Austin, Aug. 27, 2025 (GLOBE NEWSWIRE) -- The AI Data Center Market was valued at USD 14.25 billion in 2024 and is projected to reach USD 98.04 billion by 2032, registering a remarkable CAGR of 27.33% between 2025 and 2032.
This rapid growth is driven by the surging demand for high-performance computing infrastructure to support AI workloads, machine learning, and big data analytics. Increasing adoption of cloud-based AI services, expansion of hyperscale data centers, and advancements in GPUs, TPUs, and storage solutions enhance processing efficiency. Additionally, rising investments in AI-driven applications across industries such as healthcare, automotive, finance, and IT further fuel market expansion.
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The U.S. AI Data Center Market was valued at USD 3.35 billion in 2024 and is projected to reach USD 20.12 billion by 2032, growing at a CAGR of 25.21% from 2025 to 2032. Growth is driven by increasing demand for AI computing infrastructure, cloud-based AI services, advanced GPUs and storage solutions, and expanding adoption of AI applications across industries.
Key Players:
- Advanced Micro Devices, Inc.
- Amazon Web Services, Inc.
- Arista Networks, Inc.
- Cisco Systems, Inc.
- Dell Technologies
- Google LLC
- Hewlett Packard Enterprise Development LP
- Hitachi Vantara LLC
- Intel Corporation
- International Business Machines Corporation
- Juniper Networks, Inc.
- Microsoft Corporation
- NetApp
- Nutanix
- NVIDIA Corporation
AI Data Center Market Report Scope:
| Report Attributes | Details |
| Market Size in 2024 | USD 14.25 Billion |
| Market Size by 2032 | USD 98.04 Billion |
| CAGR | CAGR of 27.33% From 2025 to 2032 |
| Base Year | 2024 |
| Forecast Period | 2025-2032 |
| Historical Data | 2021-2023 |
| Report Scope & Coverage | Market Size, Segments Analysis, Competitive Landscape, Regional Analysis, DROC & SWOT Analysis, Forecast Outlook |
| Key Segments | • By Component (Hardware, Software, Services) • By Data Center Type (Hyperscale Data Centers, Enterprise Data Centers, Colocation Data Centers, Edge Data Centers, Modular & Portable Data Centers) • By AI Application (AI Model Training, AI Model Inference, Big Data Analytics, Computer Vision Processing, Natural Language Processing (NLP), Autonomous Systems & Robotics, Cybersecurity & Fraud Detection) • By Industry Vertical (IT & Telecom, BFSI, Healthcare, Retail & E-commerce, Manufacturing, Government & Defense, Energy & Utilities, Media & Entertainment, Automotive, Others) • By Deployment (On-Premises, Cloud-Based, Hybrid) |
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By Component, Hardware Segment Leads AI Data Center Market with Highest Revenue Share Driven by Specialized AI Chips and Infrastructure
The Hardware segment dominated the AI Data Center Market with around 54% revenue share in 2024, driven by strong demand for GPUs, TPUs, and FPGAs for AI training and inference. Rapid AI workload growth, investments in high-performance computing, and deployment of energy-efficient servers and advanced cooling systems further boost global hardware adoption.
By Data Center Type, Hyperscale Data Centers Segment Leads AI Data Center Market with Largest Revenue Share and Scalable AI Infrastructure
The Hyperscale Data Centers segment held the highest revenue share of approximately 35% in 2024, driven by their capacity to manage large-scale AI model training and storage. Backed by tech giants, these centers provide optimized infrastructure, integrated AI accelerators, high-throughput networking, energy efficiency, and centralized orchestration, making them essential for enterprise and cloud AI deployments.
By Deployment, Cloud-Based Segment Leads AI Data Center Market with Largest Revenue Share Driven by Scalable AI Computing Platforms
The Cloud-Based segment held the highest revenue share of approximately 62% in 2024, driven by growing preference for flexible, scalable AI computing solutions from major cloud providers. Enterprises leverage cloud AI services to reduce upfront hardware costs and rapidly deploy models, while access to advanced AI chips, toolkits, and data management capabilities strengthens cloud dominance in AI-driven digital initiatives.
By Application, AI Model Training Segment Leads AI Data Center Market with Highest Revenue Share Driven by Large-Scale Model Development
The AI Model Training segment held the largest revenue share of around 29% in 2024, driven by the growing demand to develop complex models like GPT, DALL·E, and Gemini. Training these models requires extensive computational resources, prolonged runtimes, and high infrastructure and power investments, making it the most resource-intensive and costly AI workload globally.
By Industry Vertical, BFSI Segment Leads AI Data Center Market with Largest Revenue Share Driven by AI Adoption in Financial Services
The BFSI segment accounted for the highest revenue share of approximately 29% in 2024, fueled by extensive AI use in fraud detection, algorithmic trading, credit scoring, and risk analytics. The sector’s focus on security, real-time processing, and regulatory compliance drives demand for high-performance AI infrastructure, supported by ongoing investments in AI-powered digital transformation by banks and insurers.
North America Leads AI Data Center Market with Largest Revenue Share Driven by Early AI Adoption and Hyperscaler Presence
North America accounted for the highest revenue share of approximately 37% in 2024, driven by early AI adoption, strong presence of hyperscalers like Google, Microsoft, and Amazon, and substantial investments in AI infrastructure. The region’s mature cloud ecosystem, concentration of AI talent, and rapid innovation in generative AI fuel demand for advanced, high-performance AI data center deployments.
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Exclusive Sections of the Report (The USPs)
- USP 1 – AI Adoption & Maturity Assessment by Industry
Helps clients benchmark their AI data center readiness against peers and prioritize strategic deployment for efficiency gains.
- USP 2 – Energy Efficiency & Sustainability Metrics
Provides insights on AI-driven power optimization, cooling, and resource management to reduce operational costs and carbon footprint.
- USP 3 – Infrastructure & Hardware Benchmarking (GPUs, TPUs, FPGAs)
Assists clients in selecting optimal compute architectures for AI workloads, balancing performance, cost, and scalability.
- USP 4 – AI Workload Optimization & Automation Analysis
Enables clients to leverage AI for predictive maintenance, resource allocation, and workload scheduling to maximize uptime and efficiency.
- USP 5 – Security & Compliance Framework (Data Privacy, Regulatory Adherence)
Helps clients protect sensitive AI data and comply with regulations like GDPR, HIPAA, and industry-specific standards.
- USP 6 – Vendor & Cloud Service Provider Comparison
Aids in evaluating capabilities of hyperscalers, colocation providers, and AI infrastructure vendors to support strategic partnerships.
- USP 7 – Future Trends & Innovation Roadmap
Prepares clients for emerging technologies such as AI-driven autonomous data centers, edge AI deployment, and quantum computing integration.
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