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ID : CBI_3286 | Updated on : | Author : Amit Sati | Category : IT And Telecommunications
Edge AI Hardware Market size is estimated to reach over USD 190.83 Billion by 2032 from a value of USD 26.12 Billion in 2024 and is projected to grow by USD 33.03 Billion in 2025, growing at a CAGR of 21.52% from 2025 to 2032.
Edge AI hardware refers to specialized computing devices that deploy AI algorithms and AI models directly on local edge devices, eliminating the need for cloud servers. The hardware components include CPU, GPU, and others, which help to balance performance, power efficiency, and cost to handle various tasks such as speech processing, image recognition, and others. Additionally, the key advantages, including enhanced speed, privacy, and cost-effectiveness, are driving the Edge AI Hardware Market growth. Further, the growing adoption of smartphones and industrial automation is driving the Edge AI Hardware Market demand. Furthermore, the increasing importance of data security and reduced dependency on cloud-based infrastructure is boosting the edge AI hardware industry.
AI is significantly impacting the market by enabling edge devices to process data locally, reducing reliance on cloud infrastructure, minimizing latency, and improving response times. Additionally, AI allows for faster and more efficient decision-making in applications such as autonomous vehicles, industrial automation, and smart surveillance. Moreover, the need for real-time data analysis is driving the adoption across industries. Further, the growing emphasis on developing energy-efficient hardware to support edge AI. Furthermore, AI helps to reduce latency and bandwidth requirements, eliminating the need for cloud-based processing.
The autonomous vehicles rely on AI for processing data from cameras, LiDAR, and radar in real-time, which in turn is boosting the Edge AI Hardware Market demand. The hardware enables quick decisions, eliminating the need for cloud-based processing, ensuring safety and reliability on the road is driving the Edge AI Hardware Market growth. Further, edge AI minimizes latency by processing data on the vehicle itself, improving safety and performance which results in market progress.
Therefore, the rising adoption of self-driving vehicles is driving the adoption of edge AI, in turn, proliferating the growth of the market.
Edge devices range widely in processing power, memory, and operating systems, demanding compatibility and optimization across platforms is hindering the Edge AI Hardware Market expansion. Additionally, the lack of standardized protocols and data formats makes seamless communication between diverse edge devices and existing infrastructure challenging, which in turn is restraining the market's progress. Further, Edge AI requires dedicated hardware such as AI chips or accelerators, which increases initial capital expenditure is creating a barrier for market adoption.
Therefore, the high costs and integration complexity are hindering the Edge AI Hardware Market expansion.
The expansion of 5G networks is expected to boost edge AI capabilities by offering fast, reliable connectivity, which is required for large-scale, real-time AI processing is propelling the Edge AI Hardware Market opportunities. Further, the rising adoption of edge AI across industries is creating smarter, more efficient, and secure solutions for a wide range of applications is paving the way for market progress.
Hence, the rising adoption of 5G networks is anticipated to increasethe utilization of edge devices, in turn promoting prospects for Edge AI Hardware Market opportunities during the forecast period.
Based on the components, the market is segmented into CPU, GPU, FPGA, ASIC, microcontrollers, and others.
Trends in the Components:
The CPU component accounted for the largest revenue share in the year 2024.
The GPU component is anticipated to register the fastest CAGR during the forecast period.
Based on the application, the market is segmented into smartphone, smart home appliances, industrial robots, autonomous vehicles, wearable health monitoring devices, industrial sensors, and others.
Trends in the Application:
Smartphone accounted for the largest revenue share of 36.30% in the year 2024.
Smart Home Appliances is anticipated to register the fastest CAGR during the forecast period.
Based on the end user, the market is segmented into consumer electronics, healthcare, automotive, logistics & transportation, aerospace & defence, and others.
Trends in the End User:
Consumer Electronics accounted for the largest revenue share in the year 2024.
Automotive is anticipated to register the fastest CAGR during the forecast period.
The regions covered are North America, Europe, Asia Pacific, Middle East and Africa, and Latin America.
Asia Pacific region was valued at USD 7.24 Billion in 2024. Moreover, it is projected to grow by USD 9.19 Billion in 2025 and reach over USD 54.54 Billion by 2032. Out of this, China accounted for the maximum revenue share of 27.81%. The market progress is mainly driven by the increased adoption of IoT devices. Furthermore, factors including the rising investments in AI-driven technologies are projected to drive the market growth in the Asia Pacific region during the forecast period.
North America is estimated to reach over USD 62.13 Billion by 2032 from a value of USD 8.44 Billion in 2024 and is projected to grow by USD 10.68 Billion in 2025. The North American region's ongoing advancement in high-performance hardware capabilities offers lucrative growth prospects for the market. Additionally, the growing smart infrastructure and proliferation of consumer electronic devices are driving the market progress.
The regional analysis depicts that the rising investment in Industry 4.0 initiatives is driving the market in Europe. Additionally, the key factor driving the market is the growing focus on real-time data processing in healthcare, as well as the rising adoption of IoT devices is propelling the market adoption in the Middle East and African region. Further, the miniaturization of hardware and the integration of edge AI into various sectors such as manufacturing, healthcare, and retail are paving the way for the progress of the market in the Latin American region.
The global Edge AI Hardware Market is highly competitive with major players providing edge AI hardware to the national and international markets. Key players are adopting several strategies in research and development (R&D), product innovation, and end user launches to hold a strong position in the edge AI hardware industry. Key players in the Edge AI Hardware Market include-
Partnerships & Collaborations
Report Attributes | Report Details |
Study Timeline | 2019-2032 |
Market Size in 2032 | USD 190.83 Billion |
CAGR (2025-2032) | 21.52% |
By Components |
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By Application |
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By End User |
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By Region |
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Key Players |
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North America | U.S. Canada Mexico |
Europe | U.K. Germany France Spain Italy Russia Benelux Rest of Europe |
APAC | China South Korea Japan India Australia ASEAN Rest of Asia-Pacific |
Middle East and Africa | GCC Turkey South Africa Rest of MEA |
LATAM | Brazil Argentina Chile Rest of LATAM |
Report Coverage |
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The edge AI hardware market size is estimated to reach over USD 190.83 Billion by 2032 from a value of USD 26.12 Billion in 2024 and is projected to grow by USD 33.03 Billion in 2025, growing at a CAGR of 21.52% from 2025 to 2032.
The edge AI hardware report includes specific segmentation details for components, application, end user, and regions.
In the edge AI hardware market, the smart home appliances is the fastest-growing segment during the forecast period due to the proliferation of IoT devices in homes.
The key participants in the edge AI hardware market are NVIDIA Corporation (USA), IBM (USA), Microsoft (USA), MediaTek Inc. (Taiwan), Intel Corporation (USA), Meta (USA), Horizon Robotics (China), Graphcore (UK), Samsung (South Korea), Applied Brain Research, Inc. (Canada), and others.
The edge AI hardware market is being shaped by several key trends including a surge in wearable medical devices with low latency, high security, as well as the need for real-time tracking and route optimization, and others.