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市场调查报告书
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1896165

人工智慧推理晶片市场预测至2032年：按晶片类型、部署方式、应用领域、最终用户和地区分類的全球分析

AI Inference Chips Market Forecasts to 2032 - Global Analysis By Chip Type, Deployment, 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，全球人工智慧推理晶片市场价值将达到 510 亿美元，到 2032 年将达到 2,276 亿美元，预测期内复合年增长率为 23.8%。

人工智慧推理晶片是专门设计的处理器，能够高效运行训练好的人工智慧模型，用于即时决策和资料处理。这些晶片针对低延迟、高吞吐量和高能源效率进行了最佳化，使其适用于边缘设备、自主系统、智慧摄影机和资料中心。它们的日益普及正在推动医疗保健、汽车、零售和工业自动化等行业的可扩展人工智慧部署。

根据 LinkedIn 的趋势，针对自动驾驶和智慧监控等即时任务进行推理优化的晶片的扩展，正在推动工业 4.0 各个领域的更广泛应用。

快速部署边缘人工智慧应用

边缘人工智慧应用的快速部署推动了对推理晶片的需求，这些晶片能够实现更靠近资料来源的低延迟处理。从智慧摄影机和工业IoT设备到自动驾驶汽车，边缘人工智慧都需要专为即时决策而优化的专用晶片。这一趋势降低了对云端基础设施的依赖，增强了隐私保护，并提高了回应速度。随着各行业采用边缘运算，推理晶片已成为可扩展、分散式人工智慧生态系统的关键基础，从而推动了全球市场成长。

高昂的开发和检验成本

开发人工智慧推理晶片涉及复杂的架构、先进的封装和严格的检验流程。高昂的研发成本，加上昂贵的製造和测试要求，构成了巨大的进入门槛。确保与各种人工智慧框架和工作负载的兼容性进一步增加了开发成本。这些资本密集要求使得中小企业难以与老牌半导体巨头竞争。因此，儘管对人工智慧加速发展的需求日益增长，但高成本仍然是阻碍其广泛应用的主要因素。

自主系统和智慧基础设施的扩展

自主系统和智慧基础设施的扩展为人工智慧推理晶片创造了巨大的发展机会。自动驾驶汽车、无人机和机器人依赖即时推理来实现导航、安全和决策。同样，智慧城市和互联基础设施也需要能够高效处理海量感测器资料的晶片。随着政府和企业加大对自动化和数位转型的投入，推理晶片有望在交通、能源和城市环境中实现智慧自适应系统，从而获得显着增长。

利用通用处理器提升人工智慧效能

通用处理器（包括CPU和GPU）的进步对专用推理晶片构成了威胁。随着主流处理器整合AI加速功能，某些应用对专用推理硬体的需求下降。这种融合趋势对推理晶片的差异化构成了挑战，尤其是在对成本敏感的市场。如果通用处理器持续提升大规模AI效能，可能会削弱对小众推理解决方案的需求，迫使专业供应商加快创新步伐以保持竞争力。

新冠疫情的感染疾病

新冠疫情扰乱了半导体供应链，导致人工智慧推理晶片的生产延迟和成本上升。然而，疫情也加速了数位化进程，推动了对人工智慧医疗、远端监控和自动化解决方案的需求。疫情期间，推理晶片在医疗成像、诊断支援和智慧设备领域获得了广泛应用。疫情后的復苏阶段，企业加大了对弹性供应链和本地化製造的投资。疫情也凸显了推理晶片在关键产业实现自适应资料驱动型解决方案的重要性。

预计在预测期内，图形处理器（GPU）细分市场将占据最大的市场份额。

由于其多功能性和平行处理能力，图形处理器 (GPU) 预计将在预测期内占据最大的市场份额。 GPU 可加速深度学习模型，对训练和推理任务都至关重要。其在云端、边缘和企业环境中的可扩展性确保了其广泛应用。随着人工智慧应用在各行各业的扩展，GPU 将继续成为推理运算的基础，在预测期内保持最大的市场份额，并巩固其作为人工智慧工作负载主要驱动力的地位。

预计在预测期内，云端细分市场将实现最高的复合年增长率。

受人工智慧即服务（AIaaS）平台日益普及的推动，预计云端细分市场在预测期内将实现最高成长率。企业越来越依赖云端基础架构来部署可扩展的推理工作负载，而无需投资昂贵的本地硬体。云端服务供应商正在整合专用推理晶片，以提供更快、更有效率的人工智慧服务。对灵活且经济高效的人工智慧解决方案日益增长的需求将推动云端推理的成长，使其成为人工智慧推理晶片市场中成长最快的细分市场。

比最大的地区

预计亚太地区将在整个预测期内保持最大的市场份额。这主要得益于该地区强大的半导体製造基础，以及中国、日本、韩国和台湾地区人工智慧技术的快速发展。该地区正受益于对人工智慧驱动型产业（例如家电、汽车和智慧基础设施）的大力投资。政府主导的各项措施以及不断扩大的研发中心进一步巩固了亚太地区的主导地位。随着对边缘人工智慧和云端服务需求的增长，该地区正逐步成为推理晶片的重要中心。

预计年复合成长率最高的地区

在预测期内，北美地区预计将呈现最高的复合年增长率，这主要得益于人工智慧、云端运算和国防领域的强劲需求。众多大型科技公司和半导体创新企业的存在，推动了推理晶片的快速普及。政府对人工智慧研究的资助以及国内晶片製造倡议，也将进一步促进市场成长。随着企业在医疗保健、金融和自动驾驶系统等领域扩大人工智慧的应用，北美有望成为人工智慧推理晶片市场成长最快的地区。

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目录

第一章执行摘要

第二章 前言

  • 摘要
  • 相关利益者
  • 调查范围
  • 调查方法
  • 研究材料

第三章 市场趋势分析

  • 司机
  • 抑制因素
  • 机会
  • 威胁
  • 应用分析
  • 终端用户分析
  • 新兴市场
  • 新冠疫情的感染疾病

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

5. 全球人工智慧推理晶片市场（按晶片类型划分）

  • 专用积体电路（ASIC）
  • 图形处理器（GPU）
  • 中央处理器（CPU）
  • 神经处理单元
  • 现场可程式闸阵列
  • 混合人工智慧晶片

6. 全球人工智慧推理晶片市场（以部署方式划分）

  • 基于云端的
  • 边缘设备
  • 本地资料中心
  • 嵌入式系统
  • 行动平台
  • 分散式人工智慧系统

第七章 全球人工智慧推理晶片市场（按应用划分）

  • 电脑视觉
  • 自然语言处理
  • 语音辨识
  • 自主系统
  • 建议引擎
  • 预测分析

第八章 全球人工智慧推理晶片市场（按最终用户划分）

  • 科技公司
  • OEM
  • 医疗保健提供者
  • 製造业
  • 零售与电子商务
  • 政府和国防机构

9. 全球人工智慧推理晶片市场（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
    • 其他亚太地区
  • 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

第十章：重大进展

  • 协议、伙伴关係、合作和合资企业
  • 併购
  • 新产品发布
  • 业务拓展
  • 其他关键策略

第十一章 企业概况

  • NVIDIA Corporation
  • Intel Corporation
  • Advanced Micro Devices
  • Qualcomm Incorporated
  • Google LLC
  • Amazon Web Services
  • Microsoft Corporation
  • Apple Inc.
  • Huawei Technologies
  • MediaTek Inc.
  • Graphcore Ltd.
  • Cerebras Systems
  • Groq Inc.
  • Mythic AI
  • Hailo Technologies
  • Ambarella Inc.
Product Code: SMRC32853

According to Stratistics MRC, the Global AI Inference Chips Market is accounted for $51.0 billion in 2025 and is expected to reach $227.6 billion by 2032 growing at a CAGR of 23.8% during the forecast period. AI Inference Chips are specialized processors designed to efficiently execute trained artificial intelligence models for real-time decision-making and data processing. These chips are optimized for low latency, high throughput, and energy efficiency, making them suitable for edge devices, autonomous systems, smart cameras, and data centers. Their growing adoption supports scalable AI deployment across industries such as healthcare, automotive, retail, and industrial automation.

According to LinkedIn trends, expansion of inference-optimized chips for real-time tasks like autonomous driving and smart surveillance is strengthening adoption across Industry 4.0 sectors.

Market Dynamics:

Driver:

Rapid deployment of edge AI applications

The rapid deployment of edge AI applications is fueling demand for inference chips that deliver low-latency processing closer to data sources. From smart cameras and industrial IoT devices to autonomous vehicles, edge AI requires specialized chips optimized for real-time decision-making. This trend reduces reliance on cloud infrastructure, enhances privacy, and improves responsiveness. As industries embrace edge computing, inference chips are becoming critical enablers of scalable, decentralized AI ecosystems, driving strong market growth worldwide.

Restraint:

High development and validation costs

Developing AI inference chips involves complex architectures, advanced packaging, and rigorous validation processes. High R&D costs, coupled with expensive fabrication and testing requirements, create significant barriers to entry. Ensuring compatibility with diverse AI frameworks and workloads further adds to development expenses. Smaller firms struggle to compete with established semiconductor giants due to these capital-intensive demands. As a result, high costs remain a key restraint, slowing broader adoption despite the growing need for AI acceleration.

Opportunity:

Autonomous systems & smart infrastructure expansion

The expansion of autonomous systems and smart infrastructure presents major opportunities for AI inference chips. Self-driving cars, drones, and robotics rely on real-time inference for navigation, safety, and decision-making. Similarly, smart cities and connected infrastructure demand chips capable of processing massive sensor data streams efficiently. As governments and enterprises invest in automation and digital transformation, inference chips are positioned to capture significant growth, enabling intelligent, adaptive systems across transportation, energy, and urban environments.

Threat:

General-purpose processors improving AI performance

Advances in general-purpose processors, including CPUs and GPUs, pose a threat to specialized inference chips. As mainstream processors integrate AI acceleration features, they reduce the need for dedicated inference hardware in certain applications. This convergence challenges the differentiation of inference chips, particularly in cost-sensitive markets. If general-purpose processors continue to improve AI performance at scale, they may erode demand for niche inference solutions, pressuring specialized vendors to innovate faster to maintain relevance.

Covid-19 Impact:

The COVID-19 pandemic disrupted semiconductor supply chains, delaying production and increasing costs for AI inference chips. However, it also accelerated digital adoption, boosting demand for AI-powered healthcare, remote monitoring, and automation solutions. Inference chips gained traction in medical imaging, diagnostics, and smart devices during the crisis. Post-pandemic recovery reinforced investments in resilient supply chains and localized manufacturing. Ultimately, the pandemic highlighted the importance of inference chips in enabling adaptive, data-driven solutions across critical industries.

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, owing to their versatility and parallel processing capabilities. GPUs accelerate deep learning models, making them indispensable for both training and inference tasks. Their scalability across cloud, edge, and enterprise environments ensures broad adoption. As AI applications expand across industries, GPUs remain the backbone of inference computing, securing the largest market share during the forecast period and reinforcing their role as the primary driver of AI workloads.

The cloud-based segment is expected to have the highest CAGR during the forecast period

Over the forecast period, the cloud-based segment is predicted to witness the highest growth rate, reinforced by the growing adoption of AI-as-a-service platforms. Enterprises increasingly rely on cloud infrastructure to deploy scalable inference workloads without investing in costly on-premises hardware. Cloud providers are integrating specialized inference chips to deliver faster, more efficient AI services. As demand for flexible, cost-effective AI solutions rises, cloud-based inference is expected to lead growth, making it the fastest-expanding segment in the AI inference chips market.

Region with largest share:

During the forecast period, the Asia Pacific region is expected to hold the largest market share, ascribed to its strong semiconductor manufacturing base and rapid AI adoption in China, Japan, South Korea, and Taiwan. The region benefits from robust investments in AI-driven industries such as consumer electronics, automotive, and smart infrastructure. Government-backed initiatives and expanding R&D centers further strengthen Asia Pacific's leadership. With growing demand for edge AI and cloud services, the region is positioned as the dominant hub for inference chips.

Region with highest CAGR:

Over the forecast period, the North America region is anticipated to exhibit the highest CAGR associated with strong demand from AI, cloud computing, and defense sectors. The presence of leading technology companies and semiconductor innovators drives rapid adoption of inference chips. Government funding for AI research and domestic chip manufacturing initiatives further accelerates growth. As enterprises scale AI deployments across healthcare, finance, and autonomous systems, North America is expected to emerge as the fastest-growing region in the AI inference chips market.

Key players in the market

Some of the key players in AI Inference Chips Market include Advanced Micro Devices (AMD), Intel Corporation, NVIDIA Corporation, Taiwan Semiconductor Manufacturing Company, Samsung Electronics, Marvell Technology Group, Broadcom Inc., Qualcomm Incorporated, Apple Inc., IBM Corporation, MediaTek Inc., Arm Holdings, ASE Technology Holding, Amkor Technology, Cadence Design Systems and Synopsys Inc.

Key Developments:

In November 2025, NVIDIA Corporation reported record-breaking sales of its Blackwell GPU systems, with demand "off the charts" for AI inference workloads in data centers, positioning GPUs as the backbone of generative AI deployments.

In October 2025, Intel Corporation expanded its Gaudi AI accelerator line, integrating advanced inference capabilities to compete directly with NVIDIA in cloud and enterprise AI workloads.

In September 2025, AMD (Advanced Micro Devices) introduced new MI325X accelerators optimized for inference efficiency, targeting hyperscale cloud providers and enterprise AI applications.

Chip Types Covered:

  • Application-Specific Integrated Circuits
  • Graphics Processing Units
  • Central Processing Units
  • Neural Processing Units
  • Field-Programmable Gate Arrays
  • Hybrid AI Chips

Deployments Covered:

  • Cloud-Based
  • Edge Devices
  • On-Premise Data Centers
  • Embedded Systems
  • Mobile Platforms
  • Distributed AI Systems

Applications Covered:

  • Computer Vision
  • Natural Language Processing
  • Speech Recognition
  • Autonomous Systems
  • Recommendation Engines
  • Predictive Analytics

End Users Covered:

  • Technology Companies
  • Automotive OEMs
  • Healthcare Providers
  • Manufacturing Enterprises
  • Retail & E-Commerce
  • Government & Defense

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 Application 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 Inference Chips Market, By Chip Type

  • 5.1 Introduction
  • 5.2 Application-Specific Integrated Circuits
  • 5.3 Graphics Processing Units
  • 5.4 Central Processing Units
  • 5.5 Neural Processing Units
  • 5.6 Field-Programmable Gate Arrays
  • 5.7 Hybrid AI Chips

6 Global AI Inference Chips Market, By Deployment

  • 6.1 Introduction
  • 6.2 Cloud-Based
  • 6.3 Edge Devices
  • 6.4 On-Premise Data Centers
  • 6.5 Embedded Systems
  • 6.6 Mobile Platforms
  • 6.7 Distributed AI Systems

7 Global AI Inference Chips Market, By Application

  • 7.1 Introduction
  • 7.2 Computer Vision
  • 7.3 Natural Language Processing
  • 7.4 Speech Recognition
  • 7.5 Autonomous Systems
  • 7.6 Recommendation Engines
  • 7.7 Predictive Analytics

8 Global AI Inference Chips Market, By End User

  • 8.1 Introduction
  • 8.2 Technology Companies
  • 8.3 Automotive OEMs
  • 8.4 Healthcare Providers
  • 8.5 Manufacturing Enterprises
  • 8.6 Retail & E-Commerce
  • 8.7 Government & Defense

9 Global AI Inference Chips 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 Intel Corporation
  • 11.3 Advanced Micro Devices
  • 11.4 Qualcomm Incorporated
  • 11.5 Google LLC
  • 11.6 Amazon Web Services
  • 11.7 Microsoft Corporation
  • 11.8 Apple Inc.
  • 11.9 Huawei Technologies
  • 11.10 MediaTek Inc.
  • 11.11 Graphcore Ltd.
  • 11.12 Cerebras Systems
  • 11.13 Groq Inc.
  • 11.14 Mythic AI
  • 11.15 Hailo Technologies
  • 11.16 Ambarella Inc.

List of Tables

  • Table 1 Global AI Inference Chips Market Outlook, By Region (2024-2032) ($MN)
  • Table 2 Global AI Inference Chips Market Outlook, By Chip Type (2024-2032) ($MN)
  • Table 3 Global AI Inference Chips Market Outlook, By Application-Specific Integrated Circuits (2024-2032) ($MN)
  • Table 4 Global AI Inference Chips Market Outlook, By Graphics Processing Units (2024-2032) ($MN)
  • Table 5 Global AI Inference Chips Market Outlook, By Central Processing Units (2024-2032) ($MN)
  • Table 6 Global AI Inference Chips Market Outlook, By Neural Processing Units (2024-2032) ($MN)
  • Table 7 Global AI Inference Chips Market Outlook, By Field-Programmable Gate Arrays (2024-2032) ($MN)
  • Table 8 Global AI Inference Chips Market Outlook, By Hybrid AI Chips (2024-2032) ($MN)
  • Table 9 Global AI Inference Chips Market Outlook, By Deployment (2024-2032) ($MN)
  • Table 10 Global AI Inference Chips Market Outlook, By Cloud-Based (2024-2032) ($MN)
  • Table 11 Global AI Inference Chips Market Outlook, By Edge Devices (2024-2032) ($MN)
  • Table 12 Global AI Inference Chips Market Outlook, By On-Premise Data Centers (2024-2032) ($MN)
  • Table 13 Global AI Inference Chips Market Outlook, By Embedded Systems (2024-2032) ($MN)
  • Table 14 Global AI Inference Chips Market Outlook, By Mobile Platforms (2024-2032) ($MN)
  • Table 15 Global AI Inference Chips Market Outlook, By Distributed AI Systems (2024-2032) ($MN)
  • Table 16 Global AI Inference Chips Market Outlook, By Application (2024-2032) ($MN)
  • Table 17 Global AI Inference Chips Market Outlook, By Computer Vision (2024-2032) ($MN)
  • Table 18 Global AI Inference Chips Market Outlook, By Natural Language Processing (2024-2032) ($MN)
  • Table 19 Global AI Inference Chips Market Outlook, By Speech Recognition (2024-2032) ($MN)
  • Table 20 Global AI Inference Chips Market Outlook, By Autonomous Systems (2024-2032) ($MN)
  • Table 21 Global AI Inference Chips Market Outlook, By Recommendation Engines (2024-2032) ($MN)
  • Table 22 Global AI Inference Chips Market Outlook, By Predictive Analytics (2024-2032) ($MN)
  • Table 23 Global AI Inference Chips Market Outlook, By End User (2024-2032) ($MN)
  • Table 24 Global AI Inference Chips Market Outlook, By Technology Companies (2024-2032) ($MN)
  • Table 25 Global AI Inference Chips Market Outlook, By Automotive OEMs (2024-2032) ($MN)
  • Table 26 Global AI Inference Chips Market Outlook, By Healthcare Providers (2024-2032) ($MN)
  • Table 27 Global AI Inference Chips Market Outlook, By Manufacturing Enterprises (2024-2032) ($MN)
  • Table 28 Global AI Inference Chips Market Outlook, By Retail & E-Commerce (2024-2032) ($MN)
  • Table 29 Global AI Inference Chips Market Outlook, By Government & Defense (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.