Data Center Accelerator Market Size and Share Analysis

Updated on : Oct 22, 2024

The global data center accelerator market size was valued at USD 109.9 billion in 2024 and is projected to reach USD 372.9 billion by 2029, growing at a CAGR of 27.7% during the forecast period from 2024 to 2029.

The Growing demand for cloud-based services  and surging adoption of deep learning technology in big data analytics are attributed to the ever-increasing demand for data center accelerator system industry.

Data Center Accelerator Market Forecast to 2029

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Data Center Accelerator Market Trends:

Driver: Increasing data volumes and pressing need for fast and efficient data processing

The exponential growth in data volume is significantly facilitating the development and adoption of data center accelerators, such as graphics processing units (GPUs), field-programmable gate arrays (FPGAs), and application-specific integrated circuits (ASICs).

These accelerators are designed to handle specialized and intensive computational tasks more efficiently than traditional central processing units (CPUs). The immense volume of data generated by social media platforms, IoT devices, online transactions, video streaming, and various other sources necessitates the processing power that these accelerators provide.

The sheer volume and complexity of data require robust computational power to perform real-time data analysis, machine learning (ML), and artificial intelligence (AI) tasks. GPUs, for instance, are highly effective in parallel processing, which is essential for training deep learning (DL) models.

This parallelism allows for faster processing of large datasets, leading to quicker insights and decision-making capabilities. As AI and ML applications become more prevalent, the demand for GPUs in data centers continues to rise, driving advancements in GPU technology and their integration into data center infrastructures.

Additionally, the need for low latency and high-throughput data processing in modern applications is another critical factor. Real-time applications such as autonomous vehicles, financial trading, and healthcare diagnostics depend on rapid data processing and immediate response times.

FPGAs and ASICs are particularly designed for real-time applications as they can be customized for specific tasks and provide lower latency and higher efficiency than general-purpose CPUs. These specialized hardware solutions enable data centers to meet the demands of modern applications and services by providing high throughput, minimal latency, scalability, and energy efficiency.

Moreover, the advent of edge computing, which aims to bring computation and data storage closer to the location where it is needed, further drives the need for efficient data center accelerators. With the rise in data generation at the edge, from smart devices and sensors, there is a growing need for local data processing to minimize latency and bandwidth usage. Data center accelerators are key in enabling powerful computation capabilities within smaller, localized data centers or even on-the-edge devices themselves.

Restraint: Premium pricing of accelerators

The growing use of AI in different industries has raised consumer expectations of AI technologies. However, the non-availability of affordable and energy-efficient hardware products, especially computing hardware, is slowing down the development of dedicated AI hardware, including deep learning accelerators.

Manufacturers of accelerators many times unable to meet the demand due to the high complexity and cost of producing these specialized chips. Limited supply relative to demand can drive up prices, especially during periods of high demand for AI and HPC applications.

The increasing adoption of AI across industries, coupled with the growing need for high-performance computing solutions, drives the demand for accelerators. This demand-side pressure allows manufacturers to maintain premium pricing.

Various AI technologies built to date have failed to make a larger impact on the AI market. Owing to the high cost of data center accelerators from NVIDIA and Tesla companies, most data center manufacturers are reluctant to adopt them. For cloud server inference applications, numerous FPGAs are developed to enhance security and fast computing; however, technical constraints and the high cost of reliable mechanical devices are curbing data center accelerator adoption.

Non-availability of energy-efficient hardware is a major constraint impeding the growth of the AI market. Numerous hardware manufacturing companies are working to create AI chips that can mimic the functions of the human brain and execute tasks in the cloud. However, recent advancements have highlighted the substantial power consumption of these AI chips, ranging between 50 and 75 watts. This puts them at risk of generating significant heat during prolonged use in AI applications.

Opportunity: Proliferation of MLaaS offerings

Machine learning is a subset of artificial intelligence. These algorithms are useful in many applications designed to eliminate human tasks and work independently. Deep machine learning is used to examine massive datasets, identify patterns, and outline the human interface based on the data. Data centers are equipped with sensors to filter out and provide historical data. Many research centers apply ML and AI to their historical data to improve efficiency and productivity.

The increasing demand for AI has led to a rise in the number of companies offering machine learning for cloud services. As a result, the adoption of cloud-based technology is increasing, which, in turn, is creating growth opportunities for data center accelerator market players.

Deep learning is projected to witness high demand, and machine learning-powered systems can contribute to predictive and preventive maintenance. These systems can enhance cooling efficiency, improve energy efficiency, and control temperature and cooling systems, thereby optimizing energy consumption. This is particularly important as electricity costs are crucial for data center infrastructures.

Machine learning can also be used to monitor network congestions, server performance, and disk utilization and help detect and envisage data outages. Thus, the machine learning revolution can enhance data center infrastructure and facilitate more intelligent and automated data management.

Machine Learning as a Service (MLaaS) refers to a collection of cloud-based tools designed to assist data scientists and data engineers in their everyday work, similar to how cloud-based office suites have transformed office environments. These MLaaS tools facilitate collaboration, version control, parallelization, and other processes that would otherwise be cumbersome.

Challenge: Unreliability of AI algorithms

AI is implemented through machine learning using a computer to run specific software that can be trained. Machine learning can help systems process data with the help of algorithms and identify certain features from that dataset. As AI applications grow increasingly complex and data-intensive, the performance and accuracy of AI models become critical.

However, inconsistencies in algorithmic predictions can lead to inefficiencies and errors, undermining the reliability of data center operations. These issues are exacerbated by the diverse and dynamic nature of data sets, which can further impact the stability of AI-driven processes. Consequently, ensuring robust and dependable AI algorithms is essential for the effective deployment of accelerators in data centers, necessitating ongoing advancements in algorithmic development and validation to maintain operational integrity and trustworthiness.

Data Center Accelerator Market Ecosystem

Prominent companies in this market include well-established, financially stable data center accelerator systems providers such as NVIDIA Corporation (US), Advanced Micro Devices, Inc (US), Intel Corporation (US), Alphabet Inc. (US), Qualcomm Technology, Inc. (US).

These companies have been operating in the market for several years and possess a diversified product portfolio and strong global sales and marketing networks. Along with the well-established companies, there are a large number of small and medium companies operating in this market, such as Graphcore (UK), and SambaNova Systems, Inc. (US)

Data Center Accelerator Market Segmentation

By application, enterprise inference segment is expected to grow with the highest CAGR from 2024 to 2029.

The enterprise inference segment is expected to record the highest CAGR of 45.8% during the forecast period. The growth of the segment can be attributed to the integration of AI and ML data processing capabilities, allowing enterprises to analyze large datasets more efficiently and accurately. The adoption of cloud computing also plays a crucial role by providing a scalable and flexible infrastructure for data storage and analysis. Additionally, advancements in big data analytics tools enable more sophisticated and nuanced interpretations of data.

By end-user, healthcare segment is expected to grow with the highest CAGR in 2029.

The healthcare segment is expected to exhibit the highest CAGR of 31.8% during the forecast. The exponential growth in healthcare data, driven by electronic health records (EHRs), medical imaging, genomic data, and wearable devices, necessitates advanced computational power for efficient processing and analysis.  Also, the rise of artificial intelligence (AI) and machine learning (ML) applications in healthcare, such as predictive analytics, personalized medicine, and diagnostic imaging, requires high-performance computing infrastructure to train complex models and deliver real-time insights.

Regional Analysis

In 2029, North America is projected to hold the highest CAGR of the overall data center accelerator market.

Data Center Accelerator Market by Region

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In 2029, The data center accelerator market in Asia Pacific is expected to grow at the highest CAGR. The growing trend of cloud computing has radically increased the economic impact of data center investments made by leading service providers such as Amazon.com, Inc. (US), Meta Platforms, Inc. (US), Google LLC (US), and Microsoft Corporation (US). The competition for data center projects has increased dramatically in North America. North American enterprises and research institutions are at the forefront of AI and machine learning research and applications.

Top Data Center Accelerator Companies - Key Market Players

• NVIDIA Corporation (US),
• Advanced Micro Devices, Inc (US),
• Intel Corporation (US),
• Alphabet Inc. (US),
• Qualcomm Technology, Inc. (US),
• Micron Technology, Inc. (US),
• IBM Corporation (US),
• Marvell (US),
• Achronix Semiconductor Corporation (US),
• Dell Inc. (US) are some of the key players in the data center accelerator Companies.

Data Center Accelerator Market Report Scope

Data Center Accelerator Market Highlights

This research report categorizes the data center accelerator market based processor, type, application, end-user, and region.

Recent Developments in Data Center Accelerator Industry

• In June 2024, AMD introduced next generation "Zen 5" Ryzen processors, which are designed to power advanced AI experiences. The Ryzen AI 300 Series processors feature the world's most powerful Neural Processing Unit (NPU) for next-gen AI PCs..
• In May 2024, NVIDIA Corporation collaborated with Microsoft to help developers build and deploy AI applications faster by delivering new optimizations and integrations for Windows developers.
• In April 2023, Micron Technology, Inc. has introduced the Micron 4150AT SSD, a groundbreaking automotive-grade storage solution designed for intelligent vehicles. This SSD is the world's first quad-port SSD, offering a PCIe Gen 4 interface, ruggedized automotive design, and impressive random read/write IOPS capabilities, making it suitable for diverse systems in vehicles like advanced driver-assistance systems (ADAS) and in-vehicle infotainment (IVI).
• In March 2024, NVIDIA Corporation introduced NVIDIA Blackwell. This platform introduces a new era of computing power with its Blackwell GPU architecture, featuring six transformative technologies for accelerated computing. New accelerators facilitate breakthroughs in data processing, engineering simulation, electronic design automation, computer-aided drug design, and quantum computing.
• In October2023, Micron Technology, Inc. partnered with AMD to provide memory solutions for various systems, including DDR4 and DDR5 systems with AMD EPYC processors. Micron's DDR5 RDIMM solutions have been validated for different densities, and they offer superior power efficiency compared to competitors..
• In October 2023, IBM acquired Manta Software Inc., a data lineage platform, to complement its data and AI governance capabilities within watsonx.ai, watsonx.data, and watsonx.governance. This acquisition aims to help businesses ensure trust and transparency in their products by providing visibility into data environments, data flows, sources, transformations, and dependencies.

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