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IoT 人工智能邊緣運算

市場調查報告書

商品編碼

1980036

邊緣人工智慧推理市場預測至2034年：按組件、設備類型、應用、最終用戶和地區分類的全球分析

Edge AI Inference Market Forecasts to 2034 - Global Analysis By Component (Hardware, Software and Services), Device Type, Application, End User and By Geography

出版日期: 2026年03月11日 | 出版商:

Stratistics Market Research Consulting | 英文 | 商品交期: 2-3個工作天內

價格

簡介目錄圖表

根據 Stratistics MRC 的研究，預計到 2026 年，全球邊緣 AI 推理市場將達到 1538.4 億美元，在預測期內以 19.4% 的複合年成長率成長，到 2034 年將達到 6355.1 億美元。

邊緣AI推理是指在感測器、相機、智慧型手機和工業設備等邊緣設備上本地執行人工智慧（AI）演算法，而無需依賴集中式雲端伺服器。這使得即時資料處理、低延遲決策以及透過將敏感資訊保留在設備內部來增強隱私保護成為可能。邊緣AI推理利用AI加速器和專用晶片等最佳化硬體，即使在電力和資源有限的環境中也能高效地執行複雜的計算。它正在迅速擴展到包括自動駕駛汽車、醫療保健、智慧製造和物聯網在內的各個工業領域，從而能夠提供更快、更安全、更經濟高效的智慧解決方案。

對即時情報的需求

對即時數據處理和即時決策日益成長的需求是邊緣人工智慧推理市場的主要驅動力。自動駕駛汽車、醫療保健和智慧製造等行業需要快速洞察，以提高營運效率、安全性和客戶體驗。透過在邊緣設備上本地處理人工智慧演算法，企業可以降低延遲，最大限度地減少對雲端基礎設施的依賴，並對關鍵事件做出即時回應。這使得各種應用都能實現更快、更可靠、更安全的結果。

運算能力和能源限制有限

邊緣人工智慧推理面臨諸多挑戰，其中最主要的原因是邊緣設備的運算能力和能源消耗有限。與雲端系統不同，這些設備必須在有限的處理能力、記憶體和電池續航時間內執行複雜的人工智慧操作。這些限制會阻礙效能提升、降低效率，並限制高階人工智慧模型的部署。克服這些硬體限制對於邊緣人工智慧的廣泛應用至關重要，因為各組織都在尋求能夠平衡智慧處理、能源效率和設備壽命的解決方案。

小型人工智慧晶片的技術進步

緊湊型人工智慧晶片和專用加速器的進步為邊緣人工智慧推理市場帶來了巨大的成長機會。這些創新使得在小型、節能的設備上實現高效能運算成為可能，使先進的人工智慧演算法能夠直接在邊緣執行。物聯網、醫療保健和智慧農業等行業可以利用這些晶片獲得更快、更精準的在局部洞察，同時減少對雲端處理的依賴。晶片設計和小型化技術的持續改進有望拓展應用範圍，並加速其在全球市場的推廣。

複雜的實施與維護

在分散式設備上部署和維護邊緣人工智慧系統對市場成長構成重大挑戰。管理硬體規格各異的多個設備、更新人工智慧模型以及確保效能穩定，都需要先進的技術專長和資源。此外，跨眾多邊緣節點的安全管理也增加了複雜性，並帶來營運風險。這些挑戰可能導致部署延遲、成本增加和可擴展性受限，尤其對於那些尋求與傳統基礎設施和異質邊緣環境無縫整合的公司而言更是如此。

新冠疫情的影響：

新冠疫情加速了邊緣人工智慧推理技術的應用，因為各組織都在尋求最大限度地減少人際接觸並最佳化營運效率。遠端監控、自主系統和人工智慧驅動的診斷在醫療保健、製造業和物流行業中變得至關重要。然而，供應鏈中斷和硬體生產延遲暫時阻礙了這些技術的應用。總體而言，疫情凸顯了分散式人工智慧處理的價值，並刺激了對邊緣運算解決方案的投資，以支援在動態且不確定的環境中提高韌性並加快決策速度。

在預測期內，無人機產業預計將佔據最大的市場佔有率。

在預測期內，無人機領域預計將佔據最大的市場佔有率，這主要得益於對自主導航、即時數據分析和精準操作日益成長的需求。邊緣人工智慧推理技術使無人機能夠在本地處理數據，用於空中測繪、監控和配送服務等任務，從而降低延遲並減少對雲端連接的依賴。增強的機載運算能力能夠加快決策速度、提高營運效率並增強安全性，使無人機成為邊緣人工智慧在商業、工業和國防領域部署的主要應用領域。

預計在預測期內，農業部門將呈現最高的複合年成長率。

在預測期內，隨著智慧農業解決方案的普及，農業部門預計將呈現最高的成長率。邊緣人工智慧可現場處理感測器和無人機數據，實現作物即時監測、精準灌溉、病蟲害檢測和產量最佳化。這些應用有助於提高生產力、降低資源消耗，並支持永續農業實踐。隨著對自動化和數據驅動型農場營運的需求日益成長，邊緣人工智慧推理正成為一項關鍵技術，它將把傳統農業轉變為智慧、高效且擴充性的系統。

市佔率最大的地區：

在整個預測期內，北美預計將保持最大的市場佔有率，這得益於其對先進技術的早期應用、強大的IT基礎設施以及對人工智慧研發的大量投入。汽車、醫療保健和智慧製造等關鍵產業正在擴大採用邊緣人工智慧解決方案，以實現即時智慧並提高營運效率。領先供應商的存在以及政府大力支持人工智慧應用的政策，進一步鞏固了北美在全球邊緣人工智慧推理市場的主導地位。

複合年成長率最高的地區：

在預測期內，亞太地區預計將呈現最高的複合年成長率，這主要得益於快速的工業化進程、物聯網的廣泛應用以及對人工智慧基礎設施投資的增加。中國、日本和印度等國家正在智慧製造、農業和自動駕駛系統等眾多領域部署邊緣人工智慧技術。不斷發展的技術生態系統、對低延遲解決方案日益成長的需求以及政府促進人工智慧創新的政策，都使亞太地區成為全球成長最快的邊緣人工智慧推理市場。

免費客製化服務：

購買此報告的客戶可以選擇以下免費自訂選項之一：

企業概況
- 對其他市場參與者（最多 3 家公司）進行全面分析
- 主要參與者（最多3家公司）的SWOT分析
區域細分
- 主要國家的市場估算和預測，以及根據客戶需求量身定做的複合年成長率（註：需要進行可行性測試）。
競爭性標竿分析
- 根據主要參與者的產品系列、地理覆蓋範圍和策略聯盟進行基準分析。

北美洲
- 美國
- 加拿大
- 墨西哥
歐洲
- 英國
- 德國
- 法國
- 義大利
- 西班牙
- 荷蘭
- 比利時
- 瑞典
- 瑞士
- 波蘭
- 其他歐洲國家
亞太地區
- 中國
- 日本
- 印度
- 韓國
- 澳洲
- 印尼
- 泰國
- 馬來西亞
- 新加坡
- 越南
- 其他亞太國家
南美洲
- 巴西
- 阿根廷
- 哥倫比亞
- 智利
- 秘魯
- 其他南美國家
世界其他地區（RoW）
- 中東
  - 沙烏地阿拉伯
  - 阿拉伯聯合大公國
  - 卡達
  - 以色列
  - 其他中東國家
- 非洲
  - 南非
  - 埃及
  - 摩洛哥
  - 其他非洲國家

第10章戰略市場資訊

工業價值網路和供應鏈評估
空白區域和機會地圖
產品演進與市場生命週期分析
通路、經銷商和打入市場策略的評估

第11章產業趨勢與策略舉措

併購
夥伴關係、聯盟和合資企業
新產品發布和認證
擴大生產能力和投資
其他策略舉措

第12章：公司簡介

NVIDIA Corporation
Intel Corporation
Qualcomm Technologies, Inc.
Google LLC
Microsoft Corporation
Amazon Web Services（AWS）
IBM Corporation
Huawei Technologies Co., Ltd.
Arm Holdings plc
Samsung Electronics Co., Ltd.
Apple Inc.
Dell Technologies Inc.
Cisco Systems, Inc.
Hewlett Packard Enterprise（HPE）
Advantech Co., Ltd.

簡介目錄圖表

Product Code: SMRC34200

According to Stratistics MRC, the Global Edge AI Inference Market is accounted for $153.84 billion in 2026 and is expected to reach $635.51 billion by 2034 growing at a CAGR of 19.4% during the forecast period. Edge AI Inference refers to the process of executing artificial intelligence (AI) algorithms locally on edge devices such as sensors, cameras, smartphones, or industrial equipment rather than relying on centralized cloud servers. This enables real-time data processing, low-latency decision-making and enhanced privacy by keeping sensitive information on-device. Edge AI inference leverages optimized hardware, such as AI accelerators or specialized chips, to perform complex computations efficiently within power and resource constrained environments. It is increasingly applied across industries, including autonomous vehicles, healthcare, smart manufacturing, and IoT, to deliver faster, secure, and cost effective intelligent solutions.

Market Dynamics:

Driver:

Demand for Real-Time Intelligence

The increasing need for real-time data processing and instantaneous decision-making is a primary driver for the Edge AI Inference Market. Industries such as autonomous vehicles, healthcare, and smart manufacturing require rapid insights to enhance operational efficiency, safety, and customer experience. By processing AI algorithms locally on edge devices, organizations can reduce latency, minimize reliance on cloud infrastructure, and respond immediately to critical events, enabling faster, reliable, and more secure outcomes across diverse applications.

Restraint:

Limited Compute and Energy Constraints

Edge AI inference faces significant challenges due to the limited computational capacity and energy constraints of edge devices. Unlike cloud-based systems, these devices must perform complex AI operations with restricted processing power, memory, and battery life. This limitation can hinder performance, reduce efficiency, and restrict the deployment of advanced AI models. Overcoming these hardware constraints is essential for broader adoption, as organizations seek solutions that balance intelligent processing with energy efficiency and device longevity.

Opportunity:

Tech Advancements in Compact AI Chips

Advancements in compact AI chips and specialized accelerators present a significant growth opportunity for the Edge AI Inference Market. These innovations enable high-performance computations on small, power-efficient devices, allowing sophisticated AI algorithms to run directly at the edge. Industries such as IoT, healthcare, and smart agriculture can leverage these chips to achieve faster, localized insights while reducing reliance on cloud processing. Continuous improvements in chip design and miniaturization are expected to expand applications and accelerate market adoption globally.

Threat:

Complex Deployment and Maintenance

The deployment and maintenance of Edge AI systems across distributed devices pose critical challenges for market growth. Managing multiple devices with varying hardware specifications, updating AI models, and ensuring consistent performance require substantial technical expertise and resources. Additionally, security management across numerous edge nodes increases complexity, creating operational risks. These challenges can delay adoption, raise costs, and limit scalability, particularly for enterprises seeking seamless integration with legacy infrastructure and heterogeneous edge environments.

Covid-19 Impact:

The COVID-19 pandemic accelerated the adoption of Edge AI Inference as organizations sought to minimize physical interactions and optimize operational efficiency. Remote monitoring, autonomous systems, and AI-powered diagnostics became essential across healthcare, manufacturing, and logistics sectors. However, supply chain disruptions and delayed hardware production temporarily hindered deployments. Overall, the pandemic highlighted the value of decentralized AI processing, encouraging investments in edge computing solutions to improve resilience and support rapid decision-making in dynamic and uncertain environments.

The drones segment is expected to be the largest during the forecast period

The drones segment is expected to account for the largest market share during the forecast period, due to need for autonomous navigation, real-time data analysis, and precision operations. Edge AI inference allows drones to process data locally for tasks such as aerial mapping, surveillance, and delivery services, reducing latency and dependence on cloud connectivity. Enhanced onboard computing capabilities enable faster decision-making, increased operational efficiency, and improved safety, making drones a primary application area for edge AI adoption across commercial, industrial, and defense sectors.

The agriculture segment is expected to have the highest CAGR during the forecast period

Over the forecast period, the agriculture segment is predicted to witness the highest growth rate, due to increasing adoption of smart farming solutions. Edge AI enables real-time crop monitoring, precision irrigation, pest detection, and yield optimization by processing sensor and drone data locally. These applications enhance productivity, reduce resource consumption, and support sustainable farming practices. With the growing demand for automated and data-driven agricultural operations, edge AI inference is becoming a key technology for transforming traditional farming into intelligent, efficient, and scalable systems.

Region with largest share:

During the forecast period, the North America region is expected to hold the largest market share, due to early adoption of advanced technologies, robust IT infrastructure, and significant investments in AI research and development. Key industries, including automotive, healthcare, and smart manufacturing, are increasingly deploying edge AI solutions to enable real-time intelligence and improve operational efficiency. The presence of leading technology vendors and strong government initiatives supporting AI adoption further solidifies North America's dominance in the global edge AI inference market.

Region with highest CAGR:

Over the forecast period, the Asia Pacific region is anticipated to exhibit the highest CAGR, owing to rapid industrialization, increasing IoT adoption, and growing investments in AI-powered infrastructure. Countries such as China, Japan, and India are embracing edge AI technologies across smart manufacturing, agriculture, and autonomous systems. The combination of expanding technology ecosystems, rising demand for low-latency solutions, and government initiatives promoting AI innovation positions the Asia Pacific region as the fastest-growing market for edge AI inference globally.

Key players in the market

Some of the key players in Edge AI Inference Market include NVIDIA Corporation, Intel Corporation, Qualcomm Technologies, Inc., Google LLC, Microsoft Corporation, Amazon Web Services (AWS), IBM Corporation, Huawei Technologies Co., Ltd., Arm Holdings plc, Samsung Electronics Co., Ltd., Apple Inc., Dell Technologies Inc., Cisco Systems, Inc., Hewlett Packard Enterprise (HPE), and Advantech Co., Ltd.

Key Developments:

In December 2025, IBM and AWS have deepened their strategic collaboration to accelerate enterprise adoption of agentic AI, integrating AI technologies, hybrid cloud and governance solutions to help organizations deploy scalable, secure, and business-driven autonomous systems across industries.

In October 2025, Bharti Airtel has entered a strategic partnership with IBM to enhance its newly launched Airtel Cloud, combining telco-grade reliability with IBM's advanced cloud, hybrid and AI-optimized infrastructure to help regulated enterprises scale secure, interoperable, and mission-critical workloads.

Components Covered:

Hardware
Software
Services

Device Types Covered:

Smartphones & Tablets
Industrial Robots
Smart Cameras & Surveillance Systems
Autonomous Vehicles
Drones
Wearables
Other Device Types

Applications Covered:

Healthcare & Medical Imaging
Automotive & Transportation
Retail & E-commerce
Manufacturing & Industrial Automation
Smart Home & Consumer Electronics
Security & Surveillance
Other Applications

End Users Covered:

Agriculture
Government & Defense
Energy & Utilities
Other End Users

Regions Covered:

North America
- United States
- Canada
- Mexico
Europe
- United Kingdom
- Germany
- France
- Italy
- Spain
- Netherlands
- Belgium
- Sweden
- Switzerland
- Poland
- Rest of Europe
Asia Pacific
- China
- Japan
- India
- South Korea
- Australia
- Indonesia
- Thailand
- Malaysia
- Singapore
- Vietnam
- Rest of Asia Pacific
South America
- Brazil
- Argentina
- Colombia
- Chile
- Peru
- Rest of South America
Rest of the World (RoW)
- Middle East
Saudi Arabia
United Arab Emirates
Qatar
Israel
Rest of Middle East
- Africa
South Africa
Egypt
Morocco
Rest of 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 2023, 2024, 2025, 2026, 2027, 2028, 2030, 2032 and 2034
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

1 Executive Summary

1.1 Market Snapshot and Key Highlights
1.2 Growth Drivers, Challenges, and Opportunities
1.3 Competitive Landscape Overview
1.4 Strategic Insights and Recommendations

2 Research Framework

2.1 Study Objectives and Scope
2.2 Stakeholder Analysis
2.3 Research Assumptions and Limitations
2.4 Research Methodology
- 2.4.1 Data Collection (Primary and Secondary)
- 2.4.2 Data Modeling and Estimation Techniques
- 2.4.3 Data Validation and Triangulation
- 2.4.4 Analytical and Forecasting Approach

3 Market Dynamics and Trend Analysis

3.1 Market Definition and Structure
3.2 Key Market Drivers
3.3 Market Restraints and Challenges
3.4 Growth Opportunities and Investment Hotspots
3.5 Industry Threats and Risk Assessment
3.6 Technology and Innovation Landscape
3.7 Emerging and High-Growth Markets
3.8 Regulatory and Policy Environment
3.9 Impact of COVID-19 and Recovery Outlook

4 Competitive and Strategic Assessment

4.1 Porter's Five Forces Analysis
- 4.1.1 Supplier Bargaining Power
- 4.1.2 Buyer Bargaining Power
- 4.1.3 Threat of Substitutes
- 4.1.4 Threat of New Entrants
- 4.1.5 Competitive Rivalry
4.2 Market Share Analysis of Key Players
4.3 Product Benchmarking and Performance Comparison

5 Global Edge AI Inference Market, By Component

5.1 Hardware
- 5.1.1 GPU
- 5.1.2 FPGA
- 5.1.3 ASIC
5.2 Software
- 5.2.1 AI Frameworks
- 5.2.2 Inference Engines
5.3 Services
- 5.3.1 Consulting
- 5.3.2 Support & Maintenance

6 Global Edge AI Inference Market, By Device Type

6.1 Smartphones & Tablets
6.2 Industrial Robots
6.3 Smart Cameras & Surveillance Systems
6.4 Autonomous Vehicles
6.5 Drones
6.6 Wearables
6.7 Other Device Types

7 Global Edge AI Inference Market, By Application

7.1 Healthcare & Medical Imaging
7.2 Automotive & Transportation
7.3 Retail & E-commerce
7.4 Manufacturing & Industrial Automation
7.5 Smart Home & Consumer Electronics
7.6 Security & Surveillance
7.7 Other Applications

8 Global Edge AI Inference Market, By End User

8.1 Agriculture
8.2 Government & Defense
8.3 Energy & Utilities
8.4 Other End Users

9 Global Edge AI Inference Market, By Geography

9.1 North America
- 9.1.1 United States
- 9.1.2 Canada
- 9.1.3 Mexico
9.2 Europe
- 9.2.1 United Kingdom
- 9.2.2 Germany
- 9.2.3 France
- 9.2.4 Italy
- 9.2.5 Spain
- 9.2.6 Netherlands
- 9.2.7 Belgium
- 9.2.8 Sweden
- 9.2.9 Switzerland
- 9.2.10 Poland
- 9.2.11 Rest of Europe
9.3 Asia Pacific
- 9.3.1 China
- 9.3.2 Japan
- 9.3.3 India
- 9.3.4 South Korea
- 9.3.5 Australia
- 9.3.6 Indonesia
- 9.3.7 Thailand
- 9.3.8 Malaysia
- 9.3.9 Singapore
- 9.3.10 Vietnam
- 9.3.11 Rest of Asia Pacific
9.4 South America
- 9.4.1 Brazil
- 9.4.2 Argentina
- 9.4.3 Colombia
- 9.4.4 Chile
- 9.4.5 Peru
- 9.4.6 Rest of South America
9.5 Rest of the World (RoW)
- 9.5.1 Middle East
  - 9.5.1.1 Saudi Arabia
  - 9.5.1.2 United Arab Emirates
  - 9.5.1.3 Qatar
  - 9.5.1.4 Israel
  - 9.5.1.5 Rest of Middle East
- 9.5.2 Africa
  - 9.5.2.1 South Africa
  - 9.5.2.2 Egypt
  - 9.5.2.3 Morocco
  - 9.5.2.4 Rest of Africa

10 Strategic Market Intelligence

10.1 Industry Value Network and Supply Chain Assessment
10.2 White-Space and Opportunity Mapping
10.3 Product Evolution and Market Life Cycle Analysis
10.4 Channel, Distributor, and Go-to-Market Assessment

11 Industry Developments and Strategic Initiatives

11.1 Mergers and Acquisitions
11.2 Partnerships, Alliances, and Joint Ventures
11.3 New Product Launches and Certifications
11.4 Capacity Expansion and Investments
11.5 Other Strategic Initiatives

12 Company Profiles

12.1 NVIDIA Corporation
12.2 Intel Corporation
12.3 Qualcomm Technologies, Inc.
12.4 Google LLC
12.5 Microsoft Corporation
12.6 Amazon Web Services (AWS)
12.7 IBM Corporation
12.8 Huawei Technologies Co., Ltd.
12.9 Arm Holdings plc
12.10 Samsung Electronics Co., Ltd.
12.11 Apple Inc.
12.12 Dell Technologies Inc.
12.13 Cisco Systems, Inc.
12.14 Hewlett Packard Enterprise (HPE)
12.15 Advantech Co., Ltd.

簡介目錄圖表

List of Tables

Table 1 Global Edge AI Inference Market Outlook, By Region (2023-2034) ($MN)
Table 2 Global Edge AI Inference Market Outlook, By Component (2023-2034) ($MN)
Table 3 Global Edge AI Inference Market Outlook, By Hardware (2023-2034) ($MN)
Table 4 Global Edge AI Inference Market Outlook, By GPU (2023-2034) ($MN)
Table 5 Global Edge AI Inference Market Outlook, By FPGA (2023-2034) ($MN)
Table 6 Global Edge AI Inference Market Outlook, By ASIC (2023-2034) ($MN)
Table 7 Global Edge AI Inference Market Outlook, By Software (2023-2034) ($MN)
Table 8 Global Edge AI Inference Market Outlook, By AI Frameworks (2023-2034) ($MN)
Table 9 Global Edge AI Inference Market Outlook, By Inference Engines (2023-2034) ($MN)
Table 10 Global Edge AI Inference Market Outlook, By Services (2023-2034) ($MN)
Table 11 Global Edge AI Inference Market Outlook, By Consulting (2023-2034) ($MN)
Table 12 Global Edge AI Inference Market Outlook, By Support & Maintenance (2023-2034) ($MN)
Table 13 Global Edge AI Inference Market Outlook, By Device Type (2023-2034) ($MN)
Table 14 Global Edge AI Inference Market Outlook, By Smartphones & Tablets (2023-2034) ($MN)
Table 15 Global Edge AI Inference Market Outlook, By Industrial Robots (2023-2034) ($MN)
Table 16 Global Edge AI Inference Market Outlook, By Smart Cameras & Surveillance Systems (2023-2034) ($MN)
Table 17 Global Edge AI Inference Market Outlook, By Autonomous Vehicles (2023-2034) ($MN)
Table 18 Global Edge AI Inference Market Outlook, By Drones (2023-2034) ($MN)
Table 19 Global Edge AI Inference Market Outlook, By Wearables (2023-2034) ($MN)
Table 20 Global Edge AI Inference Market Outlook, By Other Device Types (2023-2034) ($MN)
Table 21 Global Edge AI Inference Market Outlook, By Application (2023-2034) ($MN)
Table 22 Global Edge AI Inference Market Outlook, By Healthcare & Medical Imaging (2023-2034) ($MN)
Table 23 Global Edge AI Inference Market Outlook, By Automotive & Transportation (2023-2034) ($MN)
Table 24 Global Edge AI Inference Market Outlook, By Retail & E-commerce (2023-2034) ($MN)
Table 25 Global Edge AI Inference Market Outlook, By Manufacturing & Industrial Automation (2023-2034) ($MN)
Table 26 Global Edge AI Inference Market Outlook, By Smart Home & Consumer Electronics (2023-2034) ($MN)
Table 27 Global Edge AI Inference Market Outlook, By Security & Surveillance (2023-2034) ($MN)
Table 28 Global Edge AI Inference Market Outlook, By Other Applications (2023-2034) ($MN)
Table 29 Global Edge AI Inference Market Outlook, By End User (2023-2034) ($MN)
Table 30 Global Edge AI Inference Market Outlook, By Agriculture (2023-2034) ($MN)
Table 31 Global Edge AI Inference Market Outlook, By Government & Defense (2023-2034) ($MN)
Table 32 Global Edge AI Inference Market Outlook, By Energy & Utilities (2023-2034) ($MN)
Table 33 Global Edge AI Inference Market Outlook, By Other End Users (2023-2034) ($MN)