Edge AI Hardware Market - Global Industry Size, Share, Trends, Opportunity, and Forecast, 2021-2031

The Global Edge AI Hardware Market is projected to expand significantly, rising from a valuation of USD 26.11 Billion in 2025 to USD 68.85 Billion by 2031, reflecting a compound annual growth rate (CAGR) of 17.54%. This sector encompasses specialized physical components - specifically neural processing units (NPUs), graphics processing units (GPUs), and application-specific integrated circuits (ASICs) - engineered to process machine learning algorithms locally rather than depending on centralized cloud connectivity. The fundamental momentum behind this market stems from the urgent necessity for ultra-low latency in real-time decision-making processes and the drive to optimize bandwidth usage by reducing data transmission requirements. Additionally, the enforcement of strict data privacy regulations and the exponential increase in Internet of Things (IoT) devices act as primary catalysts, creating a distinct need for robust, on-device processing capabilities.

However, the market faces a substantial hurdle regarding power efficiency, as incorporating high-performance computing into resource-constrained, battery-powered devices presents significant technical difficulties. This surge in hardware demand mirrors trends in the wider chip industry; according to the Semiconductor Industry Association, global semiconductor sales hit $627.6 billion in 2024, a figure largely propelled by the explosive demand for artificial intelligence capabilities within automotive and industrial sectors. Such massive capital investment in foundational silicon underscores the industrial-scale transition toward intelligent, decentralized hardware architectures.

Market Drivers

The rapid expansion of IoT and smart connected devices serves as a major accelerator for the Edge AI Hardware market, effectively migrating processing workloads from centralized cloud infrastructures to local environments. As industries implement billions of sensors and endpoints, the costs related to latency and bandwidth for transmitting raw data become unmanageable, thereby mandating on-chip processing solutions. This decentralized strategy enables immediate data filtering and analysis, a capability essential for diverse applications from smart city infrastructure to industrial monitoring systems. The scale of this trend is highlighted by the sheer volume of connected endpoints; the "Ericsson Mobility Report" from June 2024 estimates that total cellular IoT connections will reach roughly 4.5 billion by the end of 2025, creating an urgent need for hardware that delivers low-power, high-performance inference at the network edge.

Concurrently, the increasing incorporation of AI into autonomous vehicles and robotics is compelling a hardware evolution toward inference engines that balance high performance with energy efficiency. These autonomous systems depend on advanced neural networks to safely traverse unstructured environments, fueling the demand for specialized NPUs and GPUs capable of complex logic execution without network reliance. According to the International Federation of Robotics (IFR) "World Robotics 2024" report released in September 2024, the global operational stock of industrial robots hit a record 4.28 million units in 2023, signaling a deepening base for intelligent automation. To sustain the computational intensity these applications require, memory bandwidth has become as vital as processing speed; in fact, the World Semiconductor Trade Statistics (WSTS) December 2024 forecast projected the memory integrated circuit segment would jump by 81.0% in 2024, emphasizing the infrastructure adjustments necessary to support advanced AI workloads.

Market Challenges

The issue of power efficiency remains a formidable barrier restricting the growth of the Global Edge AI Hardware Market. As manufacturers attempt to embed sophisticated machine learning features into compact devices, they encounter an inherent conflict between achieving high computational performance and maintaining low energy consumption. Edge devices, especially those utilized in remote industrial locations or wearable technology, often depend on limited battery power. The intensive processing needed for real-time AI inference rapidly depletes these energy reserves, thereby diminishing the hardware's operational lifespan and reliability. This technical limitation causes hesitation among potential buyers regarding the adoption of intelligent edge solutions for mission-critical operations where uninterrupted uptime is essential, consequently stalling widespread commercial acceptance.

The severity of this power challenge is highlighted by the massive scale of the device ecosystem awaiting upgrades. According to the GSMA, the enterprise segment accounted for 10.7 billion IoT connections in 2024, representing a vast infrastructure that necessitates energy-efficient processing to operate effectively. Unless hardware is developed that can provide high-level performance while rigorously managing power consumption, this enormous volume of connected devices will be unable to fully utilize decentralized AI, directly limiting the market's total addressable growth potential.

Market Trends

The integration of dedicated Neural Processing Units (NPUs) into Mobile SoCs is revolutionizing consumer electronics by facilitating complex on-device inference for generative AI applications. Manufacturers are increasingly embedding high-efficiency accelerators directly within smartphone processors to manage tasks such as real-time language translation and image manipulation locally, which significantly reduces latency and reliance on cloud services. This architectural transition is fueling substantial commercial upgrades, illustrated by strong consumer demand for AI-enabled flagship devices. As noted in Samsung Electronics' "Fourth Quarter and FY 2024 Results" report from January 2025, the company observed robust sales performance, with the flagship Galaxy S24 series featuring Galaxy AI achieving double-digit growth, highlighting the market's rapid shift toward hardware-enabled intelligence.

Simultaneously, the adoption of Chiplet Technology and Heterogeneous Integration is redefining semiconductor design to surpass the physical and economic scaling limitations associated with monolithic dies in edge hardware. By amalgamating smaller, modular dies manufactured on distinct process nodes into a single package, engineers can fine-tune performance and costs for specific AI workloads while enhancing yield rates. This evolution in manufacturing is essential for meeting the bandwidth and interconnect demands of next-generation edge processors utilized in high-performance computing. According to the TSMC "Fourth Quarter 2024 Earnings Conference" in January 2025, the company projected that revenue from advanced packaging technologies - which support these heterogeneous architectures - would surpass 10% of its total revenue in 2025, driven by sustained demand for high-performance computing solutions.

Key Players Profiled in the Edge AI Hardware Market

• 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

Report Scope

In this report, the Global Edge AI Hardware Market has been segmented into the following categories:

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
• More Than 10W

Edge AI Hardware Market, by Function:

• Training
• Inference

Edge AI Hardware Market, by Processor:

• CPU
• GPU
• ASIC
• Others

Edge AI Hardware Market, by Vertical:

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

Edge AI Hardware Market, by Region:

• North America
• Europe
• Asia-Pacific
• South America
• Middle East & Africa

Competitive Landscape

Company Profiles: Detailed analysis of the major companies present in the Global Edge AI Hardware Market.

Available Customization

The analyst offers customization according to your specific needs. The following customization options are available for the report:

• Detailed analysis and profiling of additional market players (up to five).

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