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

Artificial Intelligence (AI) Infrastructure Market Forecasts to 2030 - Global Analysis By Component (Hardware, Software, Services and Other Components), 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（AI）基礎設施市場規模預計在 2024 年達到 479.6 億美元，到 2030 年將達到 2,435.4 億美元，預測期內的複合年成長率為 31.1%。

人工智慧基礎設施是指支撐人工智慧應用的開發、部署和執行所需的底層技術和系統。這包括 GPU、CPU、FPGA 和 ASIC 等硬體元件，以及針對 AI 工作負載最佳化的軟體框架、雲端平台和資料儲存解決方案。 AI基礎設施實現高效的資料處理、模型訓練和推理，支援機器學習、深度學習、自然語言處理等應用。

人工智慧在各行各業的應用日益廣泛

醫療保健、汽車、金融、零售和製造等行業的公司都在使用人工智慧 (AI) 來提高業務效率、實現流程自動化並提供個人化體驗。為了管理繁重的工作負載，機器人流程自動化、影像識別、自然語言處理和預測分析等應用程式需要強大的人工智慧基礎設施。例如，汽車產業正在將人工智慧融入自動駕駛技術，而醫療保健產業正在將人工智慧用於藥物研究和診斷。如此廣泛的應用推動了對雲端基礎的解決方案、先進硬體和可擴展的高效能運算系統的需求，從而刺激對人工智慧基礎設施開發的持續投資。

資料隱私和安全問題

人工智慧系統需要大量個人資訊，包括財務、醫療和個人數據，來學習和做出決策。由於 CCPA、GDPR 和 HIPAA 等嚴格的法律，不當的資料處理可能會導致違規、未授權存取和違規。由於存在資料外洩和網路攻擊的可能性，雲端基礎的人工智慧基礎設施存在額外的漏洞。為了降低這些風險，建立強大的加密、安全的資料儲存和存取控制系統至關重要。這些擔憂不僅使人工智慧基礎設施的採用變得複雜，而且影響了公司使用人工智慧的準備情況，尤其是在受到嚴格監管的行業中。

對高效能運算 (HPC) 的需求不斷增加

人工智慧應用，尤其是使用機器學習和深度學習的應用，需要大量的處理能力來處理和分析大型資料集。 HPC 系統提供必要的處理能力，利用 GPU、平行運算和張量處理單元 (TPU) 等專用硬體來加速 AI 模型的推理和訓練。隨著人工智慧技術的發展，特別是在電腦視覺、自然語言處理和自主系統等領域，對更快、更強大的運算基礎設施的需求日益增加。對尖端基礎設施解決方案的投資是由 HPC 日益成長的需求推動的，以滿足現代 AI 工作負載的效率、可擴展性和效能需求。

實施成本高

強大的處理資源和專用設備（例如 GPU 和 TPU）可能遙不可及。此外，開發和訓練複雜的人工智慧模型、獲取和維護高品質的資料以及聘請熟練的人工智慧專家都需要大量的資金投入。將人工智慧系統與目前IT基礎設施結合非常困難、昂貴且耗時。綜合起來，這些因素使得實施人工智慧對於各種規模的企業來說都是一項沉重的成本負擔。

COVID-19 的影響

COVID-19疫情對人工智慧（AI）基礎設施市場產生了多方面的影響。一方面，遠距工作、醫療保健、電子商務和供應鏈管理對數位技術和人工智慧驅動的解決方案的日益依賴，加速了對人工智慧基礎設施的需求。同時，全球供應鏈中斷和經濟不確定性減緩了新人工智慧計劃的發展。儘管如此，這場疫情凸顯了人工智慧對業務永續營運的重要性，並刺激了各行業對人工智慧基礎設施的長期投資。

預測期內硬體部分預計將成為最大的部分

由於對支援機器學習、深度學習和資料分析等人工智慧應用的高效能運算的需求不斷成長，硬體部分估計將是最大的部分。隨著人工智慧模型變得越來越複雜，GPU、TPU 和 FPGA 等專用硬體對於加速處理速度和效率至關重要。此外，醫療保健、汽車和金融等行業擴大採用人工智慧，需要強大、可擴展且節能的硬體解決方案來處理大規模資料處理和即時推理。

預測期內，詐欺偵測領域預計將以最高複合年成長率成長

由於網路威脅日益複雜、即時決策的需求以及金融交易量的不斷增加，預計詐欺偵測領域在預測期內將以最高的複合年成長率成長。配備高效能基礎設施的人工智慧系統可以分析大量資料，比傳統方法更快、更準確地偵測模式、異常和潛在的詐欺活動。人工智慧在詐欺偵測中的應用廣泛，涵蓋銀行、電子商務、保險和金融服務領域，透過即時識別可疑活動，幫助組織防止詐欺、減少財務損失並增強安全性。

比最大的地區

由於各行業的快速市場佔有率轉型、政府對人工智慧計畫的支持不斷增加以及新興企業生態系統蓬勃發展，預計亞太地區將在預測期內佔據最大的市場佔有率。該地區龐大的人口加上不斷成長的可支配收入，推動了電子商務、金融科技、醫療保健和智慧城市等領域對人工智慧解決方案的需求。此外，5G技術和雲端運算的進步為人工智慧應用的廣泛應用提供了必要的基礎設施，進一步加速了市場成長。

複合年成長率最高的地區：

由於強勁的創業投資生態系統促進創新，北美預計將在預測期內實現最高的複合年成長率。私營和公共部門對人工智慧研究和開發的大量投資將進一步推動市場成長。該地區擁有高技能的勞動力和早期採用新興技術的文化，使其成為人工智慧基礎設施解決方案的理想市場。此外，醫療保健、金融和自動駕駛汽車等行業對人工智慧應用的需求不斷成長，推動了對先進運算能力和專用硬體的需求，從而推動市場向前發展。

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
    • 全面分析其他市場參與者（最多 3 家公司）
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  • 地理細分
    • 根據客戶興趣對主要國家進行的市場估計、預測和複合年成長率（註：基於可行性檢查）
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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
    • 資料分析
    • 資料檢驗
    • 研究途徑
  • 研究資訊來源
    • 主要研究資訊來源
    • 二手研究資料資訊來源
    • 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

5. 全球人工智慧（AI）基礎設施市場（按組件）

  • 硬體
    • 圖形處理單元 (GPU)
    • 中央處理器 (CPU)
    • 專用積體電路 (ASIC)
    • 現場可程式閘陣列(FPGA)
    • 記憶體和儲存
    • 網路元件
  • 軟體
    • 人工智慧平台
    • 作業系統
    • 人工智慧中介軟體
    • 資料管理和分析工具
  • 服務
    • 整合與部署
    • 支援和維護
    • 諮詢
  • 其他組件

6. 全球人工智慧（AI）基礎設施市場按部署模式分類

  • 雲端基礎
    • 公共雲端
    • 私有雲端
    • 混合雲端
  • 本地

7. 全球人工智慧（AI）基礎設施市場（按技術）

  • 機器學習 (ML)
    • 監督學習
    • 無監督學習
    • 強化學習
  • 自然語言處理 (NLP)
  • 電腦視覺
  • 語音辨識
  • 深度學習（DL）

第8章 全球人工智慧（AI）基礎設施市場（按應用）

  • 資料管理與處理
  • 模型訓練與開發
  • 推理與發展
  • 預測分析
  • 詐欺偵測
  • 語音和圖像識別
  • 客戶體驗管理
  • 推薦系​​統
  • 其他用途

第9章全球人工智慧（AI）基礎設施市場（按最終用戶分類）

  • 汽車與運輸
  • 教育
  • 銀行、金融服務和保險（BFSI）
  • 零售與電子商務
  • 政府和國防
  • 媒體和娛樂
  • 資訊科技和通訊
  • 醫療保健和生命科學
  • 其他最終用戶

第 10 章 全球人工智慧 (AI) 基礎設施市場（按區域）

  • 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

  • NVIDIA Corporation
  • Intel Corporation
  • Google LLC（Alphabet Inc.）
  • Microsoft Corporation
  • Amazon Web Services（AWS）
  • IBM Corporation
  • Oracle Corporation
  • Advanced Micro Devices, Inc.（AMD）
  • Huawei Technologies Co., Ltd.
  • Hewlett Packard Enterprise（HPE）
  • Dell Technologies
  • Samsung Electronics Co., Ltd.
  • Cerebras Systems
  • Graphcore
  • Qualcomm Technologies, Inc.
  • Xilinx, Inc.（AMD）
  • Fujitsu Limited
  • Cisco Systems, Inc.
  • Micron Technology, Inc.
  • Tencent Holdings Limited
Product Code: SMRC28435

According to Stratistics MRC, the Global Artificial Intelligence (AI) Infrastructure Market is accounted for $47.96 billion in 2024 and is expected to reach $243.54 billion by 2030 growing at a CAGR of 31.1% during the forecast period. Artificial Intelligence (AI) Infrastructure refers to the foundational technologies and systems required to support the development, deployment, and execution of AI applications. It encompasses hardware components such as GPUs, CPUs, FPGAs, and ASICs, along with software frameworks, cloud platforms, and data storage solutions optimized for AI workloads. AI infrastructure enables efficient data processing, model training, and inference, supporting applications like machine learning, deep learning, and natural language processing.

Market Dynamics:

Driver:

Increased adoption of AI across industries

Enterprises across industries like healthcare, automotive, finance, retail, and manufacturing are utilizing artificial intelligence (AI) to improve operational efficiency, automate procedures, and provide customized experiences. To manage demanding workloads, applications such as robotic process automation, image recognition, natural language processing, and predictive analytics need strong AI infrastructure. For instance, the automobile industry incorporates AI into autonomous driving technologies, and the healthcare sector uses AI for drug research and diagnostics. This broad use is increasing demand for cloud-based solutions, sophisticated hardware, and scalable, high-performance computing systems, which is fueling ongoing investment in the development of AI infrastructure.

Restraint:

Data privacy and security concerns

Large volumes of private information, such as financial, medical, and personal data, are necessary for AI systems to be trained and make decisions. With strict laws like the CCPA, GDPR, and HIPAA, improper data handling can result in breaches, illegal access, and noncompliance. Because of the possibility of data leaks and cyberattacks, cloud-based AI infrastructure introduces an additional degree of vulnerability. To reduce these dangers, it is crucial to have strong encryption, safe data storage, and access control systems in place. These worries not only make deploying AI infrastructure more difficult, but they also affect businesses' readiness to use AI, particularly in highly regulated sectors.

Opportunity:

Growing demand for high-performance computing (HPC)

AI applications need a lot of processing power to process and analyze large datasets, particularly those that use machine learning and deep learning. HPC systems offer the required processing power, utilizing GPUs, parallel computing, and specialized hardware such as TPUs (Tensor Processing Units) to speed up AI model inference and training. Faster and more potent computing infrastructure is becoming more and more necessary as AI technologies develop, particularly in fields like computer vision, natural language processing, and autonomous systems. Investment in cutting-edge infrastructure solutions is fueled by the growing need for HPC in order to satisfy the efficiency, scalability, and performance demands of contemporary AI workloads.

Threat:

High cost of implementation

Powerful processing resources and specialized gear, such as GPUs and TPUs, might be unaffordable. Significant financial investments are also required for the development and training of complex AI models, the acquisition and upkeep of high-quality datasets, and the employment of qualified AI specialists. It can be difficult, expensive, and time-consuming to integrate AI systems with current IT infrastructure. When taken as a whole, these elements make implementing AI a significant cost commitment for companies of all sizes.

Covid-19 Impact

The COVID-19 pandemic had a mixed impact on the Artificial Intelligence (AI) Infrastructure market. On one hand, the increased reliance on digital technologies and AI-driven solutions for remote work, healthcare, e-commerce, and supply chain management accelerated demand for AI infrastructure. On the other hand, global supply chain disruptions and economic uncertainties slowed the deployment of new AI projects. Despite this, the pandemic highlighted the importance of AI for business continuity, driving long-term investments in AI infrastructure across various sectors.

The hardware segment is expected to be the largest during the forecast period

The hardware segment is estimated to be the largest, due to the increasing demand for high-performance computing to support AI applications like machine learning, deep learning, and data analytics. As AI models become more complex, specialized hardware such as GPUs, TPUs, and FPGAs are essential for accelerating processing speed and efficiency. Additionally, the growing adoption of AI in industries like healthcare, automotive, and finance requires powerful, scalable, and energy-efficient hardware solutions to handle large-scale data processing and real-time inference.

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

The fraud detection segment is anticipated to witness the highest CAGR during the forecast period, due to the rising sophistication of cyber threats, the need for real-time decision-making, and the growing volume of financial transactions. AI-driven systems, powered by high-performance infrastructure, can analyze vast amounts of data to detect patterns, anomalies, and potential fraudulent activities faster and more accurately than traditional methods. Applications of AI in fraud detection span across banking, e-commerce, insurance, and financial services, helping organizations prevent fraud, reduce financial losses, and enhance security by identifying suspicious behavior in real time.

Region with largest share:

Asia Pacific is expected to have the largest market share during the forecast period due to rapid digital transformation across various sectors, increasing government support for AI initiatives, and a burgeoning start-up ecosystem. The region's large and growing population, coupled with rising disposable incomes, is fueling demand for AI-powered solutions in areas such as e-commerce, fintech, healthcare, and smart cities. Furthermore, advancements in 5G technology and cloud computing are providing the necessary infrastructure for the widespread adoption of AI applications, further accelerating market growth.

Region with highest CAGR:

During the forecast period, the North America region is anticipated to register the highest CAGR, owing to a robust venture capital ecosystem fostering innovation. Significant investments in AI research and development by both private and public sectors further fuel market growth. The region boasts a highly skilled workforce and a culture of early adoption of emerging technologies, making it an ideal market for AI infrastructure solutions. Additionally, the increasing demand for AI applications across various industries, such as healthcare, finance, and autonomous vehicles, is driving the need for advanced computing power and specialized hardware, propelling the market forward.

Key players in the market

Some of the key players profiled in the Artificial Intelligence (AI) Infrastructure Market include NVIDIA Corporation, Intel Corporation, Google LLC (Alphabet Inc.), Microsoft Corporation, Amazon Web Services (AWS), IBM Corporation, Oracle Corporation, Advanced Micro Devices, Inc. (AMD), Huawei Technologies Co., Ltd., Hewlett Packard Enterprise (HPE), Dell Technologies, Samsung Electronics Co., Ltd., Cerebras Systems, Graphcore, Qualcomm Technologies, Inc., Xilinx, Inc. (AMD), Fujitsu Limited, Cisco Systems, Inc., Micron Technology, Inc., and Tencent Holdings Limited.

Key Developments:

In December 2024, Intel announced the new Intel(R) Arc(TM) B-Series graphics cards. The Intel(R) Arc(TM) B580 and B570 GPUs offer best-in-class value for performance at price points that are accessible to most gamers1, deliver modern gaming features and are engineered to accelerate AI workloads.

In October 2024, Siemens is revolutionizing industrial automation with Microsoft. Through their collaboration, they have taken the Siemens Industrial Copilot to the next level, enabling it to handle the most demanding environments at scale. Combining Siemens' unique domain know-how across industries with Microsoft Azure OpenAI Service, the Copilot further improves handling of rigorous requirements in manufacturing and automation.

Components Covered:

  • Hardware
  • Software
  • Services
  • Other Components

Deployment Modes Covered:

  • Cloud-Based
  • On-Premises

Technologies Covered:

  • Machine Learning (ML)
  • Natural Language Processing (NLP)
  • Computer Vision
  • Speech Recognition
  • Deep Learning (DL)

Applications Covered:

  • Data Management and Processing
  • Model Training and Development
  • Inference and Deployment
  • Predictive Analytics
  • Fraud Detection
  • Speech and Image Recognition
  • Customer Experience Management
  • Recommendation Systems
  • Other Applications

End Users Covered:

  • Automotive and Transportation
  • Education
  • Banking, Financial Services, and Insurance (BFSI)
  • Retail and E-commerce
  • Government and Defense
  • Media and Entertainment
  • IT and Telecom
  • Healthcare and Life Sciences
  • 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 2022, 2023, 2024, 2026, and 2030
  • 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 Artificial Intelligence (AI) Infrastructure Market, By Component

  • 5.1 Introduction
  • 5.2 Hardware
    • 5.2.1 Graphics Processing Units (GPUs)
    • 5.2.2 Central Processing Units (CPUs)
    • 5.2.3 Application-Specific Integrated Circuits (ASICs)
    • 5.2.4 Field-Programmable Gate Arrays (FPGAs)
    • 5.2.5 Memory & Storage
    • 5.2.6 Networking Components
  • 5.3 Software
    • 5.3.1 AI Platforms
    • 5.3.2 Operating Systems
    • 5.3.3 AI Middleware
    • 5.3.4 Data Management and Analytics Tools
  • 5.4 Services
    • 5.4.1 Integration & Deployment
    • 5.4.2 Support & Maintenance
    • 5.4.3 Consulting
  • 5.5 Other Components

6 Global Artificial Intelligence (AI) Infrastructure Market, By Deployment Mode

  • 6.1 Introduction
  • 6.2 Cloud-Based
    • 6.2.1 Public Cloud
    • 6.2.2 Private Cloud
    • 6.2.3 Hybrid Cloud
  • 6.3 On-Premises

7 Global Artificial Intelligence (AI) Infrastructure Market, By Technology

  • 7.1 Introduction
  • 7.2 Machine Learning (ML)
    • 7.2.1 Supervised Learning
    • 7.2.2 Unsupervised Learning
    • 7.2.3 Reinforcement Learning
  • 7.3 Natural Language Processing (NLP)
  • 7.4 Computer Vision
  • 7.5 Speech Recognition
  • 7.6 Deep Learning (DL)

8 Global Artificial Intelligence (AI) Infrastructure Market, By Application

  • 8.1 Introduction
  • 8.2 Data Management and Processing
  • 8.3 Model Training and Development
  • 8.4 Inference and Deployment
  • 8.5 Predictive Analytics
  • 8.6 Fraud Detection
  • 8.7 Speech and Image Recognition
  • 8.8 Customer Experience Management
  • 8.9 Recommendation Systems
  • 8.10 Other Applications

9 Global Artificial Intelligence (AI) Infrastructure Market, By End User

  • 9.1 Introduction
  • 9.2 Automotive and Transportation
  • 9.3 Education
  • 9.4 Banking, Financial Services, and Insurance (BFSI)
  • 9.5 Retail and E-commerce
  • 9.6 Government and Defense
  • 9.7 Media and Entertainment
  • 9.8 IT and Telecom
  • 9.9 Healthcare and Life Sciences
  • 9.10 Other End Users

10 Global Artificial Intelligence (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 NVIDIA Corporation
  • 12.2 Intel Corporation
  • 12.3 Google LLC (Alphabet Inc.)
  • 12.4 Microsoft Corporation
  • 12.5 Amazon Web Services (AWS)
  • 12.6 IBM Corporation
  • 12.7 Oracle Corporation
  • 12.8 Advanced Micro Devices, Inc. (AMD)
  • 12.9 Huawei Technologies Co., Ltd.
  • 12.10 Hewlett Packard Enterprise (HPE)
  • 12.11 Dell Technologies
  • 12.12 Samsung Electronics Co., Ltd.
  • 12.13 Cerebras Systems
  • 12.14 Graphcore
  • 12.15 Qualcomm Technologies, Inc.
  • 12.16 Xilinx, Inc. (AMD)
  • 12.17 Fujitsu Limited
  • 12.18 Cisco Systems, Inc.
  • 12.19 Micron Technology, Inc.
  • 12.20 Tencent Holdings Limited

List of Tables

  • Table 1 Global Artificial Intelligence (AI) Infrastructure Market Outlook, By Region (2022-2030) ($MN)
  • Table 2 Global Artificial Intelligence (AI) Infrastructure Market Outlook, By Component (2022-2030) ($MN)
  • Table 3 Global Artificial Intelligence (AI) Infrastructure Market Outlook, By Hardware (2022-2030) ($MN)
  • Table 4 Global Artificial Intelligence (AI) Infrastructure Market Outlook, By Graphics Processing Units (GPUs) (2022-2030) ($MN)
  • Table 5 Global Artificial Intelligence (AI) Infrastructure Market Outlook, By Central Processing Units (CPUs) (2022-2030) ($MN)
  • Table 6 Global Artificial Intelligence (AI) Infrastructure Market Outlook, By Application-Specific Integrated Circuits (ASICs) (2022-2030) ($MN)
  • Table 7 Global Artificial Intelligence (AI) Infrastructure Market Outlook, By Field-Programmable Gate Arrays (FPGAs) (2022-2030) ($MN)
  • Table 8 Global Artificial Intelligence (AI) Infrastructure Market Outlook, By Memory & Storage (2022-2030) ($MN)
  • Table 9 Global Artificial Intelligence (AI) Infrastructure Market Outlook, By Networking Components (2022-2030) ($MN)
  • Table 10 Global Artificial Intelligence (AI) Infrastructure Market Outlook, By Software (2022-2030) ($MN)
  • Table 11 Global Artificial Intelligence (AI) Infrastructure Market Outlook, By AI Platforms (2022-2030) ($MN)
  • Table 12 Global Artificial Intelligence (AI) Infrastructure Market Outlook, By Operating Systems (2022-2030) ($MN)
  • Table 13 Global Artificial Intelligence (AI) Infrastructure Market Outlook, By AI Middleware (2022-2030) ($MN)
  • Table 14 Global Artificial Intelligence (AI) Infrastructure Market Outlook, By Data Management and Analytics Tools (2022-2030) ($MN)
  • Table 15 Global Artificial Intelligence (AI) Infrastructure Market Outlook, By Services (2022-2030) ($MN)
  • Table 16 Global Artificial Intelligence (AI) Infrastructure Market Outlook, By Integration & Deployment (2022-2030) ($MN)
  • Table 17 Global Artificial Intelligence (AI) Infrastructure Market Outlook, By Support & Maintenance (2022-2030) ($MN)
  • Table 18 Global Artificial Intelligence (AI) Infrastructure Market Outlook, By Consulting (2022-2030) ($MN)
  • Table 19 Global Artificial Intelligence (AI) Infrastructure Market Outlook, By Other Components (2022-2030) ($MN)
  • Table 20 Global Artificial Intelligence (AI) Infrastructure Market Outlook, By Deployment Mode (2022-2030) ($MN)
  • Table 21 Global Artificial Intelligence (AI) Infrastructure Market Outlook, By Cloud-Based (2022-2030) ($MN)
  • Table 22 Global Artificial Intelligence (AI) Infrastructure Market Outlook, By Public Cloud (2022-2030) ($MN)
  • Table 23 Global Artificial Intelligence (AI) Infrastructure Market Outlook, By Private Cloud (2022-2030) ($MN)
  • Table 24 Global Artificial Intelligence (AI) Infrastructure Market Outlook, By Hybrid Cloud (2022-2030) ($MN)
  • Table 25 Global Artificial Intelligence (AI) Infrastructure Market Outlook, By On-Premises (2022-2030) ($MN)
  • Table 26 Global Artificial Intelligence (AI) Infrastructure Market Outlook, By Technology (2022-2030) ($MN)
  • Table 27 Global Artificial Intelligence (AI) Infrastructure Market Outlook, By Machine Learning (ML) (2022-2030) ($MN)
  • Table 28 Global Artificial Intelligence (AI) Infrastructure Market Outlook, By Supervised Learning (2022-2030) ($MN)
  • Table 29 Global Artificial Intelligence (AI) Infrastructure Market Outlook, By Unsupervised Learning (2022-2030) ($MN)
  • Table 30 Global Artificial Intelligence (AI) Infrastructure Market Outlook, By Reinforcement Learning (2022-2030) ($MN)
  • Table 31 Global Artificial Intelligence (AI) Infrastructure Market Outlook, By Natural Language Processing (NLP) (2022-2030) ($MN)
  • Table 32 Global Artificial Intelligence (AI) Infrastructure Market Outlook, By Computer Vision (2022-2030) ($MN)
  • Table 33 Global Artificial Intelligence (AI) Infrastructure Market Outlook, By Speech Recognition (2022-2030) ($MN)
  • Table 34 Global Artificial Intelligence (AI) Infrastructure Market Outlook, By Deep Learning (DL) (2022-2030) ($MN)
  • Table 35 Global Artificial Intelligence (AI) Infrastructure Market Outlook, By Application (2022-2030) ($MN)
  • Table 36 Global Artificial Intelligence (AI) Infrastructure Market Outlook, By Data Management and Processing (2022-2030) ($MN)
  • Table 37 Global Artificial Intelligence (AI) Infrastructure Market Outlook, By Model Training and Development (2022-2030) ($MN)
  • Table 38 Global Artificial Intelligence (AI) Infrastructure Market Outlook, By Inference and Deployment (2022-2030) ($MN)
  • Table 39 Global Artificial Intelligence (AI) Infrastructure Market Outlook, By Predictive Analytics (2022-2030) ($MN)
  • Table 40 Global Artificial Intelligence (AI) Infrastructure Market Outlook, By Fraud Detection (2022-2030) ($MN)
  • Table 41 Global Artificial Intelligence (AI) Infrastructure Market Outlook, By Speech and Image Recognition (2022-2030) ($MN)
  • Table 42 Global Artificial Intelligence (AI) Infrastructure Market Outlook, By Customer Experience Management (2022-2030) ($MN)
  • Table 43 Global Artificial Intelligence (AI) Infrastructure Market Outlook, By Recommendation Systems (2022-2030) ($MN)
  • Table 44 Global Artificial Intelligence (AI) Infrastructure Market Outlook, By Other Applications (2022-2030) ($MN)
  • Table 45 Global Artificial Intelligence (AI) Infrastructure Market Outlook, By End User (2022-2030) ($MN)
  • Table 46 Global Artificial Intelligence (AI) Infrastructure Market Outlook, By Automotive and Transportation (2022-2030) ($MN)
  • Table 47 Global Artificial Intelligence (AI) Infrastructure Market Outlook, By Education (2022-2030) ($MN)
  • Table 48 Global Artificial Intelligence (AI) Infrastructure Market Outlook, By Banking, Financial Services, and Insurance (BFSI) (2022-2030) ($MN)
  • Table 49 Global Artificial Intelligence (AI) Infrastructure Market Outlook, By Retail and E-commerce (2022-2030) ($MN)
  • Table 50 Global Artificial Intelligence (AI) Infrastructure Market Outlook, By Government and Defense (2022-2030) ($MN)
  • Table 51 Global Artificial Intelligence (AI) Infrastructure Market Outlook, By Media and Entertainment (2022-2030) ($MN)
  • Table 52 Global Artificial Intelligence (AI) Infrastructure Market Outlook, By IT and Telecom (2022-2030) ($MN)
  • Table 53 Global Artificial Intelligence (AI) Infrastructure Market Outlook, By Healthcare and Life Sciences (2022-2030) ($MN)
  • Table 54 Global Artificial Intelligence (AI) Infrastructure Market Outlook, By Other End Users (2022-2030) ($MN)

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