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市场调查报告书
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1856892

全球人工智慧推理市场：未来预测（至2032年）－按计算类型、记忆体类型、部署模式、应用、最终用户和地区进行分析

AI Inference Market Forecasts to 2032 - Global Analysis By Compute Type, Memory Type, Deployment Mode, 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，预计到 2025 年，全球人工智慧推理市场规模将达到 1,162 亿美元，到 2032 年将达到 4,043.7 亿美元，预测期内复合年增长率为 19.5%。

人工智慧推理是指预先训练的人工智慧模型利用已学习到的模式来分析和解释新数据，从而做出预测或决策的阶段。它与训练不同，训练专注于从大型资料集中学习。推理使语音辨识、自动驾驶汽车和推荐系统等人工智慧应用能够高效运作。人工智慧推理的性能，包括其速度和可靠性，对于人工智慧技术在实际应用中产生实际效果至关重要。

根据 Appen 发布的《2020 年人工智慧现状报告》，41% 的公司表示在 COVID-19 疫情期间加快了人工智慧策略的实施，这表明在全球危机期间，组织的优先事项发生了重大转变，开始利用人工智慧。

采用生成式人工智慧和大规模语言模型

生成式人工智慧和大规模语言模式的快速整合正在改变各产业推理工作负载的管理方式。这些技术能够实现更细緻的理解、情境推理和即时决策。越来越多的企业正在将大规模语言模型（LLM）融入客户服务、内容创作和分析流程中。 LLM 处理大量资料集并产生类人回应的能力，正在推动可扩展推理解决方案的需求。随着企业寻求自动化复杂任务，他们越来越依赖人工智慧推理引擎。预计这一趋势将显着扩大各行业的市场基础。

人工智慧和机器学习营运专业人员短缺

人工智慧推理市场面临的一个关键瓶颈是人工智慧部署和机器学习运维方面专业人才的缺乏。大规模管理推理工作负载需要模型调优、基础架构编配和效能最佳化的专业知识。然而，这些专业人才仍然有限，尤其是在新兴经济体。这种人才缺口阻碍了企业充分利用人工智慧能力，并延缓了部署进度。如果没有强有力的运维支持，即使是先进的模型也可能无法提供稳定可靠的结果。弥合这一技能缺口对于充分发挥人工智慧推理平台的潜力至关重要。

人工智慧即服务（AIaaS）的成长

人工智慧即服务 (AIaaS) 平台的兴起，为可扩展、经济高效的推理部署开闢了新途径。这些云端基础的解决方案使企业无需在基础设施或人才方面投入大量资金即可存取强大的模型。凭藉灵活的 API 和计量收费，AIaaS 正在普及先进的推理能力。随着服务提供者为医疗保健、金融和零售等行业提供客製化服务，AIaaS 的应用正在不断增长。与现有企业系统的整合也日趋无缝，从而提高了营运效率。这种向基于服务的 AI 交付模式的转变，有望加速市场成长和创新。

资料隐私和监管合规

严格的资料保护法律和不断演变的法律规范为人工智慧推理技术的应用带来了重大挑战。推理引擎通常处理敏感的个人和企业数据，引发了人们对数据滥用和洩露的担忧。遵守GDPR、HIPAA等全球标准以及新兴的人工智慧特定法规需要严格的安全保障措施。为了降低风险，企业必须投资安全架构、审核追踪和可解释人工智慧。不遵守这些规定可能会导致声誉受损和经济处罚。

新冠疫情的影响：

疫情重塑了企业的优先事项，加速了数位转型和人工智慧的应用。远距办公和虚拟服务导致对自动化决策和智慧介面的需求激增。人工智慧推理平台在实现聊天机器人、诊断和跨职能预测分析方面变得至关重要。然而，供应链中断和预算限制暂时延缓了基础设施升级。疫情过后，企业在展望未来营运时，正优先考虑具有弹性的云端原生推理解决方案。

预计在预测期内，云端推断细分市场将是最大的。

由于其可扩展性和成本效益，预计在预测期内，云端推理领域将占据最大的市场份额。企业正越来越多地将工作负载迁移到云端平台，以降低延迟并提高吞吐量。云端原生推理引擎提供动态资源分配，从而能够即时处理复杂模型。与边缘设备和混合架构的整合进一步提升了效能。跨区域和跨用例部署的灵活性使云端推理极具吸引力。随着对人工智慧应用的需求不断增长，云端基础的推理有望引领市场。

预计医疗保健产业在预测期内将实现最高的复合年增长率。

预计在预测期内，医疗保健产业将迎来最高的成长率。医院和研究机构正在利用人工智慧进行诊断、影像和个人化治疗方案製定。推理引擎能够快速分析医疗数据，从而提高准确性并改善患者预后。数位化医疗和远端医疗的推进正在加速人工智慧工具的普及应用。监管机构对医疗保健领域人工智慧的支持力度不断加大，以及相关资金的投入，也推动了这一领域的成长。该行业独特的数据需求和高影响力的应用案例使其成为推理创新的理想应用领域。

占比最大的地区：

预计亚太地区将在预测期内占据最大的市场份额。该地区快速的数位化、不断扩展的技术基础设施以及政府主导的人工智慧倡议是关键的成长驱动力。中国、印度和日本等国正大力投资人工智慧研究和云端运算能力。製造业、金融业和医疗保健等行业的公司正在采用推理平台来提高生产力。该地区人工智慧新兴企业的崛起以及有利的法规环境正在推动区域竞争。

复合年增长率最高的地区：

预计北美地区在预测期内将呈现最高的复合年增长率。该地区受益于成熟的人工智慧生态系统、强劲的研发投入以及各行业的早期应用。科技巨头和新兴企业正在推动推理优化和部署方面的创新。政府对人工智慧研究的资助和伦理框架为持续成长提供了支持。企业正日益将推理引擎整合到云端、边缘和混合环境中。这些因素预计将推动人工智慧推理能力的快速发展和领先地位。

免费提供客製化服务：

订阅本报告的用户可享有以下免费客製化服务之一：

  • 公司简介
    • 对至多三家其他市场公司进行全面分析
    • 对主要企业进行SWOT分析（最多3家公司）
  • 区域细分
    • 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 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
    • 原始研究资料
    • 次级研究资讯来源
    • 先决条件

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 全球人工智慧推理市场（按计算类型划分）

  • 中央处理器（CPU）
  • 专用积体电路（ASIC）
  • 图形处理器（GPU）
  • 现场可程式闸阵列（FPGA）
  • 神经处理单元（NPU）
  • 其他的

第六章 全球人工智慧推理市场（按记忆体类型划分）

  • 高频宽记忆体（HBM）
  • 双倍资料速率（DDR）
  • 东德
  • LPDDR
  • 其他的

第七章 全球人工智慧推理市场按部署模式划分

  • 边缘推断
  • 云推断
  • 混合推理

第八章 全球人工智慧推理市场（按应用划分）

  • 自然语言处理（NLP）
  • 电脑视觉
  • 人工智慧世代
  • 机器学习
  • 机器人技术
  • 推荐系​​统
  • 预测分析
  • 其他的

第九章 全球人工智慧推理市场（按最终用户划分）

  • 卫生保健
  • 家用电器
  • 汽车与运输
  • 航太与国防
  • 零售与电子商务
  • 资讯科技/通讯
  • 银行、金融服务和保险（BFSI）
  • 製造业
  • 其他的

第十章 全球人工智慧推理市场（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
    • 其他南美洲
  • 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

第十一章 重大进展

  • 协议、伙伴关係、合作和合资企业
  • 收购与併购
  • 新产品上市
  • 业务拓展
  • 其他关键策略

第十二章 企业概况

  • NVIDIA Corporation
  • Graphcore
  • Intel Corporation
  • Baidu Inc.
  • Advanced Micro Devices（AMD）
  • Tenstorrent
  • Qualcomm Technologies
  • Huawei Technologies
  • Google
  • Samsung Electronics
  • Apple Inc.
  • IBM Corporation
  • Microsoft Corporation
  • Meta Platforms Inc.
  • Amazon Web Services（AWS）
Product Code: SMRC31764

According to Stratistics MRC, the Global AI Inference Market is accounted for $116.20 billion in 2025 and is expected to reach $404.37 billion by 2032 growing at a CAGR of 19.5% during the forecast period. AI inference refers to the stage where a pre-trained AI model utilizes its learned patterns to analyze and interpret new data, producing predictions or decisions. This differs from training, which focuses on learning from vast datasets. Inference allows AI applications like speech recognition, autonomous vehicles, and recommendation systems to operate effectively. The performance of AI inference, including its speed and reliability, is essential for ensuring that AI technologies can deliver practical results in real-world situations.

According to Appen's State of AI 2020 Report, 41% of companies reported an acceleration in their AI strategies during the COVID-19 pandemic. This indicates a significant shift in organizational priorities toward leveraging AI amidst the global crisis.

Market Dynamics:

Driver:

Adoption of generative AI and large language models

The rapid integration of generative AI and large language models is transforming how inference workloads are managed across industries. These technologies are enabling more nuanced understanding, contextual reasoning, and real-time decision-making. Enterprises are increasingly embedding LLMs into customer service, content creation, and analytics pipelines. Their ability to process vast datasets and generate human-like responses is driving demand for scalable inference solutions. As organizations seek to automate complex tasks, the reliance on AI inference engines is intensifying. This momentum is expected to significantly expand the market footprint across sectors.

Restraint:

Shortage of skilled AI and ML ops professionals

A major bottleneck in the AI inference market is the limited availability of professionals skilled in AI deployment and ML operations. Managing inference workloads at scale requires expertise in model tuning, infrastructure orchestration, and performance optimization. However, the talent pool for such specialized roles remains constrained, especially in emerging economies. This gap hampers the ability of firms to fully leverage AI capabilities and slows down implementation timelines. Without robust operational support, even advanced models may fail to deliver consistent results. Bridging this skills gap is critical to unlocking the full potential of AI inference platforms.

Opportunity:

Growth of AI-as-a-service (AIaaS)

The rise of AI-as-a-service platforms is creating new avenues for scalable and cost-effective inference deployment. These cloud-based solutions allow businesses to access powerful models without investing heavily in infrastructure or talent. With flexible APIs and pay-as-you-go pricing, AIaaS is democratizing access to advanced inference capabilities. Providers are increasingly offering tailored services for sectors like healthcare, finance, and retail, enhancing adoption. Integration with existing enterprise systems is becoming seamless, boosting operational efficiency. This shift toward service-based AI delivery is poised to accelerate market growth and innovation.

Threat:

Data privacy and regulatory compliance

Stringent data protection laws and evolving regulatory frameworks pose significant challenges to AI inference adoption. Inference engines often process sensitive personal and enterprise data, raising concerns around misuse and breaches. Compliance with global standards like GDPR, HIPAA, and emerging AI-specific regulations requires rigorous safeguards. Companies must invest in secure architectures, audit trails, and explainable AI to mitigate risks. Failure to meet compliance can result in reputational damage and financial penalties.

Covid-19 Impact:

The pandemic reshaped enterprise priorities, accelerating digital transformation and AI adoption. Remote operations and virtual services created a surge in demand for automated decision-making and intelligent interfaces. AI inference platforms became critical in enabling chatbots, diagnostics, and predictive analytics across sectors. However, supply chain disruptions and budget constraints temporarily slowed infrastructure upgrades. Post-pandemic, organizations are prioritizing resilient, cloud-native inference solutions to future-proof operations.

The cloud inference segment is expected to be the largest during the forecast period

The cloud inference segment is expected to account for the largest market share during the forecast period, due to its scalability and cost-efficiency. Enterprises are increasingly shifting workloads to cloud platforms to reduce latency and improve throughput. Cloud-native inference engines offer dynamic resource allocation, enabling real-time processing of complex models. Integration with edge devices and hybrid architectures is further enhancing performance. The flexibility to deploy across geographies and use cases makes cloud inference highly attractive. As demand for AI-powered applications grows, cloud-based inference is expected to lead the market.

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. Hospitals and research institutions are leveraging AI for diagnostics, imaging, and personalized treatment planning. Inference engines enable rapid analysis of medical data, improving accuracy and patient outcomes. The push toward digital health and telemedicine is accelerating adoption of AI-powered tools. Regulatory support and increased funding for AI in healthcare are also driving growth. This sector's unique data needs and high-impact use cases make it a prime candidate for inference innovation.

Region with largest share:

During the forecast period, the Asia Pacific region is expected to hold the largest market share. The region's rapid digitization, expanding tech infrastructure, and government-led AI initiatives are key growth drivers. Countries like China, India, and Japan are investing heavily in AI research and cloud capabilities. Enterprises across manufacturing, finance, and healthcare are adopting inference platforms to enhance productivity. The rise of local AI startups and favorable regulatory environments are boosting regional competitiveness.

Region with highest CAGR:

Over the forecast period, the North America region is anticipated to exhibit the highest CAGR. The region benefits from a mature AI ecosystem, strong R&D investments, and early adoption across industries. Tech giants and startups alike are driving innovation in inference optimization and deployment. Government funding for AI research and ethical frameworks is supporting sustainable growth. Enterprises are increasingly integrating inference engines into cloud, edge, and hybrid environments. These dynamics are expected to fuel rapid expansion and leadership in AI inference capabilities.

Key players in the market

Some of the key players in AI Inference Market include NVIDIA Corporation, Graphcore, Intel Corporation, Baidu Inc., Advanced Micro Devices (AMD), Tenstorrent, Qualcomm Technologies, Huawei Technologies, Google, Samsung Electronics, Apple Inc., IBM Corporation, Microsoft Corporation, Meta Platforms Inc., and Amazon Web Services (AWS).

Key Developments:

In October 2025, Intel announced a key addition to its AI accelerator portfolio, a new Intel Data Center GPU code-named Crescent Island is designed to meet the growing demands of AI inference workloads and will offer high memory capacity and energy-efficient performance.

In September 2025, OpenAI and NVIDIA 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.

Compute Types Covered:

  • Central Processing Unit (CPU)
  • Application-Specific Integrated Circuit (ASIC)
  • Graphics Processing Unit (GPU)
  • Field-Programmable Gate Array (FPGA)
  • Neural Processing Unit (NPU)
  • Other Compute Types

Memory Types Covered:

  • High Bandwidth Memory (HBM)
  • Double Data Rate (DDR)
  • GDDR
  • LPDDR
  • Other Memory Types

Deployment Modes Covered:

  • Edge Inference
  • Cloud Inference
  • Hybrid Inference

Applications Covered:

  • Natural Language Processing (NLP)
  • Computer Vision
  • Generative AI
  • Machine Learning
  • Robotics
  • Recommendation Systems
  • Predictive Analytics
  • Other Applications

End Users Covered:

  • Healthcare
  • Consumer Electronics
  • Automotive & Transportation
  • Aerospace & Defense
  • Retail & E-commerce
  • IT & Telecom
  • Banking, Financial Services & Insurance (BFSI)
  • Manufacturing
  • 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 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 Market, By Compute Type

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

6 Global AI Inference Market, By Memory Type

  • 6.1 Introduction
  • 6.2 High Bandwidth Memory (HBM)
  • 6.3 Double Data Rate (DDR)
  • 6.4 GDDR
  • 6.5 LPDDR
  • 6.6 Other Memory Types

7 Global AI Inference Market, By Deployment Mode

  • 7.1 Introduction
  • 7.2 Edge Inference
  • 7.3 Cloud Inference
  • 7.4 Hybrid Inference

8 Global AI Inference Market, By Application

  • 8.1 Introduction
  • 8.2 Natural Language Processing (NLP)
  • 8.3 Computer Vision
  • 8.4 Generative AI
  • 8.5 Machine Learning
  • 8.6 Robotics
  • 8.7 Recommendation Systems
  • 8.8 Predictive Analytics
  • 8.9 Other Applications

9 Global AI Inference Market, By End User

  • 9.1 Introduction
  • 9.2 Healthcare
  • 9.3 Consumer Electronics
  • 9.4 Automotive & Transportation
  • 9.5 Aerospace & Defense
  • 9.6 Retail & E-commerce
  • 9.7 IT & Telecom
  • 9.8 Banking, Financial Services & Insurance (BFSI)
  • 9.9 Manufacturing
  • 9.10 Other End Users

10 Global AI Inference Market, By Geography

  • 10.1 Introduction
  • 10.2 North America
    • 10.2.1 US
    • 10.2.2 Canada
    • 10.2.3 Mexico
  • 10.3 Europe
    • 10.3.1 Germany
    • 10.3.2 UK
    • 10.3.3 Italy
    • 10.3.4 France
    • 10.3.5 Spain
    • 10.3.6 Rest of Europe
  • 10.4 Asia Pacific
    • 10.4.1 Japan
    • 10.4.2 China
    • 10.4.3 India
    • 10.4.4 Australia
    • 10.4.5 New Zealand
    • 10.4.6 South Korea
    • 10.4.7 Rest of Asia Pacific
  • 10.5 South America
    • 10.5.1 Argentina
    • 10.5.2 Brazil
    • 10.5.3 Chile
    • 10.5.4 Rest of South America
  • 10.6 Middle East & Africa
    • 10.6.1 Saudi Arabia
    • 10.6.2 UAE
    • 10.6.3 Qatar
    • 10.6.4 South Africa
    • 10.6.5 Rest of Middle East & Africa

11 Key Developments

  • 11.1 Agreements, Partnerships, Collaborations and Joint Ventures
  • 11.2 Acquisitions & Mergers
  • 11.3 New Product Launch
  • 11.4 Expansions
  • 11.5 Other Key Strategies

12 Company Profiling

  • 12.1 NVIDIA Corporation
  • 12.2 Graphcore
  • 12.3 Intel Corporation
  • 12.4 Baidu Inc.
  • 12.5 Advanced Micro Devices (AMD)
  • 12.6 Tenstorrent
  • 12.7 Qualcomm Technologies
  • 12.8 Huawei Technologies
  • 12.9 Google
  • 12.10 Samsung Electronics
  • 12.11 Apple Inc.
  • 12.12 IBM Corporation
  • 12.13 Microsoft Corporation
  • 12.14 Meta Platforms Inc.
  • 12.15 Amazon Web Services (AWS)

List of Tables

  • Table 1 Global AI Inference Market Outlook, By Region (2024-2032) ($MN)
  • Table 2 Global AI Inference Market Outlook, By Compute Type (2024-2032) ($MN)
  • Table 3 Global AI Inference Market Outlook, By Central Processing Unit (CPU) (2024-2032) ($MN)
  • Table 4 Global AI Inference Market Outlook, By Application-Specific Integrated Circuit (ASIC) (2024-2032) ($MN)
  • Table 5 Global AI Inference Market Outlook, By Graphics Processing Unit (GPU) (2024-2032) ($MN)
  • Table 6 Global AI Inference Market Outlook, By Field-Programmable Gate Array (FPGA) (2024-2032) ($MN)
  • Table 7 Global AI Inference Market Outlook, By Neural Processing Unit (NPU) (2024-2032) ($MN)
  • Table 8 Global AI Inference Market Outlook, By Other Compute Types (2024-2032) ($MN)
  • Table 9 Global AI Inference Market Outlook, By Memory Type (2024-2032) ($MN)
  • Table 10 Global AI Inference Market Outlook, By High Bandwidth Memory (HBM) (2024-2032) ($MN)
  • Table 11 Global AI Inference Market Outlook, By Double Data Rate (DDR) (2024-2032) ($MN)
  • Table 12 Global AI Inference Market Outlook, By GDDR (2024-2032) ($MN)
  • Table 13 Global AI Inference Market Outlook, By LPDDR (2024-2032) ($MN)
  • Table 14 Global AI Inference Market Outlook, By Other Memory Types (2024-2032) ($MN)
  • Table 15 Global AI Inference Market Outlook, By Deployment Mode (2024-2032) ($MN)
  • Table 16 Global AI Inference Market Outlook, By Edge Inference (2024-2032) ($MN)
  • Table 17 Global AI Inference Market Outlook, By Cloud Inference (2024-2032) ($MN)
  • Table 18 Global AI Inference Market Outlook, By Hybrid Inference (2024-2032) ($MN)
  • Table 19 Global AI Inference Market Outlook, By Application (2024-2032) ($MN)
  • Table 20 Global AI Inference Market Outlook, By Natural Language Processing (NLP) (2024-2032) ($MN)
  • Table 21 Global AI Inference Market Outlook, By Computer Vision (2024-2032) ($MN)
  • Table 22 Global AI Inference Market Outlook, By Generative AI (2024-2032) ($MN)
  • Table 23 Global AI Inference Market Outlook, By Machine Learning (2024-2032) ($MN)
  • Table 24 Global AI Inference Market Outlook, By Robotics (2024-2032) ($MN)
  • Table 25 Global AI Inference Market Outlook, By Recommendation Systems (2024-2032) ($MN)
  • Table 26 Global AI Inference Market Outlook, By Predictive Analytics (2024-2032) ($MN)
  • Table 27 Global AI Inference Market Outlook, By Other Applications (2024-2032) ($MN)
  • Table 28 Global AI Inference Market Outlook, By End User (2024-2032) ($MN)
  • Table 29 Global AI Inference Market Outlook, By Healthcare (2024-2032) ($MN)
  • Table 30 Global AI Inference Market Outlook, By Consumer Electronics (2024-2032) ($MN)
  • Table 31 Global AI Inference Market Outlook, By Automotive & Transportation (2024-2032) ($MN)
  • Table 32 Global AI Inference Market Outlook, By Aerospace & Defense (2024-2032) ($MN)
  • Table 33 Global AI Inference Market Outlook, By Retail & E-commerce (2024-2032) ($MN)
  • Table 34 Global AI Inference Market Outlook, By IT & Telecom (2024-2032) ($MN)
  • Table 35 Global AI Inference Market Outlook, By Banking, Financial Services & Insurance (BFSI) (2024-2032) ($MN)
  • Table 36 Global AI Inference Market Outlook, By Manufacturing (2024-2032) ($MN)
  • Table 37 Global AI Inference 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.