(MENAFN- GlobeNewsWire - Nasdaq) The AI infrastructure market presents opportunities in edge AI for industrial use, driven by the need for low-latency computing. Rising demand for energy-efficient infrastructure is crucial due to AI's high power consumption. Governments are funding AI research, hyperscale data center expansions, and energy-efficient advancements.Dublin, Oct. 08, 2025 (GLOBE NEWSWIRE) -- The "AI Infrastructure Market Size and Share Outlook - Forecast Trends and Growth Analysis Report (2025-2034)" has been added to ResearchAndMarkets's offering.
The global AI infrastructure market reached a valuation of USD 26.18 Billion in 2024, and it's projected to grow at a CAGR of 23.80% from 2025 to 2034, targeting a value of USD 221.40 Billion by 2034. A significant growth driver is the rising adoption of edge AI in industrial robotics, spurring needs for infrastructure that supports low-latency operations near enterprise activities.
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Generative AI models demanding vast computational power are accelerating this market expansion. Hyperscale data centers are enhancing their capabilities with GPU-rich clusters. The European Union has allocated EUR 1.5 billion via Horizon Europe programs for AI infrastructure scaling. In parallel, China plans for AI to blossom into a USD 100 billion industry by 2030, adding considerable value across its sectors.
The necessity for energy-efficient AI infrastructure remains critical. AI operations consume significant power, with data centers potentially constituting 8% of global electricity usage by 2030, per the International Energy Agency. This has directed governments and enterprises towards sustainable infrastructure designs. Japan offers subsidies for liquid cooling systems deployment in AI clusters, while the CHIPS and Science Act in the United States directs resources towards semiconductor ventures, impacting infrastructure dynamics.
Key Trends and Developments
August 2025: SK Telecom launched a new AI infrastructure leveraging GPU-as-a-Service, based on the latest NVIDIA Blackwell GPUs. This initiative promises significant contributions to South Korean AI industry development. July 2025: Oracle committed USD 2 billion to enhance its cloud and AI infrastructure in Germany, particularly amplifying Oracle Cloud's presence in Frankfurt. This move caters to growing AI infrastructure demands in Germany. June 2025: Telangana Data Exchange, in collaboration with Indian Institute of Science, initiated an AI Solutions platform, supporting startups and providing superior data access and collaborative tools. June 2025: NVIDIA, partnering with European tech leaders, aims to establish NVIDIA Blackwell AI infrastructure to support digital sovereignty and economic growth across Europe.
Rising Government-Backed AI Infrastructure Investments
Governments are vigorously promoting AI ecosystem development. In India, the National AI Mission is setting up computation clusters across academia, while Spain's MareNostrum 5 supercomputer, launched in 2023, is dedicated to AI and genomics research. In the USA, the National Science Foundation is investing USD 140 million for new AI research institutes.
Surge in Hyperscale Data Center Expansions
Hyperscale operators continue rapid enhancements. Microsoft plans a USD 3.2 billion investment in German AI data centers. These expansions require cutting-edge GPUs, custom AI accelerators, and networking advancements.
Energy Efficiency as a Core Infrastructure Priority
AI training's high power demand is prompting shifts towards sustainability. Nvidia and Meta are leading innovations with liquid-cooled systems and AI-optimized cooling techniques, while governments enforce strict energy standards.
AI Chip Specialization Redefining Infrastructure Demand
AI infrastructure is shaped by custom silicon, like Nvidia's GPUs and Cerebras's Wafer-Scale Engine 3, essential in handling large parameter models, supported by incentives from entities like South Korea's Ministry of Trade.
Growing Adoption of Edge AI Computing
Industries like automotive and telecom are increasingly deploying AI servers for immediate operational feedback, extending AI infrastructure demand beyond traditional data centers.
Global AI Infrastructure Industry Segmentation
Market Breakup by Type:
Hardware: Core to AI tasks, requiring high-performance compute power. Server Software: Supports orchestration and scalability.
Market Breakup by Technology:
Machine Learning: Wide enterprise applications with scalable needs. Deep Learning: Demands high resources for intensive training.
Market Breakup by Deployment:
On Premises: Preferred for data control and compliance. Cloud: Gaining traction due to scalability and flexibility. Hybrid: Balances performance and security.
Market Breakup by Function:
Training: Dominant in infrastructure investment. Inference: Fuels AI application deployment and value generation.
Market Breakup by End Use:
Enterprises: Largest adopters driven by digital transformation. Cloud Service Providers: Fastest growth, expanding AI model access. Others: Including government and specialized research.
Market Breakup by Region:
North America: Leads with robust investments. Asia Pacific: Fastest-growing, driven by policy support. Europe, Latin America, Middle East, and Africa: Varying focuses on technology integration and digital projects.
Global AI Infrastructure Market Share
By type, hardware accounts for the largest share due to high-performance compute demand
Hardware is integral, bolstered by AI-dedicated servers and advanced computing necessities. Institutions like Meta and OpenAI require substantial GPU clusters. Server software's growth aligns with orchestration needs. Systems like Kubernetes enhance workload handling, vital for comprehensive AI endeavors.
By technology, machine learning registers the largest share due to versatile enterprise applications
Machine learning's adaptability fuels demand across industries, requiring moderate resources. Deep learning, pivotal in generative AI, fosters infrastructure needs with expansive data processes.
By deployment, on-premises solutions secure the largest share due to security-focused deployments
Security drives on-premises preferences, especially in regulated domains like banking and health. Alternatively, cloud models grow, powered by cost-effective scaling options.
By function, training accounts for the largest share due to compute-heavy model development
Training's demand reflects extensive resource requirements to create sophisticated models. Inference, equally critical, commands infrastructure investments for real-world applications.
By end use, enterprises clock in the largest share due to digital transformation initiatives
Enterprises innovate with AI to refine operational capacities and engage deeply with customers. Their transformations necessitate dedicated infrastructure expansion.
Global AI Infrastructure Market Regional Analysis
North America registers the largest share due to hyperscale AI investments
The region, guided by substantial data center developments and supportive policy measures, maintains market supremacy. AI commitments from the U.S. fortify its stance.
Asia Pacific's acceleration stems from strategic government programs and rapid commercial adoption. India's and China's strategic plans position them robustly in AI landscapes.
Competitive Landscape
AI infrastructure leaders focus on high-performance computing, sustainable energy, and edge AI integration. Partnerships and innovations catalyze advancements in efficient, scalable infrastructures. Key players, including Intel and Nvidia, prioritize energy-efficient and high-speed AI solutions, while Google and Microsoft expand groundbreaking infrastructure capabilities.
The key companies featured in this AI Infrastructure market report include:
Intel Corporation Nvidia Corporation Google LLC Microsoft Corporation Cisco Systems, Inc. Amazon Web Services, Inc. International Business Machines Corporation Arm Limited
Key Attributes
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