Accelerator cards are special type of expansion cards designed specifically for the purpose of acceleration in various workloads in high-performance computing, cloud servers, and data centers. Accelerator cards are used for the mining of cryptocurrency, artificial intelligence (AI), big data analysis, and other complex operations. Accelerator cards are plugged in through the PCIe slots and they are programmable, allowing the user to instruct the card to perform complex operations. Accelerator cards are much more efficient when compared with microprocessors. These cards carry additional algorithms to perform the complex operations that the host processor may not be able to carry out or it may take much longer time to perform the complex operations. CPUs and GPUs are the widely used accelerator card in high-performance computing and data centers. In data centers, to accelerate the machine learning applications, developers use FPGAs and ASICs.
The accelerator card market share is segmented on the basis of processor type, accelerator type, application and region. Based on processor type, the market is bifurcated into central processing units, graphics processing units, field programmable gate arrays, and application-specific integrated circuits. By accelerator type, it is categorized into high performance computing accelerator and cloud accelerator. By application, it is categorized into data analytics, video & image processing, machine learning, financial computing, mobile phones, and others. Based on region, the market is analyzed across North America (the U.S. and Canada), Europe (Germany, the UK, France, and Rest of Europe), Asia-Pacific (China, Japan, India, and Rest of Asia-Pacific), and LAMEA (Latin America, Middle East, and Africa).
Key players operating in the accelerator card market include Nvidia Corporation, Intel Corporation, Advanced Micro Devices Inc., Xilinx Inc., Achronix Semiconductor Corporation, Cisco Systems Inc, Fujitsu Ltd, Oracle Corporation, HP Development Company, Huawei Technologies Co. Ltd, IBM Corporation and Kalray Corporation. These players adopt collaboration, partnership, and agreement as their key developmental strategies to increase revenue of the accelerator card industry and develop new products for enhancing product portfolio.
COVID-19 Scenario Analysis
The COVID-19 pandemic has led to the nationwide lockdown, increasing the work from home culture and shutting down of businesses. However, the cloud computing market witnessed growth due to the COVID-19 impact. Besides patient data analysis, in the healthcare industry, the demand for cloud computing technologies has increased. Healthcare professionals are widely using cloud storage solutions to track a large number of cases.
The demand for high-performance computing and AI is experiencing rapid increase. Accelerator cards are widely being used in data centers for high-performance computing which is creating lucrative opportunities for accelerator card industry. This high-performance computing technology played a vital role in helping the scientist to develop a vaccine against COVID-19. Across the globe, scientists are using these accelerator cards that help them in high-performance computing to run complex mathematical models that possess a huge volume of COVID-19 pandemic data. And with this combination of simulation and modeling with the new technologies of AI, the scientists were successful in obtaining much more accurate results. Thus, HPC technology helped the scientist in intercepting various complex problems in order to develop a drug against COVID-19.
Top Impacting Factors: Market Scenario Analysis, Trends, Drivers, and Impact Analysis
Accelerator cards are widely used in cloud servers, data centers, and high-performance computing (HPC) devices to manage the workload. The use of accelerator cards in cloud services enhances the performance of cloud computing.The growth in consumer-generated data and the rise in use of AI-based services have led to the increase in demand for AI-centric data centers. AI provides personalized services by understanding customer behavior data generated from CRM systems and product reviews This is expected to act as a driving force for the Accelerator card market growth, since the accelerator cards are widely used for developing and managing AI services.
As a result of the increase in data, cloud computing has also increased drastically. This has led to the use of the accelerator card in order to compute and manage the data with high precision and accuracy. This is assisting the demand for accelerator card market growth.
The factor that impacts the growth of the Accelerator card market share are the use of AI in these cards as AI is a complex system, and for developing, managing & implementing AI specialized skill set. For instance, developers dealing with AI systems should be aware of technologies such as cognitive computing, machine learning, machine intelligence, deep learning, and image recognition. Furthermore, integrating AI with existing systems is a difficult task and is expensive as well. Even minor errors can lead to system failure or malfunctioning of the system, and this will drastically affect the result and desired outcome and is expected to hamper the accelerator card market size.
Emerging Use of AI
The increase in the use of AI and machine learning because of its proven effectiveness and efficiency has led to the increase in the Accelerator cards market share. These cards help in improving the video performance and the graphic calculation in a system. CPU and GPU are the most commonly used accelerator cards. Accelerator cards are also used in data centers to accelerate machine learning applications. The accelerator cards are designed in a way that solve customer problems, improve the customer experience, and provide high-quality accelerated performance at an excellent cost and power efficiency.
Rise in Customer Data Generation
The rise in data generation nowadays has made the use of cloud computing for data storage an important feature. Cloud computing uses the technologies such as Artificial Intelligence and machine learning in order to manage a huge amount of data. Hence, the rise in demand for artificial intelligence and machine learning technologies is anticipated to propel the growth of accelerator cards. The increase in demand for cloud services is acting as a driving force for the need for adaptable solutions that can optimize the workload, this in turn is multiplying the demand for accelerator cards. Therefore, this interconnection and rapid integration with emerging technologies helps in the expansion of the accelerator card market forecast.
Most importantly, the protection of data is very much crucial and this can only be ensured with the help of advanced technologies that arise out of these accelerator card market forecast.
Key Benefits of the Report
- This study presents the analytical depiction of the accelerator card market along with the current trends and future estimations to determine the imminent investment pockets.
- The report presents information related to key drivers, restraints, and opportunities along with detailed analysis of the accelerator card market share.
- The current market is quantitatively analyzed to highlight the accelerator card market growth scenario.
- Porter’s five forces analysis illustrates the potency of buyers and suppliers in the market.
- The report provides a detailed accelerator card market analysis based on competitive intensity and how the competition will take shape in coming years.
Questions Answered in the Research Report
- Which are the leading players active in the accelerator card market?
- What are the current trends that will influence the market in the next few years?
- What are the driving factors, restraints, and opportunities in the market?
- What are the projections for the future that would help in taking further strategic steps?
Accelerator Card Market Report Highlights
| Aspects | Details |
 By ProcessorType |
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 By Application |
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 By Region |
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 Key Market Players | AMD, IBM, Mellanox Technologies, HP, Intel, NVIDIA, Microsoft, Dell, Supermicro, Xilinx |
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