輝達 (NVDA) 2019 Q1 法說會逐字稿

Good afternoon, my name is Kelsey, and I am your conference operator for today.

下午好，我叫 Kelsey，我是您今天的會議接線員。

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 First Quarter of Fiscal 2019.

大家下午好，歡迎參加 NVIDIA 2019 財年第一季度電話會議。

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 May 16, 2018.

您可以在 2018 年 5 月 16 日之前通過電話收聽重播。

The webcast will be available for replay until the conference call to discuss our financial results for the second quarter of fiscal 2019.

在電話會議討論我們 2019 財年第二季度的財務業績之前，該網絡廣播將可供重播。

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

今天通話的內容是 NVIDIA 的財產。

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

未經我們事先書面同意，不得複製或轉錄。

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

在本次電話會議期間，我們可能會根據當前預期做出前瞻性陳述。

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

這些受到許多重大風險和不確定性的影響，我們的實際結果可能存在重大差異。

For a discussion of factors that could affect our future financial results and business, please refer to the disclosure in today's earnings release, our most recent Form 10-K, and the reports that we may file on Form 8-K with the Securities and Exchange Commission.

有關可能影響我們未來財務業績和業務的因素的討論，請參閱今天的收益發布中的披露、我們最近的 10-K 表格以及我們可能以 8-K 表格向證券交易委員會提交的報告委員會。

All our statements are made as of today, May 10, 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 5 月 10 日。

Except as required by law, we assume no obligation to update any such statements.

除法律要求外，我們不承擔更新任何此類聲明的義務。

During this call, we will discuss non-GAAP financial measures.

在本次電話會議中，我們將討論非 GAAP 財務指標。

You can find a reconciliation of these non-GAAP financial measures to GAAP financial measures in our CFO Commentary, which is posted on our website.

您可以在我們的 CFO 評論中找到這些非 GAAP 財務指標與 GAAP 財務指標的對賬，該評論發佈在我們的網站上。

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

有了這個，讓我把電話轉給科萊特。

Thanks, Simona.

謝謝，西蒙娜。

We had an excellent quarter, with growth across all our platforms, led by gaming and data center.

我們有一個出色的季度，在遊戲和數據中心的帶動下，我們所有平台都實現了增長。

Q1 revenue reached a record $3.21 billion, up 66% year-on-year, up 10% sequentially, and above our outlook of $2.9 billion.

第一季度收入達到創紀錄的 32.1 億美元，同比增長 66%，環比增長 10%，高於我們預期的 29 億美元。

Once again, all measures of profitability set records, with GAAP gross margins at 64.5%, operating margins at 40.4%, and net income at $1.24 billion.

再一次，所有的盈利能力指標都創下了記錄，GAAP 毛利率為 64.5%，營業利潤率為 40.4%，淨收入為 12.4 億美元。

From a reporting segment perspective, Q1 GPU revenue grew 77% from last year, to $2.77 billion.

從報告部門的角度來看，第一季度 GPU 收入比去年增長了 77%，達到 27.7 億美元。

Tegra Processor revenue rose 33% to $442 million.

Tegra 處理器收入增長 33% 至 4.42 億美元。

Let's start with our gaming business.

讓我們從我們的遊戲業務開始。

Revenue was $1.72 billion, up 68% year-on-year and down 1% sequentially.

收入為 17.2 億美元，同比增長 68%，環比下降 1%。

Demand was strong and broad-based across regions and products.

跨地區和產品的需求強勁且廣泛。

The gaming market remains robust and the popular Battle Royale genre is attracting a new wave of gamers to the GeForce platform.

遊戲市場依然強勁，流行的大逃殺類型正在吸引新一波遊戲玩家加入 GeForce 平台。

We also continued to see demand from upgrades, with about 35% of our installed base currently on our Pascal architecture.

我們還繼續看到升級的需求，目前我們大約 35% 的安裝基礎在我們的 Pascal 架構上。

The launch of popular titles, like Far Cry 5 and FANTASY -- FINAL FANTASY XV, continued to drive excitement in the quarter.

熱門遊戲的推出，如孤島驚魂 5 和 FANTASY - FINAL FANTASY XV，繼續推動本季度的興奮。

Gamers are increasingly engaging in social gameplay and gaming is rapidly becoming a spectator sport, while the production value of games continues to increase.

遊戲玩家越來越多地參與社交遊戲，遊戲正迅速成為一項觀賞性運動，而遊戲的產值不斷增加。

This dynamic is fueling a virtuous cycle that expands the universe of gamers and drives a mix shift to higher end GPUs.

這種動態推動了一個良性循環，擴大了遊戲玩家的範圍，並推動了向更高端 GPU 的混合轉變。

At the recent Game Developers Conference, we announced our real-time ray-tracing technology, NVIDIA RTX.

在最近的遊戲開發者大會上，我們宣布了我們的實時光線追踪技術 NVIDIA RTX。

Ray tracing is movie-quality rendering technique that delivers lifelike lighting, reflections and shadows.

光線追踪是電影質量的渲染技術，可提供逼真的照明、反射和陰影。

It has long been considered the Holy Grail of graphics and we've been working on it for over 10 years.

長期以來，它一直被認為是圖形的聖杯，我們已經為此努力了 10 多年。

We look forward to seeing amazing, cinematic games that take advantage of this technology come to the market later this year, with the pipeline building into next year and beyond.

我們期待看到利用這項技術的驚人的電影遊戲在今年晚些時候進入市場，並在明年及以後建立管道。

And we expect RTX, as well as other new technologies like 4K and virtual reality, to continue driving gamers' requirements for higher GPU performance.

我們預計 RTX 以及 4K 和虛擬現實等其他新技術將繼續推動遊戲玩家對更高 GPU 性能的需求。

While supply was tight earlier in the quarter, the situation is now easing.

雖然本季度早些時候供應緊張，但現在情況正在緩和。

As a result, we are pleased to see that channel prices for our GPUs are beginning to normalize, allowing gamers who had been priced out of the market last quarter to get their hands on the new GeForce GTX at a reasonable price.

因此，我們很高興看到我們的 GPU 的渠道價格開始正常化，讓上個季度被定價退出市場的遊戲玩家以合理的價格獲得新的 GeForce GTX。

Cryptocurrency demand was again stronger than expected, but we were able to fulfill most of it with crypto-specific GPUs, which are included in our OEM business, at $289 million.

加密貨幣需求再次強於預期，但我們能夠以 2.89 億美元的價格通過我們的 OEM 業務中包含的加密專用 GPU 來滿足大部分需求。

As a result, we could protect the vast majority of our limited gaming GPU supply for use by gamers.

因此，我們可以保護絕大多數有限的遊戲 GPU 供應供遊戲玩家使用。

Looking into Q2, we expect crypto-specific revenue to be about 1/3 of its Q1 level.

展望第二季度，我們預計加密貨幣特定收入約為第一季度水平的 1/3。

Gaming notebooks also grew well, driven by an increasing number of thin and light notebooks based on our Max-Q design.

遊戲筆記本電腦也增長良好，這得益於越來越多基於我們 Max-Q 設計的輕薄筆記本電腦。

And Nintendo Switch contributed strongly to year-on-year growth, reflecting that platform's continued success.

Nintendo Switch 對同比增長做出了巨大貢獻，反映了該平台的持續成功。

Moving to data center.

搬到數據中心。

We had another phenomenal quarter, with revenue of $701 million, up 71% year-on-year, up 16% sequentially.

我們又迎來了一個非凡的季度，收入為 7.01 億美元，同比增長 71%，環比增長 16%。

Demand was strong in all market segments, and customers increasingly embraced our GPUs and CUDA platform for high-performance computing and AI.

所有細分市場的需求都很強勁，客戶越來越多地接受我們用於高性能計算和人工智能的 GPU 和 CUDA 平台。

Adoption of our Volta architecture remained strong across a wide range of verticals and customers.

在廣泛的垂直行業和客戶中，我們的 Volta 架構的採用仍然很強勁。

In the public cloud segment, Microsoft Azure announced general availability of Tesla V100 instances, joining Amazon, IBM and Oracle.

在公共雲領域，微軟 Azure 宣布正式推出 Tesla V100 實例，加入亞馬遜、IBM 和甲骨文。

And Google Cloud announced that the V100 is now publicly available in beta.

谷歌云宣布 V100 現已公開提供測試版。

Many other hyperscale and consumer Internet companies also continued their ramp of Volta, which delivers 5x the deep learning performance of its predecessor, Pascal.

許多其他超大規模和消費互聯網公司也繼續使用 Volta，它的深度學習性能是其前身 Pascal 的 5 倍。

Volta has been chosen by every major cloud provider and server maker, reinforcing our leadership in AI deep learning.

Volta 已被各大雲提供商和服務器製造商選中，從而鞏固了我們在 AI 深度學習領域的領先地位。

In high-performance computing, strength from the broad enterprise vertical more than offset the ramp down of major supercomputing projects such as the U.S. Department of Energy's Summit System.

在高性能計算方面，來自廣泛企業垂直領域的實力足以抵消美國能源部峰會系統等主要超級計算項目的下滑。

We see a strong pipeline across a number of vertical industries from manufacturing to oil and gas, which has helped sustain the trajectory of high-performance computing next quarter and beyond.

我們看到從製造業到石油和天然氣等多個垂直行業的強大管道，這有助於維持下個季度及以後高性能計算的發展軌跡。

Traction is also increasing in AI inference.

人工智能推理的吸引力也在增加。

Inference GPU shipments to cloud service providers more than doubled from last quarter.

對雲服務提供商的推理 GPU 出貨量比上一季度增加了一倍多。

And our pipeline is growing into next quarter.

我們的管道正在增長到下個季度。

We dramatically increased our inference capabilities with the announcement of the TensorRT 4 AI Inference Accelerator Software at our recent GPU Technology Conference in San Jose.

我們最近在聖何塞舉行的 GPU 技術大會上發布了 TensorRT 4 AI 推理加速器軟件，極大地提高了推理能力。

TensorRT 4 accelerates deep learning inference up to 190 times faster than CPUs for common applications such as computer vision, neural machine translation, automatic speech recognition, speech synthesis and recommendation systems.

對於計算機視覺、神經機器翻譯、自動語音識別、語音合成和推薦系統等常見應用，TensorRT 4 將深度學習推理的速度比 CPU 快 190 倍。

It also dramatically expands the use cases prepared with the prior version.

它還極大地擴展了使用先前版本準備的用例。

With TensorRT 4, NVIDIA's market reach has expanded to approximately 30 million hyperscale servers worldwide.

借助 TensorRT 4，NVIDIA 的市場範圍已擴大到全球約 3000 萬台超大規模服務器。

At GTC, we also announced other major advancements in our deep learning platform.

在 GTC，我們還宣布了深度學習平台的其他重大進展。

We doubled the memory of Tesla V100 to 32 GB DRAM, which is a key enabler for customers building large neural networks through larger data sets.

我們將 Tesla V100 的內存翻倍至 32 GB DRAM，這是客戶通過更大的數據集構建大型神經網絡的關鍵推動力。

And we announced a new GPU interconnect fabric called NVIDIA NVSwitch.

我們還宣布了一種名為 NVIDIA NVSwitch 的新型 GPU 互連結構。

(inaudible) 16 Pascal V100 GPUs at a speed of 2.4 terabytes per second, or 5x faster than the best PCIe switch.

（聽不清）16 個 Pascal V100 GPU，速度為每秒 2.4 TB，或比最佳 PCIe 交換機快 5 倍。

We also announced our DGX-2 system, which leverages these new technologies and its updated, fully-optimized software stack to deliver a 10x performance boost beyond last year's DGX.

我們還宣布了我們的 DGX-2 系統，該系統利用這些新技術及其更新的、完全優化的軟件堆棧，提供比去年的 DGX 高出 10 倍的性能。

DGX-2 is the first single server capable of delivering 2 petaflops of computational power.

DGX-2 是第一款能夠提供 2 petaflops 計算能力的單服務器。

We are seeing strong interest from both hyperscale and (inaudible) customers, and we look forward to bringing this technology to cloud customers later this year.

我們看到超大規模和（聽不清）客戶的強烈興趣，我們期待在今年晚些時候將這項技術帶給雲客戶。

At our Investor Day in March, we updated our forecast for the data center and the rest of the market.

在 3 月的投資者日，我們更新了對數據中心和其他市場的預測。

We see the data center opportunity as very large, fueled by growing demand for accelerated computing and applications ranging from AI (inaudible) multiple market segments and vertical industries.

我們認為數據中心的機會非常大，這得益於對人工智能（聽不清）多個細分市場和垂直行業的加速計算和應用程序的需求不斷增長。

We estimate the TAM at $50 billion by 2023, which extends our previous forecast of $30 billion by 2020.

我們估計到 2023 年 TAM 將達到 500 億美元，這擴大了我們之前的預測，即到 2020 年將達到 300 億美元。

We see strong momentum in the adoption of our accelerated computing platform and the expansion of our development ecosystem to serve this rapidly growing market.

我們看到採用我們的加速計算平台和擴展我們的開發生態系統以服務於這個快速增長的市場的強勁勢頭。

About 8,500 attendees registered for GTC, up 18% from last year.

大約 8,500 名與會者註冊了 GTC，比去年增長了 18%。

CUDA downloads have continued to grow, setting a fresh record in the quarter.

CUDA 下載量持續增長，在本季度創下新紀錄。

And our total number of developers is well over 850,000, up 72% from last year.

我們的開發人員總數超過 850,000 人，比去年增長了 72%。

Moving to pro visualization.

轉向專業可視化。

Revenue grew to $251 million, up 22% from a year ago and accelerating from last quarter, driven by demand for real-time rendering as well as emerging applications like AI and VR.

收入增長至 2.51 億美元，比去年同期增長 22%，並且比上一季度有所加速，這主要得益於對實時渲染以及 AI 和 VR 等新興應用的需求。

Strength extended across several key industries, including public sector, health care and retail.

實力擴大到幾個關鍵行業，包括公共部門、醫療保健和零售。

Key wins in the quarter included Columbia University, using high-end Quadro GPUs for AI, and Siemens, using them for CT and ultrasound solutions.

本季度的主要贏家包括哥倫比亞大學，將高端 Quadro GPU 用於人工智能，以及西門子，將它們用於 CT 和超聲解決方案。

At GTC, we announced the Quadro GV100 GPU with NVIDIA RTX technology, capable of delivering real-time ray tracing to the more than 25 million artists and designers throughout the world.

在 GTC 上，我們發布了採用 NVIDIA RTX 技術的 Quadro GV100 GPU，能夠為全球超過 2500 萬藝術家和設計師提供實時光線追踪。

RTX makes computational intensive ray tracing possible in real time when running professional design and content creation applications.

在運行專業設計和內容創建應用程序時，RTX 使計算密集型光線追踪成為可能。

This allows media and entertainment professionals to see and interact with their creations with correct light and shadows and do complex renders up to 10x faster than a GPU -- a CPU alone.

這使媒體和娛樂專業人士能夠以正確的光線和陰影查看他們的創作並與之交互，並以比 GPU（僅 CPU）快 10 倍的速度進行複雜的渲染。

And the NVIDIA OptiX AI denoiser built into RTX delivers almost 100x the performance of CPUs for real-time noise-free rendering.

RTX 中內置的 NVIDIA OptiX AI 降噪器可提供幾乎 100 倍於 CPU 的性能，以實現實時無噪聲渲染。

This enabled customers to replace racks of servers in traditional render farms with GPU servers at 1/5 the cost, 1/7 the space, and 1/7 the power.

這使客戶能夠以 1/5 的成本、1/7 的空間和 1/7 的功率將傳統渲染農場中的服務器機架替換為 GPU 服務器。

Lastly, automotive.

最後，汽車。

Revenue grew 4% year-on-year to a record $145 million.

收入同比增長 4%，達到創紀錄的 1.45 億美元。

This reflects the ongoing transition from our infotainment business to our growing autonomous vehicle development and production opportunities around the globe.

這反映了從我們的信息娛樂業務到我們在全球範圍內不斷增長的自動駕駛汽車開發和生產機會的持續轉變。

At GTC and Investor Day, we made key product announcements on the advancement of autonomous vehicles and established a total addressable market opportunity of 60 billion by 2035.

在 GTC 和投資者日，我們發布了關於自動駕駛汽車發展的關鍵產品公告，並確定到 2035 年總可尋址市場機會達到 600 億。

We believe that every vehicle will be autonomous one day.

我們相信，有一天每輛車都將實現自動駕駛。

By 2035, this will encompass 100 million autonomous passenger vehicles and 10 million robo-taxis.

到 2035 年，這將包括 1 億輛自動駕駛乘用車和 1000 萬輛自動駕駛出租車。

We also introduced NVIDIA DRIVE Constellation, a platform that will help car companies, carmakers, tier 1 suppliers, and others developing autonomous vehicles test and validate their systems in a virtual world across a wide range of scenarios before deploying on the road.

我們還推出了 NVIDIA DRIVE Constellation，該平台將幫助汽車公司、汽車製造商、一級供應商和其他開發自動駕駛汽車的人在虛擬世界中跨各種場景測試和驗證他們的系統，然後再上路部署。

Each year, 10 trillion miles are driven around the world.

每年，全球行駛 10 萬億英里。

Even if test cars can eventually cover millions of miles, that's an insignificant fraction of all the scenarios that require testing to create a safe and reliable autonomous vehicle.

即使測試汽車最終可以行駛數百萬英里，在需要測試以創建安全可靠的自動駕駛汽車的所有場景中，這只是微不足道的一小部分。

DRIVE Constellation addresses this challenge by (inaudible) cars to safely drive billions of miles in virtual reality.

DRIVE Constellation 通過（聽不清）汽車在虛擬現實中安全駕駛數十億英里來應對這一挑戰。

The platform has 2 different servers.

該平台有 2 台不同的服務器。

The first is loaded with GPUs and simulates the environment that the car is driving in, as in a hyper-real video game.

第一個是加載 GPU 並模擬汽車行駛的環境，就像在超真實的視頻遊戲中一樣。

The second contains the NVIDIA DRIVE Pegasus Autonomous Vehicle Computer, which possesses the simulated data, as if it were coming from the sensors of a car driving on the road.

第二個包含 NVIDIA DRIVE Pegasus 自動駕駛汽車計算機，它擁有模擬數據，就好像它來自道路上行駛的汽車的傳感器一樣。

Real-time driving command from the DRIVE Pegasus are fed back to the simulation for true hardware-in-the-loop verification.

來自 DRIVE Pegasus 的實時駕駛命令被反饋到仿真中，以進行真正的硬件在環驗證。

Constellation will enable autonomous vehicle industry for safety test and validate their AI self-driving systems in ways that are not practical or possible with on-road testing.

Constellation 將使自動駕駛汽車行業能夠進行安全測試，並以道路測試不實用或不可能的方式驗證其人工智能自動駕駛系統。

We also extended our product roadmap to include our next-generation DRIVE Autonomous Vehicle Computer.

我們還擴展了產品路線圖，將我們的下一代 DRIVE 自動駕駛汽車計算機包括在內。

We have created a scalable AI car platform that spans the entire range of autonomous driving, from traffic jams, pilots, to level 5 robo-taxis.

我們創建了一個可擴展的人工智能汽車平台，涵蓋了自動駕駛的整個範圍，從交通擁堵、飛行員到 5 級自動駕駛出租車。

More than 370 companies and research institutions are now using NVIDIA's automotive platform.

超過 370 家公司和研究機構正在使用 NVIDIA 的汽車平台。

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

隨著這種增長勢頭，我們仍然對自動駕駛業務的中長期機遇感到興奮。

Now moving to the rest of the P&L.

現在轉到損益表的其餘部分。

Q1 GAAP gross margins were 64.5% and non-GAAP was 64.7%, records that reflect continued growth in our value-added platforms.

第一季度 GAAP 毛利率為 64.5%，非 GAAP 毛利率為 64.7%，這些記錄反映了我們增值平台的持續增長。

GAAP operating expenses were $773 million.

GAAP 運營費用為 7.73 億美元。

Non-GAAP operating expenses were $648 million, up 25% year-on-year.

非美國通用會計準則運營費用為 6.48 億美元，同比增長 25%。

We continue to invest in 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 net income was a record $1.24 billion and EPS was $1.98, up 145% and 151% respectively from a year earlier.

GAAP 淨收入達到創紀錄的 12.4 億美元，每股收益為 1.98 美元，同比分別增長 145% 和 151%。

Some of the expenses (inaudible) by a tax rate of 5% compared to our guidance of 12%.

一些費用（聽不清）的稅率為 5%，而我們的指導為 12%。

Non-GAAP net income was $1.29 billion and EPS was $2.05, both up 141% from a year ago, reflecting the revenue strength as well as gross margins and operating margin expansion on slightly lower tax.

非美國通用會計準則淨收入為 12.9 億美元，每股收益為 2.05 美元，均比一年前增長 141%，這反映了收入實力以及毛利率和營業利潤率因稅收略有下降而擴大。

Our quarterly cash flow from operations reached record levels at $1.45 billion.

我們的季度運營現金流達到創紀錄的 14.5 億美元。

Capital expenditures were $118 million.

資本支出為 1.18 億美元。

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

有了這個，讓我轉向 2019 財年第二季度的展望。

We expect revenue to be $3.1 billion plus or minus 2%.

我們預計收入為 31 億美元，正負 2%。

GAAP and non-GAAP gross margins are expected to be 63.6% and 63.5%, respectively, plus or minus 50 basis points.

GAAP 和非 GAAP 毛利率預計分別為 63.6% 和 63.5%，上下浮動 50 個基點。

GAAP and non-GAAP operating expenses are expected to be approximately $810 million and $685 million, respectively.

GAAP 和非 GAAP 運營費用預計分別約為 8.1 億美元和 6.85 億美元。

GAAP...

公認會計原則...

(technical difficulty)

（技術難度）

Capital expenditures are expected to be approximately $130 million to $150 million.

資本支出預計約為 1.3 億至 1.5 億美元。

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

更多財務細節包含在 CFO 評論和我們投資者關係網站上提供的其他信息中。

In closing, 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 JPMorgan Technology Conference next week on May 15, and at the Bank of America Global Technology Conference on June 5. We will also hold our Annual Meeting of Stockholders online on May 16.

我們將出席下週 5 月 15 日的摩根大通技術會議和 6 月 5 日的美國銀行全球技術會議。我們還將在 5 月 16 日在線舉行年度股東大會。

We will now open the call for questions.

我們現在將打開問題的電話。

Simona and I are here in Santa Clara and Jensen is dialing in from the road.

西蒙娜和我在聖克拉拉，詹森正在路上撥通電話。

Operator, would you please poll for questions?

接線員，請您投票提問嗎？

Thank you.

謝謝你。

(Operator Instructions) Your first question is from Stacy Rasgon with Bernstein Research.

（操作員說明）您的第一個問題來自 Bernstein Research 的 Stacy Rasgon。

First, I had a question on gaming seasonality.

首先，我有一個關於遊戲季節性的問題。

It's usually down pretty decently in Q1.

它通常在第一季度下降得相當不錯。

It was obviously flat this time as you were trying to fill up the channel.

當你試圖填滿頻道時，這次顯然是平的。

Now that's done.

現在已經完成了。

I was just wondering on with the supply dynamics -- supply-demand dynamics as well as like any thoughts on crypto might mean for typical -- the seasonality in the Q2 versus what would be typical or what would usually be down -- or usually be up pretty decently?

我只是想知道供應動態——供需動態以及任何關於加密貨幣的想法可能對典型意味著——第二季度的季節性與典型或通常下降——或通常是相當體面？

How are you looking at that?

你怎麼看？

And there's a question for Colette.

還有一個問題要問科萊特。

Jensen, why don't you start on the question for Stacy, and I'll follow-up afterwards, after you speak.

Jensen，你為什麼不從 Stacy 的問題開始，我會在你發言之後跟進。

Okay.

好的。

Stacy, so let's see.

斯泰西，讓我們看看。

Q1, as you probably know, Fortnite and PUBG are global phenomenons.

Q1，您可能知道，Fortnite 和 PUBG 是全球現象。

The success of Fortnite and PUBG are just beyond, beyond comprehension, really.

Fortnite 和 PUBG 的成功實在是超出了人們的理解。

Those 2 games, a combination of Hunger Games and Survivor, has just captured imaginations of gamers all over the world.

這兩款遊戲結合了飢餓遊戲和倖存者，剛剛吸引了全世界遊戲玩家的想像力。

And we saw the uptick and we saw the demand on our GPUs from all over the world.

我們看到了增長，我們看到了來自世界各地對我們 GPU 的需求。

Surely, there was scarcity as you know.

當然，如你所知，存在稀缺性。

Crypto miners bought a lot of our GPUs during the quarter, and it drove prices up.

加密礦工在本季度購買了很多我們的 GPU，這推高了價格。

And I think that a lot of the gamers weren't able to buy into the new GeForces as a result.

而且我認為許多遊戲玩家因此無法購買新的 GeForce。

And so we're starting to see the prices come down.

所以我們開始看到價格下降。

We monitor spot pricing every single day around the world.

我們每天監控世界各地的現貨價格。

And the prices are starting to normalize.

價格開始正常化。

It's still higher than where they should be.

它仍然高於他們應該在的位置。

And so obviously, the demand is still quite strong out there.

很明顯，那裡的需求仍然很強勁。

But my sense is that there's a fair amount of pent-up demand still.

但我的感覺是，仍有大量被壓抑的需求。

Fortnite is still growing in popularity.

Fortnite 的受歡迎程度仍在增長。

PUBG is doing great.

PUBG 做得很好。

And then, we've had some amazing titles coming out.

然後，我們推出了一些令人驚嘆的遊戲。

And so my sense is that the overall gaming market is just really -- is super healthy.

所以我的感覺是，整個遊戲市場真的——超級健康。

And our job is to make sure that we work as hard as we can to get supply out into the marketplace.

我們的工作是確保我們盡最大努力將供應推向市場。

And hopefully, by doing that, the pricing will normalize and the gamers can buy into their favorite graphics card at a price that we hope they can get it at.

希望通過這樣做，定價將正常化，並且遊戲玩家可以以我們希望他們能夠得到的價格購買他們最喜歡的顯卡。

And so I think there's a fair -- so I mean, the simple answer to your question is Fortnite and PUBG.

所以我認為這是一個公平的 - 所以我的意思是，你的問題的簡單答案是 Fortnite 和 PUBG。

And the demand is just really great.

而且需求真的很大。

They did a great job.

他們做的不錯。

Your next question is from Joe Moore with Morgan Stanley.

您的下一個問題來自摩根士丹利的喬摩爾。

No wonder -- Colette had talked about the inference doubling in sales quarter-over-quarter with cloud.

難怪 - Colette 曾談到雲計算的銷售額環比翻倍的推論。

Can you just talk about where you're seeing the early applications for inference?

你能談談你在哪裡看到早期的推理應用嗎？

Is that sort of as-a-service business?

那是一種即服務業務嗎？

Or are you looking at internal cloud workloads?

或者您正在查看內部雲工作負載？

And just any color you can give us on where you guys are sitting in the inference space.

你可以給我們任何顏色，告訴我們你們在推理空間中的位置。

Sure.

當然。

Joe, so as you know, there are 30 million servers around the world.

喬，如您所知，全球有 3000 萬台服務器。

And they were put in place during the time when the world didn't have deep learning.

它們是在世界沒有深度學習的時候實施的。

And now with deep learning and with machine learning approaches, the accuracy of prediction, the accuracy of recommendation has jumped so much that just about every Internet service provider in the world that has a lot of different customers and consumers are jumping onto this new software approach.

現在有了深度學習和機器學習方法，預測的準確性，推薦的準確性已經飛躍得如此之多，以至於世界上幾乎每個擁有大量不同客戶和消費者的互聯網服務提供商都在使用這種新的軟件方法.

And in order to take this newer network -- and the software that's written by deep learning, these frameworks, are massive software.

為了採用這個更新的網絡——以及由深度學習編寫的軟件，這些框架是大型軟件。

The way to think about these deep neural nets is, it has millions and millions and millions of parameters in it, and these networks are getting larger every year.

思考這些深度神經網絡的方式是，它有數以百萬計的參數，而且這些網絡每年都在變大。

And they're enormously complex.

它們非常複雜。

And the output of these neural nets had to be optimized for the computing platform that it targets.

這些神經網絡的輸出必須針對它所針對的計算平台進行優化。

How you would optimize the neural network for a CPU or a GPU is very, very different.

如何為 CPU 或 GPU 優化神經網絡是非常非常不同的。

And how you optimize for different neural networks, whether it's image recognition, speech recognition, natural language translation, recommendation systems, all of these networks have different architectures, and the optimizing compiler that's necessary to make the neural network run smoothly and fast is incredibly complex.

以及如何針對不同的神經網絡進行優化，無論是圖像識別、語音識別、自然語言翻譯、推薦系統，所有這些網絡都有不同的架構，而使神經網絡平穩快速運行所必需的優化編譯器非常複雜.

And so that's why we created TensorRT.

這就是我們創建 TensorRT 的原因。

That's what TensorRT is.

這就是 TensorRT。

TensorRT is an optimizing graph neural network compiler.

TensorRT 是一個優化的圖神經網絡編譯器。

And it optimizes for our -- each one of our platforms.

它針對我們的每個平台進行了優化。

And each -- even each one of our platforms has very different architectures.

每一個——甚至我們的每一個平台都有非常不同的架構。

For example, we invented recently -- reinvented the GPU and it's called the Tensor Core GPU, and the first of its kind is called Volta.

例如，我們最近發明了——重新發明了 GPU，它被稱為 Tensor Core GPU，其中第一個被稱為 Volta。

And so TensorRT 4.0 now supports, in addition to image recognition, all of the different types of neural network models.

因此，除了圖像識別之外，TensorRT 4.0 現在還支持所有不同類型的神經網絡模型。

The answer to your question is internal consumption.

你的問題的答案是內部消費。

Internal consumption is going to be the first users.

內部消費將成為第一批用戶。

Video recognition, detecting for inappropriate video, for example, all over the world, making recommendations from the videos that you search or the images that you're uploading, all of these types of applications are going to require an enormous amount of computation.

視頻識別，檢測不合適的視頻，例如，在世界各地，從您搜索的視頻或您上傳的圖像中提出建議，所有這些類型的應用程序都需要大量的計算。

Next question is from Vivek Arya with Bank of America.

下一個問題來自美國銀行的 Vivek Arya。

Jensen, I have 2 questions about the data center.

Jensen，我有兩個關於數據中心的問題。

One from a growth, and the second, from a competition perspective.

一是從成長的角度，二是從競爭的角度。

So from the growth side, you guys are doing about, say, $3 billion or so annualized, but you have outlined a market that could be $50 billion.

因此，從增長方面來看，你們的年化規模約為 30 億美元，但你們已經勾勒出一個可能是 500 億美元的市場。

What needs to happen for the next inflection?

下一個拐點需要發生什麼？

Is it something in the market that needs to change?

市場上有什麼需要改變的嗎？

Is it something in the product set that needs to?

它是產品集中需要的東西嗎？

How do you go and address that $50 billion market, right?

你如何去解決這個 500 億美元的市場，對吧？

Because you're only a few percent penetrated today in that large market.

因為你今天在那個大市場中只滲透了百分之幾。

So what needs to change for the next inflection point?

那麼下一個拐點需要改變什麼？

And then, on the competition side, as you are looking at that big market, how should we think about competition that is coming from some of your cloud customers, like a Google announcing a TPU 3 or perhaps others looking at other competing technologies?

然後，在競爭方面，當您看到這個大市場時，我們應該如何看待來自您的一些雲客戶的競爭，例如 Google 宣布 TPU 3 或其他人在尋找其他競爭技術？

So any color on both sort of how you look at growth and competition would be very helpful.

因此，無論您如何看待增長和競爭，任何顏色都會非常有幫助。

Thanks, Vivek.

謝謝，維維克。

First of all, at its core, this is something we all know now, that CPU scaling has really slowed.

首先，就其核心而言，這是我們現在都知道的，CPU 擴展速度確實放緩了。

And if you think about the several hundred billion dollars worth of computer equipment that's been installed in the cloud, in data centers all over the world, and as these applications for machine learning and high-performance computing approaches come along, the world needs a solution.

如果你想想價值數千億美元的計算機設備已經安裝在雲中，在世界各地的數據中心，隨著這些機器學習和高性能計算方法的應用程序的出現，世界需要一個解決方案.

CPU scaling has slowed.

CPU 擴展速度已經放緩。

And so here's the approach that we pioneered 1.5 decades ago called GPU computing.

這就是我們在 1.5 年前開創的稱為 GPU 計算的方法。

And we've been determined to continue to advance it during this time because we saw this day coming and we really believed that it was going to end.

我們決心在這段時間內繼續推進它，因為我們看到這一天即將到來，我們真的相信它會結束。

I mean, you can't deny physics.

我的意思是，你不能否認物理學。

And so we find ourselves in a great position today.

所以我們今天發現自己處於一個很好的位置。

And as Colette already mentioned, we have something close to 1 million developers on this platform now.

正如 Colette 已經提到的，我們現在在這個平台上有近 100 萬開發人員。

It is incredibly fast, speeding up CPUs by 10, 20, 50, 100x, 200x sometimes, depending on the algorithm.

它的速度非常快，有時可以將 CPU 加速 10、20、50、100 倍、200 倍，具體取決於算法。

It's everywhere.

它無處不在。

The software ecosystem is just super rich.

軟件生態系統超級豐富。

And as Colette mentioned, that there is already almost 1 billion -- 1 million developers around the world, that's grown 70% year-over-year.

正如 Colette 提到的，全世界已經有近 10 億——100 萬開發人員，同比增長 70%。

And so I think at the core, it's about the fact that the world needs a computing approach going forward.

所以我認為，核心是世界需要一種計算方法向前發展。

With respect to the -- our ability to address the TAM, there are 3 major segments.

關於我們解決 TAM 的能力，有 3 個主要部分。

There's more than that, but there's 3 major segments.

還有更多，但有3個主要部分。

One is, of course, training for deep learning.

其中之一當然是深度學習訓練。

The other is inferencing, and TRT 4 is intended to do just that, to expand our ability to address all of the different types of algorithms, machine learning algorithms that are now coming -- that are running in the data centers.

另一個是推理，TRT 4 旨在做到這一點，以擴展我們處理所有不同類型算法的能力，即現在即將出現的機器學習算法——它們正在數據中心運行。

The third is high-performance computing, and that's molecular dynamics, to medical imaging, to earth sciences, to energy sciences.

第三個是高性能計算，那就是分子動力學、醫學成像、地球科學、能源科學。

The type of algorithms that are being run in supercomputers all over the world is expanding.

在世界各地的超級計算機中運行的算法類型正在擴大。

And we're doing more and more of our product designs in virtual reality.

我們在虛擬現實中進行越來越多的產品設計。

We want to simulate our products and simulate its capabilities in simulation in this computer rather than build it in the beginning.

我們想模擬我們的產品並在這台計算機中模擬其模擬能力，而不是一開始就構建它。

And then, the last category would be graphics virtualization.

然後，最後一類是圖形虛擬化。

We've taken with GRID and our Quadro virtual workstation and now with Quadro -- with NVIDIA RTX, we turned the data center into a powerful graphic supercomputer.

我們採用了 GRID 和我們的 Quadro 虛擬工作站，現在有了 Quadro——借助 NVIDIA RTX，我們將數據中心變成了強大的圖形超級計算機。

And so these are the various applications and segments of data center that we see.

這些就是我們看到的數據中心的各種應用和細分市場。

I think, in the case of training, we're limited by the number of deep learning experts in the world.

我認為，在培訓方面，我們受到世界上深度學習專家數量的限制。

And that's growing very quickly.

而且增長非常迅速。

The frameworks are making it easier.

框架使它變得更容易。

There's a lot more open source and open documentation on sharing of knowledge.

還有更多關於知識共享的開源和開放文檔。

And so the number of AI engineers around the world is growing super fast.

因此，全球人工智能工程師的數量正在以超快的速度增長。

The second is inference.

二是推理。

And I've already talked about that.

我已經談過了。

It's really limited by our optimizing compilers and how we can target these neural network models to run on our processors.

它確實受到我們優化編譯器的限制，以及我們如何將這些神經網絡模型定位到在我們的處理器上運行。

And if we could do so, we're going to save our customers enormous amounts of money.

如果我們能做到這一點，我們將為我們的客戶節省大量資金。

We speed up applications, we speed up these neural network models 50x, 100x, 200x over a CPU.

我們加速應用程序，我們將這些神經網絡模型在 CPU 上加速 50 倍、100 倍、200 倍。

And so the more GPUs they buy, the more they're going to save.

因此，他們購買的 GPU 越多，他們節省的費用就越多。

And high-performance computing, the way to think about that is, I think, at this point, it's very clear that going forward, supercomputers are going to get built with accelerators in them.

高性能計算，我認為，在這一點上，很明顯，未來超級計算機將內置加速器。

And because of our long-term dedication to CUDA and our GPUs for acceleration, of all these codes and the nurturing of the ecosystem, I think that we're going to be -- we're going to do super well in the supercomputing world.

由於我們長期致力於 CUDA 和我們的 GPU 加速，所有這些代碼和生態系統的培育，我認為我們將 - 我們將在超級計算領域做得非常好.

And so these are the different verticals.

所以這些是不同的垂直領域。

With respect to competition, I think it all starts with the core.

關於競爭，我認為一切都是從核心開始的。

And the core is that the CPU scaling has slowed.

核心是CPU擴展速度變慢了。

And so the world needs another approach going forward.

因此，世界需要另一種方法向前發展。

And surely, because of our focus on it, we find ourselves in a great position.

當然，由於我們對它的關注，我們發現自己處於一個很好的位置。

Google announced GPU 3 and it's still behind our Tensor Core GPU.

Google 宣布了 GPU 3，它仍然落後於我們的 Tensor Core GPU。

Our Volta is our first generation of a newly reinvented approach of doing GPUs.

我們的 Volta 是我們第一代全新改造的 GPU 製造方法。

It's called Tensor Core GPUs.

它被稱為張量核心 GPU。

And we're far ahead of the competition.

我們在競爭中遙遙領先。

And -- but more than that, more than that, it's programmable.

而且——但更重要的是，它是可編程的。

It's not one function.

這不是一個功能。

It's programmable.

它是可編程的。

Not only is it faster, it's also more flexible.

它不僅更快，而且更靈活。

And as a result of the flexibility, developers could use it in all kinds of applications, whether it's medical imaging or weather simulations or deep learning or computer graphics.

由於靈活性，開發人員可以在各種應用程序中使用它，無論是醫學成像或天氣模擬，還是深度學習或計算機圖形學。

And as a result, our GPUs are available in every cloud and every data center, everywhere on the planet.

因此，我們的 GPU 可用於地球上任何地方的每個雲和每個數據中心。

And which developers need, so that accessibility so that they could develop their software.

以及需要哪些開發人員，以便他們可以開發他們的軟件的可訪問性。

And so I think that on the one hand, it's too simplistic to compare a GPU to just one of the many features that's in our Tensor Core GPU.

所以我認為，一方面，將 GPU 與我們的 Tensor Core GPU 中的眾多功能之一進行比較過於簡單。

But even if you did, we're faster.

但即使你這樣做了，我們也會更快。

We support more frameworks.

我們支持更多的框架。

We support all neural networks.

我們支持所有的神經網絡。

And as a result, if you look at GitHub, there are some 60,000 different neural network research papers that are posted that runs on NVIDIA GPUs.

因此，如果您查看 GitHub，就會發現大約 60,000 篇不同的神經網絡研究論文在 NVIDIA GPU 上運行。

And it's just a handful for the second alternative.

第二種選擇只是少數。

And so it just kind of gives you a sense of the reach and the capabilities of our GPUs.

因此，它只是讓您了解我們 GPU 的覆蓋範圍和功能。

Your next question comes from Toshiya Hari with Goldman Sachs.

您的下一個問題來自高盛的 Toshiya Hari。

Jensen, I had a question regarding your decision to pull the plug on your GeForce partner program.

Jensen，我有一個關於您決定終止 GeForce 合作夥伴計劃的問題。

I think most of us read your blog from last Friday, I think it was.

我想我們大多數人上週五都讀過你的博客，我想是的。

So we understand the basic background.

所以我們了解了基本背景。

But if you can describe what led to this decision, and perhaps talk a little bit about the potential implications, if any, in terms of your ability to compete or gain share, that will be really helpful.

但是，如果你能描述一下導致這個決定的原因，或許可以談談潛在的影響，如果有的話，就你競爭或獲得份額的能力而言，那將是非常有幫助的。

Yes.

是的。

Thanks for the question, Toshiya.

謝謝你的問題，Toshiya。

At the core, the program was about making sure that gamers who buy graphics cards knows exactly the GPU brand that's inside.

該計劃的核心是確保購買顯卡的遊戲玩家確切了解其中的 GPU 品牌。

And the reason for that is because we want gamers to -- the gaming experience of a graphics card depends so much on the GPU that is chosen.

這樣做的原因是因為我們希望遊戲玩家——顯卡的遊戲體驗在很大程度上取決於所選擇的 GPU。

And we felt that using one gaming brand, a graphics card brand and interchanging the GPU underneath causes it to be less -- causes it to be more opaque and less transparent for gamers to choose the GPU brand that they wanted.

我們認為，使用一個遊戲品牌、一個顯卡品牌並更換下面的 GPU 會導致它變得更少——導致遊戲玩家選擇他們想要的 GPU 品牌時變得更加不透明和不透明。

And most of the ecosystem loved it.

大多數生態系統都喜歡它。

And some of the people really disliked it.

有些人真的不喜歡它。

And so instead of all that distraction, we're doing so well, and we're going to continue to help the gamers choose the graphics cards like we always have, and things will sort out.

因此，我們做得很好，而不是所有的分心，我們將繼續幫助遊戲玩家選擇我們一直擁有的顯卡，事情會解決的。

And so we decided to pull the plug because the distraction was unnecessary and we had too much good stuff to go do.

所以我們決定拔掉插頭，因為分心是不必要的，而且我們有太多好事情要做。

Next question is from C.J. Muse with Evercore ISI.

下一個問題來自 C.J. Muse 和 Evercore ISI。

This is (inaudible) calling in for C.J. Muse.

這是（聽不清）呼叫 C.J. Muse。

So I had a question on HPC.

所以我有一個關於 HPC 的問題。

TSMC, on their recent call, raised their accelerator attach rate forecast in HPC to 50% from mid-teens.

台積電在最近的電話會議上，將他們在 HPC 中的加速器附加率預測從十幾歲的中期提高到了 50%。

So I would love to get further details on what exactly NVIDIA is doing to software services, et cetera, that's kind of creating this competitive positioning in HPC and AI, basically.

因此，我很想進一步了解 NVIDIA 在軟件服務等方面所做的確切工作，這基本上是在 HPC 和 AI 中創造了這種競爭定位。

And then, if I could ask a follow-up, basically, in benchmarks.

然後，如果我可以要求跟進，基本上是在基準測試中。

So there's been some news on AI benchmarks, whether it's Stanford's DAWNBench, et cetera.

所以有一些關於人工智能基準的新聞，無論是斯坦福大學的 DAWNBench 等等。

So I would love to get your thoughts on, a, the current state of benchmarks for AI workloads.

因此，我很想听聽您對 AI 工作負載基準的當前狀態的看法。

And b, the relative positioning of ASICs versus GPUs, especially as we move towards newer networks like RNN and GAN, et cetera.

b，ASIC 與 GPU 的相對定位，尤其是當我們轉向 RNN 和 GAN 等更新的網絡時。

Yes.

是的。

Thanks for the question.

謝謝你的問題。

HPC.

高性能計算。

First of all, at the core, CPU scaling has stalled and it's reached the limits of physics.

首先，在核心上，CPU 擴展已經停滯不前，它已經達到了物理極限。

And the world needs another approach to go forward.

世界需要另一種方法來前進。

We created the GPU computing approach 1.5 decades ago, and I think at this point, with the number of developers jumping on, the number of applications that's emerging, it's really clear that the future of HPC has accelerated.

我們在 1.5 年前創建了 GPU 計算方法，我認為在這一點上，隨著開發人員數量的增加和新興應用程序的數量，HPC 的未來已經加速發展。

And our GPU approach, because of its flexibility, because of its performance, because of the value that we create, that as a result of the throughput of a data center, we save people so much money just in cables alone.

而我們的 GPU 方法，因為它的靈活性，因為它的性能，因為我們創造的價值，由於數據中心的吞吐量，我們僅在電纜上就為人們節省了很多錢。

Oftentimes, more than pays for the GPUs that they buy.

通常，他們購買的 GPU 所支付的費用遠遠超過他們所購買的 GPU。

And the reason for that is because the number of servers is reduced dramatically.

其原因是服務器數量急劇減少。

And so I think the future of HPC is about acceleration, and the NVIDIA CUDA GPUs are really in a great position to serve this vacuum that's been created.

因此，我認為 HPC 的未來是關於加速的，而 NVIDIA CUDA GPU 確實處於很好的位置來服務於已經產生的這種真空。

With respect to benchmarks, you might have seen that earlier this week, we released 3 speed records.

關於基準，您可能已經看到，本週早些時候，我們發布了 3 條速度記錄。

The fastest single GPU, the fastest single node -- single computer node.

最快的單GPU，最快的單節點——單機節點。

A definition of a computer node is something that fits in a box that runs one operating system, one node.

計算機節點的定義是適合運行一個操作系統、一個節點的盒子中的東西。

And one instance, one cloud instance.

一個實例，一個雲實例。

We now have the fastest speed record for 1 GPU, 1 node and 1 instance.

我們現在擁有 1 個 GPU、1 個節點和 1 個實例的最快速度記錄。

And so we love benchmarks.

所以我們喜歡基準測試。

Nothing is more joyful than having a benchmark to demonstrate your leadership position.

沒有什麼比有一個標杆來證明你的領導地位更快樂的了。

And in the world of deep learning, the number of networks is just growing so fast because the number of different applications that deep learning is able to solve is really huge.

在深度學習的世界裡，網絡的數量增長得如此之快，因為深度學習能夠解決的不同應用程序的數量非常龐大。

And so you need a lot of software capability and you're -- the versatility of your platform needs to be great.

因此，您需要大量的軟件功能，而且您的平台的多功能性必須非常出色。

We also have a lot of expertise in the company and software.

我們在公司和軟件方面也有很多專業知識。

I mean, NVIDIA is really a full-stack computing company.

我的意思是，NVIDIA 確實是一家全棧計算公司。

From architecture, to system software, to algorithms, to applications, we have a great deal of expertise across the entire stack.

從架構到系統軟件，再到算法，再到應用程序，我們在整個堆棧中擁有大量專業知識。

And so we love these complicated benchmarking -- benchmarks that are out there, and I think this is a great way for us to simplify our leadership position.

所以我們喜歡這些複雜的基準測試——已經存在的基準測試，我認為這是我們簡化領導地位的好方法。

I think long term, the number of networks that are going to emerge will continue to grow.

我認為從長遠來看，即將出現的網絡數量將繼續增長。

And so the flexibility of ASICs is going to be its greatest downfall.

因此，ASIC 的靈活性將是其最大的失敗。

And if someone were to create a general-purpose parallel accelerating processor like ours, and had it designed to be incredibly good at deep learning, like recently what we did with our Tensor Core GPU, which is a reinvented GPU, and Volta is the first one, I -- it's going to be hard.

如果有人要創建像我們這樣的通用並行加速處理器，並且將其設計為非常擅長深度學習，就像最近我們對 Tensor Core GPU 所做的那樣，這是一個重新發明的 GPU，而 Volta 是第一個一，我——這會很難。

It's going to be expensive, and we've been doing it for a long time.

它會很昂貴，而且我們已經做了很長時間了。

And so I think this is a -- it's a great time for us.

所以我認為這對我們來說是一個偉大的時刻。

Next question is from Blayne Curtis with Barclays.

下一個問題來自巴克萊銀行的 Blayne Curtis。

Jensen, I wanted to ask on the inference side [about edge inference].

Jensen，我想問一下推理方面的問題[關於邊緣推理]。

And beyond autos, when you look at sizing that TAM, what are the other big areas that you think you can penetrate with GPUs and inference, besides auto?

除了汽車，當您考慮調整 TAM 的大小時，您認為 GPU 和推理可以滲透到哪些其他大領域，除了汽車？

Yes, Blayne.

是的，布萊恩。

The largest inference opportunity for us is actually in the cloud and the data center.

對我們來說最大的推理機會實際上是在雲和數據中心。

That's the first great opportunity.

這是第一個大好機會。

The reason for that is there's just an explosion in the number of different types of neural networks that are available.

原因是可用的不同類型神經網絡的數量激增。

There are image recognition, there's video sequencing, there's video -- there's recommender systems.

有圖像識別，有視頻排序，有視頻——有推薦系統。

There's speech recognition, speech synthesis, natural language understanding.

有語音識別、語音合成、自然語言理解。

There's just so many different types of neural networks that are being created.

有很多不同類型的神經網絡正在被創建。

And creating one ASIC that can be adapted to all of these different types of networks is just a real challenge.

創建一個可以適應所有這些不同類型網絡的 ASIC 只是一個真正的挑戰。

And by the time that you create such a thing, it's called a Tensor Core GPU, which is what we created.

當你創建這樣的東西時，它被稱為 Tensor Core GPU，這就是我們創建的。

And so I think the first opportunity for us in -- large-scale opportunity will be in the data center and the cloud.

所以我認為我們的第一個機會——大規模的機會將在數據中心和雲中。

The second will be in vertical markets.

第二個將是垂直市場。

The vertical market that you mentioned is self-driving cars.

你提到的垂直市場是自動駕駛汽車。

And we see a great opportunity in autonomous vehicles, both in the creation of autonomous vehicles.

我們看到了自動駕駛汽車的巨大機遇，無論是在自動駕駛汽車的創造方面。

And I mentioned that before, between now and the time that we ramp our AV computers we call DRIVE, we're going to be selling a whole lot of servers so that the companies could develop their neural network models for their self-driving cars as well as simulating the virtual reality -- in virtual reality, there are various test drives as well as testing their neural network and their self-driving car stack against billions and billions of miles of saved-up prerecorded videos.

我之前提到過，從現在到我們推出我們稱之為 DRIVE 的 AV 計算機，我們將出售大量服務器，以便公司可以為他們的自動駕駛汽車開發神經網絡模型除了模擬虛擬現實——在虛擬現實中，還有各種試駕，以及針對數十億英里保存的預先錄製的視頻測試他們的神經網絡和他們的自動駕駛汽車堆棧。

And so in the vertical markets, we're going to see inference both in the data center for developing the self-driving car stack as well as in the self-driving cars themselves.

因此，在垂直市場中，我們將在用於開發自動駕駛汽車堆棧的數據中心以及自動駕駛汽車本身中看到推理。

Now in the self-driving cars, the ASPs for level 2 could be a few hundred dollars to a level 5 self-driving car, taxi or driverless taxi being a few thousand dollars.

現在在自動駕駛汽車中，2 級的 ASP 可能是幾百美元，而 5 級的自動駕駛汽車、出租車或無人駕駛出租車的平均售價可能是幾千美元。

And I expect that driverless taxis will start going to market about 2019, and self-driving cars probably somewhere between 2020 and 2021.

我預計無人駕駛出租車將在 2019 年左右開始上市，而自動駕駛汽車可能會在 2020 年至 2021 年之間的某個時間點上市。

And I think the size of the market is fairly well modeled.

而且我認為市場的規模是相當好的模型。

And the simple way to think about that is, I believe that every single -- everything that moves someday will be autonomous or have autonomous capabilities.

考慮這一點的簡單方法是，我相信每一個——有朝一日移動的一切都將是自主的或具有自主能力。

And so the 100 million cars, the countless taxis, all the trucks, all the agriculture equipment, all the pizza delivery vehicles, you name it, everything is going to be autonomous.

所以一億輛汽車，無數的出租車，所有的卡車，所有的農業設備，所有的披薩送貨車，你說的，一切都將是自動的。

And the market opportunity is going to be quite large.

市場機會將非常大。

And that's the reason why we're so determined to go create that market.

這就是為什麼我們如此堅定地去創造這個市場的原因。

Your next question is from Tim Arcuri with UBS.

您的下一個問題來自瑞銀的 Tim Arcuri。

I actually wanted to go back to the question about seasonality for gaming in June.

我實際上想回到關於 6 月份遊戲季節性的問題。

Normal seasonal sounds like it's up mid-teens for June in gaming.

正常的季節性聽起來像是六月的遊戲中十幾歲。

But obviously, the comps are skewed a little bit because of the channel restock and the crypto stuff.

但很明顯，由於渠道補貨和加密貨幣的影響，comps 有點偏差。

So does the guidance for June assume that gaming is better or worse than that mid-teens normal seasonal?

那麼 6 月份的指導是否假設遊戲比青少年正常的季節更好或更差？

We are expecting the -- we are expecting Q2 to be better than seasonality, if I understand your question.

如果我理解你的問題，我們預計 - 我們預計第二季度會比季節性更好。

It should be -- we're expecting Q2 to be better than Q1 and we're expecting Q2 to be better than seasonality.

應該是——我們預計第二季度會好於第一季度，我們預計第二季度會好於季節性。

Did that answer your question?

這回答了你的問題嗎？

Your next question is from Atif Malik with Citi.

您的下一個問題來自花旗的 Atif Malik。

I have a question for Colette.

我有一個問題要問科萊特。

Colette, first thank you for breaking out crypto sales in the OEM line and guide for us.

Colette，首先感謝您在 OEM 線上打破加密貨幣銷售並為我們提供指導。

I have a question on your gross margins.

我對你們的毛利率有疑問。

Your gross margins have been expanding on product mix, despite component pricing headwinds on the DRAM side.

儘管 DRAM 方面存在組件定價逆風，但您的產品組合的毛利率一直在擴大。

When do you expect component pricing to become a tailwind to your gross margin?

您預計組件定價何時會成為您的毛利率的順風車？

Thanks so much for the question.

非常感謝這個問題。

When you think about our gross margins, just over this last quarter, as you know, we were working on stabilizing the overall supply that was out there in the market for consumer GPUs.

當您考慮我們的毛利率時，如您所知，就在上個季度，我們正在努力穩定消費 GPU 市場上的整體供應。

We benefited from that with a higher gross margin as we filled and completed that.

當我們填充並完成它時，我們受益於更高的毛利率。

You've seen us absorb a significant amount of the component pricing changes that we have seen, particularly around the memory.

您已經看到我們吸收了我們所看到的大量組件定價變化，特別是在內存方面。

We're not here to be able to forecast, generally, when those pricing of those components will stabilize.

一般來說，我們無法預測這些組件的定價何時會穩定下來。

But we believe in terms of the value added that our platforms provide, the components are an important part of finishing that.

但我們相信，就我們平台提供的附加值而言，組件是完成這一目標的重要組成部分。

But I think we have a tremendous amount more value that we are adding in terms of the software on top of our platforms, which is enabling our gross margins.

但我認為我們在平台上的軟件方面增加了巨大的價值，這使我們的毛利率得以提高。

Your next question is from Chris Caso with Raymond James.

您的下一個問題來自 Chris Caso 和 Raymond James。

My question is the progress on the deployment of Volta into the cloud service providers.

我的問題是在雲服務提供商中部署 Volta 的進展情況。

You talked in your prepared remarks about 5 deployments, including the Google beta.

您在準備好的評論中談到了 5 個部署，包括 Google 測試版。

Can you talk about the -- how soon we can expect to see some of those remaining deployments?

你能談談我們多久可以看到一些剩餘的部署嗎？

And of those already launched, how far are they along?

在那些已經推出的產品中，它們走了多遠？

What -- I guess, to say proverbially, what inning are we in, in these deployments?

什麼 - 我想，俗話說，在這些部署中，我們處於什麼局？

Yes.

是的。

So first of all, Volta is a reinvented GPU.

所以首先，Volta 是一個重新發明的 GPU。

Volta is the world's first GPU that has been designed to be incredibly good at deep learning.

Volta 是世界上第一款專為深度學習而設計的 GPU。

We call it the Tensor Core GPU.

我們稱之為 Tensor Core GPU。

It has still retained all of the flexibilities of all -- everything that CUDA has ever run is backwards compatible with everything that runs on CUDA.

它仍然保留了所有的靈活性——CUDA 曾經運行的所有東西都向後兼容在 CUDA 上運行的所有東西。

But it has new architectures designed to be incredibly good at deep learning.

但它有新的架構，旨在非常擅長深度學習。

We call it a Tensor Core GPU.

我們稱之為 Tensor Core GPU。

And that's the reason why it has all of the benefits of our GPU, but none of the ASICs can catch up to it.

這就是為什麼它具有我們 GPU 的所有優點，但沒有一個 ASIC 能夠趕上它的原因。

And so Volta is really a breakthrough.

所以 Volta 確實是一個突破。

We're going to be very successful with Volta.

Volta 我們會非常成功。

Every cloud will have it.

每朵雲都會有它。

The initial deployment is for internal consumption.

初始部署供內部使用。

Volta has been shipping to the cloud service -- cloud providers, the Internet service companies, for the vast majority of last quarter, as you guys know.

正如你們所知，Volta 在上個季度的絕大多數時間裡一直在向雲服務——雲提供商、互聯網服務公司提供服務。

And they're using it internally.

他們在內部使用它。

And now they're starting to open up Volta for external consumption, their cloud customers.

現在他們開始開放 Volta 以供外部消費，他們的雲客戶。

And they are moving as fast as they can.

他們正在盡可能快地移動。

And my expectation is that you're going to see a lot more coming online this quarter.

我的期望是，您將在本季度看到更多內容上線。

Your next question comes from Mark Lipacis with Jefferies.

您的下一個問題來自 Jefferies 的 Mark Lipacis。

I had a question about the DGX family of products.

我有一個關於 DGX 系列產品的問題。

Our own fieldwork is indicating very positive reception for DGX.

我們自己的實地調查表明，DGX 受到了非常積極的歡迎。

And I was wondering, Jensen, if can you help us understand, the high-growth we've seen in the data center business, to what extent is that being driven by the DGX?

我想知道，Jensen，您能否幫助我們了解我們在數據中心業務中看到的高速增長，在多大程度上是由 DGX 推動的？

And what, when DGX-2 starts to ramp in the back half of the year, is that -- is it something that kind of layers on top of DGX?

什麼，當 DGX-2 在今年下半年開始增長時，那是什麼 - 它是 DGX 之上的那種層嗎？

Does DGX-2 layer top of DGX?

DGX-2 是否位於 DGX 之上？

Are they (inaudible) segments, that you're kind of segmenting the market with these different products?

它們是（聽不清）細分嗎，您是在用這些不同的產品細分市場嗎？

Any color on how to think about those 2 products are -- should be -- would be helpful.

關於如何思考這兩種產品的任何顏色 - 應該 - 都會有所幫助。

Colette, could you give me a brief version of that?

Colette，你能給我一個簡短的版本嗎？

It was kind of crackling on my side.

在我身邊有點劈啪作響。

I'm going to ask the operator if they could ask for the question again because it was also, on our side, a little crackly.

我要問接線員是否可以再次問這個問題，因為在我們這邊，它也有點脆。

Yes.

是的。

Mark, your line is open.

馬克，你的線路是開放的。

Please restate your question.

請重申您的問題。

Okay.

好的。

Can you hear me better now?

你現在能聽到我的聲音了嗎？

Yes.

是的。

Much better, Mark.

好多了，馬克。

Okay.

好的。

Sorry about that.

對於那個很抱歉。

I'm at the airport.

我在機場。

So the question was on the DGX family of products.

所以問題在於 DGX 系列產品。

Our own fieldwork indicates very positive reception.

我們自己的實地考察表明接受度非常高。

I was wondering, Jensen, if you could help us understand the high-growth you've seen in the data center market.

Jensen，我想知道您能否幫助我們了解您在數據中心市場中看到的高速增長。

How much is DGX contributing to that?

DGX 對此貢獻了多少？

And then, when DGX-2 starts to ramp in the second half of the year, how do we think about DGX-1?

然後，當 DGX-2 在下半年開始爬坡時，我們如何看待 DGX-1？

Does it replace DGX -- the original DGX?

它是否取代了 DGX——原來的 DGX？

Or you're going after different segments?

或者你要追求不同的細分市場？

Or do they layer on top of one another?

還是它們相互疊加？

Any color on that would be helpful.

上面的任何顏色都會有幫助。

I see.

我懂了。

Thank you.

謝謝你。

DGX-2 and DGX-1 will both be in the market at the same time.

DGX-2 和 DGX-1 將同時上市。

And DGX is a few hundred million dollar business.

DGX 是幾億美元的業務。

It's -- it was introduced last year.

它是——它是去年推出的。

So its growth rate is obviously very high.

所以它的增長率顯然很高。

It's designed for enterprises where they don't -- they need to have their computers on-premise, but they don't want to build a supercomputer and they don't have the expertise to do so.

它是為那些不需要的企業設計的——他們需要在內部部署計算機，但他們不想構建超級計算機，而且他們沒有這樣做的專業知識。

And they would like to pull a supercomputer out of a box, plug it in, and start doing supercomputing.

他們想從盒子裡拿出一台超級計算機，插上電源，然後開始做超級計算。

And so DGX is really designed for enterprises.

所以 DGX 真的是為企業設計的。

It's designed for car companies.

它是為汽車公司設計的。

It's designed for health care companies doing life sciences work or medical imaging work.

它專為從事生命科學工作或醫學成像工作的醫療保健公司而設計。

We recently announced a project called Project Clara, which basically takes medical imaging equipment, virtualizes them, containerizes the software, and turns it into a -- and most medical imaging equipment today are computational and they -- a lot of them run on NVIDIA CUDA anyways.

我們最近宣布了一個名為 Project Clara 的項目，它基本上採用醫學成像設備，將它們虛擬化，將軟件容器化，然後將其轉變為 - 而當今大多數醫學成像設備都是計算型的，而且它們 - 其中很多都在 NVIDIA CUDA 上運行無論如何。

And so we put that -- we can put that into the data center, we can virtualize their medical instruments and it gives them the opportunity to upgrade the millions of instruments that are out in the marketplace today.

所以我們把它——我們可以把它放到數據中心，我們可以虛擬化他們的醫療儀器，這讓他們有機會升級當今市場上數百萬的儀器。

And so DGX is really designed for enterprises and we're seeing great success there.

所以 DGX 真的是為企業設計的，我們在那裡看到了巨大的成功。

It's really super easy to use and it comes with direct support from HPC and AI researchers at NVIDIA.

它非常易於使用，並且得到了 NVIDIA 的 HPC 和 AI 研究人員的直接支持。

And the answer to your question at the end is, both of them will be in the marketplace at the same time.

最後你的問題的答案是，它們都將同時在市場上。

Next question is from Mitch Steves with RBC Capital Markets.

下一個問題來自 RBC Capital Markets 的 Mitch Steves。

I'm actually going to go with a more nitty-gritty question just on the financial side, just to make sure I'm understanding this right.

實際上，我將在財務方面提出一個更具體的問題，以確保我正確理解這一點。

So the OEM beat was pretty material, given a lot of crypto revenue.

因此，考慮到大量的加密收入，OEM 擊敗非常重要。

Is it still the case that OEM is materially lower gross margin than your corporate average at this time?

此時 OEM 的毛利率是否仍遠低於您的企業平均水平？

Sure.

當然。

I'll take that question.

我會回答這個問題。

Generally, our OEM business can be a little bit volatile.

一般來說，我們的 OEM 業務可能會有些波動。

Because remember, OEM business incorporates our mainstream GPUs as well as our Tegra integrated.

因為請記住，OEM 業務包含我們的主流 GPU 以及我們的 Tegra 集成。

So we have development platforms that we sell on some of the Tegra piece of it.

所以我們有開發平台，我們在其中的一些 Tegra 上出售。

But they are slightly below, and I think you can go back and refer to our discussion at Investor Day as there's a slide there that talks about those embedded pieces and them being below.

但它們略低於，我認為你可以回去參考我們在投資者日的討論，因為那裡有一張幻燈片討論了這些嵌入式部件，它們在下面。

So yes, you're correct.

所以是的，你是對的。

Again, a very small part of our business right now.

同樣，現在我們業務的一小部分。

Your next question comes from Christopher Rolland with Susquehanna.

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

So your competitor thinks that just 10% of their sales were from crypto, or like $150 million, $160 million.

所以你的競爭對手認為他們只有 10% 的銷售額來自加密貨幣，或者像 1.5 億美元，1.6 億美元。

And you guys did almost $300 million there.

你們在那裡做了將近 3 億美元。

And perhaps I think there could actually be some in gaming as well, which would imply that you guys have 2/3 or more of that market?

也許我認為實際上游戲中也可能有一些，這意味著你們擁有該市場的 2/3 或更多？

So I guess, what's going on there?

所以我想，那裡發生了什麼？

Is there a pricing dynamic that's allowing you to have such share there?

是否有定價動態允許您在那裡擁有這樣的份額？

Or do you think it's your competitors that don't know what's actually being sold to miners versus gamers?

還是您認為是您的競爭對手不知道實際出售給礦工和遊戲玩家的是什麼？

Why such implied share in that market?

為什麼在那個市場中有如此隱含的份額？

Well, we try to as transparently reveal our numbers as we can.

好吧，我們試圖盡可能透明地披露我們的數字。

And so the thing that we -- our strategy is to create a SKU that allows the crypto miners to fulfill their needs, and we call it CMP, and to the -- as much as possible, fulfill their demand that way.

所以我們——我們的策略是創建一個 SKU，允許加密礦工滿足他們的需求，我們稱之為 CMP，並儘可能地以這種方式滿足他們的需求。

Sometimes it's just not possible because the demand is too great.

有時這是不可能的，因為需求太大。

And -- but we try to do so.

而且 - 但我們嘗試這樣做。

And we try to keep the miners on the CMP SKUs as much as we can.

我們盡量讓礦工留在 CMP SKU 上。

And so I'm not exactly sure how other people do it, but that's the way we do it.

所以我不確定其他人是如何做到的，但我們就是這樣做的。

Your next question is from Craig Ellis with B. Riley.

您的下一個問題來自 Craig Ellis 和 B. Riley。

Jensen, I just wanted to come back to an announcement that you made at GTC with ray tracing.

Jensen，我只是想回到你在 GTC 上發布的關於光線追踪的公告。

Because the technology looked like it was very high fidelity, and I think you noted at that time that it was very computationally intensive.

因為這項技術看起來保真度很高，而且我想你當時注意到它的計算量非常大。

So the question is, as we think about the gaming business and the potential for ray tracing to enter that platform group, what does it mean for dynamics that we've seen in the past, for example, the ability to really push the high end of the market with high-end capability, 1070.

所以問題是，當我們考慮遊戲業務和光線追踪進入該平台組的潛力時，對於我們過去看到的動態意味著什麼，例如，真正推動高端的能力具有高端能力的市場，1070。

Ti launched late last year.

Ti於去年年底推出。

It was very successful.

它非常成功。

Does this give you further flexibility for those types of launches as you bring exciting and very high-end technology to market?

當您將令人興奮的非常高端的技術推向市場時，這是否為您提供了更大的靈活性來進行這些類型的發布？

Yes.

是的。

I appreciate it.

我很感激。

NVIDIA RTX is the biggest computer graphics invention in the last 15 years.

NVIDIA RTX 是過去 15 年來最大的計算機圖形發明。

It took us 1 decade to do.

我們花了 1 年的時間才完成。

We literally worked on it continuously for 1 decade.

我們確實在這方面持續工作了 1 年。

And to put it into perspective, it's basically film rendering, cinematic rendering, except it's in real time.

從透視角度來看，它基本上是電影渲染，電影渲染，除了它是實時的。

It merges the style of computer graphics, rasterization, and light simulation, what people call ray tracing, as well as deep learning and AI, merged it into one unified framework so that we can achieve cinematic rendering in real time.

它融合了計算機圖形、光柵化和光模擬的風格，也就是人們所說的光線追踪，以及深度學習和人工智能，將它們融合到一個統一的框架中，這樣我們就可以實時實現電影渲染。

What it currently takes is a server about a few hours, depending on the scene, it might take as long as a full day, take a few hours to render one frame.

目前它需要一個服務器大約幾個小時，根據場景，可能需要一整天，渲染一幀需要幾個小時。

So it takes a server, one node of a server, several hours to render one frame.

所以一個服務器，一個服務器的一個節點，渲染一幀需要幾個小時。

And in order to render 30 frames per second, just imagine the number of servers you need.

為了每秒渲染 30 幀，想像一下您需要的服務器數量。

If it takes several hours per frame, and you need to render 30 frames per second in order to be real-time, it basically takes a high-performance computer, a supercomputer, a render farm, that's why they call it a render farm, it's a full data center designed just for rendering.

如果每幀需要幾個小時，而你需要每秒渲染 30 幀才能達到實時性，它基本上需要一台高性能計算機、一台超級計算機、一個渲染農場，這就是他們稱之為渲染農場的原因，這是一個專為渲染而設計的完整數據中心。

And now we've created NVIDIA RTX which makes it possible to do in real time.

現在我們已經創建了 NVIDIA RTX，它可以讓實時操作成為可能。

We demonstrated RTX on 4 Quadro GV100s.

我們在 4 個 Quadro GV100 上演示了 RTX。

It takes 4 of our latest generation Volta Tensor Core GPUs to be able to render 30 frames per second, the Star Wars cinematic that people enjoyed.

我們需要 4 個最新一代 Volta Tensor Core GPU 才能以每秒 30 幀的速度渲染人們喜歡的星球大戰電影。

And so the amount that we saved, we basically took an entire data center, reduced it into one node.

所以我們節省的數量，基本上是把整個數據中心，減少到一個節點。

And we're now doing it in real time.

我們現在正在實時進行。

And so the amount of money that we can save people who create movies, people who do commercials, people who use film rendering to create the game content, almost every single game is done that way.

所以我們可以為製作電影的人、做廣告的人、使用電影渲染來創建遊戲內容的人節省的錢，幾乎每一款遊戲都是這樣完成的。

There's quite a bit of offline rendering to create the imagery and the textures and the lighting.

有相當多的離線渲染來創建圖像、紋理和照明。

And then, of course, architectural design and car design, the number of applications, the number of industries that are built on top of modern computer graphics is really quite large.

然後，當然，建築設計和汽車設計，應用程序的數量，建立在現代計算機圖形之上的行業數量確實非常多。

And I'm certain that NVIDIA RTX is going to impact every single one of them.

而且我確信 NVIDIA RTX 將影響他們中的每一個人。

And so that's our starting point, is to dramatically reduce the cost of film rendering, dramatically reduce the time that it takes to do it, and hopefully, more GPU servers will get -- will be purchased.

所以這就是我們的出發點，就是要顯著降低電影渲染的成本，顯著減少完成它所需的時間，並希望能夠獲得更多的 GPU 服務器——將被購買。

And of course, better content will be created.

當然，將創建更好的內容。

Long-term, we've also now plotted the path towards doing it in real time.

從長遠來看，我們現在還繪製了實時實現它的路徑。

And someday, we will be able to put RTX into a GeForce gaming card and the transformation to the revolution to the gaming industry will be quite extraordinary.

有朝一日，我們將能夠將 RTX 放入 GeForce 遊戲卡中，從而為遊戲行業帶來革命性的轉變。

So we're super excited about RTX.

所以我們對 RTX 感到非常興奮。

Your next question is from Stacy Rasgon with Bernstein.

您的下一個問題來自 Stacy Rasgon 和 Bernstein。

This is a question for Colette.

這是科萊特的問題。

I want to follow up, again, on the seasonality, understanding the prior comments.

我想再次跟進季節性，了解先前的評論。

Normal seasonal for Q2 for gaming would be up in the double digits.

第二季度遊戲的正常季節性將增長兩位數。

Given your commentary on the crypto declined into Q2, given your commentary on just the general drivers around data center and the Volta ramps, I can't bring that together with the idea of gaming being above seasonal within the guidance -- the context of your guidance envelope.

鑑於您對加密貨幣的評論在第二季度下降，鑑於您僅對圍繞數據中心和 Volta 坡道的一般驅動因素發表評論，我無法將其與指南中的遊戲高於季節性的想法結合在一起——在您的背景下指導信封。

So how should I reconcile those things?

那麼我應該如何調和這些東西呢？

How are you actually thinking about seasonality for gaming into Q2 within the context of the scenarios that are currently contemplated in your guidance for next quarter?

在您下一季度的指導中目前考慮的情景背景下，您實際上如何考慮遊戲進入第二季度的季節性？

Sure, Stacy.

當然，斯泰西。

Let me see if I can bridge together Jensen and then some comments here.

讓我看看我能不能把 Jensen 聯繫起來，然後在這裡發表一些評論。

Unfortunately, they are moving quite fast to the next question, so I wasn't able to add on.

不幸的是，他們在下一個問題上進展得很快，所以我無法補充。

But let me see if I can add on here and provide a little bit of clarity in terms of the seasonality.

但是讓我看看我是否可以在這裡添加並在季節性方面提供一點清晰。

Remember, in Q1, we outgrew seasonality significantly.

請記住，在第一季度，我們明顯超過了季節性。

We left Q4 with very low inventory in terms of the channel.

就渠道而言，我們在第四季度留下了非常低的庫存。

We spent Q1 working on establishing a decent amount of inventory available.

我們在第一季度致力於建立大量可用的庫存。

We wanted to concentrate on our miners separately, and then you can see we did that in terms of Q1 by moving that to OEM and moving that to cryptocurrency-only boards.

我們想分別專注於我們的礦工，然後你可以看到我們在第一季度通過將其轉移到 OEM 並將其轉移到僅加密貨幣的板中做到了這一點。

So we left Q1 at this point with healthy overall channel inventory levels as far as where we stand.

因此，就目前而言，我們在這一點上以健康的整體渠道庫存水平離開了第一季度。

So that then takes you now to Q2.

因此，現在將您帶到第二季度。

But if we overshot in terms of seasonality in terms of Q1, we don't have to do those channel-fill dynamics again as we get into Q2.

但是，如果我們在第一季度的季節性方面超出預期，那麼當我們進入第二季度時，我們不必再次進行那些渠道填充動態。

But we do have demand out there for our gamers that we can now address very carefully with the overall inventory that we now have available.

但是我們確實對我們的遊戲玩家有需求，我們現在可以使用我們現在可用的整體庫存非常仔細地解決這些需求。

So putting together Q1 and Q2 together, yes, we are within normal seasonality, again, for our guidance.

因此，將第一季度和第二季度放在一起，是的，我們處於正常的季節性範圍內，再次作為我們的指導。

And we'll see how we'll finish in terms of the quarter.

我們將看看我們將如何在本季度完成。

But you should be in that range.

但是你應該在那個範圍內。

So yes, from a normal seasonality, at a year-to-date inclusive of Q2, yes, we're on that overall seasonality.

所以是的，從正常的季節性來看，從年初至今（包括第二季度），是的，我們處於整體季節性。

Always keep in mind, generally, our H2s are usually higher than our overall H1s, and that's what you should think about our overall guidance.

永遠記住，一般來說，我們的 H2 通常高於我們的整體 H1，這就是您應該考慮我們的整體指導的內容。

Gaming is still strong.

遊戲還是很強大的。

We have to comment that our overall drivers that have taken us to this place over the last 3 to 5 years with phenomenal growth and our ability to grow that overall market is still here and all of those things are together.

我們不得不評論說，在過去 3 到 5 年裡，我們以驚人的增長和我們發展整個市場的能力將我們帶到這個地方的整體驅動因素仍然存在，所有這些東西都在一起。

We've just had a few quarters in terms of making sure that we get the overall channel correct and put our miners separately.

在確保我們獲得正確的整體渠道並將我們的礦工分開放置方面，我們剛剛度過了幾個季度。

I hope that clarifies in terms of where we are, in terms of gaming seasonality.

我希望在遊戲季節性方面澄清我們所處的位置。

Your last question comes from Will Stein with SunTrust.

您的最後一個問題來自 SunTrust 的 Will Stein。

The question relates to the supply chain challenges that you've talked so much about in the gaming end market.

這個問題與您在遊戲終端市場上談過很多的供應鏈挑戰有關。

I'm wondering if there's something particular to that end market that is making the shortages concentrated there?

我想知道這個終端市場是否有什麼特殊的東西導致短缺集中在那裡？

Or are, in fact, other end markets in particular that the data center end market also somewhat restricted from what growth they might have achieved if there weren't the shortages that are out there?

或者，事實上，其他終端市場，特別是數據中心終端市場，如果沒有短缺，它們可能實現的增長也受到了一定的限制？

And maybe talk about the pace of recovery of those.

也許談談這些的恢復速度。

That be really helpful.

這真的很有幫助。

Let me start off here, and I'll have Jensen finish up on the last part of that question.

讓我從這裡開始，我會讓 Jensen 完成這個問題的最後一部分。

But overall, our data center business did phenomenal.

但總的來說，我們的數據中心業務表現出色。

Volta is doing extremely well.

Volta 做得非常好。

And even now with 32-bit, we're seeing tremendous adoption throughout.

即使是現在使用 32 位，我們也看到了巨大的採用率。

Again, remember it's very different than the overall consumer business.

同樣，請記住它與整體消費者業務非常不同。

You have significant amount of time for qualification, and that is moving extremely fast based on a lot of other industries and their ability to qualify.

你有大量的時間來獲得資格，而且基於許多其他行業和他們的資格能力，這進展得非常快。

So no, there is not a supply challenge at all in terms of our data center.

所以不，就我們的數據中心而言，根本不存在供應挑戰。

And our overall growth in data center, we're extremely pleased with in terms of how the quarter came out.

而我們在數據中心的整體增長，我們對本季度的表現感到非常滿意。

I'll turn it over to you, Jensen, and you can answer the rest of the part of it.

Jensen，我會把它交給你，你可以回答剩下的部分。

Yes.

是的。

The reason why miners love GeForce is because miners are everywhere in the world.

礦工之所以喜歡 GeForce，是因為礦工遍布世界各地。

One of the benefits of cryptocurrency is that it's not any sovereign currency.

加密貨幣的好處之一是它不是任何主權貨幣。

And it's, in the digital world, it's distributed.

在數字世界中，它是分佈式的。

And GeForce is the single largest distributed supercomputing infrastructure on the planet.

GeForce 是地球上最大的分佈式超級計算基礎設施。

Every gamer has a supercomputer in their PC.

每個遊戲玩家的 PC 中都有一台超級計算機。

And GeForce is so broadly distributed, it's available everywhere.

GeForce 分佈廣泛，隨處可用。

And so GeForce is really a good candidate for any new cryptocurrency or any new cryptography algorithm that comes along.

因此，GeForce 確實是任何新加密貨幣或任何新出現的加密算法的理想選擇。

We try the best we can to go directly to the major miners, and the major miners.

我們盡最大努力直接聯繫主要礦工和主要礦工。

And they represent the vast majority of the demand.

它們代表了絕大多數需求。

And to the best of our ability, serve their needs directly and we call that CMP, and that's why it's not called GeForce.

盡我們所能，直接滿足他們的需求，我們稱之為 CMP，這就是為什麼它不被稱為 GeForce。

They're called CMP.

他們被稱為CMP。

And we can serve those miners directly, hopefully, to take some of the demand pressure off of the GeForce market.

我們可以直接為這些礦工服務，希望能夠減輕 GeForce 市場的一些需求壓力。

Because ultimately, what we would like is, we would like the market for GeForce pricing to come down so that the gamers could benefit from the GeForces that we built for them.

因為最終，我們想要的是，我們希望 GeForce 定價市場能夠下降，以便遊戲玩家可以從我們為他們打造的 GeForce 中受益。

And the gaming demand is strong.

遊戲需求強勁。

I mean, the bottom line is, Fortnite is a home run.

我的意思是，底線是，Fortnite 是本壘打。

The bottom line is, PUBG is a home run.

底線是，PUBG 是本壘打。

And the number of gamers that are enjoying these games is really astronomic, as people know very well.

正如人們非常清楚的那樣，享受這些遊戲的玩家數量確實是天文數字。

And it's a global phenomenon.

這是一個全球現象。

These 2 games are equally fun in Asia as it is in Europe, as it is in the United States.

這兩個遊戲在亞洲和歐洲一樣有趣，在美國也一樣有趣。

And because you team up and this is a Battle Royale, you'd rather play with your friends.

而且因為你組隊，這是一場大逃殺，你寧願和你的朋友一起玩。

So it's incredibly social.

所以它是令人難以置信的社交。

It's incredibly sticky.

它非常粘。

And more and more -- more gamers that play, more of their friends join, and more of their friends join, more gamers that play.

而且越來越多——更多的玩家玩，更多的朋友加入，更多的朋友加入，更多的玩家玩。

And so it's this positive feedback system, and the guys at Epic did a fantastic job creating Fortnite, and it's just a wonderful game genre that people are really enjoying.

所以這是一個積極的反饋系統，Epic 的人在創建 Fortnite 方面做得非常出色，這是一種人們真正喜歡的精彩遊戲類型。

And so I think at the core of it, gaming is strong and we are looking forward to inventory normalizing in the channel so that pricing could normalize in the channel, so that gamers can come back to buy the GeForce cards that has now been in short supply for over a quarter.

所以我認為，遊戲的核心是強大的，我們期待渠道中的庫存正常化，以便渠道中的定價可以正常化，以便遊戲玩家可以回來購買現在短缺的 GeForce 卡供應超過四分之一。

And so the pent-up demand is quite significant, and I'm expecting the gamers to be able to buy new GeForces pretty soon.

因此，被壓抑的需求相當大，我預計遊戲玩家很快就能購買新的 GeForce。

Unfortunately, we have ran out of time.

不幸的是，我們已經沒有時間了。

I will now turn it back over to Jensen for any closing remarks.

我現在將把它交還給 Jensen 做任何結束語。

Let's see here.

讓我們看看這裡。

Is it my turn again?

又輪到我了？

It is.

這是。

Okay.

好的。

We had another great quarter.

我們有另一個很棒的季度。

Record revenue, record margins, record earnings, growth across every platform.

創紀錄的收入、創紀錄的利潤率、創紀錄的收益和每個平台的增長。

Data center achieved another record, with strong demand for Volta and AI inference.

數據中心再創紀錄，Volta和AI推理需求旺盛。

Gaming was strong.

遊戲很強大。

We're delighted to see prices normalizing and we can better serve pent-up gamer demand.

我們很高興看到價格正常化，我們可以更好地滿足被壓抑的遊戲玩家需求。

At the heart of our opportunity is the incredible growth of computing demand of AI, just as traditional computing has slowed.

我們機會的核心是人工智能計算需求的驚人增長，正如傳統計算已經放緩一樣。

The GPU computing approach that we've pioneered is ideal for filling this vacuum.

我們開創的 GPU 計算方法非常適合填補這一真空。

And our invention of the Tensor Core GPU has further enhanced our strong position to power the AI era.

我們發明的 Tensor Core GPU 進一步增強了我們在人工智能時代的強大地位。

I look forward to giving you another update next quarter.

我期待在下個季度為您提供另一個更新。

Thank you.

謝謝你。

This concludes today's conference call.

今天的電話會議到此結束。

Thank you, guys, for joining.

謝謝小伙伴們的加入。

You may now disconnect.

您現在可以斷開連接。