On-device AI market opportunities include real-time response, energy efficiency, and cost-effectiveness for IoT devices across consumer, industrial, and automotive sectors. Growth is driven by advances in generative AI, robotics, and autonomous applications, expanding product categories and use cases.
On-Device AI Revenue for IoT Applications, Worldwide, 2023-2029
On-Device AI Revenue for IoT Applications, Worldwide, 2023-2029
DUBLIN, Nov. 20, 2025 (Globe Newswire) — The “On-Device AI Market for IoT Applications – 1st Edition” report has been added. ResearchAndMarkets.com Offerings.
On-device AI market for IoT applications is the largest source of information on the emerging and influential on-device AI market. Whether you are a hardware vendor, OEM, ODM, enterprise AI adopter, investor, consultant, or government agency, you can gain valuable insights from this in-depth study.
Analysts estimate that the revenue generated by on-device AI solutions will reach USD 10.1 billion in 2024, an increase of approximately 22% from 2023. This number includes AI SoCs/SoMs, AI accelerators, AI MCUs, and specialized on-device AI software and platforms, but excludes revenue generated by non-IoT applications such as smartphones, tablets, and personal computers. The market is expected to grow to USD 30.6 billion in 2029, at a compound annual growth rate (CAGR) of 25%.
Revenue generated by on-device AI solutions is estimated to reach USD 10.1 billion in 2024, up 22% year over year. This number includes AI SoCs/SoMs, AI accelerators, AI MCUs, and specialized on-device AI software and platforms, but excludes revenue generated by non-IoT applications such as smartphones, tablets, and personal computers. The market is expected to grow to USD 30.6 billion in 2029, at a CAGR of 25%. Stay up to date with the latest trends and developments with this unique 90-page report.
The Internet of Things (IoT) is continually evolving and expanding into new areas. Some of the latest developments are integrating artificial intelligence (AI) capabilities directly into IoT devices to enable a new generation of applications. AI-integrated devices offer many advantages over traditional rule-based or manually programmed methods, especially in applications requiring object detection, voice recognition, predictive maintenance, anomaly detection, dynamic resource optimization, and autonomous decision-making. Running AI algorithms directly on devices, known as edge AI or on-device AI, offers many benefits, including real-time responsiveness, reduced data transfer, enhanced privacy, and increased resiliency. While cloud processing remains valid for many IoT use cases, new use cases are emerging that require the capabilities of on-device AI.
The market for on-device AI solutions is characterized by a high degree of heterogeneity in both technology and applications, in contrast to cloud-based AI, where hardware is typically designed around predefined use cases and centralized infrastructure. Embedded AI processing can be designed in a variety of ways depending on the end use case and integrated into an almost unlimited range of devices across consumer, industrial, and automotive domains. This creates a differentiated market environment with unique design constraints, performance requirements, and optimization strategies. However, the overarching goals are typically the same for all vendors. That is, achieving the highest performance per watt for the intended use case.
Analysts have identified 40 key companies shaping the on-device AI landscape. The market can be broadly divided into two tiers. The first includes hardware categories such as AI systems on chips (SoCs), systems on modules (SoMs), AI accelerators, and AI microcontroller units (MCUs), each optimized for varying levels of performance, power efficiency, and integration. While AI SoCs typically integrate components such as general-purpose and dedicated AI compute cores, on-chip memory, and connectivity onto a single chip, SoMs extend this design by incorporating external system memory, storage, and interface components on a larger board for more advanced use cases.
An AI accelerator is a specialized chip or module designed to increase the efficiency of AI inference in existing systems, typically running in parallel with another host processor in embedded applications. AI MCUs serve low-power devices by bringing neural network capabilities to sensors, wearables, and IoT endpoints where energy efficiency and cost are paramount. The second layer consists of an on-device AI platform that combines hardware, software, and developer tools to simplify model deployment and optimization.
Over the past decade, the on-device AI market has experienced steady annual growth of approximately 10%, driven primarily by traditional machine learning use cases such as computer vision and anomaly detection. In recent years, the market has reached an inflection point, with emerging technologies and applications in generative AI, robotics, and autonomous driving opening up new growth dimensions. These developments are expected to accelerate market growth and create entirely new use cases and product categories.
Report highlights:
Insights from numerous executive interviews with market-leading companies.
A 360-degree overview of the on-device AI ecosystem.
On-device AI hardware and software market value forecast to 2029.
Market share of 40 leading on-device AI hardware and software providers.
Detailed profiles of 31 leading on-device AI hardware and software providers.
Descriptions of the most important industry-wide use cases.
Detailed analysis of market trends and key developments.
Questions answered in the report:
How does on-device AI technology work?
What is the business rationale between on-device, cloud, and hybrid AI deployments?
What are the prices and pricing models for various on-device AI solutions?
What are the key success factors and challenges for stakeholders in the On-Device AI market?
Who are the leading providers of on-device AI hardware and software?
How does the market differ by industry and what are the key use cases?
How will the edge AI market evolve over the next five years?
Main topics covered:
1 Introduction 1.1 Cloud processing and on-device processing 1.1.1 Processing on the device 1.1.2 Cloud processing 1.1.3 Edge data center processing 1.1.4 Hybrid approach 1.2 AIoT: Fusion of AI and IoT 1.2.1 What is an IoT device? 1.2.2 IoT connectivity options 1.3 Overview of artificial intelligence technology 1.3.1 Artificial Intelligence 1.3.2 Machine learning 1.3.3 Deep learning 1.3.4 Generative AI 1.4 On-device AI ecosystem 1.4.1 On-device AI hardware 1.4.2 On-device AI software 1.4.3 On-device AI models 1.4.4 On-device AI platform
2 Market analysis 2.1 On-device AI industry outlook 2.1.1 AI SoC/SoM Provider 2.1.2 AI Accelerator Provider 2.1.3 AI MCU Provider 2.1.4 On-device AI platform provider 2.2 Market size and forecast 2.2.1 Automotive on-device AI market size 2.2.2 IoT on-device AI market size 2.2.3 On-device GenAI and non-GenAI market size 2.2.4 On-device AI processor shipments 2.3 Solution provider market share 2.4 Vertical deployment and use cases 2.4.1 On-device AI in automotive IoT applications 2.4.2 On-device AI in industrial IoT applications 2.4.3 On-device AI in wearables 2.4.4 On-device AI in retail IoT applications 2.4.5 On-device AI in buildings and security IoT applications 2.4.6 On-device AI in smart home applications 2.4.7 On-device AI in other IoT applications
3 Company overview and strategy 3.1 Ambarella 3.2 Ambik 3.3 Advanced Micro Devices (AMD) 3.4 Apple 3.5 Axela 3.6 Black sesame technology 3.7 Deep X 3.8 Edge Cortics 3.9 Edge Impulse 3.10 Embedded URL 3.11 Hiro 3.12 Horizon Robotics 3.13 Hug your face 3.14 Intel 3.15 MediaTek 3.16 Memly X 3.17 Mobileye 3.18 Mythic 3.19 Nota AI 3.20 Nvidia 3.21 NXP Semiconductors 3.22 Qualcomm 3.23 Renesas Electronics 3.24 Rock Chip 3.25 Sigmamaster 3.26 Shima 3.27 STMicroelectronics 3.28 Synaptics 3.29 Sintiant 3.30 Tesla 3.31 Texas Instruments
For more information on this report, please visit https://www.researchandmarkets.com/r/3cyc60.
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