세계의 온 디바이스 AI 시장 : 컴포넌트별, 배포별, 테크놀러지별, 지역별, 기회, 예측(2018-2032년)

Global on-device AI market is projected to witness a CAGR of 15.67% during the forecast period 2025-2032, growing from USD 5.40 billion in 2024 to USD 17.30 billion in 2032. The global on-device AI market is rapidly expanding due to the growing demand for real-time processing, enhanced data privacy, and reduced dependency on cloud infrastructure. Advancements in AI chipsets and model optimization are enabling seamless integration of intelligent features across smartphones, wearables, and edge devices.

With the increasing integration of smartphones, wearables, smart home systems, industrial IoT devices, and other technologies into daily life and business, the demand for local AI processing capabilities, rather than being tied to cloud infrastructure, has skyrocketed. This paradigm shift is creating a notable change in how we process, share, and secure data, ushering in a new era of intelligent edge computing. On-device AI offers compelling value in an era of growing data privacy concerns, as well as connectivity limitations in remote or real-time use cases, where lower latency, enhanced security, and offline capabilities are required. Rapid developments in AI model compression, innovative chip designs, such as Neural Processing Units (NPUs), and significant advancements in lightweight neural architectures have enabled resource-constrained devices to perform complex tasks, including speech recognition, computer vision, natural language processing, and advanced analytics, in near real-time. As more industries adopt intelligent, decentralized AI solutions, including automotive, healthcare, and consumer electronics, the global on-device AI market has nowhere to go but up, and will support the next era of innovative technology experiences.

Rising Demand for Privacy-Centric AI Solutions Drives Global On-Device AI Market Growth

As data privacy continues to be a significant issue, the demand for AI models that run directly on users' devices (without a cloud connection) is skyrocketing. On-device AI visits less data to third-party cloud services, creating less risk of data breaches and risk of sensitive data (personal messages, biological identifiers, locations) being left out in the world, while also being compliant with various global data protection regulations (GDPR, CCPA), for example. When consumers become more privacy-conscious, providing data-free AI will be viewed as a more strategic approach.

This recent emerging demand was demonstrated in March of 2025 when Qualcomm Technologies, Inc. released its latest on-device AI development at MWC 2025. Qualcomm detailed its new Snapdragon platforms, which were developed to deliver higher, faster, and more efficient AI across mobile phones, IoT devices, and vehicles. These new chipsets enable the ability to run complex tasks locally, such as voice recognition, computer vision, or contextual AI, with no reliance on the internet or external connections. Thus, it improves user privacy and latency. This new perspective, which prioritizes privacy and leans towards device-based processing, is influencing the mapping of AI development strategies.

Growing Need for Low-Latency AI Across Edge Devices Accelerates the Market Growth

Low latency is a key feature for applications running on smartphones, wearables, autonomous vehicles, and industrial IoT devices. While cloud AI is powerful, it suffers from the latency of internet connectivity and server load times. On-device AI enables users to perform final inference locally on devices, allowing for real-time decision-making in often time-sensitive applications, such as navigation, augmented reality, voice assistance, and security monitoring.

An excellent demonstration of this trend is Google LLC's launch of Gemma 3n in June 2025. Gemma 3n is a lightweight multimodal AI model that only needs 2 GB of RAM to operate. This model can accept audio, image, video, and text as inputs, all while operating directly on edge devices such as smartphones. Using the Gemma 3n model allows for quick responsiveness in offline applications. The underlying architecture of Gemma 3n ensures that edge-AI features, such as speech recognition or photo enhancement, do not lag (or appear to lag) regardless of whether the device is connected to the network. This transition from cloud AI to low-latency, high-performance AI is crucial to unlocking the next generation of intelligent edge applications. As businesses and governments explore ways to reduce reliance on cloud infrastructure while capturing speed and features, investment in on-device models and specialized chipsets is accelerating market development.

Natural Language Processing Segment Holds a Significant Share in Global On-Device AI Market

Natural Language Processing (NLP) is a crucial component of the global on-device AI ecosystem, as these applications are embedded in personal devices, enterprise tools, and embedded devices. In other words, NLP enables machines to comprehend, interpret, and generate human language. Applications such as real-time transcription services like Otter, intelligent voice assistant services like Siri, predictive text features like the "suggested text" function on smartphones, as well as AI-prompted summarization tools such as ChatGPT, are heavily reliant on NLP capabilities to operate. As demand from users for accelerated, context-based interaction escalates and the pressure regarding data privacy increases, NLP models that run directly have become a vital differentiator in an evolving AI ecosystem.

One of the most prominent examples of this transition in the market occurred in March 2025. It was at this time that Arm Limited developed on-device processing optimization of the "Stable Audio Open" model with Stability AI, utilizing Arm's KleidiAI technology. This technology enables Arm CPUs to process data 30 times faster, ultimately allowing mobile devices to create high-quality custom audio clips from natural language prompts, all without requiring an internet connection. This advancement illustrates how, when combined with modern hardware, on-device natural language processing has the potential to transform how audio is created and interacted with by users, offering reduced latency and improved privacy. As NLP advances towards transformer models and multimodal learning, it will gain influence in adding intelligence and interactions to devices at the edge. It is already the leading segment in the on-device AI ecosystem and the practical applications will continue to increase.

North America Dominates the Global On-Device AI Market

North America is currently the dominant region in the global on-device AI market, driven by its advanced technological infrastructure, concentration of AI and semiconductor companies, and increasing ownership of smart devices. This area is home to key players in this space who are investing heavily in on-device AI and driving innovation. These companies are embedding more sophisticated AI features into more user devices (smartphones, wearables, personal computing devices, and automobiles) in ways that improve the user experience while retaining users' data privacy. Regional dominance is reinforced by favorable government initiatives, a digitally literate user base, and a culture of research and development (R&D) conducive to on-device AI advancements. North America also has a highly thriving startup ecosystem that is rapidly exploring the applications of edge AI, particularly in the domains of health technologies, automotive, and industrial automation.

A notable example of this trend was demonstrated in June 2025 when Apple Inc. (California, US) announced new Apple Intelligence features at WWDC 2025, which allowed data models to run completely on-device. Meaning that these AI models run on iPhones, iPads and Macs entirely on-device, improving performance while also meeting privacy concerns, which are a prevalent consumer concern in North America. With continuous investment in this technology, North America is likely to maintain its leadership in on-device AI, as it is technologically ahead in terms of experience, has a high level of ownership penetration, and is experiencing emerging technological advancements.

Impact of U.S. Tariffs on Global On-Device AI Market

The ramifications of U.S. tariffs on on-device AI are modest but still significant. Tariff restrictions on semiconductors and other electronic components from China are a known source of cost increase for U.S.-based firms. Still, many companies have attempted to offset these impacts by diversifying their global supply chains and/or relocating production to other non-tariff countries. These costs represent additional expenses for consumers that may indirectly impact on the pricing and availability of AI-enabled consumer devices. However, the predominant domestic tech giants associated with on-device AI are Apple, Qualcomm, and Google, so the U.S. is likely to continue leading the field. In the long term, if tariffs persist, there may be increased regional production of chips to strengthen vertical integration and resilience; however, this could lead to less global collaboration and sharing of technology.

Key Players Landscape and Outlook

The global on-device AI market is fragmented, with a handful of technology companies vying to provide the public with faster, more private, and efficient AI capabilities on-device. A combination of well-established semiconductor manufacturers, cloud companies, consumer electronics companies, and AI-based companies frames the market. Notable players in the on-device AI market include Apple Inc., Google LLC, Microsoft Corporation, and others who have been working on integrating generative AI capabilities into mobile phones, laptops, smartwatches, autonomous vehicles, and IoT devices by effectively optimizing the hardware and software of on-device AI for low-power, high-performance processing.

One notable event was in May 2025, when NVIDIA Corporation launched its AI-First DGX personal computing solutions in collaboration with leading hardware manufacturers. These solutions aim to deliver data center-class AI performance on local devices, enabling enterprises and developers to build and run their advanced on-device AI workloads without relying on the cloud. Additionally, they launched NV Link Fusion, a new silicon-based architecture that enables semi-custom AI infrastructures on silicon, completing NVIDIA's strategy for decentralizing AI computation and providing scalability for edge AI across various industries.

The future for the on-device AI space looks promising. With the increasing demand for real-time AI, user privacy, and enhanced efficiency of edge computing, companies will move towards greater innovation. For customers and stakeholders, it will be essential to partner with vendors that offer scalable, secure and power-efficient on-device AI solutions. On-device AI adoption will continue to advance with improvements in AI chips, model compression, and integration at the operating system level, benefiting both developed and developing markets.

Table of Contents

1. Project Scope and Definitions

2. Research Methodology

3. Impact of U.S. Tariffs

4. Executive Summary

5. Voice of Customers

• 5.1. Respondent Demographics
• 5.2. Brand Awareness
• 5.3. Factors Considered in Purchase Decisions
• 5.4. Challenges Faced Post Purchase

6. Global On-Device AI Market Outlook, 2018-2032F

• 6.1. Market Size Analysis & Forecast
  • 6.1.1. By Value
• 6.2. Market Share Analysis & Forecast
  • 6.2.1. By Component
    • 6.2.1.1. Hardware
    • 6.2.1.2. Software
  • 6.2.2. By Deployment
    • 6.2.2.1. Cloud
    • 6.2.2.2. On-premises
  • 6.2.3. By Technology
    • 6.2.3.1. On-Device AI
    • 6.2.3.2. Natural Language Processing
    • 6.2.3.3. Computer Vision
    • 6.2.3.4. Speech Recognition
  • 6.2.4. By Region
    • 6.2.4.1. North America
    • 6.2.4.2. Europe
    • 6.2.4.3. Asia-Pacific
    • 6.2.4.4. South America
    • 6.2.4.5. Middle East and Africa
  • 6.2.5. By Company Market Share Analysis (Top 5 Companies and Others - By Value, 2024)
• 6.3. Market Map Analysis, 2024
  • 6.3.1. By Component
  • 6.3.2. By Deployment
  • 6.3.3. By Technology
  • 6.3.4. By Region

7. North America On-Device AI Market Outlook, 2018-2032F

• 7.1. Market Size Analysis & Forecast
  • 7.1.1. By Value
• 7.2. Market Share Analysis & Forecast
  • 7.2.1. By Component
    • 7.2.1.1. Hardware
    • 7.2.1.2. Software
  • 7.2.2. By Deployment
    • 7.2.2.1. Cloud
    • 7.2.2.2. On-premises
  • 7.2.3. By Technology
    • 7.2.3.1. On-Device AI
    • 7.2.3.2. Natural Language Processing
    • 7.2.3.3. Computer Vision
    • 7.2.3.4. Speech Recognition
  • 7.2.4. By Country
    • 7.2.4.1. United States
    • 7.2.4.2. Canada
    • 7.2.4.3. Mexico
• 7.3. Country Market Assessment
  • 7.3.1. United States On-Device AI Market Outlook, 2018-2032F
    • 7.3.1.1. Market Size Analysis & Forecast
      • 7.3.1.1.1. By Value
    • 7.3.1.2. Market Share Analysis & Forecast
      • 7.3.1.2.1. By Component
        • 7.3.1.2.1.1. Hardware
        • 7.3.1.2.1.2. Software
      • 7.3.1.2.2. By Deployment
        • 7.3.1.2.2.1. Cloud
        • 7.3.1.2.2.2. On-premises
      • 7.3.1.2.3. By Technology
        • 7.3.1.2.3.1. On-Device AI
        • 7.3.1.2.3.2. Natural Language Processing
        • 7.3.1.2.3.3. Computer Vision
        • 7.3.1.2.3.4. Speech Recognition

All segments will be provided for all regions and countries covered

8. Europe On-Device AI Market Outlook, 2018-2032F

• 8.1. Germany
• 8.2. France
• 8.3. Italy
• 8.4. United Kingdom
• 8.5. Russia
• 8.6. Netherlands
• 8.7. Spain
• 8.8. Turkey
• 8.9. Poland

9. Asia-Pacific On-Device AI Market Outlook, 2018-2032F

• 9.1. India
• 9.2. China
• 9.3. Japan
• 9.4. Australia
• 9.5. Vietnam
• 9.6. South Korea
• 9.7. Indonesia
• 9.8. Philippines

10. South America On-Device AI Market Outlook, 2018-2032F

• 10.1. Brazil
• 10.2. Argentina

11. Middle East and Africa On-Device AI Market Outlook, 2018-2032F

• 11.1. Saudi Arabia
• 11.2. UAE
• 11.3. South Africa

12. Porter's Five Forces Analysis

13. PESTLE Analysis

14. Market Dynamics

• 14.1. Market Drivers
• 14.2. Market Challenges

15. Market Trends and Developments

16. Case Studies

17. Competitive Landscape

• 17.1. Competition Matrix of Top 5 Market Leaders
• 17.2. SWOT Analysis for Top 5 Players
• 17.3. Key Players Landscape for Top 10 Market Players
  • 17.3.1. Advanced Micro Devices, Inc.
    • 17.3.1.1. Company Details
    • 17.3.1.2. Key Management Personnel
    • 17.3.1.3. Key Products/Services Offered
    • 17.3.1.4. Key Financials (As Reported)
    • 17.3.1.5. Key Market Focus and Geographical Presence
    • 17.3.1.6. Recent Developments/Collaborations/Partnerships/Mergers and Acquisition
  • 17.3.2. Amazon.com, Inc.
  • 17.3.3. Apple Inc.
  • 17.3.4. Google LLC
  • 17.3.5. Intel Corporation
  • 17.3.6. Microsoft Corporation
  • 17.3.7. NVIDIA Corporation
  • 17.3.8. Qualcomm Technologies, Inc.
  • 17.3.9. Untether AI
  • 17.3.10. Meta Platforms, Inc.

Companies mentioned above DO NOT hold any order as per market share and can be changed as per information available during research work.

18. Strategic Recommendations

19. About Us and Disclaimer