The global data center GPU market was valued at USD 87.32 billion in 2024. It is projected to reach USD 228.04 billion by 2030, at a CAGR of 13.7% during the forecast period of 2025 to 2030.
The data center GPU market is growing rapidly due to several key factors, including the widespread adoption of artificial intelligence (AI) and machine learning (ML), increased demand for high-performance computing, and expanding cloud services. Enterprises are utilizing GPUs to improve deep learning, large language models, and data analytics. The rise of generative AI applications and real-time inference systems further boosts the need for robust GPU infrastructure. Investments in hyperscale data centers and government initiatives to support national AI capabilities also play a role in this growth. Major cloud providers like Amazon Web Services, Google Cloud, and Microsoft Azure are enhancing their GPU offerings, while companies like NVIDIA and AMD are launching advanced GPUs tailored for training and inference workloads.
The rise of large language models, including OpenAI's GPT, Meta's LLaMA, and Google's Gemini, is increasing demand for powerful GPUs in technology, finance, and healthcare sectors. These models require extensive training over weeks and large datasets, leading to a need for dedicated GPU clusters. Companies are also creating proprietary AI models for competitive advantage. Cloud providers such as AWS, Microsoft Azure, and Google Cloud are enhancing their GPU-based training infrastructure. With AI at the forefront of business transformation, the demand for training infrastructure is set to grow significantly.
Extensive primary interviews were conducted with key industry experts in the data center GPU market space to determine and verify the market size for various segments and subsegments gathered through secondary research. The breakdown of primary participants for the report is shown below.
The study includes an in-depth competitive analysis of these key players in the data center GPU market, with their company profiles, recent developments, and key market strategies.
The data center GPU market is growing rapidly due to several key factors, including the widespread adoption of artificial intelligence (AI) and machine learning (ML), increased demand for high-performance computing, and expanding cloud services. Enterprises are utilizing GPUs to improve deep learning, large language models, and data analytics. The rise of generative AI applications and real-time inference systems further boosts the need for robust GPU infrastructure. Investments in hyperscale data centers and government initiatives to support national AI capabilities also play a role in this growth. Major cloud providers like Amazon Web Services, Google Cloud, and Microsoft Azure are enhancing their GPU offerings, while companies like NVIDIA and AMD are launching advanced GPUs tailored for training and inference workloads.
On-premises segment is expected to hold the highest CAGR during the forecast period.
On-premises solutions are expected to have the highest CAGR due to the increasing needs for data protection, low latency, and regulatory compliance in sectors like banking, automotive, retail, and healthcare. Organizations prefer to manage sensitive data with in-house GPU hardware for better control, rather than relying on third-party cloud services. On-premises data centers also allow for customized infrastructure, optimizing workloads for AI tasks that require low latency, which is crucial for real-time applications such as autonomous systems and high-frequency trading. As GPU servers become more affordable, mid-sized enterprises can invest in dedicated infrastructure. On-premises deployment is often preferred in regions with limited cloud connectivity or data sovereignty concerns, such as Asia Pacific, Europe, and the Middle East.Training segment is projected to record the second-highest CAGR during the forecast period.
The training segment is expected to see the highest growth in the data center GPU market, driven by businesses developing and optimizing large-scale machine learning and AI models. Training deep neural networks for applications like generative AI, computer vision, and natural language processing requires substantial computing power, which GPUs provide effectively.The rise of large language models, including OpenAI's GPT, Meta's LLaMA, and Google's Gemini, is increasing demand for powerful GPUs in technology, finance, and healthcare sectors. These models require extensive training over weeks and large datasets, leading to a need for dedicated GPU clusters. Companies are also creating proprietary AI models for competitive advantage. Cloud providers such as AWS, Microsoft Azure, and Google Cloud are enhancing their GPU-based training infrastructure. With AI at the forefront of business transformation, the demand for training infrastructure is set to grow significantly.
Cloud service providers (CSPs) are expected to hold the highest share of the end-user market in 2030
The Cloud Service Providers (CSPs) segment is expected to command the largest market share in the data center GPU market due to their scale, increasing AI infrastructure spending, and ability to meet the needs of enterprises and developers. Major CSPs like Amazon Web Services, Microsoft Azure, and Google Cloud are rapidly expanding their GPU data centers to meet the rising demand for AI training, inference, data analytics, and cloud gaming. They offer GPU-as-a-Service solutions, allowing companies to access advanced GPU technology without significant upfront investments. Additionally, the rise of foundation models and generative AI drives CSPs to create specialized AI supercomputers with thousands of GPUs. With their global infrastructure and robust developer ecosystems, CSPs are well-positioned to lead the data center GPU market in both revenue and volume.North America will likely register the second-highest market share in 2030.
North America is expected to lead the data center GPU market due to its advanced technological ecosystem and established cloud infrastructure. Major cloud computing companies like Amazon Web Services, Microsoft Azure, and Google Cloud are creating GPU-based data centers to support AI workloads, high-performance computing, and data analysis. North America also has a strong enterprise customer base across industries like healthcare, finance, automotive, and government, increasingly relying on AI-driven solutions that need GPU acceleration. Significant R&D investments, favorable government policies, and early technology adoption support this leadership.Extensive primary interviews were conducted with key industry experts in the data center GPU market space to determine and verify the market size for various segments and subsegments gathered through secondary research. The breakdown of primary participants for the report is shown below.
The study contains insights from various industry experts, from component suppliers to Tier 1 companies and OEMs. The break-up of the primaries is as follows:
- By Company Type: Tier 1 - 60%, Tier 2 - 10%, and Tier 3 - 30%
- By Designation: C-level executives - 10%, Directors - 30%, and Others - 60%
- By Region: Europe - 20%, North America - 70%, Asia Pacific - 5%, and RoW - 5%
The study includes an in-depth competitive analysis of these key players in the data center GPU market, with their company profiles, recent developments, and key market strategies.
Research Coverage:
The report segments the data center GPU market and forecasts its size by deployment (cloud, on-premises), function (training, inference), application (generative AI, machine learning, natural language processing, computer vision), and end user (cloud service providers, enterprises, and government organizations). It also discusses the market’s drivers, restraints, opportunities, and challenges. It gives a detailed view of the market across four main regions (North America, Europe, Asia Pacific, and RoW). The report includes an ecosystem analysis of the key players.Key Benefits of Buying the Report:
- Analysis of key drivers (growing adoption of AI and machine learning, demand for high-performance computing, cloud computing expansion, restraints (high costs of GPUs and infrastructure, short product lifecycle), opportunities (growth in autonomous systems, emergence of edge computing, advancements in quantum computing synergy), and challenges (existence of alternative technologies, stringent regulatory framework, supply chain disruptions)
- Service Development/Innovation: Detailed insights on upcoming technologies, research and development activities, and new product launches in the data center GPU market
- Market Development: Comprehensive information about lucrative markets - the report analyses the data center GPU market across varied regions
- Market Diversification: Exhaustive information about new products and services, untapped geographies, recent developments, and investments in the data center GPU market.
- Competitive Assessment: In-depth assessment of market shares, growth strategies, and service offerings of leading players, such as NVIDIA Corporation (US), Advanced Micro Devices, Inc. (US), Intel Corporation (US), Google Cloud (US), Microsoft (US), and Amazon Web Services, Inc. (US)
Table of Contents
1 Introduction
2 Research Methodology
4 Premium Insights
5 Market Overview
6 Gpu-as-a-Service (Gpuaas) Landscape
7 Data Center Gpu Market, by Deployment
8 Data Center Gpu Market, by Function
9 Data Center Gpu Market, by Application
10 Data Center Gpu Market, by End User
11 Data Center Gpu Market, by Region
12 Competitive Landscape
13 Company Profiles
14 Appendix
List of Tables
List of Figures
Companies Mentioned
- Nvidia Corporation
- Advanced Micro Devices, Inc.
- Intel Corporation
- Microsoft
- Amazon Web Services, Inc.
- Ibm
- Alibaba Cloud
- Oracle
- Coreweave.
- Tencent Cloud
- Lambda
- Vast.Ai
- Runpod
- Scalematrix Holdings, Inc.
- Digitalocean
- Jarvislabs.Ai
- Fluidstack
- Ovh Sas
- E2E Networks Limited
- Ace Cloud
- Snowcell
- Linode Llc
- Yotta Data Services Pvt Ltd.
- Vultr
- Rackspace Technology
- Gcore
- Nebius B.V.
