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市場調查報告書
商品編碼
2044415

生成式人工智慧運算和基礎設施市場預測至2034年——按組件、技術類型、部署模式、企業規模、最終用戶和地區分類的全球分析

Generative AI Compute & Infrastructure Market Forecasts to 2034 - Global Analysis By Component (Hardware, Software and Services), Technology Type, Deployment Mode, Enterprise Size, End User and By Geography
出版日期: | 出版商: Stratistics Market Research Consulting | 英文 | 商品交期: 2-3個工作天內

價格

根據 Stratistics MRC 的數據，預計到 2026 年，全球生成式人工智慧運算基礎設施市場規模將達到 645.5 億美元，在預測期內將以 35.9% 的複合年成長率成長，到 2034 年將達到 7510.4 億美元。

生成式人工智慧運算基礎設施是指開發、訓練和部署生成式人工智慧模型所需的硬體和軟體生態系統。這包括高效能GPU、專用人工智慧晶片、雲端運算平台、資料儲存系統和可擴展的網路架構。這些資源支援大規模語言模型、影像生成模型和多模態人工智慧系統的巨大計算負載。該基礎設施還包括模型編配、資料管道和最佳化框架。隨著生成式人工智慧的普及，強大的運算基礎設施對於確保效能、可擴展性和成本效益至關重要，因此吸引了全球技術供應商和企業的大量投資。

人工智慧模型的複雜性和資料量的快速成長

大規模語言模型和多模態人工智慧系統的快速發展，對強大的運算基礎設施提出了永無止境的需求。隨著模型規模和複雜性的成長，需要數兆個參數，對GPU和TPU等專用硬體的需求也隨之激增。各組織機構正大力投資可擴展的基礎設施，以處理訓練和推理所需的大量資料集。部署尖端生成式人工智慧應用的競爭迫使企業提升其資料中心能力。這種日益成長的複雜性從根本上推動了專用生成式人工智慧運算和基礎設施的擴展，以支援下一代人工智慧工作負載。

高昂的基礎設施成本和硬體短缺

部署生成式人工智慧所需的運算和基礎設施需要大量的資本投入，這構成了一大障礙，尤其對於小規模企業而言更是如此。 GPU 和 TPU 等先進處理器的高成本，加上缺乏永續的供應鏈，造成了取得方面的挑戰。此外，運行大規模人工智慧模型所需的能源消耗會導致營運成本增加，進而影響整體擁有成本 (TCO)。專用硬體組件的稀缺性通常會導致基礎設施部署前置作業時間延長。這些財務和物流方面的障礙會扼殺創新，限制市場參與企業，並阻礙中小企業在人工智慧主導的環境中進行有效競爭。

邊緣人工智慧和分散式運算的擴展

對低延遲處理和資料隱私日益成長的需求正推動生成式人工智慧（AI）能力向邊緣端擴展。在智慧型手機和物聯網感測器等邊緣設備上部署AI推理，可以減少對集中式雲端資料中心的依賴，並最大限度地降低頻寬成本。這種轉變為專用邊緣AI處理器和專為分散式環境設計的最佳化軟體框架創造了機會。自動駕駛汽車和製造業等行業正在利用邊緣基礎設施進行即時決策。隨著各組織圖平衡，分散式運算模型正在為基礎設施供應商開闢新的創新途徑，並幫助他們開拓新興市場。

不斷變化的監管環境和資料管治

人工智慧領域法規環境的快速變化對基礎設施部署策略構成重大威脅。針對人工智慧安全、資料隱私和智慧財產權的新法律法規可能會對基礎設施架構施加嚴格的合規要求。企業在訓練資料的儲存地點和方式，以及模型的部署地點和方式（尤其是在跨境部署方面）可能會面臨許多限制。未來監管的不確定性會使長期基礎設施規劃變得困難，並導致合規成本增加。未能適應這些不斷變化的法律體制可能會導致營運中斷、法律責任以及基礎設施提供者及其客戶市場進入受限。

新冠疫情的感染疾病

疫情加速了數位轉型，凸顯了擴充性且具彈性的AI基礎設施的重要性。初期，全球供應鏈中斷影響了關鍵硬體組件的供應，導致專案延長。然而，隨著各組織機構採用遠距辦公和數位化協作，這場危機刺激了對雲端AI服務的巨額投資。在醫療保健和生命科學領域，生成式AI在藥物研發和診斷支援方面迅速普及，推動了基礎設施需求。後疫情時代的策略重點在於供應鏈多元化、增加對混合雲端架構的投資，以及開發更節能的運算解決方案，以確保業務永續營運並支持AI的持續創新。

在預測期內，硬體領域預計將佔據最大的市場佔有率。

預計在預測期內，硬體領域將佔據最大的市場佔有率。這主要源自於高效能運算能力對於訓練和運行複雜的生成式人工智慧模型至關重要這一基本需求。 GPU 和 TPU 等專用元件構成了人工智慧基礎架構的基礎，能夠實現深度學習演算法所需的平行處理。隨著模型規模持續呈指數級成長，各組織機構正大力投資先進的硬體加速器和高頻寬記憶體系統。

預計在預測期內，醫療保健和生命科學產業將呈現最高的複合年成長率。

在預測期內，醫療保健和生命科學領域預計將呈現最高的成長率，這主要得益於生成式人工智慧在藥物發現、醫學影像和個人化醫療領域的巨大變革潛力。人工智慧基礎設施正助力研究人員產生新型分子結構、加速臨床試驗模擬並提高診斷準確性。人工智慧驅動的基因組分析和合成數據生成解決方案的日益普及，催生了對受監管、可擴展運算資源的強勁需求。隨著法規結構的不斷改進以適應人工智慧在臨床環境中的應用，醫療機構正大力投資於專用基礎設施。

市佔率最大的地區：

在預測期內，北美預計將佔據最大的市場佔有率，這得益於領先的技術創新者以及大量的創業投資投資。該地區擁有眾多主要的雲端服務供應商和人工智慧研究機構，它們正在推動先進基礎設施的早期應用。政府對人工智慧舉措的大力支持以及蓬勃發展的新創企業生態系統，都為北美市場的主導地位做出了貢獻。配備下一代硬體的資料中心的集中部署，確保了企業部署的擴充性。

複合年成長率最高的地區：

在預測期內，亞太地區預計將呈現最高的複合年成長率，這主要得益於快速的數位化進程和政府主導的大規模人工智慧舉措。中國、印度和日本等國正大力投資國內半導體生產和國家級人工智慧運算平台。雲端資料中心的擴張和科技型企業的不斷湧現正在加速基礎設施的部署。製造業、醫療保健和金融業對本地化人工智慧解決方案的需求日益成長，也推動了市場成長。全球技術領導者與區域供應商之間的策略合作正在促進技術轉移。

免費客製化服務：

所有購買此報告的客戶均可享受以下免費自訂選項之一：

  • 企業概況
    • 對其他市場參與者（最多 3 家公司）進行全面分析
    • 對主要公司進行SWOT分析（最多3家公司）
  • 區域細分
    • 應客戶要求，我們提供主要國家的市場估算和預測，以及複合年成長率（註：需進行可行性檢查）。
  • 競爭性標竿分析
    • 根據產品系列、地理覆蓋範圍和策略聯盟對領先公司進行基準分析。

目錄

第1章：執行摘要

  • 市場概覽及主要亮點
  • 促進因素、挑戰與機遇
  • 競爭格局概述
  • 戰略洞察與建議

第2章：研究框架

  • 研究目標和範圍
  • 相關人員分析
  • 研究假設和限制
  • 調查方法

第3章 市場動態與趨勢分析

  • 市場定義與結構
  • 主要市場促進因素
  • 市場限制與挑戰
  • 投資成長機會和重點領域
  • 產業威脅與風險評估
  • 技術與創新展望
  • 新興市場/高成長市場
  • 監管和政策環境
  • 新冠疫情的影響及復甦前景

第4章：競爭環境與策略評估

  • 波特五力分析
    • 供應商的議價能力
    • 買方的議價能力
    • 替代品的威脅
    • 新進入者的威脅
    • 競爭公司之間的競爭
  • 主要公司市佔率分析
  • 產品基準評效和效能比較

第5章：全球生成式人工智慧運算與基礎設施市場：按組件分類

  • 硬體
    • GPU
    • TPU
    • 專用積體電路（ASIC）和現場可程式閘陣列（FPGA）
    • 邊緣人工智慧處理器
  • 軟體
    • 生成式人工智慧框架
    • 模型開發工具
    • 實施和編配平台
  • 服務
    • 諮詢
    • 整合與實施
    • 支援和管理服務

第6章：全球生成式人工智慧運算與基礎設施市場：按技術類型分類

  • 深度學習
  • 變壓器模型
  • 生成對抗網路（GAN）
  • 變分自編碼器
  • 其他架構

第7章：全球生成式人工智慧運算與基礎設施市場：依部署模式分類

  • 現場
  • 雲
  • 混合

第8章：全球生成式人工智慧運算與基礎設施市場：依企業規模分類

  • 大公司
  • 小型企業

第9章：全球生成式人工智慧運算與基礎設施市場：按最終用戶分類

  • 銀行、金融服務和保險（BFSI）
  • 醫療保健和生命科學
  • 零售與電子商務
  • 電訊
  • 汽車和運輸業
  • 製造業
  • 媒體與娛樂
  • 政府/國防
  • 教育

第10章：全球生成式人工智慧運算與基礎設施市場：按地區分類

  • 北美洲
    • 美國
    • 加拿大
    • 墨西哥
  • 歐洲
    • 英國
    • 德國
    • 法國
    • 義大利
    • 西班牙
    • 荷蘭
    • 比利時
    • 瑞典
    • 瑞士
    • 波蘭
    • 其他歐洲國家
  • 亞太地區
    • 中國
    • 日本
    • 印度
    • 韓國
    • 澳洲
    • 印尼
    • 泰國
    • 馬來西亞
    • 新加坡
    • 越南
    • 其他亞太國家
  • 南美洲
    • 巴西
    • 阿根廷
    • 哥倫比亞
    • 智利
    • 秘魯
    • 其他南美國家
  • 世界其他地區（RoW）
    • 中東
      • 沙烏地阿拉伯
      • 阿拉伯聯合大公國
      • 卡達
      • 以色列
      • 其他中東國家
    • 非洲
      • 南非
      • 埃及
      • 摩洛哥
      • 其他非洲國家

第11章 策略市場資訊

  • 工業價值網路和供應鏈評估
  • 空白區域和機會地圖
  • 產品演進與市場生命週期分析
  • 通路、經銷商和打入市場策略的評估

第12章 產業趨勢與策略舉措

  • 併購
  • 夥伴關係、聯盟和合資企業
  • 新產品發布和認證
  • 擴大生產能力和投資
  • 其他策略舉措

第13章：公司簡介

  • NVIDIA
  • Microsoft
  • Google
  • Amazon Web Services（AWS）
  • IBM
  • OpenAI
  • Anthropic
  • Cohere
  • Oracle
  • AMD
  • Intel
  • SK Hynix
  • Samsung Electronics
  • Micron Technology
  • CoreWeave
Product Code: SMRC36204

According to Stratistics MRC, the Global Generative AI Compute & Infrastructure Market is accounted for $64.55 billion in 2026 and is expected to reach $751.04 billion by 2034 growing at a CAGR of 35.9% during the forecast period. Generative AI Compute & Infrastructure refers to the hardware and software ecosystem required to develop, train, and deploy generative AI models. This includes high-performance GPUs, specialized AI chips, cloud computing platforms, data storage systems, and scalable networking architectures. These resources support the intensive computational demands of large language models, image generators, and multimodal AI systems. The infrastructure also encompasses model orchestration, data pipelines, and optimization frameworks. As generative AI adoption grows, robust compute infrastructure is critical for ensuring performance, scalability, and cost efficiency, driving significant investments from technology providers and enterprises worldwide.

Market Dynamics:

Driver:

Exponential growth in AI model complexity and data volume

The rapid advancement of large language models and multimodal AI systems is creating an insatiable demand for robust computational infrastructure. As models grow in size and complexity, requiring trillions of parameters, the need for specialized hardware such as GPUs and TPUs has surged. Organizations are investing heavily in scalable infrastructure to handle the massive datasets necessary for training and inference. The competitive race to deploy cutting-edge generative AI applications is compelling enterprises to upgrade their data center capabilities. This escalating complexity is fundamentally driving the expansion of dedicated Generative AI Compute & Infrastructure to support next-generation artificial intelligence workloads.

Restraint:

High infrastructure costs and hardware scarcity

The substantial capital expenditure required for deploying Generative AI Compute & Infrastructure presents a significant barrier, particularly for smaller organizations. The high cost of advanced processors like GPUs and TPUs, coupled with persistent supply chain shortages, creates accessibility challenges. Additionally, the energy consumption associated with running large-scale AI models leads to elevated operational expenses, impacting total cost of ownership. The scarcity of specialized hardware components often results in extended lead times for infrastructure deployment. These financial and logistical hurdles can stifle innovation and limit market participation, preventing smaller enterprises from effectively competing in the AI-driven landscape.

Opportunity:

Expansion of edge AI and decentralized computing

The growing need for low-latency processing and data privacy is driving the expansion of generative AI capabilities to the edge. Deploying AI inference on edge devices, such as smartphones and IoT sensors, reduces reliance on centralized cloud data centers and minimizes bandwidth costs. This shift is creating opportunities for specialized edge AI processors and optimized software frameworks designed for distributed environments. Industries like autonomous vehicles and manufacturing are leveraging edge infrastructure for real-time decision-making. As organizations seek to balance performance with data sovereignty, decentralized computing models are opening new avenues for infrastructure providers to innovate and capture emerging market segments.

Threat:

Evolving regulatory landscape and data governance

The rapidly changing regulatory environment surrounding artificial intelligence poses a significant threat to infrastructure deployment strategies. New legislation focused on AI safety, data privacy, and intellectual property rights could impose strict compliance requirements on infrastructure architecture. Organizations may face constraints on where and how they can store training data or deploy models, particularly across international borders. Uncertainty regarding future regulations makes long-term infrastructure planning challenging and could lead to increased compliance costs. Failure to adapt to these evolving legal frameworks may result in operational disruptions, legal liabilities, and restricted market access for infrastructure providers and their clients.

Covid-19 Impact

The pandemic accelerated the digital transformation agenda, highlighting the critical need for scalable and resilient AI infrastructure. Initial disruptions in global supply chains affected the availability of essential hardware components, leading to project delays. However, the crisis spurred significant investment in cloud-based AI services as organizations embraced remote work and digital collaboration. Healthcare and life sciences sectors rapidly adopted generative AI for drug discovery and diagnostic support, driving infrastructure demand. Post-pandemic strategies now emphasize supply chain diversification, increased investment in hybrid cloud architectures, and the development of more energy-efficient computing solutions to ensure business continuity and support sustained AI innovation.

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

The hardware segment is expected to account for the largest market share during the forecast period, driven by the fundamental requirement for high-performance computing power to train and run complex generative AI models. Specialized components such as GPUs and TPUs form the backbone of AI infrastructure, enabling the parallel processing necessary for deep learning algorithms. As model sizes continue to scale exponentially, organizations are making substantial capital investments in advanced hardware accelerators and high-bandwidth memory systems.

The healthcare & life sciences segment is expected to have the highest CAGR during the forecast period

Over the forecast period, the healthcare and life sciences segment is predicted to witness the highest growth rate, fueled by the transformative potential of generative AI in drug discovery, medical imaging, and personalized medicine. AI infrastructure is enabling researchers to generate novel molecular structures, accelerate clinical trial simulations, and enhance diagnostic accuracy. The increasing adoption of AI-driven solutions for genomic analysis and synthetic data generation is creating robust demand for compliant and scalable computational resources. As regulatory frameworks evolve to accommodate AI in clinical settings, healthcare organizations are investing heavily in dedicated infrastructure.

Region with largest share:

During the forecast period, the North America region is expected to hold the largest market share, supported by the presence of major technology innovators and substantial venture capital investment. The region is home to leading cloud service providers and AI research institutions that drive early adoption of advanced infrastructure. Strong government funding for AI initiatives and a robust ecosystem of startups contribute to market dominance. The concentration of data centers equipped with next-generation hardware ensures scalability for enterprise deployments.

Region with highest CAGR:

Over the forecast period, the Asia Pacific region is anticipated to exhibit the highest CAGR, driven by rapid digitalization and massive government-backed AI initiatives. Countries like China, India, and Japan are investing heavily in domestic semiconductor production and national AI computing platforms. The expansion of cloud data centers and the proliferation of tech-savvy enterprises are accelerating infrastructure adoption. Growing demand for localized AI solutions in manufacturing, healthcare, and finance is fueling market growth. Strategic partnerships between global technology leaders and regional providers are enhancing technology transfer.

Key players in the market

Some of the key players in Generative AI Compute & Infrastructure Market include NVIDIA, Microsoft, Google, Amazon Web Services (AWS), IBM, OpenAI, Anthropic, Cohere, Oracle, AMD, Intel, SK Hynix, Samsung Electronics, Micron Technology, and CoreWeave.

Key Developments:

In March 2026, IBM and ETH Zurich announced a 10-year collaboration to advance the next generation of algorithms at the intersection of AI and quantum computing. This initiative represents the latest milestone in the long-standing collaboration between the two institutions, further strengthening a scientific exchange that has helped create the future of information technology.

In March 2026, NVIDIA and Marvell Technology, Inc. announced a strategic partnership to connect Marvell to the NVIDIA AI factory and AI-RAN ecosystem through NVIDIA NVLink Fusion(TM), offering customers building on NVIDIA architectures greater choice and flexibility in developing next-generation infrastructure. The companies will also collaborate on silicon photonics technology.

Components Covered:

  • Hardware
  • Software
  • Services

Technology Types Covered:

  • Deep Learning
  • Transformer Models
  • GANs (Generative Adversarial Networks)
  • Variational Autoencoders
  • Other Architectures

Deployment Modes Covered:

  • On Premises
  • Cloud
  • Hybrid

Enterprise Sizes Covered:

  • Large Enterprises
  • Small & Medium Enterprises

End Users Covered:

  • Banking, Financial Services, & Insurance (BFSI)
  • Healthcare & Life Sciences
  • Retail & E Commerce
  • Telecommunications
  • Automotive & Transportation
  • Manufacturing
  • Media & Entertainment
  • Government & Defense
  • Education

Regions Covered:

  • North America
    • United States
    • Canada
    • Mexico
  • Europe
    • United Kingdom
    • Germany
    • France
    • Italy
    • Spain
    • Netherlands
    • Belgium
    • Sweden
    • Switzerland
    • Poland
    • Rest of Europe
  • Asia Pacific
    • China
    • Japan
    • India
    • South Korea
    • Australia
    • Indonesia
    • Thailand
    • Malaysia
    • Singapore
    • Vietnam
    • Rest of Asia Pacific
  • South America
    • Brazil
    • Argentina
    • Colombia
    • Chile
    • Peru
    • Rest of South America
  • Rest of the World (RoW)
    • Middle East
      • Saudi Arabia
      • United Arab Emirates
      • Qatar
      • Israel
      • Rest of Middle East
    • Africa
      • South Africa
      • Egypt
      • Morocco
      • Rest of 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 2023, 2024, 2025, 2026, 2027, 2028, 2030, 2032 and 2034
  • 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

  • 1.1 Market Snapshot and Key Highlights
  • 1.2 Growth Drivers, Challenges, and Opportunities
  • 1.3 Competitive Landscape Overview
  • 1.4 Strategic Insights and Recommendations

2 Research Framework

  • 2.1 Study Objectives and Scope
  • 2.2 Stakeholder Analysis
  • 2.3 Research Assumptions and Limitations
  • 2.4 Research Methodology
    • 2.4.1 Data Collection (Primary and Secondary)
    • 2.4.2 Data Modeling and Estimation Techniques
    • 2.4.3 Data Validation and Triangulation
    • 2.4.4 Analytical and Forecasting Approach

3 Market Dynamics and Trend Analysis

  • 3.1 Market Definition and Structure
  • 3.2 Key Market Drivers
  • 3.3 Market Restraints and Challenges
  • 3.4 Growth Opportunities and Investment Hotspots
  • 3.5 Industry Threats and Risk Assessment
  • 3.6 Technology and Innovation Landscape
  • 3.7 Emerging and High-Growth Markets
  • 3.8 Regulatory and Policy Environment
  • 3.9 Impact of COVID-19 and Recovery Outlook

4 Competitive and Strategic Assessment

  • 4.1 Porter's Five Forces Analysis
    • 4.1.1 Supplier Bargaining Power
    • 4.1.2 Buyer Bargaining Power
    • 4.1.3 Threat of Substitutes
    • 4.1.4 Threat of New Entrants
    • 4.1.5 Competitive Rivalry
  • 4.2 Market Share Analysis of Key Players
  • 4.3 Product Benchmarking and Performance Comparison

5 Global Generative AI Compute & Infrastructure Market, By Component

  • 5.1 Hardware
    • 5.1.1 GPUs
    • 5.1.2 TPUs
    • 5.1.3 ASICs & FPGAs
    • 5.1.4 Edge AI Processors
  • 5.2 Software
    • 5.2.1 Generative AI Frameworks
    • 5.2.2 Model Development Tools
    • 5.2.3 Deployment & Orchestration Platforms
  • 5.3 Services
    • 5.3.1 Consulting
    • 5.3.2 Integration & Implementation
    • 5.3.3 Support & Managed Services

6 Global Generative AI Compute & Infrastructure Market, By Technology Type

  • 6.1 Deep Learning
  • 6.2 Transformer Models
  • 6.3 GANs (Generative Adversarial Networks)
  • 6.4 Variational Autoencoders
  • 6.5 Other Architectures

7 Global Generative AI Compute & Infrastructure Market, By Deployment Mode

  • 7.1 On Premises
  • 7.2 Cloud
  • 7.3 Hybrid

8 Global Generative AI Compute & Infrastructure Market, By Enterprise Size

  • 8.1 Large Enterprises
  • 8.2 Small & Medium Enterprises

9 Global Generative AI Compute & Infrastructure Market, By End User

  • 9.1 Banking, Financial Services, & Insurance (BFSI)
  • 9.2 Healthcare & Life Sciences
  • 9.3 Retail & E Commerce
  • 9.4 Telecommunications
  • 9.5 Automotive & Transportation
  • 9.6 Manufacturing
  • 9.7 Media & Entertainment
  • 9.8 Government & Defense
  • 9.9 Education

10 Global Generative AI Compute & Infrastructure Market, By Geography

  • 10.1 North America
    • 10.1.1 United States
    • 10.1.2 Canada
    • 10.1.3 Mexico
  • 10.2 Europe
    • 10.2.1 United Kingdom
    • 10.2.2 Germany
    • 10.2.3 France
    • 10.2.4 Italy
    • 10.2.5 Spain
    • 10.2.6 Netherlands
    • 10.2.7 Belgium
    • 10.2.8 Sweden
    • 10.2.9 Switzerland
    • 10.2.10 Poland
    • 10.2.11 Rest of Europe
  • 10.3 Asia Pacific
    • 10.3.1 China
    • 10.3.2 Japan
    • 10.3.3 India
    • 10.3.4 South Korea
    • 10.3.5 Australia
    • 10.3.6 Indonesia
    • 10.3.7 Thailand
    • 10.3.8 Malaysia
    • 10.3.9 Singapore
    • 10.3.10 Vietnam
    • 10.3.11 Rest of Asia Pacific
  • 10.4 South America
    • 10.4.1 Brazil
    • 10.4.2 Argentina
    • 10.4.3 Colombia
    • 10.4.4 Chile
    • 10.4.5 Peru
    • 10.4.6 Rest of South America
  • 10.5 Rest of the World (RoW)
    • 10.5.1 Middle East
      • 10.5.1.1 Saudi Arabia
      • 10.5.1.2 United Arab Emirates
      • 10.5.1.3 Qatar
      • 10.5.1.4 Israel
      • 10.5.1.5 Rest of Middle East
    • 10.5.2 Africa
      • 10.5.2.1 South Africa
      • 10.5.2.2 Egypt
      • 10.5.2.3 Morocco
      • 10.5.2.4 Rest of Africa

11 Strategic Market Intelligence

  • 11.1 Industry Value Network and Supply Chain Assessment
  • 11.2 White-Space and Opportunity Mapping
  • 11.3 Product Evolution and Market Life Cycle Analysis
  • 11.4 Channel, Distributor, and Go-to-Market Assessment

12 Industry Developments and Strategic Initiatives

  • 12.1 Mergers and Acquisitions
  • 12.2 Partnerships, Alliances, and Joint Ventures
  • 12.3 New Product Launches and Certifications
  • 12.4 Capacity Expansion and Investments
  • 12.5 Other Strategic Initiatives

13 Company Profiles

  • 13.1 NVIDIA
  • 13.2 Microsoft
  • 13.3 Google
  • 13.4 Amazon Web Services (AWS)
  • 13.5 IBM
  • 13.6 OpenAI
  • 13.7 Anthropic
  • 13.8 Cohere
  • 13.9 Oracle
  • 13.10 AMD
  • 13.11 Intel
  • 13.12 SK Hynix
  • 13.13 Samsung Electronics
  • 13.14 Micron Technology
  • 13.15 CoreWeave

List of Tables

  • Table 1 Global Generative AI Compute & Infrastructure Market Outlook, By Region (2023-2034) ($MN)
  • Table 2 Global Generative AI Compute & Infrastructure Market Outlook, By Component (2023-2034) ($MN)
  • Table 3 Global Generative AI Compute & Infrastructure Market Outlook, By Hardware (2023-2034) ($MN)
  • Table 4 Global Generative AI Compute & Infrastructure Market Outlook, By GPUs (2023-2034) ($MN)
  • Table 5 Global Generative AI Compute & Infrastructure Market Outlook, By TPUs (2023-2034) ($MN)
  • Table 6 Global Generative AI Compute & Infrastructure Market Outlook, By ASICs & FPGAs (2023-2034) ($MN)
  • Table 7 Global Generative AI Compute & Infrastructure Market Outlook, By Edge AI Processors (2023-2034) ($MN)
  • Table 8 Global Generative AI Compute & Infrastructure Market Outlook, By Software (2023-2034) ($MN)
  • Table 9 Global Generative AI Compute & Infrastructure Market Outlook, By Generative AI Frameworks (2023-2034) ($MN)
  • Table 10 Global Generative AI Compute & Infrastructure Market Outlook, By Model Development Tools (2023-2034) ($MN)
  • Table 11 Global Generative AI Compute & Infrastructure Market Outlook, By Deployment & Orchestration Platforms (2023-2034) ($MN)
  • Table 12 Global Generative AI Compute & Infrastructure Market Outlook, By Services (2023-2034) ($MN)
  • Table 13 Global Generative AI Compute & Infrastructure Market Outlook, By Consulting (2023-2034) ($MN)
  • Table 14 Global Generative AI Compute & Infrastructure Market Outlook, By Integration & Implementation (2023-2034) ($MN)
  • Table 15 Global Generative AI Compute & Infrastructure Market Outlook, By Support & Managed Services (2023-2034) ($MN)
  • Table 16 Global Generative AI Compute & Infrastructure Market Outlook, By Technology Type (2023-2034) ($MN)
  • Table 17 Global Generative AI Compute & Infrastructure Market Outlook, By Deep Learning (2023-2034) ($MN)
  • Table 18 Global Generative AI Compute & Infrastructure Market Outlook, By Transformer Models (2023-2034) ($MN)
  • Table 19 Global Generative AI Compute & Infrastructure Market Outlook, By GANs (Generative Adversarial Networks) (2023-2034) ($MN)
  • Table 20 Global Generative AI Compute & Infrastructure Market Outlook, By Variational Autoencoders (2023-2034) ($MN)
  • Table 21 Global Generative AI Compute & Infrastructure Market Outlook, By Other Architectures (2023-2034) ($MN)
  • Table 22 Global Generative AI Compute & Infrastructure Market Outlook, By Deployment Mode (2023-2034) ($MN)
  • Table 23 Global Generative AI Compute & Infrastructure Market Outlook, By On Premises (2023-2034) ($MN)
  • Table 24 Global Generative AI Compute & Infrastructure Market Outlook, By Cloud (2023-2034) ($MN)
  • Table 25 Global Generative AI Compute & Infrastructure Market Outlook, By Hybrid (2023-2034) ($MN)
  • Table 26 Global Generative AI Compute & Infrastructure Market Outlook, By Enterprise Size (2023-2034) ($MN)
  • Table 27 Global Generative AI Compute & Infrastructure Market Outlook, By Large Enterprises (2023-2034) ($MN)
  • Table 28 Global Generative AI Compute & Infrastructure Market Outlook, By Small & Medium Enterprises (2023-2034) ($MN)
  • Table 29 Global Generative AI Compute & Infrastructure Market Outlook, By End User (2023-2034) ($MN)
  • Table 30 Global Generative AI Compute & Infrastructure Market Outlook, By Banking, Financial Services, & Insurance (BFSI) (2023-2034) ($MN)
  • Table 31 Global Generative AI Compute & Infrastructure Market Outlook, By Healthcare & Life Sciences (2023-2034) ($MN)
  • Table 32 Global Generative AI Compute & Infrastructure Market Outlook, By Retail & E Commerce (2023-2034) ($MN)
  • Table 33 Global Generative AI Compute & Infrastructure Market Outlook, By Telecommunications (2023-2034) ($MN)
  • Table 34 Global Generative AI Compute & Infrastructure Market Outlook, By Automotive & Transportation (2023-2034) ($MN)
  • Table 35 Global Generative AI Compute & Infrastructure Market Outlook, By Manufacturing (2023-2034) ($MN)
  • Table 36 Global Generative AI Compute & Infrastructure Market Outlook, By Media & Entertainment (2023-2034) ($MN)
  • Table 37 Global Generative AI Compute & Infrastructure Market Outlook, By Government & Defense (2023-2034) ($MN)
  • Table 38 Global Generative AI Compute & Infrastructure Market Outlook, By Education (2023-2034) ($MN)

Note: Tables for North America, Europe, APAC, South America, and Rest of the World (RoW) are also represented in the same manner as above.