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市場調查報告書
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1689962

人工智慧基礎設施：市場佔有率分析、行業趨勢和統計、成長預測（2025-2030 年）

AI Infrastructure - Market Share Analysis, Industry Trends & Statistics, Growth Forecasts (2025 - 2030)
出版日期: | 出版商: Mordor Intelligence | 英文 200 Pages | 商品交期: 2-3個工作天內

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簡介目錄

2025年AI基礎設施市場規模預估為822.3億美元，預估至2030年將達2,056.5億美元，預測期間（2025-2030年）複合年成長率為20.12%。

人工智慧基礎設施-市場-IMG1

促進AI基礎設施市場的創新和效率

主要亮點

  • 高效能運算資料中心需求激增：AI基礎設施市場正在經歷指數級成長，這得益於高效能運算（HPC）資料中心對AI硬體的需求不斷增加。企業正在意識到人工智慧的變革潛力，並正在推動跨產業投資。
  • Nvidia 的 BlueField-3 DPU：世界上第一個 400GbE資料處理單元 (DPU)，該技術比傳統 DPU 快 10 倍，標誌著 AI 硬體的重大進步。
  • Google Cloud 與英特爾合作：這些科技巨頭已合作開發旨在增強資料中心的 AI 功能、安全性和生產力的晶片，這表明雙方正朝著戰略夥伴關係的方向發展。
  • AMD 的 MI300X 系列：AMD 發布其 MI300X 晶片系列，可執行高達 800 億個參數的生成式 AI 模型，展示了 AI 模型日益複雜的特性。
  • 工業物聯網（IIoT）和自動化技術推動成長：工業物聯網（IIoT）和自動化技術的整合正在顯著推動人工智慧基礎設施市場的發展。這些技術創新正在提高效率、最佳化流程並產生有價值的資料。
  • AFCOM 2021 調查結果：超過 40% 的參與公司計劃在 2024 年在資料中心監控和維護中實施機器人和自動化，這標誌著自動化的快速崛起。
  • 研華和 Actility Integration：這兩家公司推出了基於人工智慧的機器預後診斷和健康管理解決方案，實現了即時機器狀況監控。
  • TD SYNNEX Data-IoTSolv：此解決方案套件為合作夥伴提供利用資料分析和物聯網的工具，凸顯了對人工智慧物聯網解決方案日益成長的需求。
  • 機器學習和深度學習推動創新：機器學習和深度學習技術是人工智慧基礎設施成長的關鍵驅動力，使企業能夠從大量資料集中獲得有價值的見解。
  • TAZI.AI資金籌措成功：TAZI.AI 已籌集 460 萬美元，用於在醫療保健、保險和製藥領域部署機器學習解決方案，重點關注特定行業的 AI 採用。
  • 在政府部門：機器學習可用於業務自動化和資料分析，從而釋放人力資源以用於核心職能。
  • 疫情時代的加速：疫情加速了人工智慧和機器學習在網路自動化中的應用，網路供應商意識到人工智慧在簡化營運方面的關鍵作用。
  • 汽車和醫療保健領域的資料爆炸式成長：汽車和醫療保健等行業的資料量正在增加，對先進的人工智慧技術的需求也隨之增加，以便有效地管理和分析資料。
  • Alpine Health Systems 的人工智慧平台：該平台簡化了患有複雜疾病的患者的出院流程，展示了人工智慧在醫療管理方面的潛力。
  • Intangles Lab 的電動車環境感知 AI：這項創新解決了電動車，特別是在商用電動車領域的續航里程焦慮問題。
  • 醫療保健應用中的人工智慧：人工智慧擴大被用於臨床決策、疾病診斷和患者資料管理，展示了其在醫療保健領域的多功能性。
  • 市場現狀及未來展望：在科技巨頭、新興企業、雲端供應商等提供尖端解決方案的推動下，AI基礎設施市場預計將持續成長。
  • 雲端細分市場的成長：AI基礎設施雲市場預計在2022年價值161.2億美元，到2028年將達到492.9億美元，複合年成長率為20.22%。
  • 北美市場領導地位 北美將在 2022 年以 195.7 億美元的規模領先 AI 基礎設施市場，預計到 2028 年將達到 565.9 億美元，複合年成長率為 19.10%。
  • 新興技術：量子運算、6G 連接和先進機器人等創新將突破人工智慧基礎設施能力的界限，並支援新的應用和使用案例。

AI基礎設施市場趨勢

硬體部分：AI基礎設施的關鍵

  • 市場規模與成長：硬體部分是AI基礎設施市場的支柱。 2022年佔73.70%的市場佔有率，價值345.2億美元。預計到 2028 年將以 19.19% 的複合年成長率成長，達到 1,002.9 億美元。
  • 處理器子區隔領先：處理器的價值將在 2022 年達到 207.3 億美元，預計到 2028 年將達到 575.6 億美元，這得益於需要更強大處理能力的人工智慧演算法的複雜性不斷增加。
  • 客製化趨勢：企業正在轉向自訂AI晶片，例如華為的Ascend 910 AI處理器，它使用TensorFlow展示了比通用卡快兩倍的訓練速度。
  • 邊緣運算的影響：邊緣運算的興起正在塑造人工智慧處理器的發展。製造商正專注於能夠在使用點進行即時資料處理的處理器，尤其是對於物聯網應用。
  • 混合處理器：該公司正在開發混合 AI 處理器，將 CPU 與 GPU 或 NPU（神經處理單元）結合，以提高各種 AI 應用的多功能性和效率。

北美佔據主要市場佔有率

雲端部分：人工智慧民主化的催化劑

  • 快速成長軌跡：雲端領域預計將從 2022 年的 161.2 億美元成長到 2028 年的 492.9 億美元，複合年成長率為 20.22%。這一成長超過了整體市場的複合年成長率，並證明了雲端解決方案在人工智慧基礎設施中的重要作用。
  • 人工智慧民主化：雲端基礎的人工智慧基礎設施降低了採用的門檻，使得各種規模的企業都可以使用人工智慧技術。這種民主化加速了數位轉型並促進了創新。
  • 擴充性和靈活性：雲端平台提供無與倫比的可擴展性，使企業能夠輕鬆管理資料密集型模型訓練和推理等人工智慧工作負載。
  • 人工智慧即服務的興起：人工智慧即服務 (AIaaS) 的興起使企業能夠使用預先訓練的模型和工具集。例如，Nvidia 的 DGX Cloud 提供用於訓練 AI 模型的超級運算服務，而 Salesforce 的 AI Cloud 提供企業級 AI 工具。
  • 策略合作：AI 硬體供應商和雲端平台之間的合作，例如 Google Cloud 與新加坡智慧國家舉措的合作，正在創建特定領域的 AI 雲端解決方案。
  • 市場展望：AI基礎設施市場將隨著硬體和雲端兩部分的協同發展而持續演進。隨著人工智慧應用越來越廣泛，對可擴展且強大的基礎設施的需求將會成長，從而刺激人工智慧硬體和雲端原生解決方案的專業化。

AI基礎設施產業概覽

科技巨頭引領市場 AI基礎設施市場由英特爾、Nvidia、IBM、微軟和三星等科技巨頭主導。這些公司憑藉豐富的資源、全面的人工智慧解決方案和全球影響力佔據了相當大的市場佔有率。

NVIDIA DGX 雲端服務：這項 AI 超級運算服務使企業能夠訓練先進的生成式 AI 模型，彰顯了該公司在提供端到端 AI 基礎設施解決方案方面的領導地位。

IBM 和微軟混合解決方案：兩家公司正在開發整合 AI 功能的混合雲端解決方案，幫助企業在多種環境中有效部署 AI。

大量研發投入：領先的公司在研發方面投入大量資金，以保持競爭力並確保其處於人工智慧技術進步的前沿。

創新和專業化驅動市場成功AI基礎設施市場的成功取決於持續的創新和特定產業的專業化。

思科的生成式人工智慧解決方案思科推出了由生成式人工智慧驅動的全新網路、安全性和可觀察性解決方案，強調了創新對於獲得競爭優勢的重要性。

Mphasis.ai 的行業重點：Mphasis 專注於將 AI 功能整合到現有技術環境中，以最佳化特定領域的業務效率。

策略夥伴關係：Google Cloud 對 AI 諮詢服務的擴展說明了公司如何利用策略聯盟來擴大其產品範圍並進入新市場。

其他福利：

  • Excel 格式的市場預測 (ME) 表
  • 3 個月的分析師支持

目錄

第 1 章 簡介

  • 研究假設和市場定義
  • 研究範圍

第2章調查方法

第3章執行摘要

第4章 市場洞察

  • 市場概況
  • 產業吸引力－波特五力分析
    • 消費者議價能力
    • 供應商的議價能力
    • 新進入者的威脅
    • 替代品的威脅
    • 競爭對手之間的競爭強度
  • COVID-19 市場影響

第5章 市場動態

  • 市場促進因素
    • 高效能運算資料中心對人工智慧硬體的需求不斷增加
    • 擴大工業物聯網和自動化技術的應用
    • 拓展機器學習與深度學習技術的應用
    • 汽車和醫療保健等行業會產生大量資料
  • 市場限制
    • 業界缺乏熟練的專業人才

第6章 市場細分

  • 按產品
    • 硬體
      • 處理器
      • 貯存
      • 記憶
    • 軟體
  • 按部署
    • 本地
    • 雲
    • 混合
  • 按最終用戶
    • 企業
    • 政府
    • 雲端服務供應商
  • 按地區
    • 北美洲
      • 美國
      • 加拿大
    • 歐洲
      • 英國
      • 德國
      • 法國
      • 義大利
      • 西班牙
    • 亞洲
      • 中國
      • 印度
      • 韓國
      • 日本
    • 澳洲和紐西蘭
    • 拉丁美洲
      • 巴西
      • 墨西哥
    • 中東和非洲
      • 沙烏地阿拉伯
      • 阿拉伯聯合大公國
      • 卡達
      • 以色列
      • 南非

第7章 競爭格局

  • 公司簡介
    • Intel Corporation
    • Nvidia Corporation
    • Samsung Electronics Co. Ltd
    • Micron Technology Inc.
    • Sensetime Group Inc.
    • IBM Corporation
    • Google LLC
    • Microsoft Corporation
    • Amazon Web Services Inc.
    • Cisco Systems Inc.
    • Arm Holdings
    • Dell Inc.
    • Hewlett Packard Enterprise Development LP
    • Advanced Micro Devices
    • Synopsys Inc.

第8章投資分析

第9章：市場的未來

簡介目錄
Product Code: 69545

The AI Infrastructure Market size is estimated at USD 82.23 billion in 2025, and is expected to reach USD 205.65 billion by 2030, at a CAGR of 20.12% during the forecast period (2025-2030).

AI Infrastructure - Market - IMG1

AI Infrastructure Market: Driving Innovation and Efficiency

Key Highlights

  • Demand Surge in High-Performance Computing Data Centers: The AI Infrastructure market is experiencing exponential growth, driven by increasing demand for AI hardware in high-performance computing (HPC) data centers. Businesses are realizing the transformative potential of artificial intelligence, fueling investments across various industries.
  • Nvidia's BlueField-3 DPU: This technology, the world's first 400GbE data processing unit (DPU), is ten times faster than its predecessor, underscoring significant advancements in AI hardware.
  • Google Cloud and Intel Collaboration: These tech giants jointly developed a chip designed to enhance AI capabilities, security, and productivity in data centers, marking a trend of strategic partnerships.
  • AMD's MI300X Series: Advanced Micro Devices Inc. introduced the MI300X chip series, enabling the execution of generative AI models with up to 80 billion parameters, demonstrating the escalating complexity of AI models.
  • IIoT and Automation Technologies Propelling Growth: The integration of Industrial Internet of Things (IIoT) and automation technologies is significantly boosting the AI Infrastructure market. These innovations are enhancing efficiency, optimizing processes, and generating valuable data.
  • AFCOM 2021 Study Results: Over 40% of participants plan to deploy robotics and automation in data center monitoring and maintenance by 2024, signaling a sharp rise in automation.
  • Advantech and Actility Integration: These companies launched an AI-based solution for machine prognostics and health management, enabling real-time machine status monitoring.
  • TD SYNNEX's Data-IoTSolv: This solution suite equips partners with tools for leveraging data analytics and IoT, illustrating the growing demand for AI-powered IoT solutions.
  • Machine Learning and Deep Learning Driving Innovation: Machine learning and deep learning technologies are critical drivers of AI infrastructure growth, empowering companies to extract valuable insights from massive datasets.
  • TAZI.AI's Funding Success: The startup secured $4.6 million to roll out machine learning solutions in healthcare, insurance, and pharmaceuticals, highlighting sector-specific AI adoption.
  • Government Sector Utilization: Machine learning is increasingly used in government sectors to automate operations and analyze data, freeing human resources for core functions.
  • Pandemic-Era Acceleration: The pandemic sped up AI and ML adoption for network automation, with network providers recognizing the essential role of AI in operational streamlining.
  • Data Explosion in Automotive and Healthcare Sectors: The growing volume of data in industries like automotive and healthcare is propelling the need for advanced AI technologies to manage and analyze data efficiently.
  • Alpine Health Systems' AI-Powered Platform: This platform simplifies hospital discharge processes for patients with complex medical conditions, demonstrating AI's potential in healthcare management.
  • Intangles Lab's Ambient Cognitive AI for EVs: This innovation addresses range anxiety in electric vehicles, particularly in the commercial EV sector.
  • AI in Healthcare Applications: AI is increasingly used for clinical decision-making, disease diagnosis, and patient data management, showcasing its versatility in healthcare.
  • Market Landscape and Future Outlook: The AI Infrastructure market is poised for sustained growth, led by a mix of tech giants, startups, and cloud providers delivering cutting-edge solutions.
  • Cloud Segment Growth: The AI Infrastructure cloud market, valued at $16.12 billion in 2022, is forecasted to reach $49.29 billion by 2028, reflecting a CAGR of 20.22%.
  • North American Market Leadership: North America led the AI infrastructure market in 2022 with $19.57 billion in value, projected to hit $56.59 billion by 2028, growing at a 19.10% CAGR.
  • Emerging Technologies: Innovations like quantum computing, 6G connectivity, and advanced robotics are expected to push the boundaries of AI infrastructure capabilities, enabling new applications and use cases.

AI Infrastructure Market Trends

Hardware Segment Cornerstone of AI Infrastructure

  • Market Size and Growth: The hardware segment is the backbone of the AI Infrastructure market. In 2022, it accounted for 73.70% of the market share, valued at $34.52 billion. It is expected to grow at a CAGR of 19.19%, reaching $100.29 billion by 2028.
  • Processor Subsegment Leads: Processors were valued at $20.73 billion in 2022 and are forecasted to reach $57.56 billion by 2028, driven by the increasing complexity of AI algorithms requiring more powerful processing.
  • Customization Trend: Companies are shifting towards custom AI chips, like Huawei's Ascend 910 AI processor, which demonstrated twice the training speed of common cards using TensorFlow.
  • Edge Computing Influence: The rise of edge computing is shaping AI processor development. Manufacturers are focusing on processors that enable real-time data processing at the point of use, particularly in IoT applications.
  • Hybrid Processors: Companies are developing hybrid AI processors that combine CPUs with GPUs or Neural Processing Units (NPUs), enhancing versatility and efficiency for diverse AI applications.

North America to Hold Major Market Share

Cloud Segment: Catalyst for AI Democratization

  • Rapid Growth Trajectory: The cloud segment, valued at $16.12 billion in 2022, is projected to grow at a 20.22% CAGR, reaching $49.29 billion by 2028. This growth is outpacing the overall market CAGR, signaling the critical role of cloud solutions in AI infrastructure.
  • Democratization of AI: Cloud-based AI infrastructure lowers adoption barriers, making AI technologies accessible to businesses of all sizes. This democratization accelerates digital transformation and fosters innovation.
  • Scalability and Flexibility: Cloud platforms offer unmatched scalability, enabling enterprises to easily manage AI workloads, such as model training and inference, which are data-intensive.
  • AI-as-a-Service Proliferation: The rise of AI-as-a-Service (AIaaS) allows companies to access pre-trained models and toolsets. For example, Nvidia's DGX Cloud offers supercomputing services for AI model training, while Salesforce's AI Cloud delivers enterprise-ready AI tools.
  • Strategic Collaborations: Collaborations between AI hardware providers and cloud platforms, such as Google Cloud's partnership with Singapore's Smart Nation initiative, are creating sector-specific AI cloud solutions.
  • Market Outlook: The AI Infrastructure market will continue to evolve with the hardware and cloud segments developing synergistically. As AI applications proliferate, the demand for scalable, robust infrastructure will grow, spurring further specialization in AI hardware and cloud-native solutions.

AI Infrastructure Industry Overview

Tech Giants Lead the Market: The AI Infrastructure market is dominated by tech giants like Intel, Nvidia, IBM, Microsoft, and Samsung. These companies hold significant market share due to their extensive resources, comprehensive AI solutions, and global reach.

Nvidia's DGX Cloud Service: This AI supercomputing service enables businesses to train sophisticated generative AI models, showcasing the company's leadership in providing end-to-end AI infrastructure solutions.

IBM and Microsoft Hybrid Solutions: Both companies have developed hybrid cloud solutions that integrate AI capabilities, empowering enterprises to deploy AI across various environments efficiently.

Substantial R&D Investments: Leading players invest heavily in research and development to maintain their competitive edge, ensuring they stay at the forefront of AI technology advancements.

Innovation and Specialization Drive Market Success: Success in the AI Infrastructure market hinges on continuous innovation and industry-specific specialization.

Cisco's Generative AI Solutions: Cisco introduced new network, security, and observability offerings powered by generative AI, highlighting the importance of innovation in gaining a competitive edge.

Mphasis.ai's Industry Focus: Mphasis focuses on integrating AI capabilities into existing technological environments, optimizing operational efficiency in specific sectors.

Strategic Partnerships: Google Cloud's expansion of AI consulting services exemplifies how companies can leverage strategic collaborations to broaden their offerings and tap into new markets.

Additional Benefits:

  • The market estimate (ME) sheet in Excel format
  • 3 months of analyst support

TABLE OF CONTENTS

1 INTRODUCTION

  • 1.1 Study Assumptions and Market Definition
  • 1.2 Scope of the Study

2 RESEARCH METHODOLOGY

3 EXECUTIVE SUMMARY

4 MARKET INSIGHTS

  • 4.1 Market Overview
  • 4.2 Industry Attractiveness - Porter's Five Forces Analysis
    • 4.2.1 Bargaining Power of Consumers
    • 4.2.2 Bargaining Power of Suppliers
    • 4.2.3 Threat of New Entrants
    • 4.2.4 Threat of Substitute Products
    • 4.2.5 Intensity of Competitive Rivalry
  • 4.3 Impact of COVID-19 on the Market

5 MARKET DYNAMICS

  • 5.1 Market Drivers
    • 5.1.1 Increasing Demand for AI Hardware in High-performance Computing Data Centers
    • 5.1.2 Increasing Applications of IIoT and Automation Technologies
    • 5.1.3 Rising Application of Machine Leaning and Deep Learning Technologies
    • 5.1.4 Huge Volume of Data Being Generated in Industries such as Automotive and Healthcare
  • 5.2 Market Restraints
    • 5.2.1 Lack of Skilled Professionals in the Industry

6 MARKET SEGMENTATION

  • 6.1 By Offering
    • 6.1.1 Hardware
      • 6.1.1.1 Processor
      • 6.1.1.2 Storage
      • 6.1.1.3 Memory
    • 6.1.2 Software
  • 6.2 By Deployment
    • 6.2.1 On-premise
    • 6.2.2 Cloud
    • 6.2.3 Hybrid
  • 6.3 By End User
    • 6.3.1 Enterprises
    • 6.3.2 Government
    • 6.3.3 Cloud Service Providers
  • 6.4 By Geography
    • 6.4.1 North America
      • 6.4.1.1 United States
      • 6.4.1.2 Canada
    • 6.4.2 Europe
      • 6.4.2.1 United Kingdom
      • 6.4.2.2 Germany
      • 6.4.2.3 France
      • 6.4.2.4 Italy
      • 6.4.2.5 Spain
    • 6.4.3 Asia
      • 6.4.3.1 China
      • 6.4.3.2 India
      • 6.4.3.3 South Korea
      • 6.4.3.4 Japan
    • 6.4.4 Australia and New Zealand
    • 6.4.5 Latin America
      • 6.4.5.1 Brazil
      • 6.4.5.2 Mexico
    • 6.4.6 Middle East and Africa
      • 6.4.6.1 Saudi Arabia
      • 6.4.6.2 United Arab Emirates
      • 6.4.6.3 Qatar
      • 6.4.6.4 Israel
      • 6.4.6.5 South Africa

7 COMPETITIVE LANDSCAPE

  • 7.1 Company Profiles
    • 7.1.1 Intel Corporation
    • 7.1.2 Nvidia Corporation
    • 7.1.3 Samsung Electronics Co. Ltd
    • 7.1.4 Micron Technology Inc.
    • 7.1.5 Sensetime Group Inc.
    • 7.1.6 IBM Corporation
    • 7.1.7 Google LLC
    • 7.1.8 Microsoft Corporation
    • 7.1.9 Amazon Web Services Inc.
    • 7.1.10 Cisco Systems Inc.
    • 7.1.11 Arm Holdings
    • 7.1.12 Dell Inc.
    • 7.1.13 Hewlett Packard Enterprise Development LP
    • 7.1.14 Advanced Micro Devices
    • 7.1.15 Synopsys Inc.

8 INVESTMENT ANALYSIS

9 FUTURE OF THE MARKET