Data Center Accelerator Market size is set to surpass USD 25 billion by 2028, according to a new research report by Global Market Insights Inc.
Increased emphasis on parallel computing in AI data centers is driving the data center accelerator market. The speed of AI algorithms has grown due to the increasing usage of neural networks and machine learning that demand parallel computing solutions. Artificial neural networks perform effectively with a parallel computing framework. This model is helpful in developing deep learning training and interfaces. Deep learning accelerators with parallel processing capacities enable on-demand machine learning for augmented reality, virtual reality, and various other applications. Increased use of neural networks and machine learning has resulted in the rising demand for data center accelerators.
The COVID-19 pandemic had a moderate impact on the data center accelerator market. To prevent the COVID-19 outbreak, government authorities imposed stringent lockdowns and travel restrictions. This disrupted the overall supply chain and product development process in the first half of 2020. The market gained traction in the second half of 2020 attributed to lockdown relaxations and resumption of industrial activities. Market players witnessed a rise in demand for cognitive computing technologies owing to the increased use of machine learning and Artificial Intelligence (AI) in various industry verticals during this period. This prompted market players to offer high-performance AI-capable processors with high memory bandwidths and computational capabilities.
Increasing demand for GPU processor-based data center accelerators in hyperscale facilities will aid in market growth
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GPU processor-based data center accelerators are increasingly being used in hyperscale data center workloads. In applications ranging from energy exploration to deep learning, data scientists and researchers can analyze large magnitudes of data orders. Data center accelerator market players provide solutions with optimum horsepower that are required to conduct larger simulations.
The solution also provides maximum user density and performance for virtual desktops, apps, and workstations. Several players are launching products that provide optimal server classes for diverse Supercomputing (SCX) applications, Training (HGX-T), and Inference (HGX-I). Accelerator cards can adapt to changing acceleration requirements and algorithm standards, which can enhance any task without hardware alterations, and lower the Total Cost of Ownership (TCO).
Growing implementation of data center accelerators in deep learning training models to propel the market expansion
Data center accelerators are optimum for training Artificial Intelligence (AI) and deep learning models as they can perform numerous calculations simultaneously. This leads to the distribution of training processes, which significantly speeds up machine learning activities. Data center accelerators can effectively handle complicated processes and aid in deep learning features including matrix manipulations and computational capacities. It accelerates training procedures in many deep learning models for image classifications, video analyses, and natural language processing. The heterogeneous design of data center accelerators enables a single system to accept numerous specialized processors to handle certain tasks, offering a computational performance that deep learning applications demand.
Browse key industry insights spread across 300 pages with 297 market data tables and 33 figures & charts from the report, “Data Center Accelerator Market Size By Processor Type (CPU, GPU, FPGA, ASIC), By Type (HPC Accelerator, Cloud Accelerator), By Application (Deep Learning Training, Public Cloud Interface, Enterprise Interface), By End-Use (IT & Telecom, Healthcare, BFSI, Government, Energy, Manufacturing), COVID-19 Impact Analysis, Regional Outlook, Growth Potential, Competitive Market Share & Forecast, 2022 – 2028”, in detail along with the table of contents:
Rising use of data center accelerators in the BFSI sector to satisfy customer & regulator demands offers strong market progression opportunities
Banks to provide a customized & hassle-free experience with mobile & internet banking capabilities are quickly replacing conventional face-to-face interactions. This is encouraging financial companies to deploy data center accelerators with high processing capabilities to significantly improve time-to-insight. Data center accelerators aid in the effective implementation of financial analytics to foresee risks, make informed business decisions for clients, and deliver distinctive financial services.
Accelerators provide optimal functions to construct fast computing solutions for financial workloads such as option pricing, modeling, trading, evaluation, and risk management. Low-latency electronic trading also makes use of data center accelerators. It addresses a wide range of algorithmic trading use cases while avoiding High-Frequency Trading (HFT) losses.
Increasing investments in innovative technologies will create growth opportunities for North America region
North America data center accelerator market is projected to witness 24% growth rate through 2028 owing to the investments being done by the companies in technologies, such as AI and ML. Owing to the widespread use of AI technology to differentiate and modernize processes & products, businesses in the U.S. and Canada are installing machine learning applications such as image & speech recognition.
Companies in the market are increasingly relying on these systems to provide faster training and real-time inference. Furthermore, there is a demand for enhanced & upgraded data center accelerators that can handle larger network bandwidths created by AI and ML applications.
New product launches form a key strategy among market players
Prominent companies operating in the data center accelerator market include Achronix Semiconductor, Advanced Micro Devices, Inc., Advantech Co., Ltd., Fujitsu Ltd., Alphabet Inc. (Google LLC), Dell Inc., Huawei Technologies Co., Ltd., IBM Corporation, Intel Corporation, Lattice Semiconductor, Lenovo Ltd., Marvell Technology Inc., Microchip Technology Inc., Micron Technology, Inc., NEC Corporation, NVIDIA Corporation, Qualcomm Incorporated, Skyworks Solutions Inc., Synopsys Inc., Western Digital Corporation, and Xilinx Inc. Market leaders are introducing innovative products for AI and machine learning applications.