊戲出現時，就是新賽季開始的時候。

And so in China, there's iCafes and there's Singles' Day, November 11, there's Back to School in the United States, there's Christmas, there's Chinese New Year.

所以在中國，有 iCafes，有光棍節，11 月 11 日，在美國有返校，有聖誕節，有農曆新年。

Boy, there are so many seasons that it's kind of hard to imagine what the exact seasonality is anymore.

男孩，季節太多了，很難想像確切的季節性是什麼。

And so hopefully, over time, it becomes less of a matter.

所以希望，隨著時間的推移，它變得不那麼重要了。

But the most important thing is that we expect Pascal to continue to be the world's best gaming platform for the foreseeable future.

但最重要的是，我們預計 Pascal 在可預見的未來將繼續成為世界上最好的遊戲平台。

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

您的下一個問題來自摩根大通的 Harlan Sur。

Congratulations on the solid results and the execution.

祝賀堅實的結果和執行。

I know somebody asked a question about inferencing for the data center markets.

我知道有人問了一個關於數據中心市場推理的問題。

But on inferencing for embedded and Edge applications, on the software and firmware side, you talked about TensorRT framework; on the hardware side, you've got the Jetson TX platform; for embedded and Edge inferencing applications, things like drones and factory automation and transportation.

但是在嵌入式和邊緣應用程序的推理方面，在軟件和固件方面，您談到了 TensorRT 框架；在硬件方面，您擁有 Jetson TX 平台；用於嵌入式和邊緣推理應用，例如無人機、工廠自動化和運輸。

What else is the team doing in the embedded market to capture more of the TAM opportunity there going forward?

團隊在嵌入式市場上還做了什麼來抓住未來更多的 TAM 機會？

Yes, thanks a lot, Harlan.

是的，非常感謝，哈蘭。

The NVIDIA TensorRT is really the only optimizing inference compiler in the world today, and it targets all of our platforms.

NVIDIA TensorRT 確實是當今世界上唯一的優化推理編譯器，它針對我們所有的平台。

And we do inference in the data center that I mentioned earlier.

我們在我之前提到的數據中心進行推理。

In the embedded world, the first embedded platform we're targeting is self-driving cars.

在嵌入式世界中，我們瞄準的第一個嵌入式平台是自動駕駛汽車。

In order to drive the car, you basically inference or try to predict or perceive what's around you all the time.

為了駕駛汽車，您基本上一直在推斷或嘗試預測或感知您周圍的事物。

And that's a very complicated inference matter.

這是一個非常複雜的推理問題。

It could be extremely easy, like detecting the car in front of you and applying the brakes, or it could be incredibly hard which is trying to figure out whether you should stop at an intersection or not.

這可能非常簡單，比如檢測你前面的汽車並踩剎車，或者試圖弄清楚你是否應該在十字路口停下來可能非常困難。

If you look at most intersections, you can't just look at the lights to determine where do you stop.

如果您查看大多數十字路口，您不能只看燈來確定您在哪裡停車。

There are very few lines.

行數很少。

And so using scene understanding and using deep learning, we have the ability to recognize where to stop and whether to stop.

因此，使用場景理解和深度學習，我們有能力識別在哪裡停下來以及是否停下來。

And then for Jetson, we have a platform called Metropolis.

然後對於 Jetson，我們有一個名為 Metropolis 的平台。

And Metropolis is used for very large scale smart cities where cameras are deployed all over to keep cities safe.

Metropolis 用於超大規模的智能城市，在這些城市中到處部署攝像頭以確保城市安全。

And we've been very successful with smart cities.

我們在智慧城市方面非常成功。

Just about every major smart city provider, and what is called intelligent video analysis company, whether -- almost all over the world is using NVIDIA's platform to do inference at the Edge, AI at the Edge.

幾乎所有主要的智慧城市供應商，以及所謂的智能視頻分析公司，是否——幾乎全世界都在使用 NVIDIA 的平台在邊緣進行推理，在邊緣進行人工智能。

And then we've announced recently success with FANUC, the largest manufacturing and robotics company in the world; Komatsu, one of the largest construction equipments company in the world to apply AI at the Edge for autonomous machines.

然後我們宣布最近與世界上最大的製造和機器人公司 FANUC 取得了成功； Komatsu 是世界上最大的建築設備公司之一，在邊緣應用人工智能用於自主機器。

Drones, we have several industrial drones that are inspecting pipelines and inspecting power lines, flying over large spans of farms to figure out where to spray insecticides more accurately.

無人機，我們有幾架工業無人機正在檢查管道和檢查電力線，飛越大跨度的農場，以確定在哪裡更準確地噴灑殺蟲劑。

There's all kinds of applications.

有各種各樣的應用程序。

So you're absolutely right that inference at the Edge or AI at the Edge is a very large market opportunity for us, and that's exactly why TensorRT was created.

所以你是絕對正確的，邊緣的推理或邊緣的人工智能對我們來說是一個非常大的市場機會，這正是創建 TensorRT 的原因。

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

您的下一個問題來自摩根士丹利的 Joe Moore。

You had mentioned how lean the channel is in terms of gaming cards.

您提到過該頻道在遊戲卡方面的精簡程度。

There's been an observable increase in prices at retail.

零售價格出現了明顯的上漲。

And I'm just curious, is that a broad-based phenomenon?

我只是好奇，這是一個廣泛的現象嗎？

And is there any economic ramifications to you?

對您有任何經濟影響嗎？

Or is that just sort of retailers bringing prices up in a shortage environment?

或者這只是零售商在短缺環境中抬高價格的一種方式？

We don't set prices at the end of the market.

我們不會在市場結束時設定價格。

And the best way for us to solve this problem is work on demand -- excuse me, work on supply.

我們解決這個問題的最好方法是按需工作——對不起，按供應工作。

The demand is great.

需求很大。

And it's very likely the demand will remain great as we look throughout -- through this quarter.

正如我們在整個季度中所看到的那樣，需求很可能會保持強勁。

And so we just have to keep working on increasing supply.

所以我們只需要繼續努力增加供應。

We have -- our suppliers are the world's best and the largest semiconductor manufacturers in the world, and they're responding incredibly, and I'm really grateful for everything they're doing.

我們有——我們的供應商是世界上最好的和最大的半導體製造商，他們的反應令人難以置信，我非常感謝他們所做的一切。

We just got to catch up to that demand which is just really great.

我們必須趕上這個非常棒的需求。

Your next question comes from the line of Chris Rolland with Susquehanna.

您的下一個問題來自 Chris Rolland 和 Susquehanna。

Great quarter.

很棒的季度。

So just to clarify, Jensen, on pent-up demand.

因此，Jensen 澄清一下被壓抑的需求。

One of your GPU competitors basically said that the constraint was memory.

您的一位 GPU 競爭對手基本上說限制是內存。

I just want to make sure that, that was correct.

我只是想確保那是正確的。

And then in the CFO commentary, you mentioned opportunities for professional vis, like AI and deep learning.

然後在首席財務官的評論中，你提到了專業可視化的機會，比如人工智能和深度學習。

Can you talk about that, and what kind of applications you would use, Quadro versus Volta or GeForce?

你能談談這個嗎，你會使用什麼樣的應用程序，Quadro 與 Volta 或 GeForce？

Sure.

當然。

We are -- we're just constrained.

我們是——我們只是受到限制。

Obviously, we're 10x larger of a GPU supplier than the competition.

顯然，我們的 GPU 供應商規模是競爭對手的 10 倍。

And so we have a lot more suppliers supporting us and a lot more distributors taking our products to market and a lot more partners distributing our products all over the world.

因此，我們有更多的供應商支持我們，更多的分銷商將我們的產品推向市場，還有更多的合作夥伴在世界各地分銷我們的產品。

And so we -- I don't know how to explain it aside from the demand is just really great.

所以我們 - 除了需求真的很棒之外，我不知道如何解釋它。

And so we've just got to keep our nose to it and catch up to the demand.

所以我們只需要保持警惕並趕上需求。

With respect to Quadro, Quadro is a workstation processor.

關於 Quadro，Quadro 是一個工作站處理器。

The entire software stack is designed for all of the applications that the workstation industry uses.

整個軟件堆棧專為工作站行業使用的所有應用程序而設計。

And it's used -- the quality of the rendering is, of course, world-class because of NVIDIA and -- but the entire software stack has been designed so that mission-critical applications or long-life industrial applications and companies that are enormous and gigantic manufacturing and industrial companies in the world could rely on an entire platform which consists of processors and system and software and middleware and all the integrations into all of the CAD tools in the world to know that the supplier is going to be here and can be trusted for the entire life of the use of that product which could be several years, but the data that is generated from it has to be accountable for a couple of decades.

它被使用了——渲染的質量當然是世界級的，因為 NVIDIA 和——但是整個軟件堆棧的設計使得任務關鍵型應用程序或長壽命工業應用程序和巨大的公司世界上巨大的製造和工業公司可以依靠由處理器、系統、軟件和中間件組成的整個平台以及與世界上所有 CAD 工具的所有集成來知道供應商將在這裡並且可以在該產品的整個使用生命週期（可能是幾年）中值得信賴，但由此產生的數據必須對幾十年負責。

You need to be able to pull up an entire design of a plane or a train or a car a couple decades after it was sent to production to make sure that it's still in compliance, and if there are any questions about it, that it can be pulled up.

你需要能夠在飛機、火車或汽車投入生產幾十年後提取整個設計，以確保它仍然符合要求，如果有任何疑問，它可以被拉起來。

NVIDIA's entire platform was designed to be professional class, professional grade, long lived.

NVIDIA 的整個平台被設計為專業級、專業級、長壽命。

Now the thing that's really exciting about artificial intelligence is we now can use AI to improve images.

現在人工智能真正令人興奮的是我們現在可以使用人工智能來改善圖像。

Like, for example, you could fix a photograph using AI.

例如，您可以使用 AI 修復照片。

You could fill in damaged parts of a photograph or parts of the image that hasn't been rendered yet, you want to use AI to fill in the dots, predict the future, rendering results, which we announced and which we demonstrated at GTC recently.

您可以填充照片的損壞部分或尚未渲染的圖像部分，您想使用 AI 來填充點，預測未來，渲染結果，這是我們最近在 GTC 上宣布並展示的.

You could use that to generate designs.

您可以使用它來生成設計。

You sketch up a few strokes of what you want a car to look like.

你畫了幾筆你想要一輛汽車的樣子。

And based on the inventory, safety, physics, it could -- it has learned how to fill in the rest of it, okay, design the rest of the chassis on your behalf.

根據庫存、安全性、物理特性，它可以——它已經學會瞭如何填充其餘部分，好吧，代表你設計底盤的其餘部分。

It's called generative design.

它被稱為衍生式設計。

We're going to see generative design in product design, in building design and just about everything.

我們將在產品設計、建築設計和幾乎所有方面看到衍生式設計。

The last, if you will, 90% of the work is after the initial inspiration or the conceptual design is done.

最後，如果你願意的話，90% 的工作是在最初的靈感或概念設計完成之後。

That part of it can be highly automated through AI.

這部分可以通過人工智能實現高度自動化。

And so Quadro could be used as a platform that designs as well as generatively designs.

因此，Quadro 可以用作設計和生成設計的平台。

And then lastly, a lot of people are using our workstations to also train their neural networks for these generative designs.

最後，很多人正在使用我們的工作站來訓練他們的神經網絡以進行這些生成式設計。

And so you could train and develop your own networks and then apply it in the applications, okay?

因此，您可以訓練和開發自己的網絡，然後將其應用到應用程序中，好嗎？

So AI, think of AI really as, in the final analysis, the future way of developing software.

所以說，人工智能，歸根結底，真的是把人工智能看作是未來的軟件開發方式。

It's a brand-new capability where computers can write its own software.

這是一種全新的能力，計算機可以編寫自己的軟件。

And the software that's written is so complex and so capable that no humans could write it ourselves.

編寫的軟件非常複雜，功能如此強大，以至於沒有人可以自己編寫。

And so you could teach, you could use the data to teach a software to figure out how to write the software by itself.

所以你可以教，你可以使用數據來教一個軟件，讓它自己弄清楚如何編寫軟件。

And then when you're done developing that software, you could use it to do all kinds of stuff, including design products.

然後當你開發完那個軟件後，你可以用它來做各種各樣的事情，包括設計產品。

And so for workstations, that's how it's used.

所以對於工作站來說，就是這樣使用的。

Your next question come from the line of Craig Ellis with B. Riley.

您的下一個問題來自 Craig Ellis 和 B. Riley 的對話。

Congratulations on the very good quarterly execution.

祝賀非常好的季度執行。

A lot of near-term items here on gaming.

這裡有很多關於遊戲的近期項目。

So I'll switch it to longer term.

所以我會把它換成長期的。

Jensen, at CES, I think you said that there are now 200 million GeForce users globally.

Jensen，在 CES 上，我想你說過現在全球有 2 億 GeForce 用戶。

And if my math is correct, then that would be up about 2x over the last 3 to 4 years.

如果我的數學是正確的，那麼在過去的 3 到 4 年裡，這將增加大約 2 倍。

So the question is, is there anything that you can see that would preclude that kind of growth over a similar period?

所以問題是，你能看到有什麼東西可以阻止類似時期的這種增長嗎？

And given the recent demand dynamics, I think we've seen that NVIDIA's direct channels have been very good sources for GPUs at the prices that you intend.

鑑於最近的需求動態，我認為我們已經看到 NVIDIA 的直接渠道以您想要的價格提供了非常好的 GPU 資源。

So as we look ahead, should we expect any change in channel management from the company?

因此，展望未來，我們是否應該期待公司在渠道管理方面發生任何變化？

Yes.

是的。

Thanks a lot, Craig.

非常感謝，克雷格。

In the last several years, several dynamics happened at the same time.

在過去的幾年裡，幾個動態同時發生。

And all of it were the favorable contributions to today.

而這一切，都是對今天的有利貢獻。

First of all, gaming became a global market, and China became one of the largest gaming markets in the world.

首先，遊戲成為全球市場，中國成為全球最大的遊戲市場之一。

But second, because the market became so big, developers could invest extraordinary amounts into the production value of a video game.

但其次，由於市場變得如此之大，開發人員可以在視頻遊戲的生產價值上投入大量資金。

They could invest a few hundred million dollars and know that they're going to get the return on it.

他們可以投資幾億美元，並且知道他們會從中獲得回報。

Back when the video game industry was quite small or when PC industry -- PC gaming was small, it was too risky for a developer to invest that much.

回到視頻遊戲行業非常小的時候，或者當 PC 行業 - PC 遊戲還很小的時候，開發者投資這麼多風險太大了。

And so now an investor, a developer could invest hundreds of millions of dollars and create something that is just completely photorealistic and immersive and just beautiful.

所以現在一個投資者，一個開發商可以投資數億美元，創造出完全逼真、身臨其境、美麗的東西。

And so the production -- when a production value goes up, the GPU technology that's needed to run it well goes up.

所以生產——當生產價值上升時，運行它所需的 GPU 技術就會上升。

It's very different than music, it's very different than watching movies.

這和音樂很不一樣，和看電影也很不一樣。

Everything in video games is synthesized in real time.

視頻遊戲中的所有內容都是實時合成的。

And so when the production value goes up, the ASP or the technology has to go up.

所以當產值上升時，平均售價或技術必須上升。

And then lastly, the size of the market, people have wondered how big the video game market is going to be.

最後，市場規模，人們想知道視頻遊戲市場會有多大。

And I've always believed that the video game market is going to be literally everyone.

而且我一直相信視頻遊戲市場將是每個人的。

In 10 years' time, 15 years' time, there's going to be another 1 billion people on Earth.

再過 10 年，再過 15 年，地球上將再增加 10 億人。

And those people are going to be gamers.

這些人將成為遊戲玩家。

We're going to see more and more gamers.

我們將看到越來越多的遊戲玩家。

And not to mention that, almost every single sport could be a virtual-reality sport.

更不用說，幾乎每一項運動都可以是虛擬現實運動。

So video games is every sport.

所以電子遊戲是每一項運動。

So eSport can be any sport and every sport and every type of sport.

所以電子競技可以是任何運動，每一項運動，每一種運動。

And so I think when you consider this and put that in your mind, I think the opportunity for video games is going to be quite large, and that's essentially what we are seeing.

所以我認為，當你考慮到這一點並將其銘記於心時，我認為視頻遊戲的機會將會非常大，這基本上就是我們所看到的。

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

您的下一個問題來自 SunTrust 的 William Stein。

I'm hoping we can touch on automotive a little bit more.

我希望我們能更多地接觸汽車。

In particular, I think, in the past, you've talked about expecting sort of a low revenue growth in this market until roughly the 2020 time frame when autonomous driving kicks in, in a more meaningful way.

特別是，我認為，在過去，你曾談到預計這個市場的收入增長會有點低，直到大約 2020 年自動駕駛以更有意義的方式開始的時間框架內。

But of course, you have the AI copilot that seems to be potentially ramping sooner, and you have at least 1 marquee customer that is ramping now, I guess, but volumes aren't quite that large on the autonomous driving side.

但是，當然，您擁有的 AI 副駕駛似乎可能會更快增長，並且您至少有 1 個大客戶現在正在增長，我猜，但自動駕駛方面的交易量並沒有那麼大。

So any guidance as to when we might see these 2 factors start to accelerate revenue in that end market?

那麼，關於我們何時會看到這兩個因素開始加速該終端市場的收入的任何指導？

Yes.

是的。

Thanks a lot, Will.

非常感謝，威爾。

I wish I had more precision for you, but here are some of the dynamics that I believe in.

我希望我對你有更精確的信息，但這裡有一些我相信的動態。

I believe that autonomous capabilities -- autonomous driving, is the single greatest dynamic next to EVs in the automotive industry.

我相信自動駕駛能力——自動駕駛，是汽車行業僅次於電動汽車的最大動力。

And transportation is a $10 trillion industry.

運輸是一個價值 10 萬億美元的產業。

Between cars and shuttles and buses, delivery vehicles, I mean, it's just an extraordinary, extraordinary market.

在汽車、穿梭巴士和公共汽車、送貨車輛之間，我的意思是，這只是一個非凡的、非凡的市場。

And everything that's going to move in the future will be autonomous.

未來將要移動的一切都將是自主的。

That's for sure.

這是肯定的。

And it will be autonomous fully, or it will be autonomous partly.

它將完全自治，或者部分自治。

The size of this marketplace is quite large.

這個市場的規模相當大。

In the near term, I -- our path to that future, which I believe starts in 2020 -- 2019, 2020, but starts very strongly in 2022, I believe the path to that, in our case, has several elements.

在短期內，我——我們通向未來的道路，我相信它從 2020 年——2019 年、2020 年開始，但在 2022 年開始非常強勁，我相信在我們的案例中，通往未來的道路有幾個要素。

The first element is that in order for all these companies, whether they're Tier 1s or startups or OEMs or taxi companies or ride-hailing companies or tractor companies or shuttle companies or pizza delivery shuttles, in order to deliver -- in order to create their autonomous driving capability, the first thing you have to do is train a neural network.

第一個要素是，為了所有這些公司，無論是一級供應商、初創公司、原始設備製造商、出租車公司、叫車公司、拖拉機公司、班車公司或披薩外賣班車，為了交付——為了創造他們的自動駕駛能力，你要做的第一件事就是訓練一個神經網絡。

And we've created a platform we call the NVIDIA DGX that allows everybody to train their neural networks as quickly as possible.

我們創建了一個我們稱之為 NVIDIA DGX 的平台，讓每個人都能盡快訓練他們的神經網絡。

So that's first.

所以這是第一個。

The development of the AI requires GPUs, and we benefit first from that.

人工智能的發展需要 GPU，我們首先從中受益。

The second is -- which we'll start this year and next year, is development platforms for the cars themselves for the vehicles themselves.

第二個是——我們將在今年和明年開始，為汽車本身開發平台。

And finally, Xavier's here.

最後，澤維爾來了。

We have first silicon at Xavier's, the most complex SOC that was ever made.

我們在 Xavier's 擁有了第一塊矽片，這是有史以來最複雜的 SOC。

And we're super excited about the state of Xavier, and we're going to be sampling it in Q1.

我們對 Xavier 的狀態感到非常興奮，我們將在第一季度對其進行採樣。

And so now we'll be able to help everybody create development systems.

所以現在我們將能夠幫助每個人創建開發系統。

And there'll be thousands and tens of thousands of quite expensive development systems based on Xavier and based on Pegasus that the world is going to need.

世界將需要成千上萬個基於 Xavier 和基於 Pegasus 的相當昂貴的開發系統。

And so that's the second element.

這就是第二個要素。

The third element, in the near term, will be development agreements.

第三個要素，在短期內，將是發展協議。

Each one of these projects are engineering-intensive, and there's a development agreement that goes along with it.

這些項目中的每一個都是工程密集型的，並且伴隨著一份開發協議。

And so these 3 elements, these 3 components, are in the near term.

所以這三個元素，這三個組成部分，在短期內。

And then hopefully, starting from 2019, going forward and very strongly going from 2022 and beyond, the actual car revenues and economics will show up.

然後希望，從 2019 年開始，到 2022 年及以後，實際的汽車收入和經濟狀況將會出現。

Appreciate that question.

欣賞這個問題。

And I think this is our last question, yes?

我認為這是我們的最後一個問題，是嗎？

Well, we had a record quarter, wrapping up a record year.

好吧，我們有一個創紀錄的季度，結束了創紀錄的一年。

We have a strong -- we had strong momentum in our gaming, AI, data center and self-driving car businesses.

我們擁有強勁的勢頭——我們在遊戲、人工智能、數據中心和自動駕駛汽車業務方面擁有強勁的勢頭。

It's great to see adoption of NVIDIA's GPU computing platform increasing in so many industries.

很高興看到許多行業越來越多地採用 NVIDIA 的 GPU 計算平台。

We accomplished a great deal this last year, and we have big plans for this coming year.

去年我們取得了很大的成就，我們為來年制定了宏偉的計劃。

Next month, the brightest minds in AI and the scientific world will come together at our GPU Technology Conference in San Jose.

下個月，人工智能和科學界最聰明的人將齊聚聖何塞 GPU 技術大會。

GTC has grown tenfold in the last 5 years.

GTC 在過去 5 年中增長了十倍。

This year we expect more than 8,000 attendees.

今年我們預計將有超過 8,000 名與會者。

GTC is the place to be if you're an AI researcher or doing any field of science where computing is your essential instrument.

如果您是 AI 研究人員或從事任何以計算為基本工具的科學領域，GTC 就是您的理想之選。

There will be over 500 hours of talks of recent breakthroughs and discoveries by leaders in the field, such as Google, Amazon, Facebook, Microsoft and many others.

將有超過 500 小時的演講，討論該領域的領導者最近的突破和發現，如穀歌、亞馬遜、Facebook、微軟和許多其他公司。

Developers from industries ranging from health care to transportation to manufacturing and entertainment will come together and share state-of-the-art and AI.

來自醫療保健、交通運輸、製造和娛樂等行業的開發人員將齊聚一堂，分享最先進的人工智能技術。

This is going to be a great GTC.

這將是一個偉大的 GTC。

I hope to see all of you there.

我希望在那裡見到你們所有人。

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。

Thank you for your participation.

感謝您的參與。