Artificial Intelligence in Hardware

Global Artificial Intelligence in Hardware Market to Reach US$137.2 Billion by 2030

The global market for Artificial Intelligence in Hardware estimated at US$54.6 Billion in the year 2024, is expected to reach US$137.2 Billion by 2030, growing at a CAGR of 16.6% over the analysis period 2024-2030. AI Processors, one of the segments analyzed in the report, is expected to record a 14.2% CAGR and reach US$56.6 Billion by the end of the analysis period. Growth in the AI Accelerators segment is estimated at 18.8% CAGR over the analysis period.

The U.S. Market is Estimated at US$16.0 Billion While China is Forecast to Grow at 15.9% CAGR

The Artificial Intelligence in Hardware market in the U.S. is estimated at US$16.0 Billion in the year 2024. China, the world`s second largest economy, is forecast to reach a projected market size of US$23.9 Billion by the year 2030 trailing a CAGR of 15.9% over the analysis period 2024-2030. Among the other noteworthy geographic markets are Japan and Canada, each forecast to grow at a CAGR of 15.1% and 14.7% respectively over the analysis period. Within Europe, Germany is forecast to grow at approximately 12.5% CAGR.

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?

SCOPE OF STUDY:

The report analyzes the Artificial Intelligence in Hardware market in terms of units by the following Segments, and Geographic Regions/Countries:

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.

Select Competitors (Total 42 Featured) -

• 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 FACTOR

Our new release incorporates impact of tariffs on geographical markets as we predict a shift in competitiveness of companies based on HQ country, manufacturing base, exports and imports (finished goods and OEM). This intricate and multifaceted market reality will impact competitors by artificially increasing the COGS, reducing profitability, reconfiguring supply chains, amongst other micro and macro market dynamics.

We are diligently following expert opinions of leading Chief Economists (14,949), Think Tanks (62), Trade & Industry bodies (171) worldwide, as they assess impact and address new market realities for their ecosystems. Experts and economists from every major country are tracked for their opinions on tariffs and how they will impact their countries.

We expect this chaos to play out over the next 2-3 months and a new world order is established with more clarity. We are tracking these developments on a real time basis.

As we release this report, U.S. Trade Representatives are pushing their counterparts in 183 countries for an early closure to bilateral tariff negotiations. Most of the major trading partners also have initiated trade agreements with other key trading nations, outside of those in the works with the United States. We are tracking such secondary fallouts as supply chains shift.

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APRIL 2025: NEGOTIATION PHASE

Our April release addresses the impact of tariffs on the overall global market and presents market adjustments by geography. Our trajectories are based on historic data and evolving market impacting factors.

JULY 2025 FINAL TARIFF RESET

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