Edge AI Market Size - By Component, By Application, By End Use, Growth Forecast, 2025 – 2034

Edge AI Market Size

The edge AI market was valued at USD 12.5 billion in 2024 and is estimated to register a CAGR of 24.8% between 2025 and 2034, driven by increasing adoption of edge devices across various sectors. Businesses in healthcare, manufacturing, retail, and automotive industries have integrated edge computing solutions to enhance real-time data processing and improve efficiency. Edge devices, including IoT sensors, smart cameras, and industrial robots, allow organizations to process data closer to the source, reducing dependence on cloud infrastructure.
 

For instance, in March 2025 Arm introduced the Armv9 edge AI platform, designed to improve AI performance, efficiency, and security for IoT and edge devices. The new Cortex-A320 processor delivers faster AI processing and works alongside the Ethos-U85 AI accelerator.
 

By integrating AI-powered technologies, businesses are getting equipped with enhanced automation, improved business workflows, and better decision-making capabilities. The combination of AI with edge computing results in smarter systems capable of executing advanced analytical capabilities. This makes it possible for businesses to automate processes and increase the speed at which AI innovations are developed, consequently increasing demand for edge AI solutions. According to Statista, the market size in the Artificial Intelligence market is projected to reach USD 826.7 billion by 2030
 

The rising adoption of cloud computing for Edge AI has been instrumental in driving the market forward. While edge AI focuses on processing data locally, hybrid cloud-edge models have become increasingly popular, offering the benefits of both centralized and decentralized computing. Cloud platforms have provided scalability, centralized AI model training, and advanced data management, while edge devices have ensured real-time inferencing and localized decision-making.
 

Edge AI Market Trends

• TinyML, or machine learning on ultra-low-power microcontrollers, is gaining traction as industries look for efficient AI solutions at the edge. This technology enables small, battery-powered devices to run AI models locally without relying on cloud connectivity. Applications range from smart sensors in industrial automation to wearables in healthcare and predictive maintenance in manufacturing.
   
• For instance, Ceva's latest Ceva-NeuPro-Nano NPUs, is designed to integrate TinyML models into SoCs. These NPUs enable ultra-efficient AI processing in low-power, resource-constrained IoT devices.
   
• The convergence of AI and IoT is accelerating, leading to a surge in AI-enabled IoT devices across industries. Smart home systems, autonomous drones, and industrial IoT (IIoT) solutions are integrating AI to enhance decision-making, automate processes, and improve efficiency.
   
• AI-powered IoT devices can analyze data locally, reducing the need for constant cloud connectivity while enabling faster responses. This growth is also fueling advancements in edge AI frameworks, optimized AI chipsets, and secure AI-driven IoT networks, making intelligent automation more widespread.
   
• Edge AI hardware is evolving rapidly, with new processors, AI accelerators, and neural processing units (NPUs) designed to handle machine learning workloads efficiently. Companies are developing specialized hardware that delivers high-performance AI inference at the edge while maintaining power efficiency. These advancements enable real-time processing for applications such as robotics, autonomous vehicles, and smart surveillance.
   
• For instance, in September 2024, NXP unveiled the i.MX RT700 crossover microcontroller (MCU), designed to enhance AI capabilities at the edge. This MCU integrates two Arm Cortex-M33 cores, Cadence Tensilica HiFi DSPs, and the eIQ Neutron neural processing unit (NPU), delivering up to 172 times acceleration for AI tasks while reducing energy consumption per inference by up to 119 times.
   

Edge AI Market Analysis

Based on end use, the edge AI market is segmented into healthcare, manufacturing, BFSI, government, retail & e-commerce, telecommunication, transport & logistics, and others. The healthcare segment dominated the market in 2024, accounting for 43% of total revenue.
 

• Edge AI is widely adopted in healthcare for real-time patient monitoring, medical imaging analysis, and AI-assisted diagnostics, enabling faster decision-making and improved patient outcomes. Hospitals and clinics use AI-powered wearable devices, automated workflows, and predictive analytics to enhance efficiency and reduce costs.
   
• In March 2025, Ambiq launched the Apollo330 Plus SoC series, delivering ultra-low-power AI processing for edge devices in healthcare featuring Arm Cortex-M55 processor with AI acceleration.
   
• Manufacturing follows closely, utilizing Edge AI for predictive maintenance, process automation, and quality control, helping reduce downtime and improve productivity. In BFSI, AI supports fraud detection, automated transactions, and risk assessment, enhancing security and customer experience.
   
• Government agencies integrate Edge AI for smart surveillance, urban planning, and traffic management, while retail & e-commerce leverage AI for personalized recommendations, inventory management, and customer engagement. The telecommunication sector benefits from network optimization and faster data processing, and transport & logistics use AI for route planning, fleet monitoring, and autonomous vehicle applications.
   

Based on components, the edge AI market is segmented into software, hardware, and services, with software leading the market in 2024, accounting for 51.7% of total revenue.
 

• The increasing use of AI model optimization, real-time data processing, and edge analytics platforms has driven demand for software solutions. As industries integrate AI into their operations, software plays a key role in model deployment, data security, and system updates.
   
• Software frameworks for machine learning, AI inference, and edge deployment help devices process information locally, reducing reliance on cloud computing. The rise of low-code AI platforms has also made implementation easier for businesses, allowing them to use AI without needing deep technical expertise. With industries focusing on automation, efficiency, and real-time decision-making, software will remain the most critical component in the growth of Edge AI.
   
• The hardware segment followed, with growth fuelled by specialized AI chips, low-power processors, and embedded AI accelerators designed to enhance computing efficiency at the edge.
   
• The services segment, including AI deployment, consulting, and maintenance, saw steady growth as businesses sought expertise in implementing and managing edge AI solutions. The rising need for secure, scalable, and energy-efficient AI processing continues to shape investments across all three segments.
   

Based on application, the edge AI market is divided into video surveillance, remote monitoring, predictive maintenance, and others. In 2024, the video surveillance segment dominated the market.
 

• Edge AI in video surveillance is widely used in public safety, retail, smart cities, and industrial security. Governments and enterprises deploy AI-enabled cameras to monitor crowded areas, detect suspicious activities, and enhance law enforcement efforts.
   
• In retail, AI-driven surveillance helps with theft prevention, customer behaviour analysis, and store optimization. Industrial sites use edge-based video analytics to improve workplace safety by identifying hazards and monitoring compliance in real time.
   
• The demand for low-latency, high-resolution video processing has driven the adoption of AI-enabled security cameras and embedded vision systems. These systems not only enhance security but also reduce bandwidth costs by processing video locally instead of sending large amounts of data to centralize cloud servers.
   
• The integration of computer vision and deep learning models further strengthens their ability to recognize faces, detect anomalies, and automate decision-making in security and operational environments.
   

North America held the largest share of the edge AI market in 2024, accounting for over 30% of the global market. The U.S. dominated in the region and is projected to reach around USD 20 billion by 2034.
 

• In U.S. there is a strong adoption of AI in industries such as healthcare, and smart cities has driven market expansion, with companies integrating Edge AI for real-time decision-making and automation.
   
• For instance, in March 2025, Latent AI and Carahsoft Technology Corp. announced a partnership to enhance edge AI adoption within the U.S. public sector. Carahsoft will distribute Latent AI's Efficient Inference Platform (LEIP) software and ruggedized mobile solutions to government agencies through its reseller network and contracts.
   
• The presence of leading tech firms and semiconductor companies, including those specializing in AI chips and software solutions, has strengthened the ecosystem for Edge AI development.
   
• Government initiatives supporting AI innovation, alongside increasing investment in 5G, IoT, and cloud-edge infrastructure, have accelerated Edge AI deployments across sectors.
   

The edge AI market in Germany is expected to experience significant and promising growth from 2025 to 2034.
 

• The Edge AI market in Germany is evolving due to stricter data privacy regulations and a growing reliance on AI-powered edge computing solutions. Industries such as automotive, healthcare, and manufacturing are integrating Edge AI to enhance automation, security, and operational efficiency.
   
• As industrial AI adoption rises, there is increasing demand for low-latency computing, real-time analytics, and energy-efficient AI models to support smart factories, predictive maintenance, and autonomous systems.
   
• In the rest of Europe, EU regulations on AI ethics and data sovereignty are shaping market growth.
   
• For instance, The European Union's Artificial Intelligence Act (AI Act), the world's first comprehensive AI regulation, came into force on August 1, 2024. This legislation establishes a unified regulatory framework for AI across EU member states, aiming to mitigate AI-related risks and protect citizens' fundamental rights.
   

The edge AI market in China is expected to experience significant and promising growth from 2025 to 2034.
 

• The Edge AI market in China is expanding rapidly due to government-backed AI initiatives and strong investments in 5G and smart infrastructure. Sectors like manufacturing, retail, and autonomous driving are driving demand for real-time AI processing and intelligent automation at the edge.
   
• With rising industrial automation and smart city projects, there is increasing demand for low-latency AI models, advanced surveillance systems, and AI-powered robotics to enhance operational efficiency and security.
   
• In the rest of Asia-Pacific, countries are focusing on AI-powered IoT, edge-based healthcare solutions, and smart logistics, leveraging AI-driven automation and real-time analytics to improve productivity and innovation.
   
• For instance, in March 2025 the Korea Advanced Institute of Science and Technology (KAIST) Institute for NanoCentury (KINC) and Blaize Holdings announced a strategic partnership to advance edge AI technologies across multiple domains, including biomedical diagnostics, neuromorphic computing, and sustainable energy solutions.
   

The edge AI market in Mexico is expected to experience significant and promising growth from 2025 to 2034.
 

• The Edge AI market in Mexico is growing as industries adopt AI-driven automation, predictive analytics, and edge computing solutions to improve operational efficiency. Manufacturing, logistics, and financial services are key sectors driving adoption.
   
• As IoT adoption and digital transformation accelerate, there is increasing demand for real-time AI processing, AI-powered cybersecurity, and intelligent supply chain management to optimize business operations.
   

Edge AI Market Share

• Top 7 companies of edge AI industry are Huawei, Intel, Google LLC, Amazon, Dell, IBM, Microsoft. They collectively hold a market share of around 35% in the market.
   
• Google partners with hardware providers and uses its Edge TPU and Google Cloud to offer scalable AI solutions, particularly in industries like smart homes and autonomous vehicles
   
• For instance, in January 2025, Google and Synaptics partnered to advance edge AI. This collaboration integrates Google’s ML core with Synaptics’ Astra AI hardware to simplify IoT device development. The platform accelerates AI processing across various modalities, supporting applications in wearables, appliances, and more. It also ensures compatibility with modern compilers via MLIR-compliance.
   
• Huawei provides end-to-end AI solutions, including chips, cloud services, and AI-powered edge platforms tailored for sectors such as smart cities and transportation.
   
• Intel offers hardware solutions, aiming to drive AI inference at the edge, particularly in AI and IoT solutions. Amazon utilizes AWS IoT and Deep Learning AMIs to deploy AI at the edge for industries such as retail and logistics, enabling efficient model deployment.
   
• Dell focuses on AI-driven infrastructure with edge-to-cloud solutions, targeting real-time analytics in sectors like healthcare and manufacturing. IBM leverages its Watson AI platform for edge AI applications, supporting real-time data processing in industries like healthcare and automotive.
   
• Microsoft provides AI solutions through Azure AI, enabling businesses to deploy and manage models across edge devices in areas such as industrial automation and IoT.
   

Edge AI Market Companies

Major players operating in the edge AI industry include:

• Amazon Web Service (AWS)
• Intel
• Dell
• Google
• Gorilla technology
• Huawei Technologies
• IBM
• Microsoft
   

Edge AI market is experiencing intense competition, driven by the growing demand for efficient, low-latency computing. Leading companies are investing in AI-optimized processors, power-efficient NPUs, and advanced software frameworks to differentiate themselves.
 

Established semiconductor firms and AI startups are leveraging machine learning acceleration, real-time data processing, and cloud-edge integration to enhance performance. Innovations such as transformer-based AI models, scalable heterogeneous architectures, and AI workload optimization libraries are becoming key factors in market success.
 

Regulatory standards and cybersecurity requirements are shaping development strategies. Companies must balance performance, energy efficiency, and security to maintain trust while delivering scalable, high-performance edge AI solutions.
 

Edge AI Industry News

• In March 2025, Lanner and Arrcus announced a collaboration to deliver AI-optimized, ultra-low latency telco edge solutions. Their integration enhances AI-driven RAN and 5G connectivity for enterprises. Lanner’s MGX Edge AI platform combines GPUs, DPUs, and multi-core CPUs to accelerate AI inferencing at the edge. Arrcus’ ACE networking platform optimizes networking functions, reducing CPU overhead and improving AI workload efficiency.
   
• In March 2025, Arm launched its Armv9 Edge AI Platform, featuring the Cortex-A320 processor and Ethos-U85 NPU, to boost AI performance for IoT applications. The Cortex-A320 delivers 10× better ML performance than its predecessor, while the Ethos-U85 offers up to 4 TOPs at 1 GHz with improved efficiency. Arm Kleidi libraries optimize AI workloads without extra developer effort. Industry leaders, including AWS and Siemens, have shown interest in adopting the platform for edge AI applications.
   
• In March 2025, Weebit Nano and EMASS announced a collaboration to demonstrate ultra-low-power edge AI applications using Weebit's Resistive RAM (ReRAM) technology.
   
• In March 2025. Canonical partnered with Renesas to bring a customized Ubuntu Core OS to RZ processors, enabling efficient IoT and edge AI applications. The tailored OS enhances machine learning and computer vision performance while integrating security features tied to RZ hardware.
   

The edge AI market research report includes in-depth coverage of the industry with estimates & forecast in terms of revenue ($Bn) from 2021 to 2034, for the following segments:

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Market, By Component

• Hardware
  • Graphics Processing Unit (GPU)
  • Application Specific Integrated Circuit (ASIC)
  • Central Processing Unit (CPU)
  • Field-Programmable Gate Array (FPGA)
• Software
• Service
  • Training & consulting
  • Support & maintenance
  • System integration and testing

Market, By Application

• Video surveillance
• Remote monitoring
• Predictive maintenance
• Others

Market, By End Use

• Manufacturing
• Healthcare
• BSFI
• Government
• Retail & e-commerce
• Telecommunication
• Transport & logistics
• Others

The above information is provided for the following regions and countries:

• North America
  • U.S.
  • Canada
• Europe
  • UK
  • Germany
  • France
  • Italy
  • Spain
  • Russia
  • Nordics
• Asia Pacific
  • China
  • India
  • Japan
  • Australia
  • South Korea
  • Southeast Asia 
• Latin America
  • Brazil
  • Mexico
  • Argentina
• MEA
  • UAE
  • South Africa
  • Saudi Arabia