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市場調查報告書
商品編碼
2021626

人工智慧晶片市場預測至2034年—按晶片類型、功能、技術、應用、最終用戶和地區分類的全球分析

AI Chips Market Forecasts to 2034 - Global Analysis By Chip Type (GPU, CPU, FPGA, ASIC and Custom Accelerators), Function, Technology, Application, 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，全球人工智慧晶片市場規模將達到 396 億美元，並在預測期內以 27.3% 的複合年成長率成長，到 2034 年將達到 2732 億美元。

人工智慧晶片是專為處理人工智慧相關任務（例如神經網路、深度學習和機器學習）而開發的高階處理器。與傳統CPU不同，GPU、TPU和FPGA提供強大的平行處理能力，從而實現更快的運算速度和更高的效率。這些晶片在機器人、自動駕駛汽車、自然語言處理（NLP）和資料中心等領域至關重要。它們能夠最大限度地降低延遲、提高能源效率並實現即時分析。隨著人工智慧在各個領域變得不可或缺，對高效能、最佳化型人工智慧晶片的需求激增，推動著晶片設計和架構創新不斷向前發展。

根據印度政府的「印度人工智慧使命」舉措，印度已部署38,000個GPU以提升其人工智慧運算能力，直接支援人工智慧晶片生態系統的發展。該計劃是印度旨在減少對進口依賴、建構以人工智慧晶片為核心的本土半導體能力的更廣泛國家戰略的一部分。

高效能運算的需求

對高效能運算（HPC）在人工智慧（AI）任務中日益成長的需求是推動市場發展的動力。深度學習、自然語言處理（NLP）和電腦視覺等複雜應用需要大規模並行處理。 GPU、TPU 和其他 AI 晶片能夠有效處理這些工作負載，加速運算並提高準確性。研究、雲端運算和巨量資料分析進一步增加了對專用 AI 硬體的需求。專注於 AI 基礎設施的公司正在採用這些晶片，以實現更快的訓練、即時決策和可擴展的解決方案。因此，不斷成長的 HPC 需求持續推動著全球 AI 晶片市場的成長和創新。

人工智慧晶片高成本

高昂的AI晶片價格是限制市場發展的一大阻礙因素。開發高效能GPU、TPU和客製化處理器需要投入大量的研發和製造成本。這一價格壁壘使得中小企業難以採用AI硬體，限制了其在各類項目中的部署。此外，晶片的高成本也推高了AI基礎設施（例如資料中心和伺服器）的整體成本。因此，在對成本較為敏感的產業和地區，AI技術的應用速度正在放緩。只有當更經濟高效的AI晶片解決方案出現，使全球更多組織能夠獲得先進的運算能力時，市場成長才能有效控制。

擴展邊緣人工智慧和物聯網應用

邊緣人工智慧和物聯網的興起為人工智慧晶片創造了成長機會。在邊緣設備上進行本地數據處理可以降低延遲和網路依賴性，從而對晶片的緊湊性和能源效率提出了更高的要求。邊緣人工智慧正日益被智慧城市、製造業、零售業和物流業等行業所採用，以實現自動化、預測性維護和即時洞察。專為邊緣運算設計的人工智慧晶片支援設備端學習和高速推理。隨著互聯設備的激增和對即時處理需求的不斷成長，製造商可以開發專用於邊緣和物聯網應用的人工智慧晶片，從而進入快速成長且盈利的市場領域。

科技快速過時

人工智慧晶片市場正面臨快速技術變革的威脅。晶片架構、性能和能源效率的持續創新意味著現有晶片可能很快就會過時。投資過時技術的公司將面臨收入下降和產品生命週期縮短的風險。保持競爭力需要持續創新，這給研發團隊帶來了巨大壓力。終端用戶也面臨頻繁升級和成本增加等挑戰。這種快速變化帶來了不確定性，可能會擾亂長期商業策略並減緩人工智慧晶片的普及。製造商和用戶面臨的財務風險增加、市場更容易過時以及持續技術進步的必要性，使這個問題變得至關重要。

新型冠狀病毒（COVID-19）的影響：

新冠疫情危機對人工智慧晶片市場產生了多方面的影響。由於生產停滯、供應鏈中斷和運輸延誤，產量放緩，成本暫時上升。另一方面，疫情加劇了醫療保健、遠距辦公、雲端運算和線上服務等領域對人工智慧技術的依賴，從而推動了對先進人工智慧晶片的需求。各公司加大了對人工智慧基礎設施的投資，以實現自動化、數據分析和虛擬營運。儘管初期生產有所延誤，但疫情凸顯了人工智慧晶片的關鍵作用，加速了其應用，並推動了性能、效率和下一代晶片設計的創新，從而促進了市場的長期成長。

在預測期內，GPU（圖形處理單元）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（圖形處理器）將佔據最大的市場佔有率，因為它能夠有效地管理人工智慧和機器學習操作所必需的平行處理任務。這使得深度學習、自然語言處理 (NLP) 和電腦視覺應用中的模型訓練和推理速度更快。 GPU 在研究中心、雲端服務和企業資料中心的廣泛應用鞏固了其主導地位。憑藉卓越的運算速度、可擴展性和適應性，GPU 比 CPU、FPGA、ASIC 和客製化加速器更受人工智慧開發人員和組織的青睞。

在預測期內，3D封裝/晶片細分市場預計將呈現最高的複合年成長率。

在預測期內，3D封裝/晶片組細分市場預計將呈現最高的成長率，因為它透過垂直堆疊多個晶片元件，提高了處理速度、能源效率和整合密度。這項技術最大限度地減少了互連延遲，改善了溫度控管，並支援高要求的AI工作負載。隨著AI應用對運算複雜性和頻寬不斷成長，3D封裝能夠提供可擴展、模組化和節能的晶片解決方案。它在縮小尺寸和降低能耗的同時提升性能的能力，正在推動其在市場上的快速普及。

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，這得益於其強大的技術生態系統、先進的半導體製造能力和雄厚的研發投入。人工智慧在醫療保健、汽車、金融和雲端運算等行業的廣泛應用進一步鞏固了這一地位。完善的基礎設施、政府措施和高素質的勞動力也進一步提升了其市場主導地位。高效能、低功耗人工智慧晶片的持續進步推動了創新，而主要製造商和研究中心的集中也使北美成為人工智慧晶片開發和部署的關鍵樞紐。

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，這主要得益於人工智慧的快速普及、研發投入的增加以及半導體製造基礎設施的擴張。人工智慧正在中國、日本和韓國等主要國家的醫療保健、汽車、金融和工業等關鍵領域得到應用。政府舉措、龐大的市場基礎以及新興Start-Ups的湧現進一步推動了成長。該地區的創新、高效生產以及對先進人工智慧晶片研發的重視，正在加速市場擴張。

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
    • 對主要企業進行SWOT分析（最多3家公司）
  • 區域分類
    • 應客戶要求，我們提供主要國家和地區的市場估算和預測，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主要企業進行基準分析。

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
  • 主要企業市佔率分析
  • 產品基準評效和效能比較

第5章：全球人工智慧晶片市場：按晶片類型分類

  • GPU（圖形處理單元）
  • CPU（中央處理器）
  • FPGA（現場可程式化閘陣列）
  • ASIC（專用積體電路）
    • NPU（神經處理單元）
    • TPU（張量處理單元）
  • 自訂加速器

第6章：全球人工智慧晶片市場：按功能分類

  • 訓練負荷
  • 推理工作負載

第7章 全球人工智慧晶片市場：按技術分類

  • 系統晶片(SoC)
  • 系統級封裝 (SiP)
  • 多晶片模組（MCM）
  • 3D封裝/晶片

第8章：全球人工智慧晶片市場：按應用領域分類

  • 機器學習
    • 深度學習
    • 自然語言處理（NLP）
    • 電腦視覺
  • 機器人與自主系統
  • 邊緣人工智慧

第9章：全球人工智慧晶片市場：按最終用戶分類

  • 家用電子產品
  • 車
  • 衛生保健
  • 工業和製造業
  • 銀行、金融服務、保險業 (BFSI)
  • 資訊科技/通訊
  • 國防/航太

第10章：全球人工智慧晶片市場：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
  • Advanced Micro Devices（AMD）
  • Intel
  • Google
  • IBM
  • Apple
  • Qualcomm
  • Samsung
  • NXP Semiconductors
  • Broadcom
  • Huawei
  • Micron Technology
  • SK Hynix
  • Cerebras
  • Graphcore
  • Imagination Technologies
  • AWS（Amazon）
  • TSMC
Product Code: SMRC34966

According to Stratistics MRC, the Global AI Chips Market is accounted for $39.6 billion in 2026 and is expected to reach $273.2 billion by 2034 growing at a CAGR of 27.3% during the forecast period. AI chips are advanced processors created to handle AI-related tasks, including neural networks, deep learning, and machine learning. Different from conventional CPUs, GPUs, TPUs, and FPGAs provide extensive parallel processing, allowing quicker computations and higher efficiency. These chips are crucial for robotics, autonomous cars, NLP, and data centers. They minimize latency, improve energy usage, and enable real-time analytics. With AI becoming integral to various sectors, the need for high-performance, optimized AI chips is surging, pushing advancements in chip design and architecture innovations.

According to the IndiaAI Mission (Government of India initiative), India has already deployed 38,000 GPUs to strengthen AI compute capacity, directly supporting the AI chip ecosystem. This mission is part of a broader national strategy to reduce reliance on imports and build domestic semiconductor capability, with AI chips at the center of this effort.

Market Dynamics:

Driver:

Demand for high-performance computing

Increasing high-performance computing demands for AI tasks are a key market driver. Complex applications such as deep learning, NLP, and computer vision need extensive parallel processing. GPUs, TPUs, and other AI chips efficiently manage these workloads, accelerating computations and improving precision. Research, cloud computing, and big data analytics amplify the need for specialized AI hardware. Enterprises focusing on AI infrastructure adopt these chips for faster training, real-time decision-making, and scalable solutions. Consequently, the rising HPC requirements continue to fuel the growth and innovation in the AI chip market globally.

Restraint:

High cost of AI chips

Expensive AI chips are a major market constraint. Developing high-performance GPUs, TPUs, and custom processors involves significant R&D and production costs. This price barrier makes it difficult for smaller businesses to adopt AI hardware, restricting deployment in various projects. Additionally, the overall cost of AI infrastructure, such as data centers and servers, rises with expensive chips. As a result, the adoption of AI technology slows in cost-sensitive sectors and regions. Market growth is limited until lower-cost, efficient AI chip solutions emerge, making advanced computing accessible to a wider range of organizations globally.

Opportunity:

Expansion of edge AI and IoT applications

The rise of edge AI and IoT creates opportunities for AI chip growth. Processing data locally on edge devices reduces latency and network dependency, requiring compact and energy-efficient chips. Sectors like smart cities, manufacturing, retail, and logistics increasingly adopt edge AI for automation, predictive maintenance, and real-time insights. AI chips designed for edge computing support on-device learning and rapid inference. With the increasing adoption of connected devices and demand for instant processing, manufacturers can develop specialized AI chips for edge and IoT applications, accessing a fast-growing and lucrative market segment.

Threat:

Rapid technological obsolescence

The AI chip market faces threats from rapid technological change. Constant innovations in chip architecture, performance, and energy efficiency can render existing chips obsolete quickly. Companies investing in older technologies may see diminished returns and shortened product lifespans. Continuous innovation is required to remain competitive, pressuring R&D teams. End-users also face frequent upgrades and higher costs. This rapid pace introduces uncertainty, disrupts long-term business strategies, and may slow AI chip adoption. Financial risks for manufacturers and users increase, making the market vulnerable to obsolescence and pushing continuous technological advancement as a necessity.

Covid-19 Impact:

The COVID-19 crisis affected the AI chip market in multiple ways. Manufacturing halts, disrupted supply chains, and shipping delays slowed chip production and raised costs temporarily. At the same time, the pandemic increased reliance on AI-driven technologies in healthcare, remote work, cloud computing, and online services, raising demand for advanced AI chips. Businesses invested more in AI infrastructure to enable automation, analytics, and virtual operations. Despite initial production setbacks, the pandemic emphasized the critical role of AI chips, accelerating their adoption and encouraging innovations in performance, efficiency, and next-generation chip design for long-term market growth.

The GPU (graphics processing unit) segment is expected to be the largest during the forecast period

The GPU (graphics processing unit) segment is expected to account for the largest market share during the forecast period because they efficiently manage parallel processing tasks, crucial for AI and machine learning operations. They enable faster model training and inference in deep learning, NLP, and computer vision applications. Their extensive use in research centers, cloud services, and enterprise data centers reinforces their leading position. With superior computational speed, scalability, and adaptability, GPUs are favored by AI developers and organizations over CPUs, FPGAs, ASICs, and custom accelerators.

The 3D packaging / chiplets segment is expected to have the highest CAGR during the forecast period

Over the forecast period, the 3D packaging / chiplets segment is predicted to witness the highest growth rate because they stack multiple chip elements vertically, boosting processing speed, energy efficiency, and integration density. This technique minimizes interconnect delays, improves thermal management, and supports demanding AI workloads. As AI applications require higher computational complexity and bandwidth, 3D packaging enables scalable, modular, and power-efficient chip solutions. Its ability to enhance performance while reducing size and energy use drives its rapid market adoption.

Region with largest share:

During the forecast period, the North America region is expected to hold the largest market share due to its strong technology ecosystem, advanced semiconductor fabrication, and significant R&D spending. Widespread AI adoption in industries like healthcare, automotive, finance, and cloud computing strengthens this position. Supportive infrastructure, government initiatives, and skilled workforce further boost market leadership. Continuous advancements in high-performance, energy-efficient AI chips drive innovation, while the presence of major manufacturers and research centers makes North America a key hub for AI chip development and deployment.

Region with highest CAGR:

Over the forecast period, the Asia-Pacific region is anticipated to exhibit the highest CAGR, driven by rapid AI adoption, increasing R&D investments, and expanding semiconductor manufacturing infrastructure. Key countries such as China, Japan, and South Korea are applying AI across healthcare, automotive, finance, and industrial sectors. Government initiatives, a large market base, and emerging startups further propel growth. The region's emphasis on innovation, efficient production, and advanced AI chip development accelerates market expansion.

Key players in the market

Some of the key players in AI Chips Market include NVIDIA, Advanced Micro Devices (AMD), Intel, Google, IBM, Apple, Qualcomm, Samsung, NXP Semiconductors, Broadcom, Huawei, Micron Technology, SK Hynix, Cerebras, Graphcore, Imagination Technologies, AWS (Amazon) and TSMC.

Key Developments:

In April 2026, Intel Corp plans to invest an additional $15 million in AI chip startup SambaNova Systems, according to a Reuters review of corporate records, as the semiconductor company deepens its focus on artificial intelligence infrastructure. The proposed investment, which is subject to regulatory approval, would raise Intel's ownership stake in SambaNova to approximately 9%.

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.

In February 2025, NXP Semiconductors has acquired AI chip startup Kinara in a $307 million all-cash agreement. NXP said the acquisition would enable it to "enhance and strengthen" its ability to provide scalable AI platforms by combining Kinara's NPUs and AI software with NXP's solutions portfolio. Kinara develops programmable neural processing units (NPUs) for Edge AI applications, including multi-modal generative AI models.

Chip Types Covered:

  • GPU (Graphics Processing Unit)
  • CPU (Central Processing Unit)
  • FPGA (Field Programmable Gate Array)
  • ASIC (Application-Specific Integrated Circuit)
  • Custom Accelerators

Functions Covered:

  • Training Workloads
  • Inference Workloads

Technologies Covered:

  • System-on-Chip (SoC)
  • System-in-Package (SiP)
  • Multi-Chip Module (MCM)
  • 3D Packaging / Chiplets

Applications Covered:

  • Machine Learning
  • Robotics & Autonomous Systems
  • Edge AI

End Users Covered:

  • Consumer Electronics
  • Automotive
  • Healthcare
  • Industrial & Manufacturing
  • BFSI (Banking, Financial Services, Insurance)
  • IT & Telecom
  • Defense & Aerospace

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 Chips Market, By Chip Type

  • 5.1 GPU (Graphics Processing Unit)
  • 5.2 CPU (Central Processing Unit)
  • 5.3 FPGA (Field Programmable Gate Array)
  • 5.4 ASIC (Application-Specific Integrated Circuit)
    • 5.4.1 NPU (Neural Processing Unit)
    • 5.4.2 TPU (Tensor Processing Unit)
  • 5.5 Custom Accelerators

6 Global AI Chips Market, By Function

  • 6.1 Training Workloads
  • 6.2 Inference Workloads

7 Global AI Chips Market, By Technology

  • 7.1 System-on-Chip (SoC)
  • 7.2 System-in-Package (SiP)
  • 7.3 Multi-Chip Module (MCM)
  • 7.4 3D Packaging / Chiplets

8 Global AI Chips Market, By Application

  • 8.1 Machine Learning
    • 8.1.1 Deep Learning
    • 8.1.2 Natural Language Processing (NLP)
    • 8.1.3 Computer Vision
  • 8.2 Robotics & Autonomous Systems
  • 8.3 Edge AI

9 Global AI Chips Market, By End User

  • 9.1 Consumer Electronics
  • 9.2 Automotive
  • 9.3 Healthcare
  • 9.4 Industrial & Manufacturing
  • 9.5 BFSI (Banking, Financial Services, Insurance)
  • 9.6 IT & Telecom
  • 9.7 Defense & Aerospace

10 Global AI Chips 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 Advanced Micro Devices (AMD)
  • 13.3 Intel
  • 13.4 Google
  • 13.5 IBM
  • 13.6 Apple
  • 13.7 Qualcomm
  • 13.8 Samsung
  • 13.9 NXP Semiconductors
  • 13.10 Broadcom
  • 13.11 Huawei
  • 13.12 Micron Technology
  • 13.13 SK Hynix
  • 13.14 Cerebras
  • 13.15 Graphcore
  • 13.16 Imagination Technologies
  • 13.17 AWS (Amazon)
  • 13.18 TSMC

List of Tables

  • Table 1 Global AI Chips Market Outlook, By Region (2023-2034) ($MN)
  • Table 2 Global AI Chips Market Outlook, By Chip Type (2023-2034) ($MN)
  • Table 3 Global AI Chips Market Outlook, By GPU (Graphics Processing Unit) (2023-2034) ($MN)
  • Table 4 Global AI Chips Market Outlook, By CPU (Central Processing Unit) (2023-2034) ($MN)
  • Table 5 Global AI Chips Market Outlook, By FPGA (Field Programmable Gate Array) (2023-2034) ($MN)
  • Table 6 Global AI Chips Market Outlook, By ASIC (Application-Specific Integrated Circuit) (2023-2034) ($MN)
  • Table 7 Global AI Chips Market Outlook, By NPU (Neural Processing Unit) (2023-2034) ($MN)
  • Table 8 Global AI Chips Market Outlook, By TPU (Tensor Processing Unit) (2023-2034) ($MN)
  • Table 9 Global AI Chips Market Outlook, By Custom Accelerators (2023-2034) ($MN)
  • Table 10 Global AI Chips Market Outlook, By Function (2023-2034) ($MN)
  • Table 11 Global AI Chips Market Outlook, By Training Workloads (2023-2034) ($MN)
  • Table 12 Global AI Chips Market Outlook, By Inference Workloads (2023-2034) ($MN)
  • Table 13 Global AI Chips Market Outlook, By Technology (2023-2034) ($MN)
  • Table 14 Global AI Chips Market Outlook, By System-on-Chip (SoC) (2023-2034) ($MN)
  • Table 15 Global AI Chips Market Outlook, By System-in-Package (SiP) (2023-2034) ($MN)
  • Table 16 Global AI Chips Market Outlook, By Multi-Chip Module (MCM) (2023-2034) ($MN)
  • Table 17 Global AI Chips Market Outlook, By 3D Packaging / Chiplets (2023-2034) ($MN)
  • Table 18 Global AI Chips Market Outlook, By Application (2023-2034) ($MN)
  • Table 19 Global AI Chips Market Outlook, By Machine Learning (2023-2034) ($MN)
  • Table 20 Global AI Chips Market Outlook, By Deep Learning (2023-2034) ($MN)
  • Table 21 Global AI Chips Market Outlook, By Natural Language Processing (NLP) (2023-2034) ($MN)
  • Table 22 Global AI Chips Market Outlook, By Computer Vision (2023-2034) ($MN)
  • Table 23 Global AI Chips Market Outlook, By Robotics & Autonomous Systems (2023-2034) ($MN)
  • Table 24 Global AI Chips Market Outlook, By Edge AI (2023-2034) ($MN)
  • Table 25 Global AI Chips Market Outlook, By End User (2023-2034) ($MN)
  • Table 26 Global AI Chips Market Outlook, By Consumer Electronics (2023-2034) ($MN)
  • Table 27 Global AI Chips Market Outlook, By Automotive (2023-2034) ($MN)
  • Table 28 Global AI Chips Market Outlook, By Healthcare (2023-2034) ($MN)
  • Table 29 Global AI Chips Market Outlook, By Industrial & Manufacturing (2023-2034) ($MN)
  • Table 30 Global AI Chips Market Outlook, By BFSI (Banking, Financial Services, Insurance) (2023-2034) ($MN)
  • Table 31 Global AI Chips Market Outlook, By IT & Telecom (2023-2034) ($MN)
  • Table 32 Global AI Chips Market Outlook, By Defense & Aerospace (2023-2034) ($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.