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市场调查报告书
商品编码
1799103
全球人工智慧(AI)伺服器市场Artificial Intelligence (AI) Servers |
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到 2030 年,全球人工智慧 (AI) 伺服器市场规模将达到 846 亿美元
全球人工智慧 (AI) 伺服器市场规模预计在 2024 年达到 580 亿美元,预计 2024 年至 2030 年期间的复合年增长率为 6.5%,到 2030 年将达到 846 亿美元。本报告分析的细分市场之一——AI 训练伺服器,预计其复合年增长率将达到 7.3%,到分析期结束时规模将达到 613 亿美元。 AI 推理伺服器细分市场在分析期间的复合年增长率预计为 4.6%。
美国市场规模估计为 153 亿美元,中国市场预计复合年增长率为 6.2%
美国人工智慧 (AI) 伺服器市场规模预计在 2024 年达到 153 亿美元。作为世界第二大经济体,中国预计到 2030 年市场规模将达到 135 亿美元,在 2024-2030 年的分析期内,复合年增长率为 6.2%。其他值得关注的区域市场包括日本和加拿大,预计在分析期间内,这两个市场的复合年增长率分别为 5.9% 和 5.5%。在欧洲,预计德国市场的复合年增长率为 5.1%。
全球人工智慧 (AI) 伺服器市场 - 主要趋势和驱动因素摘要
为什么人工智慧伺服器是数据驱动世界中智慧运算的支柱
人工智慧伺服器已成为智慧运算时代的基础设施,提供应对资料爆炸性成长和日益复杂的人工智慧工作负载所需的处理能力。随着人工智慧在医疗、金融、製造、汽车和网路安全等行业的应用不断扩展,对能够支援深度学习、机器学习和神经网路训练的高效能伺服器的需求也日益迫切。与传统伺服器不同,人工智慧伺服器采用先进的架构构建,整合了 GPU、TPU、高速记忆体和互连,旨在加速并行处理任务。这些伺服器支援大规模语言模型、电脑视觉系统和预测分析引擎的即时推理和高吞吐量训练。利用人工智慧进行诈欺检测、个人化医疗、语音辨识和自主系统等任务的公司依赖这些专用伺服器的强大运算能力。此外,巨量资料和边缘人工智慧应用的兴起正在推动对分散式人工智慧基础设施的需求,在这些基础设施中,资料不仅必须在集中式资料中心处理,还必须在边缘位置处理。人工智慧伺服器在云端和本地部署中都发挥着核心作用,使企业能够灵活、安全、高效地管理工作负载。它们对于支援 TensorFlow、PyTorch 和 ONNX 等需要大量运算资源的现代开发框架也至关重要。随着企业越来越多地将人工智慧视为竞争优势,对人工智慧优化伺服器的投资也在加速成长。人工智慧日益融入国家数位转型策略、智慧城市发展和自主技术生态系统,进一步凸显了其重要性,使其成为全球数位经济不可或缺的一部分。
架构创新和元件进步如何推动伺服器效能?
人工智慧伺服器的演进源自于伺服器架构和元件技术的不断创新,从而显着提升了效能、可扩展性和能效。现代人工智慧伺服器的核心是高密度 GPU 配置,其中许多配置采用 NVIDIA A100、H100、AMD Instinct 或客製化人工智慧加速器,提供数千个核心,能够以惊人的速度执行平行运算。这些元件通常与多插槽 CPU、高频宽DDR5 和 HBM 记忆体以及 PCIe Gen 5 介面搭配使用,确保计算单元之间的快速资料传输。 NVLink、CXL 和 NVSwitch 技术整合在一起,以促进伺服器内部的无缝互连,消除延迟瓶颈并提高工作负载吞吐量。由于人工智慧工作负载需要快速处理和储存海量资料集,因此高速 NVMe SSD 和储存级内存在伺服器配置中越来越受欢迎。液冷和浸没式技术等冷却技术也正在部署,以管理高密度运算环境产生的高热量。如今,许多人工智慧伺服器在设计时都考虑到了模组化,使企业能够扩展其基础架构以满足运算需求,同时优化功耗。此外,人工智慧伺服器的设计也日益针对特定工作负载进行最佳化,例如用于训练大规模自然语言处理模型的微调模型,或用于推理和即时分析的模型。供应商正在将人工智慧主导的远端检测和管理软体融入其伺服器生态系统,以提供即时监控、预测性维护和效能参数的自动调整。这种硬体与智慧软体的融合,正在将人工智慧伺服器转变为适应性强、可自我优化的平台,以满足下一代智慧应用的独特需求。
产业特定需求、云端趋势和部署模式将如何影响市场需求?
人工智慧伺服器的需求正受到特定产业需求、云端运算的快速扩张以及部署模式偏好演进的显着影响。在医疗保健等领域,人工智慧伺服器支援诊断影像分析、药物研发和患者预后预测等关键应用。在金融领域,高频交易、诈欺检测和信用评分依赖强大的后端基础设施支援的快速主导决策。汽车产业依靠人工智慧伺服器,使用来自模拟环境和真实驾驶影像的大量资料集来训练自动驾驶演算法。同时,在零售和电子商务领域,客户行为分析和建议引擎也越来越依赖针对人工智慧优化的伺服器基础架构。这些多样化的应用程式正在推动对通用和特定产业伺服器配置的需求。云端服务供应商透过提供人工智慧即服务 (AI-as-a-Service),在扩展人工智慧功能的存取方面发挥关键作用,这使得企业无需拥有实体基础设施即可使用人工智慧伺服器。随着混合云和多重云端策略的出现,这种模式得到了显着发展,这些策略可在公有、私有和边缘环境中分配工作负载。因此,AI 伺服器供应商正在设计云端原生、容器优化的硬件,并支援 Kubernetes 和 Docker 等框架以实现灵活部署。边缘运算也正在影响设计,推动可部署在远端或移动位置的紧凑型 AI 伺服器的发展。这些边缘伺服器使即时决策更接近资料来源,从而降低延迟和频宽成本。随着 AI 渗透到越来越多的产业和业务环境中,AI 伺服器市场也日益多样化,为云端超大规模资料中心业者、企业资料中心和工业边缘应用提供了丰富的解决方案。
是什么推动了全球AI伺服器市场加速成长?
人工智慧伺服器市场的成长受到多种协同力量的推动,这些力量反映了全球向智慧主导营运、自动化和以数据为中心的创新的转变。人工智慧在商业营运、公共服务、科学研究和国防领域的广泛应用,对运算能力的需求持续增长,而只有针对人工智慧优化的伺服器才能满足这一需求。最大的驱动因素之一是数位设备、物联网感测器、社交媒体平台和企业应用程式产生的数据的指数级增长。人工智慧伺服器提供了即时处理这些数据并获得可操作洞察所需的基础设施。大规模语言模型(例如用于生成式人工智慧和对话介面的语言模型)的快速开发和部署,也推动了对能够处理数万亿个参数的超高效能训练伺服器的需求。世界各国政府正在投资人工智慧超级运算基础设施,以增强国家在科学、医疗和安全方面的能力,这进一步促进了市场扩张。晶片、记忆体和互连技术的进步正在降低每个运算单元的成本,使中小企业更容易获得人工智慧伺服器。此外,推动智慧製造、工业4.0和智慧城市基础设施发展的倡议正在将人工智慧伺服器嵌入物理环境,为机器人、自动化和预测维修系统动力来源。受网路威胁日益复杂化的推动,人工智慧伺服器正被用于运行需要快速自适应回应的威胁侦测演算法。半导体公司、伺服器製造商和云端服务供应商之间的策略合作正在加速创新和市场渗透。随着人工智慧成为全球经济的战略重点,人工智慧伺服器市场预计将继续强劲成长,并发展成为未来的智慧运算引擎。
部分
类型(AI训练伺服器、AI推理伺服器)、处理单元(基于GPU的处理单元、非基于GPU的处理单元)
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Global Artificial Intelligence (AI) Servers Market to Reach US$84.6 Billion by 2030
The global market for Artificial Intelligence (AI) Servers estimated at US$58.0 Billion in the year 2024, is expected to reach US$84.6 Billion by 2030, growing at a CAGR of 6.5% over the analysis period 2024-2030. AI Training Server, one of the segments analyzed in the report, is expected to record a 7.3% CAGR and reach US$61.3 Billion by the end of the analysis period. Growth in the AI Inference Server segment is estimated at 4.6% CAGR over the analysis period.
The U.S. Market is Estimated at US$15.3 Billion While China is Forecast to Grow at 6.2% CAGR
The Artificial Intelligence (AI) Servers market in the U.S. is estimated at US$15.3 Billion in the year 2024. China, the world's second largest economy, is forecast to reach a projected market size of US$13.5 Billion by the year 2030 trailing a CAGR of 6.2% over the analysis period 2024-2030. Among the other noteworthy geographic markets are Japan and Canada, each forecast to grow at a CAGR of 5.9% and 5.5% respectively over the analysis period. Within Europe, Germany is forecast to grow at approximately 5.1% CAGR.
Global Artificial Intelligence (AI) Servers Market - Key Trends & Drivers Summarized
Why Are AI Servers the Backbone of Intelligent Computing in a Data-Driven World?
Artificial Intelligence servers have emerged as foundational infrastructure in the era of intelligent computing, enabling the processing power required to handle the exponential growth in data and the increasing complexity of AI workloads. As AI adoption expands across industries such as healthcare, finance, manufacturing, automotive, and cybersecurity, the need for high-performance servers capable of supporting deep learning, machine learning, and neural network training has become critical. Unlike conventional servers, AI servers are built with advanced architectures that integrate GPUs, TPUs, high-speed memory, and interconnects designed to accelerate parallel processing tasks. These servers facilitate real-time inferencing and high-throughput training of large language models, computer vision systems, and predictive analytics engines. Organizations leveraging AI for tasks like fraud detection, personalized medicine, speech recognition, and autonomous systems rely on the massive computational capabilities of these specialized servers. Furthermore, the rise of big data and edge AI applications has intensified the demand for distributed AI infrastructure, where data needs to be processed not only in centralized data centers but also across edge locations. AI servers are central to both cloud and on-premise deployments, giving enterprises the flexibility to manage workloads securely and efficiently. They are also crucial in supporting modern development frameworks such as TensorFlow, PyTorch, and ONNX, which require extensive computational resources. As businesses increasingly view AI as a competitive advantage, investments in AI-optimized servers are accelerating. Their importance is further underscored by the rising integration of AI in national digital transformation strategies, smart city development, and autonomous technology ecosystems, making them indispensable to the global digital economy.
How Are Architectural Innovations and Component Advancements Driving Server Performance?
The evolution of AI servers is being propelled by continuous innovation in server architecture and component technologies, allowing for vastly improved performance, scalability, and energy efficiency. At the heart of modern AI servers are high-density GPU configurations, many featuring NVIDIA A100, H100, AMD Instinct, or custom AI accelerators that deliver thousands of cores capable of executing parallel computations at blistering speeds. These components are often paired with multi-socket CPUs, high-bandwidth DDR5 and HBM memory, and PCIe Gen 5 interfaces to ensure rapid data movement between compute units. NVLink, CXL, and NVSwitch technologies are being integrated to facilitate seamless interconnectivity within the server, eliminating latency bottlenecks and enhancing workload throughput. AI workloads require massive datasets to be processed and stored quickly, which is why high-speed NVMe SSDs and storage-class memory are being adopted widely in server configurations. Cooling innovations such as liquid cooling and immersion techniques are also being implemented to manage the intense heat generated by high-density computing environments. Many AI servers are now designed with modularity in mind, allowing enterprises to scale their infrastructure based on computational needs while optimizing power consumption. Furthermore, AI server designs are becoming increasingly optimized for specific workloads, with some models fine-tuned for training large-scale natural language processing models and others geared toward inferencing or real-time analytics. Vendors are embedding AI-driven telemetry and management software within server ecosystems to provide real-time monitoring, predictive maintenance, and automated tuning of performance parameters. This convergence of hardware and intelligent software is transforming AI servers into adaptive, self-optimizing platforms capable of meeting the unique demands of next-generation intelligent applications.
How Do Industry-Specific Needs, Cloud Trends, and Deployment Models Influence Market Demand?
The demand for AI servers is being heavily shaped by sector-specific requirements, the rapid expansion of cloud computing, and evolving preferences in deployment models. In sectors like healthcare, AI servers support critical applications such as diagnostic imaging analysis, drug discovery, and patient outcome prediction, all of which require high computational precision and data privacy. In the financial sector, high-frequency trading, fraud detection, and credit scoring rely on rapid AI-driven decision-making enabled by powerful backend infrastructure. The automotive industry is leveraging AI servers to train autonomous driving algorithms using massive datasets from simulation environments and real-world driving footage. Meanwhile, in retail and e-commerce, customer behavior analytics and recommendation engines are increasingly dependent on AI-optimized server infrastructure. These varying applications drive demand for both general-purpose and industry-specific server configurations. Cloud service providers are playing a pivotal role in expanding access to AI capabilities by offering AI-as-a-service, which allows organizations to utilize AI servers without owning physical infrastructure. This model has grown significantly with the advent of hybrid and multi-cloud strategies, where workloads are distributed across public, private, and edge environments. AI server vendors are therefore designing hardware that is cloud-native and container-optimized, supporting frameworks like Kubernetes and Docker for flexible deployment. Edge computing is also influencing design, prompting the development of compact AI servers that can be deployed in remote or mobile locations. These edge servers enable real-time decision-making close to data sources, reducing latency and bandwidth costs. As AI permeates more industries and operational environments, the market for AI servers is diversifying, with solutions tailored for cloud hyperscalers, enterprise data centers, and industrial edge applications alike.
What Is Fueling the Accelerated Growth of the AI Server Market Globally?
The growth in the AI server market is driven by several synergistic forces that reflect a global transition toward intelligence-led operations, automation, and data-centric innovation. The widespread adoption of AI in business operations, public services, scientific research, and defense is creating a relentless demand for computational power that only AI-optimized servers can meet. One of the most significant drivers is the exponential growth of data generated by digital devices, IoT sensors, social media platforms, and enterprise applications. AI servers provide the necessary infrastructure to process this data in real time and derive actionable insights. The surge in development and deployment of large language models, such as those used in generative AI and conversational interfaces, is also fueling demand for ultra-high-performance training servers that can handle trillions of parameters. Governments around the world are investing in AI supercomputing infrastructure to enhance national capabilities in science, healthcare, and security, contributing to market expansion. Technological advances in chips, memory, and interconnects are reducing the cost per compute unit, making AI servers more accessible to small and mid-sized businesses. Moreover, initiatives promoting smart manufacturing, Industry 4.0, and smart city infrastructure are embedding AI servers into physical environments where they power robotics, automation, and predictive maintenance systems. The growing sophistication of cyber threats is another factor, as AI servers are used to run threat detection algorithms that require rapid and adaptive responses. Strategic collaborations between semiconductor firms, server manufacturers, and cloud providers are accelerating innovation and market penetration. As AI becomes a strategic priority across the global economy, the AI server market is expected to continue expanding at a robust pace, evolving as the computational engine of the intelligent future.
SCOPE OF STUDY:
The report analyzes the Artificial Intelligence (AI) Servers market in terms of units by the following Segments, and Geographic Regions/Countries:
Segments:
Type (AI Training Server, AI Inference Server); Processing Unit (GPU-based Processing Unit, Non-GPU-based Processing Unit)
Geographic Regions/Countries:
World; United States; Canada; Japan; China; Europe (France; Germany; Italy; United Kingdom; and Rest of Europe); Asia-Pacific; Rest of World.
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