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  在人工智能的驱动下，2025年数据中心的建设方案将会由密集光纤系统向创新型冷却系统过渡，这些方案是值得期待的。AI-driven transformation is reshaping data center builds for 2025 – from dense fiber systems to innovative cooling solutions. Here’s what to expect. 

为满足人工能智能领域日益增长的需求，数据中心正在基础设施，电力管理制度以及冷却技术的创新方面不断发展。Data centers are evolving to meet the growing demands of AI, with new innovations in infrastructure, power management, and cooling technologies 

令人惊讶的是，去年我们注意到数据中心对人工智能计算的需求呈指数级增长。为了应对成熟IT人才持续性短缺的问题，将迫使要以更高效的运营流程、更快捷的建设流程和更具创新的方案解决问题。What a difference a year makes. Last year, we noted that the exponential growth in demand for AI compute in data centers would force more efficient processes, faster builds, and more creative problem-solving to address the persistent shortage of top IT talent.
     事实上，这些证明无疑比所有人先前的预期都要真实可信。This has certainly proven to be true – in fact, truer than anyone really expected. 

2024年5月，高盛（Goldman Sachs）发布了一份展望报告，根据报告所述，现如今，人工智能的应用预计将使得数据中心的电力需求猛增160%，这表明在资源竞争加剧今天，对于这种增长，建立规范的管理迫在眉睫。According to a May 2024 outlook published by Goldman Sachs, AI implementations are now expected to force up to a 160% spike in data center power demand, demonstrating the increased urgency in managing this growth as the race for resources heats up.

 国际能源署（IEA）估计，2022 年全球数据中心消耗了460 TWh的电力（PDF），约占总发电量的2%，预计到2026年这一数字将翻一倍。究其原因，人工智能所需的计算能力要远远高于其他形式的处理能力，因此 GPU 的计算需要不断满足增长的需求。The IEA estimated that globally, data centers consumed 460 TWh of electricity in 2022 (PDF), consuming about 2% of all generated power – and that number is expected to double by 2026. The reasons are clear; AI implementations require much greater compute power than other forms of processing, as power-hungry GPUs labor to meet growing demand. 

2024 年，对于提出更高效策略的需求显而易见。在 2025 年，我们将会看到这些策略付诸实践。目前已经有一些重大措施计划提上日程，数据中心的建设方式也将随之改变，从而推动云计算迈向新的高度。In 2024, the need for more efficient strategies became clear. In 2025, we will see those strategies put into practice. Already there are some big moves and bold plans on the table, changes in data center builds that will power cloud compute to the next level.

 人工智能驱动者——大规模计算转向小型化The AI Drivers – Big Compute Goes Small

人工智能在个人生活和职场方方面面的应用传播令人惊叹。我只能把它与万维网（World Wide Web）的早期阶段进行比较，我们在20世纪90年代末首次接触全球互联网。起初，我们对于互联网只是一种好奇，时而追捧，时而又摒弃，但在短时间内它就成为现代生活不可或缺的一部分。The spread of AI’s applications into every facet of personal and professional life has been breathtaking. I could only compare it to the earliest days of the World Wide Web, our first introduction to the global internet in the late 1990s. At first a curiosity, alternately hyped and dismissed, the internet became integral to modern life in record time. 

据说电话在发明50年后才成为普通的家庭设备。互联网的发展大约用了20年的时间。现如今，人工智能有望在更短的时间内实现同样的效果，因为它能够快速在企业中找到新的应用方向，而其中绝大部分的应用将得到数据中心的支持。It’s said that the telephone only became a common household fixture 50 years after its invention. The internet took about 20 years. Now, AI looks poised to do the same in a fraction of that time, as it quickly finds new applications in the enterprise space, and the vast majority of this will be supported by data centers. 

人工智能在创新企业的应用数量呈指数级增长——我们对人工智能在商业、科学以及社会方面产生的影响还知之甚少。具有讽刺意味的是，几十年来最大的创新成果正以越来越小的影响在企业中发挥作用。The number of inventive enterprise uses for AI is going parabolic – we have barely scratched the surface of AI’s impact on commerce, science, and society itself. Ironically, the biggest innovation in decades is making its influence felt in ever-increasingly small ways through the enterprise space. 数据中心建设正在蓬勃发展Data Center Construction is Booming
随着人工智能计算领域的淘金热愈演愈烈，科技巨头正以前所未有的速度进行扩张，不断将他们过去十年的资本支出平均值推高。The biggest names in tech are building like never before, bending their 10-year CapEx averages ever higher as the gold rush-like race to AI compute gains steam. 

不仅人工智能技术在不断发展，且交付模式也在不断发展。“AI 作为一种服务”正在为企业采用人工智能铺平道路，特别是可以承担客户服务到长期财务规划等多种角色的扮演。It’s not only the technology of AI that’s evolving, but also the delivery model. AI-as-a-Service is paving a smooth road for enterprise adoption of AI capabilities, particularly generative AI that can fill multiple roles from customer service to long-term financial planning.
    事实上，数据中心也在越来越多地利用生成式人工智能（GenAI）来解决缺乏成熟IT 员工的问题，通过使用AI来监控、管理和支持团队，从而提高团队的生产力。通过一种直观的方式来提问并获取建议，使得一个技术能力稍逊的 IT 团队也能发挥超出自身水平的作用，从而减轻数据中心所面临的部分压力。Indeed, data centers themselves are increasingly making use of GenAI to address the persistent lack of skilled IT employees by using AI to monitor, manage, and support lean IT teams so they can be more productive. With an intuitive way to ask questions and receive recommendations, a less-advanced IT team can punch above its weight and relieve some of the labor stresses data centers face. 

有了这些扩建工程之后，如何获取充足且可靠的电力仍是一个难题。最近Bain & Company在Utility Dive的一份报告中表示，数据中心在全球发电量中所占的比例越来越大，在未来，这一趋势将持续下去，到2028年，数据中心所产生的电力增量需求占比将高达44%。在多数地区，过剩能源的稀缺性正促使新的数据中心选址迁往新兴地儿、有时甚至是意想不到的位置，以确保靠近价格合理的发电源，或者租赁专用电网电力以保障供应。With these buildouts, reliable access to sufficient power remains a challenge. Data centers draw a growing percentage of generated power worldwide and the trend will continue for the foreseeable future, accounting for as much as 44% of increased electrical demand through 2028, according to Bain & Company, shared in a recent report from Utility Dive. The scarcity of excess energy supply in most areas is driving new data center builds to new and sometimes unexpected locations to secure proximity to affordable power generation sources or leasing dedicated grid power to ensure supply. 

大量阅读关于最新人工智能数据中心的新闻报道Read more of the latest AI data center news
近期，我们看到数据中心纷纷采用核能发电来支撑自身的发展。2025 年以后，我们预计会看到更多的此类现象。And we’ve all seen the stories of data centers’ recent embrace of dedicated nuclear power generation to support their growth. We expect to see even more of this in 2025 and beyond. 

选择核能是符合逻辑的：与化石燃料驱动的能源相比，核能是稳定的、可扩展的、相对可持续的。与此同时，数据中心正在通过部署水冷系统取代效率较低的强制风冷系统，尽其所能降低能耗–既考虑到经济效益，也考虑到环境责任。The choice of nuclear is logical: the source is stable, scalable, and relatively sustainable compared to fossil fuel-driven sources. At the same time, data centers are doing what they can to reduce energy consumption – both as a matter of economics and environmental responsibility – by deploying water cooling systems in place of less efficient forced air cooling. 

随着基于 GPU 的人工智能计算规模的扩大，效率优势将会愈发明显，网络运行时间的增加所带来的益处越发明显，因为过热是导致停电和组件过早失效的主要原因。As the scale of GPU-powered AI compute rises, these efficiencies will become more apparent, as will the benefits of increased network uptime, as excessive heat is a prime culprit in outages and premature component failure. 

缩减基础设施规模Shrinking the Profile of Infrastructure
鉴于对电力和冷却的需求，数据中心的光纤基础设施在人工智能计算设施中的布局越来越密集。在人工智能阵列中，AI阵列中的GPU必须完全联网——每个GPU必须能够与其他GPU通信——这会使复杂性增加一个数量级，并使冷却变得困难。为了克服所需光纤基础设施的庞大体量，数据中心将采用高度密集型的光纤系统来实现连接，将更多的光纤和连接器整合到现有的空间内，为其人工智能网络提供动力。Related to both power and cooling needs, the data center’s fiber infrastructure continues to become denser in AI compute facilities. GPUs in AI arrays must be fully networked – every GPU must be able to talk to every other GPU – which increases complexity by an order of magnitude and complicates cooling. To overcome the bulk of the required fiber infrastructure, data centers will use highly dense fiber systems to make those countless connections, packing more fibers and connectors into the existing footprint to power their AI networks. 

通过将更多的计算资源集中到更少的机架中，数据中心能够降低能耗并简化冷却系统。此外，随着超大规模数据中心从 2x400G（总计800G）迁移至原生 800G，这种先进的光纤基础设施将为应对未来可能出现的需求提供一些急需的路径容量。By forcing more compute resources into fewer racks, data centers can reduce energy use and simplify cooling needs as well. Plus, as hyperscale data centers migrate from 2x400G (aggregate 800G) to native 800G, this advanced fiber infrastructure will provide some much-needed pathway capacity to accommodate the demand yet to come. 

多架构数据中心——标准化与灵活性Multi-Tenant Data Centers – Standardization and Flexibility
我耗费大量时间研究超大规模的数据中心以及它们所采用的基于授权的 AI 服务模式，这些模式与企业相关联。但在 2025 年，还有另一个重要的业务需要加以考虑，那就是多架构数据中心（MTDC）将如何为企业客户开辟新的发展道路。无论企业处于何种发展阶段，其需求都在快速变化，因此 MTDC（多维决策中心）必须保持灵活性以适应其需求。I’ve spent a lot of time looking at the largest hyperscale data centers and their licensed AI as a service model as they relate to enterprise. But there’s another important side of the business to consider in 2025, and that’s how multi-tenant data centers (MTDCs) will forge a way forward for their enterprise customers. Whatever their vertical, enterprise needs are changing fast and MTDCs must remain flexible to accommodate their needs. 

同样，在更密集型光纤基础设施建设方面，采用标准化方法至关重要，因为它能降低 IT 人员的工作量，并简化配置变更流程。一些顶级光纤基础设施制造商正在推出或改进更简单、即插即用的技术，以帮助所有数据中心（尤其是混合型数据中心），使其具备更强的灵活性和响应能力，在精简 IT 团队的情况下仍能维持服务水平协议（SLA）。这些技术旨在帮助数据中心降低所需技术的困难程度，从而使其能够尽可能地敏捷和迅速地响应需求。Here, too, a standardized approach to denser fiber infrastructure is key because it reduces IT staff demands and simplifies configuration changes. Several top manufacturers of fiber infrastructure are in the process of launching or improving simpler, more plug-and-play technologies to help all data centers, but particularly MTDCs, to flatten the required skill curve required to be as agile and responsive as possible, maintaining SLAs even with leaner IT teams. 

2025年将会比2024更好2025 will be 2024 – Only More So
在人工智能时代的黎明，数据中心所发生的根本性变革将会是极其显著的。从规模范围来看，无论是超大规模数据中心（hyperscale）还是多架构数据中心（MTDC），都需要提升其光纤传输能力，同时缩小光纤的物理尺寸，采用新的冷却技术，并重新审视其电力采购和使用方式。不幸的是，目前顶尖的 IT 人才短缺似乎并无缓解，但人工智能本身已经在展示其能够帮助运营商填补这些人才缺失的不足，即借助生成式人工智能进行监测和管理。The fundamental changes coming to data centers in this dawn of the AI age will be truly remarkable. From location to scale, hyperscale and MTDCs alike will need to scale up their fiber capabilities while scaling down their fiber’s physical profile, adopt new cooling technologies, and take a fresh look at how they buy and use electrical power. Unfortunately, there is no end in sight to the ongoing shortage of top-skilled IT expertise, but AI itself is already demonstrating ways that it can help operators fill those gaps with GenAI-powered monitoring and management. 

随着人工智能在企业的应用不断深入，数据中心被要求将大规模的计算能力，转化为切实可行的商业效益。与人工智能一样，数据中心也将不断创新并做出调整以应对不断变化的需求，并提供行业快速发展所需要的最优方案。As AI continues to make inroads in the enterprise space, data centers will be called upon to supply the massive compute required to turn promises into practical business benefits. Like AI, data centers will innovate and adapt to meet changing needs and deliver the optimal solutions that this fast-growing industry needs.

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