輝達 (NVDA) 2024 Q4 法說會逐字稿

Good afternoon. My name is Rob, and I will be your conference operator today. At this time, I would like to welcome everyone to the NVIDIA's fourth quarter earnings call. (Operator Instructions) Thank you. Simona Jankowski, you may begin your conference.

午安.我叫羅布，今天我將擔任你們的會議操作員。此時此刻，我謹歡迎大家參加 NVIDIA 第四季財報電話會議。 （操作員說明）謝謝。 Simona Jankowski，您可以開始會議了。

Thank you. Good afternoon, everyone, and welcome to NVIDIA's conference call for the fourth quarter and fiscal 2024. With me today from NVIDIA are Jensen Huang, President and Chief Executive Officer; and Colette Kress, Executive Vice President and Chief Financial Officer.

謝謝。大家下午好，歡迎參加 NVIDIA 第四季和 2024 財年的電話會議。今天與我一起出席的有 NVIDIA 總裁兼首席執行官黃仁勳 (Jensen Huang)；執行副總裁兼財務長 Colette Kress。

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 first quarter of fiscal 2025. 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 投資者關係網站上進行網路直播。此網路廣播將在討論 2025 財年第一季財務業績的電話會議之前進行重播。今天電話會議的內容屬於 NVIDIA 的財產。未經我們事先書面同意，不得複製或轉錄。

During this call, we may make forward-looking statements based on current expectations. These are subject to a number of significant risks and uncertainties, and our actual results may differ materially. For a discussion of factors that could affect our future financial results and business, please refer to the disclosure in today's earnings release, our most recent Forms 10-K and 10-Q and the reports that we may file on Form 8-K with the Securities and Exchange Commission. All our statements are made as of today, February 21, 2024, based on information currently available to us. Except as required by law, we assume no obligation to update any such statements.

在這次電話會議中，我們可能會根據目前的預期做出前瞻性陳述。這些都受到許多重大風險和不確定性的影響，我們的實際結果可能會有重大差異。有關可能影響我們未來財務表現和業務的因素的討論，請參閱今天的收益發布中的揭露、我們最新的表格 10-K 和 10-Q 以及我們可能透過表格 8-K 提交的報告證券交易委員會。我們的所有聲明均基於我們目前掌握的信息，截至 2024 年 2 月 21 日作出。除法律要求外，我們不承擔更新任何此類聲明的義務。

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

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

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

現在，讓我把電話轉給科萊特。

Thanks, Simona. Q4 was another record quarter. Revenue of $22.1 billion was up 22% sequentially and up 265% year-on-year and well above our outlook of $20 billion. For fiscal 2024, revenue was $60.9 billion and up 126% from the prior year.

謝謝，西蒙娜。第四季又是創紀錄的季度。營收為 221 億美元，季增 22%，年增 265%，遠高於我們 200 億美元的預期。 2024 財年，營收為 609 億美元，較上年成長 126%。

Starting with Data Center. Data Center revenue for the fiscal 2024 year was $47.5 billion, more than tripling from the prior year. The world has reached a tipping point of new computing era. The trillion-dollar installed base of Data Center infrastructure is rapidly transitioning from general purpose to accelerated computing. As Moore's Law slows while computing demand continues to skywalk, companies may accelerate every workload possible to drive future improvement in performance, TCO and energy efficiency. At the same time, companies have started to build the next generation of modern Data Centers, what we refer to as AI factories, purpose-built to refine raw data and produce valuable intelligence in the era of generative AI.

從資料中心開始。 2024 財年資料中心營收為 475 億美元，比上年成長兩倍多。世界已經到達新運算時代的轉捩點。價值數萬億美元的資料中心基礎設施安裝基礎正迅速從通用運算過渡到加速運算。隨著摩爾定律的放緩，而運算需求持續飆升，公司可能會加速每項可能的工作負載，以推動未來效能、整體擁有成本和能源效率的改進。同時，企業已經開始建造下一代現代資料中心，即我們所謂的人工智慧工廠，專門用於在生成人工智慧時代提煉原始資料並產生有價值的情報。

In the fourth quarter, Data Center revenue of $18.4 billion was a record, up 27% sequentially and up 409% year-on-year, driven by the NVIDIA Hopper GPU computing platform, along with InfiniBand end-to-end networking. Compute revenue grew more than 5x and networking revenue tripled from last year. We are delighted that supply of Hopper architecture products is improving. Demand for Hopper remains very strong. We expect our next generation products to be supply constrained as demand far exceeds supply.

第四季度，在 NVIDIA Hopper GPU 運算平台以及 InfiniBand 端對端網路的推動下，資料中心營收達到創紀錄的 184 億美元，季增 27%，年增 409%。與去年相比，計算收入成長了 5 倍以上，網路收入成長了兩倍。我們很高興 Hopper 架構產品的供應正在改善。對霍珀的需求仍然非常強勁。我們預計，由於需求遠遠超過供應，我們的下一代產品將受到供應限制。

Fourth quarter Data Center growth was driven by both training and inference of generative AI and large language models across a broad set of industries, use cases and regions. The versatility and leading performance of our Data Center platform enables a high return on investment for many use cases, including AI training and inference, data processing and a broad range of CUDA accelerated workloads. We estimate in the past year, approximately 40% of Data Center revenue was for AI inference.

第四季度資料中心的成長是由跨行業、用例和地區的生成式人工智慧和大型語言模型的訓練和推理所推動的。我們的資料中心平台的多功能性和領先效能可為許多用例帶來高投資回報，包括人工智慧訓練和推理、資料處理和廣泛的 CUDA 加速工作負載。我們估計，去年資料中心收入的約 40% 來自人工智慧推理。

Building and deploying AI solutions has reached virtually every industry. Many companies across industries are training and operating their AI models and services at scale. Enterprises across NVIDIA AI infrastructure through cloud providers, including hyperscales, GPU specialized and private cloud or on-premise. NVIDIA's computing stack extends seamlessly across cloud and on-premise environments, allowing customers to deploy with a multi-cloud or hybrid cloud strategy.

建置和部署人工智慧解決方案幾乎已涉及每個行業。各行各業的許多公司都在大規模培訓和營運其人工智慧模型和服務。企業透過雲端供應商使用 NVIDIA AI 基礎設施，包括超大規模、專用 GPU 以及私有雲或本地雲。 NVIDIA 的運算堆疊可跨雲端和本地環境無縫擴展，讓客戶可以採用多雲或混合雲策略進行部署。

In the fourth quarter, large cloud providers represented more than half of our Data Center revenue, supporting both internal workloads and external public cloud customers. Microsoft recently noted that more than 50,000 organizations use GitHub Copilot Business to supercharge the productivity of their developers, contributing to GitHub revenue growth accelerating to 40% year-on-year. And Copilot for Microsoft 365 adoption grew faster in its first 2 months than the 2 previous major Microsoft 365 enterprise suite releases.

第四季度，大型雲端供應商占我們資料中心收入的一半以上，支援內部工作負載和外部公有雲客戶。微軟最近指出，超過 5 萬個組織使用 GitHub Copilot Business 來提高開發人員的工作效率，推動 GitHub 營收年增至 40%。 Copilot for Microsoft 365 採用率在前 2 個月的成長速度比之前的 2 個主要 Microsoft 365 企業套件版本還要快。

The consumer Internet companies have been early adopters of AI and represent one of our largest customer categories. Companies from search to e-commerce, social media, news and video services and entertainment are using AI for deep learning-based recommendation systems. These AI investments are generating a strong return by improving customer engagement, ad conversation and click-through rates. Meta in its latest quarter cited more accurate predictions and improved advertiser performance as contributing to the significant acceleration in its revenue.

消費性互聯網公司是人工智慧的早期採用者，也是我們最大的客戶類別之一。從搜尋到電子商務、社交媒體、新聞和視訊服務以及娛樂的公司都在使用人工智慧來建立基於深度學習的推薦系統。這些人工智慧投資透過提高客戶參與度、廣告對話和點擊率而產生了豐厚的回報。 Meta 在最新季度中表示，更準確的預測和廣告商業績的改善有助於其收入大幅增長。

In addition, consumer Internet companies are investing in generative AI to support content creators, advertisers and customers through automation tools for content and ad creation, online product descriptions and AI shopping assistance. Enterprise software companies are applying generative AI to help customers realize productivity gains. All the customers we've partnered with for both training and inference of generative AI are already seeing notable commercial success. ServiceNow's generative AI products in their latest quarter drove their largest ever net new annual contract value contribution of any new product family release. We are working with many other leading AI and enterprise software platforms as well, including Adobe, Databricks, Getty Images, SAP, and Snowflake.

此外，消費網路公司正在投資生成式人工智慧，透過內容和廣告創建、線上產品描述和人工智慧購物輔助的自動化工具為內容創作者、廣告商和客戶提供支援。企業軟體公司正在應用生成式人工智慧來幫助客戶實現生產力提升。我們在生成式人工智慧的訓練和推理方面合作的所有客戶都已經取得了顯著的商業成功。 ServiceNow 的生成式人工智慧產品在最新一個季度推動了所有新產品系列發布中有史以來最大的年度淨新合約價值貢獻。我們也與許多其他領先的人工智慧和企業軟體平台合作，包括 Adob​​e、Databricks、Getty Images、SAP 和 Snowflake。

The field of foundation of large language models is thriving, Anthropic, Google, Inflection, Microsoft, OpenAI and xAI are leading with continued amazing breakthrough in generative AI. Exciting companies like Adept, AI21, Character.AI, Cohere, Mistral, Perplexity and Runway are building platforms to serve enterprises and creators. New startups are creating LLMs to serve the specific languages, cultures and customs of the world's many regions. And others are creating foundation models to address entirely different industries like Recursion, pharmaceuticals and generative biomedicines for biology. These companies are driving demand for NVIDIA AI infrastructure through hyperscale or GPU-specialized cloud providers.

大語言模型的基礎領域正在蓬勃發展，Anthropic、Google、Inflection、Microsoft、OpenAI 和 xAI 處於領先地位，在生成 AI 領域不斷取得令人驚嘆的突破。 Adept、AI21、Character.AI、Cohere、Mistral、Perplexity 和 Runway 等令人興奮的公司正在建立為企業和創作者服務的平台。新的新創公司正在創建法學碩士，以服務世界許多地區的特定語言、文化和習俗。其他人正在創建基礎模型來解決完全不同的行業，例如遞歸、製藥和生物學的生成生物醫學。這些公司正在透過超大規模或 GPU 專業雲端供應商推動對 NVIDIA AI 基礎設施的需求。

Just this morning, we announced that we collaborated with Google to optimize its state-of-the-art new Gemma language model to accelerate their inference performance on NVIDIA GPUs in the cloud, data center, and PC. One of the most notable trends over the past year is the significant adoption of AI by enterprises across the industry verticals such as Automotive, health care, and financial services. NVIDIA offers multiple application frameworks to help companies adopt AI in vertical domains such as autonomous driving, drug discovery, low-latency machine learning for fraud detection or robotics, leveraging our full-stack accelerated computing platform.

就在今天早上，我們宣布與 Google 合作優化其最先進的新 Gemma 語言模型，以加速其在雲端、資料中心和 PC 中 NVIDIA GPU 上的推理效能。過去一年最顯著的趨勢之一是汽車、醫療保健和金融服務等垂直行業的企業大量採用人工智慧。 NVIDIA 提供多種應用框架，利用我們的全端加速運算平台，幫助企業在自動駕駛、藥物發現、用於詐欺偵測或機器人技術的低延遲機器學習等垂直領域採用人工智慧。

We estimate that Data Center revenue contribution of the Automotive vertical through the cloud or on-prem exceeded $1 billion last year. NVIDIA DRIVE infrastructure solutions include systems and software for the development of autonomous driving, including data ingestion, curation, labeling, and AI training, plus validation through simulation. Almost 80 vehicle manufacturers across global OEMs, new energy vehicles, trucking, robotaxi and Tier 1 suppliers are using NVIDIA's AI infrastructure to train LLMs and other AI models for automated driving and AI cockpit applications.

我們估計去年透過雲端或本地資料中心對汽車垂直產業的收入貢獻超過 10 億美元。 NVIDIA DRIVE 基礎設施解決方案包括用於開發自動駕駛的系統和軟體，包括資料擷取、管理、標籤和 AI 培訓，以及透過模擬進行的驗證。全球OEM、新能源汽車、卡車運輸、自動駕駛計程車和一級供應商的近80 家汽車製造商正在使用NVIDIA 的AI 基礎設施來訓練法學碩士和其他AI 模型，以實現自動駕駛和AI 座艙應用。

In effect, nearly every Automotive company working on AI is working with NVIDIA. As AV algorithms move to video transformers and more cars are equipped with cameras, we expect NVIDIA's automotive Data Center processing demand to grow significantly.

事實上，幾乎所有致力於人工智慧的汽車公司都在與 NVIDIA 合作。隨著 AV 演算法轉向視訊轉換器以及越來越多的汽車配備攝影機，我們預計 NVIDIA 的汽車資料中心處理需求將顯著增長。

In health care, digital biology and generative AI are helping to reinvent drug discovery, surgery, medical imaging, and wearable devices. We have built deep domain expertise in health care over the past decade, creating the NVIDIA Clara health care platform and NVIDIA BioNeMo, a generative AI service to develop, customize and deploy AI foundation models for computer-aided drug discovery. BioNeMo features a growing collection of pre-trained biomolecular AI models that can be applied to the end-to-end drug discovery processes. We announced Recursion is making available for the proprietary AI model through BioNeMo for the drug discovery ecosystem.

在醫療保健領域，數位生物學和生成人工智慧正在幫助重塑藥物發現、手術、醫學影像和穿戴式裝置。過去十年，我們在醫療保健領域積累了深厚的專業知識，創建了NVIDIA Clara 醫療保健平台和NVIDIA BioNeMo（一項生成式AI 服務，用於開發、定制和部署用於計算機輔助藥物發現的AI 基礎模型）。 BioNeMo 具有越來越多的預訓練生物分子 AI 模型，可應用於端到端藥物發現過程。我們宣布 Recursion 正在透過 BioNeMo 為藥物發現生態系統提供專有的 AI 模型。

In financial services, customers are using AI for a growing set of use cases from trading and risk management to customer service and fraud detection. For example, American Express improved fraud detection accuracy by 6% using NVIDIA AI.

在金融服務領域，客戶正在將人工智慧用於越來越多的用例，從交易和風險管理到客戶服務和詐欺檢測。例如，美國運通使用 NVIDIA AI 將詐欺偵測準確率提高了 6%。

Shifting to our Data Center revenue by geography. Growth was strong across all regions except for China, where our Data Center revenue declined significantly following the U.S. government export control regulations imposed in October. Although we have not received licenses from the U.S. government to ship restricted products to China, we have started shipping alternatives that don't require a license for the China market. China represented a mid-single-digit percentage of our Data Center revenue in Q4, and we expect it to stay in a similar range in the first quarter.

轉向我們按地理位置劃分的資料中心收入。除中國外，所有地區均實現強勁成長，在美國政府 10 月實施出口管制法規後，我們的資料中心收入大幅下降。儘管我們尚未獲得美國政府向中國運送受限產品的許可證，但我們已經開始向中國市場運送不需要許可證的替代品。第四季度，中國資料中心收入占我們資料中心收入的中個位數百分比，我們預計第一季將保持在類似的範圍內。

In regions outside of the U.S. and China, sovereign AI has become an additional demand driver. Countries around the world are investing in AI infrastructure to support the building of large language models in their own language on domestic data and in support of their local research and enterprise ecosystems.

在美國和中國以外的地區，主權人工智慧已成為額外的需求驅動力。世界各國都在投資人工智慧基礎設施，以支援在國內數據上用本國語言建構大型語言模型，並支持當地的研究和企業生態系統。

From a product perspective, the vast majority of revenue was driven by our Hopper architecture along with InfiniBand networking. Together, they have emerged as the de facto standard for accelerated computing and AI infrastructure. We are on track to ramp H200 with initial shipments in the second quarter. Demand is strong as H200 nearly doubled the inference performance of H100. Networking exceeded a $13 billion annualized revenue run rate. Our end-to-end networking solutions define modern AI data centers.

從產品角度來看，絕大多數的收入是由我們的 Hopper 架構和 InfiniBand 網路所推動。它們共同成為加速運算和人工智慧基礎設施的事實上的標準。我們預計在第二季度實現 H200 的首次發貨。需求強勁，H200 的推理表現幾乎是 H100 的兩倍。網路年化收入運作率超過 130 億美元。我們的端到端網路解決方案定義了現代人工智慧資料中心。

Our Quantum InfiniBand solutions grew more than 5x year-on-year. NVIDIA Quantum InfiniBand is the standard for the highest-performance AI-dedicated infrastructures. We are now entering the Ethernet networking space with the launch of our new Spectrum-X end-to-end offering designed for an AI-optimized networking for the Data Center. Spectrum-X introduces new technologies over Ethernet that are purpose-built for AI. Technologies incorporated in our Spectrum switch, BlueField DPU and software stack deliver 1.6x higher networking performance for AI processing compared with traditional Ethernet. Leading OEMs, including Dell, HPE, Lenovo and Supermicro with their global sales channels are partnering with us to expand our AI solution to enterprises worldwide. We are on track to ship Spectrum-X this quarter.

我們的 Quantum InfiniBand 解決方案年增超過 5 倍。 NVIDIA Quantum InfiniBand 是最高效能 AI 專用基礎架構的標準。我們現在正在進入乙太網路領域，推出了專為資料中心人工智慧優化網路而設計的新 Spectrum-X 端到端產品。 Spectrum-X 透過乙太網路引進了專為 AI 建構的新技術。與傳統乙太網路相比，我們的 Spectrum 交換器、BlueField DPU 和軟體堆疊中採用的技術為 AI 處理提供了 1.6 倍的網路效能。領先的原始設備製造商（包括戴爾、HPE、聯想和超微）及其全球銷售管道正在與我們合作，將我們的人工智慧解決方案擴展到全球企業。我們預計在本季推出 Spectrum-X。

We also made great progress with our software and services offerings, which reached an annualized revenue run rate of $1 billion in Q4. We announced that NVIDIA DGX Cloud will expand its list of partners to include Amazon's AWS, joining Microsoft Azure, Google Cloud, and Oracle Cloud. DGX Cloud is used for NVIDIA's own AI R&D and custom model development as well as NVIDIA developers. It brings the CUDA ecosystem to NVIDIA CSP partners.

我們的軟體和服務產品也取得了巨大進展，第四季的年化收入達到了 10 億美元。我們宣布 NVIDIA DGX Cloud 將擴大其合作夥伴名單，將亞馬遜 AWS 納入其中，加入 Microsoft Azure、Google Cloud 和 Oracle Cloud 的行列。 DGX Cloud 用於 NVIDIA 自己的 AI 研發和自訂模型開發以及 NVIDIA 開發人員。它將 CUDA 生態系統帶給 NVIDIA CSP 合作夥伴。

Okay, moving to Gaming. Gaming revenue was $2.87 billion, was flat sequentially and up 56% year-on-year, better than our outlook on solid consumer demand for NVIDIA GeForce RTX GPUs during the holidays. Fiscal year revenue of $10.45 billion was up 15%. At CES, we announced our GeForce RTX 40 Super Series family of GPUs. Starting at $599, they deliver incredible gaming performance and generative AI capabilities. Sales are off to a great start. NVIDIA AI Tensor Cores and the GPUs deliver up to 836 AI TOPS, perfect for powering AI for gaming, creating and everyday productivity.

好的，轉向遊戲。遊戲營收為 28.7 億美元，環比持平，年成長 56%，優於我們對假期期間消費者對 NVIDIA GeForce RTX GPU 強勁需求的預期。財年營收為 104.5 億美元，成長 15%。在 CES 上，我們發布了 GeForce RTX 40 Super 系列 GPU 系列。它們起價為 599 美元，提供令人難以置信的遊戲性能和生成人工智慧功能。銷售有了一個好的開始。 NVIDIA AI Tensor 核心和 GPU 提供高達 836 個 AI TOPS，非常適合為遊戲、創造和日常生產力提供 AI 動力。

The rich software stack we offer with our RTX DPUs further accelerates AI. With our DLSS technologies, 7 out of 8 pixels can be AI-generated, resulting up to 4x faster ray tracing and better image quality. And with the TensorRT LLM for Windows, our open-source library that accelerates inference performance for the latest large language models, generative AI can run up to 5x faster on RTX AI PCs. At CES, we also announced a wave of new RTX 40 Series AI laptops from every major OEM. These bring high-performance gaming and AI capabilities to a wide range of form factors, including 14-inch and thin and light laptops. With up to 686 TOPS of AI performance, these next-generation AI PCs increase generative AI performance by up to 60x, making them the best performing AI PC platforms.

我們透過 RTX DPU 提供的豐富軟體堆疊進一步加速了人工智慧。借助我們的 DLSS 技術，8 個像素中的 7 個可以由 AI 生成，從而使光線追蹤速度提高 4 倍並提高影像品質。透過 Windows 的 TensorRT LLM（我們的開源程式庫，可加速最新大型語言模型的推理效能），生成式 AI 在 RTX AI PC 上的運行速度可提高 5 倍。在 CES 上，我們也宣布了各大 OEM 推出的一系列新款 RTX 40 系列 AI 筆記型電腦。這些為各種外形尺寸帶來了高性能遊戲和人工智慧功能，包括 14 吋和輕薄筆記型電腦。這些新一代 AI PC 的 AI 性能高達 686 TOPS，將生成式 AI 性能提高了 60 倍，成為性能最佳的 AI PC 平台。

At CES, we announced NVIDIA Avatar Cloud Engine microservices, which allow developers to integrate state-of-the-art generative AI models into digital avatars. ACE won several Best of CES 2024 awards. NVIDIA has an end-to-end platform for building and deploying generative AI applications for RTX PCs and workstations. This includes libraries, SDKs, tools and services developers can incorporate into their generative AI workloads. NVIDIA is fueling the next wave of generative AI applications coming to the PC. With over 100 million RTX PCs in the installed base and over 500 AI-enabled PC applications and games, we are on our way.

在 CES 上，我們發布了 NVIDIA Avatar Cloud Engine 微服務，它允許開發人員將最先進的生成式 AI 模型整合到數位化身中。 ACE 榮獲多項 CES 2024 最佳獎項。 NVIDIA 擁有一個端對端平台，用於為 RTX PC 和工作站建置和部署生成式 AI 應用程式。這包括開發人員可以納入其產生人工智慧工作負載的程式庫、SDK、工具和服務。 NVIDIA 正在推動下一波進入 PC 的生成式 AI 應用程式。憑藉超過 1 億台 RTX PC 的安裝基數以及超過 500 個支援 AI 的 PC 應用程式和遊戲，我們正在前進。

Moving to Pro Visualization. Revenue of $463 million was up 11% sequentially, and up 105% year-on-year. Fiscal year revenue of $1.55 billion was up 1%. Sequential growth in the quarter was driven by a rich mix of RTX Ada architecture GPUs continuing to ramp. Enterprises are refreshing their workstations to support generative AI-related workloads such as data preparation, LLM fine-tuning and retrieval augmented generation. These key industrial verticals driving demand include manufacturing, Automotive, and robotics.

轉向專業可視化。營收為 4.63 億美元，季增 11%，年增 105%。財年營收為 15.5 億美元，成長 1%。本季的連續成長是由 RTX Ada 架構 GPU 的豐富組合持續成長所推動的。企業正在更新其工作站，以支援與生成型人工智慧相關的工作負載，例如資料準備、LLM 微調和檢索增強生成。這些推動需求的關鍵垂直產業包括製造業、汽車和機器人技術。

In Automotive industry has also been an early adopter of NVIDIA Omniverse as it seeks to digitize workflows from design, to build, simulate, operate and experience their factories and cars. At CES, we announced that creative partners and developers, including Brickland, WPP and ZeroLight are building Omniverse-powered car configurators. Leading automakers like Lotus are adopting the technology to bring new levels of personalization, realism and interactivity to the car buying experience.

汽車產業也是 NVIDIA Omniverse 的早期採用者，因為該產業尋求將工廠和汽車從設計到建造、模擬、操作和體驗的工作流程數位化。在 CES 上，我們宣布 Brickland、WPP 和 ZeroLight 等創意合作夥伴和開發人員正在建造 Omniverse 支援的汽車配置器。像路特斯這樣的領先汽車製造商正在採用該技術，將個人化、真實性和互動性提升到新的購車體驗水平。

Moving to Automotive. Revenue was $281 million, up 8% sequentially and down 4% year-on-year. Fiscal year revenue of $1.09 billion was up 21%, crossing the $1 billion mark for the first time on continued adoption of the NVIDIA DRIVE platform by automakers. NVIDIA DRIVE Orin is the AI car computer of choice for software-defined AV fleet. Its successor, NVIDIA DRIVE Thor, designed for vision transformers offers more AI performance and integrates a wide range of intelligent capabilities into a single AI compute platform, including autonomous driving and parking, driver and passenger monitoring, and AI cockpit functionality and will be available next year. There were several automotive customer announcements this quarter. Li Auto, Great Wall Motor, ZEEKR, the premium EV subsidiary of Geely and Xiaomi EV all announced new vehicles built on NVIDIA.

轉向汽車。營收為 2.81 億美元，季增 8%，年減 4%。由於汽車製造商繼續採用 NVIDIA DRIVE 平台，該財年營收達到 10.9 億美元，成長 21%，首次突破 10 億美元大關。 NVIDIA DRIVE Orin 是軟體定義 AV 車隊的首選 AI 車用電腦。其後繼產品NVIDIA DRIVE Thor 專為視覺變形金剛設計，可提供更多AI 性能，並將廣泛的智慧功能整合到單一AI 運算平台中，包括自動駕駛和停車、駕駛員和乘客監控以及AI 座艙功能，並將於明年推出年。本季有幾項汽車客戶公告。理想汽車、長城汽車、吉利旗下高端電動車子公司 ZEEKR 以及小米電動車都發布了基於 NVIDIA 的新車。

Moving to the rest of the P&L. GAAP gross margins expanded sequentially to 76% and non-GAAP gross margins to 76.7% on strong Data Center growth and mix. Our gross margins in Q4 benefited from favorable component costs. Sequentially, GAAP operating expenses were up 6% and non-GAAP operating expenses were up 9%, primarily reflecting higher compute and infrastructure investments and employee growth. In Q4, we returned $2.8 billion to shareholders in the form of share repurchases and cash dividends. During fiscal year '24, we utilized cash of $9.9 billion towards shareholder returns, including $9.5 billion in share repurchases.

轉向損益表的其餘部分。由於資料中心的強勁成長和組合，GAAP 毛利率連續擴大至 76%，非 GAAP 毛利率擴大至 76.7%。我們第四季的毛利率受益於有利的零件成本。隨後，GAAP 營運費用增加了 6%，非 GAAP 營運費用增加了 9%，主要反映了計算和基礎設施投資的增加以及員工的成長。第四季度，我們以股票回購和現金股利的形式向股東返還 28 億美元。在 24 財年，我們使用了 99 億美元的現金來回報股東，其中包括 95 億美元的股票回購。

Let me turn to the outlook for the first quarter. Total revenue is expected to be $24 billion, plus or minus 2%. We expect sequential growth in Data Center and ProViz, partially offset by seasonal decline in Gaming. GAAP and non-GAAP gross margins are expected to be 76.3% and 77%, respectively, plus or minus 50 basis points. Similar to Q4, Q1 gross margins are benefiting from favorable component costs. Beyond Q1, for the remainder of the year, we expect gross margins to return to the mid-70s percent range. GAAP and non-GAAP operating expenses are expected to be approximately $3.5 billion and $2.5 billion, respectively.

讓我談談第一季的展望。總收入預計為 240 億美元，上下浮動 2%。我們預計資料中心和 ProViz 業務將持續成長，但部分被遊戲業務的季節性下降所抵消。 GAAP 和非 GAAP 毛利率預計分別為 76.3% 和 77%，上下浮動 50 個基點。與第四季類似，第一季的毛利率受益於有利的零件成本。在第一季之後，在今年剩餘時間內，我們預計毛利率將恢復到 70% 左右的範圍。 GAAP 和非 GAAP 營運費用預計分別約為 35 億美元和 25 億美元。

Fiscal year 2025 GAAP and non-GAAP operating expenses are expected to grow in the mid-30% range as we continue to invest in the large opportunities ahead of us. GAAP and non-GAAP other income and expenses are expected to be an income of approximately $250 million, excluding gains and losses from nonaffiliated investments. GAAP and non-GAAP tax rates are expected to be 17%, plus or minus 1%, excluding any discrete items. Further financial details are included in the CFO commentary and other information available on our IR website.

隨著我們繼續投資眼前的巨大機遇，2025 財年 GAAP 和非 GAAP 營運費用預計將增加 30% 左右。 GAAP 和非 GAAP 其他收入和支出預計約為 2.5 億美元，不包括非關聯投資的損益。 GAAP 和非 GAAP 稅率預計為 17%，上下浮動 1%（不包括任何離散項目）。更多財務細節包含在 CFO 評論和我們的 IR 網站上提供的其他資訊中。

In closing, let me highlight some upcoming events for the financial community. We will attend the Morgan Stanley Technology and Media and Telecom Conference in San Francisco on March 4; and the TD Cowen's 44th Annual Health Care Conference in Boston on March 5. And of course, please join us for our annual GTC conference starting Monday, March 18 in San Jose, California, to be held in person for the first time in 5 years. GTC will kick off with Jensen's keynote and we will host a Q&A session for financial analysts the next day, March 19.

最後，讓我強調一下金融界即將發生的一些事件。我們將參加3月4日在舊金山舉行的摩根士丹利科技與媒體和電信會議；以及 3 月 5 日在波士頓舉行的 TD Cowen 第 44 屆年度醫療保健會議。當然，請參加我們於 3 月 18 日星期一在加利福尼亞州聖何塞舉行的年度 GTC 會議，這將是 5 年來首次親自舉行。 GTC 將以 Jensen 的主題演講拉開序幕，我們將在第二天（3 月 19 日）為金融分析師舉辦問答環節。

At this time, we are now open the call for questions. Operator, would you please poll for questions?

目前，我們正在開放式提問。接線員，請您投票詢問問題嗎？

(Operator Instructions) Your first question comes from the line of Toshiya Hari from Goldman Sachs.

（操作員說明） 您的第一個問題來自高盛的 Toshiya Hari。

Congratulations on the really strong results. My question is for Jensen on the Data Center business. Clearly, you're doing extremely well in the business. I'm curious how your expectations for calendar '24 and '25 have evolved over the past 90 days. And as you answer the question, I was hoping you can touch on some of the newer buckets within Data Center, things like software. Sovereign AI, I think you've been pretty vocal about how to think about that medium- to long term. And recently, there was an article about NVIDIA potentially participating in the ASIC market. Is there any credence to that? And if so, how should we think about you guys playing in that market over the next several years?

恭喜您取得了非常出色的成績。我的問題是問 Jensen 關於資料中心業務的問題。顯然，您的業務做得非常好。我很好奇您對日曆 '24 和 '25 的期望在過去 90 天裡發生了怎樣的變化。當您回答這個問題時，我希望您能夠接觸一下資料中心內的一些較新的內容，例如軟體。主權人工智慧，我認為您對於如何思考中長期的問題一直直言不諱。最近，有一篇關於 NVIDIA 可能參與 ASIC 市場的文章。這樣有可信度嗎？如果是這樣，我們該如何看待你們未來幾年在這個市場上的表現？

Thanks, Toshiya. Let's see. There were three questions. One more time? First question was...

謝謝，俊也。讓我們來看看。有三個問題。再一次？第一個問題是...

OpenAI.

開放人工智慧。

I guess your expectations for Data Center, how they've evolved.

我猜想您對資料中心的期望，以及它們是如何發展的。

Okay, yes. Well, you know we guide 1 quarter at a time. But fundamentally, the conditions are excellent for continued growth, calendar '24 to calendar '25 and beyond, and let me tell you why. We're at the beginning of 2 industry-wide transitions, and both of them are industry-wide. The first one is a transition from general to accelerated computing. General-purpose computing, as you know, is starting to run out of steam. And you could tell by the CSPs extending and many data centers, including our own for general-purpose computing, extending the depreciation from 4 to 6 years. There's just no reason to update with more GPUs when you can't fundamentally and dramatically enhance its throughput like you used to.

好吧，是的。嗯，你知道我們每次指導一個季度。但從根本上講，從 24 世紀到 25 世紀及以後，持續增長的條件非常好，讓我告訴你原因。我們正處於兩次全產業轉型的開始階段，而且這兩次轉型都是全產業範圍的。第一個是從通用計算到加速運算的轉變。如您所知，通用計算正開始失去動力。您可以從 CSP 的擴展和許多資料中心（包括我們自己的通用計算中心）中看出，將折舊時間從 4 年延長到 6 年。當您無法像以前那樣從根本上顯著提高其吞吐量時，就沒有理由更新更多 GPU。

And so you have to accelerate everything. This is what NVIDIA has been pioneering for some time. And with accelerated computing, you can dramatically improve your energy efficiency. You can dramatically improve your cost in data processing by 20:1, huge numbers. And of course, the speed. That speed is so incredible that we enabled a second industry-wide transition called generative AI. In generative AI, I'm sure we're going to talk plenty about it during the call. But remember, generative AI is a new application. It is enabling a new way of doing software, new types of software being created. It is a new way of computing. You can't do generative AI on traditional general-purpose computing. You have to accelerate it.

所以你必須加速一切。這是 NVIDIA 一段時間以來一直在開拓的領域。透過加速運算，您可以顯著提高能源效率。您可以將資料處理成本大幅降低 20:1，這是一個龐大的數字。當然還有速度。這種速度令人難以置信，以至於我們實現了第二次全產業轉型，稱為生成式人工智慧。在生成人工智慧方面，我確信我們會在電話會議中詳細討論。但請記住，生成式人工智慧是一種新的應用。它正在啟用一種新的軟體開發方式，新類型的軟體正在被創建。這是一種新的計算方式。你無法在傳統的通用運算上進行生成式人工智慧。你必須加速它。

And the third is, it is enabling a whole new industry. And this is something worthwhile to take a step back and look at, and it connects to your last question about sovereign AI. A whole new industry in the sense that for the very first time, a Data Center is not just about computing data and storing data and serving the employees of the company. We now have a new type of Data Center that is about AI generation, an AI generation factory, and you've heard me describe it as AI factories.

第三，它正在催生一個全新的產業。這是值得退後一步審視的事情，它與你關於主權人工智慧的最後一個問題有關。從某種意義上說，這是一個全新的行業，資料中心第一次不僅僅是計算資料和儲存資料以及為公司員工提供服務。我們現在有一個關於人工智慧生成的新型資料中心，一個人工智慧生成工廠，你已經聽過我將其描述為人工智慧工廠。

But basically, it takes raw material, which is data. It transforms it with these AI supercomputers that NVIDIA built, and it turns them into incredibly valuable tokens. These tokens are what people experience on the amazing ChatGPT or Midjourney or search these days are augmented by that. All of your recommender systems are now augmented by that, the hyper-personalization that goes along with it. All of these incredible start-ups in digital biology generating proteins and generating chemicals and the list goes on.

但基本上，它需要原料，也就是數據。它利用 NVIDIA 構建的這些 AI 超級電腦對其進行改造，並將其轉變為極其有價值的代幣。這些代幣是人們在令人驚嘆的 ChatGPT 或 Midjourney 上體驗到的，或者如今的搜尋也因此增強了。現在，您所有的推薦系統都受到了隨之而來的超個人化的增強。所有這些令人難以置信的數位生物學新創公司都在生產蛋白質和化學品，這樣的例子不勝枚舉。

And so all of these tokens are generated in a very specialized type of Data Center. And this Data Center, we call it AI supercomputers and AI generation factories. We're seeing diversity. One of the other reasons -- so the foundation is that. The way it manifests into new markets is in all of the diversity that you're seeing us in. One, the amount of inference that we do is just off the charts now. Almost every single time you interact with ChatGPT, you know that we're inferencing. Every time you use Midjourney, we're inferencing. Every time you see amazing -- these Sora videos that are being generated or Runway, the videos that they're editing, Firefly, NVIDIA's doing inferencing.

因此，所有這些令牌都是在非常專業的資料中心類型中產生的。而這個資料中心，我們稱之為AI超級電腦和AI生成工廠。我們看到了多樣性。其他原因之一——所以基礎就是這個。它在新市場中的體現方式在於您所看到的我們所處的所有多樣性。第一，我們所做的推理量現在剛剛破紀錄。幾乎每次您與 ChatGPT 互動時，您都知道我們正在推理。每次您使用 Midjourney 時，我們都會進行推理。每次你看到令人驚嘆的東西時——這些正在生成的 Sora 影片或 Runway、他們正在編輯的影片、Firefly、NVIDIA 正在做的推理。

The inference part of our business has grown tremendously, we estimate about 40%. The amount of training is continuing because these models are getting larger and larger, the amount of inference is increasing. But we're also diversifying into new industries. The large CSPs are still continuing to build out. You could see from their CapEx and their discussions. But there's a whole new category called GPU-specialized CSPs. They specialize in NVIDIA AI infrastructure, GPU-specialized CSPs. You're seeing enterprise software platforms deploying AI.

我們業務的推理部分成長巨大，我們估計約為 40%。訓練量在持續，因為這些模型變得越來越大，推理量也在增加。但我們也在向新產業多元化發展。大型 CSP 仍在繼續建設。您可以從他們的資本支出和討論中看到。但有一個全新的類別，稱為 GPU 專用 CSP。他們專注於 NVIDIA AI 基礎設施、GPU 專用 CSP。您會看到企業軟體平台部署人工智慧。

ServiceNow is just a really, really great example. You see Adobe, there's the others, SAP and others. You see consumer Internet services that are now augmenting all of their services of the past with generative AI, so they can have even more hyper-personalized content to be created. You see us talking about industrial generative AI. Now our industries represent multibillion-dollar businesses: auto, health, financial services, in total, our vertical industries are multibillion-dollar businesses now.

ServiceNow 就是一個非常非常好的例子。你可以看到 Adob​​e，還有其他公司、SAP 和其他公司。您將看到消費者網路服務現在正在透過生成式人工智慧增強過去的所有服務，因此他們可以創建更多超個人化的內容。你看到我們正在談論工業生成人工智慧。現在我們的產業代表了數十億美元的業務：汽車、健康、金融服務，總的來說，我們的垂直產業現在是數十億美元的業務。

And of course, sovereign AI. The reason for sovereign AI has to do with the fact that the language, the knowledge, the history, the culture of each region are different, and they own their own data. They would like to use their data, train it with to create their own digital intelligence and provision it to harness that raw material themselves. It belongs to them. Each one of the regions around the world, the data belongs to them. The data is most useful to their society. And so they want to protect the data, they want to transform it themselves, value-added transformation into AI and provision those services themselves.

當然，還有主權人工智慧。主權人工智慧的原因與每個地區的語言、知識、歷史、文化不同，並且擁有自己的數據有關。他們希望使用自己的數據，對其進行訓練以創建自己的數位智能，並提供數據以自行利用該原材料。它屬於他們。世界上每一個地區，數據都屬於他們。這些數據對他們的社會最有用。因此，他們想要保護數據，他們想要自己改造數據，將增值改造為人工智慧，並自己提供這些服務。

So we're seeing sovereign AI infrastructure is being built in Japan, in Canada, in France, so many other regions. And so my expectation is that what is being experienced here in the United States, in the West will surely be replicated around the world. And these AI generation factories are going to be in every industry, every company, every region. And so I think the last -- this last year, we've seen generative AI really becoming a whole new application space, a whole new way of doing computing, a whole new industry is being formed, and that's driving our growth.

因此，我們看到日本、加拿大、法國以及許多其他地區正在建造主權人工智慧基礎設施。因此，我的期望是，美國和西方正在經歷的事情肯定會在世界各地複製。這些人工智慧生成工廠將遍佈每個產業、每個公司、每個地區。所以我認為去年，我們看到生成式人工智慧真正成為一個全新的應用空間，一種全新的運算方式，一個全新的產業正在形成，這正在推動我們的成長。

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

你的下一個問題來自摩根士丹利的喬摩爾。

I wanted to follow up on the 40% of revenues coming from inference. It's a bigger number than I expected. Can you give us some sense of where that number was maybe the year before, how much you're seeing growth around LLMs from inference? And how are you measuring that? Is that -- I assume it's, in some cases, the same GPUs are used for training and inference. How solid is that measurement?

我想追蹤 40% 的收入來自推理。這個數字比我預期的要大。您能否讓我們了解一下前一年的數字，您從推論中看到法學碩士的成長有多少？你是如何衡量的？我認為在某些情況下，相同的 GPU 用於訓練和推理。這個測量有多可靠？

I'll go backwards. The estimate is probably understated and -- but we estimated it, and let me tell you why. Whenever -- a year ago, a year ago, the recommender systems that people are -- when you run the Internet, the news, the videos, the music, the products that are being recommended to you because, as you know, the Internet has trillions -- I don't know how many trillions, but trillions of things out there, and your phone is 3 inches squared. And so the ability for them to fit all of that information down to something such a small real estate is through a system, an amazing system called recommender systems.

我會向後走。這個估計可能被低估了——但我們估計了它，讓我告訴你原因。每當——一年前，一年前，人們的推薦系統——當你運行互聯網時，新聞、視頻、音樂、產品都會被推薦給你，因為正如你所知，互聯網有數萬億——我不知道有多少萬億，但外面有數萬億的東西，而你的手機只有3 英寸見方。因此，他們能夠將所有這些資訊整合到如此小的空間中，這是透過一個系統，一個稱為推薦系統的令人驚嘆的系統。

These recommender systems used to be all based on CPU approaches. But the recent migration to deep learning and now generative AI has really put these recommender systems now directly into the path of GPU acceleration. It needs GPU acceleration for the embeddings. It needs GPU acceleration for the nearest neighbor search. It needs GPU accelerating for reranking. And it needs GPU acceleration to generate the augmented information for you. So GPUs are in every single step of a recommender system now. And as you know, a recommender system is the single largest software engine on the planet.

這些推薦系統過去都是基於CPU的方法。但最近向深度學習和生成式人工智慧的遷移確實讓這些推薦系統現在直接進入了 GPU 加速的道路。嵌入需要 GPU 加速。最近鄰搜尋需要 GPU 加速。重新排序需要 GPU 加速。它需要 GPU 加速來為您產生增強資訊。所以現在推薦系統的每個步驟都用到了 GPU。如您所知，推薦系統是地球上最大的軟體引擎。

Almost every major company in the world has to run these large recommender systems. Whenever you use ChatGPT, it's being inferenced. Whenever you hear about Midjourney and just the number of things that they're generating for consumers, when you see Getty, the work that we do with Getty and Firefly from Adobe, these are all generative models. The list goes on. And none of these, as I just mentioned, existed a year ago, 100% new.

世界上幾乎每家大公司都必須運作這些大型推薦系統。每當您使用 ChatGPT 時，都會對其進行推斷。每當你聽到 Midjourney 以及他們為消費者生成的東西的數量時，當你看到 Getty、我們與 Getty 和 Adob​​e 的 Firefly 所做的工作時，這些都是生成模型。這樣的例子還在繼續。正如我剛才提到的，這些都不是一年前存在的、100% 新的。

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

您的下一個問題來自伯恩斯坦研究中心的史黛西‧拉斯貢 (Stacy Rasgon)。

I wanted to -- Colette, I wanted to touch on your comments that you expected the next generation of products, I assume that the Blackwell to be supply constrained. Can you dig into that a little bit? What is the driver of that? Why does that get constrained as Hopper is easing up? And how long do you expect that to be constrained? Like do you expect the next generation to be constrained like all the way through calendar '25? Like when do those start to ease?

我想——科萊特，我想談談您對下一代產品的期望，我認為布萊克韋爾的供應會受到限制。你能深入研究一下嗎？其驅動因素為何？為什麼隨著霍珀的放鬆，這一點會受到限制？您預計這種情況會受到限制多久？就像你預計下一代會受到像 25 年曆一樣的限制嗎？例如這些什麼時候開始緩解？

Yes. The first thing is overall, our supply is improving. Overall, our supply chain is just doing an incredible job for us. Everything from, of course, the wafers, the packaging, the memories, all of the power regulators to transceivers and networking and cables, and you name it, the list of components that we ship. As you know, people think that NVIDIA GPUs is like a chip, but the NVIDIA Hopper GPU has 35,000 parts. It weighs 70 pounds. These things are really complicated things we've built. People call it an AI supercomputer for good reason.

是的。首先，總體而言，我們的供應正在改善。總的來說，我們的供應鏈為我們做了令人難以置信的工作。當然，從晶圓、封裝、記憶體、所有電源調節器到收發器、網路和電纜，以及您能想到的一切，我們運送的組件清單。如你所知，人們認為 NVIDIA GPU 就像一塊晶片，但 NVIDIA Hopper GPU 有 35,000 個零件。它重 70 磅。我們建造的這些東西確實很複雜。人們稱其為人工智慧超級電腦是有充分理由的。

If you ever look at the back of the Data Center, the systems, the cabling system is mind-boggling. It is the most dense, complex cabling system for networking the world has ever seen. Our InfiniBand business grew 5x year-over-year. The supply chain is really doing fantastic supporting us. And so overall, the supply is improving. We expect the demand will continue to be stronger than our supply provides, and through the year and we'll do our best. The cycle times are improving and we're going to continue to do our best.

如果您觀察過資料中心、系統和佈線系統的背面，就會令人難以置信。它是世界上迄今為止最密集、最複雜的網路佈線系統。我們的 InfiniBand 業務年增 5 倍。供應鏈確實為我們提供了出色的支援。總體而言，供應正在改善。我們預計需求將繼續強於我們的供應，全年我們將盡力而為。週期時間正在改善，我們將繼續盡力而為。

However, whenever we have new products, as you know, it ramps from 0 to a very large number, and you can't do that overnight. Everything is ramped up. It doesn't step up. And so whenever we have a new generation of products and right now, we are ramping H200s, there's no way we can reasonably keep up on demand in the short term as we ramp. We're ramping Spectrum-X. We're doing incredibly well with Spectrum-X. It's our brand-new product into the world of Ethernet. InfiniBand is the standard for AI-dedicated systems. Ethernet with Spectrum-X, Ethernet is just not a very good scale-out system.

然而，正如你所知，每當我們推出新產品時，它都會從 0 上升到一個非常大的數字，而你不可能在一夜之間做到這一點。一切都在加速。它沒有進步。因此，每當我們推出新一代產品，並且現在正在增加 H200 時，我們就無法在短期內合理地滿足我們的需求。我們正在加強 Spectrum-X。我們在 Spectrum-X 方面做得非常好。這是我們進入乙太網路世界的全新產品。 InfiniBand 是人工智慧專用系統的標準。乙太網路與 Spectrum-X 相比，乙太網路並不是一個非常好的橫向擴展系統。

But with Spectrum-X, we've augmented, layered on top of Ethernet, fundamental new capabilities like adaptive routing, congestion control, noise isolation or traffic isolation so that we could optimize Ethernet for AI. And so InfiniBand will be our AI-dedicated infrastructure, Spectrum-X will be our AI-optimized networking, and that is ramping. So we'll -- with all new products, demand is greater than supply. And that's just kind of the nature of new products, and we work as fast as we can to catch up with the demand. But overall, net-net, overall, our supply is increasing very nicely.

但透過 Spectrum-X，我們在乙太網路之上增強了基本的新功能，例如自適應路由、擁塞控制、噪音隔離或流量隔離，以便我們可以針對人工智慧優化乙太網路。因此，InfiniBand 將成為我們的人工智慧專用基礎設施，Spectrum-X 將成為我們的人工智慧優化網絡，而這個網路正在不斷發展。因此，對於所有新產品，需求都會大於供應。這就是新產品的本質，我們會盡快趕上需求。但總體而言，總體而言，我們的供應量成長得非常好。

Your next question comes from the line of Matt Ramsay from TD Cowen.

您的下一個問題來自 TD Cowen 的 Matt Ramsay。

Congrats on the results. I wanted to ask, I guess, a 2-part question, and it comes at what Stacy was getting at on your demand being significantly more than your supply even though supply is improving. And I guess the 2 sides of the question are, I guess, first for Colette, like how are you guys thinking about allocation of product in terms of customer readiness to deploy and sort of monitoring if there's any kind of buildup of product that might not yet be turned on?

祝賀結果。我想，我想問一個由兩部分組成的問題，這是史黛西的意思，即儘管供應正在改善，但您的需求卻大大超過了供應。我想問題的兩個方面是，我想，首先對於科萊特來說，就像你們如何考慮在客戶準備部署方面的產品分配以及是否有任何類型的產品堆積可能不會進行監控還被打開嗎？

And then I guess, Jensen, for you. I'd be really interested to hear you speak a bit about a thought that you and your company are putting into the allocation of your product across customers, many of which compete with each other, across industries to smaller start-up companies to things in the health care arena to government. It's a very, very unique technology that you're enabling. And I'd be really interested to hear you speak a bit about how you think about "fairly allocating" sort of for the good of your company but also for the good of the industry.

然後我想，詹森，適合你。我真的很想聽你談談你和你的公司正在將你的產品分配給不同客戶的想法，其中許多客戶相互競爭，跨行業到小型新創公司，再到各個領域的事物。政府的醫療保健領域。這是您正在啟用的一項非常非常獨特的技術。我真的很想聽你談談你如何看待“公平分配”，這既是為了你公司的利益，也是為了整個行業的利益。

Let me first start with your question, thanks, about how we are working with our customers as they look into how they are building out their GPU instances and our allocation process. The folks that we work with, our customers that we work with have been partners with us for many years as we have been assisting them both in what they set up in the cloud as well as what they are setting up internally. Many of these providers have multiple products going at one time to serve so many different needs across their end customers but also what they need internally.

首先讓我從您的問題開始，謝謝，關於我們如何與客戶合作，因為他們正在研究如何建立他們的 GPU 實例和我們的分配流程。與我們合作的人員、與我們合作的客戶多年來一直是我們的合作夥伴，因為我們一直在幫助他們在雲端中設置以及在內部設置。其中許多提供者同時擁有多種產品，以滿足最終客戶的多種不同需求以及他們內部的需求。

So they are working in advance, of course, thinking about those new clusters that they will need. And our discussions with them continue not only on our Hopper architecture but helping them understand the next wave and getting their interest and getting their outlook for the demand that they want. So it's always a moving process in terms of what they will purchase, what is still being built and what is in use for end customers. But the relationships that we've built and their understanding of the sophistication of the build has really helped us with that allocation and both helped us with our communications with them.

當然，他們正在提前工作，考慮他們將需要的那些新集群。我們與他們的討論不僅繼續討論我們的 Hopper 架構，還幫助他們了解下一波浪潮並引起他們的興趣並了解他們對所需需求的展望。因此，就他們將購買什麼、仍在建造什麼以及最終客戶正在使用什麼而言，這始終是一個不斷變化的過程。但是我們建立的關係以及他們對建構複雜性的理解確實幫助了我們進行分配，也幫助了我們與他們的溝通。

First, our CSPs have a very clear view of our product road map and transitions. And that transparency with our CSPs gives them the confidence of which products to place and where and when. And so they know the timing to the best of our ability, and they know quantities and, of course, allocation. We allocate fairly. We allocate fairly, do the best of our -- best we can to allocate fairly and to avoid allocating unnecessarily.

首先，我們的 CSP 對我們的產品路線圖和過渡有非常清晰的了解。我們的 CSP 的透明度讓他們對放置哪些產品以及放置地點和時間充滿信心。因此，他們盡我們最大的能力知道時間，他們知道數量，當然還有分配。我們公平分配。我們公平分配，盡我們最大努力公平分配並避免不必要的分配。

As you mentioned earlier, why allocate something when a Data Center is not ready? Nothing is more difficult than to have anything sit around. And so allocate fairly and to avoid allocating unnecessarily. And where we do -- the question that you asked about the end markets, you know that we have an excellent ecosystem with OEMs, ODMs, CSPs, and very importantly, end markets. What NVIDIA is really unique about is that we bring our customers, we bring our partners, CSPs and OEMs, we bring them customers. The biology companies, the health care companies, financial services companies, AI developers, large language model developers, autonomous vehicle companies, robotics companies, there's just a giant suite of robotics companies that are emerging. There are warehouse robotics to surgical robotics to humanoid robotics, all kinds of really interesting robotics companies, agriculture robotics companies.

正如您之前提到的，為什麼在資料中心還沒有準備好時分配一些東西？沒有什麼比讓任何東西閒置更困難的了。因此公平分配並避免不必要的分配。在我們所做的方面—您問的有關終端市場的問題，您知道我們擁有一個優秀的生態系統，其中包括 OEM、ODM、CSP，以及非常重要的終端市場。 NVIDIA 的真正獨特之處在於，我們為客戶帶來了客戶，為合作夥伴、CSP 和 OEM 帶來了客戶，為他們帶來了客戶。生物公司、醫療保健公司、金融服務公司、人工智慧開發商、大型語言模型開發商、自動駕駛汽車公司、機器人公司，一大批機器人公司正在興起。有倉庫機器人、手術機器人、人形機器人，各種非常有趣的機器人公司、農業機器人公司。

All of these start-ups -- large companies, health care, financial services, and auto and such are working on NVIDIA's platform. We support them directly. And oftentimes, we can have a twofer by allocating to a CSP and bringing the customer to the CSP at the same time. And so this ecosystem, you're absolutely right that it's vibrant. But at the core of it, we want to allocate fairly with avoiding waste and looking for opportunities to connect partners and end users. We're looking for those opportunities all the time.

所有這些新創企業——大公司、醫療保健、金融服務和汽車等都在 NVIDIA 的平台上工作。我們直接支持他們。通常，我們可以透過分配給 CSP 並同時將客戶帶到 CSP 來實現雙重目的。所以這個生態系統是充滿活力的，你說得完全正確。但其核心是，我們希望公平分配，避免浪費，並尋找連結合作夥伴和最終用戶的機會。我們一直在尋找這些機會。

Your next question comes from the line of Timothy Arcuri from UBS.

您的下一個問題來自瑞銀 (UBS) 的 Timothy Arcuri。

I wanted to ask about how you're converting backlog into revenue. Obviously, lead times for your products have come down quite a bit. Colette, you didn't talk about the inventory purchase commitments, but if I sort of add up your inventory plus the purchase commits and your prepaid supply, sort of the aggregate of your supply, it was actually down a touch. How should we read that? Is that just you saying that you don't need to take as much of a financial commitment to your suppliers because the lead times are lower? Or is that maybe you're reaching some sort of steady state where you're closer to filling your order book and your backlog?

我想問一下你們如何將積壓訂單轉化為收入。顯然，你們產品的交貨時間已經縮短很多了。科萊特，您沒有談論庫存購買承諾，但如果我將您的庫存加上購買承諾和預付供應（您的供應總量）加起來，實際上會有所下降。我們應該怎樣讀呢？您是否只是說，由於交貨時間較短，您不需要向供應商承擔那麼多的財務承諾？或者您正在達到某種穩定狀態，更接近填滿訂單簿和積壓訂單？

Yes. So let me highlight on those three different areas of how we look at our suppliers. You're correct. Our inventory on hand, given our allocation that we're on, we're trying to, as things come into inventory, immediately work to ship them to our customers. I think our customer appreciates our ability to meet the schedules that we've looked for.

是的。因此，讓我重點介紹一下我們如何看待供應商的這三個不同領域。你是對的。我們現有的庫存，考慮到我們正在進行的分配，我們正在努力，當物品進入庫存時，立即將它們運送給我們的客戶。我認為我們的客戶很欣賞我們滿足我們所尋求的時間表的能力。

The second piece of it is our purchase commitments. Our purchase commitments have many different components into it, component that we need for manufacturing but also often we are procuring capacity that we need. The length of that need for capacity or the length of the components are all different. Some of them may be for the next 2 quarters but some of them may be for multiple years. I can say the same regarding our prepaids. Our prepaids are predesigned to make sure that we have the reserve capacity that we need as several of our manufacturing suppliers as we look forward.

第二部分是我們的購買承諾。我們的採購承諾包含許多不同的組成部分，我們製造所需的組成部分，但我們也經常採購我們需要的產能。所需容量的長度或組件的長度都不同。其中一些可能是未來兩個季度的，但有些可能是多年的。關於我們的預付費，我也可以這麼說。我們的預付款是預先設計的，以確保我們擁有我們預期的多個製造供應商所需的儲備能力。

So wouldn't read into anything regarding approximately about the same numbers as we are increasing our supply. All of them just have different lengths as we have sometimes had to buy things in long lead times or things that need a capacity to be built for us.

因此，當我們增加供應時，不會閱讀任何有關大約相同數字的內容。它們只是長度不同，因為我們有時必須購買交貨時間較長的東西或需要為我們建造容量的東西。

Your next question comes from the line of Ben Reitzes from Melius Research.

您的下一個問題來自 Melius Research 的 Ben Reitzes。

Congratulations on the results. Colette, I wanted to talk about your comment regarding gross margins and that it should go back to the mid-70s, if you don't mind unpacking that. And also, is that due to the HBM content in the new products? And what do you think are the drivers of that comment?

祝賀結果。 Colette，我想談談您對毛利率的評論，如果您不介意解開的話，它應該追溯到 70 年代中期。另外，這是因為新產品含有 HBM 嗎？您認為該評論的驅動因素是什麼？

Yes, thanks for the question. We highlighted in our opening remarks, really about our Q4 results and our outlook for Q1. Both of those quarters are unique. Those 2 quarters are unique in their gross margin as they include some benefit from favorable component cost in the supply chain kind of across both our compute and networking, and also in several different stages of our manufacturing process. So looking forward, we have visibility into a mid-70s gross margin for the rest of the fiscal year, taking us back to where we were before this Q4 and Q1 peak that we've had here. So we're really looking at just a balance of our mix. Mix is always going to be our largest driver of what we will be shipping for the rest of the year and those are really just the drivers.

是的，謝謝你的提問。我們在開場白中強調了我們第四季的業績和第一季的前景。這兩個季度都是獨一無二的。這兩個季度的毛利率是獨一無二的，因為它們包括我們的計算和網路供應鏈以及製造過程的幾個不同階段中有利的組件成本帶來的一些好處。因此，展望未來，我們可以看到本財年剩餘時間的毛利率將達到 70 年代中期，這將使我們回到第四季和第一季高峰之前的水平。所以我們實際上只是在考慮我們的組合的平衡。 Mix 始終將是我們今年剩餘時間發貨的最大驅動因素，而這些實際上只是驅動因素。

Your next question comes from the line of C.J. Muse from Cantor Fitzgerald.

你的下一個問題來自 Cantor Fitzgerald 的 C.J. Muse。

Bigger picture question for you, Jensen. When you think about the million-x improvement in GPU compute over the last decade and expectations for similar improvements in the next, how do your customers think about the long-term usability of their NVIDIA investments that they're making today? Do today's training clusters become tomorrow's inference clusters? How do you see this playing out?

詹森，給你一個更大的問題。當您考慮過去十年 GPU 計算數百萬倍的改進以及對未來類似改進的期望時，您的客戶如何看待他們今天進行的 NVIDIA 投資的長期可用性？今天的訓練集群會成為明天的推理集群嗎？您如何看待此事的進展？

C.J., thanks for the question. Yes, that's the really cool part. If you look at the reason why we're able to improve performance so much, it's because we have two characteristics about our platform. One is that it's accelerated, and two, it's programmable. It's not brittle. NVIDIA is the only architecture that has gone from the very, very beginning, literally at the very beginning when CNNs and Alex Krizhevsky and Ilya Sutskever and Geoff Hinton first revealed AlexNet, all the way through RNNs to LSTMs to every RLs to deep RLs to transformers to every single version and every species that have come along, vision transformers, multi-modality transformers that every single -- and now time sequence stuff. And every single variation, every single species of AI that has come along, we've been able to support it, optimize our stack for it and deploy it into our installed base.

C.J.，謝謝你的提問。是的，這才是真正酷的部分。如果你看看我們能夠如此大幅提高效能的原因，那是因為我們的平台有兩個特色。一是它是加速的，二是它是可編程的。它並不脆。 NVIDIA 是唯一一個從一開始就從 CNN、Alex Krizhevsky、Ilya Sutskever 和 Geoff Hinton 首次揭示 AlexNet 開始的架構，一路從 RNN 到 LSTM，再到每個 RL，再到深度 RL，再到 Transformer。到每一個版本和每一個物種，視覺變換器，多模態變換器，每一個——現在是時間序列的東西。對於每一種變更、每一種人工智慧的出現，我們都能夠支援它、優化我們的堆疊並將其部署到我們的安裝基礎中。

This is really the great amazing part. On the one hand, we can invent new architectures and new technologies like our Tensor Cores, like our Transformer Engine for Tensor Cores, improve new numerical formats and structures of processing like we've done with the different generations of Tensor Cores, meanwhile supporting the installed base at the same time. And so as a result, we take all of our new software algorithm inventions, all of the inventions, new inventions of models of the industry, and it runs on our installed base on the one hand.

這確實是最令人驚奇的部分。一方面，我們可以發明新的架構和新技術，例如我們的張量核心，例如張量核心的變壓器引擎，改進新的數值格式和處理結構，就像我們對不同代張量核心所做的那樣，同時支援同時安裝基地。因此，我們一方面採用了所有新的軟體演算法發明、所有發明、產業模型的新發明，並且它在我們的安裝基礎上運作。

On the other hand, whenever we see something revolutionary, like Transformers, we can create something brand new like the Hopper Transformer Engine and implement it into future. And so we simultaneously have this ability to bring software to the installed base and keep making it better and better and better. So our customers' installed base is enriched over time with our new software.

另一方面，每當我們看到一些革命性的東西，例如變形金剛，我們就可以創造一些全新的東西，例如霍珀變形金剛引擎，並將其應用到未來。因此，我們同時擁有將軟體引入用戶群並不斷使其越來越好的能力。因此，隨著時間的推移，我們的新軟體不斷豐富了我們客戶的安裝基礎。

On the other hand, for new technologies, create revolutionary capabilities. Don't be surprised if in our future generation, all of a sudden, amazing breakthroughs in large language models were made possible. And those breakthroughs, some of which will be in software because they run CUDA, will be made available to the installed base. And so we carry everybody with us on the one hand, we make giant breakthroughs on the other hand.

另一方面，對於新技術，創造革命性的能力。如果在我們的下一代中，大型語言模型突然實現了驚人的突破，請不要感到驚訝。這些突破（其中一些突破將出現在軟體方面，因為它們運行 CUDA）將可供安裝基礎使用。因此，我們一方面帶著大家一起，另一方面我們也取得了巨大的突破。

Your next question comes from the line of Aaron Rakers from Wells Fargo.

你的下一個問題來自富國銀行的 Aaron Rakers。

I wanted to ask about the China business. I know that in the prepared comments, you said that you started shipping some alternative solutions into China. You also pointed out that you expect that contribution to continue to be about a mid-single-digit percent of your total Data Center business. So I guess the question is, what is the extent of products that you're shipping today into the China market? And why should we not expect that maybe other alternative solutions come to the market and expand your breadth to participate in that opportunity again?

我想問一下中國業務。我知道，在準備好的評論中，您說您開始向中國運送一些替代解決方案。您也指出，您預計這項貢獻將繼續佔整個資料中心業務的中個位數百分比。所以我想問題是，你們今天向中國市場運送的產品有多大？為什麼我們不應該期待其他替代解決方案進入市場並擴大您的廣度以再次參與該機會？

At the core, remember, the U.S. government wants to limit the latest capabilities of NVIDIA's accelerated computing and AI to the Chinese market. And the U.S. government would like to see us be as successful in China as possible. Within those two constraints, within those two pillars, if you will, are the restrictions. And so we had to pause when the new restrictions came out. We immediately paused, so that we understood what the restrictions are, reconfigured our products in a way that is not software hackable in any way. And that took some time. And so we reset our product offering to China, and now we're sampling to customers in China.

請記住，核心是美國政府希望將 NVIDIA 加速運算和人工智慧的最新功能限​​制在中國市場。美國政府希望看到我們在中國盡可能取得成功。如果你願意的話，在這兩個約束之內，在這兩個支柱之內，就是限制。因此，當新的限制措施出台時，我們不得不暫停。我們立即暫停，以便了解限制是什麼，以任何軟體都無法破解的方式重新配置我們的產品。這需要一些時間。因此，我們將產品重新定位到中國，現在我們正在向中國的客戶提供樣品。

And we're going to do our best to compete in that marketplace and succeed in that marketplace within the specifications of the restriction. And so that's it. This last quarter, we -- our business significantly declined as we paused in the marketplace. We stopped shipping in the marketplace. We expect this quarter to be about the same. But after that, hopefully, we can go compete for our business and do our best, and we'll see how it turns out.

我們將盡最大努力在該市場中競爭，並在限制的規範範圍內取得成功。就是這樣。上個季度，由於我們在市場上暫停，我們的業務大幅下降。我們停止在市場上發貨。我們預計本季的情況大致相同。但在那之後，希望我們能夠去競爭我們的業務並盡力而為，我們會看看結果如何。

Your next question comes from the line of Harsh Kumar from Piper Sandler.

您的下一個問題來自 Piper Sandler 的 Harsh Kumar。

Jensen, Colette and NVIDIA team, first of all, congratulations on a stunning quarter and guide. I wanted to talk about -- a little bit about your software business, and it's pleasing to hear that it's over $1 billion. But I was hoping, Jensen or Colette, if you could just help us understand what the different parts and pieces are for the software business. In other words, just help us unpack it a little bit so we can get a better understanding of where that growth is coming from.

Jensen、Colette 和 NVIDIA 團隊首先祝賀我們取得了令人驚嘆的季度業績和指南。我想談談你們的軟體業務，很高興聽到它的價值超過 10 億美元。但我希望 Jensen 或 Colette 能夠幫助我們了解軟體業務的不同組成部分。換句話說，只需幫助我們稍微解開它，以便我們可以更好地了解成長的來源。

Let me take a step back and explain the fundamental reason why NVIDIA will be very successful in software. So first, as you know, accelerated computing really grew in the cloud. In the cloud, the cloud service providers have really large engineering teams, and we work with them in a way that allows them to operate and manage their own business. And whenever there are any issues, we have large teams assigned to them, and their engineering teams are working directly with our engineering teams, and we enhance, we fix, we maintain, we patch the complicated stack of software that's involved in accelerated computing.

讓我退後一步，解釋一下NVIDIA在軟體方面會非常成功的根本原因。首先，如您所知，加速運算確實在雲端中發展。在雲端中，雲端服務供應商擁有非常龐大的工程團隊，我們與他們合作的方式允許他們運作和管理自己的業務。每當出現任何問題時，我們都會為他們分配大型團隊，他們的工程團隊直接與我們的工程團隊合作，我們增強、修復、維護、修補涉及加速計算的複雜軟體堆疊。

As you know, accelerated computing is very different than general-purpose computing. You're not starting from a program like C++. You compile it and things run on all your CPUs. The stacks of software necessary for every domain from data processing, SQL versus SQL structured data versus all the images and text and PDF, which is unstructured, to classical machine learning to computer vision to speech to large language models, all -- recommender systems. All of these things require different software stacks. That's the reason why NVIDIA has hundreds of libraries.

如您所知，加速計算與通用計算有很大不同。您不是從 C++ 等程式開始。你編譯它，然後它就可以在你所有的 CPU 上運行。每個領域所需的軟體堆疊，從資料處理、SQL 與SQL 結構化資料與所有圖像、文字和PDF（非結構化），到經典機器學習、電腦視覺、語音到大型語言模型，所有這些都是推薦系​​統。所有這些都需要不同的軟體堆疊。這就是 NVIDIA 擁有數百個函式庫的原因。

If you don't have software, you can't open new markets. If you don't have software, you can't open and enable new applications. Software is fundamentally necessary for accelerated computing. This is the fundamental difference between accelerated computing and general-purpose computing that most people took a long time to understand. And now people understand that software is really key.

如果沒有軟體，就無法開啟新市場。如果您沒有軟體，則無法開啟和啟用新應用程式。軟體對於加速運算至關重要。這是大多數人花了很長時間才理解的加速計算和通用計算之間的根本區別。現在人們明白軟體確實是關鍵。

And the way that we work with CSPs, that's really easy. We have large teams that are working with their large teams. However, now that generative AI is enabling every enterprise and every enterprise software company to embrace accelerated computing, and when it is now essential to embrace accelerated computing because it is no longer possible, no longer likely anyhow, to sustain improved throughput through just general-purpose computing, all of these enterprise software companies and enterprise companies don't have large engineering teams to be able to maintain and optimize their software stack to run across all of the world's clouds and private clouds and on-prem.

我們與 CSP 合作的方式非常簡單。我們有大型團隊正在與他們的大型團隊合作。然而，現在生成式人工智慧正在使每個企業和每個企業軟體公司都能夠接受加速運算，而現在必須接受加速運算，因為它不再可能，無論如何也不再可能，僅透過一般性的方式來維持吞吐量的提高。就用途計算而言，所有這些企業軟體公司和企業公司都沒有大型工程團隊來維護和優化其軟體堆疊以在全球所有雲端、私有雲和本地運行。

So we are going to do the management, the optimization, the patching, the tuning, the installed base optimization for all of their software stacks. And we containerize them into our stack called NVIDIA AI Enterprise. And the way we go to market with it is think of that NVIDIA AI Enterprise now as a run time like an operating system. It's an operating system for artificial intelligence. And we charge $4,500 per GPU per year. And my guess is that every enterprise in the world, every software enterprise company that are deploying software in all the clouds and private clouds and on-prem will run on NVIDIA AI Enterprise, especially obviously, for our GPUs. And so this is going to likely be a very significant business over time. We're off to a great start. And Colette mentioned that it's already at $1 billion run rate and we're really just getting started.

因此，我們將對他們的所有軟體堆疊進行管理、最佳化、修補、調整和安裝基礎優化。我們將它們容器化到我們稱為 NVIDIA AI Enterprise 的堆疊中。我們將 NVIDIA AI Enterprise 推向市場的方式是將其視為像作業系統一樣的運作時。它是一個人工智慧作業系統。我們對每個 GPU 每年收取 4,500 美元的費用。我的猜測是，世界上每家企業、每家在所有雲端、私有雲和本地部署軟體的軟體企業都將在 NVIDIA AI Enterprise 上運行，尤其是對於我們的 GPU。因此，隨著時間的推移，這可能會成為一項非常重要的業務。我們有了一個好的開始。 Colette 提到，它的運行速度已經達到 10 億美元，而我們實際上才剛剛開始。

I will now turn the call back over to Jensen Huang, CEO, for closing remarks.

現在，我將把電話轉回給執行長黃仁勳，讓其致閉幕詞。

The computer industry is making two simultaneous platform shifts at the same time. The trillion-dollar installed base of data centers is transitioning from general purpose to accelerated computing. Every data center will be accelerated so the world can keep up with the computing demand with increasing throughput while managing cost and energy. The incredible speed-up of NVIDIA enabled -- that NVIDIA enabled a whole new computing paradigm, generative AI, where software can learn, understand and generate any information from human language to the structure of biology and the 3D world.

電腦產業正在同時進行兩個同步平台轉型。價值數萬億美元的資料中心安裝基礎正從通用運算轉向加速運算。每個資料中心都將加速，以便世界能夠透過增加吞吐量來滿足運算需求，同時管理成本和能源。 NVIDIA 實現了令人難以置信的加速——NVIDIA 實現了一種全新的計算範式，即生成式 AI，其中軟體可以學習、理解和產生從人類語言到生物結構和 3D 世界的任何資訊。

We are now at the beginning of a new industry where AI-dedicated data centers process massive raw data to refine it into digital intelligence. Like AC power generation plants of the last industrial revolution, NVIDIA AI supercomputers are essentially AI generation factories of this industrial revolution. Every company and every industry is fundamentally built on their proprietary business intelligence, and in the future, their proprietary generative AI.

我們現在正處於一個新產業的開端，人工智慧專用資料中心處理大量原始數據，將其提煉為數位智慧。與上一次工業革命的交流發電廠一樣，NVIDIA AI 超級電腦本質上也是這次工業革命的 AI 發電廠。每個公司和每個行業從根本上來說都是建立在其專有的商業智慧之上，並且在未來，其專有的生成人工智慧。

Generative AI has kicked off a whole new investment cycle to build the next trillion dollars of infrastructure of AI generation factories. We believe these two trends will drive a doubling of the world data center infrastructure installed base in the next 5 years and will represent an annual market opportunity in the hundreds of billions.

生成式人工智慧已經啟動了一個全新的投資週期，以建立下一個萬億美元的人工智慧生成工廠基礎設施。我們相信，這兩種趨勢將推動未來 5 年內全球資料中心基礎設施安裝量翻倍，並代表每年數千億的市場機會。

This new AI infrastructure will open up a whole new world of applications not possible today. We started the AI journey with the hyperscale cloud providers and consumer Internet companies. And now every industry is on board, from automotive to health care to financial services to industrial to telecom, media and entertainment. NVIDIA's full stack computing platform with industry-specific application frameworks and a huge developer and partner ecosystem gives us the speed, scale, and reach to help every company, to help companies in every industry become an AI company.

這種新的人工智慧基礎設施將開啟一個今天不可能實現的全新應用世界。我們與超大規模雲端供應商和消費者網路公司一起開啟了人工智慧之旅。現在，從汽車、醫療保健、金融服務、工業到電信、媒體和娛樂，各個行業都參與其中。 NVIDIA 的全端運算平台具有特定行業的應用框架以及龐大的開發者和合作夥伴生態系統，為我們提供了速度、規模和覆蓋範圍來幫助每個公司，幫助每個行業的公司成為人工智慧公司。

We have so much to share with you at next month's GTC in San Jose, so be sure to join us. We look forward to updating you on our progress next quarter.

在下個月於聖荷西舉行的 GTC 上，我們有很多東西要與您分享，所以請務必參加我們。我們期待向您通報下季的最新進展。

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

今天的電話會議到此結束。您現在可以斷開連線。