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全球人工智慧晶片組市場：預測至 2032 年—按組件、功能、部署方式、技術、公司類型、最終用戶和地區進行分析

AI Chipset Market Forecasts to 2032 - Global Analysis By Component, Function, Deployment, Technology, Enterprise Type, 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 的一項研究，預計到 2025 年，全球人工智慧晶片組市場價值將達到 973.5 億美元，到 2032 年將達到 6411.4 億美元，在預測期內的複合年成長率為 30.9%。

人工智慧晶片組是專為提升人工智慧（包括深度學習、神經網路處理和大規模數據分析）運作效能而設計的半導體元件。它們利用GPU、TPU和NPU等架構，以更快的速度和更高的能源效率處理並行運算任務。這些晶片組支援各種設備的人工智慧功能，從行動裝置和智慧型裝置到雲端伺服器和自主系統，從而實現即時洞察、增強運算能力並高效執行高級人工智慧演算法。

根據工業生產指數（IIP）數據，由於新冠疫情封鎖導致製造業生產進程放緩，2020 年 7 月製造業產出下降了 11.1%。

增加資料中心投資

企業正在擴展其雲端基礎設施，以支援機器學習、分析和生成式人工智慧工作負載。這種擴展需要能夠處理大規模並行運算的高效能處理器。人工智慧晶片組正在被整合，以最佳化能源效率並加速各種應用中的推理任務。超大規模資料中心供應商的策略性投資也在推動冷卻系統和硬體最佳化方面的創新。這些發展正使資料中心成為全球人工智慧晶片組部署的基礎。

開發和設計的高度複雜性

開發兼顧速度、效率和擴充性的架構需要大量的研發投入。晶片組與多樣化硬體生態系統的整合複雜性也構成了另一道障礙。快速的技術迭代縮短了產品壽命，並給工程團隊和生產流程帶來了巨大壓力。儘管企業正在採用模組化設計和模擬工具來降低風險，但准入門檻依然很高。在這種環境下，中小企業很難與老牌半導體巨頭競爭。

客製化人工智慧晶片組的興起

客製化處理器正被設計用於加速深度學習、自然語言處理和邊緣人工智慧應用。與通用GPU和CPU相比，這些晶片組可提供更最佳化的效能。半導體公司與雲端服務供應商之間的合作，正在推動針對特定產業的共同開發架構。面向醫療保健、汽車和機器人等領域的專用加速器正成為新興趨勢。這波客製化浪潮正在重新定義競爭差異化，並拓展人工智慧硬體創新的範圍。

模型壓縮技術的快速發展

能夠減小模型規模和運算需求的演算法降低了對高效能處理器的依賴。諸如剪枝、量化和知識蒸餾等技術使得在低成本硬體上高效部署成為可能。這一趨勢可能導致市場需求從高階晶片組轉向輕量級架構。廠商正透過將支援壓縮的設計納入產品藍圖來應對這一趨勢。然而，軟體最佳化領域的創新步伐持續對以硬體為中心的成長策略構成挑戰。

新冠疫情的影響：

疫情重塑了各行業人工智慧晶片組應用的優先順序。供應鏈中斷導致生產計畫延誤，硬體部署放緩。同時，對人工智慧驅動的醫療診斷和遠端協作工具的需求激增。遠端醫療、預測分析和自動化物流等領域加速了晶片組投資。各公司採用分散式測試和模擬模型來維持研發動能。

預計在預測期內，影像處理處理器（GPU）細分市場將佔據最大的市場佔有率。

預計在預測期內，影像處理(GPU) 細分市場將佔據最大的市場佔有率。 GPU 因其處理平行處理任務的能力而廣受認可，而平行處理任務對於深度學習至關重要。 GPU 在訓練和推理工作負載方面的多功能性使其成為人工智慧開發中不可或缺的一部分。記憶體頻寬和能源效率的提升進一步鞏固了 GPU 的地位。其主要應用領域包括自動駕駛汽車、醫學影像處理和自然語言處理。

在預測期內，醫療保健產業的複合年成長率將最高。

預計在預測期內，醫療保健產業將實現最高成長率，這主要得益於對基於人工智慧的診斷、藥物研發和病患監測的需求不斷成長。晶片組能夠實現醫學影像和基因組數據的即時分析。與穿戴式裝置的整合正在拓展其在預防醫學和個人化醫療領域的應用範圍。半導體公司與醫療保健機構之間的合作正在加速創新。

佔比最大的地區：

預計北美將在預測期內佔據最大的市場佔有率，這得益於該地區在雲端基礎設施和人工智慧研究方面的大力投資。領先的科技公司和大學正在推動晶片組創新。政府支持的人工智慧和半導體製造舉措進一步加強了生態系統。汽車、醫療保健和金融等行業的應用正在推動市場需求。

年複合成長率最高的地區：

預計中東和非洲地區在預測期內將實現最高的複合年成長率。各國政府正大力投資智慧城市計劃和數位轉型計畫。能源管理、安全和醫療保健領域對人工智慧日益成長的需求正在推動這一領域的擴張。與全球科技公司的合作正將先進的晶片組解決方案引入當地市場。新興新創Start-Ups正在利用人工智慧硬體開發金融科技和物流應用。這種充滿活力的環境使該地區成為人工智慧晶片組應用的快速成長前線。

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目錄

第1章執行摘要

第2章 引言

  • 概述
  • 相關利益者
  • 分析範圍
  • 分析方法
  • 分析材料

第3章 市場趨勢分析

  • 促進要素
  • 抑制因素
  • 機會
  • 威脅
  • 技術分析
  • 終端用戶分析
  • 新興市場
  • 新冠疫情的影響

第4章 波特五力分析

  • 供應商的議價能力
  • 買方議價能力
  • 替代產品的威脅
  • 新進入者的威脅
  • 競爭對手之間的競爭

第5章 全球人工智慧晶片組市場（按組件分類）

  • 中央處理器（CPU）
  • 張量處理單元（TPU）
  • 影像處理處理器（GPU）
  • 神經處理單元（NPU）
  • 專用積體電路（ASIC）
  • 現場可程式閘陣列（FPGA）
  • 其他專用處理器

第6章 全球人工智慧晶片組市場按功能分類

  • 訓練
  • 推理

第7章 全球人工智慧晶片組市場依部署方式分類

  • 雲端運算人工智慧
  • 邊緣人工智慧運算
    • 邊緣設備
    • 本地資料中心

第8章 全球人工智慧晶片組市場（按技術分類）

  • 機器學習（ML）
  • 人工智慧世代
  • 深度學習（DL）
  • 強化學習
  • 自然語言處理（NLP）
  • 電腦視覺（CV）

第9章 全球人工智慧晶片組市場（依公司類型分類）

  • 主要企業
  • 中小企業

第10章 全球人工智慧晶片組市場（按最終用戶分類）

  • 家用電子電器
  • 汽車與運輸
  • 醫學與生命科​​學
  • 資訊科技/通訊
  • 銀行、金融服務和保險（BFSI）
  • 製造業/工業
  • 零售與電子商務
  • 政府/國防
  • 農業
  • 其他最終用戶

第11章 全球人工智慧晶片組市場（按地區分類）

  • 北美洲
    • 美國
    • 加拿大
    • 墨西哥
  • 歐洲
    • 德國
    • 英國
    • 義大利
    • 法國
    • 西班牙
    • 其他歐洲
  • 亞太地區
    • 日本
    • 中國
    • 印度
    • 澳洲
    • 紐西蘭
    • 韓國
    • 亞太其他地區
  • 南美洲
    • 阿根廷
    • 巴西
    • 智利
    • 其他南美國家
  • 中東和非洲
    • 沙烏地阿拉伯
    • 阿拉伯聯合大公國
    • 卡達
    • 南非
    • 其他中東和非洲地區

第12章：主要趨勢

  • 合約、商業夥伴關係和合資企業
  • 企業合併（M&A）
  • 新產品上市
  • 業務拓展
  • 其他關鍵策略

第13章：企業概況

  • NVIDIA
  • Groq
  • Advanced Micro Devices（AMD）
  • Cerebras Systems
  • Intel Corporation
  • Huawei
  • Google
  • IBM
  • Amazon
  • Broadcom
  • Microsoft Corporation
  • TSMC
  • Qualcomm
  • Samsung Electronics
  • Apple Inc.
Product Code: SMRC32660

According to Stratistics MRC, the Global AI Chipset Market is accounted for $97.35 billion in 2025 and is expected to reach $641.14 billion by 2032 growing at a CAGR of 30.9% during the forecast period. An AI chipset refers to a purpose-built semiconductor component that boosts the performance of artificial intelligence operations, such as deep learning, neural network processing, and high-volume data analysis. Using architectures like GPUs, TPUs, and NPUs, it handles parallel computing tasks with greater speed and energy efficiency. These chipsets support AI functions in devices ranging from mobiles and smart gadgets to cloud servers and autonomous systems, enabling real-time insights, enhanced computational power, and more efficient execution of advanced AI algorithms.

According to the index of industrial production (IIP) data, in 2020, the manufacturing sector production registered a decline of 11.1% in July, as covid-19 lockdown slows down the manufacturing process.

Market Dynamics:

Driver:

Rise in data center investment

Enterprises are scaling their cloud infrastructure to support workloads in machine learning, analytics, and generative AI. This expansion requires high-performance processors capable of handling massive parallel computations. AI chipsets are being integrated to optimize energy efficiency and accelerate inference tasks across diverse applications. Strategic investments by hyperscale providers are also driving innovation in cooling systems and hardware optimization. Collectively, these developments are positioning data centers as the backbone of AI chipset adoption worldwide.

Restraint:

High development and design complexity

Developing architectures that balance speed, efficiency, and scalability requires significant R&D expenditure. Complexities in integrating chipsets with diverse hardware ecosystems add further hurdles. Rapid technological cycles often shorten product relevance, straining engineering teams and manufacturing pipelines. Companies are adopting modular design and simulation tools to mitigate risks, but the barrier to entry remains high. This environment makes it difficult for smaller players to compete with established semiconductor giants.

Opportunity:

Emergence of custom AI chipsets

Custom processors are being designed to accelerate deep learning, natural language processing, and edge AI applications. These chipsets offer optimized performance compared to general-purpose GPUs or CPUs. Partnerships between semiconductor firms and cloud providers are enabling co-developed architectures for specific industries. Emerging trends include domain-specific accelerators for healthcare, automotive, and robotics. This wave of customization is redefining competitive differentiation and expanding the scope of AI hardware innovation.

Threat:

Rapid advancements in model compression

Algorithms that reduce model size and computational requirements can lessen reliance on high-end processors. Techniques such as pruning, quantization, and knowledge distillation are enabling efficient deployment on lower-cost hardware. This trend may shift demand toward lightweight architectures rather than premium chipsets. Vendors are responding by integrating compression-aware designs into their product roadmaps. However, the pace of innovation in software optimization continues to challenge hardware-centric growth strategies.

Covid-19 Impact:

The pandemic reshaped priorities in AI chipset deployment across industries. Supply chain disruptions delayed production schedules and slowed hardware rollouts. At the same time, demand for AI-driven healthcare diagnostics and remote collaboration tools surged. Chipset investments accelerated in areas such as telemedicine, predictive analytics, and automated logistics. Companies adopted decentralized testing and simulation models to maintain development momentum.

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

The graphics processing unit (GPU) segment is expected to account for the largest market share during the forecast period. GPUs are widely recognized for their ability to handle parallel processing tasks essential for deep learning. Their versatility across training and inference workloads makes them indispensable in AI development. Advances in memory bandwidth and energy efficiency are further strengthening their role. Key applications include autonomous vehicles, healthcare imaging, and natural language processing.

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

Over the forecast period, the healthcare segment is predicted to witness the highest growth rate, due to rising demand for AI-driven diagnostics, drug discovery, and patient monitoring is fueling growth. Chipsets are enabling real-time analysis of medical imaging and genomic data. Integration with wearable devices is expanding applications in preventive care and personalized medicine. Partnerships between semiconductor firms and healthcare providers are accelerating innovation.

Region with largest share:

During the forecast period, the North America region is expected to hold the largest market share, due to the region benefits from strong investments in cloud infrastructure and AI research. Leading technology companies and universities are driving chipset innovation. Government-backed initiatives in AI and semiconductor manufacturing further strengthen the ecosystem. Adoption across industries such as automotive, healthcare, and finance is accelerating demand.

Region with highest CAGR:

Over the forecast period, the Middle East & Africa region is anticipated to exhibit the highest CAGR. Governments are investing heavily in smart city projects and digital transformation initiatives. Rising demand for AI in energy management, security, and healthcare is fueling expansion. Partnerships with global technology firms are bringing advanced chipset solutions to local markets. Emerging startups are leveraging AI hardware for fintech and logistics applications. This dynamic environment positions the region as a high-growth frontier for AI chipset deployment.

Key players in the market

Some of the key players in AI Chipset Market include NVIDIA, Groq, Advanced, Cerebras Systems, Intel Corp, Huawei, Google, IBM, Amazon, Broadcom, Microsoft, TSMC, Qualcomm, Samsung Electronics, and Apple Inc.

Key Developments:

In November 2025, IBM and the University of Dayton announced an agreement for the joint research and development of next-generation semiconductor technologies and materials. The collaboration aims to advance critical technologies for the age of AI including AI hardware, advanced packaging, and photonics.

In November 2025, Cisco, in collaboration with Intel, has announced a first-of-its-kind integrated platform for distributed AI workloads. Powered by Intel(R) Xeon(R) 6 system-on-chip (SoC), the solution brings compute, networking, storage and security closer to data generated at the edge for real-time AI inferencing and agentic workloads.

Components Covered:

  • Central Processing Unit (CPU)
  • Tensor Processing Unit (TPU)
  • Graphics Processing Unit (GPU)
  • Neural Processing Unit (NPU)
  • Application-Specific Integrated Circuit (ASIC)
  • Field-Programmable Gate Array (FPGA)
  • Other Specialized Processors

Functions Covered:

  • Training
  • Inference

Deployments Covered:

  • Cloud AI Computing
  • Edge AI Computing

Technologies Covered:

  • Machine Learning (ML)
  • Generative AI
  • Deep Learning (DL)
  • Reinforcement Learning
  • Natural Language Processing (NLP)
  • Computer Vision (CV)

Enterprise Types Covered:

  • Large Enterprises
  • Small and Medium Enterprises (SMEs)

End Users Covered:

  • Consumer Electronics
  • Automotive & Transportation
  • Healthcare & Life Sciences
  • IT & Telecommunication
  • BFSI
  • Manufacturing & Industrial
  • Retail & E-commerce
  • Government & Defense
  • Agriculture
  • 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 2024, 2025, 2026, 2028, and 2032
  • 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 End User Analysis
  • 3.8 Emerging Markets
  • 3.9 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 AI Chipset Market, By Component

  • 5.1 Introduction
  • 5.2 Central Processing Unit (CPU)
  • 5.3 Tensor Processing Unit (TPU)
  • 5.4 Graphics Processing Unit (GPU)
  • 5.5 Neural Processing Unit (NPU)
  • 5.6 Application-Specific Integrated Circuit (ASIC)
  • 5.7 Field-Programmable Gate Array (FPGA)
  • 5.8 Other Specialized Processors

6 Global AI Chipset Market, By Function

  • 6.1 Introduction
  • 6.2 Training
  • 6.3 Inference

7 Global AI Chipset Market, By Deployment

  • 7.1 Introduction
  • 7.2 Cloud AI Computing
  • 7.3 Edge AI Computing
    • 7.3.1 Edge Devices
    • 7.3.2 On-Premise Data Centers

8 Global AI Chipset Market, By Technology

  • 8.1 Introduction
  • 8.2 Machine Learning (ML)
  • 8.3 Generative AI
  • 8.4 Deep Learning (DL)
  • 8.5 Reinforcement Learning
  • 8.6 Natural Language Processing (NLP)
  • 8.7 Computer Vision (CV)

9 Global AI Chipset Market, By Enterprise Type

  • 9.1 Introduction
  • 9.2 Large Enterprises
  • 9.3 Small and Medium Enterprises (SMEs)

10 Global AI Chipset Market, By End User

  • 10.1 Introduction
  • 10.2 Consumer Electronics
  • 10.3 Automotive & Transportation
  • 10.4 Healthcare & Life Sciences
  • 10.5 IT & Telecommunication
  • 10.6 BFSI
  • 10.7 Manufacturing & Industrial
  • 10.8 Retail & E-commerce
  • 10.9 Government & Defense
  • 10.10 Agriculture
  • 10.11 Other End Users

11 Global AI Chipset Market, By Geography

  • 11.1 Introduction
  • 11.2 North America
    • 11.2.1 US
    • 11.2.2 Canada
    • 11.2.3 Mexico
  • 11.3 Europe
    • 11.3.1 Germany
    • 11.3.2 UK
    • 11.3.3 Italy
    • 11.3.4 France
    • 11.3.5 Spain
    • 11.3.6 Rest of Europe
  • 11.4 Asia Pacific
    • 11.4.1 Japan
    • 11.4.2 China
    • 11.4.3 India
    • 11.4.4 Australia
    • 11.4.5 New Zealand
    • 11.4.6 South Korea
    • 11.4.7 Rest of Asia Pacific
  • 11.5 South America
    • 11.5.1 Argentina
    • 11.5.2 Brazil
    • 11.5.3 Chile
    • 11.5.4 Rest of South America
  • 11.6 Middle East & Africa
    • 11.6.1 Saudi Arabia
    • 11.6.2 UAE
    • 11.6.3 Qatar
    • 11.6.4 South Africa
    • 11.6.5 Rest of Middle East & Africa

12 Key Developments

  • 12.1 Agreements, Partnerships, Collaborations and Joint Ventures
  • 12.2 Acquisitions & Mergers
  • 12.3 New Product Launch
  • 12.4 Expansions
  • 12.5 Other Key Strategies

13 Company Profiling

  • 13.1 NVIDIA
  • 13.2 Groq
  • 13.3 Advanced Micro Devices (AMD)
  • 13.4 Cerebras Systems
  • 13.5 Intel Corporation
  • 13.6 Huawei
  • 13.7 Google
  • 13.8 IBM
  • 13.9 Amazon
  • 13.10 Broadcom
  • 13.11 Microsoft Corporation
  • 13.12 TSMC
  • 13.13 Qualcomm
  • 13.14 Samsung Electronics
  • 13.15 Apple Inc.

List of Tables

  • Table 1 Global AI Chipset Market Outlook, By Region (2024-2032) ($MN)
  • Table 2 Global AI Chipset Market Outlook, By Component (2024-2032) ($MN)
  • Table 3 Global AI Chipset Market Outlook, By Central Processing Unit (CPU) (2024-2032) ($MN)
  • Table 4 Global AI Chipset Market Outlook, By Tensor Processing Unit (TPU) (2024-2032) ($MN)
  • Table 5 Global AI Chipset Market Outlook, By Graphics Processing Unit (GPU) (2024-2032) ($MN)
  • Table 6 Global AI Chipset Market Outlook, By Neural Processing Unit (NPU) (2024-2032) ($MN)
  • Table 7 Global AI Chipset Market Outlook, By Application-Specific Integrated Circuit (ASIC) (2024-2032) ($MN)
  • Table 8 Global AI Chipset Market Outlook, By Field-Programmable Gate Array (FPGA) (2024-2032) ($MN)
  • Table 9 Global AI Chipset Market Outlook, By Other Specialized Processors (2024-2032) ($MN)
  • Table 10 Global AI Chipset Market Outlook, By Function (2024-2032) ($MN)
  • Table 11 Global AI Chipset Market Outlook, By Training (2024-2032) ($MN)
  • Table 12 Global AI Chipset Market Outlook, By Inference (2024-2032) ($MN)
  • Table 13 Global AI Chipset Market Outlook, By Deployment (2024-2032) ($MN)
  • Table 14 Global AI Chipset Market Outlook, By Cloud AI Computing (2024-2032) ($MN)
  • Table 15 Global AI Chipset Market Outlook, By Edge AI Computing (2024-2032) ($MN)
  • Table 16 Global AI Chipset Market Outlook, By Edge Devices (2024-2032) ($MN)
  • Table 17 Global AI Chipset Market Outlook, By On-Premise Data Centers (2024-2032) ($MN)
  • Table 18 Global AI Chipset Market Outlook, By Technology (2024-2032) ($MN)
  • Table 19 Global AI Chipset Market Outlook, By Machine Learning (ML) (2024-2032) ($MN)
  • Table 20 Global AI Chipset Market Outlook, By Generative AI (2024-2032) ($MN)
  • Table 21 Global AI Chipset Market Outlook, By Deep Learning (DL) (2024-2032) ($MN)
  • Table 22 Global AI Chipset Market Outlook, By Reinforcement Learning (2024-2032) ($MN)
  • Table 23 Global AI Chipset Market Outlook, By Natural Language Processing (NLP) (2024-2032) ($MN)
  • Table 24 Global AI Chipset Market Outlook, By Computer Vision (CV) (2024-2032) ($MN)
  • Table 25 Global AI Chipset Market Outlook, By Enterprise Type (2024-2032) ($MN)
  • Table 26 Global AI Chipset Market Outlook, By Large Enterprises (2024-2032) ($MN)
  • Table 27 Global AI Chipset Market Outlook, By Small and Medium Enterprises (SMEs) (2024-2032) ($MN)
  • Table 28 Global AI Chipset Market Outlook, By End User (2024-2032) ($MN)
  • Table 29 Global AI Chipset Market Outlook, By Consumer Electronics (2024-2032) ($MN)
  • Table 30 Global AI Chipset Market Outlook, By Automotive & Transportation (2024-2032) ($MN)
  • Table 31 Global AI Chipset Market Outlook, By Healthcare & Life Sciences (2024-2032) ($MN)
  • Table 32 Global AI Chipset Market Outlook, By IT & Telecommunication (2024-2032) ($MN)
  • Table 33 Global AI Chipset Market Outlook, By BFSI (2024-2032) ($MN)
  • Table 34 Global AI Chipset Market Outlook, By Manufacturing & Industrial (2024-2032) ($MN)
  • Table 35 Global AI Chipset Market Outlook, By Retail & E-commerce (2024-2032) ($MN)
  • Table 36 Global AI Chipset Market Outlook, By Government & Defense (2024-2032) ($MN)
  • Table 37 Global AI Chipset Market Outlook, By Agriculture (2024-2032) ($MN)
  • Table 38 Global AI Chipset Market Outlook, By Other End Users (2024-2032) ($MN)

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