<p>对于许多投资者来说，当讨论转向人工智能（AI）时，<strong>Nvidia</strong>（NVDA +0.64%）是第一个被提及的话题。这家公司开创了 图形处理单元（GPUs），通过引入并行计算彻底改变了游戏行业。这种开创性的方法将大型计算任务分配到多个处理器（或核心）上，显著减少了完成任务所需的时间。事实证明，并行处理在促进 AI 所需的大型数据集上同样有效。</p><p>Nvidia 借助 AI 革命达到了新的高度，成为全球最有价值的上市公司，市值达到 4.5 万亿美元。</p><p>管理层在展望未来，Nvidia 计划在 AI 的未来上进行重大投资。</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>图片来源：Getty Images。</p><h2 id="tods33d15">260 亿美元的赌注</h2><p>根据《连线》首次报道并随后由 Nvidia 高管确认的信息，Nvidia 计划在未来五年内投资 260 亿美元开发开源 AI 模型。通过进一步深入 AI 的软件领域，Nvidia 将更好地定位自己，以开发、测试和扩展与其行业领先的 AI 芯片相一致的下一代 AI 模型和系统。</p><p>开源模型的吸引力很简单：它们是免费的，任何想使用它们的人都可以获得，并为研究人员、数据科学家和初创公司提供了一个修改和构建符合自身需求的 AI 系统的起点。</p><p>美国许多最前沿的模型是专有的，普通公众无法访问。例如，OpenAI 的旗舰产品 GPT-5.4、<strong>Alphabet</strong> 的 Google Gemini 3.1 Pro 和 Anthropic 的 Claude Opus 4.6 都是需要付费订阅或云访问才能使用的高级模型。</p><p>结果是许多研究人员和数据科学家在开源模型的基础上构建他们的 AI 系统，而许多可自由获取的模型是在中国开发的。如果 Nvidia 能够开发自己的开源模型，它可以使这些模型与其硬件更好地同步，从而提供更统一的体验。</p><h2 id="h1pgwftg">敏锐的竞争优势</h2><p>除了公司的芯片实力外，Nvidia 还通过其计算统一设备架构（CUDA）编程平台和软件架构在其硬件周围建立了广泛的护城河。CUDA 是一套编程工具，帮助开发人员优化 GPU 性能。Nvidia 拥有超过 400 个库，帮助用户 “构建、优化、部署和扩展跨 PC、工作站、云和超级计算机的应用程序，使用 CUDA 平台。”</p><p>这些编程技巧帮助开发人员、数据科学家和其他用户利用 GPU 的计算能力，创造了持久的竞争优势，因为高转换成本使用户锁定在其生态系统中。</p><p>如果 Nvidia 提供自己的针对其 GPU 调优的开源 AI 模型，这一优势将成倍增加。</p><p>展开</p><figure class="image"><img src="https://pub.pbkrs.com/uploads/2026/a508b02705b6ae1c8581c6e935a4423a?x-oss-process=style/lg" alt="Nvidia 股票报价" width="80" height="80" original-src="https://pub.pbkrs.com/uploads/2026/a508b02705b6ae1c8581c6e935a4423a"/></figure><h2 id="cvu6mjlto">NASDAQ: NVDA</h2><p>Nvidia</p><p>今日变动</p><p>(0.64%) $1.18</p><p>当前价格</p><p>$185.94</p><h3 id="0nky0ggw">关键数据点</h3><p>市值</p><p>$4.5T</p><p>日内区间</p><p>$184.45 - $187.62</p><p>52 周区间</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">铺就未来成功之路</h2><p>到目前为止，Nvidia 主要依靠其芯片制造专业知识来推动其惊人的业绩，过去几年其业绩可谓令人瞩目。自 2023 年初 AI 革命开始以来，其收入增长超过 1000%，从 60 亿美元增长到 680 亿美元。同时，其净收入飙升 2940%，从 14 亿美元增长到 430 亿美元。这反过来又推动其股价上涨 1170%。</p><p>然而，随着竞争加剧和 AI 模型变得更加分散，这一策略可能有助于 Nvidia 在未来几年保持 AI 开发的前沿。</p><p>尽管近年来表现出色，Nvidia 的股价仍然具有吸引力。该股票目前的交易价格不到 38 倍的市盈率，相较于其五年平均市盈率 74 有显著折扣。这为精明的投资者提供了 以折扣价购买股票的机会，即使 Nvidia 正在铺就未来成功之路。</p>

英伟达

2 倍做多英伟达 ETF - Direxion

高科技指数 ETF - SPDR

<p>英伟达计划在未来五年内投资 260 亿美元，以开发开源人工智能模型，增强其在人工智能市场的地位。这项投资旨在创建免费且可访问的模型，使研究人员和初创公司能够构建量身定制的人工智能系统。英伟达的竞争优势在于其 CUDA 编程平台，该平台优化了 GPU 性能。自 2023 年初以来，该公司的收入激增超过 1000%，其股票的市盈率仍低于 38 倍，给投资者提供了买入机会</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>对于许多投资者来说，当讨论转向人工智能（AI）时，<strong>Nvidia</strong>（NVDA +0.64%）是第一个被提及的话题。这家公司开创了 图形处理单元（GPUs），通过引入并行计算彻底改变了游戏行业。这种开创性的方法将大型计算任务分配到多个处理器（或核心）上，显著减少了完成任务所需的时间。事实证明，并行处理在促进 AI 所需的大型数据集上同样有效。</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 借助 AI 革命达到了新的高度，成为全球最有价值的上市公司，市值达到 4.5 万亿美元。</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>管理层在展望未来，Nvidia 计划在 AI 的未来上进行重大投资。</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>图片来源：Getty Images。</p>
</div><h2>A $26 billion bet</h2>
<div class="lb-trans"><h2>260 亿美元的赌注</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>根据《连线》首次报道并随后由 Nvidia 高管确认的信息，Nvidia 计划在未来五年内投资 260 亿美元开发开源 AI 模型。通过进一步深入 AI 的软件领域，Nvidia 将更好地定位自己，以开发、测试和扩展与其行业领先的 AI 芯片相一致的下一代 AI 模型和系统。</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>开源模型的吸引力很简单：它们是免费的，任何想使用它们的人都可以获得，并为研究人员、数据科学家和初创公司提供了一个修改和构建符合自身需求的 AI 系统的起点。</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>美国许多最前沿的模型是专有的，普通公众无法访问。例如，OpenAI 的旗舰产品 GPT-5.4、<strong>Alphabet</strong> 的 Google Gemini 3.1 Pro 和 Anthropic 的 Claude Opus 4.6 都是需要付费订阅或云访问才能使用的高级模型。</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>结果是许多研究人员和数据科学家在开源模型的基础上构建他们的 AI 系统，而许多可自由获取的模型是在中国开发的。如果 Nvidia 能够开发自己的开源模型，它可以使这些模型与其硬件更好地同步，从而提供更统一的体验。</p>
</div><h2>Keen competitive advantage</h2>
<div class="lb-trans"><h2>敏锐的竞争优势</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>除了公司的芯片实力外，Nvidia 还通过其计算统一设备架构（CUDA）编程平台和软件架构在其硬件周围建立了广泛的护城河。CUDA 是一套编程工具，帮助开发人员优化 GPU 性能。Nvidia 拥有超过 400 个库，帮助用户 “构建、优化、部署和扩展跨 PC、工作站、云和超级计算机的应用程序，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>这些编程技巧帮助开发人员、数据科学家和其他用户利用 GPU 的计算能力，创造了持久的竞争优势，因为高转换成本使用户锁定在其生态系统中。</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 提供自己的针对其 GPU 调优的开源 AI 模型，这一优势将成倍增加。</p>
</div><p>Expand</p>
<div class="lb-trans"><p>展开</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>今日变动</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>当前价格</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>关键数据点</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>日内区间</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 周区间</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>铺就未来成功之路</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>到目前为止，Nvidia 主要依靠其芯片制造专业知识来推动其惊人的业绩，过去几年其业绩可谓令人瞩目。自 2023 年初 AI 革命开始以来，其收入增长超过 1000%，从 60 亿美元增长到 680 亿美元。同时，其净收入飙升 2940%，从 14 亿美元增长到 430 亿美元。这反过来又推动其股价上涨 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，随着竞争加剧和 AI 模型变得更加分散，这一策略可能有助于 Nvidia 在未来几年保持 AI 开发的前沿。</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>尽管近年来表现出色，Nvidia 的股价仍然具有吸引力。该股票目前的交易价格不到 38 倍的市盈率，相较于其五年平均市盈率 74 有显著折扣。这为精明的投资者提供了 以折扣价购买股票的机会，即使 Nvidia 正在铺就未来成功之路。</p>
</div>

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

- Nvidia 计划在未来五年投资 260 亿美元开发开源 AI 模型。  
- 该公司的市值达到 4.5 万亿美元，年收入增长超过 1000%。  
- 开源模型将增强其硬件和开发平台的竞争优势。

英伟达正在对人工智能（AI）的未来下注 260 亿美元