AI Infrastructure Market Size, Share Analysis & Growth Research Report, 2030

Name: AI Infrastructure Market Size, Share Analysis & Growth Research Report, 2030
Creator: Mordor Intelligence
License: https://www.mordorintelligence.com/privacy-policy

AI Infrastructure Market Size and Share

Market Overview

Study Period	2019 - 2030
Market Size (2025)	USD 87.60 Billion
Market Size (2030)	USD 197.64 Billion
Growth Rate (2025 - 2030)	17.71% 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.

AI Infrastructure Market (2025 - 2030) — Image © Mordor Intelligence. Reuse requires attribution under CC BY 4.0.

AI Infrastructure Market Analysis by Mordor Intelligence

Market Analysis

The AI infrastructure market reached USD 87.6 billion in 2025 and is forecast to climb to USD 197.64 billion by 2030, registering a 17.71% CAGR over the period. Growth reflects the decisive shift from limited pilots to production-scale rollouts, especially among hyperscalers, enterprises, and public-sector agencies that now depend on purpose-built compute, high-bandwidth fabrics, and advanced thermal management to run large language and generative AI models. Rapid cloud-native accelerator availability lowers capital barriers, and combined hardware, software, and facilities investments above USD 315 billion in 2025 by the three largest hyperscalers underscore the scale imperative. Hardware retains 72.1% share in 2024, yet software’s 19.7% CAGR highlights a move toward holistic platforms rather than discrete compute islands. Regionally, North America commands a 47.7% share, but Asia-Pacific’s 19.1% CAGR signals sovereign AI strategies and manufacturing digitization initiatives accelerating local demand. End-user patterns mirror this transformation: cloud service providers account for 51.3% of spending, while the enterprise segment’s 21% CAGR shows that AI is becoming a core operational capability rather than a research exercise.

Key Report Takeaways

By offering, hardware led with 72.1% revenue share in 2024; software is expanding at a 19.7% CAGR through 2030.
By deployment, on-premises installations held 56.4% of the AI infrastructure market share in 2024, while cloud solutions are projected to grow at 20.6% CAGR to 2030.
By end user, cloud service providers contributed 51.3% of the AI infrastructure market size in 2024; enterprise demand is forecast to rise at a 21% CAGR over the same period.
By processor architecture, GPUs captured 67.4% of market revenue in 2024 and are projected to advance at 17.9% CAGR to 2030.
By geography, North America controlled 47.7% of global spending in 2024; Asia-Pacific is on course for the fastest 19.1% CAGR through 2030.

Global AI Infrastructure Market Trends and Insights

Drivers Impact Analysis

Driver	(~) % Impact on CAGR Forecast	Geographic Relevance	Impact Timeline
Soaring H100/G100 GPU backlogs among hyperscalers	+3.2%	Global, concentrated in North America and Asia-Pacific	Medium term (2-4 years)
Rapid AI-specific network fabrics (Infiniband NDR, Ethernet 800G)	+2.8%	Global, early adoption in North America and EU	Short term (≤ 2 years)
Energy-efficient liquid cooling adoption	+1.9%	Global, regulatory-driven in EU, efficiency-driven in Asia Pacific	Medium term (2-4 years)
Government CHIPS-type subsidies for AI fabs	+2.1%	North America, EU, selective Asia-Pacific markets	Long term (≥ 4 years)
Cloud-native AI accelerator instances democratise access	+4.3%	Global, strongest in North America and EU	Short term (≤ 2 years)
Open-source AI framework optimisation (e.g., Triton, TVM)	+1.5%	Global, developer-community driven	Medium term (2-4 years)

Source: Mordor Intelligence

Soaring H100/G100 GPU Backlogs Among Hyperscalers

Pre-purchase contracts now running into billions have allowed hyperscalers to secure priority access to scarce H100 and next-generation G100 accelerators. Smaller AI firms often wait 12-18 months for delivery, turning GPU allocation into a competitive moat that favors well-capitalized providers. The shortage has also catalyzed GPU-as-a-service specialists such as CoreWeave, which raised USD 11.9 billion to supply on-demand compute capacity. Alternative architectures are gaining mind-share: AMD MI300X shipments are rising, and Intel’s Gaudi 3 is expected to broaden options, but ecosystem maturity still hinges on CUDA compatibility.^{[1]NVIDIA Corporation, “Spectrum-X800 and Quantum-X800 product brief,” nvidia.com}

Rapid AI-Specific Network Fabrics (Infiniband NDR, Ethernet 800 G)

Model sizes have vaulted into the trillion-parameter realm, forcing data-center networks to remove node-to-node latency barriers. NVIDIA Quantum-X800 Infiniband and Spectrum-X800 Ethernet deliver 800 Gb/s links, providing five-fold bandwidth increases over prior iterations and enabling synchronized GPU training at scale. Early adopters such as Microsoft Azure and Oracle Cloud Infrastructure have deployed the switches, while optical cable upgrades like Corning EDGE8 ensure loss-free throughput for dense GPU pods corning.^{[2]Corning Inc., “EDGE8 solutions for 800G AI networks,” corning.com}

Energy-Efficient Liquid Cooling Adoption

Rack power densities above 40 kW have outstripped the capability of legacy air systems, pushing direct-to-chip and immersion solutions into mainstream production. Deployments cite 10–30% energy savings relative to air cooling and help data centers meet rigorous Scope-2 targets in the EU and California. Microsoft’s commitment to zero-water cooling and Google’s advanced liquid systems highlight large-scale validation of the technology microsoft. ^{[3]Microsoft Corporation, “Zero-water cooling at hyperscale,” microsoft.com}

Cloud-Native AI Accelerator Instances Democratise Access

More than 50 distinct GPU-enabled instance types across AWS, Microsoft Azure, and Google Cloud now grant enterprises elastic access to H100 clusters for both training and inference workloads. The model shifts budget from capital to operational expenditure, improves time-to-solution, and has triggered a wave of niche clouds that undercut generalist pricing by 30-50% for specific workloads.

Restraints Impact Analysis

Restraint	(~) % Impact on CAGR Forecast	Geographic Relevance	Impact Timeline
AI-class GPUs in chronic short supply through 2026	-2.8%	Global, acute in emerging markets	Medium term (2-4 years)
400 V/48 V power-conversion limits in legacy DCs	-1.9%	Global, notable in mature markets	Short term (≤ 2 years)
Sovereign-AI export controls	-1.4%	US-China, selected EU jurisdictions	Long term (≥ 4 years)
Rising Scope-2 compliance costs	-1.1%	EU, California, expanding elsewhere	Medium term (2-4 years)

Source: Mordor Intelligence

AI-Class GPUs in Chronic Short Supply Through 2026

Post-earthquake wafer disruptions and limited advanced node capacity at TSMC have pushed delivery windows to 12–18 months. Hyperscalers insulating themselves with multi-year prepayments exacerbate the imbalance, leaving smaller buyers to pay 30-50% premiums for devices like the RTX 5090. Competitors are responding: AMD is scaling MI300X output while Intel readies Gaudi 3, yet CUDA ecosystem lock-in still skews preference toward NVIDIA devices.

400 V/48 V Power-Conversion Limitations in Legacy DCs

Traditional data centers designed for 10 kW racks face efficiency losses beyond 15% when serving AI clusters requiring up to 120 kW. Upgrading to 800 V high-voltage direct-current backplanes can cost millions per facility, delaying many projects. The Open Compute Project now advocates power envelopes up to 1 MW per rack, but adoption remains uneven.

Segment Analysis

By Offering: Hardware Dominance Meets Software Acceleration

Hardware accounted for 72.1% of 2024 spending, underscoring the capital intensity of GPU clusters, high-bandwidth memory, and specialty networking. Within the AI infrastructure market size for hardware, processor expenditure remains the largest line-item as H100 clusters scale in the thousands of nodes. Storage architectures continue shifting toward NVMe over Fabrics with integrated HBM caches to avoid I/O stalls.

Software, though smaller, is the fastest-expanding category at 19.7% CAGR. Cross-vendor orchestration stacks, compiler toolchains such as Triton, and MLOps suites unlock higher utilization, pushing organizations to value integrated platforms over raw devices. As a result, total cost-of-ownership calculations increasingly assign one-third of anticipated ROI to software-driven optimization rather than incremental hardware horsepower in the AI infrastructure market.

AI Infrastructure Market — Image © Mordor Intelligence. Reuse requires attribution under CC BY 4.0.

By Deployment: On-Premises Leadership Challenged by Cloud Transformation

On-premises installations delivered 56.4% of 2024 revenue, driven by latency-sensitive use cases in finance and healthcare. Many firms continue to locate AI workloads near regulated data to navigate privacy rules and maintain deterministic response times for clinical or trading algorithms.

Cloud consumption is scaling faster at 20.6% CAGR. Elastic accelerator instances allow experimentation without capital lock-in, while hybrid approaches balance sovereignty with burst capacity. Microsoft’s pledge to invest USD 80 billion in data centers devoted to AI workloads underlines how hyperscale build-outs are recalibrating capital allocation patterns in the AI infrastructure market.

By End User: CSP Dominance Amid Enterprise Growth Surge

Cloud service providers aggregated 51.3% of the 2024 demand, leveraging scale to extract better pricing on GPUs and networking. Their curated AI-as-a-service offerings absorb complexity, making them a default choice for developers.

Enterprises, however, record a 21% CAGR as AI moves from proof-of-concept to production. Manufacturing, logistics, and medical research adopt local or edge clusters for deterministic inference. Government agencies plan sovereign clouds to keep strategic AI inside national boundaries, further diversifying share capture across the AI infrastructure market size.

By Processor Architecture: GPU Supremacy With Emerging Alternatives

GPUs held 67.4% of 2024 revenue and retain the fastest 17.9% CAGR. The AI infrastructure market share for GPU deployments should remain high through 2030 because Blackwell-class devices preserve the CUDA toolchain while lifting performance per watt.

Specialized inference ASICs and FPGAs attract interest in power-constrained data centers. Cerebras WSE-3, Google TPU v5p, and AWS Inferentia 3 target latency-sensitive or cost-per-token workloads. Optical interconnect startups such as Ayar Labs seek to remove bandwidth bottlenecks, a development that could alter node design over the long term.

Geography Analysis

North America’s 47.7% share in 2024 reflects an unrivaled concentration of hyperscaler campuses, semiconductor research hubs, and public-funded incentives like the CHIPS Act and Stargate initiative. Virginia’s Data Center Alley maintains greater than 1.5 GW active power supply, while Texas and Oregon add renewables-backed capacity that keeps PUE ratios below 1.2. The AI infrastructure market continues to benefit from a pipeline of more than 40-plus greenfield facilities scheduled to open through 2026, though export restrictions affecting China-linked developers are reshaping supply contracts.

Asia-Pacific delivers the fastest 19.1% CAGR, with China, India, and Southeast Asian states directing funds toward national AI super-nodes, smart manufacturing corridors, and regional GPU clouds. Beijing-backed “computing vouchers” subsidize AI workloads, helping the region surpass 1,000 EFLOPS combined compute capacity by 2025. India’s Digital Personal Data Protection Act pushes local processing, leading hyperscalers to co-build with domestic telecoms for edge AI clusters around 5G rollouts.

Europe balances expansion with climate goals. The Climate Neutral Data Centre Pact forces operators to hit aggressive energy-efficiency targets and source renewable power. Liquid cooling adoption, already at 20% of new builds in 2025, is expected to exceed 60% by 2027 to comply with Scope-2 reporting. Meanwhile, InvestAI financing pools accelerate Research and development partnerships across Germany, France, and the Nordics, yet budget scale still trails North American and APAC totals, compelling European operators to form cross-regional alliances such as Saudi-EU data-hub projects.

Competitive Landscape

The AI infrastructure market shows moderate concentration. NVIDIA leads in training silicon, fortified by the CUDA stack and a road-map cadence of annual double-digit performance gains. AMD accelerates through MI300X availability and a USD 10 billion partnership with Humain to stand up AI factories in Saudi Arabia and beyond. Intel widens reach by adding workstation GPUs and funding optical interconnect spin-offs that dovetail with Gaudi-class accelerators.

Hyperscalers are vertically integrating: Google’s TPUv5p, AWS’s Trainium 2, and Microsoft’s Maia chips reduce reliance on external GPU vendors, though each still buys tens of thousands of NVIDIA units for flagship model training. CoreWeave and Lambda define a “neocloud” tier that courts price-sensitive researchers with niche, bare-metal GPU offerings at up to 40% discount relative to big-three on-demand rates.

Edge infrastructure remains white space. Vendors such as Spectro Cloud and Zededa provide turnkey clusters hardened for manufacturing plants and hospitals, combining zero-touch provisioning and baked-in security. Optical interconnect innovators like Ayar Labs, funded by Intel, NVIDIA, and AMD, could alter chiplet-based package design and future-proof bandwidth needs above 100 Tb/s per node.

AI Infrastructure Industry Leaders

NVIDIA Corporation
Intel Corporation
Amazon Web Services
Microsoft (Azure)
Advanced Micro Devices
*Disclaimer: Major Players sorted in no particular order

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Recent Industry Developments

January 2025: OpenAI, SoftBank, and Oracle formed Stargate with USD 500 billion funding to erect AI-optimized data centers in the United States, targeting 100,000 jobs and national resilience.
March 2025: NVIDIA launched Blackwell Ultra GPUs and RTX PRO 6000 Blackwell Server Edition across Google Cloud and Microsoft Azure at GTC 2025.
April 2025: NVIDIA committed USD 500 billion to expand domestic chip manufacturing, signaling unmatched capital depth for AI hardware.
May 2025: AMD and Humain announced a USD 10 billion alliance to build AI factories in Saudi Arabia and elsewhere.
June 2025: NVIDIA finalized the acquisition of Run:ai to strengthen multi-cluster resource scheduling for enterprise AI deployments

Table of Contents for AI Infrastructure Industry Report

1. INTRODUCTION

1.1 Scope of the Study

2. RESEARCH METHODOLOGY

3. EXECUTIVE SUMMARY

4. MARKET LANDSCAPE

4.1 Market Drivers
- 4.1.1 Soaring H100/G100 GPU backlogs among hyperscalers
- 4.1.2 Rapid AI-specific network fabrics (Infiniband NDR, Ethernet 800 G)
- 4.1.3 Energy-efficient liquid cooling adoption
- 4.1.4 Government CHIPS-type subsidies for AI fabs
- 4.1.5 Cloud-native AI accelerator instances democratise access
- 4.1.6 Open-source AI framework optimisation (e.g., Triton, TVM)
4.2 Market Restraints
- 4.2.1 AI-class GPUs in chronic short supply through 2026
- 4.2.2 400 V / 48 V power-conversion limitations in legacy DCs
- 4.2.3 Sovereign-AI export controls (US-China, EU)
- 4.2.4 Rising Scope-2 emissions compliance costs
4.3 Supply-Chain Analysis
4.4 Regulatory Landscape
4.5 Technological Outlook
4.6 Porter's Five Force Analysis
- 4.6.1 Bargaining Power of Buyers
- 4.6.2 Bargaining Power of Suppliers
- 4.6.3 Threat of New Entrants
- 4.6.4 Threat of Substitutes
- 4.6.5 Competitive Rivalry
4.7 Assesment of Macroeconomic Trends on the Market

5. MARKET SIZE AND GROWTH FORECASTS (VALUE)

5.1 By Offering
- 5.1.1 Hardware
- 5.1.1.1 Processor
- 5.1.1.2 Storage
- 5.1.1.3 Memory
- 5.1.2 Software
- 5.1.2.1 System Optimisation
- 5.1.2.2 AI Middleware / MLOps
5.2 By Deployment
- 5.2.1 On-premise
- 5.2.2 Cloud
5.3 By End User
- 5.3.1 Enterprises
- 5.3.2 Government and Defence
- 5.3.3 Cloud Service Providers
5.4 By Processor Architecture
- 5.4.1 CPU
- 5.4.2 GPU
- 5.4.3 FPGA/ASIC (TPU, Inferentia, Gaudi, Cerebras)
- 5.4.4 Others
5.5 By Geography
- 5.5.1 North America
- 5.5.1.1 United States
- 5.5.1.2 Canada
- 5.5.1.3 Mexico
- 5.5.2 South America
- 5.5.2.1 Brazil
- 5.5.2.2 Argentina
- 5.5.2.3 Rest of South America
- 5.5.3 Europe
- 5.5.3.1 United Kingdom
- 5.5.3.2 Germany
- 5.5.3.3 France
- 5.5.3.4 Sweden
- 5.5.3.5 Rest of Europe
- 5.5.4 Asia-Pacific
- 5.5.4.1 China
- 5.5.4.2 Japan
- 5.5.4.3 India
- 5.5.4.4 Australia
- 5.5.4.5 South Korea
- 5.5.4.6 Rest of Asia-Pacific
- 5.5.5 Middle East and Africa
- 5.5.5.1 Saudi Arabia
- 5.5.5.2 United Arab Emirates
- 5.5.5.3 Turkey
- 5.5.5.4 South Africa
- 5.5.5.5 Rest of Middle East and Africa

6. COMPETITIVE LANDSCAPE

6.1 Strategic Moves
6.2 Market Share Analysis
6.3 Company Profiles (includes Global level Overview, Market level overview, Core Segments, Financials as available, Strategic Information, Market Rank/Share, Products and Services, Recent Developments)
- 6.3.1 NVIDIA Corporation
- 6.3.2 Intel Corporation
- 6.3.3 Advanced Micro Devices (AMD)
- 6.3.4 Amazon Web Services, Inc.
- 6.3.5 Microsoft Corporation
- 6.3.6 Google LLC
- 6.3.7 IBM Corporation
- 6.3.8 Cisco Systems, Inc.
- 6.3.9 Hewlett Packard Enterprise
- 6.3.10 Dell Technologies, Inc.
- 6.3.11 Samsung Electronics Co., Ltd.
- 6.3.12 Micron Technology, Inc.
- 6.3.13 Arm Holdings plc
- 6.3.14 Synopsys, Inc.
- 6.3.15 Baidu, Inc.
- 6.3.16 Alibaba Cloud
- 6.3.17 Tencent Cloud
- 6.3.18 Cerebras Systems
- 6.3.19 Graphcore
- 6.3.20 Huawei Technologies Co., Ltd.

7. MARKET OPPORTUNITIES AND FUTURE OUTLOOK

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Global AI Infrastructure Market Report Scope

AI infrastructure refers to the platforms on which organizations can build intelligent applications that are predictive, self-healing, and require minimal human intervention. It denotes using artificial intelligence and machine learning solutions to create and deploy dependable, scalable, and targeted data solutions.

The AI Infrastructure Market is segmented by offering (hardware [processor, storage, and memory] and software), by deployment (on-premise and cloud), by end users (enterprises, government, and cloud service providers), and by geography (North America [United States and Canada], Europe [United Kingdom, Germany, France, Italy, Spain, and the Rest of Europe], Asia Pacific [China, India, South Korea, Japan, and the Rest of Asia-Pacific], Latin America [Brazil, Mexico, and the Rest of Latin America], and Middle East and Africa [Saudi Arabia, United Arab Emirates, Qatar, Israel, South Africa, and the Rest of Middle East and Africa]). The market size and forecasts are provided in terms of value in USD for all the above segments.

By Offering	Hardware	Processor
		Storage
		Memory

	Software	System Optimisation
		AI Middleware / MLOps
By Deployment	On-premise
	Cloud
By End User	Enterprises
	Government and Defence
	Cloud Service Providers
By Processor Architecture	CPU
	GPU
	FPGA/ASIC (TPU, Inferentia, Gaudi, Cerebras)
	Others

By Geography	North America	United States
		Canada
		Mexico

	South America	Brazil
		Argentina
		Rest of South America

	Europe	United Kingdom
		Germany
		France
		Sweden
		Rest of Europe

	Asia-Pacific	China
		Japan
		India
		Australia
		South Korea
		Rest of Asia-Pacific

	Middle East and Africa	Saudi Arabia
		United Arab Emirates
		Turkey
		South Africa
		Rest of Middle East and Africa

By Offering

Hardware	Processor
	Storage
	Memory
Software	System Optimisation
	AI Middleware / MLOps

By Deployment

On-premise

Cloud

By End User

Enterprises

Government and Defence

Cloud Service Providers

By Processor Architecture

CPU

GPU

FPGA/ASIC (TPU, Inferentia, Gaudi, Cerebras)

Others

By Geography

North America	United States
	Canada
	Mexico
South America	Brazil
	Argentina
	Rest of South America
Europe	United Kingdom
	Germany
	France
	Sweden
	Rest of Europe
Asia-Pacific	China
	Japan
	India
	Australia
	South Korea
	Rest of Asia-Pacific
Middle East and Africa	Saudi Arabia
	United Arab Emirates
	Turkey
	South Africa
	Rest of Middle East and Africa

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Key Questions Answered in the Report

What is the current size of the AI infrastructure market?

The AI infrastructure market is valued at USD 87.6 billion in 2025 and is forecast to reach USD 197.64 billion by 2030, reflecting a 17.71% CAGR.

Which region leads spending today?

North America commands 47.7% of global spending thanks to hyperscaler campuses, semiconductor research and development, and supportive government incentives.

Which segment is growing fastest?

Software, although smaller than hardware, is expanding at 19.7% CAGR because orchestration, compilers, and MLOps platforms unlock higher hardware utilization.

How severe is the current GPU shortage?

Industry lead times stretch to 12–18 months, and pricing can exceed MSRP by up to 50% until new capacity ramps after 2026.

Page last updated on: June 18, 2025