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1725195

2030 年人工智慧基礎設施市場預測：按產品、部署模式、技術、應用、最終用戶和地區進行的全球分析

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個工作天內

價格

根據 Stratistics MRC 的數據，全球人工智慧基礎設施市場預計在 2025 年達到 402 億美元，到 2032 年將達到 2,633 億美元，預測期內的複合年成長率為 30.8%。

人工智慧基礎設施包括開發、部署和擴展人工智慧應用所需的硬體和軟體系統。這包括用於處理大型資料集的強大的 GPU、TPU 和高效能運算集群，以及用於訓練和部署模型的雲端平台和框架（如 TensorFlow 和 PyTorch）。它還支援高效、安全和可擴展的人工智慧營運的資料儲存、網路和管理工具，使農業、醫療保健和金融等行業能夠利用人工智慧進行創新和決策。

根據Cloudscene的最新數據，美國擁有2701個資料中心，德國擁有487個，英國擁有456個，中國擁有443個，為AI基礎設施的擴張奠定了堅實的基礎。

人工智慧晶片的演變

得益於GPU、TPU等AI專用晶片的發展，處理能力得到了顯著提升。這些晶片可以實現更快的數據處理，為各行業的即時人工智慧應用提供支援。晶片製造商正在不斷創新，採用節能、高效能的設計來最佳化人工智慧工作負載。增強的晶片結構為深度學習模型提供動力，使複雜的演算法能夠以最小的延遲運行。 AI晶片的持續升級是實現AI基礎設施可擴展性的主要手段。

資料隱私和安全問題

處理人工智慧系統中的大量敏感資料會引發嚴重的隱私問題。不良的安全通訊協定可能會使您的基礎設施面臨資料外洩和濫用的風險。遵守 GDPR 和 CCPA 等全球資料法規對企業來說仍然是一個挑戰。這些擔憂可能會限制人工智慧技術的採用，尤其是在醫療保健和金融等領域。公司必須在安全框架上投入大量資金，以確保用戶信任和法規遵循。

生成式人工智慧和大規模語言模式的激增

GPT 和 DALL-E 等生成式 AI 模型的日益普及正在推動對強大後端基礎設施的需求。開發人員擴大投資於大規模訓練環境來支援模型開發。人們越來越需要高通量計算來管理大規模模型推理和調整。這一趨勢為提供 AI 最佳化伺服器、儲存和網路組件的供應商創造了機會。人工智慧基礎設施供應商可以進入需要複雜內容產生和自動化的新產業。

分散式人工智慧系統中的網路安全漏洞

由於分散式資料流和端點，分散式人工智慧框架更容易受到惡意攻擊。邊緣設備上的加密和存取控制機制不足使其容易受到網路威脅。對抗性攻擊可以操縱人工智慧模型並損害其輸出和決策。人工智慧網路的規模使得即時威脅監控變得越來越複雜。持續存在的安全漏洞可能會阻礙人工智慧的採用和對系統完整性的信任。

COVID-19的影響：

疫情最初擾亂了硬體供應鏈，並減緩了各行業人工智慧基礎設施的部署。然而，這場危機加速了數位轉型，並刺激了對人工智慧業務的投資。遠端工作和虛擬服務增加了對雲端基礎的人工智慧基礎設施的需求。 COVID-19 也推動了醫療診斷和接觸者追蹤領域人工智慧應用的進步，凸顯了基礎設施需求。

預計機器學習領域將成為預測期內最大的領域。

由於機器學習在金融、零售和醫療保健等行業具有廣泛的適用性，預計在預測期內將佔據最大的市場佔有率。監督和無監督學習技術的採用正在增加，擴大了 ML使用案例的範圍。提供機器學習即服務 (MLaaS) 的雲端平台正在簡化組織的部署。公司正在使用 ML 進行模式識別、推薦系統和自動化。機器學習模型的可擴展性和成本效益佔據了這一領域的主要地位。

預計推理部分在預測期內將以最高的複合年成長率成長。

預計推理部分在預測期內將呈現最高的成長率。推理引擎對於在低延遲的實際場景中部署訓練有素的模型至關重要。成長的動力來自於邊緣和嵌入式系統對快速、節能推理的需求。硬體加速器的技術進步正在增強這一領域的能力。家用電器和自動駕駛汽車中人工智慧應用的激增正在推動這一趨勢。預計跨不同環境的最佳化推理的需求將推動高速成長。

比最大的地區

在預測期內，亞太地區預計將佔據最大的市場佔有率，這得益於其在智慧城市計畫和數位轉型方面的大量投資。中國、日本、韓國等國家正在公共和私營部門積極部署人工智慧技術。政府主導的創新計畫和資金正在推動人工智慧基礎設施的發展。主要半導體製造地的存在進一步推動了該地區的成長。此外，企業雲的快速採用正在推動市場格局的發展。

複合年成長率最高的地區：

在預測期內，由於早期採用了先進的人工智慧技術，預計北美將呈現最高的複合年成長率。大型科技公司和人工智慧研究機構的存在正在刺激創新。人工智慧基礎設施組件的高研發投入正在加速市場滲透。支持人工智慧與關鍵產業融合的法規結構也促進了成長。企業對人工智慧主導的自動化的日益關注進一步加速了市場擴張。

提供免費客製化：

訂閱此報告的客戶可享有以下免費自訂選項之一：

  • 公司簡介
    • 對其他市場公司（最多 3 家公司）進行全面分析
    • 主要企業的SWOT分析（最多3家公司）
  • 地理細分
    • 根據客戶興趣對主要國家市場進行估計、預測和複合年成長率（註：基於可行性檢查）
  • 競爭基準化分析
    • 根據產品系列、地理分佈和策略聯盟對主要企業基準化分析

目錄

第1章執行摘要

第2章 前言

  • 概述
  • 相關利益者
  • 研究範圍
  • 調查方法
    • 資料探勘
    • 數據分析
    • 數據檢驗
    • 研究途徑
  • 研究材料
    • 主要研究資料
    • 次級研究資訊來源
    • 先決條件

第3章市場走勢分析

  • 驅動程式
  • 限制因素
  • 機會
  • 威脅
  • 技術分析
  • 應用分析
  • 最終用戶分析
  • 新興市場
  • COVID-19的影響

第4章 波特五力分析

  • 供應商的議價能力
  • 買家的議價能力
  • 替代品的威脅
  • 新進入者的威脅
  • 競爭對手之間的競爭

第5章 全球人工智慧基礎設施市場（按產品提供）

  • 硬體
    • CPU、GPU、ASIC、FPGA
    • 儲存系統
    • 網路元件
  • 軟體
  • 人工智慧框架
  • 中介軟體和管理工具

第6章全球人工智慧基礎設施市場（按部署模式）

  • 本地
  • 雲端基礎

第7章 全球人工智慧基礎設施市場（按技術）

  • 機器學習
  • 深度學習
  • 自然語言處理（NLP）
  • 電腦視覺
  • 強化學習

第8章全球人工智慧基礎設施市場（按應用）

  • 訓練
  • 推理
  • 資料管理
  • 部署模型
  • 監控和編配
  • 其他應用

第9章 全球人工智慧基礎設施市場（按最終用戶）

  • 醫療保健和生命科學
  • BFSI（銀行、金融服務、保險）
  • 零售與電子商務
  • 車
  • 通訊
  • 政府和國防
  • 其他最終用戶

第10章 全球人工智慧基礎設施市場（按地區）

  • 北美洲
    • 美國
    • 加拿大
    • 墨西哥
  • 歐洲
    • 德國
    • 英國
    • 義大利
    • 法國
    • 西班牙
    • 其他歐洲國家
  • 亞太地區
    • 日本
    • 中國
    • 印度
    • 澳洲
    • 紐西蘭
    • 韓國
    • 其他亞太地區
  • 南美洲
    • 阿根廷
    • 巴西
    • 智利
    • 南美洲其他地區
  • 中東和非洲
    • 沙烏地阿拉伯
    • 阿拉伯聯合大公國
    • 卡達
    • 南非
    • 其他中東和非洲地區

第11章 重大進展

  • 協議、夥伴關係、合作和合資企業
  • 收購與合併
  • 新產品發布
  • 業務擴展
  • 其他關鍵策略

第12章 公司概況

  • 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.