Edge AI Hardware Market Size and Share
Market Overview
| Study Period | 2019 - 2030 |
|---|---|
| Market Size (2025) | USD 26.17 Billion |
| Market Size (2030) | USD 59.37 Billion |
| Growth Rate (2025 - 2030) | 17.80% CAGR |
| Fastest Growing Market | Asia Pacific |
| Largest Market | North America |
| Market Concentration | Medium |
Major Players
*Disclaimer: Major Players sorted in no particular order Image © Mordor Intelligence. Reuse requires attribution under CC BY 4.0. |
|
Edge AI Hardware Market Analysis by Mordor Intelligence
The Edge AI hardware market size stands at USD 26.17 billion in 2025 and is forecast to reach USD 59.37 billion by 2030, advancing at a 17.8% CAGR. Momentum stems from rising demand for on-device inference that cuts latency, safeguards data sovereignty, and lowers energy consumption. Premium-tier smartphones, AI-enabled personal computers, and mandatory automotive safety systems anchor near-term growth. Government incentives such as the CHIPS and Science Act encourage domestic production capacity, while 5G-powered multi-access edge computing (MEC) broadens the addressable workload. Competitive intensity is moderate as diversified semiconductor leaders defend share against application-specific chip suppliers that optimize performance per watt. Supply-chain concentration at advanced foundries and widening export controls add regional complexity but also stimulate indigenous alternatives.
Key Report Takeaways
- By processor, GPUs led with 50.7% of Edge AI hardware market share in 2024; ASICs and NPUs are projected to grow at a 19.0% CAGR through 2030.
- By device, smartphones accounted for 39.8% of the Edge AI hardware market size in 2024, whereas robots and drones are expected to expand at a 19.6% CAGR to 2030.
- By end-user industry, consumer electronics held 34.7% revenue share of Edge AI hardware market in 2024, while manufacturing and industrial IoT is advancing at a 19.8% CAGR during the forecast period.
- By deployment location, device-edge computing captured 52.28% of the Edge AI hardware market size in 2024; far-edge and MEC infrastructure is growing at 19.1% CAGR through 2030.
- By geography, North America dominated with a 39.4% share of the Edge AI hardware market in 2024; Asia-Pacific is the fastest region, expanding at a 19.5% CAGR to 2030.
Global Edge AI Hardware Market Trends and Insights
Drivers Impact Analysis
| Driver | (~) % Impact on CAGR Forecast | Geographic Relevance | Impact Timeline |
|---|---|---|---|
| AI-enabled personal computing | +3.2% | North America and Europe; expanding to Asia-Pacific | Medium term (2-4 years) |
| Smartphone upgrade cycle toward on-device AI | +4.1% | Global, early uptake in premium segments | Short term (≤ 2 years) |
| 5G/6G-driven MEC deployments | +2.8% | Asia-Pacific core; spillover to North America and Europe | Long term (≥ 4 years) |
| Automotive L2–L4 ADAS inference demand | +3.5% | Global, led by North America, Europe, China | Medium term (2-4 years) |
| Energy-efficient analog and PIM accelerators | +2.1% | Global; R&D hubs in North America and Asia | Long term (≥ 4 years) |
| Government CHIPS-style incentives | +2.3% | North America, Europe, select Asia-Pacific markets | Medium term (2-4 years) |
| Source: Mordor Intelligence | |||
Rise of AI-Enabled Personal Computing Transforms Processor Architecture
Dedicated neural processing units (NPUs) in the latest laptop chips achieve 40-50 TOPS of local AI throughput, allowing large language models and generative workloads to run offline with instant response times.[1]Intel Corporation, “Core Ultra 200V Series Processors with Integrated NPU,” intel.com New design baselines from Microsoft Copilot+ PCs compel every OEM to integrate similar acceleration, steering roadmaps toward heterogeneous compute rather than general-purpose cores. Semiconductor roadmaps through 2030 now prioritize inference-optimized tiles, driving sustained demand for edge-centric nodes.
Smartphone AI Capabilities Drive Premium Segment Refresh Cycles
Flagship mobile processors deliver 45-50 TOPS inference and extend battery life by scheduling AI tasks to dedicated engines.[2]Qualcomm Technologies, “Snapdragon 8 Elite Mobile Platform,” qualcomm.com On-device translation, generative imaging, and personal-assistant features create clear upgrade motives across premium tiers, shortening replacement intervals. Mid-range designs will inherit last year's flagship capabilities, expanding volume shipments of specialized AI silicon.
5G Infrastructure Enables Distributed Edge Computing Architectures
Operators monetize sub-10 ms latency by colocating servers at cell sites and packaging edge AI services with connectivity. MEC nodes handle video analytics, industrial control loops, and immersive XR without round-trip delay to a regional cloud. The emerging 6G vision targets sub-millisecond latency, reinforcing hardware demand at the network perimeter into the next decade.
Automotive Safety Regulations Mandate Advanced Driver Assistance Systems
EU General Safety Regulation and similar mandates in China compel every new vehicle to include automated braking and lane keeping, expanding silicon content per car. Edge inference processors delivering 2,000 TOPS within 100 watts enable Level 3–4 autonomy while meeting power budgets. Tier 1 suppliers invest in proprietary NPUs to protect margins and meet ISO 26262 functional-safety metrics.
Restraints Impact Analysis
| Restraint | (~) % Impact on CAGR Forecast | Geographic Relevance | Impact Timeline |
|---|---|---|---|
| High upfront NRE costs for advanced nodes | -2.7% | Global; greatest burden on startups and mid-tier players | Short term (≤ 2 years) |
| Fragmented toolchains and software lock-in | -1.9% | Global; slows enterprise adoption | Medium term (2-4 years) |
| Talent shortage in edge-oriented ML and silicon | -1.5% | North America and Europe | Long term (≥ 4 years) |
| Supply-chain geopolitical export controls | -2.3% | China and Russia primarily; ripple effects worldwide | Medium term (2-4 years) |
| Source: Mordor Intelligence | |||
Advanced Node Manufacturing Costs Limit Market Entry
Developing a 3 nm device demands over USD 100 million in masks and USD 20,000 per wafer, constraining access for new entrants.[3]Taiwan Semiconductor Manufacturing Co., “3 nm Platform Overview,” tsmc.com Consolidation accelerates as smaller firms seek scale or niche differentiation. Design-for-node co-optimization and chiplet partitioning partially offset cost but reinforce the advantage for incumbents with existing supply contracts.
Export Control Restrictions Fragment Global Supply Chains
Expanded U.S. control lists cap compute power per chip sold into China, forcing vendors to create region-specific derivatives. Domestic alternatives emerge yet trail leading-edge performance, producing parallel ecosystems and higher development costs. Enterprises deploying globally must qualify multiple hardware SKUs, stretching engineering resources.
Segment Analysis
By Processor: Specialized AI Chips Challenge GPU Dominance
GPU devices captured 50.7% Edge AI hardware market share in 2024 owing to mature software stacks and high parallel throughput. Over the forecast horizon, ASICs and NPUs are projected to post a 19.0% CAGR as designers emphasize performance per watt. The Edge AI hardware market size for ASICs is expected to rise sharply as automotive and industrial buyers prioritize deterministic latency and functional safety. CPUs retain value where mixed workloads require general-purpose resources, and FPGAs grow in reconfigurable roles across telecom and defense.
Chiplet packaging combines CPU, GPU, and NPU tiles on common substrates, optimizing each die for distinct tasks while sharing memory interfaces. Vendors integrate security enclaves and functional-safety monitors at the silicon layer, satisfying regulatory mandates in healthcare and automotive deployments. Multi-foundry strategies mitigate geopolitical risk, yet advanced-node dependence keeps negotiating leverage with leading fabs.
Note: Segment shares of all individual segments available upon report purchase
By Device: Robotics Applications Drive Hardware Innovation
Smartphones accounted for 39.8% of the Edge AI hardware market size in 2024, leveraging annual refresh cycles and large unit volumes. Robots and drones, however, represent the fastest trajectory, climbing at 19.6% CAGR as autonomous navigation and vision analytics demand low-latency inference. Specialized edge boards pair vision processors with depth sensors, enabling millisecond obstacle avoidance.
Cameras integrate edge AI to execute real-time detection within enclosures, reducing video backhaul costs for retail analytics and smart cities. Wearables adopt ultra-low-power neural engines that extract health insights continuously under limited battery budgets. Smart speakers consolidate voice capture, beamforming, and NLP inference on single chips, shrinking the bill of materials and enhancing privacy by keeping audio local.
By End-User Industry: Manufacturing Leads Digital Transformation
Consumer electronics held 34.7% revenue share in 2024, driven by premium handsets and emerging AI PCs. The Edge AI hardware market share for manufacturing and industrial IoT is poised to surge, with predictive-maintenance implementations raising uptime by double-digit percentages. Factory systems embed vibration and thermal sensors linked to micro-NPUs that flag anomalies on site without cloud links.
Automotive OEMs shift toward software-defined vehicles, allocating higher semiconductor budgets per car. Healthcare devices integrate edge inference for diagnostic imaging at the point of care, trimming scan-to-answer intervals. Government deployments emphasize sovereign compute paths, favoring local fabrication and cryptographic acceleration to comply with security mandates.
Note: Segment shares of all individual segments available upon report purchase
By Deployment Location: Device Edge Computing Dominates
Device-edge platforms captured 52.28% Edge AI hardware market size in 2024, reflecting immediate latency gains and data privacy compliance. Far-edge and MEC nodes are forecast to compound at a 19.1% CAGR, catalyzed by 5G rollouts that open commercial models for operators. Hybrid orchestration dynamically assigns inference between device, far edge, and cloud based on throughput targets and network congestion.
Near-edge micro-data centers support enterprise campuses, enabling aggregated analytics across fleets of connected machines. Cloud-burst modes send sporadic high-complexity tasks to regional cores when local resources peak, optimizing the total cost of ownership. Market education improves as reference architectures from hyperscalers simplify deployment choices.
Geography Analysis
North America controlled 39.4% revenue in 2024 on the back of USD 52 billion CHIPS incentives and early enterprise pilots in automotive, retail, and healthcare. Start-ups leverage venture capital density to commercialize domain-specific accelerators. Export-control policy constrains outbound sales, yet secures domestic defense and aerospace demand.
Asia-Pacific is advancing at a 19.5% CAGR, outpacing all other regions. China funds native GPU and NPU ventures to circumvent import restrictions, while South Korea allocates USD 7 billion for national AI chip lines. Japan’s Society 5.0 agenda stimulates smart-factory retrofits that require deterministic edge compute.
Europe balances sovereignty aims with budget realities under its EUR 43 billion Chips Act. Automotive hubs in Germany and France prioritize functional-safe edge inference, while GDPR compliance encourages on-premise analytics. Israel’s vibrant start-up ecosystem targets defense and medical imaging use cases, exporting boards across EMEA.
Latin America sees early adoption in agriculture drones and smart-city surveillance. The Middle East accelerates investment in sovereign data centers coupled with edge gateways to host AI for logistics and energy infrastructure. Africa remains nascent but leapfrogs legacy stacks through mobile-first deployments allied with satellite backhaul.
Competitive Landscape
Market structure is moderately concentrated, with the top five suppliers controlling roughly 55% of 2024 revenue. NVIDIA, Intel, and Qualcomm defend incumbent positions through software ecosystems and customer lock-in. AMD’s acquisition of Xilinx aligns FPGA flexibility with CPU-GPU compute, broadening offerings for industrial and telecom clients. NXP’s USD 307 million Kinara purchase signals automotive Tier 1 interest in owning inference IP.
Specialists such as Hailo and Syntiant attract capital by demonstrating 40 TOPS inference within 5-watt power budgets.[4]Hailo Technologies, “Hailo-10 Product Announcement,” hailo.ai Groq’s language-processing architecture claims deterministic latency advantages for generative AI workloads. Foundries race toward 2 nm gate-all-around nodes; Samsung plans USD 44 billion U.S. capacity to match TSMC’s timeline. Vertical integration by Apple and Tesla underscores the strategic weight of proprietary silicon.
Strategic alliances blossom: cloud providers bundle hardware reference designs with managed edge services, and automotive suppliers co-design chips with intellectual-property vendors to streamline ASIL certifications. Patent cross-licensing rises as heterogeneous chiplet topologies intersect across incumbents and start-ups.
Edge AI Hardware Industry Leaders
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NVIDIA Corporation
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Intel Corporation
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Qualcomm Incorporated
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Samsung Electronics Co., Ltd.
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Apple Inc.
- *Disclaimer: Major Players sorted in no particular order
Recent Industry Developments
- September 2025: Apple rolled out the iPhone 16 family powered by the A18 Pro chip. Its redesigned Neural Engine pushes 35 TOPS of on-device AI yet consumes 20% less power, enabling instant language translation and richer computational photography.
- August 2025: Intel revealed the Core Ultra 300 series for AI PCs and workstations. Each processor integrates an NPU that supplies up to 50 TOPS, allowing local execution of language models with as many as 13 billion parameters, no cloud needed.
- July 2025: Qualcomm introduced the Snapdragon X Elite platform for premium AI laptops. Featuring an Oryon CPU, Adreno GPU, and 45 TOPS NPU, the chip meets Microsoft Copilot+ requirements while still delivering all-day battery life.
- June 2025: NVIDIA debuted Jetson Thor, an automotive development board that serves 2,000 TOPS of compute within a sub-100-watt envelope, supporting real-time sensor fusion for Level 4 autonomous driving.
- May 2025: Samsung commenced 2 nm Gate-All-Around production at its Taylor, Texas fab, becoming the second U.S. foundry after TSMC to manufacture leading-edge chips aimed at automotive and mobile AI workloads.
- April 2025: MediaTek shipped the Dimensity 9400+ system-on-chip. Its NPU 890 sustains 50 TOPS and runs Meta’s Llama 3.2 models entirely on the handset, giving Android devices feature parity with Apple’s on-device AI.
- March 2025: Huawei announced the Ascend 910D training processor, built on a 7 nm node yet rivaling NVIDIA’s H100 in performance, underscoring China’s progress in indigenous AI silicon despite export restrictions.
- February 2025: AMD launched the Instinct MI350 accelerators that blend GPU cores with Xilinx FPGA fabric, providing adaptive compute for AI workloads that evolve in real time.
- January 2025: Aumovio, Continental’s chip unit, committed USD 500 million to develop proprietary processors and vision systems for Level 3–4 autonomous vehicles, deepening the supplier’s vertical integration strategy.
Global Edge AI Hardware Market Report Scope
The scope for the edge AI hardware market primarily includes processors, sensors, and cameras that address the need for cognitive computing needs. These devices are used to power and process various AI-based devices. Multiple types of processors used in edge AI devices include semiconductor products such as central processing units (CPU), graphic processing units (GPU), field-programmable gate arrays (FPGA), and application-specific integrated circuits (ASICs).
The edge AI hardware market is segmented by processor (CPU, GPU, FPGA, ASIC), by device (smartphones, cameras, robots, wearables, smart speakers), by end-user industry (government, consumer electronics, real estate, automotive, transportation, healthcare, manufacturing, others), and by geography (North America, Europe, Asia Pacific, Latin America, Middle East and Africa). The market sizes and forecasts are provided in terms of value in USD for all the above segments.
| CPU |
| GPU |
| FPGA |
| ASIC and NPU |
| Smartphones |
| Cameras and Smart Vision Sensors |
| Robots and Drones |
| Wearables |
| Smart Speakers and Home Hubs |
| Other Edge Devices |
| Consumer Electronics |
| Automotive and Transportation |
| Manufacturing and Industrial IoT |
| Healthcare |
| Government and Public Safety |
| Other End-User Industries |
| Device Edge |
| Near Edge Servers |
| Far Edge / MEC |
| Cloud-Assisted Hybrid |
| North America | United States | |
| Canada | ||
| Mexico | ||
| South America | Brazil | |
| Argentina | ||
| Rest of South America | ||
| Europe | Germany | |
| United Kingdom | ||
| France | ||
| Italy | ||
| Spain | ||
| Rest of Europe | ||
| Asia-Pacific | China | |
| Japan | ||
| South Korea | ||
| India | ||
| Singapore | ||
| Australia | ||
| Rest of Asia-Pacific | ||
| Middle East and Africa | Middle East | Saudi Arabia |
| United Arab Emirates | ||
| Turkey | ||
| Rest of Middle East | ||
| Africa | South Africa | |
| Nigeria | ||
| Egypt | ||
| Rest of Africa | ||
| By Processor | CPU | ||
| GPU | |||
| FPGA | |||
| ASIC and NPU | |||
| By Device | Smartphones | ||
| Cameras and Smart Vision Sensors | |||
| Robots and Drones | |||
| Wearables | |||
| Smart Speakers and Home Hubs | |||
| Other Edge Devices | |||
| By End-User Industry | Consumer Electronics | ||
| Automotive and Transportation | |||
| Manufacturing and Industrial IoT | |||
| Healthcare | |||
| Government and Public Safety | |||
| Other End-User Industries | |||
| By Deployment Location | Device Edge | ||
| Near Edge Servers | |||
| Far Edge / MEC | |||
| Cloud-Assisted Hybrid | |||
| By Geography | North America | United States | |
| Canada | |||
| Mexico | |||
| South America | Brazil | ||
| Argentina | |||
| Rest of South America | |||
| Europe | Germany | ||
| United Kingdom | |||
| France | |||
| Italy | |||
| Spain | |||
| Rest of Europe | |||
| Asia-Pacific | China | ||
| Japan | |||
| South Korea | |||
| India | |||
| Singapore | |||
| Australia | |||
| Rest of Asia-Pacific | |||
| Middle East and Africa | Middle East | Saudi Arabia | |
| United Arab Emirates | |||
| Turkey | |||
| Rest of Middle East | |||
| Africa | South Africa | ||
| Nigeria | |||
| Egypt | |||
| Rest of Africa | |||
Key Questions Answered in the Report
How large will the Edge AI hardware market be by 2030?
It is projected to reach USD 59.37 billion by 2030, rising from USD 26.17 billion in 2025 at a 17.8% CAGR.
Which processor type is growing fastest?
ASIC and NPU devices are forecast to expand at 19.0% CAGR through 2030 as they optimize power per inference for edge workloads.
Why is Asia-Pacific the fastest-growing region?
Government funding in China, South Korea, and Japan for domestic semiconductor capacity drives a 19.5% regional CAGR.
What share do smartphones hold in 2024?
Smartphones contributed 39.8% of revenue in 2024, benefitting from premium handset refresh cycles.
Which deployment tier currently dominates?
Device-edge computing leads with 52.28% revenue share, providing immediate latency and privacy advantages.
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