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市场调查报告书
商品编码
1725195

2030 年人工智慧基础设施市场预测：按产品、部署模式、技术、应用、最终用户和地区进行的全球分析

AI Infrastructure Market Forecasts to 2030 - Global Analysis By Offering (Hardware, Software, AI Frameworks and Middleware & Management Tools), Deployment Mode, Technology, Application, End User and By Geography
出版日期: | 出版商: Stratistics Market Research Consulting | 英文 200+ Pages | 商品交期: 2-3个工作天内

价格

根据 Stratistics MRC 的数据，全球人工智慧基础设施市场预计在 2025 年达到 402 亿美元，到 2032 年将达到 2,633 亿美元，预测期内的复合年增长率为 30.8%。

人工智慧基础设施包括开发、部署和扩展人工智慧应用所需的硬体和软体系统。这包括用于处理大型资料集的强大的 GPU、TPU 和高效能运算集群，以及用于训练和部署模型的云端平台和框架（如 TensorFlow 和 PyTorch）。它还支援高效、安全和可扩展的人工智慧营运的资料储存、网路和管理工具，使农业、医疗保健和金融等行业能够利用人工智慧进行创新和决策。

根据Cloudscene的最新数据，美国拥有2701个数据中心，德国拥有487个，英国拥有456个，中国拥有443个，为AI基础设施的扩张奠定了坚实的基础。

人工智慧晶片的演变

得益于GPU、TPU等AI专用晶片的发展，处理能力得到了显着提升。这些晶片可以实现更快的数据处理，为各行业的即时人工智慧应用提供支援。晶片製造商正在不断创新，采用节能、高效能的设计来优化人工智慧工作负载。增强的晶片结构为深度学习模型提供动力，使复杂的演算法能够以最小的延迟运行。 AI晶片的持续升级是实现AI基础设施可扩展性的主要手段。

资料隐私和安全问题

处理人工智慧系统中的大量敏感资料会引发严重的隐私问题。不良的安全通讯协定可能会使您的基础设施面临资料外洩和滥用的风险。遵守 GDPR 和 CCPA 等全球资料法规对企业来说仍然是一个挑战。这些担忧可能会限制人工智慧技术的采用，尤其是在医疗保健和金融等领域。公司必须在安全框架上投入大量资金，以确保用户信任和法规遵循。

生成式人工智慧和大规模语言模式的激增

GPT 和 DALL-E 等生成式 AI 模型的日益普及正在推动对强大后端基础设施的需求。开发人员越来越多地投资于大规模训练环境来支援模型开发。人们越来越需要高通量计算来管理大规模模型推理和调整。这一趋势为提供 AI 优化伺服器、储存和网路组件的供应商创造了机会。人工智慧基础设施供应商可以进入需要复杂内容产生和自动化的新产业。

分散式人工智慧系统中的网路安全漏洞

由于分散式资料流和端点，分散式人工智慧框架更容易受到恶意攻击。边缘设备上的加密和存取控制机制不足使其容易受到网路威胁。对抗性攻击可以操纵人工智慧模型并损害其输出和决策。人工智慧网路的规模使得即时威胁监控变得越来越复杂。持续存在的安全漏洞可能会阻碍人工智慧的采用和对系统完整性的信任。

COVID-19的影响：

疫情最初扰乱了硬体供应链，并减缓了各行业人工智慧基础设施的部署。然而，这场危机加速了数位转型，并刺激了对人工智慧业务的投资。远端工作和虚拟服务增加了对云端基础的人工智慧基础设施的需求。 COVID-19 也推动了医疗诊断和接触者追踪领域人工智慧应用的进步，凸显了基础设施需求。

预计机器学习领域将成为预测期内最大的领域。

由于机器学习在金融、零售和医疗保健等行业具有广泛的适用性，预计在预测期内将占据最大的市场占有率。监督和无监督学习技术的采用正在增加，扩大了 ML使用案例的范围。提供机器学习即服务 (MLaaS) 的云端平台正在简化组织的部署。公司正在使用 ML 进行模式识别、推荐系统和自动化。机器学习模型的可扩展性和成本效益占据了这一领域的主要地位。

预计推理部分在预测期内将以最高的复合年增长率成长。

预计推理部分在预测期内将呈现最高的成长率。推理引擎对于在低延迟的实际场景中部署训练有素的模型至关重要。成长的动力来自于边缘和嵌入式系统对快速、节能推理的需求。硬体加速器的技术进步正在增强这一领域的能力。家用电器和自动驾驶汽车中人工智慧应用的激增正在推动这一趋势。预计跨不同环境的最佳化推理的需求将推动高速成长。

比最大的地区

在预测期内，亚太地区预计将占据最大的市场占有率，这得益于其在智慧城市计画和数位转型方面的大量投资。中国、日本、韩国等国家正在公共和私营部门积极部署人工智慧技术。政府主导的创新计画和资金正在推动人工智慧基础设施的发展。主要半导体製造地的存在进一步推动了该地区的成长。此外，企业云的快速采用正在推动市场格局的发展。

复合年增长率最高的地区：

在预测期内，由于早期采用了先进的人工智慧技术，预计北美将呈现最高的复合年增长率。大型科技公司和人工智慧研究机构的存在正在刺激创新。人工智慧基础设施组件的高研发投入正在加速市场渗透。支持人工智慧与关键产业融合的法规结构也促进了成长。企业对人工智慧主导的自动化的日益关注进一步加速了市场扩张。

提供免费客製化：

订阅此报告的客户可享有以下免费自订选项之一：

  • 公司简介
    • 对其他市场公司（最多 3 家公司）进行全面分析
    • 主要企业的SWOT分析（最多3家公司）
  • 地理细分
    • 根据客户兴趣对主要国家市场进行估计、预测和复合年增长率（註：基于可行性检查）
  • 竞争基准化分析
    • 根据产品系列、地理分布和策略联盟对主要企业基准化分析

目录

第一章执行摘要

第二章 前言

  • 概述
  • 相关利益者
  • 研究范围
  • 调查方法
    • 资料探勘
    • 数据分析
    • 数据检验
    • 研究途径
  • 研究材料
    • 主要研究资料
    • 次级研究资讯来源
    • 先决条件

第三章市场走势分析

  • 驱动程式
  • 限制因素
  • 机会
  • 威胁
  • 技术分析
  • 应用分析
  • 最终用户分析
  • 新兴市场
  • COVID-19的影响

第四章 波特五力分析

  • 供应商的议价能力
  • 买家的议价能力
  • 替代品的威胁
  • 新进入者的威胁
  • 竞争对手之间的竞争

第五章 全球人工智慧基础设施市场（按产品提供）

  • 硬体
    • CPU、GPU、ASIC、FPGA
    • 储存系统
    • 网路元件
  • 软体
  • 人工智慧框架
  • 中介软体和管理工具

第六章全球人工智慧基础设施市场（按部署模式）

  • 本地
  • 云端基础

第七章 全球人工智慧基础设施市场（按技术）

  • 机器学习
  • 深度学习
  • 自然语言处理（NLP）
  • 电脑视觉
  • 强化学习

第八章全球人工智慧基础设施市场（按应用）

  • 训练
  • 推理
  • 资料管理
  • 部署模型
  • 监控和编配
  • 其他应用

第九章 全球人工智慧基础设施市场（按最终用户）

  • 医疗保健和生命科学
  • BFSI（银行、金融服务、保险）
  • 零售与电子商务
  • 车
  • 通讯
  • 政府和国防
  • 其他最终用户

第十章 全球人工智慧基础设施市场（按地区）

  • 北美洲
    • 美国
    • 加拿大
    • 墨西哥
  • 欧洲
    • 德国
    • 英国
    • 义大利
    • 法国
    • 西班牙
    • 其他欧洲国家
  • 亚太地区
    • 日本
    • 中国
    • 印度
    • 澳洲
    • 纽西兰
    • 韩国
    • 其他亚太地区
  • 南美洲
    • 阿根廷
    • 巴西
    • 智利
    • 南美洲其他地区
  • 中东和非洲
    • 沙乌地阿拉伯
    • 阿拉伯聯合大公国
    • 卡达
    • 南非
    • 其他中东和非洲地区

第十一章 重大进展

  • 协议、伙伴关係、合作和合资企业
  • 收购与合併
  • 新产品发布
  • 业务扩展
  • 其他关键策略

第十二章 公司概况

  • Advanced Micro Devices, Inc
  • Amazon Web Service
  • Cadence Design Systems
  • Cisco
  • Dell
  • Google
  • Graphcore
  • Gyrfalcon Technology
  • Hewlett Packard Enterprise Development LP
  • IBM
  • Imagination Technologies
  • INTEL
  • Micron Technology
  • Microsoft
  • NVIDIA
Product Code: SMRC29341

According to Stratistics MRC, the Global AI Infrastructure Market is accounted for $40.2 billion in 2025 and is expected to reach $263.3 billion by 2032 growing at a CAGR of 30.8% during the forecast period. AI Infrastructure encompasses the hardware and software systems required to develop, deploy, and scale artificial intelligence applications. This includes powerful GPUs, TPUs, and high-performance computing clusters for processing large datasets, alongside cloud platforms and frameworks like TensorFlow or PyTorch for model training and deployment. It supports data storage, networking, and management tools to ensure efficient, secure, and scalable AI operations, enabling industries like agriculture, healthcare, and finance to leverage AI for innovation and decision-making.

According to Cloudscene's recent data, there are 2,701 data centers in the United States, 487 in Germany, 456 in the United Kingdom, and 443 in China, creating a robust foundation for AI infrastructure expansion.

Market Dynamics:

Driver:

Advancements in AI chips

The evolution of AI-specific chips, such as GPUs and TPUs, is significantly enhancing processing capabilities. These chips allow for faster data processing, facilitating real-time AI applications across industries. Chipmakers are increasingly innovating with energy-efficient and high-performance designs, optimizing AI workloads. Enhanced chip architectures are empowering deep learning models, enabling complex algorithm executions with minimal latency. The continuous upgrade in AI chipsets is a major enabler for the scalability of AI infrastructure.

Restraint:

Data privacy & security concerns

The handling of vast volumes of sensitive data within AI systems raises critical privacy issues. Inadequate security protocols can expose infrastructure to data breaches and misuse. Compliance with global data regulations, such as GDPR and CCPA, remains a challenge for enterprises. These concerns can limit the adoption of AI technologies, particularly in sectors like healthcare and finance. Companies must invest heavily in secure frameworks to ensure user trust and regulatory compliance.

Opportunity:

Surge in generative AI and large language models

The growing popularity of generative AI models like GPT and DALL*E is driving demand for powerful backend infrastructure. Enterprises are increasingly investing in large-scale training environments to support model development. There is a rising need for high-throughput computing to manage model inference and tuning at scale. This trend creates opportunities for vendors offering AI-optimized servers, storage, and networking components. AI infrastructure providers can tap into new verticals requiring complex content generation and automation.

Threat:

Cybersecurity vulnerabilities in distributed AI systems

Decentralized AI frameworks are more exposed to malicious attacks due to dispersed data flows and endpoints. Inadequate encryption and access control mechanisms in edge devices increase susceptibility to cyber threats. Adversarial attacks can manipulate AI models, compromising their outputs and decision-making. The growing scale of AI networks makes real-time threat monitoring increasingly complex. Persistent security loopholes can hinder trust in AI deployment and system integrity.

Covid-19 Impact:

The pandemic initially disrupted hardware supply chains, delaying AI infrastructure rollouts across sectors. However, the crisis accelerated digital transformation, spurring investments in AI-enabled operations. Remote work and virtual services led to increased demand for cloud-based AI infrastructure. COVID-19 also triggered advancements in AI applications for healthcare diagnostics and contact tracing, highlighting infrastructure needs.

The machine learning segment is expected to be the largest during the forecast period

The machine learning segment is expected to account for the largest market share during the forecast period due to its widespread applicability across industries like finance, retail, and healthcare. Increasing adoption of supervised and unsupervised learning techniques is expanding ML use cases. Cloud platforms offering ML-as-a-Service (MLaaS) are simplifying deployment for organizations. Enterprises are leveraging ML for pattern recognition, recommendation systems, and automation. The scalability and cost-effectiveness of ML models make this segment dominant.

The inference segment is expected to have the highest CAGR during the forecast period

Over the forecast period, the inference segment is predicted to witness the highest growth rate, inference engines are becoming vital for deploying trained models in real-world scenarios with low latency. The need for fast and energy-efficient inference in edge and embedded systems is driving growth. Technological advancements in hardware accelerators are boosting the segment's capabilities. The proliferation of AI-powered applications in consumer electronics and autonomous vehicles supports this trend. The demand for optimized inference across diverse environments is expected to fuel high growth.

Region with largest share:

During the forecast period, the Asia Pacific region is expected to hold the largest market share due to massive investments in smart city initiatives and digital transformation. Countries like China, Japan, and South Korea are actively deploying AI technologies across public and private sectors. Government-led innovation programs and funding are boosting AI infrastructure development. The presence of major semiconductor manufacturing hubs further supports the region's growth. Additionally, rapid enterprise cloud adoption is enhancing the market landscape.

Region with highest CAGR:

Over the forecast period, the North America region is anticipated to exhibit the highest CAGR owing to its early adoption of advanced AI technologies. The presence of major tech giants and AI research institutions is fostering innovation. High R&D investments in AI infrastructure components are accelerating market penetration. Regulatory frameworks supporting AI integration in critical industries are also contributing to growth. The increasing focus on AI-driven automation across enterprises further amplifies market expansion.

Key players in the market

Some of the key players in AI Infrastructure Market include Advanced Micro Devices, Inc, Amazon Web Services, Cadence Design Systems, Cisco, Dell, Google, Graphcore, Gyrfalcon Technology, Hewlett Packard Enterprise Development LP, IBM, Imagination Technologies, Intel, Micron Technology, Microsoft and NVIDIA.

Key Developments:

In March 2025, NVIDIA unveiled the DGX H200 AI Supercomputer, a high-performance infrastructure solution optimized for large-scale generative AI model training with enhanced energy efficiency.

In March 2025, Intel launched the Xeon 7 Series AI Accelerator, a next-generation processor with integrated AI cores for edge and data center applications, improving performance for real-time AI analytics.

In February 2025, Amazon Web Services announced the AWS Graviton4 Processor, a new AI-optimized chip designed for cost-effective, high-throughput inference workloads in cloud-based AI infrastructure.

Offerings Covered:

  • Hardware
  • Software
  • AI Frameworks
  • Middleware & Management Tools

Deployment Modes Covered:

  • On-Premise
  • Cloud-Based

Technologies Covered:

  • Machine Learning
  • Deep Learning
  • Natural Language Processing (NLP)
  • Computer Vision
  • Reinforcement Learning

Applications Covered:

  • Training
  • Inference
  • Data Management
  • Model Deployment
  • Monitoring & Orchestration
  • Other Applications

End Users Covered:

  • Healthcare & Life Sciences
  • BFSI (Banking, Financial Services, and Insurance)
  • Retail & E-commerce
  • Automotive
  • Telecommunications
  • Government & Defense
  • Other End Users

Regions Covered:

  • North America
    • US
    • Canada
    • Mexico
  • Europe
    • Germany
    • UK
    • Italy
    • France
    • Spain
    • Rest of Europe
  • Asia Pacific
    • Japan
    • China
    • India
    • Australia
    • New Zealand
    • South Korea
    • Rest of Asia Pacific
  • South America
    • Argentina
    • Brazil
    • Chile
    • Rest of South America
  • Middle East & Africa
    • Saudi Arabia
    • UAE
    • Qatar
    • South Africa
    • Rest of Middle East & Africa

What our report offers:

  • Market share assessments for the regional and country-level segments
  • Strategic recommendations for the new entrants
  • Covers Market data for the years 2024, 2025, 2026, 2028, and 2032
  • Market Trends (Drivers, Constraints, Opportunities, Threats, Challenges, Investment Opportunities, and recommendations)
  • Strategic recommendations in key business segments based on the market estimations
  • Competitive landscaping mapping the key common trends
  • Company profiling with detailed strategies, financials, and recent developments
  • Supply chain trends mapping the latest technological advancements

Free Customization Offerings:

All the customers of this report will be entitled to receive one of the following free customization options:

  • Company Profiling
    • Comprehensive profiling of additional market players (up to 3)
    • SWOT Analysis of key players (up to 3)
  • Regional Segmentation
    • Market estimations, Forecasts and CAGR of any prominent country as per the client's interest (Note: Depends on feasibility check)
  • Competitive Benchmarking
    • Benchmarking of key players based on product portfolio, geographical presence, and strategic alliances

Table of Contents

1 Executive Summary

2 Preface

  • 2.1 Abstract
  • 2.2 Stake Holders
  • 2.3 Research Scope
  • 2.4 Research Methodology
    • 2.4.1 Data Mining
    • 2.4.2 Data Analysis
    • 2.4.3 Data Validation
    • 2.4.4 Research Approach
  • 2.5 Research Sources
    • 2.5.1 Primary Research Sources
    • 2.5.2 Secondary Research Sources
    • 2.5.3 Assumptions

3 Market Trend Analysis

  • 3.1 Introduction
  • 3.2 Drivers
  • 3.3 Restraints
  • 3.4 Opportunities
  • 3.5 Threats
  • 3.6 Technology Analysis
  • 3.7 Application Analysis
  • 3.8 End User Analysis
  • 3.9 Emerging Markets
  • 3.10 Impact of Covid-19

4 Porters Five Force Analysis

  • 4.1 Bargaining power of suppliers
  • 4.2 Bargaining power of buyers
  • 4.3 Threat of substitutes
  • 4.4 Threat of new entrants
  • 4.5 Competitive rivalry

5 Global AI Infrastructure Market, By Offering

  • 5.1 Introduction
  • 5.2 Hardware
    • 5.2.1 CPUs, GPUs, ASICs, FPGAs
    • 5.2.2 Storage Systems
    • 5.2.3 Networking Components
  • 5.3 Software
  • 5.4 AI Frameworks
  • 5.5 Middleware & Management Tools

6 Global AI Infrastructure Market, By Deployment Mode

  • 6.1 Introduction
  • 6.2 On-Premise
  • 6.3 Cloud-Based

7 Global AI Infrastructure Market, By Technology

  • 7.1 Introduction
  • 7.2 Machine Learning
  • 7.3 Deep Learning
  • 7.4 Natural Language Processing (NLP)
  • 7.5 Computer Vision
  • 7.6 Reinforcement Learning

8 Global AI Infrastructure Market, By Application

  • 8.1 Introduction
  • 8.2 Training
  • 8.3 Inference
  • 8.4 Data Management
  • 8.5 Model Deployment
  • 8.6 Monitoring & Orchestration
  • 8.7 Other Applications

9 Global AI Infrastructure Market, By End User

  • 9.1 Introduction
  • 9.2 Healthcare & Life Sciences
  • 9.3 BFSI (Banking, Financial Services, and Insurance)
  • 9.4 Retail & E-commerce
  • 9.5 Automotive
  • 9.6 Telecommunications
  • 9.7 Government & Defense
  • 9.8 Other End Users

10 Global AI Infrastructure Market, By Geography

  • 10.1 Introduction
  • 10.2 North America
    • 10.2.1 US
    • 10.2.2 Canada
    • 10.2.3 Mexico
  • 10.3 Europe
    • 10.3.1 Germany
    • 10.3.2 UK
    • 10.3.3 Italy
    • 10.3.4 France
    • 10.3.5 Spain
    • 10.3.6 Rest of Europe
  • 10.4 Asia Pacific
    • 10.4.1 Japan
    • 10.4.2 China
    • 10.4.3 India
    • 10.4.4 Australia
    • 10.4.5 New Zealand
    • 10.4.6 South Korea
    • 10.4.7 Rest of Asia Pacific
  • 10.5 South America
    • 10.5.1 Argentina
    • 10.5.2 Brazil
    • 10.5.3 Chile
    • 10.5.4 Rest of South America
  • 10.6 Middle East & Africa
    • 10.6.1 Saudi Arabia
    • 10.6.2 UAE
    • 10.6.3 Qatar
    • 10.6.4 South Africa
    • 10.6.5 Rest of Middle East & Africa

11 Key Developments

  • 11.1 Agreements, Partnerships, Collaborations and Joint Ventures
  • 11.2 Acquisitions & Mergers
  • 11.3 New Product Launch
  • 11.4 Expansions
  • 11.5 Other Key Strategies

12 Company Profiling

  • 12.1 Advanced Micro Devices, Inc
  • 12.2 Amazon Web Service
  • 12.3 Cadence Design Systems
  • 12.4 Cisco
  • 12.5 Dell
  • 12.6 Google
  • 12.7 Graphcore
  • 12.8 Gyrfalcon Technology
  • 12.9 Hewlett Packard Enterprise Development LP
  • 12.10 IBM
  • 12.11 Imagination Technologies
  • 12.12 INTEL
  • 12.13 Micron Technology
  • 12.14 Microsoft
  • 12.15 NVIDIA

List of Tables

  • Table 1 Global AI Infrastructure Market Outlook, By Region (2024-2032) ($MN)
  • Table 2 Global AI Infrastructure Market Outlook, By Offering (2024-2032) ($MN)
  • Table 3 Global AI Infrastructure Market Outlook, By Hardware (2024-2032) ($MN)
  • Table 4 Global AI Infrastructure Market Outlook, By CPUs, GPUs, ASICs, FPGAs (2024-2032) ($MN)
  • Table 5 Global AI Infrastructure Market Outlook, By Storage Systems (2024-2032) ($MN)
  • Table 6 Global AI Infrastructure Market Outlook, By Networking Components (2024-2032) ($MN)
  • Table 7 Global AI Infrastructure Market Outlook, By Software (2024-2032) ($MN)
  • Table 8 Global AI Infrastructure Market Outlook, By AI Frameworks (2024-2032) ($MN)
  • Table 9 Global AI Infrastructure Market Outlook, By Middleware & Management Tools (2024-2032) ($MN)
  • Table 10 Global AI Infrastructure Market Outlook, By Deployment Mode (2024-2032) ($MN)
  • Table 11 Global AI Infrastructure Market Outlook, By On-Premise (2024-2032) ($MN)
  • Table 12 Global AI Infrastructure Market Outlook, By Cloud-Based (2024-2032) ($MN)
  • Table 13 Global AI Infrastructure Market Outlook, By Technology (2024-2032) ($MN)
  • Table 14 Global AI Infrastructure Market Outlook, By Machine Learning (2024-2032) ($MN)
  • Table 15 Global AI Infrastructure Market Outlook, By Deep Learning (2024-2032) ($MN)
  • Table 16 Global AI Infrastructure Market Outlook, By Natural Language Processing (NLP) (2024-2032) ($MN)
  • Table 17 Global AI Infrastructure Market Outlook, By Computer Vision (2024-2032) ($MN)
  • Table 18 Global AI Infrastructure Market Outlook, By Reinforcement Learning (2024-2032) ($MN)
  • Table 19 Global AI Infrastructure Market Outlook, By Application (2024-2032) ($MN)
  • Table 20 Global AI Infrastructure Market Outlook, By Training (2024-2032) ($MN)
  • Table 21 Global AI Infrastructure Market Outlook, By Inference (2024-2032) ($MN)
  • Table 22 Global AI Infrastructure Market Outlook, By Data Management (2024-2032) ($MN)
  • Table 23 Global AI Infrastructure Market Outlook, By Model Deployment (2024-2032) ($MN)
  • Table 24 Global AI Infrastructure Market Outlook, By Monitoring & Orchestration (2024-2032) ($MN)
  • Table 25 Global AI Infrastructure Market Outlook, By Other Applications (2024-2032) ($MN)
  • Table 26 Global AI Infrastructure Market Outlook, By End User (2024-2032) ($MN)
  • Table 27 Global AI Infrastructure Market Outlook, By Healthcare & Life Sciences (2024-2032) ($MN)
  • Table 28 Global AI Infrastructure Market Outlook, By BFSI (Banking, Financial Services, and Insurance) (2024-2032) ($MN)
  • Table 29 Global AI Infrastructure Market Outlook, By Retail & E-commerce (2024-2032) ($MN)
  • Table 30 Global AI Infrastructure Market Outlook, By Automotive (2024-2032) ($MN)
  • Table 31 Global AI Infrastructure Market Outlook, By Telecommunications (2024-2032) ($MN)
  • Table 32 Global AI Infrastructure Market Outlook, By Government & Defense (2024-2032) ($MN)
  • Table 33 Global AI Infrastructure Market Outlook, By Other End Users (2024-2032) ($MN)

Note: Tables for North America, Europe, APAC, South America, and Middle East & Africa Regions are also represented in the same manner as above.