Austin, March 24, 2026 (GLOBE NEWSWIRE) -- The AI Data Center Market size is estimated at USD 18.14 billion in 2025 and is expected to reach USD 203.26 billion by 2035, growing at a CAGR of 27.33% over 2026-2035.
The growth of the market is driven by increasing generative AI workload volumes, enterprise investment in large language model (LLM) training infrastructure, and hyperscaler demand for dedicated AI compute capacity at scale.
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The U.S. AI Data Center Market is estimated at USD 7.14 billion in 2025 and is expected to reach USD 79.98 billion by 2035, growing at a CAGR of 27.49% Over 2026-2035.
The concentration of hyperscale cloud providers, federal investment in AI infrastructure through the CHIPS and Science Act, and well-established semiconductor and data center supply chains supporting large-scale AI compute deployments are the main reasons why the United States is the largest market for AI data centers.
Segmentation Analysis:
By Component
The hardware segment accounted for the highest revenue share of approximately 58.76% in 2025, owing to the capital-intensive nature of GPU server procurement and high-bandwidth networking switch deployments globally. The software segment is anticipated to achieve the highest CAGR of nearly 29.14% during the 2026–2035 period, driven by the increasing demand for AI workload orchestration platforms globally.
By Data Center Type
By 2025, the hyperscale data centers segment contributed the largest revenue share of 46.32% due to the concentration of AI compute investment among top-tier cloud service providers. The edge data centers segment is projected to grow at the highest CAGR of about 31.47% between 2026 and 2035 due to the growing need for real-time AI inference processing in manufacturing, retail, autonomous vehicles, and smart city applications globally.
By Deployment
The cloud-based deployment segment accounted for the largest share of the AI data center market with about 52.18%, owing to the established hyperscaler AI platform ecosystems and flexible pay-per-use GPU instance pricing models globally. The hybrid deployment segment is slated to grow at the fastest rate with a CAGR of around 28.93% throughout the forecast period of 2026–2035, as enterprises in regulated industries seek comprehensive AI data center frameworks that combine on-premises data sovereignty controls with cloud-based AI compute scalability.
By Application
The AI model training application segment accounted for the highest revenue share of approximately 34.57% in 2025, owing to the extreme compute intensity of foundation model pre-training runs and the growing number of enterprises fine-tuning open-source LLMs on proprietary datasets globally. The autonomous systems & robotics segment is anticipated to achieve the highest CAGR of nearly 32.61% during the 2026–2035 period, driven by the increasing deployment of AI-powered industrial robots globally.
By Industry Vertical
The IT & telecom segment accounted for the largest share of the AI data center market with about 27.43%, owing to the direct role of technology and communications companies as both AI data center operators and primary consumers of AI compute infrastructure for network optimization, customer experience AI, and internal AI platform development. The healthcare segment is slated to grow at the fastest rate with a CAGR of around 30.18% throughout the forecast period of 2026–2035, as hospitals, pharmaceutical companies, and health insurance providers seek comprehensive AI data center capabilities for medical imaging analysis globally.
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Regional Insights:
Asia Pacific is the fastest-growing region in the AI data center market with a CAGR of 29.67%, as the awareness about AI compute infrastructure investment, government-backed national AI development programs, and data center construction activity in China, Japan, Singapore, and India is growing.
North America held the largest revenue share of over 41.53% in 2025 of the AI data center market due to the concentration of hyperscale cloud providers, a well-established data center real estate and power infrastructure ecosystem, and increased enterprise and government investment in AI-specific computing environments.
Surging Generative AI and Large Language Model Workload Demand is Augmenting Market Expansion Globally
Growing enterprise adoption of AI-powered application programming interfaces (APIs), exponential growth in LLM parameter counts, and hyperscaler investment in dedicated AI training clusters to support foundation model development and fine-tuning operations are the main factors driving the AI data center market share. The base of the market, the penetration of GPU-dense hyperscale and colocation data center segments, and the expansion of the worldwide market share are all being driven by these solutions for AI compute scalability and enterprise AI deployment readiness.
Key Players:
- NVIDIA Corporation
- Microsoft Corporation (Azure)
- Amazon Web Services, Inc. (AWS)
- Alphabet Inc. (Google Cloud)
- Meta Platforms, Inc.
- Intel Corporation
- Advanced Micro Devices, Inc. (AMD)
- IBM Corporation
- Dell Technologies Inc.
- Hewlett Packard Enterprise (HPE)
- Equinix, Inc.
- Digital Realty Trust, Inc.
- Vertiv Holdings Co.
- Schneider Electric SE
- Supermicro (Super Micro Computer, Inc.)
- Arista Networks, Inc.
- Cisco Systems, Inc.
- Broadcom Inc.
- Lenovo Group Limited
- Huawei Technologies Co., Ltd.
Recent Developments:
In March 2025, NVIDIA Corporation launched the NVIDIA GB300 NVL72 AI data center rack system delivering over 1.4 exaflops of AI compute performance, with confirmed procurement commitments from multiple top-tier hyperscalers for AI training cluster deployments targeting 2025 and 2026 facility buildouts.
In January 2025, Microsoft Corporation announced a USD 80 billion AI data center infrastructure investment commitment for fiscal year 2025, with the majority of new AI-optimized facility capacity planned for deployment across the United States to support growing Azure AI and OpenAI platform workload demands.
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Exclusive Sections of the Report (The USPs):
- AI Compute Utilization & GPU Efficiency Metrics – helps you understand GPU cluster utilization, training throughput, and idle compute waste, enabling optimization of performance and cost across AI workloads.
- AI Inference Performance & Latency Benchmarking – helps you evaluate inference speed across edge, colocation, and hyperscale environments, supporting deployment decisions for real-time AI applications.
- Power Efficiency & Sustainability Metrics – helps you assess PUE trends, cooling technologies, renewable energy adoption, and water usage efficiency to align with sustainability and cost-efficiency goals.
- AI Workload Growth & Capacity Planning Analysis – helps you identify the gap between rising AI compute demand and available infrastructure, enabling better long-term capacity planning and investment strategies.
- Infrastructure Scaling & Enterprise Migration Trends – helps you track foundation model growth, GPU cluster scaling requirements, and enterprise shift from on-premises to cloud/hybrid environments.
- Capex, Pricing & ROI Benchmarking – helps you evaluate investment efficiency through capex per megawatt, pricing differentials, total cost of ownership, and return on AI infrastructure investments.
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