A quick peek into the report
Table of Contents
1.1 Industry Outlook
1.1.1 Ongoing Trends
1.1.1.1 Timeline of GPU and Server Design Upgrades
1.1.1.2 Data Center Capacities: Current and Future
1.1.1.3 Data Center Power Consumption Scenario
1.1.1.4 Other Industrial Trends
1.1.1.4.1 HPC Cluster Developments
1.1.1.4.2 Blockchain Initiatives
1.1.1.4.3 Super Computing
1.1.1.4.4 5G and 6G Developments
1.1.1.4.5 Impact of Server/Rack Density
1.1.2 Equipment Upgrades and Process Improvements
1.1.3 Adaptive Cooling Solutions for Evolving Server Capacities
1.1.3.1 Traditional Cooling Techniques
1.1.3.2 Hot and Cold Aisle Containment
1.1.3.3 Free Cooling and Economization
1.1.3.4 Liquid Cooling Systems
1.1.4 Budget and Procurement Model of Data Center End Users
1.1.5 Stakeholder Analysis
1.1.5.1 Value Chain Analysis
1.1.6 Ecosystem/Ongoing Programs
1.1.6.1 Consortiums, Associations, and Regulatory Bodies
1.2 Business Dynamics
1.2.1 Business Drivers
1.2.1.1 Surging Demand for Cryptocurrency Mining
1.2.1.2 Rising Enterprise Adoption of Data Center GPUs for High-Performance Computing Applications
1.2.2 Business Challenges
1.2.2.1 High Bargaining Power of GPU Manufacturers
1.2.3 Market Strategies and Developments
1.2.4 Business Opportunities
1.2.4.1 Technological Advancement in High-Performing Computing (HPC)
1.2.4.2 Government Support for Smart City Development and Digitalization
2.1 Asia-Pacific
2.1.1 Asia-Pacific (by Country)
2.1.1.1 Japan
2.1.1.2 China
2.1.1.3 Australia
2.1.1.4 Singapore
2.1.1.5 India
2.1.1.6 Rest-of-Asia-Pacific
3.1 Competitive Benchmarking
3.2 Market Share Analysis
3.2.1 By GPU Manufacturer
3.2.2 By GPU Server Manufacturer
3.3 Company Profiles
3.3.1 Server GPU Manufacturer
3.3.1.1 ASUSTeK Computer Inc.
3.3.1.1.1 Company Overview
3.3.1.1.2 Product and Customer Portfolio Analysis
3.3.1.2 INSPUR Co., Ltd.
3.3.1.2.1 Company Overview
3.3.1.2.2 Product and Customer Portfolio Analysis
3.3.1.3 Huawei Technologies Co., Ltd.
3.3.1.3.1 Company Overview
3.3.1.3.2 Product and Customer Portfolio Analysis
3.3.1.4 GIGA-BYTE Technology Co., Ltd.
3.3.1.4.1 Company Overview
3.3.1.4.2 Product and Customer Portfolio Analysis
3.3.1.5 Advantech Co., Ltd.
3.3.1.5.1 Company Overview
3.3.1.5.2 Product and Customer Portfolio Analysis
3.3.1.6 Fujitsu
3.3.1.6.1 Company Overview
3.3.1.6.2 Product and Customer Portfolio Analysis
4.1 Primary Data Sources
4.2 Secondary Data Sources
4.3 Market Estimation and Forecast
Table 1: Summary of Trends
Table 2: Comparison of Air Cooling and Liquid Cooling
Table 3: Comparison between Cost-Sensitive Buyers and Premium/High-End Buyers
Table 4: Key Consortiums, Associations, and Regulatory Bodies in the AI and Semiconductors - A Server GPU Market
Figure 1: Asia-Pacific AI and Semiconductors - A Server GPU Market, $Billion, 2022-2028
Figure 2: Asia-Pacific AI and Semiconductors - A Server GPU Market (by End User), $Billion, 2022-2028
Figure 3: Asia-Pacific AI and Semiconductors - A Server GPU Market (by Facility Type), $Billion, 2022-2028
Figure 4: Asia-Pacific AI and Semiconductors - A Server GPU Market (by Configuration Type), $Billion, 2022-2028
Figure 5: Asia-Pacific AI and Semiconductors - A Server GPU Market (by Form Factor), $Billion, 2022-2028
Figure 6: AI and Semiconductors - A Server GPU Market (by Region), $Million, 2022
Figure 7: Nvidia’s Official Roadmap
Figure 8: Domestic Electricity Consumption of Different Selected Countries vs. Data Centers in 2020 in TWh
Figure 9: HPC Cluster Project Growth, 2019-2020
Figure 10: Crypto Transaction Volume Growth, 2020-2022
Figure 11: Distribution of 500 Supercomputers, June 2019
Figure 12: Average Server Rack Density in Data Center, 2016-2020
Figure 13: Ripple Effects in Server GPU Market due to Equipment Upgradation and Process Improvements
Figure 14: Stakeholder Analysis
Figure 15: Value Chain Analysis
Figure 16: Cryptocurrency Ownership by Country, 2021-2022
Figure 17: Dynamics of Data Generation, Replication, and Utilization, 2019-2023
Figure 18: Share of Key Market Strategies and Developments, 2019-2023
Figure 19: Product Development and Innovations (by Company)
Figure 20: Partnerships, Collaborations, Joint Ventures, and Alliances
Figure 21: Competitive Benchmarking Matrix for Key AI and Semiconductors - A Server GPU Manufacturers
Figure 22: Competitive Benchmarking Matrix for Key AI and Semiconductors - A Server GPU Server Manufacturers
Figure 23: Market Share Analysis of AI and Semiconductors - A Server GPU Market (by GPU Manufacturer), 2022
Figure 24: Market Share Analysis of AI and Semiconductors - A Server GPU Market (by GPU Server Manufacturer), 2022
Figure 25: AI and Semiconductors - A Server GPU Market: Research Methodology
Figure 26: Data Triangulation
Figure 27: Top-Down and Bottom-Up Approach
Figure 28: Assumptions and Limitations
Asia-Pacific AI and Semiconductors - A Server GPU Market Report Coverage
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Asia-Pacific AI and Semiconductors - A Server GPU Market |
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Base Year |
2022 |
Market Size in 2023 |
$xxx Billion |
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Forecast Period |
2023-2033 |
Value Projection and Estimation by 2033 |
$xxx Billion |
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CAGR During Forecast Period |
xxx % |
|
|
Key Market Players and Competition Synopsis
The companies that are profiled have been selected based on inputs gathered from primary experts and analysing company coverage, product portfolio, and market penetration.
How can this report add value to an organization?
Product/Innovation Strategy: In the ever-evolving realm of server GPU technology, notable advancements are reshaping the landscape, with a focus on optimizing data center performance. Leveraging cutting-edge solutions, including AI-driven analytics and real-time monitoring platforms, server GPU technology offers intricate insights into server health, workload management, and resource utilization. Innovations such as advanced data center cooling techniques contribute to efficient temperature regulation, ensuring optimal server functionality. The market encompasses a diverse range of solutions, from high-performance computing platforms to precision cooling systems, enabling businesses to elevate operational efficiency and minimize resource utilization effectively.
Growth/Marketing Strategy: The AI and semiconductor – a server GPU market has experienced notable growth strategies adopted by key players. Business expansions, collaborations, and partnerships have played a crucial role. Companies are expanding their reach to multiple markets, establishing alliances, and entering research collaborations to strengthen their technological capabilities. Collaborative initiatives between technology companies and domain experts are propelling the advancement of cutting-edge monitoring tools. Furthermore, strategic joint ventures are facilitating the integration of diverse expertise, significantly enhancing the market presence of these solutions. This collaborative approach is pivotal in creating comprehensive, user-friendly, and efficient server GPU solutions.
Competitive Strategy: In the dynamic realm of the AI and semiconductor – a server GPU market, manufacturers are expanding their product portfolios to cater to diverse computing needs and applications. Rigorous competitive benchmarking reveals the distinct strengths of market players, highlighting their unique solutions and regional expertise. Strategic collaborations with research institutions and technology organizations are pivotal drivers of innovation, shaping the evolution of the server GPU landscape.
Introduction to Asia-Pacific AI and Semiconductors - A Server GPU Market
The Asia-Pacific AI and semiconductor - a server GPU market accounted for $3,284.2 million in 2023 and is expected to grow at a CAGR of 33.97% and reach $14,171.0 million by 2028. The rise of edge computing, which processes data near its source instead of relying solely on centralized cloud servers, is fueling the demand for GPU servers. Additionally, the growing trend of virtualization in data centers and enterprise environments is a major factor driving the need for GPU servers.
Market Introduction
The Asia-Pacific AI and semiconductors market, particularly for server GPUs, is experiencing rapid growth driven by several key factors. The proliferation of edge computing, which processes data near its source, is significantly boosting the demand for high-performance GPU servers. Additionally, the increasing trend toward virtualization in data centers and enterprise environments is propelling market expansion. The region's strong focus on AI development and integration across various sectors, including automotive, healthcare, and finance, is further enhancing the need for advanced GPU solutions. Government initiatives and substantial investments in technology infrastructure are also supporting market growth. Leading companies in the APAC region are investing in innovative GPU technologies to meet the rising demand, ensuring the market's continued expansion and technological advancement.
Market Segmentation:
Segmentation 1: by Application (End User)
• Cloud Computing
• HPC Application
Segmentation 2: by Product (Configuration Type)
• Single GPU
• Dual to Quad GPU
• High-Density GPU
Segmentation 3: by Country
• Japan
• China
• India
• Australia
• Singapore
• Rest-of-Asia-Pacific
Asia-Pacific AI and Semiconductors - A Server GPU Market
Focus on Application, Product, and Country - Analysis and Forecast, 2023-2028
Frequently Asked Questions
The global AI and semiconductor - a server GPU market accounted for $15.4 billion in 2023 and is expected to grow at a CAGR of 31.99% and reach $61.7 billion by 2028.
GPUs (Graphics Processing Units) are essential for AI applications because they can process multiple tasks simultaneously, making them ideal for training and inference in machine learning models. Their parallel processing capabilities significantly speed up complex computations required for deep learning algorithms.
Semiconductors are the fundamental building blocks of GPUs. They determine the performance, power efficiency, and computational capabilities of GPUs. Advances in semiconductor technology enable the production of more powerful and efficient GPUs, which are crucial for handling the demanding workloads of AI applications in servers.
Advancements in semiconductor technology, such as smaller node sizes (e.g., 7nm, 5nm) and new materials, allow for more transistors to be packed into a chip, enhancing GPU performance and energy efficiency. This leads to more powerful server GPUs capable of handling increasingly complex AI workloads.
GPUs are generally more efficient than CPUs for AI and machine learning tasks due to their parallel processing capabilities. While CPUs are designed for general-purpose computing and can handle a wide range of tasks, GPUs excel at handling the large-scale, repetitive calculations required for training and running AI models.