엣지 AI 개발 솔루션 : 피지컬 및 온 디바이스 AI 변혁을 추진

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

Table of Contents

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