Edge AI Hardware Market – Global Industry Size, Share, Trends, Opportunity, and Forecast, Segmented By Device (Smartphones, Robots, Surveillance Cameras, Wearables, Smart Speakers, Automotive, Smart Mirrors, Others), By Power Consumption (Less Than 1W, 1-3W, 3-5W, 3-5W and More Than 10W), By Function (Training, Inference), By Processor (CPU, GPU, ASIC, and Others), By Vertical (Healthcare & Life Science, Retail & Consumer Electronics, Automotive & Transportation, Aerospace & Defense, Government, Construction and Others), By Region, Competition 2021-2031F

Forecast Period	2027-2031
Market Size (2025)	USD 26.11 Billion
CAGR (2026-2031)	17.54%
Fastest Growing Segment	Smartphones
Largest Market	North America
Market Size (2031)	USD 68.85 Billion

Market Overview

The Global Edge AI Hardware Market will grow from USD 26.11 Billion in 2025 to USD 68.85 Billion by 2031 at a 17.54% CAGR. Edge AI hardware comprises specialized physical components, such as neural processing units (NPUs), graphics processing units (GPUs), and application-specific integrated circuits (ASICs), designed to execute machine learning algorithms directly on local devices without relying on centralized cloud connectivity. The market’s growth is fundamentally supported by the critical need for ultra-low latency in real-time decision-making and the requirement to optimize bandwidth utilization by minimizing data transmission. Furthermore, stringent data privacy mandates and the rapid proliferation of Internet of Things (IoT) devices serve as primary drivers, necessitating robust on-device processing capabilities distinct from broader technological trends.

However, a significant challenge impeding broader market expansion is the constraint of power efficiency, as integrating high-performance computing into resource-limited, battery-operated devices remains technically demanding. This hardware demand is reflected in the broader chip sector's performance, where, according to the Semiconductor Industry Association, in 2024, global semiconductor sales reached $627.6 billion, a surge driven substantially by the explosive requirement for artificial intelligence capabilities across automotive and industrial applications. This massive investment in foundational silicon highlights the industrial scale of the shift toward intelligent, decentralized hardware architectures.

Key Market Drivers

The rapid proliferation of IoT and smart connected devices acts as a primary catalyst for the Edge AI Hardware market, shifting processing loads from centralized clouds to local environments. As industries deploy billions of sensors and endpoints, the latency and bandwidth costs associated with transmitting raw data become unsustainable, necessitating on-chip processing. This decentralized approach allows devices to filter and analyze information instantaneously, a requirement for applications ranging from smart cities to industrial monitoring. This trend is quantified by the sheer volume of connected endpoints; according to Ericsson, June 2024, in the 'Ericsson Mobility Report', total cellular IoT connections are expected to reach approximately 4.5 billion by the end of 2025. This massive installed base creates an immediate necessity for hardware capable of executing low-power, high-performance inference at the network edge.

Simultaneously, the rising integration of AI in autonomous vehicles and robotics is forcing a hardware evolution towards high-performance, energy-efficient inference engines. These autonomous systems require sophisticated neural networks to navigate unstructured environments safely, driving demand for specialized NPUs and GPUs capable of executing complex logic without network dependence. According to the International Federation of Robotics (IFR), September 2024, in the 'World Robotics 2024' report, the global operational stock of industrial robots reached a record 4.28 million units in 2023, reflecting the deepening embedded base for intelligent automation. To support the computational intensity of such applications, memory bandwidth is becoming as critical as processing speed; according to World Semiconductor Trade Statistics (WSTS), December 2024, in its updated market forecast, the memory integrated circuit segment was projected to surge by 81.0% in 2024, highlighting the infrastructure shift required to support advanced AI workloads.

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Key Market Challenges

The constraint of power efficiency stands as a formidable obstacle limiting the expansion of the Global Edge AI Hardware Market. As manufacturers strive to integrate complex machine learning capabilities into compact environments, they face a conflict between computational performance and energy consumption. Edge devices, particularly those deployed in remote industrial settings or wearable applications, frequently rely on finite battery reserves. The intense processing required for real-time AI inference drains these power sources rapidly, reducing the operational lifespan and reliability of the hardware. This technical limitation makes potential buyers hesitant to adopt intelligent edge solutions for mission-critical tasks where consistent uptime is non-negotiable, thereby stalling broader commercial adoption.

The magnitude of this power challenge is underscored by the immense scale of the device ecosystem waiting to be upgraded. According to the GSMA, in 2024, the enterprise segment reached 10.7 billion IoT connections, representing a vast infrastructure that demands energy-efficient processing to function effectively. Without hardware that can deliver high-level performance while strictly managing power draw, this massive volume of connected devices cannot fully leverage decentralized AI, directly restricting the market's total addressable growth.

Key Market Trends

The Integration of Dedicated Neural Processing Units (NPUs) in Mobile SoCs is transforming consumer electronics by enabling complex on-device inference for generative AI applications. Manufacturers are embedding high-efficiency accelerators directly into smartphone processors to handle tasks like real-time language translation and image manipulation locally, thereby reducing latency and cloud dependency. This architectural shift is driving significant commercial upgrades, as evidenced by consumer demand for AI-capable flagship devices. According to Samsung Electronics, January 2025, in the 'Fourth Quarter and FY 2024 Results' report, the company recorded robust sales performance with the flagship Galaxy S24 series featuring Galaxy AI achieving double-digit growth, underscoring the market's rapid transition toward hardware-enabled intelligence.

The Adoption of Chiplet Technology and Heterogeneous Integration is reshaping semiconductor design to overcome the physical and economic scaling limits of monolithic dies in edge hardware. By combining smaller, modular dies fabricated on different process nodes into a single package, engineers can optimize performance and cost for specific AI workloads while improving yield rates. This manufacturing evolution is critical for supporting the bandwidth and interconnect requirements of next-generation edge processors used in high-performance computing. According to TSMC, January 2025, in the 'Fourth Quarter 2024 Earnings Conference', the company projected that its revenue from advanced packaging technologies, which enable these heterogeneous architectures, would exceed 10% of its total revenue in 2025 due to sustained demand for high-performance computing.

Segmental Insights

The smartphone segment is currently recognized as the fastest-growing category within the Global Edge AI Hardware Market. This expansion is driven by the increasing integration of dedicated neural processing units into mobile processors, allowing devices to execute artificial intelligence tasks locally rather than relying on cloud servers. Manufacturers prioritize this on-device processing to improve user data privacy and reduce latency for applications such as image recognition and real-time language translation. Consequently, the consistent consumer demand for responsive, intelligent features in mobile devices continues to accelerate the adoption of specialized AI hardware components in this sector.

Regional Insights

North America dominates the Global Edge AI Hardware Market, driven by the concentration of key semiconductor manufacturers and advanced digital infrastructure within the region. The widespread adoption of Internet of Things devices in industrial automation and autonomous vehicles fuels the demand for efficient, real-time local processing. Strategic government initiatives, specifically the CHIPS and Science Act, provide crucial funding to strengthen domestic chip production and research capabilities. Additionally, the National Institute of Standards and Technology plays a vital role by establishing standards that enhance hardware interoperability and security. This robust ecosystem of innovation and policy support secures the region's leading market position.

Recent Developments

• In August 2025, NVIDIA announced the general availability of the Jetson AGX Thor developer kit and production modules, a computing platform built for the era of generative AI and humanoid robotics. Based on the Blackwell architecture, the new system delivers a substantial increase in AI performance and energy efficiency compared to its predecessor. The company highlighted that the platform is designed to handle complex tasks such as interaction and autonomous operation in unstructured environments. Major robotics manufacturers adopted the technology to power next-generation autonomous machines in manufacturing, logistics, and healthcare, leveraging its capability to run multimodal foundation models locally.
• In July 2025, Hailo announced the commercial availability of its Hailo-10H AI accelerator, a processor specifically optimized for running generative AI models at the edge without reliance on cloud connectivity. The new chip features a proprietary architecture capable of delivering up to 40 tera-operations per second, enabling efficient execution of large language and vision-language models on personal computers and automotive infotainment systems. The company stated that the processor supports industry-standard frameworks and achieves high throughput with low power consumption. This release targeted the growing market for privacy-centric, low-latency AI applications in environments with limited or intermittent internet access.
• In September 2024, SiMa.ai introduced the MLSoC Modalix, a new family of multi-modal edge AI system-on-chips designed to process varying data types, including text, image, and audio. The product line, which offers performance options ranging from 25 to 200 TOPS, was engineered to support generative AI workloads and large language models directly on edge devices. The company emphasized that this software-centric platform enables developers to deploy complex multi-modal applications with high power efficiency. This launch marked a significant expansion of the company's offerings, aiming to facilitate the shift of generative AI from the cloud to the embedded edge across sectors like smart retail and industrial robotics.
• In April 2024, AMD expanded its adaptive system-on-chip portfolio with the launch of the Versal AI Edge Series Gen 2 during the Embedded World exhibition. These new devices integrate FPGA programmable logic, next-generation AI engines, and embedded Arm processors to accelerate edge AI applications such as autonomous driving and industrial systems. The company revealed that a major automotive manufacturer selected the new devices for its advanced driver-assistance system to achieve safety-critical vision processing. The Gen 2 series was designed to deliver significantly improved AI performance per watt compared to previous generations, addressing the growing demand for real-time data processing in power-constrained edge environments.

Key Market Players

• Qualcomm Technologies, Inc.
• Huawei Technologies Co. Ltd.
• Samsung Electronics Co. Ltd.
• MediaTek Inc.
• International Business Machines Corporation
• Microsoft Corporation
• NVIDIA Corporation
• Google LLC
• Apple Inc.
• Intel Corporation

By Device	By Power Consumption	By Function	By Processor	By Vertical	By Region
Smartphones Robots Surveillance Cameras Wearables Smart Speakers Automotive Smart Mirrors Others	Less Than 1W 1-3W 3-5W 3-5W and More Than 10W	Training Inference	CPU GPU ASIC and Others	Healthcare & Life Science Retail & Consumer Electronics Automotive & Transportation Aerospace & Defense Government Construction and Others	North America Europe Asia Pacific South America Middle East & Africa

Report Scope:

In this report, the Global Edge AI Hardware Market has been segmented into the following categories, in addition to the industry trends which have also been detailed below:

• Edge AI Hardware Market, By Device:

• Smartphones
• Robots
• Surveillance Cameras
• Wearables
• Smart Speakers
• Automotive
• Smart Mirrors
• Others

• Edge AI Hardware Market, By Power Consumption:

• Less Than 1W
• 1-3W
• 3-5W
• 3-5W and More Than 10W

• Edge AI Hardware Market, By Function:

• Training
• Inference

• Edge AI Hardware Market, By Processor:

• CPU
• GPU
• ASIC
• and Others

• Edge AI Hardware Market, By Vertical:

• Healthcare & Life Science
• Retail & Consumer Electronics
• Automotive & Transportation
• Aerospace & Defense
• Government
• Construction and Others

• Edge AI Hardware Market, By Region:

• North America
• United States
• Canada
• Mexico
• Europe
• France
• United Kingdom
• Italy
• Germany
• Spain
• Asia Pacific
• China
• India
• Japan
• Australia
• South Korea
• South America
• Brazil
• Argentina
• Colombia
• Middle East & Africa
• South Africa
• Saudi Arabia
• UAE