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市场调查报告书
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1889200

全球人工智慧晶片组市场：预测至 2032 年—按组件、功能、部署方式、技术、公司类型、最终用户和地区进行分析

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个工作天内

价格

根据 Stratistics MRC 的一项研究，预计到 2025 年，全球人工智慧晶片组市场价值将达到 973.5 亿美元，到 2032 年将达到 6411.4 亿美元，在预测期内的复合年增长率为 30.9%。

人工智慧晶片组是专为提升人工智慧（包括深度学习、神经网路处理和大规模数据分析）运作效能而设计的半导体元件。它们利用GPU、TPU和NPU等架构，以更快的速度和更高的能源效率处理并行运算任务。这些晶片组支援各种设备的人工智慧功能，从行动装置和智慧型装置到云端伺服器和自主系统，从而实现即时洞察、增强运算能力并高效执行高级人工智慧演算法。

根据工业生产指数（IIP）数据，由于新冠疫情封锁导致製造业生产进程放缓，2020 年 7 月製造业产出下降了 11.1%。

增加资料中心投资

企业正在扩展其云端基础设施，以支援机器学习、分析和生成式人工智慧工作负载。这种扩展需要能够处理大规模并行运算的高效能处理器。人工智慧晶片组正在被集成，以优化能源效率并加速各种应用中的推理任务。超大规模资料中心供应商的策略性投资也在推动冷却系统和硬体优化方面的创新。这些发展正使资料中心成为全球人工智慧晶片组部署的基础。

开发和设计的高度复杂性

开发兼顾速度、效率和扩充性的架构需要大量的研发投入。晶片组与多样化硬体生态系统的整合复杂性也构成了另一道障碍。快速的技术迭代缩短了产品寿命，并给工程团队和生产流程带来了巨大压力。儘管企业正在采用模组化设计和模拟工具来降低风险，但准入门槛依然很高。在这种环境下，中小企业很难与老牌半导体巨头竞争。

客製化人工智慧晶片组的兴起

客製化处理器正被设计用于加速深度学习、自然语言处理和边缘人工智慧应用。与通用GPU和CPU相比，这些晶片组可提供更最佳化的效能。半导体公司与云端服务供应商之间的合作，正在推动针对特定产业的共同开发架构。面向医疗保健、汽车和机器人等领域的专用加速器正成为新兴趋势。这波客製化浪潮正在重新定义竞争差异化，并拓展人工智慧硬体创新的范围。

模型压缩技术的快速发展

能够减小模型规模和运算需求的演算法降低了对高效能处理器的依赖。诸如剪枝、量化和知识蒸馏等技术使得在低成本硬体上高效部署成为可能。这一趋势可能导致市场需求从高阶晶片组转向轻量级架构。厂商正透过将支援压缩的设计纳入产品蓝图来应对这一趋势。然而，软体优化领域的创新步伐持续对以硬体为中心的成长策略构成挑战。

新冠疫情的影响：

疫情重塑了各行业人工智慧晶片组应用的优先顺序。供应链中断导致生产计画延误，硬体部署放缓。同时，对人工智慧驱动的医疗诊断和远端协作工具的需求激增。远端医疗、预测分析和自动化物流等领域加速了晶片组投资。各公司采用分散式测试和模拟模型来维持研发动能。

预计在预测期内，影像处理处理器（GPU）细分市场将占据最大的市场份额。

预计在预测期内，影像处理(GPU) 细分市场将占据最大的市场份额。 GPU 因其处理平行处理任务的能力而广受认可，而平行处理任务对于深度学习至关重要。 GPU 在训练和推理工作负载方面的多功能性使其成为人工智慧开发中不可或缺的一部分。记忆体频宽和能源效率的提升进一步巩固了 GPU 的地位。其主要应用领域包括自动驾驶汽车、医学影像处理和自然语言处理。

在预测期内，医疗保健产业的复合年增长率将最高。

预计在预测期内，医疗保健产业将实现最高成长率，这主要得益于对基于人工智慧的诊断、药物研发和病患监测的需求不断增长。晶片组能够实现医学影像和基因组数据的即时分析。与穿戴式装置的整合正在拓展其在预防医学和个人化医疗领域的应用范围。半导体公司与医疗保健机构之间的合作正在加速创新。

占比最大的地区：

预计北美将在预测期内占据最大的市场份额，这得益于该地区在云端基础设施和人工智慧研究方面的大力投资。领先的科技公司和大学正在推动晶片组创新。政府支持的人工智慧和半导体製造倡议进一步加强了生态系统。汽车、医疗保健和金融等行业的应用正在推动市场需求。

年复合成长率最高的地区：

预计中东和非洲地区在预测期内将实现最高的复合年增长率。各国政府正大力投资智慧城市计划和数位转型计画。能源管理、安全和医疗保健领域对人工智慧日益增长的需求正在推动这一领域的扩张。与全球科技公司的合作正将先进的晶片组解决方案引入当地市场。新兴新创Start-Ups正在利用人工智慧硬体开发金融科技和物流应用。这种充满活力的环境使该地区成为人工智慧晶片组应用的快速成长前线。

免费客製化服务：

订阅本报告的用户可从以下免费自订选项中选择一项：

  • 公司简介
    • 对最多三家其他公司进行全面分析
    • 对主要企业进行SWOT分析（最多3家公司）
  • 区域分类
    • 根据客户兴趣对主要国家进行市场估算、预测和复合年增长率分析（註：基于可行性检查）
  • 竞争基准化分析
    • 基于产品系列、地域覆盖和策略联盟对主要企业基准化分析

目录

第一章执行摘要

第二章 引言

  • 概述
  • 相关利益者
  • 分析范围
  • 分析方法
  • 分析材料

第三章 市场趋势分析

  • 司机
  • 抑制因素
  • 机会
  • 威胁
  • 技术分析
  • 终端用户分析
  • 新兴市场
  • 新冠疫情的影响

第四章 波特五力分析

  • 供应商的议价能力
  • 买方议价能力
  • 替代产品的威胁
  • 新进入者的威胁
  • 竞争对手之间的竞争

第五章 全球人工智慧晶片组市场（按组件划分）

  • 中央处理器（CPU）
  • 张量处理单元（TPU）
  • 影像处理处理器（GPU）
  • 神经处理单元（NPU）
  • 专用积体电路（ASIC）
  • 现场可程式闸阵列（FPGA）
  • 其他专用处理器

第六章 全球人工智慧晶片组市场按功能划分

  • 训练
  • 推理

第七章 全球人工智慧晶片组市场依部署方式划分

  • 云端运算人工智慧
  • 边缘人工智慧运算
    • 边缘设备
    • 本地资料中心

第八章 全球人工智慧晶片组市场（按技术划分）

  • 机器学习（ML）
  • 人工智慧世代
  • 深度学习（DL）
  • 强化学习
  • 自然语言处理（NLP）
  • 计算机视觉（CV）

第九章 全球人工智慧晶片组市场（依公司类型划分）

  • 大公司
  • 中小企业

第十章 全球人工智慧晶片组市场（按最终用户划分）

  • 家用电子电器
  • 汽车与运输
  • 医学与生命科​​学
  • 资讯科技/通讯
  • 银行、金融服务和保险（BFSI）
  • 製造业/工业
  • 零售与电子商务
  • 政府/国防
  • 农业
  • 其他最终用户

第十一章 全球人工智慧晶片组市场（按地区划分）

  • 北美洲
    • 美国
    • 加拿大
    • 墨西哥
  • 欧洲
    • 德国
    • 英国
    • 义大利
    • 法国
    • 西班牙
    • 其他欧洲
  • 亚太地区
    • 日本
    • 中国
    • 印度
    • 澳洲
    • 纽西兰
    • 韩国
    • 亚太其他地区
  • 南美洲
    • 阿根廷
    • 巴西
    • 智利
    • 其他南美国家
  • 中东和非洲
    • 沙乌地阿拉伯
    • 阿拉伯聯合大公国
    • 卡达
    • 南非
    • 其他中东和非洲地区

第十二章：主要趋势

  • 合约、商业伙伴关係和合资企业
  • 企业合併（M&A）
  • 新产品上市
  • 业务拓展
  • 其他关键策略

第十三章：企业概况

  • 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.