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市场调查报告书
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2030 年人工智慧 (AI) 基础设施市场预测：按组件、部署模式、技术、应用、最终用户和地区进行的全球分析

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个工作天内

价格

根据 Stratistics MRC 的数据，全球人工智慧（AI）基础设施市场规模预计在 2024 年达到 479.6 亿美元，到 2030 年将达到 2,435.4 亿美元，预测期内的复合年增长率为 31.1%。

人工智慧基础设施是指支撑人工智慧应用的开发、部署和执行所需的底层技术和系统。这包括 GPU、CPU、FPGA 和 ASIC 等硬体元件，以及针对 AI 工作负载最佳化的软体框架、云端平台和资料储存解决方案。 AI基础设施实现高效的资料处理、模型训练和推理，支援机器学习、深度学习、自然语言处理等应用。

人工智慧在各行各业的应用日益广泛

医疗保健、汽车、金融、零售和製造等行业的公司都在使用人工智慧 (AI) 来提高业务效率、实现流程自动化并提供个人化体验。为了管理繁重的工作负载，机器人流程自动化、影像识别、自然语言处理和预测分析等应用程式需要强大的人工智慧基础设施。例如，汽车产业正在将人工智慧融入自动驾驶技术，而医疗保健产业正在将人工智慧用于药物研究和诊断。如此广泛的应用推动了对云端基础的解决方案、先进硬体和可扩展的高效能运算系统的需求，从而刺激对人工智慧基础设施开发的持续投资。

资料隐私和安全问题

人工智慧系统需要大量个人信息，包括财务、医疗和个人数据，来学习和做出决策。由于 CCPA、GDPR 和 HIPAA 等严格的法律，不当的资料处理可能会导致违规、未授权存取和违规。由于存在资料外洩和网路攻击的可能性，云端基础的人工智慧基础设施存在额外的漏洞。为了降低这些风险，建立强大的加密、安全的资料储存和存取控制系统至关重要。这些担忧不仅使人工智慧基础设施的采用变得复杂，而且影响了公司使用人工智慧的准备情况，尤其是在受到严格监管的行业中。

对高效能运算 (HPC) 的需求不断增加

人工智慧应用，尤其是使用机器学习和深度学习的应用，需要大量的处理能力来处理和分析大型资料集。 HPC 系统提供必要的处理能力，利用 GPU、平行运算和张量处理单元 (TPU) 等专用硬体来加速 AI 模型的推理和训练。随着人工智慧技术的发展，特别是在电脑视觉、自然语言处理和自主系统等领域，对更快、更强大的运算基础设施的需求日益增加。对尖端基础设施解决方案的投资是由 HPC 日益增长的需求推动的，以满足现代 AI 工作负载的效率、可扩展性和效能需求。

实施成本高

强大的处理资源和专用设备（例如 GPU 和 TPU）可能遥不可及。此外，开发和训练复杂的人工智慧模型、获取和维护高品质的资料以及聘请熟练的人工智慧专家都需要大量的资金投入。将人工智慧系统与目前IT基础设施结合非常困难、昂贵且耗时。综合起来，这些因素使得实施人工智慧对于各种规模的企业来说都是一项沉重的成本负担。

COVID-19 的影响

COVID-19疫情对人工智慧（AI）基础设施市场产生了多方面的影响。一方面，远距工作、医疗保健、电子商务和供应链管理对数位技术和人工智慧驱动的解决方案的日益依赖，加速了对人工智慧基础设施的需求。同时，全球供应链中断和经济不确定性减缓了新人工智慧计划的发展。儘管如此，这场疫情凸显了人工智慧对业务永续营运的重要性，并刺激了各行业对人工智慧基础设施的长期投资。

预测期内硬体部分预计将成为最大的部分

由于对支援机器学习、深度学习和资料分析等人工智慧应用的高效能运算的需求不断增长，硬体部分估计将是最大的部分。随着人工智慧模型变得越来越复杂，GPU、TPU 和 FPGA 等专用硬体对于加速处理速度和效率至关重要。此外，医疗保健、汽车和金融等行业越来越多地采用人工智慧，需要强大、可扩展且节能的硬体解决方案来处理大规模资料处理和即时推理。

预测期内，诈欺侦测领域预计将以最高复合年增长率成长

由于网路威胁日益复杂、即时决策的需求以及金融交易量的不断增加，预计诈欺侦测领域在预测期内将以最高的复合年增长率成长。配备高效能基础设施的人工智慧系统可以分析大量资料，比传统方法更快、更准确地侦测模式、异常和潜在的诈欺活动。人工智慧在诈欺侦测中的应用广泛，涵盖银行、电子商务、保险和金融服务领域，透过即时识别可疑活动，帮助组织防止诈欺、减少财务损失并增强安全性。

比最大的地区

由于各行业的快速市场占有率转型、政府对人工智慧计画的支持不断增加以及新兴企业生态系统蓬勃发展，预计亚太地区将在预测期内占据最大的市场份额。该地区庞大的人口加上不断增长的可支配收入，推动了电子商务、金融科技、医疗保健和智慧城市等领域对人工智慧解决方案的需求。此外，5G技术和云端运算的进步为人工智慧应用的广泛应用提供了必要的基础设施，进一步加速了市场成长。

复合年增长率最高的地区：

由于强劲的创业投资生态系统促进创新，北美预计将在预测期内实现最高的复合年增长率。私营和公共部门对人工智慧研究和开发的大量投资将进一步推动市场成长。该地区拥有高技能的劳动力和早期采用新兴技术的文化，使其成为人工智慧基础设施解决方案的理想市场。此外，医疗保健、金融和自动驾驶汽车等行业对人工智慧应用的需求不断增长，推动了对先进运算能力和专用硬体的需求，从而推动市场向前发展。

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
    • 全面分析其他市场参与者（最多 3 家公司）
    • 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进行的市场估计、预测和复合年增长率（註：基于可行性检查）
  • 竞争性基准化分析
    • 根据产品系列、地理分布和策略联盟对主要企业进行基准化分析

目录

第一章执行摘要

第 2 章 前言

  • 概述
  • 相关利益者
  • 研究范围
  • 调查方法
    • 资料探勘
    • 资料分析
    • 资料检验
    • 研究途径
  • 研究资讯来源
    • 主要研究资讯来源
    • 二手研究资料资讯来源
    • 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 4 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

5. 全球人工智慧（AI）基础设施市场（按组件）

  • 硬体
    • 图形处理单元 (GPU)
    • 中央处理器 (CPU)
    • 专用积体电路 (ASIC)
    • 现场可程式闸阵列(FPGA)
    • 记忆体和储存
    • 网路元件
  • 软体
    • 人工智慧平台
    • 作业系统
    • 人工智慧中介软体
    • 资料管理和分析工具
  • 服务
    • 整合与部署
    • 支援和维护
    • 咨询
  • 其他组件

6. 全球人工智慧（AI）基础设施市场按部署模式划分

  • 云端基础
    • 公共云端
    • 私有云端
    • 混合云端
  • 本地

7. 全球人工智慧（AI）基础设施市场（按技术）

  • 机器学习 (ML)
    • 监督学习
    • 无监督学习
    • 强化学习
  • 自然语言处理 (NLP)
  • 电脑视觉
  • 语音辨识
  • 深度学习（DL）

第八章 全球人工智慧（AI）基础设施市场（按应用）

  • 资料管理与处理
  • 模型训练与开发
  • 推理与发展
  • 预测分析
  • 诈欺侦测
  • 语音和图像识别
  • 客户体验管理
  • 推荐系​​统
  • 其他用途

第九章全球人工智慧（AI）基础设施市场（按最终用户划分）

  • 汽车与运输
  • 教育
  • 银行、金融服务和保险（BFSI）
  • 零售与电子商务
  • 政府和国防
  • 媒体和娱乐
  • 资讯科技和通讯
  • 医疗保健和生命科学
  • 其他最终用户

第 10 章 全球人工智慧 (AI) 基础设施市场（按区域）

  • 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

  • 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.