<p>對於許多投資者來説，當討論轉向人工智能（AI）時，<strong>Nvidia</strong>（NVDA +0.64%）是第一個被提及的話題。這家公司開創了 圖形處理單元（GPUs），通過引入並行計算徹底改變了遊戲行業。這種開創性的方法將大型計算任務分配到多個處理器（或核心）上，顯著減少了完成任務所需的時間。事實證明，並行處理在促進 AI 所需的大型數據集上同樣有效。</p><p>Nvidia 藉助 AI 革命達到了新的高度，成為全球最有價值的上市公司，市值達到 4.5 萬億美元。</p><p>管理層在展望未來，Nvidia 計劃在 AI 的未來上進行重大投資。</p><figure class="image"><img src="https://pub.pbkrs.com/uploads/2026/bbc1272b69d053b7725fa3a3ed181e0e?x-oss-process=style/lg" width="2000" height="1200" original-src="https://pub.pbkrs.com/uploads/2026/bbc1272b69d053b7725fa3a3ed181e0e"/></figure><p>圖片來源：Getty Images。</p><h2 id="tods33d15">260 億美元的賭注</h2><p>根據《連線》首次報道並隨後由 Nvidia 高管確認的信息，Nvidia 計劃在未來五年內投資 260 億美元開發開源 AI 模型。通過進一步深入 AI 的軟件領域，Nvidia 將更好地定位自己，以開發、測試和擴展與其行業領先的 AI 芯片相一致的下一代 AI 模型和系統。</p><p>開源模型的吸引力很簡單：它們是免費的，任何想使用它們的人都可以獲得，併為研究人員、數據科學家和初創公司提供了一個修改和構建符合自身需求的 AI 系統的起點。</p><p>美國許多最前沿的模型是專有的，普通公眾無法訪問。例如，OpenAI 的旗艦產品 GPT-5.4、<strong>Alphabet</strong> 的 Google Gemini 3.1 Pro 和 Anthropic 的 Claude Opus 4.6 都是需要付費訂閲或雲訪問才能使用的高級模型。</p><p>結果是許多研究人員和數據科學家在開源模型的基礎上構建他們的 AI 系統，而許多可自由獲取的模型是在中國開發的。如果 Nvidia 能夠開發自己的開源模型，它可以使這些模型與其硬件更好地同步，從而提供更統一的體驗。</p><h2 id="h1pgwftg">敏鋭的競爭優勢</h2><p>除了公司的芯片實力外，Nvidia 還通過其計算統一設備架構（CUDA）編程平台和軟件架構在其硬件周圍建立了廣泛的護城河。CUDA 是一套編程工具，幫助開發人員優化 GPU 性能。Nvidia 擁有超過 400 個庫，幫助用户 “構建、優化、部署和擴展跨 PC、工作站、雲和超級計算機的應用程序，使用 CUDA 平台。”</p><p>這些編程技巧幫助開發人員、數據科學家和其他用户利用 GPU 的計算能力，創造了持久的競爭優勢，因為高轉換成本使用户鎖定在其生態系統中。</p><p>如果 Nvidia 提供自己的針對其 GPU 調優的開源 AI 模型，這一優勢將成倍增加。</p><p>展開</p><figure class="image"><img src="https://pub.pbkrs.com/uploads/2026/a508b02705b6ae1c8581c6e935a4423a?x-oss-process=style/lg" alt="Nvidia 股票報價" width="80" height="80" original-src="https://pub.pbkrs.com/uploads/2026/a508b02705b6ae1c8581c6e935a4423a"/></figure><h2 id="cvu6mjlto">NASDAQ: NVDA</h2><p>Nvidia</p><p>今日變動</p><p>(0.64%) $1.18</p><p>當前價格</p><p>$185.94</p><h3 id="0nky0ggw">關鍵數據點</h3><p>市值</p><p>$4.5T</p><p>日內區間</p><p>$184.45 - $187.62</p><p>52 周區間</p><p>$86.62 - $212.19</p><p>成交量</p><p>4.7M</p><p>平均成交量</p><p>176M</p><p>毛利率</p><p>71.07%</p><p>股息收益率</p><p>0.03%</p><h2 id="up8oxt3jv">鋪就未來成功之路</h2><p>到目前為止，Nvidia 主要依靠其芯片製造專業知識來推動其驚人的業績，過去幾年其業績可謂令人矚目。自 2023 年初 AI 革命開始以來，其收入增長超過 1000%，從 60 億美元增長到 680 億美元。同時，其淨收入飆升 2940%，從 14 億美元增長到 430 億美元。這反過來又推動其股價上漲 1170%。</p><p>然而，隨着競爭加劇和 AI 模型變得更加分散，這一策略可能有助於 Nvidia 在未來幾年保持 AI 開發的前沿。</p><p>儘管近年來表現出色，Nvidia 的股價仍然具有吸引力。該股票目前的交易價格不到 38 倍的市盈率，相較於其五年平均市盈率 74 有顯著折扣。這為精明的投資者提供了 以折扣價購買股票的機會，即使 Nvidia 正在鋪就未來成功之路。</p>

英偉達

2 倍做多英偉達 ETF - Direxion

高科技指數 ETF - SPDR

<p>英偉達計劃在未來五年內投資 260 億美元，以開發開源人工智能模型，增強其在人工智能市場的地位。這項投資旨在創建免費且可訪問的模型，使研究人員和初創公司能夠構建量身定製的人工智能系統。英偉達的競爭優勢在於其 CUDA 編程平台，該平台優化了 GPU 性能。自 2023 年初以來，該公司的收入激增超過 1000%，其股票的市盈率仍低於 38 倍，給投資者提供了買入機會</p>

<p>For many investors, when the discussion turns to artificial intelligence (AI), <strong>Nvidia</strong>( NVDA +0.64%) is the first topic of conversation. The company pioneered the graphics processing units (GPUs) that revolutionized gaming with the introduction of parallel computing. This groundbreaking approach divided large computational tasks across multiple processors (or cores), dramatically reducing the time needed to complete the task. It turned out that parallel processing worked equally well on the large datasets necessary to facilitate AI.</p>
<div class="lb-trans"><p>對於許多投資者來説，當討論轉向人工智能（AI）時，<strong>Nvidia</strong>（NVDA +0.64%）是第一個被提及的話題。這家公司開創了 圖形處理單元（GPUs），通過引入並行計算徹底改變了遊戲行業。這種開創性的方法將大型計算任務分配到多個處理器（或核心）上，顯著減少了完成任務所需的時間。事實證明，並行處理在促進 AI 所需的大型數據集上同樣有效。</p>
</div><p>Nvidia has ridden the AI revolution to new heights, helping it become the world's most valuable publicly traded company, with a market cap of $4.5 trillion.</p>
<div class="lb-trans"><p>Nvidia 藉助 AI 革命達到了新的高度，成為全球最有價值的上市公司，市值達到 4.5 萬億美元。</p>
</div><p>Management is looking ahead, and Nvidia is planning a big bet on the future of AI.</p>
<div class="lb-trans"><p>管理層在展望未來，Nvidia 計劃在 AI 的未來上進行重大投資。</p>
</div><p><img src="https://imageproxy.pbkrs.com/https://g.foolcdn.com/image//query-b3A9cmVzaXplJnVybD1odHRwczovL2cuZm9vbGNkbi5jb20vZWRpdG9yaWFsL2ltYWdlcy84NjA0MTUvZXh0ZXJpb3Itb2YtbnZpZGlhLWhlYWRxdWFydGVycy13aXRoLWJsYWNrLXNpZ24tc2hvd2luZy1udmlkaWEtbG9nby5qcGcmdz0zODQw" alt="Exterior of Nvidia headquarters with black sign showing Nvidia logo." original-src="https://g.foolcdn.com/image/?url=https%3A%2F%2Fg.foolcdn.com%2Feditorial%2Fimages%2F860415%2Fexterior-of-nvidia-headquarters-with-black-sign-showing-nvidia-logo.jpg&amp;w=3840&amp;op=resize"/></p>
<p>Image source: Getty Images.</p>
<div class="lb-trans"><p>圖片來源：Getty Images。</p>
</div><h2>A $26 billion bet</h2>
<div class="lb-trans"><h2>260 億美元的賭注</h2>
</div><p>Over the next five years, Nvidia plans to invest $26 billion to develop open-source AI models, according to a report that first appeared in Wired and later confirmed by Nvidia executives. By delving further into the software side of AI, Nvidia would be better positioned to develop, test, and scale next-generation AI models and systems aligned with its industry-leading AI chips.</p>
<div class="lb-trans"><p>根據《連線》首次報道並隨後由 Nvidia 高管確認的信息，Nvidia 計劃在未來五年內投資 260 億美元開發開源 AI 模型。通過進一步深入 AI 的軟件領域，Nvidia 將更好地定位自己，以開發、測試和擴展與其行業領先的 AI 芯片相一致的下一代 AI 模型和系統。</p>
</div><p>The attraction of open-source models is simple: they are free and available to anyone who wants to use them, and give researchers, data scientists, and start-ups a jumping-off point to modify and build AI systems tailored to their own needs.</p>
<div class="lb-trans"><p>開源模型的吸引力很簡單：它們是免費的，任何想使用它們的人都可以獲得，併為研究人員、數據科學家和初創公司提供了一個修改和構建符合自身需求的 AI 系統的起點。</p>
</div><p>Many of the foremost models in the U.S. are proprietary and not accessible to the general public. For example, OpenAI's flagship GPT-5.4, <strong>Alphabet</strong>'s Google Gemini 3.1 Pro, and Anthropic's Claude Opus 4.6 are premium models that generally require a paid subscription or cloud access to use.</p>
<div class="lb-trans"><p>美國許多最前沿的模型是專有的，普通公眾無法訪問。例如，OpenAI 的旗艦產品 GPT-5.4、<strong>Alphabet</strong> 的 Google Gemini 3.1 Pro 和 Anthropic 的 Claude Opus 4.6 都是需要付費訂閲或雲訪問才能使用的高級模型。</p>
</div><p>The result is that many researchers and data scientists are building their AI systems on top of open-source models, and many of the freely available models were developed in China. If Nvidia were to develop its own open-source models, it could sync them to perform better with its hardware, providing a more unified experience.</p>
<div class="lb-trans"><p>結果是許多研究人員和數據科學家在開源模型的基礎上構建他們的 AI 系統，而許多可自由獲取的模型是在中國開發的。如果 Nvidia 能夠開發自己的開源模型，它可以使這些模型與其硬件更好地同步，從而提供更統一的體驗。</p>
</div><h2>Keen competitive advantage</h2>
<div class="lb-trans"><h2>敏鋭的競爭優勢</h2>
</div><p>Beyond the company's chip prowess, Nvidia has built a wide moat around its hardware through its Compute Unified Device Architecture (CUDA) programming platform and software architecture. CUDA is a suite of programming tools that helps developers optimize GPU performance. Nvidia boasts more than 400 libraries that help users &#34;build, optimize, deploy, and scale applications across PCs, workstations, the cloud, and supercomputers using the CUDA platform.&#34;</p>
<div class="lb-trans"><p>除了公司的芯片實力外，Nvidia 還通過其計算統一設備架構（CUDA）編程平台和軟件架構在其硬件周圍建立了廣泛的護城河。CUDA 是一套編程工具，幫助開發人員優化 GPU 性能。Nvidia 擁有超過 400 個庫，幫助用户 “構建、優化、部署和擴展跨 PC、工作站、雲和超級計算機的應用程序，使用 CUDA 平台。”</p>
</div><p>These programming hacks help developers, data scientists, and other users harness the computational horsepower of the GPU, creating a durable competitive advantage as high switching costs keep users locked into its ecosystem.</p>
<div class="lb-trans"><p>這些編程技巧幫助開發人員、數據科學家和其他用户利用 GPU 的計算能力，創造了持久的競爭優勢，因為高轉換成本使用户鎖定在其生態系統中。</p>
</div><p>If Nvidia offered its own open-source AI models tuned to its GPUs, this advantage would be increased by an order of magnitude.</p>
<div class="lb-trans"><p>如果 Nvidia 提供自己的針對其 GPU 調優的開源 AI 模型，這一優勢將成倍增加。</p>
</div><p>Expand</p>
<div class="lb-trans"><p>展開</p>
</div><p><img src="https://imageproxy.pbkrs.com/https://g.foolcdn.com/image//query-b3A9cmVzaXplJnVybD1odHRwczovL2cuZm9vbGNkbi5jb20vYXJ0L2NvbXBhbnlsb2dvcy9tYXJrL05WREEucG5nJnc9MTI4" alt="Nvidia Stock Quote" original-src="https://g.foolcdn.com/image/?url=https%3A%2F%2Fg.foolcdn.com%2Fart%2Fcompanylogos%2Fmark%2FNVDA.png&amp;w=128&amp;op=resize"/></p>
<h2>NASDAQ: NVDA</h2>
<div class="lb-trans"><h2>NASDAQ: NVDA</h2>
</div><p>Nvidia</p>
<div class="lb-trans"><p>Nvidia</p>
</div><p>Today's Change</p>
<div class="lb-trans"><p>今日變動</p>
</div><p>(0.64%) $1.18</p>
<div class="lb-trans"><p>(0.64%) $1.18</p>
</div><p>Current Price</p>
<div class="lb-trans"><p>當前價格</p>
</div><p>$185.94</p>
<div class="lb-trans"><p>$185.94</p>
</div><h3>Key Data Points</h3>
<div class="lb-trans"><h3>關鍵數據點</h3>
</div><p>Market Cap</p>
<div class="lb-trans"><p>市值</p>
</div><p>$4.5T</p>
<div class="lb-trans"><p>$4.5T</p>
</div><p>Day's Range</p>
<div class="lb-trans"><p>日內區間</p>
</div><p>$184.45 - $187.62</p>
<div class="lb-trans"><p>$184.45 - $187.62</p>
</div><p>52wk Range</p>
<div class="lb-trans"><p>52 周區間</p>
</div><p>$86.62 - $212.19</p>
<div class="lb-trans"><p>$86.62 - $212.19</p>
</div><p>Volume</p>
<div class="lb-trans"><p>成交量</p>
</div><p>4.7M</p>
<div class="lb-trans"><p>4.7M</p>
</div><p>Avg Vol</p>
<div class="lb-trans"><p>平均成交量</p>
</div><p>176M</p>
<div class="lb-trans"><p>176M</p>
</div><p>Gross Margin</p>
<div class="lb-trans"><p>毛利率</p>
</div><p>71.07%</p>
<div class="lb-trans"><p>71.07%</p>
</div><p>Dividend Yield</p>
<div class="lb-trans"><p>股息收益率</p>
</div><p>0.03%</p>
<div class="lb-trans"><p>0.03%</p>
</div><h2>Paving the road to future success</h2>
<div class="lb-trans"><h2>鋪就未來成功之路</h2>
</div><p>Until now, Nvidia has relied primarily on its chipmaking expertise to power its blistering results, which have been nothing short of astonishing over the past few years. Since the dawn of the AI revolution in early 2023, its revenue has grown more than 1,000%, from $6 billion to $68 billion. At the same time, its net income has soared 2,940%, from $1.4 billion to $43 billion. This, in turn, has pushed its stock price up 1,170%.</p>
<div class="lb-trans"><p>到目前為止，Nvidia 主要依靠其芯片製造專業知識來推動其驚人的業績，過去幾年其業績可謂令人矚目。自 2023 年初 AI 革命開始以來，其收入增長超過 1000%，從 60 億美元增長到 680 億美元。同時，其淨收入飆升 2940%，從 14 億美元增長到 430 億美元。這反過來又推動其股價上漲 1170%。</p>
</div><p>However, as competition heats up and AI models become more fragmented, this strategy could help keep Nvidia at the cutting edge of AI development for years to come.</p>
<div class="lb-trans"><p>然而，隨着競爭加劇和 AI 模型變得更加分散，這一策略可能有助於 Nvidia 在未來幾年保持 AI 開發的前沿。</p>
</div><p>Despite its impressive run in recent years, Nvidia is still attractively priced. The stock is currently trading for less than 38 times earnings, a significant markdown compared to its five-year average multiple of 74. This gives astute investors the opportunity to pick up shares at a discount, even as Nvidia paves the road to future success.</p>
<div class="lb-trans"><p>儘管近年來表現出色，Nvidia 的股價仍然具有吸引力。該股票目前的交易價格不到 38 倍的市盈率，相較於其五年平均市盈率 74 有顯著折扣。這為精明的投資者提供了 以折扣價購買股票的機會，即使 Nvidia 正在鋪就未來成功之路。</p>
</div>

Nvidia Is Making a Massive $26 Billion Bet on the Future of Artificial Intelligence (AI)

- Nvidia 計劃在未來五年投資 260 億美元開發開源 AI 模型。  
- 該公司的市值達到 4.5 萬億美元，年收入增長超過 1000%。  
- 開源模型將增強其硬件和開發平台的競爭優勢。

英偉達正在對人工智能（AI）的未來下注 260 億美元