Data Center GPU Market

[253 Pages Report] The data center GPU market was valued at USD 14.3 billion in 2023 and is estimated to reach USD 63.0 billion by 2028, registering a CAGR of 34.6% during the forecast period.

The growth of the data center GPU market is governed by rising focus on parallel computing in artificial intelligence (AI) data centers, growing use of deep learning technology in big data analytics, increasing data traffic and need for high computing power.

Data Center GPU Market Forecast to 2028

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Market Dynamics:

Drivers: Rising adoption of data center GPUs in enterprises

On- premise technology investments, are being made by most of the organizations, so for cloud adoption, operating in a hybrid architecture is necessary. Hence, cloud providers are selected by organizations for implementing hybrid cloud architecture without further investments in on-premise hardware and software in order to achieve their business goals. Amazon Web Services (US) entered in to a partnership with on-premise platform providers, such as Intel (US), Microsoft (US), and VMware (US), for developing hybrid capabilities across storage, networking, security, and management tools; and application deployments for making the integration of cloud services easy. Enterprises can run their existing applications with high performance on Amazon Web Services due to these hybrid capabilities. Growth opportunities are being sought by key tech giants in enterprise inference applications by installing enterprise solutions in data centers. NVIDIA’S Apache Spark 3.0 is the first release of Spark that offers fully integrated and seamless GPU acceleration for analytics and AI workloads. The breakthrough performance of GPUs empowers enterprises and researchers to train bigger models more frequently, ultimately realising the value of big data with the power of AI.

Restraint: High costs associated with data center GPUs

Graphics Processing Units (GPUs) have been originally designed for the purpose of rendering graphics and images, however they have emerged as powerful accelerators for data analytics tasks, in recent years,. Even though GPUs offer better speed and performance, they are more expensive compared to CPUs. . This is particularly true in the case of high-end GPUs with specialized architectures and features. On the other hand, CPUs, are typically less expensive, which makes them a more budget-friendly option for some data analytics projects.

High-performance data center GPUs can be expensive, both in terms of initial acquisition costs and ongoing operational expenses related to power and cooling.

Operational costs associated with the data center GPUs include acquisition costs, implementation costs of the GPUs, network integration costs, costs for system management, maintenance costs, costs for troubleshooting, and repair and training costs. network integration costs, costs for system management, maintenance costs, costs for troubleshooting, and repair and training costs. Integrating GPUs into existing data center infrastructure may require upgrades or modifications to support the additional power and cooling requirements. This can add to the initial investment costs.

GPUs generate significant heat when operating at full capacity. Data centers require efficient cooling systems to maintain optimal operating temperatures. Cooling can account for a significant portion of operational expenses, including electricity costs and maintenance. Software licensing costs for GPU-accelerated applications and frameworks can add to operational expenses. Enterprises may need to invest in specialized software or pay for GPU usage in cloud services.

Opportunity: Growing potential of GPUs in the healthcare sector

Aging is associated with progressive deterioration in the structure and function of organs. With the growth in the geriatric population, the incidence of various age-related diseases is expected to increase worldwide. Governments in several countries are increasingly focusing on adopting novel technologies to counter this and efficiently handle the growing burden on their respective healthcare systems. In scenarios like these, the introduction of Graphics Processing Units (GPUs) could potentially revolutionize the game. GPUs prove to be particularly well-suited for situations where the intricacy of tasks has historically hindered the swift and accurate generation of insights. Their remarkable capacity for parallelizing tasks allows for an improvement of a magnitude previously unattainable, far surpassing the incremental gains achieved by even the most high-powered traditional computing systems. With the emergence of more recent enterprise GPU chipsets, exemplified by the likes of the NVIDIA Ampere A100, the potential is unlocked to possess up to 70 times more cores at a fraction of the cost when compared to equivalent Central Processing Units (CPUs). Technological advancements have lowered the barriers to harnessing GPUs, providing healthcare companies with a valuable opportunity to accelerate tasks and harness data as never before.

Challenge: Security concerns associated with data center GPUs

Since data centers store sensitive information, including financial transactions, personnel records, and other corporate data that are both crucial and confidential, data center network connections are required to be reliable, safe, and, in many cases, encrypted to avoid costly breaches and data losses. Data Center GPUs, like any other hardware components in a data center environment, come with specific security concerns that need to be addressed to protect sensitive data and maintain the integrity of the infrastructure. Unauthorized physical access to Data Center GPUs can lead to tampering, theft, or replacement with compromised hardware. Data centers must implement strict access controls, surveillance, and physical security measures to prevent unauthorized individuals from accessing the hardware.

For example, recently, ESEA, a paid subscription service that offers competitive matchmaking, league play and cheat prevention, admitted that their users' graphic cards had been hijacked to mine Bitcoin virtual currency. A malicious entity has hidden aa bitcoin miner in ESEA (a video game service) software. This miner used the GPUs in users’ machines to earn cryptocurrency without their knowledge. The miner overheated and harmed the machines by overloading the GPUs.

GPUs have firmware that can potentially be targeted by malicious actors. Attacks on GPU firmware can lead to data exfiltration or compromised performance. Regular firmware updates and security audits are essential to mitigate this risk.

Data Center GPU Ecosystem

Healthcare segment to exhibit the second-highest growth in terms of data center GPU market for enterprises during the forecast period

The healthcare segment is expected to exhibit the second-highest growth for the data center GPU market for enterprises  during the forecast period. Data center GPUs are currently being used in many aspects of the healthcare industry, including medical imaging and diagnosis, drug discovery and development, clinical data analysis, radiotherapy planning, medical simulation and training, telemedicine and remote diagnostics, and healthcare analytics.

The market for training segment to witness higher growth during the forecast period

The market for training segment is expected to grow a higher CAGR during the forecast period. Training is computationally costly and is best accelerated with GPUs. The time taken to go through all the training samples can be reduced using GPU compared with CPU, even when using a small dataset.

Automotive enterprise segment to exhibit the highest growth in terms of data center GPU market during the forecast period

The data center GPU market for the automotive enterprise segment is expected to exhibit the highest growth during the forecast period.  The automotive industry relies on high-performance GPUs to manage graphics processing, including video and imaging, for a range of applications. Autonomous Driving and ADAS (Advanced Driver Assistance Systems), training and testing of AI models, data processing and analytics, infotainment and user experience, cybersecurity are some of the applications in which GPUs are being leveraged. Most autonomous vehicle manufacturers currently use GPU for their core AI processing. In March 2022, NVIDIA announced its new H100 AI processor, a successor to the A100, used for plotting a self-driving car’s route through traffic. The company also announced working on a new generation of its Hyperion car chip family, which is due in 2026.

Data Center GPU market in Asia Pacific estimated to grow at the fastest rate during the forecast period

The data center GPU market, in Asia Pacific is expected to grow at the highest CAGR during the forecast period. Asia Pacific is the host to a few of the fastest-growing and leading industrialized economies, such as China, Japan, and India in the world. It is witnessing dynamic changes in the adoption of new technologies and advancements in organizations across industries. Increasing adoption of deep learning and NLP technologies for finance, agriculture, marketing, and law applications is also driving the market in this region

Data Center GPU Market by Region

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Key Market Players

Major vendors in the data center GPU companies include NVIDIA Corporation (NVIDIA) (US), Intel Corporation (Intel) (US), Advanced Micro Devices, Inc (AMD) (US), Samsung Electronics Co., Ltd. (Samsung) (South Korea), Micron Technology, Inc. (Micron) (US), Qualcomm Technologies, Inc. (US), International Business Machines Corporation (IBM) (US), Google Inc. (Google) (US), Microsoft Corporation (Microsoft) (US), Imagination Technologies (UK), Advantech Co., Ltd. (Taiwan), Huawei Technologies Co., Ltd. (Huawei) (China), ZOTAC Technology Ltd. (Hong Kong), Apple Inc. (US), GIGABYTE Technology Co., Ltd. (Taiwan), Arm Ltd. (UK), and Graphcore (UK) are some of the key players in the data center GPU market.

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Scope of the Report

Data Center GPU Market Highlights

This research report categorizes the data center GPU market based on deployment type, function, end user, and Region.

Recent Developments

•   In August 2023, NVIDIA Corporation (US) announced NVIDIA OVX servers featuring the new NVIDIA L40S GPU, a powerful, universal data center processor designed to accelerate the most compute-intensive, complex applications, including AI training and inference, 3D designs and visualization, video processing and industrial digitalization with the NVIDIA Omniverse platform.
• In June 2023, Intel Corporation (US) introduced the Intel Arc Pro A60 and Pro A60M as new members of the Intel Arc™ Pro A-series professional range of graphics processing units (GPUs). The new products are a significant step up in performance in the Intel Arc Pro family and are carefully designed for professional workstations users with up to 12GB of video memory (VRAM) and support for four displays with high dynamic range (HDR) and Dolby Vision support.

Key Questions Addressed in the Report :

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