Artificial Intelligence in Hardware - Global Strategic Business Report

The global market for Artificial Intelligence in Hardware was valued at US$54.6 Billion in 2024 and is projected to reach US$137.2 Billion by 2030, growing at a CAGR of 16.6% from 2024 to 2030. This comprehensive report provides an in-depth analysis of market trends, drivers, and forecasts, helping you make informed business decisions. The report includes the most recent global tariff developments and how they impact the Artificial Intelligence in Hardware market.

Global Artificial Intelligence in Hardware Market - Key Trends & Drivers Summarized

Why Is Hardware Innovation Critical to Unlocking the Full Potential of Artificial Intelligence?

Artificial Intelligence (AI) hardware is emerging as a foundational pillar in enabling the widespread deployment and acceleration of AI workloads across industries. As AI models become more complex, compute-intensive, and data-hungry, traditional hardware architectures - especially general-purpose CPUs - are no longer sufficient to handle real-time processing, inference, and training tasks efficiently. This has spurred the development and adoption of specialized AI-optimized hardware, including graphics processing units (GPUs), tensor processing units (TPUs), field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), and edge AI processors. These technologies are purpose-built to deliver the parallel processing, low latency, and high throughput essential for next-generation AI applications such as computer vision, deep learning, natural language processing, and autonomous systems.

AI hardware is now seen as a strategic enabler not just for data centers, but for edge devices, robotics, IoT platforms, and high-performance computing environments. Cloud service providers, semiconductor firms, and hyperscale enterprises are investing heavily in custom AI chips to improve energy efficiency, accelerate AI training cycles, and reduce total cost of ownership. As AI penetrates sectors such as automotive, healthcare, defense, and industrial automation, demand for AI hardware that balances performance, power efficiency, and scalability is rising sharply - positioning this segment as a linchpin of the global AI ecosystem.

How Are Hardware Architectures Evolving to Meet AI-Specific Computational Demands?

To support rapidly evolving AI workloads, hardware architectures are undergoing a paradigm shift from general-purpose processing to domain-specific acceleration. GPUs, originally designed for rendering graphics, have become the workhorse for AI training due to their massive parallel processing capabilities. Nvidia, AMD, and Intel continue to innovate in this space with AI-optimized GPU architectures featuring higher memory bandwidth, software stack integration (CUDA, ROCm), and tensor cores specifically tailored for matrix multiplications critical in deep learning.

Beyond GPUs, custom silicon such as Google’s TPUs and Amazon’s Inferentia chips are pushing performance boundaries for cloud-based inference tasks. These ASICs offer optimized throughput-per-watt ratios and minimal latency, addressing AI workloads at hyperscale levels. FPGAs are gaining traction for their configurability and balance between performance and flexibility, making them suitable for low-latency edge inference and prototyping. Moreover, neuromorphic computing architectures, inspired by the human brain, and photonic chips leveraging light for computation are being explored for ultra-low-power and high-speed AI execution. These innovations are giving rise to heterogeneous computing platforms where AI hardware is purpose-built for workload-specific acceleration - ushering in a new era of hardware-defined intelligence.

Where Is Demand for AI-Optimized Hardware Growing and Which End-Use Segments Are Leading Adoption?

Demand for AI-optimized hardware is growing rapidly across cloud data centers, autonomous systems, mobile devices, and edge infrastructure. North America leads the global market, driven by massive investments from hyperscale cloud providers such as Google, Amazon, Microsoft, and Meta. These companies are developing or procuring AI-specific chips to handle enormous training workloads, large language models, and multimodal inference at scale. Europe and Asia-Pacific - particularly China, South Korea, and Taiwan - are also expanding AI hardware adoption through national AI strategies, semiconductor self-sufficiency initiatives, and smart manufacturing programs.

Industries leading adoption include automotive, where AI chips power autonomous driving systems, advanced driver assistance systems (ADAS), and in-vehicle infotainment; healthcare, where AI processors enable real-time medical imaging, diagnostics, and remote patient monitoring; and industrial automation, where edge AI chips support robotics, predictive maintenance, and visual inspection. Consumer electronics companies are integrating AI accelerators into smartphones, wearables, and smart home devices to enhance voice recognition, facial authentication, and contextual computing. Military and aerospace sectors are also leveraging secure, mission-critical AI hardware for surveillance, situational awareness, and autonomous mission planning. As AI use cases diversify, demand is shifting from centralized training to decentralized, real-time inference, driving strong growth in edge AI hardware.

What Is Fueling the Global Expansion of the AI in Hardware Market?

The growth in the artificial intelligence in hardware market is driven by several converging factors, including the exponential growth of AI applications, demand for real-time inference at the edge, and advancements in semiconductor fabrication. A major driver is the expanding scale and sophistication of AI models - such as generative AI, multimodal models, and reinforcement learning systems - that require increasingly powerful hardware to train and deploy efficiently. The rise of edge computing and latency-sensitive applications - ranging from smart surveillance and predictive analytics to autonomous mobility - is creating robust demand for compact, power-efficient AI chips capable of on-device inference.

Government funding and strategic partnerships aimed at achieving semiconductor sovereignty are further bolstering R&D in AI chip development, particularly in the U.S., China, and the EU. The convergence of AI with 5G, IoT, and robotics is amplifying the need for vertically integrated hardware-software stacks that can scale across cloud-to-edge architectures. Additionally, the commercial push toward open hardware ecosystems and modular accelerators is expanding accessibility for startups, researchers, and niche AI developers. As hardware becomes the bottleneck and enabler of AI innovation simultaneously, a strategic question emerges:Can the global AI hardware ecosystem keep pace with the computational and efficiency demands of tomorrow’s AI-driven economies without compromising accessibility, sustainability, and interoperability?

Report Scope

The report analyzes the Artificial Intelligence in Hardware market, presented in terms of market value (US$ Thousand). The analysis covers the key segments and geographic regions outlined below.

Segments: Hardware (AI Processors, AI Accelerators, AI Chips, AI-enabled Servers); End-Use (IT & Telecommunications, Manufacturing, Retail, Automotive, Healthcare, Other End-Uses).

Geographic Regions/Countries: World; United States; Canada; Japan; China; Europe (France; Germany; Italy; United Kingdom; and Rest of Europe); Asia-Pacific; Rest of World.

Key Insights:

• Market Growth: Understand the significant growth trajectory of the AI Processors segment, which is expected to reach US$56.6 Billion by 2030 with a CAGR of a 14.2%. The AI Accelerators segment is also set to grow at 18.8% CAGR over the analysis period.
• Regional Analysis: Gain insights into the U.S. market, valued at $16.0 Billion in 2024, and China, forecasted to grow at an impressive 15.9% CAGR to reach $23.9 Billion by 2030. Discover growth trends in other key regions, including Japan, Canada, Germany, and the Asia-Pacific.

Why You Should Buy This Report:

• Detailed Market Analysis: Access a thorough analysis of the Global Artificial Intelligence in Hardware Market, covering all major geographic regions and market segments.
• Competitive Insights: Get an overview of the competitive landscape, including the market presence of major players across different geographies.
• Future Trends and Drivers: Understand the key trends and drivers shaping the future of the Global Artificial Intelligence in Hardware Market.
• Actionable Insights: Benefit from actionable insights that can help you identify new revenue opportunities and make strategic business decisions.

Key Questions Answered:

• How is the Global Artificial Intelligence in Hardware Market expected to evolve by 2030?
• What are the main drivers and restraints affecting the market?
• Which market segments will grow the most over the forecast period?
• How will market shares for different regions and segments change by 2030?
• Who are the leading players in the market, and what are their prospects?

Report Features:

• Comprehensive Market Data: Independent analysis of annual sales and market forecasts in US$ Million from 2024 to 2030.
• In-Depth Regional Analysis: Detailed insights into key markets, including the U.S., China, Japan, Canada, Europe, Asia-Pacific, Latin America, Middle East, and Africa.
• Company Profiles: Coverage of players such as Advanced Micro Devices (AMD), Ambarella Inc., Ampere Computing, Apple Inc., Arm Ltd. and more.
• Complimentary Updates: Receive free report updates for one year to keep you informed of the latest market developments.

Some of the 42 companies featured in this Artificial Intelligence in Hardware market report include:

• Advanced Micro Devices (AMD)
• Ambarella Inc.
• Ampere Computing
• Apple Inc.
• Arm Ltd.
• Blaize
• Broadcom Inc.
• Cambricon Technologies
• Cerebras Systems
• Enfabrica
• Graphcore
• Groq
• Hailo
• Huawei Technologies
• IBM
• Imagination Technologies
• Intel Corporation
• MediaTek Inc.
• Meta Platforms
• Micron Technology

Tariff Impact Analysis: Key Insights for 2025

Global tariff negotiations across 180+ countries are reshaping supply chains, costs, and competitiveness. This report reflects the latest developments as of April 2025 and incorporates forward-looking insights into the market outlook.

The analysts continuously track trade developments worldwide, drawing insights from leading global economists and over 200 industry and policy institutions, including think tanks, trade organizations, and national economic advisory bodies. This intelligence is integrated into forecasting models to provide timely, data-driven analysis of emerging risks and opportunities.

What's Included in This Edition:

• Tariff-adjusted market forecasts by region and segment
• Analysis of cost and supply chain implications by sourcing and trade exposure
• Strategic insights into geographic shifts

Buyers receive a free July 2025 update with:

• Finalized tariff impacts and new trade agreement effects
• Updated projections reflecting global sourcing and cost shifts
• Expanded country-specific coverage across the industry