輝達 (NVDA) 2018 Q4 法說會逐字稿

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

午安.我叫維多利亞，今天我將擔任你們的會議接線生。歡迎參加英偉達財務業績電話會議。（操作說明）

I'll now turn the call over to Simona Jankowski, Vice President of Investor Relations, to begin your conference.

現在我將把電話交給投資者關係副總裁西蒙娜·揚科夫斯基，由她來開始你們的會議。

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

謝謝。各位下午好，歡迎參加英偉達2018財年第四季業績電話會議。今天與我一起參加電話會議的英偉達總裁兼執行長黃仁勳，以及執行副總裁兼財務長科萊特·克雷斯。

I'd like to remind you that our call is being webcast live on NVIDIA's Investor Relations website. It's also being recorded. You can hear a replay by telephone until February 16, 2018. The webcast will be available for replay up until next quarter's conference call to discuss our fiscal first quarter financial results. The content of today's call is NVIDIA's property. It can't be reproduced or transcribed without our prior written consent.

我想提醒各位，我們的電話會議正在英偉達投資者關係網站上進行網路直播。整個過程都被錄影了。您可以在 2018 年 2 月 16 日之前透過電話收聽重播。網路直播將提供回放，直至下個季度召開電話會議討論我們第一財季的財務業績為止。本次電話會議的內容歸英偉達所有。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 report that we may file on Form 8-K with the Securities and Exchange Commission.

在本次電話會議中，我們可能會根據目前的預期發表一些前瞻性聲明。這些都存在著許多重大風險和不確定性，我們的實際結果可能與預期有重大差異。有關可能影響我們未來財務表現和業務的因素的討論，請參閱今天發布的收益報告中的披露資訊、我們最新的 10-K 表格和 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。

All our statements are made as of today, February 8, 2018, based on information currently available to us. Except as required by law, we assume no obligation to update any such statements. 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 and our CFO commentary, which is posted on our website.

我們所有聲明均截至2018年2月8日，並基於我們目前掌握的資訊。除法律另有規定外，我們不承擔更新任何此類聲明的義務。在本次電話會議中，我們將討論非GAAP財務指標。您可以在我們的網站上找到這些非GAAP財務指標與GAAP財務指標的調節表以及我們的財務長評論。

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

接下來，我將把電話交給科萊特。

Thanks, Simona. We had an outstanding quarter and fiscal 2018 led by strong growth in our gaming and data center businesses. Q4 revenue reached $2.91 billion, up 34% year-on-year, up 10% sequentially, and above our outlook of $2.65 billion. All measures of profitability set records. They also hit important milestones. For the first time, gross margins strongly exceeded 60%, non-GAAP operating margins exceeded 40% and net income exceeded $1 billion.

謝謝你，西蒙娜。在遊戲和資料中心業務強勁成長的帶動下，我們取得了出色的季度業績和 2018 財年業績。第四季營收達 29.1 億美元，年增 34%，季增 10%，高於我們先前預測的 26.5 億美元。所有獲利指標均創下新紀錄。他們也取得了重要的里程碑式成就。公司毛利率首次大幅超過 60%，非 GAAP 營業利潤率首次超過 40%，淨收入首次超過 10 億美元。

Fiscal 2018 revenue was $9.71 billion, up 41% or $2.8 billion above the previous year. Each of our platforms posted record full year revenue, with data center growing triple digits. From a reporting segment perspective, Q4 GPU revenue grew 33% from last year to $2.46 billion. Tegra Processor revenue rose 75% to $450 million.

2018 財年營收為 97.1 億美元，較上年成長 41%，即 28 億美元。我們每個平台都實現了創紀錄的全年收入，其中資料中心實現了三位數的成長。從報告分部來看，第四季 GPU 營收比去年同期成長 33%，達到 24.6 億美元。Tegra處理器營收成長75%，達到4.5億美元。

Let's start with our gaming business. Q4 revenue was $1.74 billion, up 29% year-on-year and up 11% sequentially with growth across all regions. 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.

讓我們先從遊戲業務說起。第四季營收為 17.4 億美元，年增 29%，季增 11%，所有地區均成長。假期期間，許多優秀遊戲推高了 GPU 的需求，包括《絕地求生》（PlayerUnknown's Battlegrounds）、《PUBG》、《命運2》、《決勝時刻：二戰》、《星際大戰：前線2》。

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

PUBG 繼續保持著驚人的成長勢頭，玩家人數接近 3000 萬，同時線上玩家超過 300 萬。這些遊戲呈現出令人驚嘆的視覺效果，需要強大的圖形性能，這促使我們向高階遊戲產品組合轉型，並採用了我們的 Pascal 架構。

eSports continues to grow, expanding the overall industry and our business. In one sign of their popularity, Activision's Overwatch League launched in January and reached 10 million viewers globally in its first week.

電子競技持續發展，帶動了整個產業和我們業務的擴張。從其受歡迎程度來看，動視的守望先鋒聯賽在1月推出後，第一周就吸引了全球1000萬觀眾。

We had a busy start to the year with a number of announcements at the annual Consumer Electronics Show in Las Vegas. We introduced NVIDIA BFGDs, big-format gaming displays, in a partnership with Acer, ASUS and HP. 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. The BFGD won 9 Best of Show awards for various publications.

今年伊始，我們就非常忙碌，在拉斯維加斯舉行的年度消費電子展上發布了一系列公告。我們與宏碁、華碩和惠普合作，推出了 NVIDIA BFGD（大尺寸遊戲顯示器）。這些高階 65 吋 4K 顯示器可實現超低延遲遊戲，並整合我們的 SHIELD 串流媒體設備，提供 Netflix、遊戲影片（原文如此）[亞馬遜影片]、YouTube 和 Hulu 等熱門應用程式。BFGD 憑藉各種出版物獲得了 9 項最佳展示獎。

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. And updated NVIDIA Ansel's photo mode and support for new titles with ShadowPlay highlights for capturing gaming achievements. Additionally, the Nintendo Switch gaming console contributed to our growth as it became the fastest-selling console of all time in the U.S.

我們將 GeForce NOW 的免費測試版從 Mac 擴展到了基於 Windows 的 PC，並且我們透過新功能增強了 GeForce Experience，包括 NVIDIA Freestyle，可以使用各種濾鏡自訂遊戲體驗。此外，NVIDIA Ansel 的照片模式也進行了更新，並新增了對 ShadowPlay 高光功能的兼容性，可用於捕捉遊戲精彩時刻。此外，任天堂Switch遊戲機也促進了我們的發展，因為它成為美國有史以來銷售速度最快的遊戲機。

Strong demand in the cryptocurrency market exceeded our expectations. We met some of this demand with a dedicated board in our OEM business, and some was met with our gaming GPUs. This contributed to lower than historical channel inventory levels of our gaming GPUs throughout the quarter. 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. That said, our main focus remains on our core gaming market as cryptocurrency trends will likely remain volatile.

加密貨幣市場的強勁需求超出了我們的預期。我們透過 OEM 業務中的專用電路板滿足了部分需求，並透過我們的遊戲 GPU 滿足了部分需求。這導致我們整個季度的遊戲GPU渠道庫存水準低於歷史水準。雖然加密貨幣對我們業務的整體貢獻仍然難以量化，但我們相信它佔收入的比例比上一季更高。儘管如此，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繼續波動。

Moving to data center. Revenue of $606 million was up 105% year-on-year and up 20% sequentially. 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. V100s are available through every major computer maker and have been chosen by every major cloud provider to deliver AI and high-performance computing. Hyperscale and cloud customers adopting the V100 include Alibaba, Amazon Web Services, Baidu, Google, IBM, Microsoft Azure, Oracle and Tencent.

遷移至資料中心。營收達 6.06 億美元，年增 105%，季增 20%。這一優異表現反映了基於​​我們 Volta 架構的 Tesla V100 GPU 的強勁普及，該產品於第二季度開始出貨，並在第三季和第四季繼續擴大出貨量。V100 已透過各大電腦製造商銷售，並被各大雲端服務供應商選用，用於提供人工智慧和高效能運算。採用 V100 的超大規模和雲端客戶包括阿里巴巴、亞馬遜網路服務、百度、Google、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. 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. Our newer-generation V100s delivered even higher performance, with the Volta architecture offering 10x the deep learning performance of Pascal.

我們在人工智慧潮流市場繼續保持領先地位，我們的 GPU 仍然是訓練深度學習網路的首選平台。本季度，日本 Preferred Networks 公司使用 1,024 個 Tesla P100 GPU，在創紀錄的 15 分鐘內訓練了用於影像分類的 ResNet-50 神經網路。我們新一代的 V100 效能更高，Volta 架構的深度學習效能是 Pascal 的 10 倍。

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. We continue to view AI inference as a significant new opportunity for our data center GPUs. Hyperscale inference applications that run on GPUs include speech recognition, image and video analytics, recommender systems, translation, search and natural language processing.

我們也看到，在人工智慧推理市場，NVIDIA 的平台能夠比 CPU 將效能和效率提高幾個數量級，其發展勢頭越來越強勁。我們仍然認為人工智慧推理是我們資料中心GPU的一個重要新機會。在 GPU 上運行的超大規模推理應用程式包括語音辨識、圖像和視訊分析、推薦系統、翻譯、搜尋和自然語言處理。

The data center business also benefited from strong growth in high-performance computing. The HPC community has increasingly moved to accelerated computing in recent years as Moore's Law has begun to level off. Indeed, more than 500 HPC applications are now GPU-accelerated, including all of the top 15. NVIDIA added a record 34 new GPU-accelerated systems to the latest TOP500 supercomputer list, bringing our total to 87 systems. 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.

資料中心業務也受益於高效能運算的強勁成長。近年來，隨著摩爾定律逐漸趨於平緩，高效能運算領域越來越多地轉向加速運算。事實上，目前已有超過 500 個 HPC 應用程式採用 GPU 加速，其中包括排名前 15 的所有應用程式。NVIDIA 在最新的 TOP500 超級電腦名單中新增了創紀錄的 34 個 GPU 加速系統，使我們的總數達到 87 個系統。我們的總 petaflops 排名提高了 28%，並在 Green500 全球最節能超級電腦排行榜的前 20 名中佔據了 14 個席位。

During the quarter, we continued to support the buildout of major next-generation supercomputers. 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. We also announced new wins such as Japan's fastest AI supercomputer, the ABCI system, which leverages more than 4,000 Tesla V100 GPUs.

本季度，我們繼續支持下一代大型超級電腦的建設。其中包括美國能源部的 Summit 系統，該系統預計將於今年稍後上線，屆時將成為世界上最強大的超級電腦。我們還宣布了新的勝利，例如日本最快的 AI 超級電腦 ABCI 系統，該系統利用了 4000 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. Modern supercomputers will need to support multi-precision computation for applying deep learning together with simulation and testing. 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.

重要的是，我們開始看到高效能運算和人工智慧的融合，科學家開始利用人工智慧更快解決問題。現代超級電腦需要支援多精度計算，以便將深度學習與模擬和測試相結合。透過將人工智慧與高效能運算相結合，超級電腦可以在從粒子物理學到藥物發現再到天文物理學等各種運算領域提供比以往高幾個數量級的效能提升。

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

我們也看到人工智慧在越來越多的垂直產業中得到應用，例如交通、能源、製造業、智慧城市和醫療保健。我們宣布與 GE Health 和 Nuance 在醫療影像領域合作；與 GE 旗下的 Baker Hughes 在石油和天然氣領域合作；並與日本的小松公司在建築和採礦領域合作。

Moving to professional visualization. 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. These emerging applications now represent approximately 30% of pro visualization sales. We saw strength across several key industries including defense, manufacturing, energy, health care and Internet service providers. 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.

邁向專業可視化。第四季營收創下歷史新高，達到創紀錄的 2.54 億美元，比去年同期增長 13%，環比增長 6%，這主要得益於對實時渲染的需求以及人工智能和虛擬現實等新興應用的推動。這些新興應用目前約佔專業視覺化銷售額的 30%。我們看到國防、製造業、能源、醫療保健和網路服務供應商等幾個關鍵產業表現強勁。在主要客戶中，葛蘭素史克公司將 Quadro 的高端產品用於人工智慧，墨西哥國家石油天然氣公司則將其用於地震資料處理和視覺化。

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

轉向汽車產業。在汽車產業，第四季營收年增 3% 至 1.32 億美元，季減 8%。這一連續下降反映了我們從資訊娛樂（正變得越來越商品化）向下一代人工智慧駕駛艙系統以及基於 NVIDIA 硬體和軟體構建的完全自動駕駛汽車平台的轉型。

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. 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.

在 CES 上，我們透過幾個關鍵里程碑和新的合作夥伴關係，展示了我們在自動駕駛汽車領域的領導地位，這表明人工智慧自動駕駛汽車正在從部署走向生產。在吸引了近 8000 名與會者的座無虛席的主題演講中，詹森宣布，全球首款自主機器處理器 DRIVE Xavier 將於本季向客戶推出。

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

DRIVE Xavier 擁有超過 90 億個晶體管，是迄今為止最複雜的晶片系統。我們也宣布，NVIDIA DRIVE 是全球首個功能安全的 AI 自動駕駛平台，使汽車製造商能夠打造安全運行的自動駕駛汽車，這是汽車上市的必要條件。

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

此外，我們在 CES 上宣布了一系列合作，包括與 Uber 的合作，Uber 一直在其自動駕駛汽車和貨運卡車車隊的 AI 計算系統中使用 NVIDIA 技術。我們宣布，採埃孚和百度正在使用 NVIDIA DRIVE 自動駕駛技術，為全球最大的汽車市場——中國——打造一個可量產的 AI 自動駕駛汽車平台。採用這項技術的量產車型，包括奇瑞汽車的車型，預計將於 2020 年上路。

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

我們也宣布與 Aurora 建立合作夥伴關係，Aurora 致力於打造模組化、可擴展的 L4 和 L5 自動駕駛硬體平台，該平台整合了 NVIDIA DRIVE Xavier 處理器。大眾汽車執行長 Herbert Diess 與詹森一起上台。他們宣布，新一代智慧大眾汽車將採用 NVIDIA DRIVE 智慧體驗平台（或稱 DRIVE IX）來打造全新的人工智慧駕駛艙體驗，並提升安全性。

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

在後來的 CES 展會上，梅賽德斯-奔馳宣布其基於人工智慧的新型智慧座艙 MBUX 採用了英偉達的圖形和人工智慧技術。MBUX 使用者體驗系統包括精美的觸控螢幕顯示器和全新的語音助手，上週在梅賽德斯-奔馳 A 級緊湊型轎車上首次亮相，並將於今年春季上市。本週早些時候，我們宣布與大陸集團建立合作夥伴關係，共同打造從增強型 L2 到 L5 級別的 AI 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. 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. With this growing momentum, we remain excited about the intermediate to long-term opportunities for autonomous driving.

目前已有超過 320 家公司和研究機構使用 NVIDIA DRIVE 平台。這比一年前成長了 50%，幾乎涵蓋了自動駕駛汽車生態系統中的每一家汽車製造商、卡車製造商、無人計程車公司、地圖公司、感測器製造商和軟體新創公司。隨著這一發展勢頭日益強勁，我們對自動駕駛的中長期機會仍然充滿信心。

Now turning to the rest of the P&L. Q4 GAAP gross margins was 61.9%, and non-GAAP was 62.1%, records that reflect continued growth in our value-added platforms. GAAP operating expenses were $728 million, and non-GAAP operating expenses were $607 million, up 28% and 22% year-on-year, respectively. We continue to invest in the key platforms driving our long-term growth, including gaming, AI and automotive.

現在來看損益表的其餘部分。第四季 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成長。GAAP 營運費用為 7.28 億美元，非 GAAP 營運費用為 6.07 億美元，分別年增 28% 和 22%。我們將繼續投資於推動我們長期成長的關鍵平台，包括遊戲、人工智慧和汽車。

GAAP EPS was $1.78, up 80% from a year earlier. 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. Our fourth quarter GAAP effective tax rate was a benefit of 3.7% compared with our expectation of a tax rate of 17.5%.

GAAP每股收益為1.78美元，較上年同期成長80%。部分上漲是由美國稅制改革導致的稅率低於預期以及與股票選擇權相關的超額稅收優惠所推動的。我們第四季的 GAAP 實際稅率比我們預期的 17.5% 的稅率低了 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%. We returned $1.25 billion to shareholders in the fiscal year through a combination of quarterly dividends and share repurchases.

非GAAP每股盈餘為1.72美元，比去年同期成長52%，反映出季度稅率為10.5%，而我們預期為17.5%。本財年，我們透過季度分紅和股票回購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. 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.

我們的季度經營活動現金流達到創紀錄的 13.6 億美元，使我們本財年的現金流量總額達到創紀錄的 35 億美元。第四季資本支出為 4.16 億美元，其中包括與購買我們先前融資的聖克拉拉校區大樓相關的 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. In Q4, we recorded a GAAP-only one-time net tax benefit of $133 million or $0.21 per diluted share. 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. 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.

讓我花點時間更詳細說明美國企業稅制改革對本季及我們未來財務狀況的影響。第四季度，我們確認了僅以美國通用會計準則計算的一次性淨稅優惠 1.33 億美元，即每股攤薄收益 0.21 美元。這主要與《減稅與就業法案》相關的累計海外收益過渡稅的暫定稅額以及某些遞延所得稅資產和負債的重新計量有關。我們先前已就超過過渡稅暫定稅額的部分預期收益提列了稅款，因此產生了一次性收益。

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

2019 財年，我們預期 GAAP 和非 GAAP 稅率約為 12%，低於先前的約 17%。這並未考慮股票選擇權激勵帶來的超額稅收優惠，而根據股票價格和歸屬時間表，這可能會在特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. 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. In addition, we remain committed to returning cash to shareholders, with our plan remaining at $1.25 billion for fiscal 2019.

就我們的資本配置優先事項而言，我們將繼續把重點放在投資自身業務上，因為我們看到了未來巨大的發展機會。較低的稅率增強了我們投資營運支出（例如增加工程人才）和資本支出（例如投資用於內部人工智慧開發的超級電腦）的能力。此外，我們仍致力於向股東返還現金，2019 財年的返還計劃仍為 12.5 億美元。

With that, let me turn to the outlook for the first quarter of fiscal 2019. We expect revenue to be $2.9 billion, plus or minus 2%. GAAP and non-GAAP gross margins are expected to be 62.7% and 63%, respectively, plus or minus 50 basis points. GAAP and non-GAAP operating expenses are expected to be approximately $770 million and $645 million, respectively. GAAP and non-GAAP OI&E are both expected to be nominal. GAAP and non-GAAP tax rates are both expected to be 12%, plus or minus 1%, excluding discrete items.

接下來，我將展望一下 2019 財年第一季的情況。我們預計營收為 29 億美元，上下浮動 2%。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 和非 GAAP 營運費用預計分別約為 7.7 億美元和 6.45 億美元。GAAP及非GAAP經營損益預計均為名目值。GAAP 和非 GAAP 稅率預計均為 12%，上下浮動 1%，不包括特殊項目。

For the full fiscal year 2019, we expect our operating expenses to grow at a similar pace as in Q1. Further financial details are included in the CFO commentary and other information available in our IR website.

預計 2019 財年全年營運支出將以與第一季類似的速度成長。更多財務細節請參閱財務長評論以及我們投資者關係網站上的其他資訊。

In closing, I'd like to highlight a few upcoming events for the financial community. 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. 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.

最後，我想重點介紹一下金融界即將發生的一些事件。我們將於 2 月 13 日在高盛科技與網路大會上演講，並於 2 月 26 日在摩根士丹利科技、媒體與電信大會上進行演講。我們也將於 3 月 27 日在聖荷西舉辦年度投資者日活動，該活動將與我們的年度 GPU 技術大會同期舉行，我們對此感到非常興奮。

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

現在開始接受提問。操作員，請問能否進行一次投票，徵求大家的意見？

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

（操作說明）你的第一個問題來自 Evercore 的 C.J. Muse 的一句台詞。

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. 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.

我想問的第一個問題是，考慮到遊戲產業的正常季節性，這意味著資料中心在下一個季度的支出可能會超過 7 億美元。所以，我很好奇我的想法是否正確，或者你們對加密貨幣的建模是否更加保守，很想聽聽你們的看法。

Which way is more conservatively, C.J.?

C.J.，哪種方式比較保守？

Yes? Sorry.

是的？對不起。

When you say conservatively, which direction were you saying it was. Are you implying up or down?

你說保守的話，指的是哪個方向？你是說上漲還是下跌？

Well, just curious to hear your thoughts there.

我只是好奇想聽聽你的看法。

We model crypto approximately flat.

我們把加密貨幣建模為近似平坦的。

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

好的。那麼，作為更大問題的一部分，您如何看待遊戲產業在接下來的季度中的季節性變化？

Well, there's a lot of dynamics going on in gaming. One dynamic, of course, is that there's a fairly sizable pent-up demand going into this quarter. But I think the larger dynamics that are happening relate to just the really amazing games that are out right now.

嗯，遊戲領域有很多動態因素在運作。當然，其中一個因素是，本季存在相當大的潛在需求。但我認為，目前發生的更大趨勢與現在市面上那些非常優秀的遊戲息息相關。

PUBG is just -- is doing incredibly well, as you might have known, and it's become a global phenomenon. And whether it's here in the United States or in Europe, or in China, in Asia, PUBG is just doing incredibly well. And we expect other developers to come up with similar genre, like PUBG, that are going to be coming out in the near future. And I'm super excited about these games.

正如你可能已經知道的那樣，PUBG 的表現非常出色，已經成為一種全球現象。無論是在美國、歐洲、中國或亞洲，PUBG 都取得了巨大的成功。我們預計其他開發人員也會推出類似 PUBG 的遊戲類型，這些遊戲將在不久的將來面世。我對這些比賽感到無比興奮。

And then, of course, there's Call of Duty, there's Star Wars. There's just so many great games that are out in the marketplace today, Overwatch and League of Legends, still doing well. There's just a countless number of great franchises that are out in the marketplace. And the gaming market is growing, and production value is going up. And that's driving increased unit sales of GPUs as well as ASPs of GPUs. And so I think those are -- that's probably the larger dynamic of gaming.

當然，還有《決勝時刻》和《星際大戰》。如今市面上有許多優秀的遊戲，像是《鬥陣特攻》和《英雄聯盟》，它們至今仍然很受歡迎。市場上有很多優秀的特許經營項目。遊戲市場正在成長，製作水準也在提高。這推動了GPU的銷量和平均售價的成長。所以我覺得這些——這大概就是遊戲領域更大的動態了。

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

你的下一個問題來自傑富瑞集團的馬克·利帕西斯。

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

第一個問題，我們所做的檢查表明，您在 Volta 中安裝的 Tensor Core 使其在資料中心的神經網路應用中具有巨大的優勢。我很好奇 Tensor Cores 在遊戲市場是否也有類似的用途。

Yes, first of all, I appreciate you asking a Tensor Core question. It is probably the single biggest innovation we had last year in data centers.

首先，感謝您提出關於 Tensor Core 的問題。這可能是我們去年在資料中心領域取得的最大創新。

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. 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.

我們的 GPU，要達到與我們一個 Volta GPU 相當的效能，需要 20 多個 CPU 或 10 多個節點。因此，只有 1 個 GPU 就能以極快的速度進行深度學習，需要 10 個以上的 CPU 伺服器節點才能跟上。

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. 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.

然後，去年 Tensor Core 的出現，將深度學習的吞吐量提高了 8 倍，計算吞吐量又提高了 8 倍。因此，Tensor Core 真正體現了 GPU 的強大功能。這與 CPU 非常不同，CPU 的指令集會長時間保持鎖定狀態，因此很難——很難取得進展。

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

就我們的 GPU 和 CUDA 而言，這是它的基本優勢之一，我們可以年復一年地持續為其添加新功能。因此，Tensor Core 對我們 GPU 原有出色效能的提升，在去年真正提高了標準。

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. 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.

正如科萊特之前所說，我們的 Volta GPU 現在已被世界各地廣泛採用，無論是在中國的阿里巴巴、騰訊、百度、科大訊飛，還是在美國的亞馬遜、Facebook、谷歌、微軟、IBM 和甲骨文，以及在歐洲和日本。採用 Volta 的雲端服務供應商數量非常可觀，我認為大家都非常讚賞我們與 Tensor Core 合作所做的工作。

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. And the deep learning developers have really jumped on it. And almost every deep learning framework is being optimized to take advantage of Tensor Core.

現在框架中推出的所有更新，Tensor Core 都是新的指令集，一個新的架構。深度學習開發者們已經迅速投入其中。幾乎所有深度學習框架都在進行最佳化，以充分利用 Tensor Core 的優勢。

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. And we've been demonstrating some of that at GTC, if you've seen some of that. 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. And that's where Tensor Core could be a real advantage.

在推理方面，也就是它在電子遊戲中發揮作用的地方，現在可以使用深度學習來合成和生成新的藝術作品。我們在 GTC 上已經示範過其中的一些內容，如果你看過的話。無論是提高紋理品質、生成人工角色、製作角色動畫，還是製作臉部動畫（用於語音或身體動畫），深度學習在電玩遊戲中的應用範圍都在不斷擴大。而這正是 Tensor Core 的真正優勢所在。

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. 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.

如果你看一下 Tensor Core 的運算能力，並將其與未經優化的 GPU 甚至 CPU 進行比較，你會發現它的運算吞吐量現在提高了兩個數量級以上。這使我們能夠即時合成圖像、合成虛擬世界、製作角色動畫、製作臉部動畫，並將虛擬實境和人工智慧帶到這些視訊遊戲中，從而實現這些功能。

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

你的下一個問題來自美國銀行的 Vivek Arya。

Congratulations on the strong growth and the consistent execution. Jensen, just a near- and longer-term question on the data center. Near term, you had, had a number of strong quarters in data center. How is the utilization of these GPUs? And how do you measure whether you're over or under from a supply perspective?

祝賀你們取得強勁成長和持續穩定的業績。Jensen，關於資料中心，我有一個近期和長期的問題。短期來看，資料中心業務曾有過幾個強勁的季度。這些GPU的利用率如何？從供給角度來看，如何衡量供應是過剩還是不足？

And then longer term, there seems to be a lot of money going into startups developing silicon for deep learning. Is there any advantage they have in taking a clean-sheet approach? Or is GPU the most optimal answer? Like if you were starting a new company looking at AI today, would you make another GPU? Or would you make another ASIC or some other format? Just any color would be helpful.

從長遠來看，似乎有很多資金投入開發深度學習晶片的新創公司。採取零封對手的策略對他們有什麼優勢嗎？或者說，GPU是最佳解決方案？比如說，如果你今天創辦一家專注於人工智慧的新公司，你會再生產GPU嗎？還是你會開發另一款ASIC晶片或其他類型的晶片？任何顏色都可以。

Sure. In the near term, the best way to measure customers that are already using our GPUs for deep learning is repeat customers. When they come back another quarter, another quarter, and they continue to buy GPUs, that would suggest that their workload has continued to increase.

當然。短期內，衡量已在使用我們 GPU 進行深度學習的客戶的最佳方法是統計回頭客數量。如果他們又過了一個季度，又過了一個季度，並且繼續購買 GPU，那就表示他們的工作負載一直在增加。

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厚市場基礎的現有客戶而言，我們的另一個機會是利用我們的 GPU 進行推理，這對我們公司來說是一個尚未開發的成長機會，這真的非常令人興奮，而且我們已經看到了這方面的進展。

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

對於那些並非處於深度學習前沿，或者說絕對前沿的公司而言——除了 1、2 或 3 家超大規模資料中心之外，我幾乎把其他所有公司都歸入這一類——它們部署、採用深度學習，並將深度學習應用於所有應用程式的工作仍在進行中。所以我認為第二波顧客正在陸續到來。

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. They have enormous customer bases and -- that they could apply artificial intelligence to. But they run their application in hyperscale clouds. That third phase of growth is now really spiking, and I'm excited about that.

然後還有第三波客戶——他們不是超大規模資料中心，他們是網路服務應用，是面向消費者的網路應用。他們擁有龐大的客戶群，而且可以將人工智慧應用於這些客戶群。但他們的應用程式運行在超大規模雲端平台。第三階段的成長現在正處於爆發式增長期，我對此感到非常興奮。

And so that's kind of the way to think about it. There's the pioneers, the first phase, are the returning customers. Then there's the second phase that's now ramping. The third phase that's now ramping. And then for everybody, we have an opportunity to apply our GPUs for inference.

所以，這大概就是思考這個問題的方式。第一階段是開拓者，也就是回頭客。接下來是正在加速推進的第二階段。第三階段目前正在加速推進。然後，對於所有人來說，我們都有機會利用我們的 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. And the reason for that is because the GPU was already inherently the world's best high-throughput computational processor.

如果我擁有世界上所有的錢，例如數十億美元的研發資金，我會把它捐給英偉達的GPU團隊，而這正是我所做的。原因在於，GPU 本身已經是世界上最好的高吞吐量運算處理器。

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. 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.

高吞吐量處理器比你從概要工具中實例化的線性代數要複雜得多，它並沒有那麼簡單。計算吞吐量，讓所有數據在晶片內以極高的能效運行，同時還要運行所有必要的軟體來保持數據流動，以及對每個框架進行的所有優化，其複雜程度真的非常巨大。

The networks are changing all the time. It started out with just basically CNNs, and then all kinds of versions of CNNs now. 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. It started out with just 8 layers, and now it's 152 layers going to 1,000 layers. It started with mostly recognition, and now it's moving to synthesis with GANs. And there's so many versions of GANs.

網路一直在變化。最初只有捲積神經網路（CNN），現在出現了各種各樣的捲積神經網路版本。它最初是 RNN 和簡單的 RNN，現在出現了各種各樣的 LSTM 和門控 RNN，以及各種各樣有趣的網絡，它們都在不斷發展壯大。它最初只有 8 層，現在已經有 152 層，目標是 1000 層。它最初主要應用於識別，現在正轉向使用 GAN 進行合成。生成對抗網路（GAN）有很多不同的版本。

And so all of these different types of networks are really, really hard to nail down. And we're still at the beginning of AI. So the ability for our GPUs to be programmable to all of these different architectures and networks is just an enormous advantage. You don't ever have to guess whether NVIDIA GPUs could be used for one particular network or another. 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. And so the more GPUs you buy, the more money you save.

因此，所有這些不同類型的網路都很難準確定義。我們仍處於人工智慧發展的初期階段。因此，我們的 GPU 能夠對所有這些不同的架構和網路進行編程，這是一個巨大的優勢。您再也不用擔心 NVIDIA GPU 是否適用於某個特定的網路了。因此，您可以隨意購買我們的 GPU，並且知道您購買的每一塊 GPU 都可以讓您的資料中心減少 22 個節點、10 個節點或 22 個 CPU 的伺服器數量。因此，你購買的GPU越多，節省的錢就越多。

And so I think that capability is really quite unique. 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. This year -- this last year, we introduced Tensor Core, which increased it by another factor of nearly 10. Meanwhile, our GPUs get more complex, energy-efficient. Efficiency gets better and better every single year, and the software richness gets more amazing.

所以我認為這種能力確實非常獨特。然後，如果我可以舉一個去年或前年的例子，我們推出了 16 位元混合精準度，我們推出了 8 位元整數，前年我們推出了 NVLink。今年——也就是去年，我們推出了 Tensor Core，使其效能提高了近 10 倍。同時，我們的GPU變得越來越複雜，能源效率也越來越高。效率逐年提高，軟體功能也越來越豐富，令人驚嘆。

And so it's a much harder problem than just a multiply accumulator. Artificial intelligence is the single most complex mode of software that the world has ever known. That's the reason why it's taken us so long to get here. And these high-performance supercomputers is an essential ingredient and an essential instrument in advancing AI. And so I don't think it's nearly as simple as linear algebra. But if I had all the money in the world, I would invest it in the team that we have.

因此，這比一個簡單的乘法累加器問題要困難得多。人工智慧是世界上已知最複雜的軟體模式。這就是我們花了這麼長時間才走到今天的原因。這些高效能超級電腦是推動人工智慧發展的重要因素和重要工具。所以我覺得它遠遠沒有線性代數那麼簡單。但如果我擁有全世界的財富，我也會把它投資到我們現有的團隊。

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

你的下一個問題來自伯恩斯坦研究公司的史黛西·拉斯貢。

I have a question for Colette. 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. In your Q3 commentary, you did not call out crypto as a driver, you are calling it out in Q4. Is it fair to say that like that incremental growth is all crypto?

我有個問題想問科萊特。所以，如果我將 Switch 的營收成長計入本季度，這意味著遊戲業務 [x] 成長了，我不知道，也許是 1.4 億美元，1.5 億美元。在第三季評論中，你沒有將加密貨幣列為驅動因素，但在第四季評論中，你卻將其列為驅動因素。是否可以說，這種漸進式成長完全是加密貨幣的功勞？

And I guess going forward, you mentioned pent-up demand. Normally, your seasonality for gaming will be down probably double digits. Do you think that pent-up demand is enough to reverse that normal seasonal pattern -- or normally down? 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?

我想，展望未來，您提到了被壓抑的需求。通常情況下，遊戲產業的季節性波動可能會出現兩位數的下降。你認為被壓抑的需求足以扭轉正常的季節性模式——或者說，扭轉通常的下降趨勢嗎？坦白說，你認為遊戲玩家現在還能在零售店買到顯示卡來滿足這種被壓抑的需求嗎？

So let me comment on the first one. We did talk about our overall crypto business last quarter as well. We indicated how much we had in OEM boards, and we also indicated that there was definitely some also in our GTX business.

那麼，讓我對第一個問題發表一下看法。上個季度我們也談到了我們的整體加密貨幣業務。我們指出了我們在 OEM 主機板方面的投入，同時也指出了我們在 GTX 業務方面也投入了一定比例的資金。

Keep in mind, that's very difficult for us to quantify down to the end customer. It is. But yes, there is also some in our Q3, and we did comment on it. So here we are commenting in terms of what we saw in terms of Q4. It's up a bit from what we saw in Q3, and we do again expect probably going forward. I'll let Jensen answer regarding the demand for gamers as we move forward.

請記住，我們很難將這種影響量化到最終客戶層面。這是。是的，我們第三季也有一些問題，我們也對此發表了評論。所以，我們在這裡是根據我們在第四季度所看到的情況進行評論的。這比第三季的情況略有上升，我們預計未來可能會繼續上升。關於未來對遊戲玩家的需求，我會讓 Jensen 來回答。

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

是的。所以，如果你——思考被壓抑的需求的一種方式是，我們通常在通路中會有 6 到 8 週的庫存。我認為您可以確定，目前全球範圍內通路相對匱乏。

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. And so we're doing everything we can. But I think the most important thing is we just got to catch up with supply.

我們正在努力將GPU推向市場，以滿足遊戲玩家的需求，並且我們正在盡一切努力為電商和系統整合商提供建議，以便更好地服務遊戲玩家。所以我們正在盡一切努力。但我認為最重要的是，我們必須趕上供應速度。

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

你的下一個問題來自 RBC 的 Mitch Steves。

I actually want to circle back on the autos, since I was at CES. 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. And just to clarify, the expected ASP uplift is somewhere around $1,000. Is that about right?

我其實想再聊聊汽車，因為我之前去過CES展。所以，照計畫來看，到 2019 年底，我們還是會看到自主 ASP 的提升。需要澄清的是，預計平均售價將上漲約 1000 美元。這樣差不多嗎？

Yes, it just depends on mix. I think the -- for autonomous vehicles that still have drivers, passenger cars, branded cars, ASPs anywhere from $500 to $1,000 make sense. For robot taxis, where they're driverless, they're not autonomous vehicles, they're actually driverless vehicles, the ASP will be several thousand dollars.

是的，這取決於配料比例。我認為對於仍然需要駕駛員的自動駕駛汽車、乘用車、品牌汽車來說，500 到 1000 美元的平均售價是合理的。對於無人駕駛的機器人計程車（它們不是自動駕駛車輛，而是真正的無人駕駛車輛），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. And then with autonomous vehicles, cars that have autonomous driving capability, automatic driving capability starts late 2019. You could see a lot more in 2020. And just almost every premium car by 2022 will have autonomous automatic driving capabilities.

就時間安排而言，我認為從今年開始到明年，你肯定會看到越來越大規模的部署，尤其是在無人駕駛計程車方面。然後是自動駕駛汽車，即具備自動駕駛能力的汽車，自動駕駛能力將於 2019 年底開始普及。2020年你可能會看到更多。到 2022 年，幾乎所有高階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. Jensen, I was hoping to ask a little bit about inferencing. How big was inferencing within data center in Q4 or fiscal '18? And more importantly, how do you expect it to trend over the next 12 to 18 months?

偉大的。Jensen，我想問你一些關於推理的問題。2018財年第四季資料中心內的推理規模有多大？更重要的是，您預計它在未來 12 到 18 個月內的發展趨勢如何？

Yes, thanks a lot, Toshi. First of all, just a comment about inference. The way that it works is you take the output of these frameworks. And the output of these frameworks is a really complex, large computational graph.

是的，非常感謝你，Toshi。首先，我想就推理問題提一點看法。它的工作原理是，你獲取這些框架的輸出結果。這些框架的輸出結果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. And these parameters are waves and activation layers and -- activation functions, and there are millions of them. And it's millions of them that composes -- consists of this computational graph. And this computational graph has all kinds of interesting and complicated layers.

當你想到這些神經網路時，你會發現它們有數百萬個參數，任何事物的數百萬個參數都是非常複雜的。這些參數是波、激活層和激活函數，而且有數百萬個。而構成這個計算圖的正是其中的數百萬個元素。這個計算圖包含了各種有趣而複雜的層次。

And so you take this computational graph that comes out of each one of these frameworks, and they're all different. They're in different formats, they're in different styles, they have different architectures. They're all different. 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.

因此，你會發現每個框架產生的計算圖都各不相同。它們格式不同，風格不同，架構也不同。它們各不相同。然後，你需要找到一種方法來編譯這些計算圖，最佳化這個圖，合理化所有可以組合和折疊的東西，減少 GPU 或處理器中所有資源之間的衝突。

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. 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. 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.

這些衝突可能涉及片上記憶體、暫存器檔案和資料路徑，也可能涉及晶片結構、幀緩衝區介面或記憶體容量。我的意思是，這台電腦非常複雜，它由各種不同的處理器、GPU 之間的互連以及連接多個節點的網路組成。所以你必須弄清楚所有這些不同的衝突、資源是什麼，並進行編譯和最佳化，以利用它們來保持它一直運作下去。

And so TensorRT is basically a very sophisticated optimizing graph compilation -- graph compiler. And it targets each one of our processors. 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. All of that targeting is different. And so first of all, TensorRT, the software of inference, that's really where the magic is.

因此，TensorRT 本質上是一個非常複雜的最佳化圖編譯—圖編譯器。它針對的是我們每一個處理器。它針對 Xavier 的方式與針對 Volta 的方式不同，它針對我們的推斷的方式不同，它針對低能量的方式不同，針對不同的精度。所有這些目標定位都各不相同。首先，TensorRT，也就是推理軟體，這才是真正的神奇之處。

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. 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. Okay, so that's kind of the background.

其次，我們優化 GPU 以實現極高的吞吐量並支援不同的精度，因為有些網路可以承受 8 位元整數甚至更低的精度，有些網路勉強可以使用 16 位元浮點數，而有些網路則希望保持 32 位元浮點數，這樣就不必擔心在傳輸過程中丟失的任何精度。因此，我們創建了一種架構，該架構由最佳化圖、計算圖編譯器和高吞吐量、混合精度的處理器組成。好的，以上就是背景狀況。

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. And this quarter, we started shipping. We -- looking outwards, my sense is that the inference market is probably about as large in the data centers as training. And the wonderful thing is everything that you train on our processor will inference wonderfully on our processors as well.

我們開始——我們一直在對我們的特斯拉 P4（我們的資料中心推理處理器）進行取樣，而且——我們看到了非常令人興奮的回饋。本季度，我們開始出貨。從外部來看，我的感覺是，資料中心的推理市場可能與訓練市場規模相當。更棒的是，你在我們的處理器上訓練的任何東西，在我們的處理器上也能很好地進行推理。

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. And the amount of money they save is not 20% or 50%, it's factors of 10. The money savings for all of these data centers that are becoming increasingly capital constrained is really quite dramatic.

資料中心也逐漸意識到，購買越多的 GPU 用於卸載推理和訓練，就能節省越多的錢。而且他們節省的金額不是 20% 或 50%，而是 10 倍。對於這些資金日益緊張的資料中心而言，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. TensorRT also targets Xavier. TensorRT targets our Pegasus robot taxi computer. 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? So I think inference is a very important work for us. It is very complicated work, and we're making great progress.

此外，我們還有機會探索自主機器，也就是自動駕駛汽車。TensorRT 也以 Xavier 為目標。TensorRT 的目標是我們的 Pegasus 機器人計程車電腦。它們都必須極其有效率地進行推理，這樣我們才能維持即時性，保持低能耗，降低汽車公司的成本，懂嗎？所以我認為推理對我們來說是一項非常重要的工作。這是一項非常複雜的工作，但我們正在取得巨大進展。

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

你的下一個問題來自巴克萊銀行的布萊恩‧柯蒂斯。

Just kind of curious, as you look at the Gaming business -- I've kind of lost track of what seasonality is. You clearly have a big ramp ahead of you. 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.

我只是有點好奇，當你觀察遊戲產業時——我已經有點搞不清楚季節性是什麼了。你面前顯然有一段很長的路要走。我有點好奇，在考慮 Pascal 與 Volta 之前的季節性因素時，你是否可以大致推斷一下四月甚至七月的情況。

I -- well, we haven't announced anything for April or July. And so the best way to think about that is Pascal is the best gaming platform on the planet. It is the most feature-rich, the best software, the most energy-efficient. And from $99 to $1,000, you could buy the world's best GPUs, the most advanced GPUs. And if you buy Pascal, you know you've got the best.

我——嗯，我們還沒有公佈四月或七月的任何消息。因此，最好的理解方式就是：Pascal 是地球上最好的遊戲平台。它功能最豐富，軟體最好，能效最高。99 美元到 1000 美元，你就可以買到世界上最好的 GPU，最先進的 GPU。如果你購買 Pascal，你就知道你擁有的東西是最好的。

Seasonality is a good question and increasingly because gaming is a global market and because people play games every day. It's just part of their life. There's no -- I don't think there's much seasonality in TV or books or music. People just -- whenever new titles come out, that's when a new season starts.

季節性是一個值得探討的問題，而且這個問題越來越重要，因為遊戲是一個全球市場，人們每天都在玩遊戲。這只是他們生活的一部分。我覺得電視、書籍或音樂沒有太大的季節性。人們通常認為——每當有新遊戲上映，新一季就開始了。

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. Boy, there are so many seasons that it's kind of hard to imagine what the exact seasonality is anymore. And so hopefully, over time, it becomes less of a matter. But the most important thing is that we expect Pascal to continue to be the world's best gaming platform for the foreseeable future.

所以，在中國，有網咖和雙十一購物節；在美國，有返校季；在美國，有聖誕節；在美國，有中國新年。哎，季節這麼多，現在很難想像確切的季節劃分是什麼了。所以，希望隨著時間的推移，這個問題能逐漸變得不那麼重要。但最重要的是，我們預計 Pascal 在可預見的未來仍將是世界上最好的遊戲平台。

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

你的下一個問題來自哈蘭·蘇爾與摩根大通的合作。

Congratulations on the solid results and the execution. I know somebody asked a question about inferencing for the data center markets. 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. What else is the team doing in the embedded market to capture more of the TAM opportunity there going forward?

恭喜你們取得了紮實的成果，執行得也很出色。我知道有人問過關於資料中心市場推理的問題。但是，對於嵌入式和邊緣應用的推理，在軟體和韌體方面，您談到了 TensorRT 框架；在硬體方面，您有 Jetson TX 平台；對於嵌入式和邊緣推理應用，例如無人機、工廠自動化和交通運輸。為了在未來抓住嵌入式市場更大的潛在市場機遇，團隊還在做哪些工作？

Yes, thanks a lot, Harlan. The NVIDIA TensorRT is really the only optimizing inference compiler in the world today, and it targets all of our platforms. And we do inference in the data center that I mentioned earlier. In the embedded world, the first embedded platform we're targeting is self-driving cars.

是的，非常感謝你，哈蘭。NVIDIA TensorRT 是目前世界上唯一真正意義上的最佳化推理編譯器，並且它面向我們所有的平台。我們在我之前提到的資料中心進行推理。在嵌入式領域，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. And that's a very complicated inference matter. 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.

為了駕駛汽車，你基本上需要不斷推斷、預測或感知周圍的情況。這是一個非常複雜的推論問題。這可能非常簡單，例如發現前面的車並踩剎車；也可能非常困難，例如判斷是否應該在十字路口停車。

If you look at most intersections, you can't just look at the lights to determine where do you stop. There are very few lines. And so using scene understanding and using deep learning, we have the ability to recognize where to stop and whether to stop.

如果你觀察大多數十字路口，你會發現你不能只看信號燈來決定在哪裡停車。線路很少。因此，利用場景理解和深度學習，我們能夠識別在哪裡停止以及是否應該停止。

And then for Jetson, we have a platform called Metropolis. And Metropolis is used for very large scale smart cities where cameras are deployed all over to keep cities safe. And we've been very successful with smart cities. 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.

此外，對於 Jetson，我們有一個名為 Metropolis 的平台。Metropolis 則用於超大型智慧城市，在這些城市中部署攝影機以確保城市安全。我們在智慧城市建設方面取得了巨大成功。幾乎所有主要的智慧城市供應商，以及所謂的智慧視訊分析公司，無論在世界範圍內，都在使用 NVIDIA 的平台進行邊緣推理，即邊緣人工智慧。

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. 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. There's all kinds of applications.

最近，我們宣布與全球最大的製造和機器人公司發那​​科（FANUC）以及全球最大的工程機械公司之一小松（Komatsu）成功合作，將人工智慧應用於邊緣自主機器。我們有幾架工業無人機，用於檢查管道和電力線路，飛越大片農田，以更準確地確定在哪裡噴灑殺蟲劑。應用程式種類繁多。

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.

所以您說得完全正確，邊緣推理或邊緣人工智慧對我們來說是一個非常大的市場機會，而這正是 TensorRT 誕生的原因。

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

你的下一個問題來自摩根士丹利的喬摩爾。

You had mentioned how lean the channel is in terms of gaming cards. There's been an observable increase in prices at retail. And I'm just curious, is that a broad-based phenomenon? And is there any economic ramifications to you? Or is that just sort of retailers bringing prices up in a shortage environment?

你之前提到過，這個管道在遊戲顯示卡的內容非常匱乏。零售價格出現了明顯的上漲。我只是好奇，這是普遍現象嗎？那對你來說會有經濟上的影響嗎？或者這只是零售商在供應短缺的情況下提高價格的一種方式？

We don't set prices at the end of the market. And the best way for us to solve this problem is work on demand -- excuse me, work on supply. The demand is great. And it's very likely the demand will remain great as we look throughout -- through this quarter.

我們不負責在市場末端定價。解決這個問題的最佳方法是按需生產——抱歉，是按需供應。需求量很大。而且，我們預計本季需求將持續旺盛。

And so we just have to keep working on increasing supply. 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. We just got to catch up to that demand which is just really great.

因此，我們必須繼續努力增加供應。我們的供應商是世界上最好、最大的半導體製造商，他們的反應非常積極，我真的很感激他們所做的一切。我們只需要滿足這種需求，真是太好了。

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

你的下一個問題來自克里斯羅蘭和薩斯奎哈納樂團。

Great quarter. So just to clarify, Jensen, on pent-up demand. One of your GPU competitors basically said that the constraint was memory. I just want to make sure that, that was correct.

很棒的季度。所以澄清一下，Jensen，這是由於積壓的需求。你的一位GPU競爭對手基本上是這麼說的：瓶頸在於記憶體。我只是想確認一下，那是否正確。

And then in the CFO commentary, you mentioned opportunities for professional vis, like AI and deep learning. Can you talk about that, and what kind of applications you would use, Quadro versus Volta or GeForce?

然後，在財務長的評論中，您提到了專業視覺化的機會，例如人工智慧和深度學習。您能談談這方面嗎？您會在哪些應用程式情境中使用 Quadro、Volta 或 GeForce 顯示卡？

Sure. We are -- we're just constrained. Obviously, we're 10x larger of a GPU supplier than the competition. 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. And so we -- I don't know how to explain it aside from the demand is just really great. And so we've just got to keep our nose to it and catch up to the demand.

當然。我們是——我們只是受到限制。顯然，我們的GPU供應商規模是競爭對手的10倍。因此，我們有更多的供應商支持我們，更多的經銷商將我們的產品推向市場，更多的合作夥伴在世界各地分銷我們的產品。所以——除了需求真的很大之外，我不知道該如何解釋。所以我們必須緊盯形勢，努力滿足市場需求。

With respect to Quadro, Quadro is a workstation processor. The entire software stack is designed for all of the applications that the workstation industry uses. 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.

Quadro 是一款工作站處理器。整個軟體堆疊都是為工作站行業使用的所有應用程式而設計的。當然，渲染品質是世界一流的，這要歸功於 NVIDIA 等技術——但整個軟體堆疊的設計宗旨是，讓任務關鍵型應用或長期工業應用，以及世界上規模龐大的製造和工業公司，都能依賴一個完整的平台，該平台由處理器、系統、軟體、中間件以及與全球所有 CAD工具的整合組成，從而確保供應商在產品的整個生命週期內（可能長達數年）始終存在並值得信賴，而由此產生的數據則必須能夠保證數十年的安全可靠。

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

你需要能夠在飛機、火車或汽車投入生產幾十年後調出其完整的設計圖，以確保它仍然符合規範，並且如果對它有任何疑問，都可以調出來進行解答。NVIDIA 的整個平台設計目標是專業級、專業品質、長壽命。

Now the thing that's really exciting about artificial intelligence is we now can use AI to improve images. Like, for example, you could fix a photograph using AI. 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.

現在人工智慧真正令人興奮的地方在於，我們現在可以利用人工智慧來改善影像。例如，你可以使用人工智慧修復照片。您可以填充照片中損壞的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 上宣布和演示的。

You could use that to generate designs. You sketch up a few strokes of what you want a car to look like. 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. It's called generative design. We're going to see generative design in product design, in building design and just about everything.

你可以用它來產生設計圖。你用幾筆勾勒出你想要的汽車外觀。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。它被稱為生成式設計。我們將會在產品設計、建築設計以及幾乎所有領域看到生成式設計。

The last, if you will, 90% of the work is after the initial inspiration or the conceptual design is done. That part of it can be highly automated through AI. And so Quadro could be used as a platform that designs as well as generatively designs. And then lastly, a lot of people are using our workstations to also train their neural networks for these generative designs. And so you could train and develop your own networks and then apply it in the applications, okay?

最後，或者說90%的工作，是在最初的靈感或概念設計完成後進行的。這部分工作可以透過人工智慧高度自動化完成。因此，Quadro 可以用作設計平台以及生成式設計平台。最後，許多人也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。這樣你就可以訓練和開發自己的網絡，然後將其應用到應用程式中，好嗎？

So AI, think of AI really as, in the final analysis, the future way of developing software. It's a brand-new capability where computers can write its own software. And the software that's written is so complex and so capable that no humans could write it ourselves. 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，從根本上來說，人工智慧可以被視為未來軟體開發的方式。這是電腦能夠編寫自身軟體的全新功能。而且寫出來的軟體非常複雜、功能強大，是任何人類都無法寫出來的。因此，你可以教軟體，你可以使用數據來教軟體如何自己寫軟體。

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

然後，當你完成軟體開發後，你可以用它來做各種各樣的事情，包括設計產品。所以，在工作站上，就是這樣使用的。

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

你的下一個問題來自 Craig Ellis 和 B. Riley 的系列。

Congratulations on the very good quarterly execution. A lot of near-term items here on gaming. So I'll switch it to longer term. Jensen, at CES, I think you said that there are now 200 million GeForce users globally. And if my math is correct, then that would be up about 2x over the last 3 to 4 years.

恭喜你們季度業績出色。這裡有許多近期遊戲相關的內容。所以我把它改為長期持有。Jensen，我記得你在 CES 上說過，現在全球有 2 億 GeForce 用戶。如果我沒算錯的話，在過去三、四年裡，這個數字應該增加了大約兩倍。

So the question is, is there anything that you can see that would preclude that kind of growth over a similar period? 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. So as we look ahead, should we expect any change in channel management from the company?

所以問題是，您認為在類似的時期內，是否有任何可能阻止這種成長的因素？鑑於最近的需求動態，我認為我們已經看到，NVIDIA 的直接管道一直是以您預期價格購買 GPU 的非常好的來源。展望未來，我們是否應該期待該公司在通路管理方面做出任何改變？

Yes. Thanks a lot, Craig. In the last several years, several dynamics happened at the same time. And all of it were the favorable contributions to today. First of all, gaming became a global market, and China became one of the largest gaming markets in the world.

是的。非常感謝，克雷格。近幾年來，多種動態同時發生。所有這些都對今天的成就做出了有利貢獻。首先，遊戲已成為全球市場，而中國已成為世界上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. They could invest a few hundred million dollars and know that they're going to get the return on it. 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.

但其次，由於市場規模如此龐大，開發商可以投入大量資金來提高電子遊戲的製作價值。他們可以投資數億美元，並且知道會獲得回報。在電子遊戲產業規模還很小，或者說PC遊戲產業規模還很小的時​​候，對開發商來說，投入那麼多資金風險太大了。

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. And so the production -- when a production value goes up, the GPU technology that's needed to run it well goes up. It's very different than music, it's very different than watching movies. Everything in video games is synthesized in real time. And so when the production value goes up, the ASP or the technology has to go up.

因此，現在投資者或開發商可以投入數億美元，創造出完全逼真、身臨其境且美輪美奐的作品。因此，當製作水準提高時，運行流暢所需的 GPU 技術也會提高。它和音樂很不一樣，也和看電影很不一樣。電子遊戲中的一切都是即時合成的。因此，當生產價值提高時，平均售價或技術也必須提高。

And then lastly, the size of the market, people have wondered how big the video game market is going to be. And I've always believed that the video game market is going to be literally everyone. In 10 years' time, 15 years' time, there's going to be another 1 billion people on Earth. And those people are going to be gamers. We're going to see more and more gamers.

最後，關於市場規模，人們一直想知道電子遊戲市場究竟會有多大。我一直認為，電子遊戲市場最終會涵蓋所有人。10年後、15年後，地球上的人口將會增加10億。這些人將會是遊戲玩家。我們將會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. So video games is every sport. So eSport can be any sport and every sport and every type of sport. 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.

更不用說，幾乎每一項運動都可以變成虛擬實境運動。所以電子遊戲涵蓋了所有體育運動。所以電子競技可以是任何運動，可以是所有運動，也可以是所有類型的運動。所以我覺得，當你考慮到這一點並牢記在心時，我認為電子遊戲的機會將會非常巨大，而這基本上就是我們現在所看到的。

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

你的下一個問題來自威廉‧史坦與太陽信託銀行的淵源。

I'm hoping we can touch on automotive a little bit more. 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. 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. So any guidance as to when we might see these 2 factors start to accelerate revenue in that end market?

我希望我們能再多聊聊汽車方面的話題。特別是，我認為，您過去曾談到，預計到 2020 年左右自動駕駛技術真正普及之前，這個市場的收入成長將比較低。當然，人工智慧副駕駛技術似乎有可能更快普及，而且至少有一位知名客戶正在加速推進，但自動駕駛方面的規模還沒有那麼大。那麼，關於這兩個因素何時才能開始加速終端市場的收入成長，有什麼指導嗎？

Yes. Thanks a lot, Will. I wish I had more precision for you, but here are some of the dynamics that I believe in. I believe that autonomous capabilities -- autonomous driving, is the single greatest dynamic next to EVs in the automotive industry. And transportation is a $10 trillion industry. Between cars and shuttles and buses, delivery vehicles, I mean, it's just an extraordinary, extraordinary market. And everything that's going to move in the future will be autonomous. That's for sure. And it will be autonomous fully, or it will be autonomous partly. The size of this marketplace is quite large. 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. 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. And we've created a platform we call the NVIDIA DGX that allows everybody to train their neural networks as quickly as possible. So that's first. The development of the AI requires GPUs, and we benefit first from that. The second is -- which we'll start this year and next year, is development platforms for the cars themselves for the vehicles themselves. And finally, Xavier's here. We have first silicon at Xavier's, the most complex SOC that was ever made. And we're super excited about the state of Xavier, and we're going to be sampling it in Q1. And so now we'll be able to help everybody create development systems. 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. And so that's the second element. The third element, in the near term, will be development agreements. Each one of these projects are engineering-intensive, and there's a development agreement that goes along with it. And so these 3 elements, these 3 components, are in the near term. 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. Appreciate that question. And I think this is our last question, yes?

是的。非常感謝，威爾。我希望我能更精確地解釋清楚，但以下是我相信的一些動態。我認為，自動駕駛能力是汽車產業中僅次於電動車的最大發展動力。而交通運輸業是一個價值 10 兆美元的產業。從汽車、接駁車、巴士到送貨車輛，我的意思是，這真是一個非同尋常、非同尋常的市場。未來所有移動的物體都將是自主的。那當然。它將完全自主運行，或部分自主運行。這個市場的規模相當大。就近期而言，我認為我們通往未來的道路始於 2020 年——2019 年、2020 年，但會在 2022 年強勢開局。我認為，就我們而言，通往未來的道路包含幾個要素。第一個要素是，對於所有這些公司，無論是一級供應商、新創公司、原始設備製造商、計程車公司、叫車公司、拖拉機公司、班車公司或披薩外送班車，為了交付——為了創建他們的自動駕駛能力，你首先要做的是訓練神經網路。我們創建了一個名為 NVIDIA DGX 的平台，讓每個人都能以最快的速度訓練他們的神經網路。這是第一點。人工智慧的開發需要GPU，而我們首先從中受益。第二點——我們將在今年和明年啟動——是汽車本身的開發平台。最後，澤維爾也來了。我們在澤維爾大學獲得了首個矽晶片，這是有史以​​來製造的最複雜的系統單晶片 (SoC)。我們對 Xavier 的現狀感到非常興奮，我們將在第一季進行試用。因此，現在我們將能夠幫助所有人創建開發系統。世界將會需要成千上萬套基於 Xavier 和 Pegasus 的昂貴開發系統。這就是第二個要素。第三個要素，就近期而言，將是發展協議。這些項目都是工程密集型項目，並且都附帶一份開發協議。因此，這 3 個要素，這 3 個組成部分，都是近期需要關注的。然後，希望從 2019 年開始，並持續發展，特別是從 2022 年及以後，汽車的實際收入和經濟效益能夠顯現出來。感謝您的提問。我想這是我們最後一個問題了吧？

Well, we had a record quarter, wrapping up a record year. We have a strong -- we had strong momentum in our gaming, AI, data center and self-driving car businesses. It's great to see adoption of NVIDIA's GPU computing platform increasing in so many industries. We accomplished a great deal this last year, and we have big plans for this coming year. Next month, the brightest minds in AI and the scientific world will come together at our GPU Technology Conference in San Jose. GTC has grown tenfold in the last 5 years. This year we expect more than 8,000 attendees. GTC is the place to be if you're an AI researcher or doing any field of science where computing is your essential instrument. 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. Developers from industries ranging from health care to transportation to manufacturing and entertainment will come together and share state-of-the-art and AI. This is going to be a great GTC. I hope to see all of you there.

我們剛剛經歷了一個創紀錄的季度，也為創紀錄的一年畫下了圓滿的句點。我們在遊戲、人工智慧、資料中心和自動駕駛汽車業務方面都擁有強勁的發展勢頭。很高興看到英偉達的GPU運算平台在眾多產業中的應用日益廣泛。去年我們取得了巨大的成就，對即將到來的一年我們也有宏偉的計劃。下個月，人工智慧和科學界最傑出的人才將齊聚聖荷西，參加我們的GPU技術會議。GTC在過去5年中成長了十倍。今年我們預計將有超過8000名與會者。如果你是人工智慧研究人員，或從事任何以計算為主要工具的科學領域，那麼 GTC 就是你該來的地方。屆時將有超過 500 小時的演講，內容涵蓋該領域領導者（如Google、亞馬遜、Facebook、微軟等）的最新突破和發現。來自醫療保健、交通運輸、製造業和娛樂等各行各業的開發者將齊聚一堂，分享最先進的技術和人工智慧。這將會是一場精彩的GTC大會。希望到那時能見到你們所有人。

This concludes today's conference call. You may now disconnect. Thank you for your participation.

今天的電話會議到此結束。您現在可以斷開連線了。感謝您的參與。