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1856892

全球人工智慧推理市場：未來預測（至2032年）－按計算類型、記憶體類型、部署模式、應用、最終用戶和地區進行分析

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個工作天內

價格

根據 Stratistics MRC 的數據，預計到 2025 年，全球人工智慧推理市場規模將達到 1,162 億美元，到 2032 年將達到 4,043.7 億美元，預測期內複合年成長率為 19.5%。

人工智慧推理是指預先訓練的人工智慧模型利用已學習到的模式來分析和解釋新數據，從而做出預測或決策的階段。它與訓練不同，訓練專注於從大型資料集中學習。推理使語音辨識、自動駕駛汽車和推薦系統等人工智慧應用能夠高效運作。人工智慧推理的性能，包括其速度和可靠性，對於人工智慧技術在實際應用中產生實際效果至關重要。

根據 Appen 發布的《2020 年人工智慧現狀報告》，41% 的公司表示在 COVID-19 疫情期間加快了人工智慧策略的實施，這表明在全球危機期間，組織的優先事項發生了重大轉變，開始利用人工智慧。

採用生成式人工智慧和大規模語言模型

生成式人工智慧和大規模語言模式的快速整合正在改變各產業推理工作負載的管理方式。這些技術能夠實現更細緻的理解、情境推理和即時決策。越來越多的企業正在將大規模語言模型（LLM）融入客戶服務、內容創作和分析流程中。 LLM 處理大量資料集並產生類人回應的能力，正在推動可擴展推理解決方案的需求。隨著企業尋求自動化複雜任務，他們越來越依賴人工智慧推理引擎。預計這一趨勢將顯著擴大各行業的市場基礎。

人工智慧和機器學習營運專業人員短缺

人工智慧推理市場面臨的一個關鍵瓶頸是人工智慧部署和機器學習運維方面專業人才的缺乏。大規模管理推理工作負載需要模型調優、基礎架構編配和效能最佳化的專業知識。然而，這些專業人才仍然有限，尤其是在新興經濟體。這種人才缺口阻礙了企業充分利用人工智慧能力，並延緩了部署進度。如果沒有強力的運維支持，即使是先進的模型也可能無法提供穩定可靠的結果。彌合這一技能缺口對於充分發揮人工智慧推理平台的潛力至關重要。

人工智慧即服務（AIaaS）的成長

人工智慧即服務 (AIaaS) 平台的興起，為可擴展、經濟高效的推理部署開闢了新途徑。這些雲端基礎的解決方案使企業無需在基礎設施或人才方面投入大量資金即可存取強大的模型。憑藉靈活的 API 和計量收費，AIaaS 正在普及先進的推理能力。隨著服務提供者為醫療保健、金融和零售等行業提供客製化服務，AIaaS 的應用正在不斷成長。與現有企業系統的整合也日趨無縫，從而提高了營運效率。這種向基於服務的 AI 交付模式的轉變，有望加速市場成長和創新。

資料隱私和監管合規

嚴格的資料保護法律和不斷演變的法律規範為人工智慧推理技術的應用帶來了重大挑戰。推理引擎通常處理敏感的個人和企業數據，引發了人們對數據濫用和洩露的擔憂。遵守GDPR、HIPAA等全球標準以及新興的人工智慧特定法規需要嚴格的安全保障措施。為了降低風險，企業必須投資安全架構、審核追蹤和可解釋人工智慧。不遵守這些規定可能會導致聲譽受損和經濟處罰。

新冠疫情的影響：

疫情重塑了企業的優先事項，加速了數位轉型和人工智慧的應用。遠距辦公和虛擬服務導致對自動化決策和智慧介面的需求激增。人工智慧推理平台在實現聊天機器人、診斷和跨職能預測分析方面變得至關重要。然而，供應鏈中斷和預算限制暫時延緩了基礎設施升級。疫情過後，企業在展望未來營運時，正優先考慮具有彈性的雲端原生推理解決方案。

預計在預測期內，雲端推斷細分市場將是最大的。

由於其可擴展性和成本效益，預計在預測期內，雲端推理領域將佔據最大的市場佔有率。企業正擴大將工作負載遷移到雲端平台，以降低延遲並提高吞吐量。雲端原生推理引擎提供動態資源分配，從而能夠即時處理複雜模型。與邊緣設備和混合架構的整合進一步提升了效能。跨區域和跨用例部署的靈活性使雲端推理極具吸引力。隨著對人工智慧應用的需求不斷成長，雲端基礎的推理有望引領市場。

預計醫療保健產業在預測期內將實現最高的複合年成長率。

預計在預測期內，醫療保健產業將迎來最高的成長率。醫院和研究機構正在利用人工智慧進行診斷、影像和個人化治療方案製定。推理引擎能夠快速分析醫療數據，從而提高準確性並改善患者預後。數位化醫療和遠端醫療的推進正在加速人工智慧工具的普及應用。監管機構對醫療保健領域人工智慧的支持力度不斷加大，以及相關資金的投入，也推動了這一領域的成長。該行業獨特的數據需求和高影響力的應用案例使其成為推理創新的理想應用領域。

佔比最大的地區：

預計亞太地區將在預測期內佔據最大的市場佔有率。該地區快速的數位化、不斷擴展的技術基礎設施以及政府主導的人工智慧舉措是關鍵的成長驅動力。中國、印度和日本等國正大力投資人工智慧研究和雲端運算能力。製造業、金融業和醫療保健等行業的公司正在採用推理平台來提高生產力。該地區人工智慧新興企業的崛起以及有利的法規環境正在推動區域競爭。

複合年成長率最高的地區：

預計北美地區在預測期內將呈現最高的複合年成長率。該地區受益於成熟的人工智慧生態系統、強勁的研發投入以及各行業的早期應用。科技巨頭和新興企業正在推動推理最佳化和部署方面的創新。政府對人工智慧研究的資助和倫理框架為持續成長提供了支持。企業正日益將推理引擎整合到雲端、邊緣和混合環境中。這些因素預計將推動人工智慧推理能力的快速發展和領先地位。

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目錄

第1章執行摘要

第2章 前言

  • 概述
  • 相關利益者
  • 調查範圍
  • 調查方法
    • 資料探勘
    • 數據分析
    • 數據檢驗
    • 研究途徑
  • 研究材料
    • 原始研究資料
    • 次級研究資訊來源
    • 先決條件

第3章 市場趨勢分析

  • 促進要素
  • 抑制因素
  • 機會
  • 威脅
  • 應用分析
  • 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
  • 買方的議價能力
  • 替代品的威脅
  • 新進入者的威脅
  • 競爭對手之間的競爭

第5章 全球人工智慧推理市場（按計算類型分類）

  • 中央處理器（CPU）
  • 專用積體電路（ASIC）
  • 圖形處理器（GPU）
  • 現場可程式閘陣列（FPGA）
  • 神經處理單元（NPU）
  • 其他

第6章 全球人工智慧推理市場（按記憶體類型分類）

  • 高頻寬記憶體（HBM）
  • 雙倍資料速率（DDR）
  • 東德
  • LPDDR
  • 其他

第7章 全球人工智慧推理市場按部署模式分類

  • 邊緣推斷
  • 雲推斷
  • 混合推理

第8章 全球人工智慧推理市場（按應用分類）

  • 自然語言處理（NLP）
  • 電腦視覺
  • 人工智慧世代
  • 機器學習
  • 機器人技術
  • 推薦系​​統
  • 預測分析
  • 其他

第9章 全球人工智慧推理市場（按最終用戶分類）

  • 衛生保健
  • 家用電器
  • 汽車與運輸
  • 航太與國防
  • 零售與電子商務
  • 資訊科技/通訊
  • 銀行、金融服務和保險（BFSI）
  • 製造業
  • 其他

第10章 全球人工智慧推理市場（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
    • 其他南美洲
  • 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11章 重大進展

  • 協議、夥伴關係、合作和合資企業
  • 收購與併購
  • 新產品上市
  • 業務拓展
  • 其他關鍵策略

第12章 企業概況

  • 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.