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市場調查報告書
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人工智慧運算硬體市場預測至2034年—按硬體類型、運算能力、部署模式、應用、最終用戶和地區分類的全球分析

AI Compute Hardware Market Forecasts to 2034 - Global Analysis By Hardware Type (GPUs, AI Accelerators, CPUs, AI ASICs, FPGA Accelerators, AI Compute Systems and Servers), Compute Function, Deployment, 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，預計到 2026 年，全球人工智慧計算硬體市場規模將達到 928 億美元，並在預測期內以 17.0% 的複合年成長率成長，到 2034 年將達到 3259 億美元。

人工智慧運算硬體是指專用於運行人工智慧工作負載（包括訓練和推理）的處理器、加速器和系統。該市場涵蓋GPU、AI加速器、CPU、AI專用積體電路（ASIC）、FPGA加速器以及用於AI訓練、AI推理和訓練與推理相結合的AI計算系統和伺服器。這些系統部署在資料中心、邊緣運算、企業運算和高效能運算環境中，並應用於生成式人工智慧、機器學習、電腦視覺、自然語言處理、建議和預測分析以及自主系統等應用。該市場的目標客戶包括雲端服務供應商、超大規模資料中心業者、企業、政府和國防機構、研究和學術機構以及汽車和旅遊公司。

生成式人工智慧和大規模語言模型工作負載的爆炸性成長

生成式人工智慧應用和大規模語言模型的快速普及是人工智慧運算硬體市場的主要驅動力。訓練和運行這些模型需要龐大的運算資源，對專用人工智慧硬體產生了前所未有的需求。訓練擁有數百億個參數的大規模語言模型需要數千個GPU。人工智慧聊天機器人和生成式應用的推理工作負載也需要大規模的專用加速器。雲端服務供應商和企業正在大力投資人工智慧基礎設施以應對這些工作負載。隨著生成式人工智慧在各行業的應用加速，對人工智慧運算硬體的需求持續呈指數級成長，從而支撐著市場的持續擴張。

先進人工智慧晶片高成本且供應鏈受限

先進人工智慧硬體的高昂成本和持續的供應鏈限制是市場發展的主要障礙。高階人工智慧GPU和加速器單價高達數萬美元，使得一些機構難以負擔人工智慧基礎設施的投資。人工智慧晶片先進封裝能力的不足造成了供應瓶頸。出口限制和地緣政治緊張局勢也對全球供應鏈產生影響。高密度人工智慧運算集群的電力和散熱需求推高了營運成本。這些成本和供應方面的因素可能會限制市場成長，尤其小規模的機構和難以獲得先進晶片的地區而言更是如此。

開發客製化人工智慧專用積體電路和特定領域架構

客製化人工智慧專用積體電路（ASIC）和領域特定架構的持續創新為人工智慧運算硬體市場的擴張帶來了巨大機會。超大規模資料中心業者正在開發針對其特定人工智慧工作負載最佳化的客製化晶片，從而提升效能並降低成本。與通用GPU相比，用於推理的領域特定架構具有更高的效率。神經處理單元（NPU）和張量處理架構則針對特定人工智慧任務進行了效能最佳化。隨著人工智慧工作負載日益多樣化，客製化變得愈發重要，專用人工智慧硬體的市場佔有率正在不斷擴大。

人工智慧資料中心面臨的電力和溫度控管挑戰

人工智慧計算硬體極高的功率密度和溫度控管需求對市場成長構成重大威脅。人工智慧集群消耗大量電力，對資料中心電力基礎設施造成巨大壓力。高密度GPU機架的冷卻需求需要先進的液冷解決方案。對電力供應和永續性的擔憂可能會限制某些地區人工智慧資料中心的擴張。這些基礎設施挑戰可能會減緩人工智慧硬體的普及，尤其是在電力供應緊張的市場。

新型冠狀病毒（COVID-19）的影響：

新冠疫情提升了數位轉型和自動化的優先級，加速了人工智慧運算硬體市場的成長。遠距辦公和線上服務的普及增加了對人工智慧應用的需求。疫情期間，供應鏈中斷影響了硬體供應。然而，疫情凸顯了人工智慧能力的戰略重要性，並刺激了投資成長。後疫情時代，生成式人工智慧的突破性進展正在推動人工智慧運算硬體的爆炸性成長。

在預測期內，GPU細分市場預計將佔據最大的市場佔有率。

預計在預測期內，GPU領域將佔據最大的市場佔有率，這主要得益於其在人工智慧訓練和推理工作負載方面的領先地位、成熟的軟體生態系統以及持續的效能提升。 GPU提供深度學習和神經網路運算所需的平行處理能力。雲端服務供應商、企業和研究機構的廣泛採用正推動著這一領域的發展。 NVIDIA的GPU平台在人工智慧硬體市場佔據主導地位。隨著人工智慧模型複雜性的增加，對GPU的需求持續成長，使其在硬體類型細分市場中保持最大的佔有率。

預計在預測期內，人工智慧推理領域將呈現最高的複合年成長率。

在預測期內，人工智慧推理領域預計將呈現最高的成長率，這主要得益於人工智慧應用在生產環境中的快速部署、對即時人工智慧處理需求的不斷成長以及邊緣人工智慧應用的日益普及。隨著人工智慧模型從開發階段過渡到部署階段，推理工作負載的成長速度超過了訓練工作負載。該領域受益於人工智慧在消費應用、自動駕駛系統和企業工作流程中日益廣泛的應用。隨著對人工智慧推理需求的加速成長，這種運算能力正在推動該領域內最快的成長。

市佔率最大的地區：

在整個預測期內，北美預計將保持最大的市場佔有率，這得益於領先的人工智慧硬體製造商、超大規模雲端服務供應商和主要人工智慧研究機構的存在。美國透過對人工智慧基礎設施的大量投資，正推動該地區的成長。主要科技公司總部設在該地區，推動創新和應用。跨產業的大量人工智慧投資也支撐著市場成長。技術領先地位和集中投資使北美能夠保持其市場主導地位。

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，這主要得益於中國、日本、韓國和印度人工智慧技術的快速普及、政府主導的人工智慧舉措以及資料中心基礎設施的擴張。該地區龐大且持續成長的科技產業正在催生對人工智慧運算硬體的巨大需求。政府推動人工智慧發展的項目正在加速投資。隨著人工智慧應用和基礎設施投資的不斷擴大，亞太地區的人工智慧運算硬體市場正以全球最快的速度成長。

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）
  • 區域分類
    • 根據客戶的興趣，我們將提供主要國家的市場估算和預測，以及複合年成長率（註：需經可行性確認）。
  • 競爭性標竿分析
    • 根據產品系列、企業發展和策略聯盟對重點公司進行基準分析。

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：全球人工智慧運算硬體市場：按硬體類型分類

  • GPU
  • 人工智慧加速器
  • CPU
  • AI ASIC
  • FPGA加速器
  • 人工智慧運算系統和伺服器

第6章 全球人工智慧運算硬體市場：依運算功能分類

  • 人工智慧訓練
  • 人工智慧推理
  • 訓練與推理

第7章 全球人工智慧運算硬體市場：依部署方式分類

  • 資料中心
  • 邊緣運算
  • 企業運算
  • 高效能運算

第8章 全球人工智慧運算硬體市場：按應用領域分類

  • 人工智慧世代
  • 機器學習
  • 電腦視覺
  • 自然語言處理
  • 推薦和預測分析
  • 自主系統

第9章 全球人工智慧運算硬體市場：依最終用戶分類

  • 雲端服務供應商
  • 超大規模資料中心業者
  • 公司
  • 政府/國防
  • 研究和學術機構
  • 汽車和旅遊公司

第10章：全球人工智慧運算硬體市場：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 Corporation
  • Advanced Micro Devices, Inc.
  • Intel Corporation
  • Qualcomm Incorporated
  • Broadcom Inc.
  • Arm Holdings plc
  • Alphabet Inc.
  • Amazon Web Services, Inc.
  • Huawei Technologies Co., Ltd.
  • Cerebras Systems
  • Groq, Inc.
  • Marvell Technology, Inc.
  • Micron Technology, Inc.
  • Samsung Electronics Co., Ltd.
  • SK hynix Inc.
Product Code: SMRC40223

According to Stratistics MRC, the Global AI Compute Hardware Market is accounted for $92.8 billion in 2026 and is expected to reach $325.9 billion by 2034 growing at a CAGR of 17.0% during the forecast period. AI compute hardware refers to the specialized processors, accelerators, and systems designed to execute artificial intelligence workloads including training and inference. The market encompasses GPUs, AI accelerators, CPUs, AI ASICs, FPGA accelerators, and AI compute systems and servers utilized for AI training, AI inference, and combined training and inference functions. These systems are deployed across data centers, edge computing, enterprise computing, and high-performance computing environments, serving applications including generative AI, machine learning, computer vision, natural language processing, recommendation and predictive analytics, and autonomous systems. The market serves cloud service providers, hyperscalers, enterprises, government and defense organizations, research and academic institutions, and automotive and mobility companies.

Market Dynamics:

Driver:

Explosive growth in generative AI and large language model workloads

The rapid proliferation of generative AI applications and large language models is a primary driver for the AI compute hardware market. Training and running these models requires massive computational resources, driving unprecedented demand for specialized AI hardware. Large language models with hundreds of billions of parameters require thousands of GPUs for training. Inference workloads for AI chatbots and generative applications require dedicated accelerators at scale. Cloud service providers and enterprises are investing heavily in AI infrastructure to support these workloads. As generative AI adoption accelerates across industries, demand for AI compute hardware continues growing exponentially, supporting sustained market expansion.

Restraint:

High costs and supply chain constraints for advanced AI chips

The significant costs associated with advanced AI hardware and ongoing supply chain constraints represent a major restraint for the market. High-end AI GPUs and accelerators cost tens of thousands of dollars each, making AI infrastructure investment prohibitive for some organizations. Advanced packaging capacity for AI chips is limited, creating supply bottlenecks. Export controls and geopolitical tensions affect global supply chains. Power and cooling requirements for dense AI compute clusters add operational costs. These cost and supply factors may constrain market growth, particularly for smaller organizations and in regions with limited access to advanced chips.

Opportunity:

Development of custom AI ASICs and domain-specific architectures

Continuous innovation in custom AI ASICs and domain-specific architectures presents significant opportunities for AI compute hardware market expansion. Hyperscalers are developing custom silicon optimized for their specific AI workloads, improving performance and reducing costs. Domain-specific architectures for inference deliver superior efficiency compared to general-purpose GPUs. Neural processing units and tensor processing architectures offer optimized performance for specific AI tasks. As AI workloads diversify and customization priorities grow, specialized AI hardware captures growing market share.

Threat:

Power and thermal management challenges in AI data centers

The extreme power density and thermal management requirements of AI compute hardware pose significant threats to market growth. AI clusters consume enormous amounts of electricity, straining data center power infrastructure. Cooling requirements for high-density GPU racks require advanced liquid cooling solutions. Power availability and sustainability concerns may limit AI data center expansion in some regions. These infrastructure challenges may slow AI hardware deployment, particularly in power-constrained markets.

Covid-19 Impact:

The COVID-19 pandemic accelerated AI compute hardware market growth as digital transformation and automation priorities intensified. Remote work and online services increased demand for AI-powered applications. Supply chain disruptions affected hardware availability during the pandemic. However, the pandemic also highlighted the strategic importance of AI capabilities, driving increased investment. Post-pandemic, generative AI breakthroughs have driven explosive demand for AI compute hardware.

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

The GPUs segment is expected to account for the largest market share during the forecast period, driven by their dominance in AI training and inference workloads, established software ecosystems, and continuous performance improvements. GPUs offer parallel processing capabilities essential for deep learning and neural network computations. The segment benefits from widespread adoption across cloud service providers, enterprises, and research institutions. NVIDIA's GPU platforms dominate the AI hardware market. As AI model complexity increases, GPU demand continues growing, maintaining the largest hardware type segment share.

The AI Inference segment is expected to have the highest CAGR during the forecast period

Over the forecast period, the AI Inference segment is predicted to witness the highest growth rate, fueled by the rapid deployment of AI applications in production, growing demand for real-time AI processing, and the expansion of edge AI deployments. Inference workloads are growing faster than training as AI models move from development to deployment. The segment benefits from increasing AI adoption across consumer applications, autonomous systems, and enterprise workflows. As AI inference demand accelerates, this compute function delivers the fastest segment growth.

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 AI hardware manufacturers, hyperscale cloud providers, and leading AI research institutions. The United States leads regional growth with substantial investment in AI infrastructure. Major technology companies are headquartered in the region, driving innovation and adoption. High AI investment across industries supports market growth. With technology leadership and investment concentration, North America maintains its dominant market position.

Region with highest CAGR:

Over the forecast period, the Asia-Pacific region is anticipated to exhibit the highest CAGR, driven by rapid AI adoption, government AI initiatives, and expanding data center infrastructure across China, Japan, South Korea, and India. The region's large and growing technology sector creates substantial demand for AI compute hardware. Government programs promoting AI development are accelerating investment. As AI adoption and infrastructure investment expand, Asia Pacific delivers the fastest AI compute hardware market growth globally.

Key players in the market

Some of the key players in AI Compute Hardware Market include NVIDIA Corporation, Advanced Micro Devices, Inc., Intel Corporation, Qualcomm Incorporated, Broadcom Inc., Arm Holdings plc, Alphabet Inc., Amazon Web Services, Inc., Huawei Technologies Co., Ltd., Cerebras Systems, Groq, Inc., Marvell Technology, Inc., Micron Technology, Inc., Samsung Electronics Co., Ltd., and SK hynix Inc.

Key Developments:

In October 2026, NVIDIA detailed production milestones for its Rubin platform, demonstrating up to 50 petaflops of NVFP4 computing power via the Vera CPU and Rubin GPU architectures to lower mixture-of-experts model inference costs.

In October 2026, Intel streamlined its data-center AI roadmap, focusing commercial volume deliveries on Gaudi 3 while designating the Falcon Shores platform as an internal development vehicle for future programmable architectures.

In September 2026, AMD announced an agreement to acquire World Labs to integrate spatial AI model capabilities directly into its ROCm software stack and accelerator hardware roadmap.

In September 2026, Qualcomm announced its next-generation Snapdragon X2 Elite Extreme processors at the Snapdragon Summit, targeting agentic AI PC computing and enterprise multi-modal inference workloads.

Hardware Types Covered:

  • GPUs
  • AI Accelerators
  • CPUs
  • AI ASICs
  • FPGA Accelerators
  • AI Compute Systems and Servers

Compute Functions Covered:

  • AI Training
  • AI Inference
  • Training and Inference

Deployments Covered:

  • Data Centers
  • Edge Computing
  • Enterprise Computing
  • High-Performance Computing

Applications Covered:

  • Generative AI
  • Machine Learning
  • Computer Vision
  • Natural Language Processing
  • Recommendation and Predictive Analytics
  • Autonomous Systems

End Users Covered:

  • Cloud Service Providers
  • Hyperscalers
  • Enterprises
  • Government and Defense
  • Research and Academic Institutions
  • Automotive and Mobility Companies

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 AI Compute Hardware Market, By Hardware Type

  • 5.1 GPUs
  • 5.2 AI Accelerators
  • 5.3 CPUs
  • 5.4 AI ASICs
  • 5.5 FPGA Accelerators
  • 5.6 AI Compute Systems and Servers

6 Global AI Compute Hardware Market, By Compute Function

  • 6.1 AI Training
  • 6.2 AI Inference
  • 6.3 Training and Inference

7 Global AI Compute Hardware Market, By Deployment

  • 7.1 Data Centers
  • 7.2 Edge Computing
  • 7.3 Enterprise Computing
  • 7.4 High-Performance Computing

8 Global AI Compute Hardware Market, By Application

  • 8.1 Generative AI
  • 8.2 Machine Learning
  • 8.3 Computer Vision
  • 8.4 Natural Language Processing
  • 8.5 Recommendation and Predictive Analytics
  • 8.6 Autonomous Systems

9 Global AI Compute Hardware Market, By End User

  • 9.1 Cloud Service Providers
  • 9.2 Hyperscalers
  • 9.3 Enterprises
  • 9.4 Government and Defense
  • 9.5 Research and Academic Institutions
  • 9.6 Automotive and Mobility Companies

10 Global AI Compute Hardware 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 Corporation
  • 13.2 Advanced Micro Devices, Inc.
  • 13.3 Intel Corporation
  • 13.4 Qualcomm Incorporated
  • 13.5 Broadcom Inc.
  • 13.6 Arm Holdings plc
  • 13.7 Alphabet Inc.
  • 13.8 Amazon Web Services, Inc.
  • 13.9 Huawei Technologies Co., Ltd.
  • 13.10 Cerebras Systems
  • 13.11 Groq, Inc.
  • 13.12 Marvell Technology, Inc.
  • 13.13 Micron Technology, Inc.
  • 13.14 Samsung Electronics Co., Ltd.
  • 13.15 SK hynix Inc.

List of Tables

  • Table 1 Global AI Compute Hardware Market Outlook, By Region (2023-2034) ($MN)
  • Table 2 Global AI Compute Hardware Market Outlook, By Hardware Type (2023-2034) ($MN)
  • Table 3 Global AI Compute Hardware Market Outlook, By GPUs (2023-2034) ($MN)
  • Table 4 Global AI Compute Hardware Market Outlook, By AI Accelerators (2023-2034) ($MN)
  • Table 5 Global AI Compute Hardware Market Outlook, By CPUs (2023-2034) ($MN)
  • Table 6 Global AI Compute Hardware Market Outlook, By AI ASICs (2023-2034) ($MN)
  • Table 7 Global AI Compute Hardware Market Outlook, By FPGA Accelerators (2023-2034) ($MN)
  • Table 8 Global AI Compute Hardware Market Outlook, By AI Compute Systems and Servers (2023-2034) ($MN)
  • Table 9 Global AI Compute Hardware Market Outlook, By Compute Function (2023-2034) ($MN)
  • Table 10 Global AI Compute Hardware Market Outlook, By AI Training (2023-2034) ($MN)
  • Table 11 Global AI Compute Hardware Market Outlook, By AI Inference (2023-2034) ($MN)
  • Table 12 Global AI Compute Hardware Market Outlook, By Training and Inference (2023-2034) ($MN)
  • Table 13 Global AI Compute Hardware Market Outlook, By Deployment (2023-2034) ($MN)
  • Table 14 Global AI Compute Hardware Market Outlook, By Data Centers (2023-2034) ($MN)
  • Table 15 Global AI Compute Hardware Market Outlook, By Edge Computing (2023-2034) ($MN)
  • Table 16 Global AI Compute Hardware Market Outlook, By Enterprise Computing (2023-2034) ($MN)
  • Table 17 Global AI Compute Hardware Market Outlook, By High-Performance Computing (2023-2034) ($MN)
  • Table 18 Global AI Compute Hardware Market Outlook, By Application (2023-2034) ($MN)
  • Table 19 Global AI Compute Hardware Market Outlook, By Generative AI (2023-2034) ($MN)
  • Table 20 Global AI Compute Hardware Market Outlook, By Machine Learning (2023-2034) ($MN)
  • Table 21 Global AI Compute Hardware Market Outlook, By Computer Vision (2023-2034) ($MN)
  • Table 22 Global AI Compute Hardware Market Outlook, By Natural Language Processing (2023-2034) ($MN)
  • Table 23 Global AI Compute Hardware Market Outlook, By Recommendation and Predictive Analytics (2023-2034) ($MN)
  • Table 24 Global AI Compute Hardware Market Outlook, By Autonomous Systems (2023-2034) ($MN)
  • Table 25 Global AI Compute Hardware Market Outlook, By End User (2023-2034) ($MN)
  • Table 26 Global AI Compute Hardware Market Outlook, By Cloud Service Providers (2023-2034) ($MN)
  • Table 27 Global AI Compute Hardware Market Outlook, By Hyperscalers (2023-2034) ($MN)
  • Table 28 Global AI Compute Hardware Market Outlook, By Enterprises (2023-2034) ($MN)
  • Table 29 Global AI Compute Hardware Market Outlook, By Government and Defense (2023-2034) ($MN)
  • Table 30 Global AI Compute Hardware Market Outlook, By Research and Academic Institutions (2023-2034) ($MN)
  • Table 31 Global AI Compute Hardware Market Outlook, By Automotive and Mobility Companies (2023-2034) ($MN)

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