Edge AI Accelerator Market Size, Share & Trends Analysis Report By Processor (CPU, GPU, ASIC, FPGA), By Device (Smartphones, IoT Devices), By End-use (Healthcare, Automotive), By Region, And Segment Forecasts, 2025 - 2030

Edge AI Accelerator Market Size & Trends

The global edge AI accelerator market size was estimated at USD 7.71 billion in 2024 and is projected to grow at a CAGR of 30.8% from 2025 to 2030. The edge AI accelerator industry is experiencing significant growth due to the increasing demand for real-time data processing. As more devices connect to the internet, the need for instant analysis becomes critical across various industries. Traditional cloud computing methods face challenges like latency, bandwidth limitations, and data privacy concerns, pushing organizations to seek localized solutions.

For instance, NVIDIA Jetson, developed by NVIDIA Corporation, is an AI edge computing platform for real-time processing in robotics, automation, and industry. It is widely used in autonomous vehicles, robotics, and industrial automation. Jetson's edge AI accelerators enable real-time data processing by running AI models directly on devices, reducing the need for cloud computing. This capability enhances operational efficiency and responsiveness in applications ranging from autonomous vehicles to smart factories. Furthermore, advancements in AI algorithms and hardware technologies continue to improve the performance and affordability of these accelerators. As a result, more companies are using edge AI solutions to stay ahead of the competition.

AI accelerators are being integrated into compact devices, enabling real-time processing at the edge. This reduces dependence on external GPUs and cloud computing, allowing for faster and more efficient operations. By processing data locally, devices experience lower latency, improved security, and reduced bandwidth usage. These benefits make edge AI more practical for applications across various industries, including IoT, robotics, and industrial automation. As adoption increases, businesses are leveraging edge AI to enhance decision-making, optimize resource utilization, and enable autonomous systems.

The growing demand for intelligent, low-power computing solutions is driving further advancements in AI hardware and software. For instance, in September 2024, Raspberry Pi Foundation, a U.K. nonprofit promoting computing education, and Sony launched a $70 AI-powered camera module featuring the Sony IMX500 image sensor and an onboard AI accelerator for real-time image processing. It enables neural network models to run directly on the device, simplifying edge AI applications such as object detection and pose estimation without requiring external GPUs.

The growth of the Internet of Things (IoT) significantly contributes to the expansion of the edge AI accelerator market. The proliferation of IoT devices generates massive amounts of data that require immediate processing for effective insights and actions. Edge AI accelerators enable these devices to perform complex computations locally, reducing reliance on cloud infrastructure. This shift helps manage data more securely and efficiently, addressing privacy concerns associated with transmitting sensitive information over the internet. As industries embrace digital transformation, integrating AI capabilities into IoT devices becomes essential for achieving smart operations. The combination of IoT and edge AI accelerators opens up new opportunities for automation, predictive maintenance, and enhanced user experiences. Consequently, businesses are investing in edge AI technologies to capitalize on the potential of their IoT ecosystems fully.

The increasing focus on energy efficiency is driving the adoption of edge AI accelerators across various industries. These accelerators consume less power than traditional cloud-based processing, making them ideal for resource-constrained environments. Google’s Coral Edge TPU supports real-time AI processing with minimal energy use, benefiting applications such as IoT, smart cameras, and industrial automation. Reducing reliance on cloud computing helps lower energy consumption and enhances data privacy by processing information locally. Industries face mounting pressure to adopt sustainable technologies that align with environmental regulations and corporate sustainability goals. As organizations focus on energy efficiency, demand for edge AI accelerators is expected to grow, driving AI adoption.

Global Edge AI Accelerator Market Report Segmentation

This report forecasts revenue growth at global, regional, and country levels and provides an analysis of the latest industry trends and opportunities in each of the sub-segments from 2018 to 2030. For this study, Grand View Research has segmented the global edge AI accelerator market report based on processor, device, end use, and region:

• Processor Outlook (Revenue, USD Million, 2018 - 2030)
• Central Processing Unit (CPU)
• Graphics Processing Unit (GPU)
• Application-Specific Integrated Circuits (ASICs)
• Field-Programmable Gate Array (FPGA)
• Device Outlook (Revenue, USD Million, 2018 - 2030)
• Smartphones
• IoT Devices
• Robots
• Cameras
• End-use Outlook (Revenue, USD Million, 2018 - 2030)
• Healthcare
• Automotive
• Retail
• Manufacturing
• Security and Surveillance
• Others
• Regional Outlook (Revenue, USD Million, 2018 - 2030)
• North America
• U.S.
• Canada
• Mexico
• Europe
• UK
• Germany
• France
• Asia Pacific
• China
• Japan
• India
• Australia
• South Korea
• Latin America
• Brazil
• Middle East & Africa (MEA)
• KSA
• UAE
• South Africa