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市场调查报告书
商品编码
1848391

2032 年 AI 加速器市场预测：按类型、技术、应用、最终用户和地区进行的全球分析

AI Accelerator Market Forecasts to 2032 - Global Analysis By Type, Technology, 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个工作天内

价格

根据 Stratistics MRC 的数据，全球 AI 加速器市场规模预计在 2025 年达到 335.6 亿美元，到 2032 年将达到 2,257.7 亿美元，预测期内的复合年增长率为 31.3%。

AI加速器是专门设计的硬体单元，旨在提高人工智慧（包括机器学习和深度学习）运算的速度和效率。 GPU、TPU和NPU等设备增强了资料处理和运算能力，支援更快的AI模型训练和推理。这些加速器广泛应用于云端服务、自动驾驶技术和边缘运算等领域，能够处理密集型演算法和大量数据，同时提升效能、能源效率和系统可扩展性。

业内专家预测，到 2025 年底，生成式人工智慧晶片市场规模将达到 500 亿美元，到 2027 年将增加至约 7,000 亿美元。

对高效能运算（HPC）的需求不断增长

即时数据处理和复杂模拟的需求日益增长，推动着各行各业对高效能运算的采用。自动驾驶、基因组学和金融建模等领域需要庞大的运算吞吐量，这推动了人们对人工智慧加速器的兴趣。企业越来越多地采用平行处理架构来高效​​处理大规模工作负载。随着人工智慧模型日益复杂，对更快训练和推理速度的需求也日益增长。云端服务供应商和超大规模企业正在大力投资客製化晶片，以优化效能并降低延迟。运算需求的指数级成长使得人工智慧加速器成为下一代数位基础设施的关键推动者。

整合复杂性

将 AI 加速器整合到现有 IT 生态系统中，将为企业带来巨大的技术障碍。与旧系统、软体堆迭和资料管道的相容性问题往往会延迟部署进度。开发人员必须驾驭各种框架、API 和硬体配置，才能确保无缝运​​作。缺乏标准化的介面和工具链会给整合带来沉重的负担，尤其对于中小型企业而言。为了充分利用加速器的功能，需要投入大量资金来培训员工并重组工作流程。这些挑战可能会减缓 AI 增强解决方案的采用速度，并限制其在整个组织中的可扩展性。

节能晶片设计的进步

低功耗架构和热优化的突破为部署AI加速器开启了新的可能性。晶片製造商正在利用先进的封装、3D堆迭和新材料来降低消费量，同时又不牺牲性能。这些创新使边缘设备和行动平台能够持续运行复杂的AI工作负载。监管压力和企业永续性目标正在进一步推动向更绿色的计算解决方案的转变。新兴企业和成熟企业都在探索神经形态和模拟运算范式，以突破效率的界限。随着能源成本的上升，对高性能且环保的加速器的需求为市场扩张创造了肥沃的土壤。

与通用 CPU/GPU 的激烈竞争

通用处理器的普及和不断发展对专用AI加速器构成了竞争威胁。 CPU和GPU日益针对AI工作负载进行最佳化，缩小了与专用晶片之间的效能差距。它们的多功能性和广泛的开发者支援使其对成本敏感的应用具有吸引力。主流供应商正在将AI功能捆绑到主流处理器中，从而减少了某些用例对单独加速器的需求。这种商品化可能会削弱利基加速器解决方案的差异化。如果在性能或效率方面没有明显的优势，AI加速器可能难以维持市场发展动能。

COVID-19的影响：

疫情扰乱了全球供应链，减缓了人工智慧加速器组件的製造和交付。封锁和远端办公要求将需求转向云端基础的推理和边缘运算解决方案。晶片短缺和物流瓶颈影响了生产和部署计划。然而，这场危机加速了数位转型，企业纷纷投资人工智慧以实现营运自动化和最佳化。医疗保健、物流和网路安全领域越来越多地采用人工智慧加速器来应对疫情相关的挑战。后疫情时代策略如今强调供应链韧性、分散式运算模式和灵活的部署架构。

资料中心部分预计将成为预测期内最大的部分

资料中心领域预计将在预测期内占据最大的市场份额，因为它在动力来源大规模人工智慧应用方面发挥核心作用。超大规模资料中心业者和云端服务供应商正在整合客製化加速器，以提高吞吐量并降低能源成本。这些设施支援各种需要高效能运算的工作负载，从自然语言处理到推荐引擎。冷却系统和工作负载编配的创新正在提高加速器的利用率和效率。人工智慧即服务平台的兴起进一步推动了对可扩展、富含建议的基础设施的需求。随着企业转向云端原生架构，资料中心仍是人工智慧部署的支柱。

预计医疗保健产业在预测期内将实现最高复合年增长率

预计医疗保健产业将在预测期内实现最高成长率，这得益于人工智慧诊断和个人化医疗的蓬勃发展。医院和研究机构正在利用加速器进行影像分析、基因组学和药物研发。人工智慧与临床工作流程的整合正在增强决策能力和病患预后。对数位医疗和远端医疗的监管支持正在推动基础设施投资。加速器正在推动穿戴式装置和远端监控系统的即时数据处理。

比最大的地区

由于数位化进程的快速推进和基础设施的扩张，预计亚太地区将在预测期内占据最大的市场份额。中国、印度和韩国等国正大力投资半导体製造和人工智慧研究。政府支持的倡议正在推动国内晶片研发，减少对进口的依赖。该地区在金融、製造业和智慧城市领域的人工智慧应用正呈现强劲成长势头。全球科技公司与本地企业之间的策略合作伙伴关係正在加速技术创新和部署。凭藉庞大的用户群和不断增长的运算需求，亚太地区正逐渐成为加速器领域的主导力量。

复合年增长率最高的地区：

在预测期内，北美预计将呈现最高的复合年增长率，这得益于其在人工智慧创新和创业投资资金筹措的主导。美国拥有主要的晶片设计公司、云端服务供应商和人工智慧新兴企业，推动下一代加速器的发展。监管机构正在简化新计算技术的核准途径，以促进其更快的商业化。企业正在将加速器整合到混合云和边缘环境中，以提高效能和灵活性。该地区受益于由开发者、研究机构和企业采用者组成的成熟生态系统。随着人工智慧应用的多样化，北美将继续引领全球加速器的应用。

免费客製化服务：

此报告的订阅者可以使用以下免费自订选项之一：

  • 公司简介
    • 全面分析其他市场参与者（最多 3 家公司）
    • 主要企业的SWOT分析（最多3家公司）
  • 区域细分
    • 根据客户兴趣对主要国家进行的市场估计、预测和复合年增长率（註：基于可行性检查）
  • 竞争基准化分析
    • 根据产品系列、地理分布和策略联盟对主要企业进行基准化分析

目录

第一章执行摘要

第二章 前言

  • 概述
  • 相关利益者
  • 调查范围
  • 调查方法
    • 资料探勘
    • 数据分析
    • 数据检验
    • 研究途径
  • 研究材料
    • 主要研究资料
    • 二手研究资料
    • 先决条件

第三章市场走势分析

  • 驱动程式
  • 抑制因素
  • 机会
  • 威胁
  • 技术分析
  • 应用分析
  • 最终用户分析
  • 新兴市场
  • COVID-19的影响

第四章 波特五力分析

  • 供应商的议价能力
  • 买方的议价能力
  • 替代品的威胁
  • 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加速器市场类型

  • 图形处理单元（GPU）
  • 现场可程式闸阵列（FPGA）
  • 专用积体电路（ASIC）
  • 张量处理单元（TPU）
  • 其他类型

第六章 全球人工智慧加速器市场（按技术）

  • 云端基础的AI加速器
  • 基于边缘的AI加速器
  • 本地 AI 加速器

第七章全球人工智慧加速器市场（按应用）

  • 资料中心
  • 机器人技术
  • 云端运算
  • 自动驾驶汽车
  • 消费性电子产品
  • 医疗保健和生命科学
  • 其他用途

第八章全球人工智慧加速器市场（按最终用户）

  • 资讯科技/通讯
  • 製造业
  • 车
  • 航太/国防
  • 卫生保健
  • 零售
  • 其他最终用户

第九章全球人工智慧加速器市场（按地区）

  • 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
    • 其他欧洲国家
  • 亚太地区
    • 日本
    • 中国
    • 印度
    • 澳洲
    • 纽西兰
    • 韩国
    • 其他亚太地区
  • 南美洲
    • 阿根廷
    • 巴西
    • 智利
    • 南美洲其他地区
  • 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

第十章：重大进展

  • 协议、伙伴关係、合作和合资企业
  • 收购与合併
  • 新产品发布
  • 业务扩展
  • 其他关键策略

第十一章 公司概况

  • NVIDIA Corporation
  • Amazon Web Services
  • Advanced Micro Devices, Inc.（AMD）
  • Alphabet Inc.
  • Intel Corporation
  • Graphcore Limited
  • Google LLC
  • Axelera AI
  • Qualcomm Technologies, Inc.
  • Meta Platforms, Inc.
  • Apple Inc.
  • Samsung Electronics Co., Ltd.
  • Microsoft Corporation
  • IBM Corporation
  • Taiwan Semiconductor Manufacturing Company（TSMC）
Product Code: SMRC31636

According to Stratistics MRC, the Global AI Accelerator Market is accounted for $33.56 billion in 2025 and is expected to reach $225.77 billion by 2032 growing at a CAGR of 31.3% during the forecast period. An AI Accelerator is a dedicated hardware unit created to boost the speed and efficiency of artificial intelligence operations, including machine learning and deep learning. Devices like GPUs, TPUs, and NPUs enhance data handling and computational power, supporting faster AI model training and inference. Widely applied in areas such as cloud services, autonomous technologies, and edge computing, these accelerators handle intensive algorithms and vast data volumes while improving performance, energy efficiency, and system scalability.

According to Industry Experts, the market for chips powering generative AI will hit USD 50 billion by the end of 2025, with projections to rise to approximately USD 700 billion by 2027.

Market Dynamics:

Driver:

Growing need for high-performance computing (HPC)

The escalating demand for real-time data processing and complex simulations is propelling the adoption of high-performance computing across industries. Sectors such as autonomous driving, genomics, and financial modeling require immense computational throughput, driving interest in AI accelerators. Enterprises are increasingly deploying parallel processing architectures to handle large-scale workloads efficiently. As AI models become more sophisticated, the need for faster training and inference speeds is intensifying. Cloud providers and hyperscalers are investing heavily in custom silicon to optimize performance and reduce latency. This surge in computational requirements is positioning AI accelerators as critical enablers of next-gen digital infrastructure.

Restraint:

Complexity of integration

Integrating AI accelerators into existing IT ecosystems presents significant technical hurdles for enterprises. Compatibility issues with legacy systems, software stacks, and data pipelines often slow deployment timelines. Developers must navigate diverse frameworks, APIs, and hardware configurations to ensure seamless operation. The lack of standardized interfaces and toolchains adds to the integration burden, especially for smaller firms. Training personnel and rearchitecting workflows to leverage accelerator capabilities requires substantial investment. These challenges can delay adoption and limit the scalability of AI-enhanced solutions across organizations.

Opportunity:

Advancements in energy-efficient chip designs

Breakthroughs in low-power architecture and thermal optimization are unlocking new possibilities for AI accelerator deployment. Chipmakers are leveraging advanced packaging, 3D stacking, and novel materials to reduce energy consumption without compromising performance. These innovations are enabling edge devices and mobile platforms to run complex AI workloads sustainably. Regulatory pressure and corporate sustainability goals are further incentivizing the shift toward greener compute solutions. Startups and incumbents alike are exploring neuromorphic and analog computing paradigms to push efficiency boundaries. As energy costs rise, demand for high-performance yet eco-friendly accelerators is creating fertile ground for market expansion.

Threat:

Intense competition from general-purpose CPUs/GPUs

The widespread availability and continual evolution of general-purpose processors pose a competitive threat to specialized AI accelerators. CPUs and GPUs are increasingly optimized for AI workloads, narrowing the performance gap with dedicated chips. Their versatility and broad developer support make them attractive for cost-sensitive applications. Major vendors are bundling AI capabilities into mainstream processors, reducing the need for discrete accelerators in some use cases. This commoditization risks eroding the differentiation of niche accelerator solutions. Without clear performance or efficiency advantages, AI accelerators may struggle to maintain market momentum.

Covid-19 Impact:

The pandemic disrupted global supply chains, delaying fabrication and delivery of AI accelerator components. Lockdowns and remote work mandates shifted demand toward cloud-based inference and edge computing solutions. Chip shortages and logistics bottlenecks impacted production schedules and deployment timelines. However, the crisis accelerated digital transformation, with enterprises investing in AI to automate and optimize operations. Healthcare, logistics, and cybersecurity sectors saw increased adoption of AI accelerators to manage pandemic-related challenges. Post-Covid strategies now emphasize supply chain resilience, distributed compute models, and flexible deployment architectures.

The data centers segment is expected to be the largest during the forecast period

The data centers segment is expected to account for the largest market share during the forecast period, due to its central role in powering large-scale AI applications. Hyperscalers and cloud providers are integrating custom accelerators to enhance throughput and reduce energy costs. These facilities support diverse workloads, from natural language processing to recommendation engines, requiring high-performance compute. Innovations in cooling systems and workload orchestration are improving accelerator utilization and efficiency. The rise of AI-as-a-service platforms is further driving demand for scalable, accelerator-rich infrastructure. As enterprises migrate to cloud-native architectures, data centers remain the backbone of AI deployment.

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 driven by the surge in AI-powered diagnostics and personalized medicine. Hospitals and research institutions are leveraging accelerators for imaging analysis, genomics, and drug discovery. The integration of AI into clinical workflows is enhancing decision-making and patient outcomes. Regulatory support for digital health and telemedicine is boosting infrastructure investments. Accelerators are enabling real-time data processing in wearable devices and remote monitoring systems.

Region with largest share:

During the forecast period, the Asia Pacific region is expected to hold the largest market share, fueled by rapid digitization and infrastructure expansion. Countries like China, India, and South Korea are investing heavily in semiconductor manufacturing and AI research. Government-backed initiatives are promoting domestic chip development and reducing reliance on imports. The region is witnessing strong growth in AI adoption across finance, manufacturing, and smart cities. Strategic collaborations between global tech firms and local players are accelerating innovation and deployment. With a vast user base and rising compute needs, Asia Pacific is emerging as a dominant force in the accelerator landscape.

Region with highest CAGR:

Over the forecast period, the North America region is anticipated to exhibit the highest CAGR, owing to its leadership in AI innovation and venture capital funding. The U.S. is home to major chip designers, cloud providers, and AI startups driving next-gen accelerator development. Regulatory bodies are streamlining approval pathways for emerging compute technologies, fostering rapid commercialization. Enterprises are integrating accelerators into hybrid cloud and edge environments to boost performance and agility. The region benefits from a mature ecosystem of developers, research institutions, and enterprise adopters. As AI applications diversify, North America continues to set the pace for global accelerator adoption.

Key players in the market

Some of the key players in AI Accelerator Market include NVIDIA Corporation, Amazon Web Services, Advanced Micro Devices, Inc. (AMD), Alphabet Inc., Intel Corporation, Graphcore Limited, Google LLC, Axelera AI, Qualcomm Technologies, Inc., Meta Platforms, Inc., Apple Inc., Samsung Electronics Co., Ltd., Microsoft Corporation, IBM Corporation, and Taiwan Semiconductor Manufacturing Company (TSMC).

Key Developments:

In September 2025, NVIDIA and OpenAI announced a letter of intent for a landmark strategic partnership to deploy at least 10 gigawatts of NVIDIA systems for OpenAI's next-generation AI infrastructure to train and run its next generation of models on the path to deploying superintelligence. To support this deployment including data center and power capacity, NVIDIA intends to invest up to $100 billion in OpenAI as the new NVIDIA systems are deployed.

In September 2025, Intel Corporation and NVIDIA announced a collaboration to jointly develop multiple generations of custom datacenter and PC products that accelerate applications and workloads across hyperscale, enterprise and consumer markets. The companies will focus on seamlessly connecting NVIDIA and Intel architectures using NVIDIA NVLink - integrating the strengths of NVIDIA's AI and accelerated computing with Intel's leading CPU technologies and x86 ecosystem to deliver cutting-edge solutions for customers.

Types Covered:

  • Graphics Processing Unit (GPU)
  • Field-Programmable Gate Array (FPGA)
  • Application-Specific Integrated Circuit (ASIC)
  • Tensor Processing Unit (TPU)
  • Other Types

Technologies Covered:

  • Cloud-Based AI Accelerators
  • Edge-Based AI Accelerators
  • On-Premise AI Accelerators

Applications Covered:

  • Data Centers
  • Robotics
  • Cloud Computing
  • Autonomous Vehicles
  • Consumer Electronics
  • Healthcare & Life Sciences
  • Other Applications

End Users Covered:

  • IT & Telecom
  • Manufacturing
  • Automotive
  • Aerospace & Defense
  • Healthcare
  • Retail
  • 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 Application Analysis
  • 3.8 End User Analysis
  • 3.9 Emerging Markets
  • 3.10 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 Accelerator Market, By Type

  • 5.1 Introduction
  • 5.2 Graphics Processing Unit (GPU)
  • 5.3 Field-Programmable Gate Array (FPGA)
  • 5.4 Application-Specific Integrated Circuit (ASIC)
  • 5.5 Tensor Processing Unit (TPU)
  • 5.6 Other Types

6 Global AI Accelerator Market, By Technology

  • 6.1 Introduction
  • 6.2 Cloud-Based AI Accelerators
  • 6.3 Edge-Based AI Accelerators
  • 6.4 On-Premise AI Accelerators

7 Global AI Accelerator Market, By Application

  • 7.1 Introduction
  • 7.2 Data Centers
  • 7.3 Robotics
  • 7.4 Cloud Computing
  • 7.5 Autonomous Vehicles
  • 7.6 Consumer Electronics
  • 7.7 Healthcare & Life Sciences
  • 7.8 Other Applications

8 Global AI Accelerator Market, By End User

  • 8.1 Introduction
  • 8.2 IT & Telecom
  • 8.3 Manufacturing
  • 8.4 Automotive
  • 8.5 Aerospace & Defense
  • 8.6 Healthcare
  • 8.7 Retail
  • 8.8 Other End Users

9 Global AI Accelerator Market, By Geography

  • 9.1 Introduction
  • 9.2 North America
    • 9.2.1 US
    • 9.2.2 Canada
    • 9.2.3 Mexico
  • 9.3 Europe
    • 9.3.1 Germany
    • 9.3.2 UK
    • 9.3.3 Italy
    • 9.3.4 France
    • 9.3.5 Spain
    • 9.3.6 Rest of Europe
  • 9.4 Asia Pacific
    • 9.4.1 Japan
    • 9.4.2 China
    • 9.4.3 India
    • 9.4.4 Australia
    • 9.4.5 New Zealand
    • 9.4.6 South Korea
    • 9.4.7 Rest of Asia Pacific
  • 9.5 South America
    • 9.5.1 Argentina
    • 9.5.2 Brazil
    • 9.5.3 Chile
    • 9.5.4 Rest of South America
  • 9.6 Middle East & Africa
    • 9.6.1 Saudi Arabia
    • 9.6.2 UAE
    • 9.6.3 Qatar
    • 9.6.4 South Africa
    • 9.6.5 Rest of Middle East & Africa

10 Key Developments

  • 10.1 Agreements, Partnerships, Collaborations and Joint Ventures
  • 10.2 Acquisitions & Mergers
  • 10.3 New Product Launch
  • 10.4 Expansions
  • 10.5 Other Key Strategies

11 Company Profiling

  • 11.1 NVIDIA Corporation
  • 11.2 Amazon Web Services
  • 11.3 Advanced Micro Devices, Inc. (AMD)
  • 11.4 Alphabet Inc.
  • 11.5 Intel Corporation
  • 11.6 Graphcore Limited
  • 11.7 Google LLC
  • 11.8 Axelera AI
  • 11.9 Qualcomm Technologies, Inc.
  • 11.10 Meta Platforms, Inc.
  • 11.11 Apple Inc.
  • 11.12 Samsung Electronics Co., Ltd.
  • 11.13 Microsoft Corporation
  • 11.14 IBM Corporation
  • 11.15 Taiwan Semiconductor Manufacturing Company (TSMC)

List of Tables

  • Table 1 Global AI Accelerator Market Outlook, By Region (2024-2032) ($MN)
  • Table 2 Global AI Accelerator Market Outlook, By Type (2024-2032) ($MN)
  • Table 3 Global AI Accelerator Market Outlook, By Graphics Processing Unit (GPU) (2024-2032) ($MN)
  • Table 4 Global AI Accelerator Market Outlook, By Field-Programmable Gate Array (FPGA) (2024-2032) ($MN)
  • Table 5 Global AI Accelerator Market Outlook, By Application-Specific Integrated Circuit (ASIC) (2024-2032) ($MN)
  • Table 6 Global AI Accelerator Market Outlook, By Tensor Processing Unit (TPU) (2024-2032) ($MN)
  • Table 7 Global AI Accelerator Market Outlook, By Other Types (2024-2032) ($MN)
  • Table 8 Global AI Accelerator Market Outlook, By Technology (2024-2032) ($MN)
  • Table 9 Global AI Accelerator Market Outlook, By Cloud-Based AI Accelerators (2024-2032) ($MN)
  • Table 10 Global AI Accelerator Market Outlook, By Edge-Based AI Accelerators (2024-2032) ($MN)
  • Table 11 Global AI Accelerator Market Outlook, By On-Premise AI Accelerators (2024-2032) ($MN)
  • Table 12 Global AI Accelerator Market Outlook, By Application (2024-2032) ($MN)
  • Table 13 Global AI Accelerator Market Outlook, By Data Centers (2024-2032) ($MN)
  • Table 14 Global AI Accelerator Market Outlook, By Robotics (2024-2032) ($MN)
  • Table 15 Global AI Accelerator Market Outlook, By Cloud Computing (2024-2032) ($MN)
  • Table 16 Global AI Accelerator Market Outlook, By Autonomous Vehicles (2024-2032) ($MN)
  • Table 17 Global AI Accelerator Market Outlook, By Consumer Electronics (2024-2032) ($MN)
  • Table 18 Global AI Accelerator Market Outlook, By Healthcare & Life Sciences (2024-2032) ($MN)
  • Table 19 Global AI Accelerator Market Outlook, By Other Applications (2024-2032) ($MN)
  • Table 20 Global AI Accelerator Market Outlook, By End User (2024-2032) ($MN)
  • Table 21 Global AI Accelerator Market Outlook, By IT & Telecom (2024-2032) ($MN)
  • Table 22 Global AI Accelerator Market Outlook, By Manufacturing (2024-2032) ($MN)
  • Table 23 Global AI Accelerator Market Outlook, By Automotive (2024-2032) ($MN)
  • Table 24 Global AI Accelerator Market Outlook, By Aerospace & Defense (2024-2032) ($MN)
  • Table 25 Global AI Accelerator Market Outlook, By Healthcare (2024-2032) ($MN)
  • Table 26 Global AI Accelerator Market Outlook, By Retail (2024-2032) ($MN)
  • Table 27 Global AI Accelerator 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.