Introduction to AI and Semiconductors A Server GPU Market
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. The proliferation of edge computing, where data processing occurs closer to the source of data generation rather than relying solely on centralized cloud servers, is driving the demand for GPU servers. The increasing trend toward virtualization in data centers and enterprise environments is also a significant driver for GPU servers.

The rapid development of machine learning and artificial intelligence applications is a major driver of this trend. A key element of AI and ML is the training of sophisticated neural networks, which is accelerated in large part by GPU servers. Companies such as Nvidia, for instance, have noticed a spike in demand for their GPU products, such as the Nvidia A100 Tensor Core GPU, which is intended especially for AI tasks. The global AI and semiconductor – server GPU market is growing as a result of the use of GPU servers by a variety of industries, including healthcare, finance, and autonomous cars, to handle large datasets and increase the precision of AI models.

The end-use application segment is a part of the application segment for the worldwide AI and semiconductor – server GPU market. Cloud computing (private, public, and hybrid clouds) and HPC applications (scientific research, machine learning, artificial intelligence, and other applications) are included in the end-use application sector. The global AI and Semiconductor – a server GPU market has also been divided into segments based on the kind of facility, which includes blockchain mining facilities, HPC clusters, and data centers (including hyperscale, colocation, enterprise, modular, and edge data centers).

According to estimates, the data center category will have the biggest market share in 2022 and will continue to lead the market during the projection period. The push toward GPU-accelerated computing in data centers is fueled by GPU technological breakthroughs that provide increased energy efficiency and performance. GPU servers can transfer certain computations from conventional CPUs to GPU servers, which improves overall performance and reduces energy consumption. Consequently, the increasing use of GPU servers in data centers is in line with the changing requirements of companies and institutions that want to manage the sustainability and efficiency of their data center operations while achieving higher levels of processing capacity.

The push toward GPU-accelerated computing in data centers is fueled by GPU technological breakthroughs that provide increased energy efficiency and performance. GPUs offer an efficient way to strike a balance between processing capacity and power consumption, which is something that data center operators are looking for in solutions. GPU servers can transfer certain computations from conventional CPUs to GPU servers, which improves overall performance and reduces energy consumption. Consequently, the increasing use of GPU servers in data centers is in line with the changing requirements of companies and institutions that want to manage the sustainability and efficiency of their data center operations while achieving higher levels of processing capacity.

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 Region

  • North America - U.S. and Rest-of-North America
  • Europe - Germany, France, Netherlands, Italy, Ireland, U.K., and Rest-of-Europe
  • Asia-Pacific - Japan, China, India, Australia, Singapore, and Rest-of-Asia-Pacific
  • Rest-of-the-World - Middle East and Africa and Latin America


Data center expansion and the rise of cloud computing services have further propelled the demand for GPU servers in North America. Cloud service providers, including industry giants such as Amazon Web Services (AWS), Microsoft Azure, and Google Cloud, are investing heavily in GPU infrastructure to offer customers high-performance computing capabilities on a scalable and cost-effective basis. This trend is particularly prominent as businesses increasingly rely on cloud-based resources for AI training, simulation, and other GPU-intensive tasks.

Recent Developments in the Global AI and Semiconductor- A Server GPU Market

  • In November 2023, the AMD Ryzen Embedded 7000 Series processor family, optimized for the high-performance demands of industrial markets, was unveiled by AMD today at Smart Production Solutions 2023. By fusing integrated Radeon graphics with ""Zen 4"" architecture, Ryzen Embedded 7000 Series processors offer performance and functionality that was not previously available for the embedded market.
  • In November 2023, Imagination Technologies introduced IMG DXD, the first model in a new range of DirectX-compatible high-performance GPU IP. The new IMG DXD has the API coverage to run well-known PC games in addition to other Windows-based apps and mobile games, starting with a hardware-based version of DirectX 11. The desktop market has already granted it a license to operate.
  • In September 2023, Advantech announced the acquisition of a 100% ownership position in BitFlow, Inc., a North American business specializing in AI machine vision and high-end picture collection. In the future, Advantech plans to expand BitFlow’s fundamental technology for high-speed image acquisition as well as its broad product line and market basis.
  • In November 2023, Expanding on its previously released high-density server product line, GIGABYTE announced the DLC-ready servers, H263-S63-LAN1 & H273-Z80-LAN1, which support NVIDIA GraceTM CPU & GraceTM HopperTM Superchip.



Demand – Drivers, Challenges, and Opportunities

Market Drivers:
GPU server producers can capitalize on this need by providing customized cryptocurrency mining solutions, including rigs specifically designed for mining, cloud-based mining services, or GPU-as-a-service platforms. By charging fees, charging subscriptions, or entering into contracts, these systems can make money for the makers while giving miners access to strong and scalable GPU resources.

The need for data center GPUs derives from their key role in AI model training and execution, which is especially advantageous for businesses engaged in computationally demanding tasks like engineering simulations and scientific research. Manufacturers of GPU servers can take advantage of this demand by providing specialized solutions for high-performance computing (HPC) applications, such as GPU-as-a-service platforms, cloud-based GPU services, and dedicated GPU servers. In addition to giving businesses scalable GPU resources, these customized services bring in money for the manufacturers through fees, subscriptions, or contracts.

Market Challenges:
The economies of scale provided by GPU manufacturers, most notably Nvidia, create a significant barrier to entry for manufacturers of data center GPU servers wishing to integrate backward. A company trying to backward integrate into the GPU production process, for example, would find it difficult to achieve equivalent economies of scale. This has an impact on the business’s capacity to maintain overall competitiveness, engage in research and development, and match prices. As a result, it might be difficult for producers of data center GPU servers to achieve comparable economies of scale, which could limit their efficacy in the extremely competitive market. Additionally, a recurring problem for manufacturers of data center GPU servers is the continual innovation by GPU manufacturers, demonstrated by the ongoing development of GPUs, CPUs, and data processing units (DPUs).

Market Opportunities:
OpenAI’s GPT-4, the latest and largest language model, is one specific real-time illustration of how GPU servers may help HPC and AI. It needed a lot of processing power to train on a huge dataset with over 1 trillion words. A significant contribution was made by GPU servers, more especially by Nvidia H100 Tensor Core GPUs, which sped up the training process up to 60 times faster than CPUs alone. Mixed-precision training was used to achieve this acceleration by optimizing both calculation performance and memory use. Because of this, GPT-4 might be trained in a few short weeks and accomplish cutting-edge results in challenges involving natural language processing.

Artificial intelligence (AI) and advanced analytics play a crucial role in smart cities as they optimize resource allocation, enhance public safety, and improve overall quality of life. Due to their suitability for AI and analytics workloads, GPU servers are becoming an essential part of the infrastructure for the development of smart cities.

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 global 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 global 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 global 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.

Research Methodology
The research methodology design adopted for this specific study includes a mix of data collected from primary and secondary research. Both primary sources (in-house experts, industry leaders, and market players) and secondary research (a host of paid and unpaid databases), along with analytical tools, are employed to build the forecast and predictive models.

Primary Data Sources
The primary sources involve global AI and semiconductors - a server GPU industry experts and stakeholders such as equipment and device manufacturers, suppliers, and others. Respondents such as vice presidents, CEOs, marketing directors, and technology and innovation directors have been interviewed to verify this research study’s qualitative and quantitative aspects.

The key data points taken from primary sources include:

  • validation and triangulation of all the numbers and graphs
  • validation of reports segmentation and key qualitative findings
  • understanding the competitive landscape
  • validation of the numbers of various markets for market type
  • percentage split of individual markets for region-wise analysis



Secondary Data Sources
This research study involves the usage of extensive secondary research, directories, company websites, and annual reports. It also makes use of databases, such as Hoovers, Bloomberg, Businessweek, and Factiva, to collect useful and effective information for an extensive, technical, market-oriented, and commercial study of the global market. In addition to the aforementioned data sources, the study has been undertaken with the help of other data sources and websites, such as the National Institute of Standards and Technology and the International Telecommunication Union.

Secondary research was done to obtain crucial information about the industry’s value chain, revenue models, the market’s monetary chain, the total pool of key players, and the current and potential use cases and applications.

The key data points taken from secondary research include:

  • segmentations and percentage shares
  • data for market value
  • key industry trends of the top players in the market
  • qualitative insights into various aspects of the market, key trends, and emerging areas of innovation
  • quantitative data for mathematical and statistical calculations



Some prominent names established in this market are:

Company Type 1: GPU Manufacturer

  • Nvidia Corporation (Nvidia)
  • Advanced Micro Devices, Inc. (AMD)
  • Intel Corporation (Intel)



Company Type 2: Server GPU Manufacturer

  • Dell Inc.
  • Penguin Computing, Inc.
  • Exxact Corporation


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