資料中心 GPU 市場：2025-2030 年全球預測（按產品、記憶體容量、應用、部署模型和最終用戶）

預計2024年資料中心GPU市場規模將達251.3億美元，2025年將達304.4億美元，複合年成長率為21.55%，到2030年將達810.7億美元。

主要市場統計數據
基準年2024年	251.3億美元
預計2025年	304.4億美元
預測年份 2030	810.7億美元
複合年成長率（%）	21.55%

簡介技術創新和不斷變化的需求模式正在推動企業轉型，為資料中心 GPU 市場前所未有的成長動態奠定基礎

資料中心 GPU 已成為推動下一波企業運算能力發展的基礎引擎，重塑了企業處理和分析大量資料的方式。人工智慧工作負載、高效能運算模擬和進階虛擬需求的融合，將 GPU 推向了資料中心架構的核心。隨著工作負載的多樣化以及企業尋求加速數位轉型，GPU 的角色也不斷擴展，從傳統的圖形渲染任務擴展到機器學習模型訓練、推理和即時分析等關鍵領域。

變革性技術和市場變化重新定義了資料中心 GPU 在人工智慧、高效能虛擬、虛擬化和新興邊緣使用案例中的應用

人工智慧和深度學習的興起是 GPU 在資料中心快速普及的重要催化劑。神經網路訓練和推理任務需要 GPU 才能提供的大規模平行處理能力。這種需求正推動解決方案供應商創新處理器架構，增加核心數量、最佳化張量運算，並配備專用加速器，以縮短訓練時間並提高吞吐量。

評估美國關稅對 2025 年資料中心 GPU 供應鏈採購成本和策略採購決策的累積影響

我們的分析檢驗了美國對 GPU 進口徵收新關稅的累積影響，以及這些措施將如何影響全球供應鏈。半導體零件關稅的不斷上漲，增加了資料中心營運商和超大規模資料中心營運商的採購成本，促使許多企業重新評估其戰略採購實踐，尋找替代供應商並重新談判契約，以減輕貿易政策變化帶來的財務負擔。

關鍵市場細分洞察揭示了產品類型、記憶體容量、部署模型和最終用戶部門如何影響資料中心 GPU 採用模式

我們採用精細的細分方法，揭示資料中心 GPU 市場中微妙的促進因素和採用模式。透過分析產品架構、記憶體配置、部署類型和最終用戶垂直領域，相關人員可以自訂其策略，以滿足特定的工作負載需求和組織目標。

影響亞太資料中心 GPU 需求的區域動態：地緣政治經濟影響與成長動力

美洲地區主要雲端服務供應商和大型企業資料中心的佈局，正在推動先進 GPU 解決方案的需求。超大規模資料中心營運商正在大力投資 GPU 加速架構，以支援虛擬開發、高效能運算以及身臨其境型虛擬和擴增實境應用程式。該地區成熟的IT基礎設施和成熟的供應鏈網路將協助下一代 GPU 平台的快速普及，同時，對永續能源消耗的日益關注，也推動了對節能液冷 GPU 系統的投資。

為行業領導者提供可行的策略建議，以應對資料中心 GPU 市場的複雜性、最佳化具有成本效益的部署策略並最大限度地提高營運效率

為了應對資料中心 GPU 生態系統的複雜性，產業領導者必須採取積極主動的多維度方法，平衡效能需求、成本管理和長期可擴展性。建議企業進行全面的工作負載分析，以確定獨立和整合 GPU 解決方案的最佳組合，確保處理能力、記憶體配置和能耗目標之間的協調一致。

結合專家初步研究、二手資料整合和定量分析的綜合調查方法確保深入了解資料中心 GPU 市場。

本分析基於嚴謹的調查方法，旨在提供對資料中心 GPU 市場的全面可靠的洞察。我們的方法融合了定性和定量分析技術，以捕捉影響產業發展軌蹟的多元動態。

快速發展的資料中心 GPU 生態系統中的關鍵趨勢。總結性見解概述了利害關係人面臨的策略挑戰和新機會。

摘要：對於尋求駕馭人工智慧、高效能運算和虛擬的組織而言，資料中心 GPU 已成為不可或缺的資源。 GPU 架構的持續演進、記憶體的增強以及能源效率的提升，正在為各行各業的運算密集型應用開闢新的前景。

目錄

第1章：前言

第2章調查方法

第3章執行摘要

第4章 市場概述

第5章市場動態

• 加大研發投入，推動GPU效能與應用的突破
• 資料中心 GPU 的增強安全功能解決了日益嚴重的資料保護問題
• GPU製造商與雲端供應商之間的策略夥伴關係關係加速創新和市場滲透
• 開發針對深度學習和科學模擬等特定工作負載的專用 GPU
• 將 GPU 整合到邊緣運算中以支援即時資料處理和分析
• GPU 架構的進步提高了資料中心的運算能力和效率
• GPU虛擬技術的興起，提高了資源配置和可擴充性。
• 節能 GPU 對於降低資料中心營運成本的重要性日益凸顯
• 雲端服務供應商的擴張將加強資料中心GPU基礎設施的全球影響力。
• 人工智慧和機器學習應用的日益普及將推動對高效能資料中心 GPU 的需求

第6章 市場洞察

• 波特五力分析
• PESTEL分析

第7章 2025年美國關稅的累積影響

第 8 章：資料中心 GPU 市場（按產品）

• 離散的
• 融合的

9.資料中心 GPU 市場（按記憶體容量）

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

第 10 章：資料中心 GPU 市場（按應用）

• 人工智慧和機器學習
• 數據分析
  • 巨量資料分析
  • 流分析
• 高效能運算
• 渲染
  • 3D渲染
  • 影片渲染
• 虛擬和雲端圖形

第 11 章：資料中心 GPU 市場（依部署模式）

• 雲
• 本地

第 12 章：資料中心 GPU 市場（依最終使用者分類）

• BFSI
• 雲端服務供應商
• 政府和國防
• 醫療保健和生命科學
• 通訊

13.美洲資料中心GPU市場

• 美國
• 加拿大
• 墨西哥
• 巴西
• 阿根廷

14.歐洲、中東和非洲資料中心GPU市場

• 英國
• 德國
• 法國
• 俄羅斯
• 義大利
• 西班牙
• 阿拉伯聯合大公國
• 沙烏地阿拉伯
• 南非
• 丹麥
• 荷蘭
• 卡達
• 芬蘭
• 瑞典
• 奈及利亞
• 埃及
• 土耳其
• 以色列
• 挪威
• 波蘭
• 瑞士

15.亞太資料中心 GPU 市場

• 中國
• 印度
• 日本
• 澳洲
• 韓國
• 印尼
• 泰國
• 菲律賓
• 馬來西亞
• 新加坡
• 越南
• 台灣

第16章競爭格局

• 2024年市場佔有率分析
• 2024年FPNV定位矩陣
• 競爭分析
  • 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17章 研究人工智慧

第18章 研究統計

第19章 研究聯絡人

第20章 研究報導

第21章 附錄

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