輝達 (NVDA) 2027 Q2 法說會逐字稿

Good afternoon. My name is Tiffany, and I will be your conference operator today. At this time, I would like to welcome everyone to NVIDIA's second quarter earnings call (Operator Instructions) Toshiya Hari, you may begin your conference.

下午好。我叫 Tiffany，今天將擔任本次會議的接線員。此刻，我謹代表 NVIDIA 歡迎各位參加第二季財報電話會議（接線員指示）。Toshiya Hari，您可以開始會議。

Thank you. Good afternoon, and welcome to NVIDIA's conference call for the second quarter of fiscal 2027. With me today from NVIDIA are Jensen Huang, President and Chief Executive Officer; and Colette Kress, Executive Vice President and Chief Financial Officer. 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 third quarter of fiscal 2027.

謝謝。下午好，歡迎各位參加 NVIDIA 2027 會計年度第二季財報電話會議。今天與我一同出席的 NVIDIA 主管包括：總裁暨執行長黃仁勳（Jensen Huang）；以及執行副總裁暨財務長 Colette Kress。本次電話會議將於 NVIDIA 投資人關係網站進行即時網路直播。網路直播回放將提供至我們討論 2027 會計年度第三季財務結果的電話會議為止。

The content of today's call is NVIDIA's property. It can't be reproduced or transcribed without our prior written consent. 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.

今日電話會議內容為 NVIDIA 之財產。未經我們事先書面同意，不得重製或逐字轉錄。在本次電話會議中，我們可能會基於目前的預期發表前瞻性陳述。此類陳述受多項重大風險與不確定性影響，實際結果可能出現重大差異。關於可能影響我們未來財務結果與業務之因素，請參閱今日財報新聞稿、我們最近一期的 10-K 與 10-Q 表格，以及我們可能向美國證券交易委員會以 8-K 表格提交的報告中的揭露。

All our statements are made as of today, August 26, 2026, based on information currently available to us. Except as required by law, we assume no obligation to update any such statements. During this call, we will discuss non-GAAP financial measures. You can find a reconciliation of these non-GAAP financial measures to GAAP financial measures in our CFO commentary, which is posted on our website.

我們所有陳述均以今日（2026 年 8 月 26 日）為準，並以目前可得資訊為基礎。除法律另有規定外，我們不承擔更新任何此類陳述之義務。在本次電話會議中，我們將討論非 GAAP 財務衡量指標。您可在我們網站上發布的財務長評論中，查閱非 GAAP 財務衡量指標與 GAAP 財務衡量指標之調節表。

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

接下來，我把電話交給 Colette。

Thanks, Toshiya. We delivered another outstanding quarter with record revenue, operating income and EPS. Total revenue of $96 billion more than doubled year-over-year as growth accelerated for the fourth consecutive quarter. The surge in AI demand is driving a global infrastructure buildout, supported by an expanding and diverse set of growth opportunities, spanning hyperscalers, AI labs, AI natives, enterprises and sovereign customers. We expect to grow revenue by approximately 70% in fiscal 2028.

謝謝你，Toshiya。我們再度交出亮眼的一季，營收、營業利益與每股盈餘（EPS）皆創新高。總營收達 960 億美元，年增逾一倍，且成長加速已連續第四季。AI 需求激增正推動全球基礎設施建置，在一系列擴大且多元的成長機會支撐下，涵蓋超大規模雲端業者、AI 實驗室、AI 原生公司、企業與主權客戶。我們預期在 2028 會計年度營收約成長 70%。

This is a supply-constrained outlook. Q2 data center revenue increased 18% quarter-over-quarter to $89 billion with strong contributions from both sub segments, hyperscale and ACIE, which includes our neocloud, industrial and enterprise customers. Hyperscale revenue of $49 billion grew 13% sequentially, driven by sustained strength in Blackwell, reinforcing that more compute drives more revenue as new GPU capacity comes online, our hyperscale customers delivered strong financial results in the quarter with accelerating revenue growth and expanding margins.

這是一個受供應限制的展望。第二季資料中心營收季增 18% 至 890 億美元，兩個子業務（超大規模與 ACIE）皆有強勁貢獻；ACIE 包含我們的新雲（neocloud）、工業與企業客戶。超大規模營收為 490 億美元，季增 13%，主要受 Blackwell 持續強勁帶動，進一步印證隨著新的 GPU 產能上線，更多算力帶來更多營收；我們的超大規模客戶本季亦繳出強勁財務表現，營收成長加速且利潤率擴張。

With cloud industry backlog now greater than $2 trillion, CapEx by the top 5 hyperscalers is expected to reach nearly $800 billion in 2026 and $1.3 trillion in 2027. Today, we are delighted to announce an expansion of our partnership with AWS. Building on its already vast installed base of NVIDIA compute, AWS is deploying an additional 2 million GPUs starting this quarter through the second quarter of fiscal '29, along with Vera CPUs, some integrated with Rubin, other standalone.

隨著雲端產業積壓訂單現已超過 2 兆美元，預期前五大超大規模業者的資本支出（CapEx）將在 2026 年接近 8,000 億美元，並於 2027 年達到 1.3 兆美元。今天，我們很高興宣布擴大與 AWS 的合作夥伴關係。在其既有龐大的 NVIDIA 算力裝機基礎之上，AWS 將自本季起至 2029 會計年度第二季部署額外 200 萬顆 GPU，並搭配 Vera CPU，其中部分與 Rubin 整合，其他則為獨立配置。

AWS will serve NVIDIA Nemotron family of open models on Amazon Bedrock and SageMaker. Amazon will also adopt our full physical AI stack, Omniverse, Cosmos, Isaac and Jetson to power its fleet of warehouse robots. ACIE revenue of $40 billion increased 25% sequentially and 138% year-over-year. Growth was driven by neocloud capacity additions to meet the rising demand from enterprises, AI start-ups and sovereigns as well as hyperscalers purchasing capacity to supplement their own buildouts.

AWS 將在 Amazon Bedrock 與 SageMaker 上提供 NVIDIA Nemotron 系列開放模型。Amazon 也將採用我們完整的實體 AI 堆疊（physical AI stack），包括 Omniverse、Cosmos、Isaac 與 Jetson，為其倉儲機器人車隊提供動力。ACIE 營收為 400 億美元，季增 25%，年增 138%。成長主要來自新雲擴增產能，以滿足企業、AI 新創與主權客戶日益上升的需求，以及超大規模業者為補足其自建擴張而採購的產能。

Using NVIDIA DSX reference designs, our neocloud partners are bringing capacity online faster and at lower token cost. They are expected to exit the year with 8 gigawatts in total installed capacity, up from approximately 3 gigawatts at the end of 2025. Incredibly, we are seeing demand acceleration even at our scale. Customers' forecasts point to our growth doubling next year. However, as I mentioned earlier, we expect to grow approximately 70% as we are supply constrained.

透過 NVIDIA DSX 參考設計，我們的新雲合作夥伴能以更快速度、並以更低的 token 成本將產能上線。預期他們在年底時的總裝置容量將達 8 吉瓦，高於 2025 年底約 3 吉瓦。令人驚訝的是，即使在我們這樣的規模下，我們仍看到需求加速。客戶的預測顯示，我們明年的成長將倍增。然而，如我先前所提，我們預期約成長 70%，因為我們受供應限制。

NVIDIA compute is fully utilized across every cloud we serve, the economic value it generates for hyperscale, neocloud and AI lab partners keeps rising. Besides building the best AI computing technologies and the most capable supply chain, NVIDIA has three unique capabilities that are engines powering our growth. First, NVIDIA's architecture runs every model, and we're growing share as closed and open model adoption grow. Closed and open models alike, adoption is skyrocketing.

我們所服務的每一個雲端中，NVIDIA 算力都已滿載運行；其為超大規模、新雲與 AI 實驗室夥伴所創造的經濟價值持續上升。除了打造最佳 AI 運算技術與最具能力的供應鏈之外，NVIDIA 還具備三項獨特能力，成為推動我們成長的引擎。第一，NVIDIA 的架構可運行所有模型，且隨著封閉式與開放式模型採用增加，我們的市占率也在提升。無論封閉或開放模型，採用速度都在飆升。

NVIDIA runs the leading closed models, OpenAI, Anthropic, Grok, Meta, Gemini and the leading open models, TML, Mistral, Qwen, Kimi, GLM, DeepSeek, MiniMax and Nemotron. We're great at small models and giant ones, large or video, autoregressive or diffusion in the cloud or in the edge. NVIDIA is great at training, great at inference, great at agentic workloads.

NVIDIA 支援領先的封閉式模型：OpenAI、Anthropic、Grok、Meta、Gemini；以及領先的開放式模型：TML、Mistral、Qwen、Kimi、GLM、DeepSeek、MiniMax 與 Nemotron。我們擅長小模型也擅長巨型模型；無論是大型或影片模型、自回歸或擴散模型，在雲端或邊緣端皆能勝任。NVIDIA 擅長訓練、擅長推論，也擅長代理式（agentic）工作負載。

One platform, fungible for every model and workload, durable for the entire life cycle of AI. That combination of performance, fungibility and durability is what makes NVIDIA the productive and financeable compute infrastructure. Our second unique capability is our full-stack AI factory platform that is expanding our share of the data center TAM.

單一平台，可在各種模型與工作負載間通用（fungible），並在 AI 全生命週期中具備長久耐用性（durable）。性能、通用性與耐用性的結合，使 NVIDIA 成為高生產力且可融資（financeable）的運算基礎設施。我們第二項獨特能力，是全堆疊 AI 工廠平台，正擴大我們在資料中心總可服務市場（TAM）中的占比。

Since Hopper, our revenue opportunity has grown from roughly $18 billion per gigawatt to $25 billion with Blackwell, to $40 billion with Vera Rubin, which now spans Vera CPU, Rubin GPU, NVLink, InfiniBand or Ethernet and Groq LPU announced earlier this week. Our ability to extreme co-design across GPU, CPU, NVLink scale-up networking, scale-out networking, systems, algorithms and software enables us to deliver X factor performance gain every generation.

自 Hopper 以來，我們每吉瓦的營收機會已從約 180 億美元，隨 Blackwell 提升至 250 億美元，並隨 Vera Rubin 提升至 400 億美元；Vera Rubin 現已涵蓋 Vera CPU、Rubin GPU、NVLink、InfiniBand 或乙太網路，以及本週稍早宣布的 Groq LPU。我們在 GPU、CPU、NVLink 擴展式（scale-up）網路、橫向擴展式（scale-out）網路、系統、演算法與軟體之間進行極致協同設計（extreme co-design）的能力，使我們每一代都能帶來 X 因子等級的效能提升。

Vera Rubin exemplifies this, delivering 30 times higher throughput per megawatt and 35 times lower token costs relative to Grace Blackwell Ultra. We commenced production shipments of Vera Rubin earlier this month. Having already received purchase orders from every major hyperscaler, AI cloud and system OEM, we expect Vera Rubin to mark the fastest product ramp in NVIDIA's history.

Vera Rubin 正是此能力的典範，相較於 Grace Blackwell Ultra，每兆瓦吞吐量提升 30 倍，token 成本降低 35 倍。我們已於本月稍早開始量產出貨 Vera Rubin。在已收到所有主要超大規模業者、AI 雲端與系統 OEM 的採購訂單後，我們預期 Vera Rubin 將成為 NVIDIA 歷史上最快的產品放量（ramp）。

Our networking business had another record quarter with revenue growing 18% on a sequential basis. Spectrum-X Ethernet, which grew 2.6x on a year-over-year basis is already helping us become the largest and fastest-growing network company in the world. Rising adoption of agentic AI is driving an acceleration in demand for data center CPUs. Our Grace CPU introduced in 2021 has been a great success, with revenue on a trailing 12-month basis, exceeding $5 billion. Today, we are in full production of our next-generation Vera CPU.

我們的網路業務再創單季新高，營收季增 18%。Spectrum-X 乙太網路年增 2.6 倍，已在協助我們成為全球最大且成長最快的網路公司。代理式 AI 的採用提升，正推動資料中心 CPU 需求加速。我們於 2021 年推出的 Grace CPU 已取得巨大成功，過去 12 個月（TTM）營收已超過 50 億美元。今天，我們的下一代 Vera CPU 已全面量產。

As a stand-alone product, Vera expands our TAM even further. Vera completes agentic task 1.8 times faster on the SPEC benchmark and provides 5 times the bandwidth per watt than any other data center CPU. We expect Vera to be deployed by every major hyperscaler, neocloud, AI lab, and system OEM with shipments already underway to our lead partners, including OCI, SpaceXAI and starting this quarter, AWS. We continue to see demand for approximately $20 billion in total server CPUs.

作為一項獨立產品，Vera 進一步擴大了我們的 TAM。在 SPEC 基準測試上，Vera 完成代理式任務的速度快 1.8 倍，且每瓦可提供的頻寬是其他任何資料中心 CPU 的 5 倍。我們預期 Vera 將被所有主要超大規模雲端業者（hyperscaler）、新型雲端（neocloud）、AI 實驗室與系統 OEM 部署；目前已開始向我們的領先合作夥伴出貨，包括 OCI、SpaceXAI，並且自本季起也將出貨給 AWS。我們持續看到總計約 200 億美元的伺服器 CPU 需求。

And based on our customer demand and improving supply outlook, our preliminary expectation is for CPU revenue to more than double in fiscal '28, positioning us as one of the world's leading server CPU suppliers. Since the announcement of our Groq partnership last year, we've been working to unite NVIDIA's high throughput and Groq's high interactivity architectures. At Hot Chips earlier this week, we announced that Groq 3 LPX, our first rack scale LPU system is in full production and already setting records demonstrating nearly 4 times the number of tokens per second against the next best alternative on our artificial analysis benchmark.

並且基於客戶需求以及供應前景改善，我們的初步預期是 2028 會計年度 CPU 營收將超過倍增，使我們躋身全球領先的伺服器 CPU 供應商之列。自去年宣布與 Groq 的合作夥伴關係以來，我們一直致力於整合 NVIDIA 的高吞吐量架構與 Groq 的高互動性架構。在本週稍早的 Hot Chips 大會上，我們宣布 Groq 3 LPX——我們第一套機櫃級（rack scale）LPU 系統——已全面量產，並且已創下紀錄：在我們的人工分析基準上，其每秒 token 數幾乎是次佳替代方案的 4 倍。

We expect to ship Groq 3 LPX in volume later this quarter to early adopters. Nebius will be the first. Today, we're not just selling the best chips, we're selling a full-stack AI factory platform, offering superior economics for customers and capturing a bigger share of the data center TAM. Our third unique capability is the combination of our full-stack AI factory and rich CUDA ecosystem, allowing us to extend AI into markets a single chip alone can never reach. Beyond the hyperscalers lies a massive market anxious to adopt AI, customers with no interest in designing their own custom silicon.

我們預期將於本季稍晚向早期採用者大量出貨 Groq 3 LPX。Nebius 將是第一家。今天，我們不只是銷售最好的晶片，我們也在銷售全棧 AI 工廠平台，為客戶提供更優的經濟效益，並在資料中心 TAM 中取得更大的份額。我們第三項獨特能力，是將全棧 AI 工廠與豐富的 CUDA 生態系結合，使我們能把 AI 延伸到僅靠單一晶片永遠無法觸及的市場。在超大規模雲端業者之外，還有一個龐大的市場急於採用 AI——這些客戶無意自行設計客製化矽晶片。

NVIDIA's fully proven full-stack platform is uniquely suited to help sovereigns, neoclouds and enterprises build their AI infrastructure, bring it to full operation, continuously optimize it through CUDA software and connect it to offtake demand from our vast developer ecosystem. Hyperscalers will remain a major growth driver, but non-hyperscaler growth, our ACIE segment, spanning sovereign, regional neoclouds, enterprise, edge and air-gap data centers, will represent roughly half of our data center business. Our AI native start-up ecosystem developed and running primarily on the NVIDIA compute platform is scaling at a rapid pace.

NVIDIA 經充分驗證的全棧平台，特別適合協助主權國家、新型雲端與企業建置其 AI 基礎設施，使其全面投入運作，透過 CUDA 軟體持續最佳化，並連結到我們龐大開發者生態系所帶來的承購（offtake）需求。超大規模雲端業者仍將是主要成長動能，但非超大規模的成長——我們的 ACIE 部門，涵蓋主權、區域型新型雲端、企業、邊緣與隔離（air-gap）資料中心——將約占我們資料中心業務的一半。我們的 AI 原生新創生態系主要在 NVIDIA 運算平台上開發與運行，正以極快速度擴張。

Global VC funding in AI, roughly 70% of which is spent on compute, exceeded $400 billion in the first half of 2026, surpassing the $265 billion raised in all of 2025. Nearly 20 companies, including Cursor owned by SpaceX, Figma and Together AI, now exceed $1 billion in annualized run rate revenue, up from 13 companies in Q4 of last year, with vertical enterprise software logging the fastest growth. In enterprise, on a trailing 12-month basis, on-prem revenue in the automotive vertical reached $8 billion while financial services, manufacturing and health care combined contributed $7 billion in revenue.

全球 AI 領域的創投資金（VC）在 2026 年上半年超過 4,000 億美元，其中約 70% 用於運算，已超越 2025 年全年募集的 2,650 億美元。目前約有 20 家公司（包括 SpaceX 旗下的 Cursor、Figma 與 Together AI）年化營收（annualized run rate）已超過 10 億美元，高於去年第四季的 13 家，其中垂直型企業軟體成長最快。在企業端，以過去 12 個月（TTM）計算，汽車垂直領域的地端（on-prem）營收達 80 億美元，而金融服務、製造與醫療保健合計貢獻 70 億美元營收。

Hudson River Trading and Jane Street are leveraging NVIDIA's powered AI factories to accelerate quantitative trading. Samsung Electronics is using NVIDIA cuLitho to achieve up to 20 times greater performance in computational lithography, while Bristol Myers Squibb is investing in Vera Rubin AI factory, a fast follow to the Roche and Lilly buildouts as drug R&D time lines compress from years to months.

Hudson River Trading 與 Jane Street 正在運用 NVIDIA 驅動的 AI 工廠，加速量化交易。Samsung Electronics 正使用 NVIDIA cuLitho，使計算式微影（computational lithography）效能最高提升 20 倍；同時 Bristol Myers Squibb 正投資 Vera Rubin AI 工廠，作為 Roche 與 Lilly 建置案之後的快速跟進，因藥物研發時程正由數年壓縮至數月。

In sovereign AI, our business primarily through the regional neoclouds, grew 35% sequentially and more than tripled year-over-year in Q2. A country or region can allocate land and power directly to a regional cloud partner in ways it never would to a foreign hyperscaler. We don't own a cloud ourselves, we are a neutral partner to every sovereign and neocloud. Because NVIDIA compute is productive, fungible, rentable and durable, regional cloud interest is surging around the world.

在主權 AI 方面，我們的業務主要透過區域型新型雲端推動，第二季季增 35%，且年增超過 3 倍。一個國家或地區可以將土地與電力直接配置給區域雲端合作夥伴，而這是它絕不會對外國超大規模雲端業者採取的方式。我們自己不經營雲端；我們是每一個主權國家與新型雲端的中立合作夥伴。由於 NVIDIA 運算具備高生產力、可互換（fungible）、可出租且耐用，全球對區域雲端的興趣正快速升溫。

We helped CoreWeave, Nebius and Nscale build entire infrastructure businesses, and neoclouds are emerging everywhere. Firebird in Armenia, Cassava Technologies across Africa, GMI Cloud in Taiwan, Yotta and Neysa in India, Firmus in Australia, YTL AI Cloud in Malaysia, pairing local land, power and operating expertise with our platform. Last month, we announced a partnership with Noetra, Japan's national AI company, to build an NVIDIA DSX AI factory that will create open models to power AI agents, digital twins, robotics and physical AI applications.

我們協助 CoreWeave、Nebius 與 Nscale 建立完整的基礎設施業務，而新型雲端正在各地湧現。亞美尼亞的 Firebird、橫跨非洲的 Cassava Technologies、台灣的 GMI Cloud、印度的 Yotta 與 Neysa、澳洲的 Firmus、馬來西亞的 YTL AI Cloud，皆將在地土地、電力與營運專業與我們的平台結合。上個月，我們宣布與日本國家級 AI 公司 Noetra 合作，建置 NVIDIA DSX AI 工廠，將打造開放模型以驅動 AI 代理、數位孿生、機器人與實體 AI 應用。

South Korea's LG and Hyundai Motor Group are partnering with NVIDIA to build and scale AI. And in Europe, a record 35 new NVIDIA-powered AI supercomputers were unveiled to advance industry and scientific breakthroughs. Neoclouds are seeing strong demand pipelines for many diverse offtakers. Rather than allocating their entire capacity to a single long-term offtake guarantee that lenders typically require to finance a data center independently, we have introduced a revenue-sharing structure.

南韓的 LG 與 Hyundai Motor Group 正與 NVIDIA 合作建置並擴展 AI。在歐洲，創紀錄的 35 台全新 NVIDIA 驅動 AI 超級電腦亮相，以推動產業與科學突破。新型雲端正看到來自多元承購方（offtakers）的強勁需求管線。不同於將全部產能配置給單一長期承購保證（這通常是放款方在資料中心獨立融資時所要求的），我們已導入一種收益分成結構。

NVIDIA provides a take-or-pay commitment on a portion of the facility's capacity, a minimum revenue guarantee that gives lenders the confidence to underwrite the project, and in exchange, we share in a portion of the neoclouds' revenue earned above that floor. Independent capital still underwrites every deal on its own merits. We're not making loans. In this model, we get paid twice, once on the hardware sale and again, through the share of rental revenue, a highly reoccurring stream layered on top of a onetime equipment purchase.

NVIDIA 對設施部分產能提供「承購或付款」（take-or-pay）承諾，亦即最低營收保證，讓放款方有信心承作（underwrite）該專案；作為交換，我們分享新型雲端在該底線之上所賺取的一部分營收。獨立資本仍會依每筆交易自身條件承作。我們不提供貸款。在此模式下，我們可獲得兩次報酬：一次來自硬體銷售，另一次則透過租賃營收分成——這是一條高度經常性（recurring）的收入來源，疊加在一次性設備採購之上。

Over time, this model can expand our addressable market and create reoccurring usage-linked revenue stream alongside our core platform revenue, with the potential to drive billions in revenue over the medium to long term. Together, NVIDIA's three unique capabilities, a platform that runs every model, a full-stack AI factory platform capturing more of the data center TAM, and a CUDA ecosystem that extends AI into markets no single chip could reach alone, reinforce one another and are the engines of our growth.

隨著時間推進，此模式可擴大我們的可服務市場，並在核心平台營收之外，建立與使用量連動的經常性收入來源，於中長期具備帶來數十億美元營收的潛力。綜合而言，NVIDIA 的三項獨特能力——可運行所有模型的平台、可攫取更多資料中心 TAM 的全棧 AI 工廠平台，以及將 AI 延伸至單一晶片無法獨自觸及市場的 CUDA 生態系——彼此相互強化，並成為我們成長的引擎。

Let me update you on our progress with our frontier AI labs. The frontier AI labs have extraordinary demand for training and inference compute, but they are growing faster than what their balance sheets and credit profiles can support. They have rapidly growing customer demand, yet still lack the decades-long infrastructure contracts and investment-grade financing capacity needed to secure the AI factory infrastructure independently. In other words, their growth isn't limited by their technology or customer demand. It's limited by compute.

我來向各位更新我們在前沿 AI 實驗室（frontier AI labs）方面的進展。前沿 AI 實驗室對訓練與推論運算有極高需求，但其成長速度快於其資產負債表與信用狀況所能支撐。它們的客戶需求快速成長，卻仍缺乏取得 AI 工廠基礎設施所需的、長達數十年的基礎設施合約與投資等級的融資能力。換言之，它們的成長並非受限於技術或客戶需求。而是受限於運算。

For these companies, more compute means more and more intelligence, more users and more revenue. NVIDIA is needed to help power this flywheel. First, we've invested nearly $50 billion in the frontier AI labs. This was a meaningful commitment, but it represented a small fraction of our expected free cash flow over the same period. Further, to support the frontier labs infrastructure buildouts, we recently announced partnerships with 6 of the world's leading infrastructure capital providers, Apollo, BlackRock, Blackstone, Brookfield, Goldman Sachs and KKR, to establish financing platforms that will raise over $500 billion of third-party capital.

對這些公司而言，更多運算意味著更多且更強的智慧、更多使用者與更多營收。NVIDIA 需要協助驅動這個飛輪效應。首先，我們已在前沿 AI 實驗室投資近 500 億美元。這是一項重要承諾，但相較於同期間我們預期的自由現金流，僅占很小一部分。此外，為支持前沿實驗室的基礎設施建置，我們近期宣布與全球 6 家領先的基礎設施資本提供者——Apollo、BlackRock、Blackstone、Brookfield、Goldman Sachs 與 KKR——建立合作夥伴關係，以設立融資平台，將募集超過 5,000 億美元的第三方資本。

With these partnerships, building on our unique fungible and durable computing platform, the AI labs will be able to build and assess AI infrastructure funded by long-term institutional capital at relatively attractive rates. Last week, we announced that we secured land, power, shell capacity through our partnership with SoftBank Energy to exclusively host NVIDIA compute at their Portsmouth campus.

透過這些合作，並建立在我們獨特、可互換且耐用的運算平台之上，AI 實驗室將能以相對具吸引力的利率，建置並擴充由長期機構資本資助的 AI 基礎設施。上週，我們宣布透過與 SoftBank Energy 的合作，在其 Portsmouth 園區取得土地、電力與機殼（shell）容量，以專屬方式託管 NVIDIA 運算。

The initial deployment expected to support 4.25 gigawatts of AI factory capacity will be utilized by OpenAI. Each generation of NVIDIA AI factory systems deployed at PORTS-Pike could represent approximately 1.5 million NVIDIA GPUs. And over 20 years, the site could support multiple upgrade cycles. Here's the essential economic point. The LPS commitment secures a long-lived AI factory site, while the NVIDIA compute within the data center can be upgraded repeatedly.

預期支援 4.25 吉瓦 AI 工廠產能的初始部署將由 OpenAI 使用。在 PORTS-Pike 部署的每一代 NVIDIA AI 工廠系統，可能約相當於 150 萬顆 NVIDIA GPU。而在 20 年期間，該場址可支援多個升級週期。以下是關鍵的經濟要點。LPS 的承諾確保了一個長期存續的 AI 工廠場址，而資料中心內的 NVIDIA 運算能力可反覆升級。

This project deepens our long-standing partnership with OpenAI. OpenAI has committed to substantial deployments of NVIDIA AI infrastructure through 2030. OpenAI's existing and planned commitments represent approximately 12 gigawatts of NVIDIA compute. For another frontier AI lab, we will provide selective credit enhancement for nearly 2 gigawatts of compute. This complements the substantial NVIDIA compute capacity they've secured independently without NVIDIA's credit support.

此專案深化了我們與 OpenAI 長期以來的合作夥伴關係。OpenAI 已承諾在 2030 年前大規模部署 NVIDIA AI 基礎設施。OpenAI 既有與規劃中的承諾合計約代表 12 吉瓦的 NVIDIA 運算能力。對於另一家前沿 AI 實驗室，我們將為近 2 吉瓦的運算能力提供選擇性的信用增強。這也補足了他們在未獲得 NVIDIA 信用支持的情況下，自行取得的大量 NVIDIA 運算產能。

We recognize the scale of this support, and we know some will call this circular financing. We see it differently. We're going through a major computing platform shift, the creation of one of the most important technologies in human history and these are once in a generation companies. The technology leadership is proven and their customer traction and usage are skyrocketing. We expect them to become the largest technology companies in history.

我們理解這項支持的規模，也知道有人會稱之為循環融資。但我們的看法不同。我們正經歷一次重大的運算平台轉移，這是在創造人類歷史上最重要的技術之一，而這些都是一代才會出現一次的公司。其技術領先地位已獲證明，客戶牽引力與使用量正急速攀升。我們預期他們將成為史上最大的科技公司。

We believe these investments measured against the strength of their demand, the business they create for us, the ecosystem they build on NVIDIA's platform and the equity returns on our invested capital will be excellent, and our risk is limited. The NVIDIA compute platform is fungible and durable and can be redeployed to support other customers. For context, we expect demand from the AI labs for which we expect to leverage our balance sheet to contribute toward roughly a quarter of our business next year.

我們相信，將這些投資與其需求強度、為我們創造的業務、在 NVIDIA 平台上建立的生態系，以及我們投入資本的股權回報相衡量，表現將非常出色，且我們的風險有限。NVIDIA 的運算平台具可替代性與耐久性，並可重新部署以支援其他客戶。作為背景說明，我們預期，對於我們預計運用資產負債表提供支持的 AI 實驗室需求，明年將約占我們業務的四分之一。

This remains compute we ship will be consumed by investment-grade customers or those that are backed by one. In Q2, we shipped less than 1% of our total data center revenue in Hopper 200 products to customers based in China in accordance with the US government licenses. Current Hopper shipments are dilutive to corporate gross margins. And given ongoing geopolitical uncertainty, there is no China data center compute revenue in our forward outlook.

我們出貨的這些運算能力仍將由投資級客戶，或由投資級客戶背書的客戶所消化。在第二季，我們依照美國政府許可，向中國客戶出貨的 Hopper 200 產品占我們資料中心總營收不到 1%。目前 Hopper 的出貨對公司整體毛利率具有稀釋效應。且鑑於持續的地緣政治不確定性，我們的前瞻展望中不包含任何中國資料中心運算營收。

Moving to the rest of the P&L. GAAP and non-GAAP gross margins were both 75%, largely unchanged from last quarter due to a similar product mix. GAAP and non-GAAP operating expenses were up 10% and 11% sequentially, primarily due to high compute infrastructure costs and compensation and benefits costs. Our non-GAAP effective tax rate of 16% increased from a year ago, primarily due to higher revenue. On our balance sheet, inventory increased to $32 billion as we prepared for the Vera Rubin launch.

接著談損益表其餘部分。GAAP 與非 GAAP 毛利率皆為 75%，由於產品組合相近，較上季大致持平。GAAP 與非 GAAP 營業費用較前一季分別增加 10% 與 11%，主要因高額運算基礎設施成本，以及薪酬與福利成本。我們的非 GAAP 有效稅率為 16%，較去年提高，主要因營收增加。在資產負債表方面，為了準備 Vera Rubin 上市，我們的存貨增加至 320 億美元。

Days of sales outstanding increased to 60 days, reflecting extended payment terms for large purchases by certain investment-grade customers to be shipped over multiple quarters. In Q2, we returned a record $26 billion to shareholders, $20 billion through share repurchases and $6 billion through our quarterly dividend of $0.25 per share. Relative to our plan to return 50% or more of free cash flow, we returned 60% on a year-to-date basis. And going forward, we intend to increase and return excess free cash flow net of strategic uses.

應收帳款週轉天數增加至 60 天，反映部分投資級客戶對於跨多季出貨的大額採購取得了延長付款條件。在第二季，我們向股東回饋創紀錄的 260 億美元，其中 200 億美元透過庫藏股回購，60 億美元透過每股 0.25 美元的季度股利。相較於我們回饋至少 50% 自由現金流的計畫，我們年初至今已回饋 60%。展望未來，在扣除策略性用途後，我們計畫提高並返還超額自由現金流。

Let me turn to the outlook for the third quarter. Total revenue is expected to be $108 billion, plus or minus 2%. We expect sequential growth to be driven primarily by ACIE with data center, while growth in hyperscale is expected to reaccelerate in Q4 and into fiscal year '28, as supply of Vera Rubin grows over time. We see Vera Rubin accounting for about 20% of data center revenue in Q3. Looking ahead, our preliminary expectation is for fiscal year '28, revenue to grow approximately 70% year-over-year.

接下來談第三季展望。預期總營收為 1,080 億美元，上下浮動 2%。我們預期季增主要由 ACIE 與資料中心帶動；而超大規模客戶（hyperscale）的成長預期將在第四季並延續至 2028 會計年度重新加速，因 Vera Rubin 的供應將隨時間增加。我們預期 Vera Rubin 在第三季約占資料中心營收的 20%。往前看，我們對 2028 會計年度的初步預期是，營收年增約 70%。

Although we will work to close the supply-demand gap, we expect supply to remain a bottleneck at least through the end of fiscal year '28. Many of you have expressed concerns regarding our gross margins as component costs have risen significantly. As you are already aware, we are experiencing extreme pricing conditions in memory. The magnitude of the price increase has exceeded our prior expectations and are headed even higher into next year. As a result, we are resetting expectations today.

儘管我們將努力縮小供需缺口，但我們預期至少到 2028 會計年度年底之前，供應仍將是瓶頸。許多人對於我們的毛利率表示擔憂，因為零組件成本已大幅上升。如各位已知，我們正面臨記憶體的極端定價環境。漲價幅度已超出我們先前的預期，且明年還將進一步走高。因此，我們今天將重新設定市場預期。

For Q3, we expect GAAP and non-GAAP gross margins to be 74% plus or minus 50 basis points. We expect margins to bottom in Q4 in the 71% to 72% range before settling at 72% to 73% in fiscal year '28, as executed price increases take effect in Q1. We want to be direct about this rather than let it linger as an open question. Memory scarcity today is being driven in large part by the AI buildout itself and unlike a component that simply raises our cost with no offset benefit, tighter memory supply is a symptom of the same demand surge that's driving our own growth.

就第三季而言，我們預期 GAAP 與非 GAAP 毛利率為 74%，上下 50 個基點。我們預期毛利率將在第四季觸底，落在 71% 至 72% 區間，之後在 2028 會計年度穩定於 72% 至 73%，因已執行的漲價將於第一季開始反映。我們希望直接說清楚，而不是讓這件事長期懸而未決。當前的記憶體短缺在很大程度上是由 AI 建置本身所驅動；不同於僅提高成本且沒有對應效益的零組件，記憶體供應趨緊其實是同一波需求激增的症狀，而這波需求也正推動我們自身的成長。

We have a long-standing deep relationships with all three major memory suppliers and we're working closely with them to further increase the capacity our road map requires. GAAP and non-GAAP operating expenses are expected to be approximately $9.2 billion and $9.0 billion, respectively. For the full year, we now expect OpEx to grow in the low 50s driven by a broadening of our product portfolio and further increase in the usage of AI tools, which is already and will continue to enhance engineering productivity. For full year fiscal year '27, we continue to expect GAAP and non-GAAP taxes to be between 16% and 18%, excluding any discrete items and material changes to our tax environment.

我們與三大記憶體供應商皆維持長期且深厚的合作關係，並正與他們密切合作，以進一步提升我們路線圖所需的產能。預期 GAAP 與非 GAAP 營業費用分別約為 92 億美元與 90 億美元。就全年而言，我們目前預期營業費用將成長約 50% 出頭，主要由產品組合擴大，以及 AI 工具使用量進一步提升所帶動；這些工具已經並將持續提升工程生產力。就 2027 會計年度全年而言，在不含任何一次性項目及稅務環境重大變動的前提下，我們仍預期 GAAP 與非 GAAP 稅率介於 16% 至 18%。

With that, we will now transition to Q&A. Operator, please poll for questions.

接下來，我們將進入問答環節。接線員，請開始徵詢提問。

(Operator Instructions)

(接線員指示)

Joseph Moore, Morgan Stanley.

Joseph Moore，摩根士丹利。

Great. Thank you. I wonder if you could give us color on the 70%, and what gives you the confidence to guide a full year out if you haven't been doing that? And then what's the gap between that amount of growth in the 100% demand growth. What is the kind of key constraint that separates those numbers? And could you close those gaps over time?

很好。謝謝。我想請您就這個 70% 提供更多說明：如果你們過去並未這樣做，為何現在有信心給出一整年的指引？另外，這個成長幅度與 100% 的需求成長之間差距有多大？是什麼關鍵限制因素造成這兩個數字的差異？而你們能否隨時間推進逐步縮小這些差距？

Yeah, thanks, Joe. As you probably are aware, AI has become useful. And the AI agents that are being adopted everywhere use an enormous amount of compute. First of all, the large language models are larger than ever because they're smarter than ever. And these agents go through reasoning and planning multiple turns of tool use. The amount of compute necessary for an agent versus a human using it is probably 15 times to 100 times depending on the type of problem you're trying to solve.

是的，謝謝你，Joe。如你可能已經知道，AI 已經變得有用。而正在各處被採用的 AI 代理（agents）會使用龐大的運算量。首先，大型語言模型比以往更大，因為它們比以往更聰明。而這些代理會進行推理與規劃，並在多輪工具使用中反覆運作。相較於人類使用，代理完成同樣任務所需的運算量，可能高出 15 倍到 100 倍，取決於你要解決的問題類型。

And so the amount of compute necessary is just extraordinary. That's a factor that almost everybody sees. The part that people don't see about our growth because we're practically singular because of the nature of how we deliver products. I mean we're the only company in the world that creates and builds, offers an entire AI factory platform, a full-stack system. Customers can still mix and match. However, most companies just don't have the skills to do that or desire to do that.

因此，所需的運算量真的是極其驚人。這是一個幾乎所有人都看得到的因素。但人們看不到的是，我們的成長有一部分原因在於我們交付產品的方式，使得我們幾乎是獨一無二的。我的意思是，我們是全球唯一一家打造並建置、提供完整 AI 工廠平台、全棧系統的公司。客戶仍然可以混搭組合。然而，大多數公司就是沒有那樣做的技能，或也不想那樣做。

And so there's an entire part of the market that we experienced growth. There is sovereign AI, there're regional AIs, there're neoclouds, there're AI start-ups, there're enterprises, where we're seeing -- which represents about half of our business, and that's growing 100% a year. That part of the world's computing is likely to be larger over time than even what we're currently experiencing in the cloud. And so I think the demand that we see is driven by all of those factors. It is also the case that you can no longer procure technology per se and stand up this infrastructure.

因此，我們在市場上經歷成長的還有一整個部分。包括主權 AI、區域型 AI、新型雲端（neocloud）、AI 新創、以及企業客戶——我們正在看到的這些（約占我們業務的一半），而且每年成長 100%。隨著時間推移，這部分世界的運算規模很可能會比我們目前在雲端所經歷的還要更大。因此，我認為我們看到的需求是由所有這些因素所驅動。另外一點是，你已經不能再只是採購技術本身，然後把這套基礎設施搭起來。

You've got to go secure the land, power and shell, which oftentimes is a couple two, three years out. All of the rest of the supply chain necessary to align the construction, the power, the cooling, all of the labor that's necessary. AI infrastructure is creating so many jobs all over the United States and all around the world. It just takes a lot more planning. And so we're involved in securing infrastructure now further down the pipeline. Just as a long time ago, people asked me why is it that we're working with memory suppliers when we're a chip company.

你必須先去確保土地、電力與機房外殼（shell），而這往往要兩、三年之後才到位。其餘供應鏈也都必須到位，才能協調建設、供電、散熱，以及所需的全部勞動力。AI 基礎設施正在美國各地以及全球創造大量工作機會。這一切都需要更多規劃。因此，我們現在也更早介入，在管線更下游處去確保基礎設施。就像很久以前，人們問我：我們是一家晶片公司，為什麼要和記憶體供應商合作。

And today, people understand it's really quite genius that we were working on our supply chain so far upstream. We work with power generator companies, we -- downstream. We work with land, power and shell companies all around the world. And that helps prepare all of this computing that's going to be built that will ultimately deploy for our ecosystem and our customers. And so we just have a lot greater visibility now upstream and downstream. It is the case that we've never forecasted or never guided to a year in advance.

而今天，人們明白我們把供應鏈往上游做得那麼深，其實非常高明。我們也與發電機公司合作——在下游。我們與全球各地的土地、電力與機房外殼公司合作。這有助於為即將建置的所有運算能力做好準備，最終會部署到我們的生態系與客戶端。因此，我們現在在上游與下游都擁有更高的可視性。而且事實上，我們從來沒有做過、也從來沒有對外指引過提前一年（的預測）。

And even though our demand is much greater than 70%, our supply allows us to confidently deliver 70%. And we're going to continue to work with our supply chain to increase on that. But what we wanted to do is to be consistent with everybody, from our customers, our shareholders, our supply chain, everybody sees the same view. And the reason why that's important is because everybody is putting a lot of resources at play. And so we wanted to make sure that everybody has the same set of information.

即便我們的需求遠高於 70%，我們的供給也讓我們能有信心交付 70%。我們也會持續與供應鏈合作，把這個數字再提高。但我們想做的是，對所有人保持一致——我們的客戶、股東、供應鏈，所有人看到的都是同一個視角。這之所以重要，是因為每個人都在投入大量資源。因此，我們希望確保每個人都擁有同一套資訊。

And we've got a huge year coming up next year, and it's going to be pretty extraordinary.

而明年我們將迎來非常重要的一年，會相當非凡。

C.J. Muse, Cantor Fitzgerald.

C.J. Muse，Cantor Fitzgerald。

Yeah, good afternoon. Thank you for taking the question. There's tremendous investor focus on your inference market share. Can you speak to the evolving workloads you're seeing with the agentic AI and how you see your share evolving here over time, particularly when you reflect on the growing value of the TAM you're seeing with each new full-stack generation, your expectation for greater growth from ACIE, and then also including Groq 3 LPX? I would love to hear your thoughts.

是的，午安。謝謝讓我提問。投資人非常關注你們在推論市場的市占率。能否談談你們在代理式 AI（agentic AI）方面看到的工作負載如何演進，以及你們認為市占率隨時間將如何變化？特別是考量到你們每一代新的全棧產品都讓你們看到的 TAM 價值持續提升、你們預期 ACIE 將帶來更高成長，並且也把 Groq 3 LPX 納入考量。很想聽聽你的看法。

Yeah, thanks, C.J. The AI life cycle is getting way more complex than it used to be. And it's playing into NVIDIA's architecture much more greatly than it used to be. And so you could kind of see it as 4 phases. There's the first phase, which is preparing all of the data that you need. Some of it is synthetic, some of it is real, some of it is human labeled and generated, pre-train the models. And then there's post training, the third phase and then there's the agentic inference. Agentic inference is extremely complicated. And so every one of those phases are complicated.

好的，謝謝你，C.J.。AI 的生命週期正變得比過去複雜得多。而這也比以前更大幅度地發揮在 NVIDIA 的架構優勢上。所以你可以把它大致看成四個階段。第一個階段是準備你所需要的所有資料。其中一部分是合成資料，一部分是真實資料，一部分是人工標註與生成的資料，用來做模型的預訓練。接著是後訓練，第三個階段，然後是代理式推論。代理式推論極其複雜。因此，這些階段每一個都很複雜。

The thing that's really great about the NVIDIA architecture, and we created this with NVLink 72, and it was a big surprise on the world when we first created the first -- the world's first rack-scale architecture. It was hardly easy, and it was very challenging building the first generation. We're now in our third generation of NVLink 72 rack-scale systems. We had to reinvent the entire supply chain, reinvent systems, reinvent the technology, redistribute our software, refactor our software, everything, every aspect of it was hard.

NVIDIA 架構真正厲害之處在於——我們透過 NVLink 72 做到這點——當我們首次打造出全球第一個機櫃級（rack-scale）架構時，讓全世界都非常驚訝。那絕非易事，打造第一代非常具挑戰。我們現在已經來到 NVLink 72 機櫃級系統的第三代。我們必須重塑整個供應鏈、重塑系統、重塑技術，重新分配我們的軟體、重構我們的軟體——所有事情、每一個面向都很難。

But what it allowed us to do was to create one fungible system that allows us to transition from data creation, data preparation, the pre-training, the post-training to agentic inference. The benefits to customers is incredible. And the reason for that is because you've just spent, and we just mentioned, each gigawatt of technology and NVIDIA's revenue exposure in the Hopper time frame with Hopper plus InfiniBand and now Vera Rubin and CPU and three types of different networking.

但它讓我們能打造出一個可互換（fungible）的單一系統，使我們可以從資料生成、資料準備、預訓練、後訓練一路轉換到代理式推論。對客戶而言，效益非常驚人。原因在於你剛剛投入了——我們也提到過——每一吉瓦（gigawatt）的技術，以及 NVIDIA 在 Hopper 時期的營收曝險：Hopper 加上 InfiniBand，現在再加上 Vera Rubin、CPU，以及三種不同的網路。

Because it takes that many types of networking to address the entire world's data center, not to mention the scale in security networking and the scale across multicampus networking, so you could argue five different types of networking systems and then, of course, Groq. And all of that increased our revenue contribution or revenue opportunity per gigawatt to $40 billion. So each gigawatt of data center increased from, say, $30 billion about five years ago to now $60 billion today. Of course, the productivity is tremendous.

因為要涵蓋全世界的資料中心，就需要那麼多種類的網路，更不用說安全網路的規模，以及跨多園區（multicampus）網路的規模；所以你可以說是五種不同的網路系統，然後當然還有 Groq。而這一切把我們每吉瓦的營收貢獻或營收機會提高到 400 億美元。因此，每吉瓦資料中心的價值，從大約五年前的 300 億美元提升到今天的 600 億美元。當然，生產力提升非常巨大。

The performance is incredible in comparison, but you're talking about a $60 billion investment. And to the extent that you could use it across multiple phases of the AI life cycle, run every single type of model you can imagine running on it, whether it's diffusion or autoregressive or state space or some hybrid version of that, every version of attention mechanism, you can think of small or large models, like the investment that you make will be preserved and useful and productive for a lot longer time. And so I think our advantage in this new world is really quite extraordinary.

相較之下，效能也令人難以置信，但你談的是一筆 600 億美元的投資。如果你能把它用在 AI 生命週期的多個階段，在上面跑你能想像的每一種模型——不論是擴散模型（diffusion）、自回歸（autoregressive）、狀態空間（state space）或其混合版本，各種注意力機制（attention mechanism），小模型或大模型——那麼你所做的投資就能被保留，並在更長時間內保持有用且高產出。因此，我認為在這個新世界裡，我們的優勢確實非常非凡。

And it could explain why it is that our growth is actually accelerating. It was already large, but now it's accelerating. Let's see, you asked about Groq. Super excited about Groq 3. We achieved record token interactivity rate, extremely low latency performance generation. The team is doing fantastically. We spent the last several months using the NVLink architecture, which will be the core and it will be the core engine.

這也可以解釋為什麼我們的成長其實正在加速。原本就已經很大，現在還在加速。我們來看，你問到 Groq。我們對 Groq 3 非常興奮。我們達成了創紀錄的 token 互動速率，以及極低延遲的生成效能。團隊表現非常出色。過去幾個月我們一直在使用 NVLink 架構，它將會是核心，也將會是核心引擎。

And then for services that would like to have super high interactivity, super high-speed token generation done, the throughput is going to be a lot lower. The cost per token will be higher, but you could associate it with high ASP services. And so for those companies, you could bolt on one of our Groq accelerators. I'm super excited about that. But the vast majority of the world's data centers will just be Vera Rubin NVLink 72.

然後，對於希望具備超高互動性、超高速 token 生成的服務而言，其吞吐量會低很多。每個 token 的成本會更高，但你可以把它對應到高 ASP 的服務。因此，對那些公司來說，你可以外掛我們的一個 Groq 加速器。我對此非常興奮。但全球絕大多數的資料中心將會是 Vera Rubin NVLink 72。

Stacy Rasgon, Bernstein Research.

Stacy Rasgon，Bernstein Research。

Hi, guys. Thanks for taking my question. So the 70% growth in fiscal '28, which I guess is sort of calendar '27. So that's something like, I don't know, a $200 billion uptick versus the prior outlook, if I back it out, the prior outlook was $1 trillion over the three years. So this is probably $200 billion more. I was just wondering if you could talk us through the contributors, does that increase across the different products, the Vera and CPUs and Groq and everything else?

嗨，各位。謝謝讓我提問。所以，2028 會計年度的 70% 成長，我想大概相當於曆年 2027 年。如果我倒推的話，這大概是相較於先前展望多出約 2,000 億美元的增量；先前的展望是三年合計 1 兆美元。所以這可能多了約 2,000 億美元。我想請你們談談主要貢獻來源：這個增加是分散在不同產品上嗎，例如 Vera、CPU、Groq 以及其他所有項目？

And also you talked about your price increase that takes effect in Q1. So I assume some of this is pricing. And I guess I'm also curious, maybe I'm squeezing too many questions in here, but I'm also curious just it's a constrained number. What would it be if it wasn't constrained?

另外你們也提到將在第一季生效的漲價。所以我假設其中一部分是來自定價。我想我可能在這裡塞了太多問題，但我也很好奇：既然這是一個受限（constrained）的數字，如果不受限的話會是多少？

The unconstrained would be a lot higher. It's we grew 100% year-over-year this year. The unconstrained is significant. And so we're just going to have to go work hard to get more capacity. And we have a large supply chain. We have a really gigantic supply chain. And so we have incredible partners and we've secured a lot of supply, but we just need a lot more. To break it down, the way to think about that is most people see just hyperscalers. And that's half of the picture.

不受限的話會高得多。我們今年年增 100%。不受限的需求是相當可觀的。所以我們必須努力去取得更多產能。我們有很大的供應鏈。我們的供應鏈非常龐大。因此我們有很棒的合作夥伴，也已經確保了大量供應，但我們還需要更多。拆解來看，多數人只看到超大規模雲端業者（hyperscalers）。而那只是一半的圖像。

The other half of the picture is what we call ACIE and that's all the enterprise, the neoclouds, the sovereign AIs, that part of the world is invisible to everybody. And the reason for that is because they don't buy custom chips, they don't buy chips one at a time. They really need an entire factory platform built for them. And so that's a space that we add just a tremendous amount of value. Now of course, back in this hyperscale space, that's growing incredibly too, right? You know that they now have backlogs of $2 trillion.

另一半我們稱為 ACIE，涵蓋所有企業客戶、新型雲（neoclouds）、主權 AI（sovereign AIs）；這一塊對大家而言是看不見的。原因在於他們不買客製晶片，也不是一顆一顆買晶片。他們真正需要的是為他們打造一整套工廠級平台。因此在這個領域我們能創造極其巨大的價值。當然，回到超大規模這個領域，它也在高速成長，對吧？你們知道他們現在有 2 兆美元的積壓訂單（backlog）。

You know that when they stand up NVIDIA compute, when that happens, the revenues go up, their earnings contribution go up. Compute is profitable, very profitable today. And compute directly translates into increased revenues. And so there's just a race to want to bring more NVIDIA compute online, both at the hyperscalers, but what you don't see is just really tremendous opportunities outside the hyperscalers. But the other part of it is, and it's the reason why we mapped it out for you.

你們知道，當他們部署 NVIDIA 運算資源時，一旦上線，營收就會上升，對獲利的貢獻也會上升。運算現在是有利可圖的，而且非常有利可圖。而運算會直接轉化為營收增加。因此大家都在競賽，想要讓更多 NVIDIA 運算上線，不僅是在超大規模雲端業者那邊；但你們看不到的是，在超大規模之外其實也有非常巨大的機會。不過另一部分原因——也就是我們為什麼把它替你們畫出來——是這樣。

In the case of Hopper, we were at about $18 billion per gigawatt. For Grace Blackwell, we're at about $25 billion per gigawatt. And for Vera Rubin, it's about $40 billion per gigawatt. And the productivity is X factors increase in each generation. And so customers want to race to the next generation as fast as they can. Meanwhile, because NVIDIA's compute is so productive, the tokens they are generating, the GPU hours they're renting out is insanely profitable, as you know. Their margins are fantastic. And so -- so all of that is just simultaneously happening.

以 Hopper 來看，我們大約是每吉瓦 180 億美元。Grace Blackwell 大約是每吉瓦 250 億美元。而 Vera Rubin 大約是每吉瓦 400 億美元。而且每一代的生產力都以倍數（X factors）提升。因此客戶都想盡可能快地競速升級到下一代。同時，由於 NVIDIA 的運算生產力極高，他們所產生的 tokens、出租的 GPU 小時數，獲利高得驚人，如你所知。他們的毛利率非常出色。所以——所以這一切都在同時發生。

I think the big picture is that we're going through this platform shift, and it affects every computer company, and every industry in the world uses computers. So therefore, every industry is affected. Every company is affected. And this new way of doing computing is intelligent. It's not based on retrieval of files, but it's now generative, generating intelligence. And that requires compute, but the results you get is phenomenal. The results you get is tremendously better. And so we're just seeing that across the world. Everybody wants to be part of the AI revolution. Everybody will have to be part of this computing shift, and everybody has to build infrastructure.

我認為大方向是：我們正在經歷一次平台轉移（platform shift），它影響每一家電腦公司，而世界上每個產業都使用電腦。因此，每個產業都會受到影響。每家公司都會受到影響。而這種新的運算方式是智慧化的。它不再以檔案檢索為基礎，而是生成式的，生成智慧。這需要運算能力，但你得到的結果是驚人的。你得到的結果會好非常多。因此我們在全球各地都看到這種趨勢。每個人都想成為 AI 革命的一部分。每個人都必須成為這次運算轉移的一部分，而每個人都必須建置基礎設施。

Vivek Arya, Bank of America Securities.

Vivek Arya，美國銀行證券（Bank of America Securities）。

Thanks for providing all the transparency and all the commitments and guarantees that you have for a number of years. When I just add up everything that's in the CFO commentary, I get to a number of about $500 billion or so obviously, over the next several years. So I had a few questions related to that. First is, is that the takeaway that the sum of all your ecosystem investments over the next several years is in that ballpark, or are there other equity or other investments that could still be ahead? That's one.

感謝你們提供如此透明的資訊，以及你們多年期的各項承諾與保證。當我把 CFO 評論裡的所有內容加總起來，我得到的數字大約是未來幾年合計 5,000 億美元左右，顯然是跨越接下來數年。所以我有幾個相關問題。第一，是否可以這樣解讀：你們未來幾年在整個生態系的投資總和大致就在這個區間？還是說仍可能有其他股權或其他投資在後面？這是第一個。

Secondly, if there is a specific cash part of that that we should think about in fiscal '28. And then, Jensen, a lot of these investments are designed to help the frontier labs, especially OpenAI and Anthropic, but both of them are designing their own custom chips. In fact, OpenAI just in the last few days spoke about Jalapeno and their claims about being better than Blackwell and so forth. So how are you balancing this dynamic where you want to invest a lot in the ecosystem, but part of that ecosystem wants to develop competitive solutions?

第二，如果其中有一個特定的現金（cash）部分，我們在 2028 會計年度應該如何看待？另外 Jensen，這些投資很多是為了協助前沿實驗室（frontier labs），尤其是 OpenAI 和 Anthropic，但他們兩家都在設計自己的客製晶片。事實上，OpenAI 在過去幾天才談到 Jalapeno，並宣稱它比 Blackwell 更好等等。所以你們如何在這種動態之間取得平衡：一方面你們想在生態系投入很多，但生態系的一部分又想開發具競爭性的解決方案？

Well, we're building something very different. Whereas many of these XPUs are inference-specific chips for one cloud or one service, NVIDIA is a platform, an entire AI factory platform that spans the entire AI life cycle that you can use in any cloud, it's in every cloud. You can run anywhere. We'll help you set it up anywhere. And so we built something very different.

嗯，我們打造的是非常不同的東西。許多這些 XPU 是針對推論（inference）的特定晶片，服務於某一家雲或某一項服務；而 NVIDIA 是一個平台，是一整套 AI 工廠平台，涵蓋整個 AI 生命週期，你可以在任何雲上使用，它存在於每一朵雲。你可以在任何地方運行。我們也會協助你在任何地方把它建置起來。所以我們打造的是非常不同的東西。

These all of the AI services at some point are going to want to go around the world. And those data centers won't necessarily be just built by them. And also, I fully expect -- and so they're going to run in -- I think they're going to run on NVIDIA all around the world. And of course, I think our technology, I have 100% confidence that our technology will continue to be extraordinary for them, and that the economics of using our technology, whether it's from data processing to training to post training to agentic processing, our technology is going to be extraordinary for them.

這些 AI 服務終究都會想要走向全球。而那些資料中心不一定都會由他們自己來建置。而且我完全預期——因此他們將會在——我認為他們會在全球各地都跑在 NVIDIA 上。當然，我對我們的技術有 100% 的信心：我們的技術會持續對他們而言非常卓越；而使用我們技術的經濟性，無論是從資料處理、訓練、訓練後（post training）到代理式處理（agentic processing），我們的技術對他們都會非常出色。

They're going to use it. And so I'm very confident that they're going to be customers and partners of ours for a very long time. Now having said that, taking a step backwards, investing in these two companies or there are several AI labs that we've invested in, investing in these companies are once in a generation opportunity. I think the only regret that I have is that I didn't invest more and sooner. And both of the two -- of the companies will likely go public soon, and others will follow. And these will be some of the most consequential technology companies in history.

他們會使用它。所以我非常有信心，他們會在很長一段時間內成為我們的客戶與合作夥伴。不過話說回來，退一步看，投資這兩家公司——或我們投資的其他幾家 AI 實驗室——投資這些公司是世代難逢的機會。我唯一的遺憾是我沒有更早、也沒有投更多。而這兩家公司很可能很快就會上市，其他公司也會跟進。而它們將會是歷史上最具關鍵影響力的一些科技公司。

And so I'm delighted to be their friend. I'm delighted to partner with them. I'm delighted that they're building an ecosystem on top of the NVIDIA architecture. I'm delighted that they're counting on us to scale up, and I have 100% confidence that through quite a long period of time, they're going to be utilizing NVIDIA compute for a lot of their computing. And so I feel great about it.

因此，我很高興能成為他們的朋友。我很高興能與他們合作。我很高興他們正在 NVIDIA 架構之上打造一個生態系統。我很高興他們仰賴我們來擴大規模，而且我有 100% 的信心，在相當長的一段時間裡，他們將會在大量運算工作上使用 NVIDIA 的運算能力。所以我對此感到非常好。

So Vivek, let me add a little bit more regarding the commitments and the portion within those commitments, which is our supply commitments. This is essential. This is essential for the raising of Vera Rubin today as well as all next year. You can see that those commitments, the biggest parts of them are in the first three years, and we will use that to build the products that we need. This is what also gives us the confidence in terms of our growth in revenue, given how much we have already aligned in commitment in terms of our supply as well as capacity that we would need.

所以 Vivek，讓我再補充一些關於承諾以及這些承諾中的組成部分，也就是我們的供應承諾。這很關鍵。這對於今天 Vera Rubin 的拉升（ramp）以及明年全年都很關鍵。你可以看到，這些承諾中最大的一部分集中在前三年，而我們會利用這些承諾來打造我們所需要的產品。這也讓我們對營收成長更有信心，因為就供應以及我們所需的產能而言，我們已經在承諾上做了大量對齊。

Timothy Arcuri, UBS.

Timothy Arcuri，瑞銀（UBS）。

Jensen, I wanted to ask about open source. There's a lot of talk about that these models could gain share for workload in the US. You're obviously well positioned with Nemotron, but on the other hand, a lot of the end demand is being driven by these big frontier model companies. So there's a lot of investors that equate open models as being negative for the growth of those companies. So how do you sort of put and take that? Do you see the rise of open models as being good for NVIDIA or ultimately negative?

Jensen，我想問一下開源。市場上有很多討論，認為這些模型可能會在美國的工作負載中提升市占。你們在 Nemotron 上顯然佈局得很好，但另一方面，終端需求很大一部分是由這些大型前沿模型公司所帶動。因此有不少投資人把開放模型視為對那些公司的成長不利。那你如何看待這其中的取捨？你認為開放模型的崛起對 NVIDIA 是利多，還是最終會是利空？

The world will need both closed models and open models. And both closed models and open models are skyrocketing in use. Near most, I would say, nearly all open models run on NVIDIA, and the reason for that is because NVIDIA's footprint around the world is the highest, and our architecture is the most fungible. It's everywhere. It's in PCs and edge devices like DGX Spark, which is doing great, all the way to robots and workstations and your on-prem data centers.

世界同時需要封閉模型與開放模型。而且封閉模型與開放模型的使用量都在飆升。我會說，幾乎所有開放模型都跑在 NVIDIA 上，原因是 NVIDIA 在全球的部署覆蓋率最高，而我們的架構可移植性（fungible）最強。它無所不在。從 PC 與像 DGX Spark 這類邊緣裝置（表現非常好），一路到機器人、工作站，以及你們自建（on-prem）的資料中心。

Open models are doing incredibly well. Closed models, we know are doing incredibly well. The frontier labs, their scale -- their sales are skyrocketing, their margins are fantastic. They're generating profitable tokens. They are only limited by the amount of compute. That is equally true for open models. And our position in open models is very good because the CUDA ecosystem is literally everywhere. The open models are also foundational to just about every AI start-up and every enterprise company around the world is vital to them.

開放模型表現非常出色。封閉模型，我們也知道表現非常出色。前沿實驗室的規模——他們的銷售正在飆升，利潤率非常亮眼。他們正在產生可獲利的 token。他們唯一的限制就是運算量（compute）的多寡。這對開放模型同樣成立。而我們在開放模型上的位置非常好，因為 CUDA 生態系幾乎無所不在。開放模型同時也是幾乎每一家 AI 新創與全球每一家企業不可或缺的基礎。

And the reason for that is because you should rent intelligence, strong intelligence, smart intelligence wherever you can, which is the reason why we rented, and I encourage my employees to use the cloud services as much as they can. But every major company and every -- surely, every country and every start-up needs to build their domain specific, their proprietary AI, their proprietary alpha.

原因在於，你應該在任何可行之處租用智慧——強大的智慧、聰明的智慧——這也是為什麼我們會去租用，我也鼓勵員工盡可能多使用雲端服務。但每一家大型公司，以及每一個——當然，每一個國家與每一家新創——都需要打造其領域專屬的、專有的 AI，以及他們專有的 alpha。

And the open models reaching frontier levels has made it possible, has enabled them to all do that. One of the areas where frontier models is vital is cybersecurity. You see the number of cybersecurity companies that are enabled by frontier models so that they could have distributed, massively distributed, continuously running autonomous cybersecurity systems to defend. Those companies are emerging. There are some amazing companies.

而開放模型達到前沿水準，使這一切成為可能，也讓他們都能做到。前沿模型至關重要的一個領域是資安。你會看到許多資安公司因前沿模型而被賦能，從而能夠建立分散式、超大規模分散、持續運行的自主資安系統來進行防禦。這些公司正在湧現。有一些非常了不起的公司。

They couldn't do it without open models. And so open models is both incredibly successful and has finally reached the frontier, but it's also vital to the American economy. It's vital to the world economy. It's vital to companies to build their own proprietary AI. You can't do without one or the other.

沒有開放模型，他們做不到。因此，開放模型不僅非常成功、也終於達到前沿水準，同時它對美國經濟至關重要。它對全球經濟至關重要。它對企業打造自身專有 AI 至關重要。兩者缺一不可。

Both are going to be extraordinarily successful. And lastly, as you know, our market footprint of all AI models, we're the only I think we're the only platform. I'm fairly certain we're the only platform that runs every frontier model, whether it's closed or open. Most of them were built on NVIDIA and so they run great on NVIDIA. And so we're delighted by any model succeeding. So long as models succeed, I'm very happy. And both closed and open models are going to succeed and they're both simultaneously driving our sales.

兩者都將取得非凡的成功。最後，如你所知，在所有 AI 模型的市場覆蓋上，我們是唯一——我想我們是唯一的平台。我相當確定我們是唯一能運行每一個前沿模型的平台，不論是封閉或開放。其中大多數都是在 NVIDIA 上建構的，因此在 NVIDIA 上運行得非常好。所以任何模型的成功都讓我們感到高興。只要模型成功，我就非常開心。而封閉與開放模型都會成功，並且兩者同時在推動我們的銷售。

Ben Reitzes, Melius Research.

Ben Reitzes，Melius Research。

I wanted to ask you a question, Jensen, about demand in a different way. You talked about demand growing 100% next year, and I wanted to kind of get a sense for a couple of things driving that and even beyond that. And there's two concepts here. There's recursive self-improvement, which apparently at Anthropic and OpenAI is going very well with AI that improves itself. And even OpenAI said they could hit AGI by the end of this year.

Jensen，我想用不同的方式問你一個關於需求的問題。你提到明年需求將成長 100%，我想更了解推動這件事的幾個因素，甚至包括更長遠的影響。這裡有兩個概念。一個是遞迴式自我改進（RSI），據說在 Anthropic 和 OpenAI 那邊進展非常順利，AI 能夠自我改進。甚至 OpenAI 也說他們可能在今年底達到 AGI。

And with the developments in RSI as well as AGI, what happens to industry demand? Does it inflect further? And what does it mean for NVIDIA when those things take place? And how are you looking at that as a demand catalyst? Thanks.

隨著 RSI 以及 AGI 的發展，產業需求會發生什麼事？會不會進一步出現拐點、加速上升？當這些事情發生時，對 NVIDIA 意味著什麼？你如何把它們視為需求的催化劑？謝謝。

I appreciate that. It's going to inflect further. Today, the vast majority of AI is prompted by people. I believe that this last month, it has crossed. Most AI are now agentic. But in the future, every company will have a whole bunch of agents. We have 40,000 employees roughly. In the future, we'll have 400,000 agents, 4 million agents. And those agents are running continuously. They're running in the background.

我很感謝這個問題。它會進一步出現拐點。今天，絕大多數 AI 是由人來下提示（prompt）。我相信就在上個月，它已經跨過了一個門檻。現在大多數 AI 都是代理式（agentic）的。但未來，每家公司都會有一大堆代理。我們大約有 40,000 名員工。未來，我們會有 400,000 個代理、400 萬個代理。而這些代理會持續運行。它們會在背景中運行。

If you have anybody who builds -- if you know anybody who builds edge personal AI agents and they run it on their like a DGX Spark, and I know a lot of people who run it on DGX Stations, this incredible workstation that we've built, and you can buy it from Dell, and they're incredible. And these AI agents running on a DGX Station runs 24/7, because you've got stuff for them to do for it to do all the time.

如果你認識有人在打造——如果你認識有人在打造邊緣端的個人 AI 代理，並把它跑在像 DGX Spark 這樣的裝置上；我也認識很多人把它跑在 DGX Station 上，這是我們打造的一款令人驚豔的工作站，你可以從 Dell 購買，它們非常棒。而這些在 DGX Station 上運行的 AI 代理是 24/7 運作的，因為你總是有事情要它去做、讓它一直有任務可做。

And so the -- when the world goes to agentic, fully agentic systems, you're going to have agents running all the time, working with other agents running all the time. And those will be working in the background, improving your company, improving your lives. In a lot of ways, we're kind of recursive at this point.

因此——當世界走向代理式、完全代理式的系統時，你會有代理一直在運行，並與其他一直在運行的代理協作。而它們會在背景中工作，改善你的公司、改善你的生活。在很多方面，我們此刻某種程度上已經是遞迴的了。

And you could argue it's coarse-grained, but every time you run through an agent, it reflects on how it could do a better job next time and it updates the skill file. And so the skills document, the markdown is updated at the end of every single one of them. And so next time you run it, it's going to get better. It's kind of a loosely coarse-grained self-improvement. And so you see that all over always.

你可以說這是粗粒度（coarse-grained）的，但每次你跑完一個代理，它都會反思下次如何做得更好，並更新技能檔案。因此，每一次結束時，技能文件、markdown 都會被更新。所以下次你再跑它時，它就會變得更好。這有點像是一種鬆散、粗粒度的自我改進。因此你會到處、一直看到這種情況。

And so in a lot of ways, and for many tasks, we could say that we've already achieved AGI. I think all of those milestones and all those -- they're kind of senseless at this point. I think the most important thing that matters for the industry is that; one, AI is now doing productive and useful work; two, AI is generating profitable tokens; and three, if we had more compute, we could generate more profitable tokens, which results in more profit for all of the services.

因此在很多方面、以及對許多任務而言，我們可以說我們其實已經達成了 AGI。我認為所有那些里程碑以及那些——在這個時間點其實有點沒有意義。我認為對產業最重要的是：第一，AI 現在正在做具生產力且有用的工作；第二，AI 正在產生可獲利的 token；第三，如果我們有更多算力，我們就能產生更多可獲利的 token，進而讓所有服務獲得更多利潤。

This is the exact phase where we're at, which is the reason why everybody is leaning in.

這正是我們所處的階段，也正因如此大家才會全力投入。

Jim Schneider, Goldman Sachs.

Jim Schneider，高盛。

If you think about the 100% growth you talked about in terms of the plus unconstrained demand growth you're expecting, the 70% you expect to fulfill in terms of supply, can you maybe talk about some of the -- or rank order some of the most acute constraints, whether that be things like data center, power and shell availability, DRAM, wafer foundry availability, et cetera? If you could maybe help us understand which are the biggest among those, that would be very helpful. Thank you.

如果你把你提到的 100% 成長，連同你預期的額外不受約束的需求成長一起考量，以及你預期在供給端能滿足的 70%，你能否談談——或按重要性排序——一些最嚴峻的限制因素？例如資料中心、電力與機房殼體（shell）可用性、DRAM、晶圓代工產能可用性等等？如果你能幫我們理解其中哪些是最大的瓶頸，會非常有幫助。謝謝。

There's something funny I could say, but I'm going to just not. The -- last year, one of the funnest things to do is just to go figure out where I go for dinner and who I have dinner with, and their stock price doubles the next day. I think the answer is our entire supply chain is challenged, and everybody is really running flat out. And more capacity is coming online all the time, which is one of the advantages, what's going to happen this year. It's not going to come online in just an instance in time, but it's going to come online every day.

我可以講個好笑的，但我就先不講了。——去年最好玩的事情之一，就是去看看我去哪裡吃晚餐、跟誰吃晚餐，然後他們的股價隔天就翻倍。我想答案是：我們整個供應鏈都面臨挑戰，而大家真的都在全速運轉。而且更多產能一直在持續上線，這也是今年將會發生的其中一個優勢。它不會在某個瞬間一次性上線，但會每天都逐步上線。

Yields are going to get improved. We're going to be doing yield improvement. We're going to work hard on working with every one of our suppliers. And so we're just we have a -- it's not even next year yet, and so we've got lots and lots of time to work hard every day. And so at this moment, we have supply for 70%.

良率會提升。我們會做良率改善。我們會努力與每一位供應商合作。所以我們就是——現在甚至還沒到明年，因此我們還有非常非常多的時間每天努力。而在此刻，我們的供給能滿足 70%。

We have more supply than 70%, but about 70%. Our demand is much higher than that. And we've got to go work hard or we're going to be disappointing customers. And we like not to disappoint our customers, and we like to work hard for them and so I've got -- I'm going to need the help of the entire supply chain to help me out here.

我們的供給其實超過 70%，但大約是 70%。我們的需求遠高於此。我們必須努力，否則就會讓客戶失望。而我們不喜歡讓客戶失望，我們也喜歡為他們努力，所以我——我需要整個供應鏈的協助來幫我度過這一關。

But they all know that. What I'm telling you about our needs for next year is exactly consistent with what I've told them. Everybody is on the exact same song sheet. And I'm trying to be as transparent as we can because we're talking about big numbers.

但他們都知道。我跟你說的我們明年的需求，與我告訴他們的完全一致。大家都在同一份「歌譜」上。我也試著盡可能透明，因為我們談的是很大的數字。

Aaron Rakers, Wells Fargo.

Aaron Rakers，富國銀行。

Yeah, thanks for taking the question. I want to go back to the gigawatts, the 25 to 40. And maybe trying to understand like, I think, Jensen, you said at some recent conferences that that's going to further scale. So as we think about the path even beyond Vera Rubin, we think about Vera Rubin Ultra and so on and so forth. Should we really conceptualize like 40 billion goes to 60 billion, 80 billion. And then I guess underneath of that question is, how do we kind of think about your ability to scale the capacity deployments? Is it a linear function? Or is there something that kind of unlocks your ability to supply more demand as we look through fiscal '27 -- '28, sorry?

好的，謝謝讓我提問。我想回到你提到的 25 到 40 吉瓦（gigawatts）。我想試著理解，我記得 Jensen 你在最近一些會議上說過，這還會進一步擴大。所以當我們思考甚至超越 Vera Rubin 的路徑，例如 Vera Rubin Ultra 等等。我們是否應該把它概念化為：400 億會走到 600 億、800 億？然後我想這個問題背後是：我們該如何看待你們擴大部署產能的能力？它是線性函數嗎？或者在我們展望 2027 會計年度——2028，不好意思——時，有什麼因素能解鎖你們供應更多需求的能力？

Yes, great question, great question. And very simple. Is our goal to put as much compute on the plot of land? Is our goal to put more compute into 1 gigawatt or less? And so obviously, we would like -- the speed of light answer, the perfect answer is actually infinity per gigawatt. And so if we could literally get $1 trillion of compute into 1 gigawatt and 1 piece of land, power, shell, it would be a fantastic outcome. And so the answer is directionally in that direction.

是的，很好的問題，很好的問題。而且非常簡單。我們的目標是把盡可能多的算力放在同一塊土地上嗎？我們的目標是在 1 吉瓦或更少的電力下放入更多算力嗎？所以很顯然，我們希望——用「光速」來回答，完美答案其實是每吉瓦無限大。因此如果我們真的能把 1 兆美元的算力塞進 1 吉瓦、以及同一塊土地、電力、機房殼體（shell）裡，那會是非常棒的結果。所以答案在方向上就是朝那個方向前進。

We started in the world of general-purpose computing during Moore's Law. We were probably pick your favorite number, but I'm going to go with something like $5 billion, $3 billion per gigawatt of compute with general-purpose computing. And then eventually, with Hopper, it was 18. Now Grace Blackwell is 25. Next, Vera Rubin is 40. And after that, it's going to be higher. And that's excellent. That's fantastic for the industry.

我們起步於摩爾定律時代的一般用途運算世界。我們當時大概——你可以選你喜歡的數字，但我就用像是每吉瓦算力 50 億美元、30 億美元這樣的一般用途運算。然後最終到了 Hopper，是 18。現在 Grace Blackwell 是 25。下一代 Vera Rubin 是 40。而在那之後，還會更高。這很棒。這對產業來說非常好。

It's fantastic for customers so long as the productivity of it continues to grow, the durability and the fungibility continues to grow. Then people are happy to invest in assets that generates revenues, generates profits and helps them recoup their returns so incredibly fast. I heard the other day that return on invested capital is now less than a year. And we're talking about $50 billion data centers. And so that tells you something about the productivity of NVIDIA's technology and the rentability of it. I want to thank all of you for joining us today.

只要其生產力持續成長、耐久性與可替代性（fungibility）持續提升，這對客戶也非常好。那麼人們就樂於投資能帶來營收、產生利潤，並幫助他們極快回收報酬的資產。我前幾天聽說，投入資本報酬率（ROIC）的回收期現在已經不到一年。而我們談的是 500 億美元的資料中心。所以這能說明 NVIDIA 技術的生產力，以及其可出租獲利能力。感謝各位今天加入我們。

There are no further questions at this time. Toshiya Hari, I turn the call back over to you.

目前沒有進一步的問題。Toshiya Hari，我把電話交回給你。

Thank you. Before we close, please note that Jensen will be participating in a keynote fireside chat at the Goldman Sachs Communacopia & Technology Conference in San Francisco on September 10. He'll also be giving a keynote at GTC Berlin on October 21. Our earnings call to discuss the results of our third quarter of fiscal 2027 is scheduled for November 17. Thank you for joining us today. Operator, please close the call.

謝謝。在我們結束之前，請注意 Jensen 將於 9 月 10 日在舊金山舉行的高盛 Communacopia & Technology Conference 參與一場主題爐邊談話（keynote fireside chat）。他也將於 10 月 21 日在 GTC Berlin 發表主題演講。我們用於討論 2027 會計年度第三季業績結果的財報電話會議，預定於 11 月 17 日舉行。感謝各位今天加入我們。接線員，請結束本次電話會議。

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

今天的電話會議到此結束。您現在可以掛線。