PUBLISHER: QYResearch | PRODUCT CODE: 1861304
PUBLISHER: QYResearch | PRODUCT CODE: 1861304
The global market for Artificial Intelligence for Edge Devices was estimated to be worth US$ 5008 million in 2024 and is forecast to a readjusted size of US$ 20907 million by 2031 with a CAGR of 22.4% during the forecast period 2025-2031.
Artificial intelligence (AI) processing today is mostly done in a cloud-based data center. The majority of AI processing is dominated by training of deep learning models, which requires heavy compute capacity. Artificial intelligence for edge devices means that AI software algorithms are processed locally on a hardware device. The algorithms are using data (sensor data or signals) that are created on the device. A device using Edge AI software does not need to be connected in order to work properly, it can process data and take decisions independently without a connection. In this report, artificial intelligence for edge devices contains software tools, platforms, artificial intelligence chip.
Many emerging applications-from autonomous driving to robotics and industrial automation-require instantaneous insights that cloud-based systems cannot always provide due to latency issues. AI at the edge eliminates delays by processing data locally, enabling real-time decision-making essential for safety-critical and time-sensitive operations.
The explosive growth of IoT ecosystems is generating massive data volumes. Transmitting all of this data to cloud servers is expensive and inefficient. Edge AI allows devices to analyze data locally, reducing bandwidth requirements and ensuring faster, scalable, and cost-effective IoT deployments across smart homes, factories, healthcare, and agriculture.
The rollout of 5G technology is enabling edge devices to achieve higher bandwidth and ultra-low latency communication. This synergy allows AI-enabled edge devices to support advanced applications like augmented/virtual reality (AR/VR), smart cities, connected healthcare, and intelligent transportation systems, further accelerating adoption.
With stricter regulations such as GDPR, HIPAA, and CCPA, organizations are under pressure to ensure data security. Edge AI enhances privacy compliance by keeping sensitive information (e.g., medical data, financial transactions, personal identifiers) on local devices instead of transmitting it to centralized servers. This makes it particularly valuable in healthcare, finance, and government applications.
The development of AI-specific chips (such as GPUs, TPUs, and NPUs) and optimized microcontrollers is making it feasible to run complex AI models directly on edge devices. Innovations in low-power AI accelerators and neuromorphic computing are enhancing performance while reducing energy consumption, broadening the range of devices that can leverage AI capabilities.
This report aims to provide a comprehensive presentation of the global market for Artificial Intelligence for Edge Devices, focusing on the total sales revenue, key companies market share and ranking, together with an analysis of Artificial Intelligence for Edge Devices by region & country, by Type, and by Application.
The Artificial Intelligence for Edge Devices market size, estimations, and forecasts are provided in terms of sales revenue ($ millions), considering 2024 as the base year, with history and forecast data for the period from 2020 to 2031. With both quantitative and qualitative analysis, to help readers develop business/growth strategies, assess the market competitive situation, analyze their position in the current marketplace, and make informed business decisions regarding Artificial Intelligence for Edge Devices.
Market Segmentation
By Company
Segment by Type
Segment by Application
By Region
Chapter Outline
Chapter 1: Introduces the report scope of the report, global total market size. This chapter also provides the market dynamics, latest developments of the market, the driving factors and restrictive factors of the market, the challenges and risks faced by manufacturers in the industry, and the analysis of relevant policies in the industry.
Chapter 2: Detailed analysis of Artificial Intelligence for Edge Devices company competitive landscape, revenue market share, latest development plan, merger, and acquisition information, etc.
Chapter 3: Provides the analysis of various market segments by Type, covering the market size and development potential of each market segment, to help readers find the blue ocean market in different market segments.
Chapter 4: Provides the analysis of various market segments by Application, covering the market size and development potential of each market segment, to help readers find the blue ocean market in different downstream markets.
Chapter 5: Revenue of Artificial Intelligence for Edge Devices in regional level. It provides a quantitative analysis of the market size and development potential of each region and introduces the market development, future development prospects, market space, and market size of each country in the world.
Chapter 6: Revenue of Artificial Intelligence for Edge Devices in country level. It provides sigmate data by Type, and by Application for each country/region.
Chapter 7: Provides profiles of key players, introducing the basic situation of the main companies in the market in detail, including product revenue, gross margin, product introduction, recent development, etc.
Chapter 8: Analysis of industrial chain, including the upstream and downstream of the industry.
Chapter 9: Conclusion.