輝達 (NVDA) 2023 Q1 法說會逐字稿

Good afternoon. My name is David, and I'll be your conference operator today. At this time, I'd like to welcome everyone to NVIDIA's first quarter earnings call. Today's conference is being recorded. (Operator Instructions) Thank you.

下午好。我的名字是大衛，今天我將成為您的會議接線員。在這個時候，我想歡迎大家參加 NVIDIA 的第一季度財報電話會議。今天的會議正在錄製中。 （操作員說明）謝謝。

Simona Jankowski, you may begin your conference.

Simona Jankowski，你可以開始你的會議了。

Thank you. Good afternoon, everyone, and welcome to NVIDIA's conference call for the first quarter of fiscal 2023. With me today from NVIDIA are Jen-Hsun Huang, President and Chief Executive Officer; and Colette Kress, Executive Vice President and Chief Financial Officer.

謝謝你。大家下午好，歡迎參加 NVIDIA 2023 財年第一季度電話會議。今天與我一起來自 NVIDIA 的是總裁兼首席執行官黃仁勳；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. The webcast will be available for replay until the conference call to discuss our financial results for the second quarter of fiscal 2023. 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。網絡直播將在電話會議召開之前進行重播，以討論我們 2023 財年第二季度的財務業績。今天電話會議的內容是 NVIDIA 的財產。未經我們事先書面同意，不得複製或轉錄。

During this call, we may make forward-looking statements based on current expectations. These are subject to a number of significant risks and uncertainties and our actual results may differ materially. For a discussion of factors that could affect our future financial results and business, please refer to the disclosure in today's earnings release, our most recent Forms 10-K and 10-Q and the reports that we may file on Form 8-K with the Securities and Exchange Commission. All our statements are made as of today, May 25, 2022, based on information currently available to us. Except as required by law, we assume no obligation to update any such statements.

在本次電話會議期間，我們可能會根據當前預期做出前瞻性陳述。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。有關可能影響我們未來財務業績和業務的因素的討論，請參閱今天的收益發布中的披露、我們最近的 10-K 和 10-Q 表格以及我們可能在 8-K 表格中與證券交易委員會。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，即 2022 年 5 月 25 日。除法律要求外，我們不承擔更新任何此類聲明的義務。

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

在本次電話會議中，我們將討論非 GAAP 財務指標。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. We delivered a strong quarter driven by record revenue in both Data Center and Gaming, with strong fundamentals and execution against a challenging macro backdrop.

謝謝，西蒙娜。在數據中心和遊戲業務創紀錄的收入的推動下，我們在充滿挑戰的宏觀背景下擁有強勁的基本面和執行力，實現了強勁的季度業績。

Total revenue of $8.3 billion was a record, up 8% sequentially and up 46% year-on-year. Data Center has become our largest market platform, and we see continued strong momentum going forward.

總收入達到創紀錄的 83 億美元，環比增長 8%，同比增長 46%。數據中心已成為我們最大的市場平台，我們看到了持續強勁的發展勢頭。

Starting with Gaming. Revenue of $3.6 billion rose 6% sequentially and 31% year-on-year, powered by the GeForce RTX 30 Series product cycle. Since launching in the fall of 2020, the RTX 30 Series has been our best Gaming product cycle ever. The gaming industry has grown tremendously, with 100 million new PC gamers added in the past 2 years according to Newzoo. And NVIDIA RTX has set new standard for the industry, with demand from both first-time GPU buyers as well as those upgrading their PCs to experience the 250-plus RTX-optimized games and apps, double from last year. We estimate that almost 1/3 of the GeForce Gaming GPU installed base is now on RTX.

從遊戲開始。在 GeForce RTX 30 系列產品週期的推動下，收入 36 億美元，環比增長 6%，同比增長 31%。自 2020 年秋季推出以來，RTX 30 系列一直是我們有史以來最好的遊戲產品週期。根據 Newzoo 的數據，遊戲行業發展迅猛，過去 2 年新增了 1 億 PC 遊戲玩家。 NVIDIA RTX 為行業樹立了新標準，首次購買 GPU 的用戶以及升級 PC 以體驗 250 多種 RTX 優化遊戲和應用程序的需求比去年翻了一番。我們估計，現在有近 1/3 的 GeForce 遊戲 GPU 安裝基數在 RTX 上。

RTX has brought tremendous energy into the gaming world, and has helped drive a sustained expansion in our higher-end platforms and installed base with significant runway still ahead. Overall, end demand remained solid, though mixed by region, and demand in Americas remain strong. However, we started seeing softness in parts of Europe related to the war in the Ukraine and parts of China due to the COVID lockdowns.

RTX 為遊戲世界帶來了巨大的能量，並幫助推動了我們的高端平台和安裝基礎的持續擴張，並且仍有重要的發展空間。總體而言，終端需求保持穩健，儘管各地區喜憂參半，美洲的需求依然強勁。但是，由於 COVID 封鎖，我們開始看到與烏克蘭戰爭和中國部分地區有關的歐洲部分地區疲軟。

As we expect some ongoing impact as we prepare for a new architectural transition later in the year, we are projecting Gaming revenue to decline sequentially in Q2. Channel inventory has nearly normalized, and we expect it to remain around these levels in Q2.

由於我們預計在今年晚些時候為新的架構過渡做準備時會產生一些持續的影響，我們預計遊戲收入將在第二季度連續下降。渠道庫存已接近正常化，我們預計第二季度將保持在這些水平附近。

The extent in which cryptocurrency mining contributed to Gaming demand is difficult for us to quantify with any reasonable degree of precision. The reduced pace of increase in Ethereum network hash rate likely reflects lower mining activity on GPUs. We expect a diminishing contribution going forward.

我們很難以任何合理的精確度來量化加密貨幣挖掘對遊戲需求的貢獻程度。以太坊網絡哈希率增長速度放緩可能反映了 GPU 上的挖礦活動減少。我們預計未來的貢獻會減少。

Laptop Gaming revenue posted strong sequential and year-on-year growth, driven by the ramp of the NVIDIA RTX 30 Series lineup. With this year's spring refresh and ahead of the upcoming back-to-school season, there are now over 180 laptop models featuring RTX 30 Series GPUs and our energy-efficient thin and light Max-Q technologies, up from 140 at this time last year.

在 NVIDIA RTX 30 系列產品陣容的推動下，筆記本電腦遊戲收入實現了強勁的環比和同比增長。隨著今年春季更新和即將到來的返校季，現在有超過 180 款筆記本電腦型號採用 RTX 30 系列 GPU 和我們的節能輕薄 Max-Q 技術，高於去年這個時候的 140 款.

Driving this growth are not just gamers, but also the fast-growing category of content creators from whom we offer dedicated NVIDIA studio drivers. We've also developed applications and tools to empower artists, from Omniverse for advanced 3D and collaboration to broadcast for live streaming to canvas for painting landscapes with AI. The creator economy is estimated at $100 billion and powered by 80 million individual creators and broadcasters.

推動這一增長的不僅是遊戲玩家，還有快速增長的內容創作者類別，我們為他們提供專門的 NVIDIA 工作室驅動程序。我們還開發了應用程序和工具來增強藝術家的能力，從用於高級 3D 和協作的 Omniverse 到用於直播的廣播，再到用於使用 AI 繪製風景的畫布。創作者經濟估計為 1000 億美元，由 8000 萬個人創作者和廣播公司提供支持。

We continued to build out our GeForce NOW cloud gaming service. Gamers can now access RTX 3080 class streaming, our new top-tier offering with subscription plans of $19.99 a month. We added over 100 games to the GeForce NOW library, bringing the total to over 1,300 games. And last week, we launched Fortnite on GeForce NOW with touch controls for mobile devices, streaming through the Safari web browser on iOS and the GeForce NOW Android app.

我們繼續構建我們的 GeForce NOW 雲遊戲服務。遊戲玩家現在可以訪問 RTX 3080 類流媒體，這是我們新的頂級產品，訂閱計劃為每月 19.99 美元。我們在 GeForce NOW 庫中添加了 100 多款遊戲，使遊戲總數達到 1,300 多款。上週，我們在 GeForce NOW 上推出了 Fortnite，它帶有用於移動設備的觸摸控制，通過 iOS 上的 Safari 網絡瀏覽器和 GeForce NOW Android 應用程序進行流式傳輸。

Moving to Pro Visualization. Q1 revenue was $622 million, was down sequentially 3% and up 67% from a year ago. Demand remains strong as enterprises continued to build out their employee's remote office infrastructure to support hybrid work. Sequential growth in the mobile workstation GPUs was offset by lower desktop revenue. Strong year-on-year growth was supported by the NVIDIA RTX Ampere architecture product cycle. Top use cases include: digital content creation at customers such as Sony Pictures Animation and medical imaging at customers such as Medtronic.

轉向專業可視化。第一季度收入為 6.22 億美元，環比下降 3%，同比增長 67%。隨著企業繼續構建員工的遠程辦公基礎設施以支持混合工作，需求依然強勁。移動工作站 GPU 的連續增長被台式機收入的下降所抵消。 NVIDIA RTX Ampere 架構產品週期支持了強勁的同比增長。主要用例包括：Sony Pictures Animation 等客戶的數字內容創建和 Medtronic 等客戶的醫學成像。

In just its second quarter of general availability, our Omniverse enterprise software is being adopted by some of the world's largest companies. Amazon is using Omniverse to create digital twins to better optimize warehouse design and flow and to train more intelligent robots. Kroger is using Omniverse to optimize store efficiency with digital twin store simulation. And PepsiCo is using Omniverse digital twins to improve the efficiency and environmental sustainability of the supply chain.

我們的 Omniverse 企業軟件僅在第二季度全面上市，就被一些世界上最大的公司採用。亞馬遜正在使用 Omniverse 創建數字雙胞胎，以更好地優化倉庫設計和流程，並訓練更智能的機器人。 Kroger 正在使用 Omniverse 通過數字孿生商店模擬優化商店效率。百事可樂正在使用 Omniverse 數字雙胞胎來提高供應鏈的效率和環境可持續性。

Omniverse is also expanding our GPU sales pipeline, driving higher-end and multiple GPU configurations. The Omniverse ecosystem continues to rapidly expand with third-party developers in the robotics, industrial automation, 3D design and rendering ecosystems developing connections to Omniverse.

Omniverse 還在擴展我們的 GPU 銷售渠道，推動更高端和多 GPU 配置。 Omniverse 生態系統繼續快速擴展，機器人、工業自動化、3D 設計和渲染生態系統中的第三方開發人員正在開發與 Omniverse 的連接。

Moving to Automotive. Q1 revenue of $138 million increased 10% sequentially and declined 10% from the year ago quarter. Our DRIVE O-RAN SoC is now in production and kicks off a major product cycle with auto customers ramping in Q2 and beyond. O-RAN has great traction in the marketplace with over 35 customer wins from automakers, truck makers and robo taxi companies.

轉向汽車。第一季度收入為 1.38 億美元，環比增長 10%，同比下降 10%。我們的 DRIVE O-RAN SoC 現已投入生產，並隨著汽車客戶在第二季度及以後的增長而開始了一個主要的產品週期。 O-RAN 在市場上擁有巨大的吸引力，贏得了來自汽車製造商、卡車製造商和機器人出租車公司的超過 35 個客戶。

In Q1, BYD, China's largest EV maker, and Lucid, an award winning EV pioneer were the latest to announce that they are building their next-generation fleets on DRIVE O-RAN. Our Automotive design win pipeline now exceeds $11 billion over the next 6 years, up from $8 billion just a year ago.

在第一季度，中國最大的電動汽車製造商比亞迪和屢獲殊榮的電動汽車先驅 Lucid 是最新宣布他們將在 DRIVE O-RAN 上建立下一代車隊。在未來 6 年內，我們的汽車設計贏得渠道現已超過 110 億美元，高於一年前的 80 億美元。

Moving to Data Center. Record revenue of $3.8 billion grew 15% sequentially and accelerated to 83% growth year-on-year. Revenue from hyperscale and cloud computing customers more than doubled year-on-year, driven by strong demand for both external and internal workloads. Customers remain supply constrained in their infrastructure needs and continue to add capacity as they try to keep pace with demand.

遷移到數據中心。創紀錄的 38 億美元收入環比增長 15%，同比增長 83%。由於對外部和內部工作負載的強勁需求，來自超大規模和雲計算客戶的收入同比增長了一倍以上。客戶的基礎設施需求仍然受到供應限制，並在努力跟上需求的同時繼續增加容量。

Revenue from vertical industries grew a strong double-digit percentage from last year. Top verticals driving growth this quarter include: consumer Internet companies, financial services and telecom. Overall, Data Center growth was driven primarily by strong adoption of our A100 GPU for both training and inference with large volume deployments by hyperscale customers and broadening adoption across the vertical industries. Top workloads include: recommender systems, conversational AI, large language models and cloud graphics.

垂直行業的收入比去年強勁增長了兩位數。本季度推動增長的主要垂直領域包括：消費互聯網公司、金融服務和電信。總體而言，數據中心的增長主要是由於我們的 A100 GPU 在訓練和推理方面的大力採用，以及超大規模客戶的大規模部署以及在垂直行業中的廣泛採用。主要工作負載包括：推薦系統、對話式 AI、大型語言模型和雲圖形。

Networking revenue accelerated on strong broad-based demand for our next-generation 25-, 50- and 100-gig ethernet adapters. Customers are choosing NVIDIA's networking products for their leading performance and robust software functionality. In addition, networking revenue is benefiting from growing demand for DGX super pods and cross-selling opportunities. Customers are increasingly combining our compute and networking products to build what are essentially modern AI factories with data as the raw material input and intelligence as the output.

由於對我們下一代 25、50 和 100 千兆以太網適配器的廣泛需求，網絡收入加速增長。客戶選擇 NVIDIA 的網絡產品是因為它們具有領先的性能和強大的軟件功能。此外，網絡收入受益於對 DGX 超級 pod 的需求不斷增長和交叉銷售機會。客戶越來越多地將我們的計算和網絡產品結合起來，以構建本質上以數據為原材料輸入、以智能為輸出的現代人工智能工廠。

Our networking products are still supply constrained though we expect continued improvement throughout the rest of the year. One of the biggest workloads driving adoption of NVIDIA AI is natural language processing, which has been revolutionized by transformer-based models. Recent industry breakthroughs traced to transformers include: large language models like GPT-3, NVIDIA MegaMolBART for drug discovery and DeepMine alpha fold for protein structure prediction.

我們的網絡產品仍然受到供應限制，儘管我們預計在今年餘下時間會繼續改善。推動採用 NVIDIA AI 的最大工作負載之一是自然語言處理，它已被基於轉換器的模型徹底改變。可追溯到轉換器的最新行業突破包括：大型語言模型，如 GPT-3、用於藥物發現的 NVIDIA MegaMolBART 和用於蛋白質結構預測的 DeepMine alpha fold。

Transformers allow self-supervised learning without the need for human-labeled data. They enable unprecedented levels of accuracy for TAF such as text generation, translation, summarization and answering questions. To do that, Transformers use enormous training data sets and very large neuro networks well into the hundreds of billions of parameters. To run these giant models without sacrificing low inference times, customers like Microsoft are increasingly deploying NVIDIA AI, including our NVIDIA Ampere architecture-based GPUs and full software stack. In addition, we are seeing a rising wave of customer innovation using large language models that is driven by increased demand for NVIDIA AI and GPU instances in the cloud.

Transformers 允許自我監督學習，而不需要人工標記的數據。它們使 TAF 的準確性達到前所未有的水平，例如文本生成、翻譯、摘要和回答問題。為此，Transformers 使用龐大的訓練數據集和非常龐大的神經網絡，將其融入數千億個參數中。為了在不犧牲低推理時間的情況下運行這些巨型模型，微軟等客戶越來越多地部署 NVIDIA AI，包括我們基於 NVIDIA Ampere 架構的 GPU 和完整的軟件堆棧。此外，由於對雲中 NVIDIA AI 和 GPU 實例的需求增加，我們看到使用大型語言模型的客戶創新浪潮正在上升。

At GTC, we announced our next-generation Data Center GPU, the H100 based on the new or upper architecture. Packed with 80 billion transistors, H100 is the world's largest, most powerful accelerator, offering an order of magnitude leap in performance over the A100. We believe H100 is hitting the market at the perfect time. H100 is ideal for advancing large language models and deep recommender systems, the 2 largest-scale AI workloads today. We are working with leading server makers and hyperscale customers to qualify and ramp H100. It as well as the new DGX H100 AI supercomputing system will ramp in volume late in the calendar year.

在 GTC，我們發布了我們的下一代數據中心 GPU，即基於新架構或上層架構的 H100。 H100 包含 800 億個晶體管，是世界上最大、最強大的加速器，與 A100 相比，性能實現了一個數量級的飛躍。我們相信 H100 在完美的時機上市。 H100 非常適合推進大型語言模型和深度推薦系統，這是當今最大規模的兩種 AI 工作負載。我們正在與領先的服務器製造商和超大規模客戶合作，以獲得 H100 的資格和升級。它以及新的 DGX H100 AI 超級計算系統將在日曆年末量產。

Building on the H100 product cycle, we are on track to launch our first-ever Data Center CPU, Grace, in the first half of 2023. Grace is the ideal CPU for AI factories. This week at Computex, we announced that dozens of server models based on Grace will be brought to market by the first wave of system builders, including ASUS, Foxconn, GIGABYTE, QCT, Supermicro and [Y-Win]. These servers will be powered by the NVIDIA Grace CPU Super Chip, which features 2 CPUs and the Grace Hopper Super Chip, which pairs an NVIDIA Hopper GPU with an NVIDIA Grace CPU in an integrated model.

在 H100 產品週期的基礎上，我們有望在 2023 年上半年推出我們的首款數據中心 CPU Grace。Grace 是 AI 工廠的理想 CPU。本週在 Computex 上，我們宣布數十款基於 Grace 的服務器型號將由第一波系統製造商推向市場，包括華碩、富士康、技嘉、QCT、Supermicro 和 [Y-Win]。這些服務器將由具有 2 個 CPU 的 NVIDIA Grace CPU 超級芯片和將 NVIDIA Hopper GPU 與 NVIDIA Grace CPU 在集成模型中配對的 Grace Hopper 超級芯片提供動力。

We've introduced new reference designs based on Grace for the massive new workloads of next-generation data centers: DGX for cloud graphics and gaming, OVX for digital twins or Omniverse, and HGX for HPC and AI. These server designs are all optimized for NVIDIA's rich accelerated computing software stacks and can be qualified as part of our NVIDIA-certified systems lineup.

我們為下一代數據中心的大量新工作負載推出了基於 Grace 的新參考設計：用於雲圖形和遊戲的 DGX、用於數字孿生或 Omniverse 的 OVX，以及用於 HPC 和 AI 的 HGX。這些服務器設計都針對 NVIDIA 豐富的加速計算軟件堆棧進行了優化，並且可以成為我們 NVIDIA 認證系統系列的一部分。

The enabler for the Grace Hopper and Grace Super Chips is our ultra energy-efficient, low-latency, high-speed memory coherent interconnect called NVLink, which scales from die to die, chip to chip and system to system. With NVLink, we can configure Grace and Hopper to address a broad range of workloads.

Grace Hopper 和 Grace Super Chips 的推動者是我們稱為 NVLink 的超節能、低延遲、高速內存相干互連，它可以從芯片到芯片、芯片到芯片以及系統到系統進行擴展。借助 NVLink，我們可以配置 Grace 和 Hopper 來處理廣泛的工作負載。

Future in video chips, the CPUs, GPUs, DPUs, NICs and SoCs will integrate NVLink just like Grace and Hopper based on our world-class SerDes technology. We're making NVLink open to customers and partners to implement custom chips that connect to NVIDIA's platforms.

視頻芯片的未來，CPU、GPU、DPU、NIC 和 SoC 將像 Grace 和 Hopper 一樣基於我們世界級的 SerDes 技術集成 NVLink。我們正在向客戶和合作夥伴開放 NVLink，以實施連接到 NVIDIA 平台的定制芯片。

In networking, we're kicking off a major product cycle with the introduction of Spectrum-4, the world's first 400-gigabit per second end-to-end Ethernet networking platform, including the Spectrum-4 switch, ConnectX-7 SmartNIC, BlueField-3 DPU and the DOCA software. Built for AI, NVIDIA's Spectrum-4 arrives as data centers are growing exponentially and demanding extreme performance, advanced security and powerful features to enable high-performance advanced virtualization and simulation at scale. Across our businesses, we are launching multiple new GPU, CPU, DPU and SoC products over the coming quarters, with a ramp in supply to support the customer demand.

在網絡方面，我們推出了 Spectrum-4，這是世界上第一個每秒 400 Gb 端到端以太網網絡平台，包括 Spectrum-4 交換機、ConnectX-7 SmartNIC、BlueField -3 DPU 和 DOCA 軟件。 NVIDIA Spectrum-4 專為 AI 打造，正值數據中心呈指數級增長並需要極致性能、高級安全性和強大功能以實現大規模高性能高級虛擬化和模擬的情況下推出。在我們的業務中，我們將在未來幾個季度推出多種新的 GPU、CPU、DPU 和 SoC 產品，並增加供應以支持客戶需求。

Moving to the rest of the P&L, GAAP gross margin for the first quarter was 65.5%, and non-GAAP gross margin was up 67.1%, up 90 basis points from a year ago and up 10 basis points sequentially. We have been able to offset rising costs and supply chain pressures. We expect to maintain gross margins at current levels in Q2.

轉向損益表的其餘部分，第一季度的 GAAP 毛利率為 65.5%，非 GAAP 毛利率上升 67.1%，比去年同期上升 90 個基點，環比上升 10 個基點。我們已經能夠抵消不斷上升的成本和供應鏈壓力。我們預計第二季度毛利率將維持在當前水平。

Going forward, as new products ramp and software becomes a larger percent of revenue, we have opportunities to increase gross margins longer term. GAAP operating margin was 22.5%, impacted by a $1.35 billion acquisition termination charge related to the ARM transaction. Non-GAAP operating margin was 47.7%.

展望未來，隨著新產品的興起和軟件在收入中所佔的比例越來越大，我們有機會長期提高毛利率。 GAAP 營業利潤率為 22.5%，受到與 ARM 交易相關的 13.5 億美元收購終止費用的影響。非美國通用會計準則營業利潤率為 47.7%。

We are closely managing our operating expenses to balance the current macro environment with our growth opportunities, and we've been very successful in hiring so far this year and are now slowing to integrate these new employees. This also enables us to focus our budget on taking care of our existing employees as inflation persist.

我們正在密切管理我們的運營費用，以平衡當前的宏觀環境和我們的增長機會，今年到目前為止，我們在招聘方面非常成功，現在正在放緩整合這些新員工的速度。這也使我們能夠在通貨膨脹持續的情況下將預算集中在照顧現有員工上。

We are still on track to grow our non-GAAP operating expenses in the high 20s range this year. we expect sequential increases to level off after Q2 as the first half of the year includes a significant amount of expenses related to the bring-up of multiple new products, which should not reoccur in the second half.

我們仍有望在今年將非 GAAP 運營費用提高到 20 多歲。我們預計第二季度之後的連續增長將趨於平穩，因為今年上半年包括與推出多個新產品相關的大量費用，這在下半年不會再次發生。

During Q1, we repurchased $2 billion of our stock. Our Board of Directors increased and extended our share repurchase program to repurchase an additional common stock up to a total of $15 billion through December 2023.

在第一季度，我們回購了 20 億美元的股票。我們的董事會增加並擴大了我們的股票回購計劃，以在 2023 年 12 月之前回購總額達 150 億美元的額外普通股。

Let me now turn to the outlook for the second quarter of fiscal 2023. Our outlook assumes an estimated impact of approximately $500 million relating to Russia and China COVID lockdowns. We estimate the impact of lower sell-through in Russia and China to affect our Q2 Gaming sell-in by $400 million. Furthermore, we estimate the absence of sales to Russia to have a $100 million impact on Q2 in Data Center. We expect strong sequential growth in Data Center and Automotive to be more than an offset by the sequential decline in Gaming.

現在讓我談談 2023 財年第二季度的展望。我們的展望假設與俄羅斯和中國 COVID 封鎖有關的估計影響約為 5 億美元。我們估計俄羅斯和中國的銷售額下降對我們的第二季度遊戲銷售額造成 4 億美元的影響。此外，我們估計缺少對俄羅斯的銷售將對數據中心的第二季度產生 1 億美元的影響。我們預計數據中心和汽車的強勁連續增長將被遊戲的連續下滑所抵消。

Revenue is expected to be $8.1 billion, plus or minus 2%. GAAP and non-GAAP gross margins are expected to be 65.1% and 67.1%, respectively, plus or minus 50 basis points. GAAP operating expenses are expected to be $2.46 billion. Non-GAAP operating expenses are expected to be $1.75 billion. GAAP and non-GAAP Other income and expenses are expected to be an expense of approximately $40 million, excluding gains and losses on nonaffiliated investments.

收入預計為 81 億美元，正負 2%。 GAAP 和非 GAAP 毛利率預計分別為 65.1% 和 67.1%，上下浮動 50 個基點。 GAAP 運營費用預計為 24.6 億美元。非美國通用會計準則運營費用預計為 17.5 億美元。 GAAP 和非 GAAP 其他收入和支出預計約為 4000 萬美元，不包括非關聯投資的損益。

GAAP and non-GAAP tax rates are expected to be 12.5% plus or minus 1%, excluding discrete items. And capital expenditures are expected to be approximately $400 million to $450 million. Further financial details are included in the CFO commentary and other information available on our IR website.

GAAP 和非 GAAP 稅率預計為 12.5% 正負 1%，不包括離散項目。資本支出預計約為 4 億至 4.5 億美元。更多財務細節包含在 CFO 評論和我們投資者關係網站上提供的其他信息中。

In closing, let me highlight the upcoming events for the financial community. We will be attending the BofA Securities Technology Conference in person on June 7, where Jen-Hsun will participate in a keynote fireside chat. Our earnings call to discuss the results of our second quarter of fiscal 2023 is scheduled for Wednesday, August 24.

最後，讓我強調一下金融界即將發生的事件。我們將親自參加 6 月 7 日舉行的美國銀行證券技術會議，Jen-Hsun 將參加主題爐邊談話。我們將在 8 月 24 日星期三召開財報電話會議，討論我們 2023 財年第二季度的業績。

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

我們現在將打開問題的電話。接線員，您可以投票提問嗎？

(Operator Instructions) We'll take our first question from C.J. Muse with Evercore ISI.

（操作員說明）我們將回答 C.J. Muse 和 Evercore ISI 的第一個問題。

I guess would love to get an update on how you're thinking about the Gaming cycle from here. The business has essentially doubled over the last 2 years. And now we've got some crosswinds with crypto falling off, channel potentially clearing ahead of a new product cycle. You talked about macro challenges. But at the same time, only 1/3 of the installed base has RTX and we're moving out from under supply. So we'd love to hear your thoughts from here once we get beyond kind of the challenges around COVID lockdown in the July quarter. How are you thinking about Gaming trends?

我想我很想從這裡獲得有關您對遊戲週期的看法的最新信息。在過去的兩年中，該業務基本上翻了一番。現在我們遇到了一些不利因素，加密貨幣下降，渠道可能會在新產品週期之前清理。你談到了宏觀挑戰。但與此同時，只有 1/3 的安裝基數擁有 RTX，我們正在擺脫供應不足的局面。因此，一旦我們在 7 月季度克服了圍繞 COVID 鎖定的各種挑戰，我們很想听聽您的想法。您如何看待遊戲趨勢？

Yes, C.J., thanks for the question. The -- you captured a lot of the dynamics well in your question. The underlying dynamics of the Gaming industry is really solid, net of the situation with COVID lockdown in China and Russia. The rest of the market is fairly robust. And we expect the Gaming dynamics to be intact.

是的，C.J.，謝謝你的提問。 - 你在你的問題中很好地捕捉到了很多動態。遊戲行業的潛在動力非常穩固，不包括中國和俄羅斯的 COVID 封鎖情況。其餘市場相當強勁。我們預計遊戲動態將保持不變。

There are several things that are driving the Gaming industry. In the last 2 years alone, 100 million new gamers came into the PC industry. The format has expanded tremendously. And the ways that people are using their PCs to connect with friends, to be an influencer as a platform for themselves, use it for broadcast. So many people are now using their home PCs as their second workstation, if you will, second studio. Because they're also working from home. It is our primary way of communicating these days. The need for GeForce PCs have never been greater.

有幾件事正在推動遊戲行業。僅在過去 2 年，就有 1 億新遊戲玩家進入 PC 行業。格式已大大擴展。人們使用個人電腦與朋友聯繫、成為影響者作為自己平台的方式，將其用於廣播。如此多的人現在將他們的家用 PC 作為他們的第二個工作站，如果你願意的話，第二個工作室。因為他們也在家里工作。這是我們這些天的主要交流方式。對 GeForce PC 的需求從未如此強烈。

And so I think the fundamental dynamics are really good. And so as we look into the second half of the year, we look -- it's hard to predict exactly what -- when COVID and the war in Russia is going to be behind us, but nonetheless, the governing dynamics of the gaming industry is great.

所以我認為基本動力非常好。因此，當我們展望今年下半年時，我們會看到 - 很難準確預測 - 什麼時候 COVID 和俄羅斯的戰爭將在我們身後，但儘管如此，遊戲行業的治理動態是偉大的。

Next, we'll go to Matt Ramsay with Cowen.

接下來，我們將與 Cowen 一起前往 Matt Ramsay。

Jen-Hsun, I wanted to ask a bit of a question on the Data Center business. In this upcoming cycle with H100, there's some I/O upgrades that are happening in servers that I think are going to be a fairly strong driver for you, in addition to what's going on with Hopper and the huge performance leaps that are there.

Jen-Hsun，我想問一個關於數據中心業務的問題。在即將到來的 H100 週期中，除了 Hopper 的進展和巨大的性能飛躍之外，我認為服務器中正在進行一些 I/O 升級，這對你來說將是一個相當強大的驅動力。

I wanted to ask a longer-term question, though, around your move to NVLink with Grace and Hopper and what's going on with your whole portfolio. Do you envision the business continuing to be sort of card-driven attached to third-party servers? Or do you think revenue shifts dramatically? Or in a small way, over time, to be more sort of vertically integrated all of the chips together on NVLink? And how is the industry sort of responding to that potential move?

不過，我想問一個長期的問題，關於您與 Grace 和 Hopper 一起遷移到 NVLink 以及您的整個投資組合的情況。您是否設想業務繼續以卡驅動的方式連接到第三方服務器？還是您認為收入會發生巨大變化？或者在某種程度上，隨著時間的推移，將所有芯片垂直集成到 NVLink 上？該行業如何應對這一潛在舉措？

Yes, I appreciate the question. The -- let's see, the first point that you made is a very big point. The next generation of servers that are being teed up right now are all gen 5. The I/O performance is substantially higher than what was available before. And so you're going to see a pretty large refresh as a result of that. Brand-new networking cards from our company and others. Gen 5, of course, drives new platform refresh. And so we're perfectly timed to ramp into the Gen 5 generation with Hopper.

是的，我很欣賞這個問題。 - 讓我們看看，你提出的第一點非常重要。目前正在開發的下一代服務器都是第 5 代。I/O 性能大大高於以前的可用性能。因此，您將看到一個相當大的刷新結果。我們公司和其他公司的全新網卡。當然，第 5 代推動了新平台的更新。因此，我們正處於使用 Hopper 進入第 5 代的完美時機。

There are a lot of different system configurations you want to make. If you take a step back and look at the type of systems that are necessary for data processing, scientific computing, machine learning and training, inference done in the cloud for hyperscale nature, done on-prem for enterprise computing, done at the edge. Each one of these workloads and deployment locations, the way that you manage would dictate a different system architecture.

您想要進行許多不同的系統配置。如果您退後一步，看看數據處理、科學計算、機器學習和訓練所必需的系統類型，在雲中為超大規模性質進行推理，為企業計算在本地完成，在邊緣完成。這些工作負載和部署位置中的每一個，您的管理方式都將決定不同的系統架構。

So there isn't one size that fits all, which is one of the reasons why it's so terrific that we support PCI Express, that we innovate a chip-to-chip interconnect for the very first time, before anybody else did. This is now some 7 years ago. We're in our fourth generation of NVLink that allows us to connect 2 chips next to each other, 2 dies, 2 chips, 2 modules, 2 SXM modules to 2 systems to multiple systems.

因此，沒有一種適合所有人的尺寸，這就是為什麼我們支持 PCI Express 如此出色的原因之一，我們在其他任何人之前首次創新了芯片到芯片的互連。這已經是大約 7 年前的事了。我們的第四代 NVLink 允許我們將 2 個芯片、2 個芯片、2 個芯片、2 個模塊、2 個 SXM 模塊連接到 2 個系統到多個系統。

And so our coherent chip-to-chip link, NVLink, has made it possible for us to mix and match chips, dies, packages, systems and all of these different types of configurations. And I think that, over time, you're going to see even more types of configurations. And the reason for that has to do with a couple of very important new type of data centers that are emerging.

因此，我們一致的芯片到芯片鏈接 NVLink 使我們能夠混合和匹配芯片、裸片、封裝、系統以及所有這些不同類型的配置。而且我認為，隨著時間的推移，你會看到更多類型的配置。其原因與正在出現的一些非常重要的新型數據中心有關。

And you're starting to see that now with fairly large installations, infrastructures with NVIDIA HPC and NVIDIA AI. These are really AI factories, where you're processing the data, refining the data and turning that data into intelligence. These AI factories are essentially running one major workload and they're running at 24/7. Deep recommender systems is a good example of that.

現在，您開始看到具有 NVIDIA HPC 和 NVIDIA AI 的相當大的安裝、基礎設施。這些是真正的人工智能工廠，你在其中處理數據、提煉數據並將數據轉化為智能。這些人工智能工廠本質上是在運行一種主要的工作負載，並且它們全天候運行。深度推薦系統就是一個很好的例子。

In the future, you're going to see large language models essentially becoming a platform themselves. That would be running 24/7, hosting a whole bunch of applications. And then on the other end, you're seeing data centers at the edge that are going to be robotics or autonomous data centers that are running 24/7. They are going to be running in factories and retail stores and warehouses, logistics warehouses all over the world. So these 2 new type of data centers are just emerging, and they also have different architectures.

將來，您將看到大型語言模型本身本質上成為一個平台。那將是 24/7 全天候運行，託管一大堆應用程序。然後在另一端，您會看到邊緣的數據中心將成為機器人或自主數據中心，它們將 24/7 全天候運行。它們將在世界各地的工廠、零售商店和倉庫、物流倉庫中運行。所以這兩種新型數據中心才剛剛興起，它們也有不同的架構。

So I think the net of it all is that our ability to support every single workload, because we have a universal accelerator running every single workload from data processing to data analytics to high-performance computing to training to inference, that we can support ARM and x86, that we support PCI Express to multisystem NVlink to multi-chip NVLink to multi-die NVLink. That capability for us is -- makes it possible for us to really be able to serve all of these different segments.

所以我認為這一切的關鍵在於我們支持每一個工作負載的能力，因為我們有一個通用加速器運行每一個工作負載，從數據處理到數據分析，從高性能計算到訓練到推理，我們可以支持 ARM 和x86，我們支持 PCI Express 到多系統 NVlink 到多芯片 NVLink 到多芯片 NVLink。對我們來說，這種能力是——使我們能夠真正為所有這些不同的細分市場服務。

With respect to vertical integration, I think that's system integration. The better way to make me saying that is that system integration is going to come in all kinds of different ways. We're going to do semi-custom chips, as we've done with many companies in the past, including Nintendo. We'll do semi-custom chiplets as we do with NVLink. NVLink is open to our partners, and they could bring it to any fab and connect it coherently into our chip. We could do multi-module packages. We could do multi-package systems. So there's a lot of different ways to do system integration.

關於垂直整合，我認為是系統整合。讓我這麼說的更好的方法是系統集成將以各種不同的方式出現。我們將做半定制芯片，就像我們過去對包括任天堂在內的許多公司所做的那樣。我們將像使用 NVLink 一樣製作半定制小芯片。 NVLink 對我們的合作夥伴開放，他們可以將其帶到任何晶圓廠並將其連貫地連接到我們的芯片中。我們可以做多模塊包。我們可以做多包系統。所以有很多不同的方法來進行系統集成。

Next, we'll go to Stacy Rasgon with Bernstein Research.

接下來，我們將與 Bernstein Research 一起前往 Stacy Rasgon。

I wanted to follow up on the sequential. So Colette, I know you said the $500 million was a $400 million hit to Gaming and a $100 million hit to data. So I'm assuming, that doesn't mean the Gaming is down $400 million. I mean, is Gaming -- do you see Gaming actually down more than the actual Russia and lockdown hit? And I guess just how do I think about the relative sequentials of the businesses in light of those constraints that you guys are facing?

我想跟進順序。所以科萊特，我知道你說 5 億美元對遊戲造成了 4 億美元的打擊，對數據造成了 1 億美元的打擊。所以我假設，這並不意味著 Gaming 減少了 4 億美元。我的意思是，遊戲是不是——你認為遊戲實際上比俄羅斯和封鎖打擊更嚴重嗎？我想，鑑於你們面臨的這些限制，我如何看待業務的相對順序？

Sure. Let me start first with what does that mean to gaming. What does that mean to Gaming for Q2, we do expect Gaming to decline into Q2. We still believe our end demand remains very strong. Ampere has just been a great architecture, and there's many areas where we continue to see strength and growth in both our sell-through and probably what we will see added into that channel as well.

當然。讓我先談談這對遊戲意味著什麼。這對第二季度的遊戲意味著什麼，我們確實預計遊戲將下降到第二季度。我們仍然相信我們的最終需求仍然非常強勁。 Ampere 剛剛成為一個偉大的架構，在許多領域，我們繼續看到我們的銷售量和可能我們將看到添加到該渠道的內容的實力和增長。

But in total, Q2 Gaming will decline from last quarter from Q1, that it will probably decline in the teens, as we try and work through some of these lockdowns in China, which are holding us up. So overall, the demand for Gaming is still strong. We still expect end demand to grow year-over-year in Q2.

但總的來說，Q2 遊戲將比上個季度從 Q1 下降，它可能會在青少年時期下降，因為我們試圖通過中國的一些封鎖來解決這些問題，這些封鎖阻礙了我們。因此，總體而言，對遊戲的需求仍然強勁。我們仍然預計第二季度的終端需求將同比增長。

Next, we'll go to Mark Lipacis with Jefferies.

接下來，我們將與 Jefferies 一起前往 Mark Lipacis。

If you listen to the networking OEMs, this earnings season, it seems that there's a lot of talk about increased spending by enterprises on their data centers and sometimes you hear them talking about how this is being driven by AI. You talked about your year-over-year growth in your cloud versus enterprise spending.

如果你聽聽網絡 OEM 的聲音，在這個財報季，似乎有很多關於企業增加數據中心支出的討論，有時你會聽到他們談論人工智能如何推動這一點。您談到了您的雲計算與企業支出的同比增長。

I wonder if you could talk about what you are seeing sequentially? Are you seeing a sequential inflection in the enterprise? And can you talk about the attach rate of software for enterprise versus data centers? And which software is -- are you seeing the most interest? I know you talked about, is it Omniverse? Is it natural language processing? Or is there one big driver? Or is it a bunch of drivers for the various different software packages you have?

我想知道您是否可以依次談談您所看到的內容？您是否看到企業出現連續變化？您能談談企業與數據中心的軟件附加率嗎？哪個軟件是你最感興趣的？我知道你談到過，是 Omniverse 嗎？是自然語言處理嗎？還是有一個大驅動？還是您擁有的各種不同軟件包的一堆驅動程序？

Yes. Thanks, Mark. We had a record Data Center business this last quarter. We expect to have a record -- another record quarter this quarter, and we're fairly enthusiastic about the second half. AI and data-driven machine learning techniques developed for writing software and extracting insight from the vast amount of data that companies have is incredibly strategic to all the companies that we know. Because in a funnel analysis, AI is about automation of intelligence, and most companies are about domain-specific intelligence.

是的。謝謝，馬克。上個季度，我們的數據中心業務創紀錄。我們預計會有一個創紀錄的季度——本季度另一個創紀錄的季度，我們對下半年相當熱情。為編寫軟件和從公司擁有的大量數據中提取洞察力而開發的人工智能和數據驅動的機器學習技術對我們所知道的所有公司都具有難以置信的戰略意義。因為在漏斗分析中，人工智能是關於智能的自動化，而大多數公司都是關於特定領域的智能。

We want to produce intelligence. And there are several techniques now that have been created to make it possible for most companies to apply their data to extract insight and to automate a lot of the predictive things that they have to do and do it quickly. And so I think the trend that you hear other people are experiencing about machine learning, data analytics, data-driven insights, artificial intelligence, however, it's described, is all exactly the same thing. And it's sweeping just about every industry and every company.

我們想產生智能。現在已經創建了幾種技術，使大多數公司能夠應用他們的數據來提取洞察力，並使他們必須做的許多預測性事情自動化并快速完成。所以我認為你聽到的其他人正在經歷的關於機器學習、數據分析、數據驅動的洞察力、人工智能的趨勢，然而，它被描述過，都是完全一樣的。它席捲了幾乎所有行業和每家公司。

Our networking business is also highly supply constrained. Our demand is really, really high. And it requires a lot of components aside from just our chips. Components and transceivers and connectors and cables. And just -- it's a really -- it's a complicated system, the network, and there are many physical components. And so the supply chain has been problematic. We're doing our best. And our supply has been increasing from Q4 to Q1. We're expecting it to increase in Q2 and increase in Q3 and Q4. And so we're really, really grateful for the support from the components industry around us, and we'll be able to increase that.

我們的網絡業務也受到高度供應限制。我們的要求非常非常高。除了我們的芯片之外，它還需要很多組件。組件和收發器以及連接器和電纜。只是 - 它真的是 - 它是一個複雜的系統，網絡，並且有許多物理組件。因此，供應鏈一直存在問題。我們正在盡力而為。從第四季度到第一季度，我們的供應量一直在增加。我們預計它會在第二季度增加，並在第三季度和第四季度增加。因此，我們非常非常感謝我們周圍組件行業的支持，我們將能夠增加這種支持。

With respect to software, there are 2 -- first of all, there are all kinds of machine learning models: computer vision, speech AI, natural language understanding, all kinds of robotics applications, the most -- probably the largest -- the most visible one is self-driving cars, which is essentially a robotic AI. And then recently, this incredible breakthrough from an AI model called Transformers that has led to really, really significant advances in natural language understanding.

軟件方面，有2——首先，有各種機器學習模型：計算機視覺、語音AI、自然語言理解、各種機器人應用，最多——可能最大——最多可見的一個是自動駕駛汽車，它本質上是一個機器人人工智能。然後最近，一個名為 Transformers 的 AI 模型取得了令人難以置信的突破，它在自然語言理解方面帶來了非常非常重要的進步。

And so they're all these different types of models. There are thousands and thousands of species of AI models and -- in all these different industries. One of my favorite -- I'll just say it very quickly, and I'll answer that question about the software. One of my favorites is using Transformers to understand the language of chemistry or using Transformers and using AI models to understand the language of proteins, amino acids, which is genomics. To apply AI to understand -- to recognize the patterns, to understand the sequence and essentially understand the language of chemistry and biology is a really, really important breakthrough. And all of this excitement around synthetic biology, much of it stands back to the -- some of these inventions.

所以它們都是這些不同類型的模型。在所有這些不同的行業中，有成千上萬種 AI 模型。我最喜歡的一個——我會很快說出來，我會回答這個關於軟件的問題。我的最愛之一是使用 Transformers 來理解化學語言，或者使用 Transformers 並使用 AI 模型來理解蛋白質、氨基酸的語言，即基因組學。應用人工智能來理解——識別模式、理解序列並從本質上理解化學和生物學的語言是一個非常非常重要的突破。所有這些圍繞合成生物學的興奮，其中大部分都可以追溯到這些發明中的一些。

But anyhow, all of these different models need an engine to run on. And that engine is called NVIDIA AI. In the case of hyperscalers, they can cobble together a lot of open source, and we provide a lot of our source to them and a lot of our engines to them for them to operate their AI. But for enterprises, they need someone to package it together and be able to support it and refresh it, update it for new architecture, support old architectures in their installed base, et cetera, and all the different use cases that they have.

但無論如何，所有這些不同的模型都需要一個引擎來運行。該引擎稱為 NVIDIA AI。就超大規模生產者而言，他們可以拼湊出大量開源代碼，我們為他們提供了很多源代碼，並為他們提供了很多引擎，以供他們操作人工智能。但是對於企業來說，他們需要有人將它打包在一起，並且能夠支持和更新它，為新架構更新它，在他們的安裝基礎中支持舊架構等等，以及他們擁有的所有不同的用例。

And so that engine is called NVIDIA AI. It's almost like a SQL engine, if you will. And except this is an engine for artificial intelligence. There's another engine that we provide and that engine is called Omniverse and it's designed for the next wave of AI, where artificial intelligence has to not just manipulate information like recommender systems and conversational systems and such, but it has to interact with physical systems.

所以這個引擎被稱為 NVIDIA AI。如果你願意的話，它幾乎就像一個 SQL 引擎。除了這是一個人工智能引擎。我們提供了另一個引擎，該引擎稱為 Omniverse，它是為下一波人工智能設計的，人工智能不僅要操縱推薦系統和對話系統等信息，而且還必須與物理系統交互。

Whether it's interacting with physics directly, meaning robotics or being able to automate physical systems like heat-recovery steam generators, which is really important today. And so Omniverse is designed to be able to sit at that interface, that intersection between simulation and artificial intelligence, and that's what Omniverse is about.

無論是直接與物理交互，即機器人技術，還是能夠自動化物理系統，如熱回收蒸汽發生器，這在今天非常重要。所以 Omniverse 被設計成能夠坐在那個界面上，模擬和人工智能之間的交叉點，這就是 Omniverse 的意義所在。

Omniverse has now -- let's see some -- we're still early in the deployment of Omniverse for commercial license. It's been a couple of quarters now since we've released Omniverse enterprise. And I think, at this point, we have 10% of the world's top 100 companies that are already customers, licensing customers, substantially more who we're evaluating. I think it's been downloaded nearly 200,000 times. It has been tried in some 700 companies. And Colette highlighted some of the companies, you might see some of the companies that are using it in all kinds of interesting applications at GTC.

Omniverse 現在 - 讓我們看一些 - 我們仍處於部署 Omniverse 以獲得商業許可的早期階段。自從我們發布 Omniverse 企業版以來，已經過去了幾個季度。而且我認為，在這一點上，我們有 10% 的世界前 100 家公司已經是客戶，許可客戶，我們正在評估的人要多得多。我認為它已被下載近 200,000 次。它已在大約 700 家公司中試用。 Colette 重點介紹了一些公司，您可能會在 GTC 上看到一些公司在各種有趣的應用程序中使用它。

And so I fully expect that the NVIDIA AI engine, the Omniverse engine, are going to be very successful for us in the future and contribute greatly to our earnings.

因此，我完全期望 NVIDIA AI 引擎，Omniverse 引擎，在未來對我們來說會非常成功，並為我們的收入做出巨大貢獻。

Next, we'll go to Vivek Arya with BofA Securities.

接下來，我們將與 BofA Securities 一起前往 Vivek Arya。

Just wanted to clarify, Colette, if your Q2 outlook includes any destocking benefits from the new products that you're planning to launch this year.

只是想澄清一下，科萊特，如果你的第二季度展望包括你計劃今年推出的新產品的任何去庫存效益。

And then, Jen-Hsun, my question is for you. You're still guiding Data Center to a very strong, I think, close to 70% or so year-on-year growth despite all the headwinds. Are you worried at all about all the headlines about the slowdown in the macro economy? Like is there any cyclical impact on Data Center growth that we should keep in mind as we think about the second half of the year?

然後，仁勳，我的問題是給你的。我認為，儘管面臨所有不利因素，您仍在引導數據中心實現非常強勁的同比增長，接近 70% 左右。您是否擔心所有關於宏觀經濟放緩的頭條新聞？考慮到下半年，我們應該牢記對數據中心增長的任何週期性影響嗎？

Yes. Vivek, let me first answer the question that you asked regarding any new products as we look at Q2. As we discussed about it, most of the ramp that we have of our new architectures, we're going to see in the back half of the year. We're going to start to see, for example, Hopper will probably be here in Q3, but starting to ramp closer to the end of the calendar year. So you should think about most of our product launches to be ramping in the second half of the year on that part.

是的。 Vivek，讓我首先回答您在我們查看第二季度時提出的有關任何新產品的問題。正如我們所討論的那樣，我們新架構的大部分斜坡，我們將在今年下半年看到。例如，我們將開始看到，Hopper 可能會在第三季度出現，但在接近日曆年年底時開始增加。因此，您應該考慮到我們的大部分產品發布都將在下半年增加。

I'll turn it over for Jen-Hsun for the rest.

剩下的交給仁勳吧。

Thanks. Our Data Center demand is strong and remains strong. Hyperscale and cloud computing revenues, as you mentioned, has grown significantly, has doubled year-over-year. And we're seeing really strong adoption of A100. A100 is really quite special and unique in the world of accelerators. And this is one of the really, really great innovations as we extended our GPU from graphics to CUDA to Tensor Core GPUs. It's now a universal accelerator.

謝謝。我們的數據中心需求強勁並且依然強勁。正如您所提到的，超大規模和雲計算收入顯著增長，同比翻了一番。我們看到 A100 的採用率非常高。 A100 在加速器領域確實非常特別和獨特。這是我們將 GPU 從圖形擴展到 CUDA 再到 Tensor Core GPU 的真正非常偉大的創新之一。它現在是一個通用加速器。

And so you could use it for data processing. For ETL, for example, extract, transform and load. You could use it for database acceleration. Many SQL functions are accelerated on NVIDIA GPUs. We accelerate Rapids, we accelerate -- which is the Python version, Data Center scale version of Pandas, We accelerate Spark 3.0. And so from database queries to data processing to extraction and transform and loading of data before you do training and inference and, whatever, image processing or other algorithmic processing you need to do can be fully accelerated on A100. And so we're seeing great success there.

所以你可以用它來處理數據。例如，對於 ETL，提取、轉換和加載。您可以將其用於數據庫加速。許多 SQL 函數在 NVIDIA GPU 上得到加速。我們加速 Rapids，我們加速——這是 Python 版本，Pandas 的數據中心規模版本，我們加速 Spark 3.0。因此，在您進行訓練和推理之前，從數據庫查詢到數據處理，再到數據的提取、轉換和加載，以及您需要做的任何圖像處理或其他算法處理，都可以在 A100 上完全加速。所以我們在那裡看到了巨大的成功。

At the core and closer to what is happening today, you're seeing several different very important new AI models that are being invested in at very, very large scale and with great urgency.

在核心和更接近今天發生的情況下，您會看到幾個不同的非常重要的新 AI 模型正在以非常、非常大的規模和非常緊迫的方式進行投資。

You probably have heard about deep recommender systems. This is the economic engine, the information-filtering engine of the Internet. If not for the recommender system, it would be practically impossible for us to enjoy our Internet experience, shopping experience with trillions of things that are changing in the world every day constantly and be able to use your 3-inch phone to even engage the Internet. And so all of that magic is made possible by this incredible thing called recommender system.

您可能聽說過深度推薦系統。這是經濟引擎，是互聯網的信息過濾引擎。如果沒有推薦系統，我們幾乎不可能享受我們的互聯網體驗，在世界上每天都在不斷變化的數万億事物中享受購物體驗，甚至無法用你的 3 英寸手機上網。 .因此，所有這些神奇的東西都可以通過這個令人難以置信的東西稱為推薦系統來實現。

The second thing is conversational AI. You're seeing chat bots and website customer service, even live customer service being now supported by AI. Conversational AI has an opportunity to enhance the customer service, on the one hand. On the other hand, supplement for a lot of labor shortage.

第二件事是對話式人工智能。你會看到聊天機器人和網站客戶服務，甚至現在人工智能支持的實時客戶服務。一方面，對話式人工智能有機會增強客戶服務。另一方面，補充大量勞動力短缺。

And then the third is this groundbreaking piece of work that's related to Transformers that led to natural language understanding breakthrough. But within it is this incredible thing called large language models, which embeds human knowledge because it's been trained and so much data. And we recently announced Megatron 530B. And it was a collaboration we did with Microsoft, the foundation of, I think they call it Turing. And this language model and others like it, like open AI, GPD 3 are really transformative, and they take an enormous amount of computation. However, the net result is a pre-trained model that is really quite remarkable.

第三個是與變形金剛相關的開創性工作，它導致了自然語言理解的突破。但其中有一個令人難以置信的東西，稱為大型語言模型，它嵌入了人類知識，因為它已經過訓練和大量數據。我們最近發布了威震天 530B。這是我們與微軟的合作，我認為他們稱之為圖靈的基礎。這種語言模型和其他類似的語言模型，如開放式 AI、GPD 3 確實具有變革性，它們需要大量的計算。然而，最終結果是一個非常出色的預訓練模型。

Now we're working with thousands of start-ups, large companies that are building, who are using the public cloud. And so it's driving a lot of demand for us in the public cloud. I think we have now 10,000 AI inception startups that are working with us and using NVIDIA AI. Whether it's on-prem or in the cloud, it saves money because the computation time is significantly reduced. The quality of service is a lot better, and they could do greater things. And so that's driving AI in the cloud.

現在，我們正在與數以千計的初創企業、正在建設的大公司合作，他們正在使用公共雲。因此，它在公共雲中推動了對我們的大量需求。我認為我們現在有 10,000 家 AI 初創公司正在與我們合作並使用 NVIDIA AI。無論是在本地還是在雲端，它都可以節省資金，因為計算時間顯著減少。服務質量要好得多，他們可以做更多的事情。這就是在雲端推動人工智能。

And so all of these different factors, whether it's just the industrial recognition of the importance of AI, the transformative nature of these new AI models, recommender systems, large language models, conversational AI. The thousands of companies around the world that are using NVIDIA AI in the cloud -- driving public cloud demand, all of these things are driving our Data Center growth. And so we expect to see Data Center demand remain strong.

所以所有這些不同的因素，無論是工業界對人工智能重要性的認識，這些新的人工智能模型的變革性質，推薦系統，大型語言模型，對話式人工智能。全球數以千計的公司在雲中使用 NVIDIA AI——推動公共雲需求，所有這些都在推動我們的數據中心增長。因此，我們預計數據中心需求將保持強勁。

Next, we'll go to Tim Arcuri with UBS.

接下來，我們將與瑞銀一起前往 Tim Arcuri。

I had a question about this $500 million impact for July and whether it's more supply related or demand related. And that's because most others in semis are sort of citing this China stuff, in particular, as more of a logistics issue, so more of a supply issue. But the language, Colette, you were using in your commentary side of lower sell-through in Gaming and sort of the absence of sales in Russia, to me, that sounds a little more demand, which would make sense in the context of this new freeze on hiring that you have.

我有一個關於 7 月份 5 億美元影響的問題，以及它與供應相關還是與需求相關。那是因為半成品中的大多數其他人都在引用中國的東西，尤其是更多的物流問題，所以更多的是供應問題。但是，科萊特，你在評論方面使用的語言是遊戲銷售率較低，以及俄羅斯沒有銷售，對我來說，這聽起來有點需求，這在這個新的背景下是有意義的凍結你的招聘。

So I ask because if it's supply related, then you could argue that it's not really perishable and really just timing. but if demand related, that might never come back, and it could be the beginning of falling knife. So I'm wondering if you could sort of walk through that for me.

所以我問，因為如果它與供應有關，那麼你可以爭辯說它不是真正易腐爛的，真的只是時機。但如果與需求有關，那可能永遠不會回來，它可能是落刀的開始。所以我想知道你是否可以為我介紹一下。

Thanks, Tim, for the question. Let me try a bit here on the China and Russia, two very different things. The current China lockdowns that we are seeing, interestingly, has implications to both supply and demand. We have seen challenges in terms of the logistics throughout the country, things, going in and out of the country. It puts a lot of pressure on just logistics that were already under pressure.

謝謝蒂姆的問題。讓我在這裡嘗試一下中國和俄羅斯，兩個截然不同的事物。有趣的是，我們看到的當前中國的封鎖對供需都有影響。我們在全國各地的物流、事物、進出該國都看到了挑戰。它給已經承受壓力的物流帶來了很大壓力。

From a demand perspective, it has also been hit from the Gaming side. you have very large cities that are in full lockdown focusing really on other important things for the citizens there. So it's impacting our demand. We do believe that they will come out of COVID and the demand for our products will come back. We do believe that will occur. The supply, we'll sort it out. It's very difficult to determine how.

從需求的角度來看，它也受到了遊戲方面的打擊。您有非常大的城市處於完全封鎖狀態，真正專注於對那裡的公民來說其他重要的事情。所以它影響了我們的需求。我們確實相信它們會擺脫 COVID 的影響，並且對我們產品的需求會回來。我們相信這會發生。供應，我們會解決的。很難確定如何。

Now in the case of Russia, we're not selling to Russia. That's something that we had announced earlier last quarter. But there were plans and Russia has been a part of our overall company revenue of probably about 2% of our company revenue historically. And a little larger percentage when you look at our Gaming business. I hope that helped.

現在就俄羅斯而言，我們不賣給俄羅斯。這是我們上個季度早些時候宣布的。但是有一些計劃，俄羅斯一直是我們公司總收入的一部分，歷史上可能占我們公司收入的 2% 左右。當您查看我們的遊戲業務時，百分比會更大一些。我希望這有幫助。

Next, we'll go to Ambrish Srivastava with BMO.

接下來，我們將與 BMO 一起前往 Ambrish Srivastava。

And I actually really appreciate it that you called out demand. For most ship companies, it seems like it's heresy to say. Demand is a problem, so refreshing to hear that.

我真的很感激你提出了需求。對於大多數船公司來說，這樣說似乎是異端。需求是一個問題，聽到這個消息令人耳目一新。

I had a question on the second half, and it relates to both Data Center as well as Gaming. So last couple of times you have talked publicly, you have made comments that your visibility into the Data Center has never been better. So I was wondering, if you just take out the Russia impact, is that still true? All the orders that you have been getting, they're intact. And you did say that business will see a strong momentum. I just want to make sure that statement of confidence you have made stays.

下半場我有一個問題，它既涉及數據中心，也涉及遊戲。因此，在最近幾次公開談話中，您曾發表評論說您對數據中心的了解從未像現在這樣好。所以我想知道，如果你只是排除俄羅斯的影響，那仍然是真的嗎？您收到的所有訂單都完好無損。您確實說過，業務將出現強勁勢頭。我只是想確保您所做的信心聲明得以保留。

And then on Gaming, Colette, do we expect second half to be up year-over-year just based on the guide for second quarter? It seems like it could be up sequentially but may not return to year-over-year growth in Q3.

然後在遊戲方面，科萊特，僅根據第二季度的指南，我們是否預計下半年會同比增長？看起來它可能會連續上升，但可能不會在第三季度恢復到同比增長。

Yes. Ambrish, thanks for the question. On first principles, it should be the case that our visibility of Data Centers is vastly better, vastly better than a couple of years ago. And the reason for that is several. One, if you recall a couple 2, 3 years ago, deep learning and AI was starting to accelerate in the most computer science deep companies in the world with CSPs and hyperscalers. And but just about everywhere else, it was still quite nascent. And there was a couple of reasons for that. Obviously, the understanding of the technology is not as pervasive at the time. The type of industrial use cases for artificial intelligence requires labeling of data that's really quite difficult.

是的。 Ambrish，謝謝你的提問。首先，我們對數據中心的可見性應該比幾年前好得多，好得多。原因有很多。第一，如果您還記得 2 年、3 年前的情況，深度學習和 AI 在世界上擁有 CSP 和超大規模計算機的大多數計算機科學深度公司中開始加速。但就在其他任何地方，它仍然是新生事物。這有幾個原因。顯然，當時對該技術的理解並不那麼普遍。人工智能的工業用例類型需要對數據進行標記，這非常困難。

And then now with Transformers, you have unsupervised learning and other techniques, zero-shot learning that allows us to do all kinds of interesting things without having to have human-labeled data. We even have synthetic-generated data with Omniverse that helps customers do data generation without having to label data, which is either too costly or, quite frankly, oftentimes impossible. And so now, the knowledge and the technology has evolved to a place that most of the industries could use artificial intelligence at a fairly effective way and in many industries rather transformative.

然後現在有了變形金剛，你有無監督學習和其他技術，零樣本學習讓我們可以做各種有趣的事情，而不必擁有人工標記的數據。我們甚至擁有 Omniverse 合成生成的數據，可以幫助客戶生成數據而無需標記數據，這要么成本太高，要么坦率地說，通常是不可能的。所以現在，知識和技術已經發展到大多數行業可以以相當有效的方式使用人工智能的地方，並且在許多行業中相當具有變革性。

And so I think, number one, we went from clouds and hyperscalers to all of industries. Second, we went from training-focused to inference. Most people thought that inference was going to be easy. It turns out the inference is by far the harder. And the reason for that is because there are so many different models and there are so many different use cases and so many quality of service requirements. And you want to run these inference models in a small of a footprint as you can.

所以我認為，第一，我們從雲計算和超大規模企業擴展到所有行業。其次，我們從以訓練為中心轉向推理。大多數人認為推理會很容易。事實證明，推理要困難得多。原因是因為有很多不同的模型，有很多不同的用例和很多服務質量要求。並且您希望盡可能在很小的空間內運行這些推理模型。

And so when you scale out the number of users that use the service, it's really quite high. So using acceleration and using NVIDIA's platform, we could inference any model from computer vision to speech to chemistry to biology, you name it. And we do it so quickly and so fast that the cost is very low. And so the more acceleration you do, the more money you will save. And that -- I think, that wisdom is absolutely true.

因此，當您擴大使用該服務的用戶數量時，它確實非常高。因此，使用加速和使用 NVIDIA 的平台，我們可以推斷任何模型，從計算機視覺到語音，從化學到生物學，應有盡有。而且我們做的如此之快，如此之快，以至於成本非常低。所以你做的加速越多，你節省的錢就越多。而且——我認為，這種智慧是絕對正確的。

And so the second dimension is training to inference. The third dimension is that we now have so many different types of configurations of systems that we can go from high-performance computing systems all the way to cloud to on-prem to edge.

所以第二個維度是訓練推理。第三個維度是，我們現在擁有如此多不同類型的系統配置，我們可以從高性能計算系統一直到雲，從本地到邊緣。

And then the final concept is really this industrial deployment now of AI that's causing us to be able to, in just about every industry, find growth. And so as you know, our cloud and hyperscalers are growing very, very quickly. However, the vertical part, vertical industries, which is the financial services and retail and telco, all of those vertical industries have also grown very, very nicely.

最後的概念是現在人工智能的這種工業部署，它使我們能夠在幾乎每個行業中找到增長。如您所知，我們的雲計算和超大規模計算正在快速增長。但是，垂直部分，垂直行業，即金融服務、零售和電信，所有這些垂直行業也發展得非常非常好。

And so in all of those different dimensions, our visibility should be a lot better. And then starting a couple of years ago, adding the Mellanox portfolio to our company, we're able to provide a lot more solution-oriented, end-to-end platform solutions for companies that don't have the skills and don't have the technical depth to be able to stand up these sophisticated systems. And so our networking business is growing very, very nicely as well.

所以在所有這些不同的維度上，我們的能見度應該會好很多。然後從幾年前開始，將 Mellanox 產品組合添加到我們的公司中，我們能夠為不具備相關技能的公司提供更多以解決方案為導向的端到端平台解決方案。擁有足以支撐這些複雜系統的技術深度。因此，我們的網絡業務也在非常非常好地增長。

Next, we'll go to Harlan Sur with JPMorgan.

接下來，我們將與摩根大通一起前往 Harlan Sur。

Let me ask a question. I just want to maybe just ask this question a little bit more directly. So it's good to see the team being able to drive -- navigate the dynamic supply chain environment, right? You booked strong sequential growth in Data Center in April, here in the July quarter, even with some demand impact from Russia, right? And so as we think about the second half of the year, cloud spending is strong, and it's actually, I think, accelerating.

讓我問一個問題。我只是想更直接地問這個問題。所以很高興看到團隊能夠推動——駕馭動態的供應鏈環境，對嗎？您在 4 月（在 7 月季度）預訂了數據中心的強勁連續增長，即使受到俄羅斯的一些需求影響，對嗎？因此，當我們考慮今年下半年時，雲支出強勁，而且我認為它實際上正在加速。

You're getting ready to ramp H100 later in the year. Mellanox, I think, is getting more supply as you move through the year. And in general, I think previously, you guys were anticipating sequential supply and revenue growth for the business through this entire year. I understand the uncertainty around Gaming. But does the team expect continued sequential growth in Data Center through the remainder of the year?

您正準備在今年晚些時候推出 H100。我認為，隨著這一年的發展，Mellanox 正在獲得更多的供應。總的來說，我認為以前，你們預計該業務全年的連續供應和收入增長。我理解圍繞遊戲的不確定性。但該團隊是否預計數據中心將在今年剩餘時間內繼續保持連續增長？

Either one of those -- the answer is yes. The answer is yes. We see a strong demand in Data Center. Hyperscale to cloud computing to vertical industries. Ampere is going to continue to scale out. It's been qualified in every single company in the world. And so after 2 years, it remains the best universal accelerator on the planet. And it's going to continue to scale out in all these different domains and different markets.

其中任何一個——答案是肯定的。答案是肯定的。我們看到數據中心的需求強勁。從超大規模到雲計算到垂直行業。安培將繼續擴大規模。它在世界上每一家公司都獲得了資格。因此，2 年後，它仍然是地球上最好的通用加速器。它將繼續在所有這些不同的領域和不同的市場中擴展。

We're going to layer on top of that a brand-new architecture, Hopper. I'm going to layer on top of that brand-new networking architectures. Quantum 3, CX-7, BlueField 3, and we have increasing supply. And so we're looking forward to an excellent quarter next quarter again for Data Centers and going into the second half.

我們將在此之上添加一個全新的架構 Hopper。我將在全新的網絡架構之上進行分層。 Quantum 3、CX-7、BlueField 3，我們的供應也在增加。因此，我們期待數據中心在下個季度再次迎來一個出色的季度，並進入下半年。

Next, we'll go to Chris Caso with Raymond James.

接下來，我們將和雷蒙德詹姆斯一起去克里斯卡索。

Wonder if you could speak a little bit about the purchase obligations, which seemed like they were up again in the quarter, and how that -- was that a function of longer-dated obligations? Or a higher magnitude of obligations? And maybe you could just speak to supply constraints in general. You've mentioned a couple of times in the call about continued constraints in the networking business. What about the other parts of the business? Where are you still constrained?

想知道您是否可以談談購買義務，這似乎在本季度再次上漲，以及那是如何 - 這是長期義務的功能？還是更高級別的義務？也許你可以只談談一般的供應限制。您在電話會議中多次提到網絡業務的持續限制。業務的其他部分呢？你還在哪裡受限？

Yes. So let me start here, and I'll see if Jen-Hsun wants to add more of that. Our purchase obligations as well as our prepaids have 2 major things to keep in mind. One, for the first time ever, we are pre-paying to make sure that we have that supply and those commitments long term. And additionally, on our purchase obligations, many of them are for long-lead-time items that are a must for us to procure to make sure that we have the products coming to market.

是的。所以讓我從這裡開始，我會看看 Jen-Hsun 是否想添加更多內容。我們的購買義務以及我們的預付款有兩點需要牢記。一，有史以來第一次，我們預付費用，以確保我們有長期的供應和這些承諾。此外，在我們的採購義務方面，其中許多是我們必須採購的長交貨期物品，以確保我們的產品上市。

A good percentage of our purchase commitments is for our Data Center business, which you can imagine, are much larger systems, much more complex systems. And those things that we are procuring to make sure we can feed the demand, both in the upcoming quarters and further.

我們的採購承諾中有很大一部分用於我們的數據中心業務，您可以想像，它是更大的系統，更複雜的系統。以及我們正在採購的那些東西，以確保我們能夠滿足未來幾個季度和未來幾個季度的需求。

Areas in terms of where we are still a little bit supply constrained are networking. Our demand is quite strong. We've been improving it each time. But yes, we still have demand -- excuse me, supply concerns with networking still.

我們仍然有點供應受限的領域是網絡。我們的需求相當強烈。我們每次都在改進它。但是，是的，我們仍然有需求——對不起，仍然存在對網絡的供應問題。

Is there others that you want to add on, Jen-Hsun?

仁勳，還有其他要補充的嗎？

No, I thought you were perfect. That's perfect.

不，我以為你很完美。那很完美。

Our final question comes from Aaron Rakers with Wells Fargo.

我們的最後一個問題來自富國銀行的 Aaron Rakers。

And most of my questions around Gaming and Data Center have been answered. But I guess I'll ask about the Auto segment. While it's still small, clearly, you guys sound confident in that business, starting to see "significant sequential growth" into this next quarter. I'm wondering if you could help us kind of think about the trajectory of that business over the next couple of quarters.

我關於遊戲和數據中心的大部分問題都已得到解答。但我想我會問一下汽車部分。雖然它仍然很小，但顯然，你們對這項業務充滿信心，開始看到下個季度的“顯著連續增長”。我想知道您是否可以幫助我們思考該業務在接下來幾個季度的發展軌跡。

And I think, in the past, you've said that, that should start to really inflect higher as we move into the second half of the year. Just curious if you could help us think about that piece of the business.

而且我認為，在過去，您曾說過，隨著我們進入下半年，這應該會開始真正走高。只是好奇您是否可以幫助我們考慮這部分業務。

Several data points. We are just starting. We have just started shipping O-RAN in the first quarter of shipping production O-RAN. O-RAN is a robotics processor. It's designed for a software-defined robotic card or robotic pick and placer or a robotic mover, logistics mover.

幾個數據點。我們才剛剛開始。我們剛剛開始出貨 O-RAN，第一季度出貨量產 O-RAN。 O-RAN 是一種機器人處理器。它專為軟件定義的機器人卡片或機器人拾放器或機器人搬運器、物流搬運器而設計。

We've been designed into 35 car and trucks and robo taxi companies and more others, if you include logistics movers and last mile delivery systems and farming equipment. And the number of design wins for O-RAN is really quite fantastic. O-RAN is a revolutionary processor. And it's designed as a -- if you will, a Data Center on a chip. And it is the first Data Center on a chip that is robotic, processes sensor information, it's safe, it has the ability to be rather resilient. It has confidential computing. It is designed to be secure, designed to be all those things because these data centers are going to be everywhere.

如果您包括物流搬運工和最後一英里交付系統和農業設備，我們已經被設計成 35 家汽車和卡車以及機器人出租車公司和更多其他公司。 O-RAN 的設計獲獎數量確實非常驚人。 O-RAN 是一款革命性的處理器。它被設計成一個——如果你願意的話，一個芯片上的數據中心。它是第一個機器人芯片上的數據中心，處理傳感器信息，它是安全的，它具有相當彈性的能力。它具有機密計算。它被設計成安全的，被設計成所有這些東西，因為這些數據中心將無處不在。

And so O-RAN is really a technological marvels in production. We experienced very likely the lowest auto quarter in some time for some time. And the reason for that is because over the next 6 years-or-so, we have $11 billion and counting of business that we've secured estimated. And so I think it's a fairly safe thing to say now that O-RAN and our autonomous vehicle and robotics business is going to be our next multibillion dollar business. It's on its way surely there.

因此，O-RAN 確實是生產中的技術奇蹟。我們很可能經歷了一段時間以來最低的汽車季度。這樣做的原因是因為在接下來的 6 年左右，我們有 110 億美元，而且我們已經獲得了估計的業務。因此，我認為現在說 O-RAN 以及我們的自動駕駛汽車和機器人業務將成為我們下一個數十億美元的業務是一件相當安全的事情。它肯定在路上。

The robotics and autonomous systems and autonomous machines, whether they move or not move, but AI systems that are at the physical edge is surely going to be the next major computing segment. It is surely going to be the next major Data Center segment. We've been working in this area, as you know, for a decade. We have a fair amount of expertise in this area. And O-RAN is just one example of our work here.

機器人、自主系統和自主機器，無論它們是否移動，但處於物理邊緣的人工智能係統肯定會成為下一個主要的計算領域。它肯定會成為下一個主要的數據中心領域。如您所知，我們已經在這個領域工作了十年。我們在這方面擁有相當多的專業知識。 O-RAN 只是我們在這里工作的一個例子。

We have 4 pillars to our strategy for autonomous systems. Starting from the data processing and the AI training part of it to train robotics AIs; second, to simulate robotics AIs, which is omniverse; third, to the memory of the robotics AI otherwise known as mapping; and then finally, the actual robotics application and the robotics processor in the system. And that's where O-RAN goes.

我們的自主系統戰略有 4 個支柱。從數據處理和其中的人工智能訓練部分開始訓練機器人人工智能；第二，模擬機器人人工智能，即全能；第三，機器人人工智能的記憶，也稱為映射；最後是實際的機器人應用程序和系統中的機器人處理器。這就是 O-RAN 的發展方向。

But O-RAN is just 1 of our 4 pillars of our robotics strategy and the next wave of AI. And so I am really optimistic and really enthusiastic about the next phase of the computer industry's growth. And I think a lot of it is going to be at the edge. A lot of it's going to be about robotics.

但 O-RAN 只是我們機器人戰略和下一波 AI 的 4 個支柱之一。所以我對計算機行業下一階段的發展非常樂觀和熱情。而且我認為其中很多都將處於邊緣。其中很多將與機器人技術有關。

Thank you. I'll now turn it back over to Jen-Hsun Huang for any additional closing remarks.

謝謝你。我現在將把它轉回給 Jen-Hsun Huang 以獲取任何額外的結束語。

Thanks, everyone.

謝謝大家。

The full impact and duration of the war in Ukraine and COVID lockdowns in China is difficult to predict. However, the impact of our technology and our market opportunities remain unchanged. The effectiveness of deep learning AI continues to astound. The Transformer model, which led to the natural language understanding breakthroughs, is being advanced to learn patterns with great spatial, sequential and temporal complexity. Researchers are creating Transformer models that are revolutionizing applications from robotics to drug discovery.

難以預測烏克蘭戰爭和中國 COVID 封鎖的全部影響和持續時間。然而，我們技術的影響和我們的市場機會保持不變。深度學習人工智能的有效性繼續令人震驚。導致自然語言理解突破的 Transformer 模型正在被推進以學習具有高度空間、順序和時間複雜性的模式。研究人員正在創建 Transformer 模型，這些模型正在徹底改變從機器人技術到藥物發現的應用。

The effectiveness of deep learning AI is driving companies across industries to adopt NVIDIA for AI computing. We're focused on 4 major initiatives: First, ramping our next generation of AI infrastructure chips and platforms, Hopper GPU, BlueField DPU, NVLink, InfiniBand, Quantum InfiniBand, Spectrum Ethernet networking. And all this to help customers build their AI factories and take advantage of new AI breakthroughs like Transformers.

深度學習 AI 的有效性正在推動各行各業的公司採用 NVIDIA 進行 AI 計算。我們專注於 4 個主要舉措：首先，提升我們的下一代 AI 基礎設施芯片和平台、Hopper GPU、BlueField DPU、NVLink、InfiniBand、Quantum InfiniBand、Spectrum Ethernet 網絡。而這一切都是為了幫助客戶建立他們的人工智能工廠，並利用變形金剛等新的人工智能突破。

Second, ramping our system and software industry partners to launch Grace, our first CPU.

其次，讓我們的系統和軟件行業合作夥伴推出我們的第一個 CPU 格雷斯。

Third, ramping O-RAN, our new robotics processor and nearly 40 customers building cars, robo taxis, trucks, delivery robots, logistics robots, farming robots to medical instruments.

第三，增加 O-RAN、我們的新機器人處理器和近 40 家客戶製造汽車、機器人出租車、卡車、送貨機器人、物流機器人、農業機器人和醫療器械。

And fourth, with our software platforms, adding new value to our ecosystem with NVIDIA AI and NVIDIA Omniverse, and expanding into new markets with new CUDA acceleration libraries.

第四，借助我們的軟件平台，通過 NVIDIA AI 和 NVIDIA Omniverse 為我們的生態系統增加新價值，並通過新的 CUDA 加速庫拓展新市場。

These initiatives will greatly advance AI, and while continuing to extend this most impactful technology of our time to scientists in every field and companies in every industry.

這些舉措將極大地推動人工智能發展，同時繼續將這一當今最具影響力的技術推廣到各個領域的科學家和各個行業的公司。

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

我們期待在下個季度向您通報我們的進展情況。謝謝你。

This concludes today's conference call. You may now disconnect.

今天的電話會議到此結束。您現在可以斷開連接。