NEWARK, Del. — The global Data Center GPU Market is projected to increase from USD 27.8 billion in 2026 to USD 341.2 billion by 2036, registering a compound annual growth rate (CAGR) of 28.5%, based on the forecast figures provided for this release. The outlook reflects growing demand for accelerated computing as businesses, cloud service providers, and research institutions expand their use of artificial intelligence (AI), machine learning, and high-performance computing.

Graphics processing units (GPUs) enable parallel processing for computationally intensive workloads. They support AI model training, inference, advanced analytics, and scientific simulations. As organizations deploy increasingly demanding applications, GPU infrastructure has become an important consideration in data center capacity planning.

FMI's currently published report identifies NVIDIA Corporation, Advanced Micro Devices (AMD), Intel Corporation, Google, and Samsung Electronics among the companies covered in its competitive analysis. However, the supplied 2026–2036 market figures require confirmation against an updated FMI forecast before publication.

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Data Center GPU Market Forecast and Key Figures

The forecast figures supplied for this release indicate significant expansion over the next decade.

• Market size in 2026: USD 27.8 billion
• Projected market size in 2036: USD 341.2 billion
• Forecast CAGR, 2026–2036: 28.5%
• Published report's leading deployment model: On-premises, with a reported 58.4% share in 2025
• Published report's leading function: Training, with a reported 61.7% share in 2025
• Published report's leading end-user category: Cloud service providers, with a reported 54.9% share in 2025

The segment shares above refer to FMI's currently available 2025 analysis, not verified 2026 segment estimates. They provide historical context for understanding the market's structure.

AI Training and Inference Drive Accelerated Computing Demand

AI model training is a major application for data center GPUs because it requires substantial parallel processing power and memory bandwidth. Organizations developing machine learning models rely on accelerated infrastructure to process large datasets and execute complex computations.

Inference creates another source of demand as AI models move into production. Businesses need computing resources to generate responses, analyze information, and support AI-enabled applications. Training and inference have different performance and utilization requirements, influencing how operators select and allocate GPU resources.

The commercial opportunity extends beyond the accelerator itself. Buyers also evaluate memory capacity, software compatibility, interconnect performance, power consumption, and the ability to scale workloads across multiple GPUs.

For technology suppliers, these requirements create opportunities to provide integrated computing platforms that combine hardware, software, networking, and system-management capabilities.

Cloud Expansion and Data Center Infrastructure Requirements

Cloud service providers are important participants in the data center GPU ecosystem because they offer accelerated computing resources to businesses that do not want to purchase and maintain all their own hardware.

Cloud-based GPU access can help organizations scale computing capacity according to workload requirements. On-premises infrastructure remains relevant for businesses that prioritize direct control over data, predictable access to resources, or specialized operating environments.

Both approaches require careful infrastructure planning. High-performance GPUs consume substantial electricity and generate heat, making power availability and thermal management important considerations. Large GPU clusters also require efficient networking to coordinate computation across accelerators.

Operators evaluating new deployments should consider total system performance, utilization, cooling requirements, and operating costs rather than assessing GPUs by processing specifications alone.

Regional Opportunities in GPU-Accelerated Computing

Regional demand depends on AI adoption, cloud infrastructure investment, enterprise modernization, and access to suitable computing capacity.

China and India are important markets to examine for their expanding digital infrastructure and AI-related computing requirements. The United States remains a significant market for cloud services, enterprise computing, and AI development. Germany and other European markets provide additional opportunities across industrial computing, research, and enterprise applications.

These factors are relevant to market planning, but the country growth rates published in FMI's current report relate to its 2025–2035 forecast period. They should not be presented as verified 2026–2036 projections without an updated source.

For companies entering new regions, practical considerations include data center capacity, power availability, supply-chain access, customer requirements, and the availability of technical support.

Competitive Landscape: Top Companies in the Data Center GPU Market

FMI's current competitive coverage identifies the following ten companies:

1. NVIDIA Corporation
2. Advanced Micro Devices, Inc.
3. Advantech Co., Ltd.
4. Google Inc.
5. Huawei Technologies Co., Ltd.
6. IBM Corporation
7. Imagination Technologies
8. Intel Corporation
9. Qualcomm Technologies, Inc.
10. Samsung Electronics Co., Ltd.

These companies participate in different parts of the computing and semiconductor ecosystem. NVIDIA and AMD provide GPU accelerator platforms, while Intel participates in data center computing and accelerator technologies. Google and IBM have roles in cloud and enterprise computing infrastructure. The remaining companies contribute through computing hardware, semiconductor technologies, and related platforms.

This list reflects FMI's published company coverage; it is not a ranking of market share or proof that all companies compete directly across every GPU category.

Competitive differentiation can depend on processing performance, memory bandwidth, software support, power efficiency, system compatibility, and availability. Buyers should assess these factors against the requirements of their specific workloads.

Strategic Priorities for Businesses and Technology Providers

For data center operators: Assess power, cooling, and networking capacity alongside GPU procurement to support reliable deployments.

For cloud providers: Match accelerator availability to training, inference, and enterprise workloads while managing utilization and operating costs.

For enterprise buyers: Compare cloud and on-premises options based on data governance, workload consistency, scalability, and total cost of ownership.

For technology suppliers: Prioritize software integration, efficient system design, and solutions that simplify deployment and management of GPU-accelerated infrastructure.

Data Center GPU Market Outlook Through 2036

Based on the figures supplied for this release, the Data Center GPU Market is projected to reach USD 341.2 billion by 2036, growing at a 28.5% CAGR from 2026. AI development, cloud computing, and high-performance workloads provide important demand drivers for accelerated computing infrastructure.

Access the full market assessment: Visit FMI's Data Center GPU Market report for the published forecast, segmentation, regional analysis, and competitive coverage: https://www.futuremarketinsights.com/reports/data-center-gpu-market

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