资料中心 GPU 市场：2025-2030 年全球预测（按产品、记忆体容量、应用、部署模型和最终用户）

预计2024年资料中心GPU市场规模将达251.3亿美元，2025年将达304.4亿美元，复合年增长率为21.55%，到2030年将达810.7亿美元。

主要市场统计数据
基准年2024年	251.3亿美元
预计2025年	304.4亿美元
预测年份 2030	810.7亿美元
复合年增长率（%）	21.55%

简介技术创新和不断变化的需求模式正在推动企业转型，为资料中心 GPU 市场前所未有的成长动态奠定基础

资料中心 GPU 已成为推动下一波企业运算能力发展的基础引擎，重塑了企业处理和分析大量资料的方式。人工智慧工作负载、高效能运算模拟和进阶虚拟需求的融合，将 GPU 推向了资料中心架构的核心。随着工作负载的多样化以及企业寻求加速数位转型，GPU 的角色也不断扩展，从传统的图形渲染任务扩展到机器学习模型训练、推理和即时分析等关键领域。

变革性技术和市场变化重新定义了资料中心 GPU 在人工智慧、高效能虚拟、虚拟化和新兴边缘使用案例中的应用

人工智慧和深度学习的兴起是 GPU 在资料中心快速普及的重要催化剂。神经网路训练和推理任务需要 GPU 才能提供的大规模平行处理能力。这种需求正推动解决方案供应商创新处理器架构，增加核心数量、最佳化张量运算，并配备专用加速器，以缩短训练时间并提高吞吐量。

评估美国关税对 2025 年资料中心 GPU 供应链采购成本和策略采购决策的累积影响

我们的分析检验了美国对 GPU 进口征收新关税的累积影响，以及这些措施将如何影响全球供应链。半导体零件关税的不断上涨，增加了资料中心营运商和超大规模资料中心营运商的采购成本，促使许多企业重新评估其战略采购实践，寻找替代供应商并重新谈判合同，以减轻贸易政策变化带来的财务负担。

关键市场细分洞察揭示了产品类型、记忆体容量、部署模型和最终用户部门如何影响资料中心 GPU 采用模式

我们采用精细的细分方法，揭示资料中心 GPU 市场中微妙的驱动因素和采用模式。透过分析产品架构、记忆体配置、部署类型和最终用户垂直领域，相关人员可以自订其策略，以满足特定的工作负载需求和组织目标。

影响亚太资料中心 GPU 需求的区域动态：地缘政治经济影响与成长动力

美洲地区主要云端服务供应商和大型企业资料中心的布局，正在推动先进 GPU 解决方案的需求。超大规模资料中心营运商正在大力投资 GPU 加速架构，以支援虚拟开发、高效能运算以及身临其境型虚拟和扩增实境应用程式。该地区成熟的IT基础设施和成熟的供应链网路将协助下一代 GPU 平台的快速普及，同时，对永续能源消耗的日益关注，也推动了对节能液冷 GPU 系统的投资。

为行业领导者提供可行的策略建议，以应对资料中心 GPU 市场的复杂性、优化具有成本效益的部署策略并最大限度地提高营运效率

为了应对资料中心 GPU 生态系统的复杂性，产业领导者必须采取积极主动的多维度方法，平衡效能需求、成本管理和长期可扩展性。建议企业进行全面的工作负载分析，以确定独立和整合 GPU 解决方案的最佳组合，确保处理能力、记忆体配置和能耗目标之间的协调一致。

结合专家初步研究、二手资料整合和定量分析的综合调查方法确保深入了解资料中心 GPU 市场。

本分析基于严谨的调查方法，旨在提供对资料中心 GPU 市场的全面可靠的洞察。我们的方法融合了定性和定量分析技术，以捕捉影响产业发展轨蹟的多元动态。

快速发展的资料中心 GPU 生态系统中的关键趋势。总结性见解概述了利害关係人面临的策略挑战和新机会。

摘要：对于寻求驾驭人工智慧、高效能运算和虚拟的组织而言，资料中心 GPU 已成为不可或缺的资源。 GPU 架构的持续演进、记忆体的增强以及能源效率的提升，正在为各行各业的运算密集型应用开闢新的前景。

目录

第一章：前言

第二章调查方法

第三章执行摘要

第四章 市场概述

第五章市场动态

• 加大研发投入，推动GPU效能与应用的突破
• 资料中心 GPU 的增强安全功能解决了日益严重的资料保护问题
• GPU製造商与云端供应商之间的策略伙伴关係关係加速创新和市场渗透
• 开发针对深度学习和科学模拟等特定工作负载的专用 GPU
• 将 GPU 整合到边缘运算中以支援即时资料处理和分析
• GPU 架构的进步提高了资料中心的运算能力和效率
• GPU虚拟技术的兴起，提高了资源配置和可扩充性。
• 节能 GPU 对于降低资料中心营运成本的重要性日益凸显
• 云端服务供应商的扩张将加强资料中心GPU基础设施的全球影响力。
• 人工智慧和机器学习应用的日益普及将推动对高效能资料中心 GPU 的需求

第六章 市场洞察

• 波特五力分析
• PESTEL分析

第七章 2025年美国关税的累积影响

第 8 章：资料中心 GPU 市场（按产品）

• 离散的
• 融合的

9.资料中心 GPU 市场（按记忆体容量）

• 4GB 至 8GB
• 8GB 至 16GB
• 16GB 或更大
• 小于 4 GB

第 10 章：资料中心 GPU 市场（按应用）

• 人工智慧和机器学习
• 数据分析
  • 巨量资料分析
  • 流分析
• 高效能运算
• 渲染
  • 3D渲染
  • 影片渲染
• 虚拟和云端图形

第 11 章：资料中心 GPU 市场（依部署模式）

• 云
• 本地

第 12 章：资料中心 GPU 市场（依最终使用者划分）

• BFSI
• 云端服务供应商
• 政府和国防
• 医疗保健和生命科学
• 通讯

13.美洲资料中心GPU市场

• 美国
• 加拿大
• 墨西哥
• 巴西
• 阿根廷

14.欧洲、中东和非洲资料中心GPU市场

• 英国
• 德国
• 法国
• 俄罗斯
• 义大利
• 西班牙
• 阿拉伯聯合大公国
• 沙乌地阿拉伯
• 南非
• 丹麦
• 荷兰
• 卡达
• 芬兰
• 瑞典
• 奈及利亚
• 埃及
• 土耳其
• 以色列
• 挪威
• 波兰
• 瑞士

15.亚太资料中心 GPU 市场

• 中国
• 印度
• 日本
• 澳洲
• 韩国
• 印尼
• 泰国
• 菲律宾
• 马来西亚
• 新加坡
• 越南
• 台湾

第十六章竞争格局

• 2024年市场占有率分析
• 2024年FPNV定位矩阵
• 竞争分析
  • Advanced Micro Devices, Inc.
  • Analog Devices, Inc.
  • Arm Holdings PLC
  • ASUSTeK Computer Inc.
  • Broadcom Inc.
  • Fujitsu Limited
  • Google LLC by Alphabet Inc.
  • Hewlett Packard Enterprise Company
  • Huawei Investment & Holding Co., Ltd.
  • Imagination Technologies Limited
  • Intel Corporation
  • International Business Machines Corporation
  • Microsoft Corporation
  • NVIDIA Corporation
  • Oracle Corporation
  • VeriSilicon Microelectronics（Shanghai）Co., Ltd.

第十七章 研究人工智慧

第十八章 研究统计

第十九章 研究联络人

第二十章 研究报导

第21章 附录

The Data Center GPU Market was valued at USD 25.13 billion in 2024 and is projected to grow to USD 30.44 billion in 2025, with a CAGR of 21.55%, reaching USD 81.07 billion by 2030.

KEY MARKET STATISTICS
Base Year [2024]	USD 25.13 billion
Estimated Year [2025]	USD 30.44 billion
Forecast Year [2030]	USD 81.07 billion
CAGR (%)	21.55%

Introduction Setting the Stage for Unprecedented Growth Dynamics in the Data Center GPU Market Through Technological Innovation and Evolving Demand Patterns Driving Enterprise Transformation

Data center GPUs have emerged as the foundational engines driving the next wave of enterprise computing capabilities, reshaping how organizations process and analyze massive volumes of data. The convergence of artificial intelligence workloads, high performance computing simulations, and advanced virtualization demands has propelled GPUs to the core of data center architectures. As workloads diversify and organizations seek to accelerate their digital transformation initiatives, the role of GPUs continues to expand beyond traditional graphics rendering tasks into critical domains such as machine learning model training, inferencing, and real-time analytics.

Furthermore, ongoing advancements in GPU microarchitecture, memory bandwidth, and energy efficiency are expanding the boundaries of what is possible within server environments. Leading solution providers are optimizing GPU platforms to meet stringent performance and scalability requirements, while infrastructure architects are balancing cost considerations with the need for enhanced throughput and reduced latency. As enterprises embark on multi-cloud and hybrid deployment strategies, GPUs will play a pivotal role in enabling consistent, high-performance compute across heterogeneous environments.

In this executive summary, we explore the transformative shifts influencing the data center GPU landscape, assess the impact of evolving trade policies, and provide in-depth segmentation and regional insights. We conclude with strategic recommendations designed to help decision makers navigate the evolving ecosystem and capitalize on emerging opportunities.

Transformative Technological and Market Shifts Redefining Data Center GPU Applications in Artificial Intelligence High Performance Computing Virtualization and Emerging Edge Use Cases

The rise of AI and deep learning has been the primary catalyst behind the surge in data center GPU deployments. Neural network training and inference tasks require massive parallel processing capabilities that only GPUs can deliver at scale. This demand is driving solution providers to innovate processor architectures with higher core counts, tensor operation optimizations, and specialized accelerators that further reduce training times and improve throughput.

Parallel to advancements in AI, high performance computing workloads in scientific research, engineering simulations, and financial modeling have evolved to leverage GPU-accelerated compute clusters. By offloading compute-intensive tasks to GPUs, organizations can achieve significant gains in performance per watt and reduce time-to-insight for complex simulations. This convergence of AI and HPC is blurring traditional workload distinctions, leading to unified GPU platforms designed for both domains.

Virtualization technologies are also undergoing a transformation, as GPUs become increasingly virtualized to support multiple concurrent workloads across tenants and applications. Enhanced resource partitioning, hardware-level isolation, and quality of service controls enable more efficient utilization of GPU resources in multi-tenant data centers. Meanwhile, the ascent of edge computing is extending GPU capabilities to distributed infrastructure, where latency-sensitive applications such as autonomous systems and real-time analytics demand localized acceleration.

These transformative shifts underscore the dynamic evolution of the data center GPU market. As workloads continue to diversify and new use cases emerge, organizations must adapt their infrastructure strategies to harness the full potential of GPU acceleration across cloud, hybrid, and edge environments.

Assessing the Cumulative Impact of United States Tariffs on Data Center GPU Supply Chains Procurement Costs and Strategic Sourcing Decisions in 2025

Our analysis examines the cumulative impact of newly imposed United States tariffs on GPU imports and how these measures reverberate throughout the global supply chain. Increased duty rates on semiconductor components have amplified procurement costs for data center operators and hyperscalers. This has prompted strategic sourcing reviews, with many organizations seeking alternative suppliers or renegotiating contracts to mitigate the financial burden imposed by trade policy changes.

Beyond immediate cost implications, tariffs have influenced long-term supply chain resilience strategies. Providers are exploring geographically diversified manufacturing footprints and consolidating relationships with foundries and packaging partners. These adjustments aim to alleviate risks associated with single-source dependencies, while ensuring consistent delivery schedules amid shifting regulatory landscapes. In some cases, build-to-order models and buffer inventory tactics have emerged as practical countermeasures to counteract potential component shortages.

Moreover, the tariffs have accelerated conversations around domestic semiconductor manufacturing initiatives in the United States. Policymakers and industry consortiums are evaluating incentives and subsidies designed to revitalize local fabrication capabilities. Although these efforts represent strategic bets on long-term supply independence, they are unlikely to yield immediate relief for data center operators. In the interim, companies must navigate higher entry costs and optimize budget allocations to preserve performance benchmarks within the constraints of new tariffs.

As these trade policies stabilize, data center operators will need to balance cost management with performance imperatives. Organizations that proactively adapt their procurement frameworks and invest in supply chain agility will be best positioned to maintain competitive GPU infrastructure without compromising service level agreements or innovation roadmaps

Key Market Segmentation Insights Uncovering How Product Types Memory Capacities Deployment Models and End User Verticals Shape Data Center GPU Adoption Patterns

A granular segmentation approach reveals the nuanced drivers and adoption patterns within the data center GPU market. By dissecting the landscape across product architectures, memory configurations, deployment modalities, and end-user verticals, stakeholders can tailor their strategies to specific workload requirements and organizational objectives.

When considering product architectures, the differentiation between discrete GPU cards and integrated GPU solutions becomes critical. Discrete GPUs deliver dedicated high-performance processing cores and expanded memory interfaces that excel in compute-intensive applications, whereas integrated GPUs embed processing units within existing server chipsets, offering a more cost-effective and compact solution for lighter workloads and edge scenarios with strict space and cooling constraints.

Memory capacity segmentation further delineates use case suitability, with below four gigabytes catering to lightweight inference and edge analytics, four to eight gigabytes serving entry-level acceleration tasks, eight to sixteen gigabytes balancing cost and performance for mixed AI workloads, and above sixteen gigabytes addressing the most demanding deep learning model training and large-scale data processing scenarios. These tiers enable organizations to match GPU memory footprints with their specific data throughput and model complexity requirements.

Deployment model considerations span cloud-based and on-premise implementations, each presenting distinct advantages. Cloud-only deployments offer elasticity and pay-as-you-go flexibility, enabling organizations to scale GPU resources in alignment with fluctuating computational demands. Conversely, on-premise solutions provide tighter control over data security, latency, and integration with existing IT environments, which is essential for regulated industries or organizations with stringent compliance and performance requirements.

End-user vertical analysis uncovers tailored GPU adoption patterns across industries. Financial services institutions leverage GPUs for generation tasks, including content creation, synthetic data generation, and text generation, as well as inference scenarios such as real-time image and video analytics, recommender systems, and speech recognition and translation, alongside learning applications encompassing data analytics and big data processing, deep learning model training, and reinforcement learning. Education, energy and utilities, government, healthcare, IT and telecommunications, manufacturing, media and entertainment, and retail sectors each deploy similar generation, inference, and learning use cases, adapting GPU configurations to sector-specific performance, regulatory, and cost considerations

Regional Dynamics Shaping Data Center GPU Demand Trends Across the Americas EMEA and Asia Pacific Highlighting Geopolitical Economic Influences and Growth Drivers

In the Americas, the presence of leading cloud service providers and large enterprise data centers has propelled robust demand for advanced GPU solutions. Hyperscale operators are investing heavily in GPU-accelerated architectures to support artificial intelligence development, high performance computing, and immersive virtual and augmented reality applications. This region's mature IT infrastructure and established supply chain networks enable rapid adoption of next-generation GPU platforms, while rising emphasis on sustainable energy consumption is driving investments in energy efficient and liquid-cooled GPU systems.

The Europe, Middle East & Africa region presents a diverse landscape shaped by stringent data sovereignty regulations, varying levels of infrastructure maturity, and distinct economic dynamics. Regulatory frameworks such as data protection mandates influence deployment decisions, particularly in sectors like finance and healthcare where compliance is paramount. In parallel, emerging markets within this region are pursuing digital transformation initiatives, embracing cloud and hybrid GPU deployments to drive innovation in smart city projects, industrial automation, and academic research. Collaborative initiatives between governments and private consortia are fostering local GPU deployment and skills development programs, laying the groundwork for future growth.

Asia-Pacific stands out as a high-momentum market characterized by rapid technology adoption in both developed and emerging economies. Investment in artificial intelligence research and smart manufacturing has accelerated GPU uptake across data centers in countries such as China, Japan, South Korea, and India. The drive toward domestic semiconductor manufacturing and favorable government incentives has further bolstered the region's strategic importance. Additionally, the proliferation of 5G networks and edge computing deployments is creating a new frontier for GPU-accelerated applications in areas such as autonomous vehicles, intelligent surveillance, and real-time language processing.

Across all regions, geopolitical factors, energy infrastructure considerations, and local regulatory environments will continue to shape market dynamics. Organizations that align their GPU investment strategies with regional nuances and leverage collaborative ecosystem partnerships are poised to secure sustainable competitive advantages in the global data center GPU landscape

Leading technology providers have intensified their efforts to establish differentiated positions within the data center GPU market by pursuing targeted innovation, strategic partnerships, and ecosystem collaborations. A prominent player in this space has expanded its GPU line-up with specialized tensor cores and multi-chip module designs that deliver exceptional performance for both training and inference workloads. Another major vendor has focused on heterogeneous compute architectures, integrating GPU, CPU, and programmable logic to create highly adaptable acceleration platforms suitable for cloud, enterprise, and edge deployments. A third key contender has leveraged its extensive manufacturing scale to optimize production costs and secure supply chain resilience, enabling competitive pricing and rapid fulfillment.

In addition to product development, these companies are forging partnerships with hyperscale cloud providers, software vendors, and system integrators to enhance platform interoperability and streamline deployment. Collaborative engagements with artificial intelligence frameworks and high performance computing middleware providers are accelerating time-to-value for end-users by delivering pre-validated solutions and comprehensive developer tools. Furthermore, select providers are investing in industry consortiums and standards bodies to promote open GPU interfaces and facilitate cross-platform compatibility.

Research and development investments remain a critical differentiator, with the leading firms allocating significant resources toward next-generation microarchitectures, memory innovations, and power efficiency improvements. Strategic acquisitions of specialized AI accelerator startups and intellectual property portfolios have augmented internal capabilities, while joint ventures with foundries and packaging firms have strengthened long-term production pipelines. These combined efforts underscore the competitive intensity within the market and highlight the rapid pace of technological evolution.

As new entrants emerge and incumbent players refine their offerings, market participants must continuously monitor competitive developments and adapt their partnerships, product roadmaps, and go-to-market strategies. By aligning with the innovation trajectories of leading GPU providers, technology buyers can capitalize on best-in-class performance, reliability, and total cost of ownership benefits

Actionable Strategic Recommendations for Industry Leaders to Navigate Data Center GPU Market Complexity Optimize Cost-Effective Deployment Strategies and Maximize Operational Efficiency

To navigate the complexities of the data center GPU ecosystem, industry leaders should adopt a proactive and multi-dimensional approach that balances performance requirements, cost management, and long-term scalability. Organizations are advised to conduct comprehensive workload analyses to determine the optimal blend of discrete and integrated GPU solutions, ensuring alignment between processing power, memory configurations, and energy consumption targets.

It is imperative to diversify procurement strategies in light of geopolitical risks and tariff fluctuations. This entails establishing relationships with multiple suppliers, exploring alternate sourcing regions, and negotiating flexible contracts that incorporate volume discounts and contingency provisions. Concurrently, companies should consider hybrid and multi-cloud deployment models to leverage the unique advantages of public cloud elasticity while maintaining critical workloads on-premise for security and compliance mandates.

Strategic partnerships with ecosystem stakeholders can accelerate deployment and reduce integration overheads. By collaborating with software vendors, cloud service providers, and systems integrators, organizations can access optimized reference architectures and pre-certified solutions that streamline development pipelines. Additionally, allocating dedicated resources to research and pilot emerging GPU architectures, such as those optimized for edge inference or specialized AI accelerators, will position enterprises to rapidly adopt innovations that deliver competitive differentiation.

Finally, continuous performance monitoring and capacity planning must be institutionalized to ensure GPU infrastructures deliver sustained value. Implementing advanced analytics and predictive maintenance frameworks will optimize resource utilization, drive operational efficiencies, and enable data-driven decision making. By adopting these actionable recommendations, industry leaders can effectively harness the transformative power of data center GPUs and secure a strategic advantage in an increasingly compute-intensive digital landscape.

Comprehensive Research Methodology Employing Primary Expert Interviews Secondary Data Synthesis and Quantitative Analysis to Ensure Robust Data Center GPU Market Insights

This analysis is grounded in a rigorous research methodology designed to deliver comprehensive and reliable insights into the data center GPU market. Our approach integrates both qualitative and quantitative techniques to capture the multifaceted dynamics influencing industry trajectories.

Primary research involved structured interviews with over one hundred industry executives, technology architects, supply chain experts, and end-user decision makers. These conversations provided firsthand perspectives on emerging use cases, procurement challenges, and deployment best practices. Additionally, roundtable discussions with academic researchers and technology consortium representatives offered deep insights into R&D advancements and regulatory implications.

Secondary research encompassed an extensive review of publicly available resources, including corporate financial reports, patent filings, technical white papers, and regulatory filings. Data from trade associations, industry benchmarks, and public filings were synthesized to validate market trends, competitive strategies, and technology roadmaps. This secondary data served as a critical cross-reference to corroborate and contextualize findings from primary sources.

Quantitative analysis employed statistical modeling and trend extrapolation techniques to identify correlations between market drivers, pricing dynamics, and adoption rates. Segmentation analysis was conducted across product types, memory capacities, deployment models, and end-user verticals to reveal distinct demand patterns. Regional analyses were performed to uncover geopolitically influenced variations in adoption and investment behaviors.

To ensure accuracy and impartiality, all findings underwent a multi-tiered validation process. Peer review sessions with subject matter experts and cross-functional data reconciliation ensured that conclusions were both robust and actionable. The result is a comprehensive, evidence-based perspective on the data center GPU ecosystem that supports strategic decision making for technology providers and end-users alike.

Conclusive Insights Summarizing Key Trends Strategic Challenges and Emerging Opportunities for Stakeholders in the Rapidly Evolving Data Center GPU Ecosystem

In summary, data center GPUs have become indispensable for organizations striving to harness the power of artificial intelligence, high performance computing, and virtualization. The continuous evolution of GPU architectures, memory enhancements, and energy efficiency improvements is driving new horizons for compute-intensive applications across industries.

Transformative technological shifts, including the convergence of AI and HPC workloads, virtualization advancements, and the rise of edge use cases, are redefining the value proposition of GPU acceleration. Segmentation analysis highlights the importance of aligning product architectures, memory capacities, deployment models, and end-user requirements to optimize performance and cost profiles. Regional dynamics further underscore the need for localized strategies that account for regulatory environments, infrastructure maturity, and geopolitical considerations.

Competitive landscape insights reveal that leading GPU providers are aggressively enhancing their offerings through innovation, partnerships, and strategic investments. Industry leaders must remain vigilant in tracking these developments and adapting their procurement, deployment, and ecosystem collaboration approaches to maintain a competitive edge. By embracing forward-looking recommendations and leveraging data-driven methodologies, organizations can successfully navigate market uncertainties and capitalize on emerging opportunities.

Ultimately, the strategic deployment of data center GPUs will be a critical determinant of organizational agility and innovation capacity. Decision makers that proactively address cost pressures, supply chain complexities, and technological evolution will be best positioned to realize the full potential of GPU-accelerated computing in an increasingly digital world.

Table of Contents

1. Preface

• 1.1. Objectives of the Study
• 1.2. Market Segmentation & Coverage
• 1.3. Years Considered for the Study
• 1.4. Currency & Pricing
• 1.5. Language
• 1.6. Stakeholders

2. Research Methodology

• 2.1. Define: Research Objective
• 2.2. Determine: Research Design
• 2.3. Prepare: Research Instrument
• 2.4. Collect: Data Source
• 2.5. Analyze: Data Interpretation
• 2.6. Formulate: Data Verification
• 2.7. Publish: Research Report
• 2.8. Repeat: Report Update

3. Executive Summary

4. Market Overview

• 4.1. Introduction
• 4.2. Market Sizing & Forecasting

5. Market Dynamics

• 5.1. Increasing investments in research and development driving breakthroughs in GPU performance and applications
• 5.2. Enhanced security features in data center GPUs addressing rising concerns over data protection
• 5.3. Strategic partnerships between GPU manufacturers and cloud providers accelerating innovation and market penetration
• 5.4. Development of specialized GPUs tailored for specific workloads such as deep learning and scientific simulations
• 5.5. Integration of GPUs in edge computing to support real-time data processing and analytics
• 5.6. Advancements in GPU architecture boosting computational capabilities and efficiency in data centers
• 5.7. Rise of GPU virtualization technologies enabling better resource allocation and scalability
• 5.8. Growing importance of energy-efficient GPUs to reduce operational costs in data centers
• 5.9. Expansion of cloud service providers enhancing the deployment of data center GPU infrastructure globally
• 5.10. Increasing adoption of AI and machine learning applications driving demand for high-performance data center GPUs

6. Market Insights

• 6.1. Porter's Five Forces Analysis
• 6.2. PESTLE Analysis

7. Cumulative Impact of United States Tariffs 2025

8. Data Center GPU Market, by Product

• 8.1. Introduction
• 8.2. Discrete
• 8.3. Integrated

9. Data Center GPU Market, by Memory Capacity

• 9.1. Introduction
• 9.2. 4GB to 8GB
• 9.3. 8GB to 16GB
• 9.4. Above 16GB
• 9.5. Below 4 GB

10. Data Center GPU Market, by Application

• 10.1. Introduction
• 10.2. AI & Machine Learning
• 10.3. Data Analytics
  • 10.3.1. Big Data Analytics
  • 10.3.2. Streaming Analytics
• 10.4. High Performance Computing
• 10.5. Rendering
  • 10.5.1. 3D Rendering
  • 10.5.2. Video Rendering
• 10.6. Virtualization & Cloud Graphics

11. Data Center GPU Market, by Deployment Model

• 11.1. Introduction
• 11.2. Cloud
• 11.3. On-premise

12. Data Center GPU Market, by End User

• 12.1. Introduction
• 12.2. BFSI
• 12.3. Cloud Service Providers
• 12.4. Government & Defense
• 12.5. Healthcare & Life Sciences
• 12.6. Telecommunication

13. Americas Data Center GPU Market

• 13.1. Introduction
• 13.2. United States
• 13.3. Canada
• 13.4. Mexico
• 13.5. Brazil
• 13.6. Argentina

14. Europe, Middle East & Africa Data Center GPU Market

• 14.1. Introduction
• 14.2. United Kingdom
• 14.3. Germany
• 14.4. France
• 14.5. Russia
• 14.6. Italy
• 14.7. Spain
• 14.8. United Arab Emirates
• 14.9. Saudi Arabia
• 14.10. South Africa
• 14.11. Denmark
• 14.12. Netherlands
• 14.13. Qatar
• 14.14. Finland
• 14.15. Sweden
• 14.16. Nigeria
• 14.17. Egypt
• 14.18. Turkey
• 14.19. Israel
• 14.20. Norway
• 14.21. Poland
• 14.22. Switzerland

15. Asia-Pacific Data Center GPU Market

• 15.1. Introduction
• 15.2. China
• 15.3. India
• 15.4. Japan
• 15.5. Australia
• 15.6. South Korea
• 15.7. Indonesia
• 15.8. Thailand
• 15.9. Philippines
• 15.10. Malaysia
• 15.11. Singapore
• 15.12. Vietnam
• 15.13. Taiwan

16. Competitive Landscape

• 16.1. Market Share Analysis, 2024
• 16.2. FPNV Positioning Matrix, 2024
• 16.3. Competitive Analysis
  • 16.3.1. Advanced Micro Devices, Inc.
  • 16.3.2. Analog Devices, Inc.
  • 16.3.3. Arm Holdings PLC
  • 16.3.4. ASUSTeK Computer Inc.
  • 16.3.5. Broadcom Inc.
  • 16.3.6. Fujitsu Limited
  • 16.3.7. Google LLC by Alphabet Inc.
  • 16.3.8. Hewlett Packard Enterprise Company
  • 16.3.9. Huawei Investment & Holding Co., Ltd.
  • 16.3.10. Imagination Technologies Limited
  • 16.3.11. Intel Corporation
  • 16.3.12. International Business Machines Corporation
  • 16.3.13. Microsoft Corporation
  • 16.3.14. NVIDIA Corporation
  • 16.3.15. Oracle Corporation
  • 16.3.16. VeriSilicon Microelectronics (Shanghai) Co., Ltd.

17. ResearchAI

18. ResearchStatistics

19. ResearchContacts

20. ResearchArticles

21. Appendix