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市場調查報告書
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2071381

邊緣人工智慧市場機會、成長促進因素、產業趨勢分析及2026-2035年預測

Edge AI Market Opportunity, Growth Drivers, Industry Trend Analysis, and Forecast 2026 - 2035
出版日期: | 出版商: Global Market Insights Inc. | 英文 255 Pages | 商品交期: 2-3個工作天內

價格
簡介目錄

全球邊緣人工智慧市場預計到 2025 年將達到 252 億美元，並以 24.7% 的複合年成長率成長，到 2035 年將達到 2,255 億美元。

邊緣人工智慧市場-IMG1

市場成長的驅動力在於互聯環境中對即時資料處理、低延遲決策和增強資料隱私的日益成長的需求。邊緣人工智慧 (Edge AI) 使人工智慧工作負載能夠在感測器、攝影機、工業設備和自主系統等設備本地進行處理，從而減少對集中式雲端基礎設施的依賴。隨著物聯網、智慧製造、汽車、醫療和監控應用領域資料量的爆炸性成長，這種能力變得愈發重要。邊緣人工智慧透過最大限度地減少延遲和頻寬使用，提高了系統的響應速度和可靠性，使其成為任務關鍵型和時間敏感型應用場景的理想選擇。

市場範圍
開始年份 2025
預測期 2026-2035
起始金額 252億美元
預測金額 2255億美元
複合年成長率 24.7%

連網型設備、5G網路和智慧自動化技術的日益普及，進一步加速了邊緣人工智慧在企業中的部署。各組織正利用邊緣智慧來提高營運效率、實現預測性維護並增強安全和監控能力。此外，對資料安全性和合規性的日益關注，促使企業選擇在本地處理敏感數據，而不是將其發送到集中式資料中心。這些優勢推動了邊緣人工智慧解決方案在工業和商業領域的廣泛應用，使市場走上了長期永續成長的道路。

從組件來看，預計到2025年，硬體部分將佔據47.2%的市場。邊緣AI硬體，包括AI處理器、GPU、ASIC、FPGA和邊緣伺服器，構成了網路邊緣即時推理和分析的基礎。汽車、製造和智慧城市等領域對能夠在本地運行複雜AI模型的高性能、高能源效率晶片的需求正在迅速成長。半導體公司正致力於開發專用的邊緣AI加速器，這些加速器針對低功耗和高運算吞吐量進行了最佳化。由於智慧攝影機、工業機器人和自主設備的快速普及，對邊緣AI硬體解決方案的投資依然強勁。

到2025年，影像監控產業將佔據顯著的市場佔有率，這主要得益於公共和私人環境中對即時影像分析、增強安全性和智慧監控日益成長的需求。邊緣人工智慧（Edge AI）支援在攝影機和邊緣裝置上對影像資料進行本地處理，無需依賴雲端連接即可實現即時威脅偵測、臉部辨識、物件追蹤和行為分析。這顯著降低了延遲、頻寬佔用和資料傳輸成本，同時縮短了緊急情況下的回應時間。利用邊緣人工智慧的影像監控技術正日益廣泛應用於智慧城市、交通樞紐、零售商店、工業設施和關鍵基礎設施等領域。

在快速工業化、智慧基礎設施大規模部署以及政府對人工智慧和數位轉型（DX）的大力支持舉措，預計到2025年，中國邊緣人工智慧市場規模將達到39億美元。隨著雲端運算技術、先進的5G連接和分散式人工智慧處理能力的融合，中國邊緣人工智慧市場正經歷強勁成長。企業和通訊業者正在加速部署以邊緣為中心的基礎設施，以實現即時數據處理、超低延遲分析和快速現場決策。包括華為和中興在內的領先科技公司持續加大對整合邊緣人工智慧解決方案的投入，這些解決方案融合了運算能力、網路管理和人工智慧整合能力，以支援大規模5G部署和工業IoT應用。

目錄

第1章：調查方法

第2章執行摘要

第3章 行業洞察

  • 產業生態系分析
    • 供應商情況
    • 利潤率分析
    • 成本結構
    • 每個階段增加的價值
    • 影響價值鏈的因素
    • 中斷
  • 影響產業的因素
    • 成長促進因素
      • 邊緣設備在各終端用戶產業的應用日益普及
      • 加大對人工智慧技術的投資
      • 5G網路正變得越來越普及。
      • 雲端運算技術的應用正在迅速成長。
    • 產業潛在風險與挑戰
      • 隱私和安全問題
      • 互通性問題
    • 市場機遇
      • 擴展支援 5G 的邊緣運算基礎設施
      • 物聯網和連網型設備的應用正在各個產業不斷擴展。
      • 對即時分析和低延遲處理的需求日益成長
      • 自主系統與智慧工業自動化的發展
  • 成長潛力分析
  • 技術與創新展望
    • 最新科技趨勢
      • 人工智慧邊緣運算設備
      • 5G整合邊緣基礎設施
      • 邊緣型電腦視覺系統
      • 物聯網邊緣分析平台
    • 新興技術
      • 邊緣聯邦學習
      • 面向邊緣人工智慧的神經形態運算
      • 邊緣人工智慧晶片和加速器（NPU/ASIC）
      • 部署在邊緣設備上的生成式人工智慧模型
  • 價格分析
    • 對過去價格趨勢的分析
    • 定價策略：按業務類型分類
  • 監理情勢
  • 波特的分析
  • PESTLE分析
  • 專利分析
  • 成本細分分析
  • 人工智慧和生成式人工智慧對市場的影響
    • 利用人工智慧改造現有經營模式
    • 按細分市場分類的生成式人工智慧用例和部署藍圖
    • 風險、限制和監管考量
  • 永續性和環境方面
    • 永續計劃
    • 減少廢棄物策略
    • 生產中的能源效率
    • 具有環保意識的舉措
    • 考慮碳足跡
  • 預測假設和情境分析
    • 基本案例：驅動複合年成長率的關鍵宏觀經濟與產業變量
    • 樂觀情境：宏觀經濟與產業的順風
    • 悲觀情景：宏觀經濟放緩或產業逆風

第4章 競爭情勢

  • 介紹
  • 企業市佔率分析
    • 北美洲
    • 歐洲
    • 亞太地區
    • 拉丁美洲
    • 中東和非洲
  • 主要市場公司的競爭分析
  • 競爭定位矩陣
  • 主要進展
    • 併購
    • 夥伴關係和聯盟
    • 新產品發布
    • 業務拓展計劃及資金籌措
  • 按公司規模進行基準測試
    • 排名分類標準與選擇標準
    • 按銷售額、地區和創新能力分類的層級定位矩陣。

第5章 市場估計與預測：依組件分類，2022-2035年

  • 硬體
    • 圖形處理器（GPU）
    • 專用積體電路（ASIC），
    • 中央處理器（CPU）
    • 現場可程式閘陣列（FPGA）
  • 軟體
  • 服務
    • 培訓和諮詢
    • 支援與維護
    • 系統整合與測試

第6章 市場估計與預測：依應用領域分類，2022-2035年

  • 影像監控
  • 遠端監控
  • 預測性保護
  • 其他

第7章 市場估計與預測：依最終用途分類，2022-2035年

  • 製造業
  • 衛生保健
  • BSFI
  • 政府
  • 零售與電子商務
  • 電訊
  • 運輸/物流
  • 其他

第8章 市場估計與預測：依地區分類，2022-2035年

  • 北美洲
    • 美國
    • 加拿大
  • 歐洲
    • 德國
    • 英國
    • 法國
    • 義大利
    • 西班牙
    • 俄羅斯
    • 挪威
    • 荷蘭
    • 瑞典
  • 亞太地區
    • 中國
    • 印度
    • 日本
    • 澳洲
    • 韓國
    • 新加坡
    • 泰國
    • 印尼
    • 越南
  • 拉丁美洲
    • 巴西
    • 墨西哥
    • 阿根廷
  • 中東和非洲
    • 南非
    • 沙烏地阿拉伯
    • UAE
    • 土耳其

第9章：公司簡介

  • 世界公司
    • NVIDIA Corporation
    • Intel Corporation
    • Microsoft Corporation
    • Amazon Web Services（AWS）
    • Alphabet（Google）
    • IBM
    • Qualcomm
    • Apple Inc.
    • Huawei Technologies
  • 當地公司
    • ADLINK Technology Inc.
    • Synaptics Incorporated
    • Gorilla Technology Group
    • Robert Bosch GmbH
    • Siemens AG
    • Dell Technologies
    • Nutanix Inc.
    • Edge Impulse Inc.
    • FogHorn Systems
  • 新興企業/顛覆者
    • Kneron Inc.
    • Ambiq Micro
    • SiMa.ai
    • Viso.ai
簡介目錄
Product Code: 5390

The Global Edge AI Market was valued at USD 25.2 billion in 2025 and is estimated to grow at a CAGR of 24.7% to reach USD 225.5 billion by 2035.

Edge AI Market - IMG1

Market growth is driven by the rising need for real-time data processing, low-latency decision-making, and enhanced data privacy across connected environments. Edge AI enables artificial intelligence workloads to be processed locally on devices such as sensors, cameras, industrial equipment, and autonomous systems, reducing dependence on centralized cloud infrastructure. This capability is increasingly critical as data volumes surge across IoT, smart manufacturing, automotive, healthcare, and surveillance applications. By minimizing latency and bandwidth usage, edge AI improves system responsiveness and reliability, making it ideal for mission-critical and time-sensitive use cases.

Market Scope
Start Year2025
Forecast Year2026-2035
Start Value$25.2 Billion
Forecast Value$225.5 Billion
CAGR24.7%

The growing adoption of connected devices, 5G networks, and intelligent automation is further accelerating edge AI deployment across enterprises. Organizations are leveraging edge-based intelligence to improve operational efficiency, enable predictive maintenance, and enhance safety and monitoring capabilities. Additionally, increasing concerns around data security and regulatory compliance are encouraging enterprises to process sensitive data locally rather than transmitting it to centralized data centers. These advantages are driving widespread adoption of edge AI solutions across both industrial and commercial applications, positioning the market for sustained long-term growth.

Based on the component, the hardware segment held a 47.2% share in 2025. Edge AI hardware, including AI-enabled processors, GPUs, ASICs, FPGAs, and edge servers, forms the backbone of real-time inference and analytics at the network edge. Demand for high-performance, energy-efficient chips capable of running complex AI models locally is rising rapidly across automotive, manufacturing, and smart city deployments. Semiconductor companies are focusing on developing specialized edge AI accelerators optimized for low power consumption and high computational throughput. The rapid proliferation of smart cameras, industrial robots, and autonomous devices continues to drive strong investment in edge AI hardware solutions.

The video surveillance segment captured significant share in 2025, driven by the growing need for real-time video analytics, enhanced security, and intelligent monitoring across public and private environments. Edge AI enables video data to be processed locally on cameras and edge devices, allowing instant threat detection, facial recognition, object tracking, and behavioral analysis without relying on cloud connectivity. This significantly reduces latency, bandwidth usage, and data transmission costs while improving response times in critical situations. Video surveillance powered by edge AI is increasingly deployed in smart cities, transportation hubs, retail stores, industrial facilities, and critical infrastructure.

China Edge AI Market generated USD 3.9 billion in 2025, driven by rapid industrialization, large-scale deployment of smart infrastructure, and strong government support for AI and digital transformation initiatives. China's edge AI landscape is experiencing strong growth as cloud-based technologies, advanced 5G connectivity, and decentralized AI processing capabilities increasingly converge. Businesses and telecommunications providers are accelerating the adoption of edge-centric infrastructures to enable real-time data processing, ultra-low-latency analytics, and faster on-site decision-making. Major technology companies, including Huawei and ZTE, continue to expand investments in integrated edge AI solutions that combine computing power, network management, and AI coordination capabilities to support large-scale 5G deployments and industrial IoT applications.

Key players operating in the Global Edge AI Market include NVIDIA Corporation, Intel Corporation, Qualcomm Technologies, Advanced Micro Devices (AMD), Arm Ltd., Google LLC, IBM Corporation, Amazon Web Services, Microsoft Corporation, and Huawei Technologies. These companies compete through innovation in AI chips, edge platforms, and integrated hardware-software solutions, while expanding partnerships across automotive, industrial, and telecom ecosystems. Companies in the edge AI market are strengthening their market position through continuous innovation in AI-specific hardware and optimized edge software platforms. Leading players are investing heavily in developing low-power, high-performance processors tailored for real-time inference at the edge. Strategic partnerships with OEMs, industrial automation providers, and telecom operators help accelerate solution deployment across key industries. Firms are also expanding end-to-end edge AI ecosystems by integrating hardware, software, and cloud orchestration capabilities.

Table of Contents

Chapter 1 Methodology

  • 1.1 Research approach
  • 1.2 Quality Commitments
    • 1.2.1 GMI AI policy & data integrity commitment
      • 1.2.1.1 Source consistency protocol
  • 1.3 Research Trail & Confidence Scoring
    • 1.3.1 Research Trail Components
    • 1.3.2 Scoring Components
  • 1.4 Data Collection
    • 1.4.1 Partial list of primary sources
  • 1.5 Data mining sources
    • 1.5.1 Paid sources
      • 1.5.1.1 Sources, by region
  • 1.6 Base estimates and calculations
    • 1.6.1 Base year calculation for any one approach
  • 1.7 Forecast model
    • 1.7.1 Quantified market impact analysis
      • 1.7.1.1 Mathematical impact of growth parameters on forecast
  • 1.8 Research transparency addendum
    • 1.8.1 Source attribution framework
    • 1.8.2 Quality assurance metrics
    • 1.8.3 Our commitment to trust

Chapter 2 Executive Summary

  • 2.1 Industry 360° synopsis, 2022 - 2035
  • 2.2 Key market trends
    • 2.2.1 Regional
    • 2.2.2 Component
    • 2.2.3 Application
    • 2.2.4 End Use
  • 2.3 TAM Analysis, 2026-2035
  • 2.4 CXO perspectives: Strategic imperatives

Chapter 3 Industry Insights

  • 3.1 Industry ecosystem analysis
    • 3.1.1 Supplier landscape
    • 3.1.2 Profit margin analysis
    • 3.1.3 Cost structure
    • 3.1.4 Value addition at each stage
    • 3.1.5 Factor affecting the value chain
    • 3.1.6 Disruptions
  • 3.2 Industry impact forces
    • 3.2.1 Growth drivers
      • 3.2.1.1 Increasing adoption of edge devices across various end-user verticals.
      • 3.2.1.2 Growing investment in the AI technology.
      • 3.2.1.3 Growing adoption of 5G network.
      • 3.2.1.4 Surging adoption of cloud computing technology.
    • 3.2.2 Industry pitfalls and challenges
      • 3.2.2.1 Privacy and security concerns
      • 3.2.2.2 Interoperability issues
    • 3.2.3 Market opportunities
      • 3.2.3.1 Expansion of 5G-enabled edge computing infrastructure
      • 3.2.3.2 Rising adoption of IoT and connected devices across industries
      • 3.2.3.3 Increasing demand for real-time analytics and low-latency processing
      • 3.2.3.4 Growth in autonomous systems and smart industrial automation
  • 3.3 Growth potential analysis
  • 3.4 Technology and innovation landscape
    • 3.4.1 Current technological trends
      • 3.4.1.1 AI-enabled edge computing devices
      • 3.4.1.2 5G-integrated edge infrastructure
      • 3.4.1.3 Edge-based computer vision systems
      • 3.4.1.4 IoT-enabled edge analytics platforms
    • 3.4.2 Emerging technologies
      • 3.4.2.1 Federated learning at the edge
      • 3.4.2.2 Neuromorphic computing for edge AI
      • 3.4.2.3 Edge AI chips and accelerators (NPUs/ASICs)
      • 3.4.2.4 Generative AI models deployed on edge devices
  • 3.5 Pricing Analysis (Driven by primary research)
    • 3.5.1 Historical Price Trend Analysis
    • 3.5.2 Pricing Strategy by Player Type
  • 3.6 Regulatory landscape
    • 3.6.1 North America
    • 3.6.2 Europe
    • 3.6.3 Asia Pacific
    • 3.6.4 Latin America
    • 3.6.5 Middle East & Africa
  • 3.7 Porter's analysis
  • 3.8 PESTEL analysis
  • 3.9 Patent analysis (Driven by primary research)
  • 3.10 Cost breakdown analysis
  • 3.11 Impact of AI and Generative AI on the Market
    • 3.11.1 AI Driven Disruption of Existing Business Models
    • 3.11.2 GenAI Use Cases and Adoption Roadmap by Segment
    • 3.11.3 Risks Limitations and Regulatory Considerations
  • 3.12 Sustainability and environmental aspects
    • 3.12.1 Sustainable practices
    • 3.12.2 Waste reduction strategies
    • 3.12.3 Energy efficiency in production
    • 3.12.4 Eco-friendly Initiatives
    • 3.12.5 Carbon footprint considerations
  • 3.13 Forecast assumptions & scenario analysis (Driven by Primary Research)
    • 3.13.1 Base Case- Key Macro & Industry Variables Driving CAGR
    • 3.13.2 Optimistic Scenarios- Favorable macro and industry tailwinds
    • 3.13.3 Pessimistic Scenario - Macroeconomic slowdown or industry headwinds

Chapter 4 Competitive Landscape, 2025

  • 4.1 Introduction
  • 4.2 Company market share analysis
    • 4.2.1 North America
    • 4.2.2 Europe
    • 4.2.3 Asia Pacific
    • 4.2.4 LATAM
    • 4.2.5 MEA
  • 4.3 Competitive analysis of major market players
  • 4.4 Competitive positioning matrix
  • 4.5 Key developments
    • 4.5.1 Mergers & acquisitions
    • 4.5.2 Partnerships & collaborations
    • 4.5.3 New Product Launches
    • 4.5.4 Expansion Plans and funding
  • 4.6 Company tier benchmarking
    • 4.6.1 Tier classification criteria & qualifying thresholds
    • 4.6.2 Tier positioning matrix by revenue, geography & innovation

Chapter 5 Market Estimates & Forecast, By Component, 2022 - 2035 (USD Mn)

  • 5.1 Key trends
  • 5.2 Hardware
    • 5.2.1 Graphics Processing Unit (GPU)
    • 5.2.2 Application Specific Integrated Circuit (ASIC),
    • 5.2.3 Central Processing Unit (CPU)
    • 5.2.4 Field-Programmable Gate Array (FPGA)
  • 5.3 Software
  • 5.4 Service
    • 5.4.1 Training & consulting
    • 5.4.2 Support & maintenance
    • 5.4.3 System integration and testing

Chapter 6 Market Estimates & Forecast, By Application, 2022 - 2035 (USD Mn)

  • 6.1 Key trends
  • 6.2 Video surveillance
  • 6.3 Remote monitoring
  • 6.4 Predictive maintenance
  • 6.5 Others

Chapter 7 Market Estimates & Forecast, By End Use, 2022 - 2035 (USD Mn)

  • 7.1 Key trends
  • 7.2 Manufacturing
  • 7.3 Healthcare
  • 7.4 BSFI
  • 7.5 Government
  • 7.6 Retail & e-commerce
  • 7.7 Telecommunication
  • 7.8 Transport & logistics
  • 7.9 Others

Chapter 8 Market Estimates & Forecast, By Region, 2022 - 2035 (USD Mn)

  • 8.1 Key trends
  • 8.2 North America
    • 8.2.1 US
    • 8.2.2 Canada
  • 8.3 Europe
    • 8.3.1 Germany
    • 8.3.2 UK
    • 8.3.3 France
    • 8.3.4 Italy
    • 8.3.5 Spain
    • 8.3.6 Russia
    • 8.3.7 Norway
    • 8.3.8 Netherlands
    • 8.3.9 Sweden
  • 8.4 Asia Pacific
    • 8.4.1 China
    • 8.4.2 India
    • 8.4.3 Japan
    • 8.4.4 Australia
    • 8.4.5 South Korea
    • 8.4.6 Singapore
    • 8.4.7 Thailand
    • 8.4.8 Indonesia
    • 8.4.9 Vietnam
  • 8.5 Latin America
    • 8.5.1 Brazil
    • 8.5.2 Mexico
    • 8.5.3 Argentina
  • 8.6 MEA
    • 8.6.1 South Africa
    • 8.6.2 Saudi Arabia
    • 8.6.3 UAE
    • 8.6.4 Turkey

Chapter 9 Company Profiles

  • 9.1 Global Players
    • 9.1.1 NVIDIA Corporation
    • 9.1.2 Intel Corporation
    • 9.1.3 Microsoft Corporation
    • 9.1.4 Amazon Web Services (AWS)
    • 9.1.5 Alphabet (Google)
    • 9.1.6 IBM
    • 9.1.7 Qualcomm
    • 9.1.8 Apple Inc.
    • 9.1.9 Huawei Technologies
  • 9.2 Regional Players
    • 9.2.1 ADLINK Technology Inc.
    • 9.2.2 Synaptics Incorporated
    • 9.2.3 Gorilla Technology Group
    • 9.2.4 Robert Bosch GmbH
    • 9.2.5 Siemens AG
    • 9.2.6 Dell Technologies
    • 9.2.7 Nutanix Inc.
    • 9.2.8 Edge Impulse Inc.
    • 9.2.9 FogHorn Systems
  • 9.3 Emerging Players / Disruptors
    • 9.3.1 Kneron Inc.
    • 9.3.2 Ambiq Micro
    • 9.3.3 SiMa.ai
    • 9.3.4 Viso.ai