邊緣人工智慧開發解決方案：推動實體和設備端人工智慧的轉型

Edge AI Development Solutions: Empowering the Physical and On-device AI Revolution

出版日期: 2026年02月28日 | 出版商:

VDC Strategy | 英文 36 Pages/430 Exhibits | 商品交期: 最快1-2個工作天內

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簡介目錄

本報告內容

人工智慧正迅速地向邊緣端遷移。在設備上部署模型具有雲端無法比擬的諸多優勢，例如低延遲、低成本和更高的隱私保護。因此，各種規模的設備製造商和原始設備製造商 (OEM) 都在大力投資邊緣人工智慧。近年來，邊緣人工智慧開發解決方案的商業市場發展迅猛，因為為邊緣設備開發和部署模型需要先進的技術專長。除了模型最佳化、部署和管理工具之外，工程團隊還需要特定領域的專業知識和人工智慧技術。每種硬體目標都面臨獨特的模型最佳化挑戰，供應商必須幫助客戶解決這些挑戰，才能加速人工智慧開發並確保長期競爭力。

本報告詳細分析了邊緣人工智慧開發解決方案市場的關鍵工具、趨勢和策略考慮。報告涵蓋了2024年至2029年的市場規模和預測，以及按產品類型（解決方案或服務）、地區（美洲、歐洲、中東和非洲、亞太地區）、垂直行業、工作負載類型（電腦視覺、基於感測器、LLM/VLM、控制與自主）和主要供應商分類的細分市場分析。定性分析部分包括相關併購的影響、宏觀和產品趨勢分析以及主要供應商概況。此外，報告也基於VDC的「工程師之聲」調查，深入剖析了工程組織的說明、偏好和意見。

本報告探討的主要問題

哪些類型的組織正在將人工智慧作為其產品策略的核心要素？
解決方案供應商應該如何與開放原始碼和免費軟體解決方案競爭？
哪個垂直市場蘊藏最大的成長機會？
機器人技術何時才能得到廣泛的商業性應用？
哪些人工智慧工作負載最受歡迎？到 2029 年，工作負載的選擇將會發生怎樣的變化？
為什麼混合量化對於市場進入至關重要？
在那些主要專注於資料中心相關業務的公司中，哪些公司已經進入邊緣人工智慧市場？

技術提供者正接受調查

Altair
alwaysAI
AMD
AnythingLLM
AWS
Advantech
Agility Robotics
Aptiv
Arm
Blaize
Boston Dynamics
Bosch
Brium
Cadence
DataRobot
DEEPX
Edge Impulse
Everseen
Google
Hexagon
Infineon
Intel
Kinara
Latent AI
MathWorks
MediaTek
Microchip
Microsoft
ModelCat
MovianAI
Nordic Semiconductor
Nota AI
NXP
NVIDIA
Nutanix
PyTorch Foundation
Qualcomm
QuickLogic
QNX
Roboflow
Samsung
Scale AI
SensiML
Siemens
SiMa.ai
Squint
STMicroelectronics
Synaptics
Texas Instruments
Wind River

Inside this Report

AI is rapidly moving to the edge. On-device model deployments offer several enticing advantages that the cloud cannot match, including reduced latency, lower cost, and enhanced privacy. As a result, device makers and OEMs of all sizes are investing heavily in edge AI. Due to the elevated technical expertise required to develop and deploy models for edge devices, the commercial market for edge AI development solutions has grown rapidly over the past few years. Engineering organizations need model optimization, deployment, and management tools alongside access to domain-specific professional services and AI expertise. Each distinct hardware target creates unique model optimization challenges that solution vendors must solve for their customers, accelerating AI development and ensuring long-term competitiveness.

This report includes an in-depth analysis of the leading tools, trends, and strategic considerations relevant to the market for edge AI development solutions. It includes market sizing and forecasts from 2024 to 2029 with commentary and segmentations by product type (solution or service), region (Americas, EMEA, APAC), vertical market, workload type (computer vision, sensor-based, LLM/VLM, control & autonomy), and leading vendors. Qualitatively, the report includes coverage of the impact of relevant mergers and acquisitions, an analysis of macro and product trends, and profiles of leading vendors. This report also features insights into the needs, preferences, and opinions of engineering organizations from VDC’s Voice of the Engineer survey.

What Questions are Addressed?

What types of organizations have embraced AI as a core part of their product strategy?
How can solution providers compete with open source and freeware solutions?
Which vertical markets present the best opportunity for growth?
When will robotics gain meaningful commercial traction?
Which AI workloads are most popular, and how will workload selection change through 2029?
Why is hybrid quantization essential to market entry?
Which companies historically focused on datacenter applications have targeted the edge AI market?

Who Should Read this Report?

This report was written for those making critical decisions regarding product, marketing, channel, and competitive strategy and tactics. This report is intended for senior decision-makers who are developing embedded and edge AI solutions, including:

CEO or other C-level executives
Corporate development and M&A teams
Marketing executives
Business development and sales leaders
Product development and product strategy leaders
Channel management and channel strategy leaders

Technology Providers in this Research

Altair
alwaysAI
AMD
AnythingLLM
AWS
Advantech
Agility Robotics
Aptiv
Arm
Blaize
Boston Dynamics
Bosch
Brium
Cadence
DataRobot
DEEPX
Edge Impulse
Everseen
Google
Hexagon
Infineon
Intel
Kinara
Latent AI
MathWorks
MediaTek
Microchip
Microsoft
ModelCat
MovianAI
Nordic Semiconductor
Nota AI
NXP
NVIDIA
Nutanix
PyTorch Foundation
Qualcomm
QuickLogic
QNX
Roboflow
Samsung
Scale AI
SensiML
Siemens
SiMa.ai
Squint
STMicroelectronics
Synaptics
Texas Instruments
Wind River

Inside this Report

Executive Summary

Key Findings

Global Market Overview

Professional Services Remain Key for Effective Market Entry
Hardware Vendors Raise Standards For Commercial Solution Providers
ExecuTorch Adoption Grows at the High-performance Edge
Recent Mergers and Acquisitions
Regulatory Pressures Increase Across Europe

Regional Trends & Forecast

Americas
Europe, Middle East, and Africa
Asia-Pacific

Vertical Market Trends & Forecast

AI Workload Trends & Forecast

Computer Vision Sensor-based AI
LLMs/VLMs
Real-time Control & Autonomy

End-user Insights

AI/ML Features Will Permeate Throughout Safety-critical Systems
GPUs Remain the Primary Inference and Training Architecture
Constant Model Retraining Creates Demand for Model Maintenance Features
Incumbent Model Zoos Will Hold User Share

Competitive Landscape

Vendor & Technology Provider Profiles

alwaysAI
AMD AWS
Edge Impulse

Intel
MathWorks
NVIDIA
NXP

Roboflow
Scale
Siemens
Wind River

About the Authors

About VDC Research

List of Exhibits

Exhibit 1 Global Revenue of Edge AI Tools & Related Services Segmented by Product Type, 2024-2029
Exhibit 2 Percentage of Global Revenue from Edge AI Tools & Professional Services Segmented by Product Type, 2024-2029
Exhibit 3 Global Revenue of Edge AI Development Tools & Related Services Segmented by Geographic Region, 2024-2029
Exhibit 4 Percentage of Global Revenue from Edge AI Development Tools & Related Services Segmented by Geographic Region, 2024-2029
Exhibit 5 Global Revenue of Edge AI Development Tools & Related Services Segmented by Vertical Market, 2024-2029
Exhibit 6 Percentage of Global Revenue from Edge AI Development Tools & Related Services Segmented by Vertical Market, 2024-2029
Exhibit 7 Global Revenue of Edge AI Development Tools & Related Services Segmented by Workload Type, 2024-2029
Exhibit 8 Percentage of Global Revenue from Edge AI Development Tools & Related Services Segmented by Workload Type, 2024-2029
Exhibit 9 Global Revenue of Edge AI Development Tools & Related Services Segmented by Leading Vendors, 2024
Exhibit 10 Global Revenue of Edge AI Development Tools & Related Services Segmented by Leading Vendors, 2025 Estimated
Exhibit 11 Current Use of AI/ML in Current Project and Expected in Three Years Segmented by Vertical Market
Exhibit 12 Target Architectures Used for Inference and Training and Expected Architecture Used in Three Years
Exhibit 13 Frequency for Which Models are Fine-tuned or Re-trained
Exhibit 14 Primary Source for Pre-trained Models and Expected Source in Three Years

IoT & Embedded Engineering Survey

Exhibit 77 CPU/MCU Suppliers for Current Project
Exhibit 140 Types of Artificial Intelligence Workloads Used in Current Projects
Exhibit 141 Types of Artificial Intelligence Workloads Expected to be Used Three Years From Now
Exhibit 142 Types of Machine Learning Used for AI in Current Projects
Exhibit 143 Types of Machine Learning Expected to be Used for AI Three Years From Now
Exhibit 144 Location Current Machine Learning Models are Trained
Exhibit 145 Location Machine Learning Models are Expected to be Trained in Future Projects
Exhibit 146 Target Architecture(s) Used for Training In Current Project
Exhibit 147 Target Architecture(s) Used for Inferencing In Current Project
Exhibit 148 Target Architecture(s) Expected to be Used for Training and Inferencing In Future Projects
Exhibit 149 ASICs Used for AI Training
Exhibit 151 Current Level of AI/ML Performance Expected for Target Systems to Run Typical End User/Customer Workloads
Exhibit 152 Expected Level of AI/ML Performance for Target Systems Three Years From Now
Exhibit 153 Location Deep Learning Models are Often Fine-tuned or Re-trained
Exhibit 154 Underlying Hardware Primarily Used for Fine-tuning or Re-training Deep Learning Models
Exhibit 155 Frequency for Which Models are Fine-tuned or Re-trained
Exhibit 157 Use of Pre-trained AI Models from Public Model Repositories
Exhibit 158 Software Frameworks and Tools Used for Training AI Models
Exhibit 161 AI Software Resources or APIs Used in Current Projects
Exhibit 162 AI Software Resources or APIs Expected to be Used Three Years From Now
Exhibit 163 Greatest Challenges in Developing Software for AI
Exhibit 164 Most Preferred Development Component Would Like Reduced to Make AI Workloads More Cost Efficient