Data Center Accelerator Market by Processor Type (CPU, GPU, FPGA, ASIC), Type (HPC Accelerator, Cloud Accelerator), Application (Deep Learning Training, Public Cloud Interface, Enterprise Interface), and Geography - Global Forecast to 2026
[209 Pages Report] The global data center accelerator market size is projected to grow from USD 13.7 billion in 2021 to USD 65.3 billion by 2026; it is expected to grow at a CAGR of 36.7% from 2021 to 2026. Factors such as growing demand for deep learning and surge in demand for cloud-based services are driving the growth of the market during the forecast period.
To know about the assumptions considered for the study, Request for Free Sample Report
COVID-19 Impact on the Global Data center accelerator market
Post COVID-19, the manufacturing sector is expected to scale up smart manufacturing processes using AI, IoT, and blockchain technologies. By adopting these technologies, companies can cut costs, increase process efficiency, and reduce human contact significantly. Currently, AI is being used for predictive maintenance and will further be implemented to forecast demand and returns in the supply chain.
COVID-19 has impacted the educational industries rather positively, with ed-tech companies adopting AI technology to impart education during the lockdown. Ed-tech firms have deployed AI tools to enhance online learning and virtual classroom experience for students. For instance, Coursera has launched an AI-powered tool called the CourseMatch that helps schools and universities identify courses on the platform that matches their curriculum. Furthermore, a personalized online tutorial company Squirrel AI uses AI-based adaptive learning to curate lessons for a student.
Several industries are worse hit by this pandemic, but some industries are benefiting from this pandemic. However, the adoption of AI is expected to grow. Therefore, we can say the COVID-19 will drive the data center accelerator market for certain industries.
Data center accelerator Market Dynamics
Driver: Growth of cloud-based services
Deep learning services being made available over the cloud are reducing the initial costs associated with executing business operations and curtailing server maintenance tasks. A growing number of tech giants and startups have begun offering machine learning as a cloud service due to the burgeoning demand for AI-based computation. Most companies and startups do not develop their own specialized hardware or software to apply deep learning to their specific business needs. Cloud-based solutions are ideal for small and midsized businesses that find on-premises solutions costlier. Thus, the increasing adoption of cloud-based technology is necessitating the need for deep learning.
Big data analytics has also played a pivotal role in the growth of cloud services. Big data analytics is the process of scrutinizing large datasets to uncover hidden patterns, unknown correlations, market trends, customer preferences, and other actionable insights. Big data has become important to many public and private organizations wherein massive amounts of domain-specific information is generated, which can contain useful information on national intelligence, cybersecurity, fraud detection, marketing, and medical informatics. The deep learning technique is used to extract high-level, complex abstractions from data through a hierarchical learning process. It is an important technique used for analyzing massive amounts of unsupervised data, making it a valuable tool for big data analytics wherein the raw data is largely unstructured. Deep learning is also used for extracting complex patterns from massive volumes of data, semantic indexing, data tagging, fast information retrieval, and simplifying discriminative tasks.
The evolution of technologies, namely machine learning and artificial intelligence (AI), has generated the demand for cognitive computing technology across various verticals such as automotive, industrial, and consumer. Rapid developments in the video analytics domain and increasing adoption of advanced technologies in the security and surveillance industry have resulted in the development of high-performance AI-capable processors such as GPU and TPU, which have higher memory bandwidth and computational capability as compared to traditional processors, i.e., central processing units (CPUs). Creative professionals, gamers, designers, and video enthusiasts require deep learning accelerators with parallel processing capabilities that can facilitate the provisioning of on-demand machine learning for augmented reality, virtual reality, and several other application areas.
Restraint: Limited AI hardware expertss
AI is a complex system, and for developing, managing, and implementing AI systems, companies require personnel with certain skill sets. For instance, people dealing with AI systems should be aware of technologies such as cognitive computing, ML and machine intelligence, deep learning, and image recognition. In addition, integrating AI solutions with existing systems is a difficult task that requires well-funded in-house R&D and patent filling. Even minor errors can translate into system failure or malfunctioning of a solution, which can drastically affect the outcome and desired result.
Professional services of data scientists and developers are needed to customize existing ML-enabled AI processors. AI is a technology that is still growing and emerging, and hence workforce possessing in-depth knowledge of this technology is limited. The impact of this restraining factor will likely remain high during the initial years of the forecast period.
Opportunity: Demand in the market for FPGA-based accelerators
An FPGA is an integrated circuit that can be configured by a customer or designer after it is manufactured (field programmable). FPGAs are programmed using hardware description languages such as VHSIC hardware description language (VHDL) or Verilog. FPGAs offer advantages such as rapid prototyping, short time to market, ability to be reprogramed in the field for debugging, and long product life cycle. They contain individual programmable logic blocks known as configurable logic blocks (CLBs). These logic blocks are interconnected in such a manner that a user can configure the computing system multiple times. FPGAs contain large resources of logic gates and RAM for complex digital computation.
In 2017, Intel (US) acquired field-programmable gate array (FPGA) chip designer Altera (US). With this, Intel is expected to further leverage FPGA accelerators into its primary data center server business. In May 2020, Aldec, Inc., a pioneer in mixed HDL language simulation and hardware-assisted verification for FPGA and ASIC designs, has launched a new FPGA accelerator board for high-performance computing (HPC), high-frequency trading (HFT) applications, and high-speed FPGA prototyping. The HES-XCKU11P-DDR4 is a 1U form factor board featuring a Xilinx Kintex® UltraScale+™ FPGA, a PCIe inference, and two QSFP-DD connectors (providing a total of up to 400 Gbit/s bandwidth), and which hits the ideal sweet spot between speed, logic cells, low power draw, and price.
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. However, a concern associated with such systems is that it is unclear as to what is going on inside algorithms; the internal workings remain inaccessible, and unlike humans, the answers provided by these systems are uncontextualized. Researchers at the Facebook AI Research (FAIR) lab found that the chatbots they created had deviated from their predefined script and were communicating in a language created by themselves, which humans could not understand. While one of the important goals of current research is to improve AI-to-human communication, the possibility that an AI system can create its own unique language that humans cannot understand could be a setback. Moreover, several scientists and tech influencers, such as Stephen Hawking, Elon Musk, Bill Gates, and Steve Wozniak, have already warned that future AI technology could lead to unintended consequences.
APAC held the largest market for the data center accelerator market in 2026 owing to growing demand for data center accelerator in China
As multinational and domestic enterprises increasingly transition to cloud services providers (CSPs) and colocation solutions, the data center market in China continues to evolve. The demand for data centers in the country has now exceeded the available supply as organizations seek enhanced connectivity and scalable solutions for their growing businesses. Investments by the Chinese government for stimulating technological developments have led to an increase in the adoption of cloud-based services, such as Big Data Analytics and Internet of Things (IoT). Various government reforms, such as the establishment of free trade in Shanghai, are attracting international investors. The growing demand for high-density, redundant facilities is triggering a shift in the design and development of the country’s data centers.
For instance, in June 2017, AMD (US) collaborated with Baidu (China) to create a comprehensive and open ecosystem to address the growing demand for data center workloads and provide enhanced human-computer interaction. Similarly, in August 2019, Intel and Lenovo (China) announced a multi-year collaboration focused on the rapidly-growing opportunity in the convergence of high-performance computing (HPC) and artificial intelligence (AI) to help accelerate solutions for the world’s most challenging problems. Building on the companies’ long-standing partnership in data centers, the multi-year global collaboration will accelerate the convergence of HPC and AI, creating solutions for organizations of all sizes. Also, in December 2019, NVIDIA and Didi Chuxing (DiDi) (China), the world’s leading mobile transportation platform, announced that DiDi would leverage NVIDIA GPUs and AI technology to develop autonomous driving and cloud computing solutions. DiDi will use NVIDIA GPUs in data centers for training machine learning algorithms and NVIDIA DRIVE for inference on its Level 4 autonomous driving vehicles. The above-mentioned key developments of companies are driving the demand for the data center accelerator market in China.
To know about the assumptions considered for the study, download the pdf brochure
The data center accelerator market is dominated by a few globally established players such as Intel Corporation (US), Google. Inc (US), NVIDIA Corporation (US), Xilinx Inc. (US), IBM Corporation (US), Advanced Micro Devices, Inc (US), Marvell Technology (Hamilton), and Qualcomm Technology (US).
Scope of the report
|
Report Metric |
Details |
|
Market size available for years |
2018–2026 |
|
Base year considered |
2020 |
|
Forecast period |
2021–2026 |
|
Forecast units |
Value (USD Million) and Volume (Million Units) |
|
Segments covered |
By Processor, By Type, By Application |
|
Geographies covered |
Asia Pacific, Europe, the Americas (North America, South America), and Rest of World |
|
Companies covered |
The key players operating in the data center accelerator market are Intel Corporation (US), Google. Inc (US), NVIDIA Corporation (US), Xilinx Inc. (US), IBM Corporation (US), Advanced Micro Devices, Inc (US), Marvell Technology (Hamilton), and Qualcomm Technology (US). |
The study categorizes the data center accelerator market based on processor, type, application at the regional and global levels.
By Processor:
- CPU
- GPU
- FPGA
- ASIC
By Application:
- Deep learning training
- Public cloud interface
- Enterprise interface
By Type:
- Cloud data center
- HPC data center
By Region:
- North America
- Europe
- APAC
- RoW
Recent Developments
- In April 2021, Intel announced the launch of a 3rd Gen Intel Xeon Scalable processor that will deliver a balanced architecture with built-in AI, crypto acceleration, and advanced security capabilities.
- In May 2020, NVIDIA announced two powerful products for its EGX Edge AI platform — the EGX A100 for larger commercial off-the-shelf servers and the tiny EGX Jetson Xavier NX for micro-edge servers, delivering high-performance, secure AI processing. With the NVIDIA EGX Edge AI platform, hospitals, stores, farms, and factories can carry out real-time processing and the protection of massive amounts of data streaming from trillions of edge sensors. The platform makes it possible to securely deploy, manage, and update fleets of servers remotely.
- In May 2020, NVIDIA announced that the first GPU based on the NVIDIA Ampere architecture, the NVIDIA A100, which is in full production and shipping to customers worldwide. A100 draws on design breakthroughs in the NVIDIA Ampere architecture, offering the company the largest leap in performance to date within its eight generations of GPUs. This, in turn, unify AI training and inference and boost performance by up to 20x over its predecessors. A universal workload accelerator, A100 is also built for data analytics, scientific computing, and cloud graphics.
Frequently Asked Questions (FAQ):
What is the current size of the global data center accelerator market?
The data center accelerator market is projected to grow from USD 13.7 billion in 2021 to USD 65.3 billion by 2026; it is expected to grow at a CAGR of 36.7% from 2021 to 2026.
Why data center accelerator are used?
In data centers, co-processors or accelerators are used for the work offload of computationally intensive applications. Accelerators are designed to perform complex computational functions in parallel with the general-purpose processor, with higher execution speed. The data center accelerators facilitate a significant increase in the performance of these data centers
What is the impact of covid-19 on the data center accelerator market?
The impact of the COVID-19 pandemic on the growth of the data center accelerator market is low. From a regional point of view, the outbreak of COVID-19 has caused a major setback to the exports-oriented economies, owing to the temporary shutting down of manufacturing plants. The automotive industry of Germany has been severely affected as most of the automobile trade takes place with China and Italy. Besides the issues related to exports and imports, the growth of the manufacturing industry in Germany has been hampered due to the closure of manufacturing plants and slowdown in operations to contain the spread of COVID-19.
On the other hand, the use of AI in manufacturing, enterprises, as well as in organizations has become essential owing to remote working and the opportunities offered by AI. Companies are focusing on adopting newer means of technologies and ease their processes. This has had positive growth for the data center accelerator market as the adoption of AI has been higher during the pandemic. Hence, the data center accelerator market is projected to witness strong growth in the next five years, with most of the processes getting automated.
What are the technological trends going in the data center accelerator market?
GPU technology is transforming and advancing at a rapid pace. As the demand for accelerated computing is rising, businesses and enterprises are finding it difficult to achieve the same through regular CPU-based servers due to on-premises hosting limitations. As a result, the adoption of on-demand GPU cloud computing-based solutions is increasing. These can run compute-intensive HPC workloads and offer bandwidth speeds comparable to those offered by on-premises GPU. Companies such as Microsoft, Amazon, and IBM offer cloud GPU solutions in partnership with firms such as NVIDIA and AMD. The advent of cloud gaming is further projected to improve the penetration of cloud GPUs as companies, such as Shadow, Google, and NVIDIA, expand their online cloud gaming services to support real PC gameplay by using hardware-based GPU virtualization.
The growing adoption of GPU cloud for machine learning is projected to increase the future applications of GPU in various nascent HPC applications across sectors. Further, the improvements in data security due to hardware-level virtualization and the increase in the number of certifications related to performance, safety, and efficiency being offered by the cloud GPU could positively impact their adoption in the coming years in several sectors, such as aerospace and defense, healthcare, and BFSI. For instance, Fujifilm made use of Nvidia Quadro GPUs to power its 3D mammography solution, the AMULET tool. The use of Quadro K2000 helped deliver high image quality, which meets the demand of the doctors to derive accurate reads. Moreover, using a single GPU to power a multi-display AMULET system results in developing a solution that is simple and affordable. Using the Quadro GPU, Fujifilm has made the AMULET system ultra-fast, responsive, and consistently reliable in critical conditions.
Which are the major companies in the data center accelerator market?
Intel Corporation (US), Google. Inc (US), NVIDIA Corporation (US), Xilinx Inc. (US), IBM Corporation (US), Advanced Micro Devices, Inc (US), Marvell Technology (Hamilton), and Qualcomm Technology (US) are the players dominating the global data center accelerator market. .
To speak to our analyst for a discussion on the above findings, click Speak to Analyst
TABLE OF CONTENTS
1 INTRODUCTION (Page No. - 25)
1.1 STUDY OBJECTIVES
1.2 MARKET DEFINITION
1.2.1 INCLUSIONS AND EXCLUSIONS
1.3 STUDY SCOPE
1.3.1 MARKETS COVERED
FIGURE 1 SEGMENTATION OF THE DATA CENTER ACCELERATOR MARKET
1.3.2 DATA CENTER ACCELERATOR MARKET, BY GEOGRAPHY
1.3.3 YEARS CONSIDERED
1.4 CURRENCY
1.5 LIMITATIONS
1.6 STAKEHOLDERS
1.7 SUMMARY OF CHANGES
2 RESEARCH METHODOLOGY (Page No. - 31)
2.1 RESEARCH DATA
FIGURE 2 RESEARCH DESIGN
2.1.1 SECONDARY DATA
2.1.1.1 Secondary sources
2.1.2 PRIMARY DATA
2.1.2.1 Key industry insights
2.1.2.2 Breakdown of primaries
2.2 MARKET SIZE ESTIMATION
2.2.1 MARKET SIZE ESTIMATION OF THE DATA CENTER ACCELERATOR MARKET
2.2.1.1 Approach for arriving at the market share by the bottom-up analysis (demand side)
2.2.2 BOTTOM-UP APPROACH
2.2.3 TOP-DOWN APPROACH
2.2.3.1 Approach for arriving at the market share by the top-down analysis (supply side)
2.3 MARKET BREAKDOWN & DATA TRIANGULATION
FIGURE 3 DATA TRIANGULATION
2.4 RESEARCH ASSUMPTION
2.5 RISK ASSESSMENT
2.6 LIMITATION OF RESEARCH
TABLE 1 MARKET FORECASTING METHODOLOGY ADOPTED FROM 2019 TO 2026
3 EXECUTIVE SUMMARY (Page No. - 41)
FIGURE 4 GPU TO ACCOUNT FOR THE LARGEST SIZE OF THE DATA CENTER ACCELERATOR MARKET
FIGURE 5 DATA CENTER ACCELERATOR MARKET FOR ENTERPRISE INFERENCE TO GROW AT THE HIGHEST CAGR BETWEEN 2021 AND 2026
FIGURE 6 HPC DATA CENTER ACCELERATOR MARKET IN APAC TO GROW AT THE HIGHEST CAGR
FIGURE 7 DATA CENTER ACCELERATOR MARKET IN APAC PROJECTED TO GROW AT THE HIGHEST CAGR
3.1 IMPACT OF COVID-19 ON THE DATA CENTER ACCELERATOR MARKET
FIGURE 8 IMPACT OF COVID-19 ON THE DATA CENTER ACCELERATOR MARKET, 2018–2025 (USD MILLION)
3.1.1 REALISTIC SCENARIO (POST-COVID-19)
3.1.2 OPTIMISTIC SCENARIO (POST-COVID-19)
3.1.3 PESSIMISTIC SCENARIO (POST-COVID-19)
4 PREMIUM INSIGHTS (Page No. - 46)
4.1 ATTRACTIVE OPPORTUNITIES IN THE DATA CENTER ACCELERATOR MARKET
FIGURE 9 RISING NEED FOR CO-PROCESSORS IN DATA CENTERS DUE TO SLOW DOWN IN MOORE’S LAW AS POTENTIAL OPPORTUNITY FOR THE MARKET
4.2 DATA CENTER ACCELERATOR MARKET, BY TYPE
FIGURE 10 MARKET FOR CLOUD DATA CENTER ACCELERATOR TO GROW AT A HIGHER CAGR DURING THE FORECAST PERIOD
4.3 MARKET FOR CLOUD DATA CENTER ACCELERATOR, BY COUNTRY
FIGURE 11 CLOUD DATA CENTER ACCELERATOR MARKET IN INDIA TO GROW AT THE HIGHEST CAGR DURING THE FORECAST PERIOD
4.4 APAC: DATA CENTER ACCELERATOR MARKET, BY APPLICATION & COUNTRY
FIGURE 12 PUBLIC CLOUD INFERENCE TO HOLD THE LARGEST SHARE OF THE DATA CENTER ACCELERATOR MARKET IN APAC BY 2026
4.5 DATA CENTER ACCELERATOR MARKET, BY PROCESSOR TYPE
FIGURE 13 MARKET FOR ASIC TO GROW AT A HIGHER CAGR BETWEEN 2021 AND 2026
5 MARKET OVERVIEW (Page No. - 49)
5.1 INTRODUCTION
5.2 MARKET DYNAMICS
FIGURE 14 DATA CENTER ACCELERATOR MARKET: DRIVERS, RESTRAINTS, OPPORTUNITIES, AND CHALLENGES
5.2.1 DRIVERS
5.2.1.1 Growth of cloud-based services
TABLE 2 COMPANIES OFFERING CLOUD SERVICES FOR DEEP/MACHINE LEARNING
5.2.1.2 Focus on parallel computing in AI data centers
5.2.1.3 Deep learning usage in big data analytics
TABLE 3 DRIVERS: IMPACT ANALYSIS
5.2.2 RESTRAINTS
5.2.2.1 Premium pricing of accelerators
5.2.2.2 Limited AI hardware experts
TABLE 4 RESTRAINTS: IMPACT ANALYSIS
5.2.3 OPPORTUNITIES
5.2.3.1 Demand in the market for FPGA-based accelerators
5.2.3.2 Rising need for co-processors due to the slowdown of Moore’s Law
FIGURE 15 SLOWDOWN IN MOORE’S LAW
TABLE 5 OPPORTUNITIES: IMPACT ANALYSIS
5.2.4 CHALLENGES
5.2.4.1 Unreliability of AI algorithms
5.2.4.2 Complex AI mechanisms
TABLE 6 CHALLENGES: IMPACT ANALYSIS
5.3 IMPACT OF COVID-19
5.4 ECOSYSTEM
TABLE 7 COMPANIES AND THEIR ROLE IN THE ECOSYSTEM
FIGURE 16 ECOSYSTEM VIEW
5.5 TECHNOLOGY ANALYSIS
5.5.1 CLOUD GPU
5.5.2 ARTIFICIAL INTELLIGENCE
TABLE 8 COMPARISON OF SMART PROCESSORS TYPE
5.6 CASE STUDIES
5.6.1 ACHIEVING FASTER AI-BASED MEDICAL DIAGNOSIS SUPPORT WITH XILINX ALVEO ACCELERATOR CARDS
5.6.2 XILINX PROVIDES TWITCH WITH PLUG AND PLAY VP9 TRANSCODING SOLUTION FOR LIVE VIDEO STREAMING
5.6.3 XILINX POWERS ALIBABA CLOUD FAAS WITH AI ACCELERATION SOLUTION FOR E-COMMERCE BUSINESS
5.6.4 ARTIFICIAL INTELLIGENCE ACCELERATES DARK MATTER SEARCH
5.6.5 CFD ACCELERATION WITH XILINX ALVEO FPGA
5.6.6 OTHER USE CASES
5.6.6.1 FPGA Use Cases; FPGAs as Offload Accelerators
5.6.6.2 Intel’s deep learning solutions brings touch to Somatic’s robots
5.6.6.3 AI catalyzes operational improvements for manufacturing and smart cities
5.6.6.4 Accelerate Adoption of AI in Diagnostic Radiology
5.7 VALUE CHAIN ANALYSIS
FIGURE 17 VALUE CHAIN ANALYSIS OF THE DATA CENTER ACCELERATOR MARKET
5.8 STANDARDS AND GUIDELINES FOR THE DATA CENTER MARKET
5.8.1 STORAGE NETWORKING INDUSTRY ASSOCIATION (SNIA)
5.8.2 TELECOMMUNICATIONS INFRASTRUCTURE STANDARD FOR DATA CENTERS
5.8.3 DISTRIBUTED MANAGEMENT TASK FORCE (DMTF) STANDARDS
5.8.4 ELECTRONICS INDUSTRY ASSOCIATION (EIA)
5.8.5 TELECOMMUNICATION INDUSTRY ASSOCIATION (TIA)
5.8.6 DATA CENTER SITE INFRASTRUCTURE TIER STANDARD (UPTIME INSTITUTE)
5.8.7 SNIA AND CLOUD DATA MANAGEMENT INFERENCE (CDMI)
5.9 REGULATIONS
5.9.1 EXPORT-IMPORT REGULATIONS
5.9.2 RESTRICTION OF HAZARDOUS SUBSTANCES (ROHS) AND WASTE ELECTRICAL AND ELECTRONIC EQUIPMENT (WEEE)
5.9.3 REGISTRATION, EVALUATION, AUTHORIZATION, AND RESTRICTION OF CHEMICALS (REACH)
5.9.4 GENERAL DATA PROTECT ION REGULATION (GDPR)
5.10 PORTER’S FIVE FORCES ANALYSIS
TABLE 9 IMPACT OF EACH FORCE ON THE MARKET
5.10.1 BARGAINING POWER OF SUPPLIERS
5.10.2 BARGAINING POWER OF BUYERS
5.10.3 THREAT OF NEW ENTRANTS
5.10.4 THREAT OF SUBSTITUTES
5.10.5 INTENSITY OF COMPETITIVE RIVALRY
TABLE 10 IMPACT OF EACH FORCE ON THE MARKET, 2020 VS 2026
5.11 PRICING ANALYSIS
TABLE 11 PRODUCT AND PRICING ANALYSIS
FIGURE 18 ASP OF CLOUD DATA CENTER ACCELERATOR PROCESSOR, 2018–2021 (USD)
TABLE 12 ASP RANGES OF PROCESSOR TYPES
5.12 PATENT ANALYSIS
FIGURE 19 PATENT ANALYSIS
TABLE 13 LIST OF PATENTS
5.13 YC-YCC SHIFT - DATA CENTER ACCELERATOR
FIGURE 20 YC-YCC SHIFT: DATA CENTER ACCELERATOR TO PRESENT NEW GROWTH OPPORTUNITIES
5.14 TRADE ANALYSIS
TABLE 14 IMPORT DATA OF PARTS AND ACCESSORIES OF THE MACHINES OF HEADING, BY COUNTRY, 2016–2020 (USD THOUSANDS)
TABLE 15 EXPORT DATA PARTS AND ACCESSORIES OF THE MACHINES OF HEADING, BY COUNTRY, 2016–2020 (USD THOUSANDS)
6 DATA CENTER ACCELERATOR MARKET, BY PROCESSOR TYPE (Page No. - 79)
6.1 INTRODUCTION
FIGURE 21 DATA CENTER ACCELERATOR MARKET, BY PROCESSOR TYPE
FIGURE 22 DATA CENTER ACCELERATOR MARKET FOR ASIC TO GROW AT THE HIGHEST CAGR DURING THE FORECAST PERIOD
TABLE 16 DATA CENTER ACCELERATOR MARKET, BY PROCESSOR TYPE, 2018–2020 (USD MILLION)
TABLE 17 DATA CENTER ACCELERATOR MARKET, BY PROCESSOR TYPE, 2021–2026 (USD MILLION)
6.2 CPU
6.2.1 CPU CONSISTS OF A FEW CORES TO OPTIMIZE FOR SEQUENTIAL SERIAL PROCESSING
TABLE 18 DATA CENTER ACCELERATOR MARKET FOR CPU, BY APPLICATION, 2018–2020 (USD MILLION)
TABLE 19 DATA CENTER ACCELERATOR MARKET FOR CPU, BY APPLICATION, 2021–2026 (USD MILLION)
TABLE 20 DATA CENTER ACCELERATOR MARKET FOR CPU, BY TYPE, 2018–2020 (USD MILLION)
TABLE 21 DATA CENTER ACCELERATOR MARKET FOR CPU, BY TYPE, 2021–2026 (USD MILLION)
6.3 GPU
6.3.1 GPUS DEVELOPED TO DELIVER HIGH-PERFORMANCE TO THE DATA CENTERS
TABLE 22 DATA CENTER ACCELERATOR MARKET FOR GPU, BY APPLICATION, 2018–2020 (USD MILLION)
TABLE 23 DATA CENTER ACCELERATOR MARKET FOR GPU, BY APPLICATION, 2021–2026 (USD MILLION)
TABLE 24 DATA CENTER ACCELERATOR MARKET FOR GPU, BY TYPE, 2018–2020 (USD MILLION)
TABLE 25 DATA CENTER ACCELERATOR MARKET FOR GPU, BY TYPE, 2021–2026 (USD MILLION)
6.4 FPGA
6.4.1 FPGAS USED PRIMARILY IN MACHINE LEARNING INFERENCE, VIDEO ALGORITHMS, AND SMALL-VOLUME SPECIALIZED APPLICATIONS
TABLE 26 DATA CENTER ACCELERATOR MARKET FOR FPGA, BY APPLICATION, 2018–2020 (USD MILLION)
TABLE 27 DATA CENTER ACCELERATOR MARKET FOR FPGA, BY APPLICATION, 2021–2026 (USD MILLION)
TABLE 28 DATA CENTER ACCELERATOR MARKET FOR FPGA, BY TYPE, 2018–2020 (USD MILLION)
TABLE 29 DATA CENTER ACCELERATOR MARKET FOR FPGA, BY TYPE, 2021–2026 (USD MILLION)
6.5 ASIC
6.5.1 BEING AFFORDABLE, ASIC OFFERS LITTLE PROGRAMMABILITY BUT PROVIDES MAXIMUM PERFORMANCE AT A GIVEN POWER
TABLE 30 DATA CENTER ACCELERATOR MARKET FOR ASIC, BY APPLICATION, 2018–2020 (USD MILLION)
TABLE 31 DATA CENTER ACCELERATOR MARKET FOR ASIC, BY APPLICATION, 2021–2026 (USD MILLION)
TABLE 32 DATA CENTER ACCELERATOR MARKET FOR ASIC, BY TYPE, 2018–2020 (USD MILLION)
TABLE 33 DATA CENTER ACCELERATOR MARKET FOR ASIC, BY TYPE, 2021–2026 (USD MILLION)
7 DATA CENTER ACCELERATOR MARKET, BY TYPE (Page No. - 89)
7.1 INTRODUCTION
FIGURE 23 DATA CENTER ACCELERATOR MARKET, BY TYPE
FIGURE 24 DATA CENTER ACCELERATOR MARKET FOR CLOUD TO GROW AT THE HIGHEST CAGR DURING THE FORECAST PERIOD
TABLE 34 DATA CENTER ACCELERATOR MARKET, BY TYPE, 2018–2020 (USD MILLION)
TABLE 35 DATA CENTER ACCELERATOR MARKET, BY TYPE, 2021–2026 (USD MILLION)
7.2 CLOUD DATA CENTER
7.2.1 ARTIFICIAL INTELLIGENCE TO DRIVE THE GROWTH OF CLOUD DATA CENTER
TABLE 36 CLOUD DATA CENTER ACCELERATOR MARKET, BY PROCESSOR TYPE, 2018–2020 (USD MILLION)
TABLE 37 CLOUD DATA CENTER ACCELERATOR MARKET, BY PROCESSOR TYPE, 2021–2026 (USD MILLION)
TABLE 38 CLOUD DATA CENTER ACCELERATOR MARKET, BY REGION, 2018–2020 (USD MILLION)
TABLE 39 CLOUD DATA CENTER ACCELERATOR MARKET, BY REGION, 2021–2026 (USD MILLION)
7.3 HPC DATA CENTER
7.3.1 HPC USED FOR RUNNING HIGH LEVELS OF COMPUTATIONS ALGORITHMS THAT REQUIRE COMPLEX ANALYTICS
TABLE 40 HPC DATA CENTER ACCELERATOR MARKET, BY PROCESSOR TYPE, 2018–2020 (USD MILLION)
TABLE 41 HPC DATA CENTER ACCELERATOR MARKET, BY PROCESSOR TYPE, 2021–2026 (USD MILLION)
TABLE 42 HPC DATA CENTER ACCELERATOR MARKET, BY REGION, 2018–2020 (USD MILLION)
TABLE 43 HPC DATA CENTER ACCELERATOR MARKET, BY REGION, 2021–2026 (USD MILLION)
8 DATA CENTER ACCELERATOR MARKET, BY APPLICATION (Page No. - 95)
8.1 INTRODUCTION
FIGURE 25 DATA CENTER ACCELERATOR MARKET, BY APPLICATION
FIGURE 26 DATA CENTER ACCELERATOR MARKET FOR ENTERPRISE INFERENCE TO GROW AT THE HIGHEST CAGR DURING THE FORECAST PERIOD
TABLE 44 DATA CENTER ACCELERATOR MARKET, BY APPLICATION, 2018–2020 (USD MILLION)
TABLE 45 DATA CENTER ACCELERATOR MARKET, BY APPLICATION, 2021–2026 (USD MILLION)
8.2 DEEP LEARNING TRAINING
8.2.1 DEEP LEARNING USES ARTIFICIAL NEURAL NETWORKS TO LEARN MULTIPLE LEVELS OF DATA
TABLE 46 DATA CENTER ACCELERATOR MARKET FOR DEEP LEARNING TRAINING, BY REGION, 2018–2020 (USD MILLION)
TABLE 47 DATA CENTER ACCELERATOR MARKET FOR DEEP LEARNING TRAINING, BY REGION, 2021–2026 (USD MILLION)
8.3 PUBLIC CLOUD INFERENCE
8.3.1 BEING ECONOMIC TO USE, PUBLIC CLOUD TO DRIVE THE NEED OF PUBLIC CLOUD SERVICE PROVIDERS
TABLE 48 DATA CENTER ACCELERATOR MARKET FOR PUBLIC CLOUD INFERENCE, BY REGION, 2018–2020 (USD MILLION)
TABLE 49 DATA CENTER ACCELERATOR MARKET FOR PUBLIC CLOUD INFERENCE, BY REGION, 2021–2026 (USD MILLION)
8.4 ENTERPRISE INFERENCE
8.4.1 COMPANIES SUCH AS IBM TO RAPIDLY FOCUS ON BUILDING SOLUTIONS FOR ENTERPRISE INFERENCE
TABLE 50 DATA CENTER ACCELERATOR MARKET FOR ENTERPRISE INFERENCE, BY REGION, 2018–2020 (USD MILLION)
TABLE 51 DATA CENTER ACCELERATOR MARKET FOR ENTERPRISE INFERENCE, BY REGION, 2021–2026 (USD MILLION)
9 GEOGRAPHIC ANALYSIS (Page No. - 101)
9.1 INTRODUCTION
FIGURE 27 DATA CENTER ACCELERATOR MARKET SEGMENTATION, BY GEOGRAPHY
FIGURE 28 GEOGRAPHIC SNAPSHOT: DATA CENTER ACCELERATOR MARKET IN APAC TO GROW AT THE HIGHEST CAGR DURING FORECAST PERIOD
FIGURE 29 DATA CENTER ACCELERATOR MARKET IN INDIA TO GROW AT THE HIGHEST CAGR FROM 2021 TO 2026
TABLE 52 DATA CENTER ACCELERATOR MARKET, BY REGION, 2018–2020 (USD MILLION)
TABLE 53 DATA CENTER ACCELERATOR MARKET, BY REGION, 2021–2026 (USD MILLION)
FIGURE 30 DATA CENTER ACCELERATOR MARKET, 2018–2020 (THOUSAND UNITS)
FIGURE 31 DATA CENTER ACCELERATOR MARKET, 2021–2026 THOUSAND UNITS)
9.2 NORTH AMERICA
FIGURE 32 SEGMENTATION: NORTH AMERICA, BY COUNTRY
FIGURE 33 NORTH AMERICA: DATA CENTER ACCELERATOR MARKET SNAPSHOT
TABLE 54 DATA CENTER ACCELERATOR MARKET FOR HPC IN NORTH AMERICA, BY COUNTRY, 2018–2020 (USD MILLION)
TABLE 55 DATA CENTER ACCELERATOR MARKET FOR HPC IN NORTH AMERICA, BY COUNTRY, 2021–2026 (USD MILLION)
TABLE 56 DATA CENTER ACCELERATOR MARKET FOR CLOUD IN NORTH AMERICA, BY COUNTRY, 2018–2020 (USD MILLION)
TABLE 57 DATA CENTER ACCELERATOR MARKET FOR CLOUD IN NORTH AMERICA, BY COUNTRY, 2021–2026 (USD MILLION)
TABLE 58 DATA CENTER ACCELERATOR MARKET IN NORTH AMERICA, BY PROCESSOR TYPE, 2018–2020 (USD MILLION)
TABLE 59 DATA CENTER ACCELERATOR MARKET IN NORTH AMERICA, BY PROCESSOR TYPE, 2021–2026 (USD MILLION)
TABLE 60 DATA CENTER ACCELERATOR MARKET IN NORTH AMERICA, BY APPLICATION, 2018–2020 (USD MILLION)
TABLE 61 DATA CENTER ACCELERATOR MARKET IN NORTH AMERICA, BY APPLICATION, 2021–2026 (USD MILLION)
TABLE 62 DATA CENTER ACCELERATOR MARKET IN NORTH AMERICA, BY TYPE, 2018–2020 (USD MILLION)
TABLE 63 DATA CENTER ACCELERATOR MARKET IN NORTH AMERICA, BY TYPE, 2021–2026 (USD MILLION)
TABLE 64 DATA CENTER ACCELERATOR MARKET IN NORTH AMERICA, BY COUNTRY, 2018–2020 (USD MILLION)
TABLE 65 DATA CENTER ACCELERATOR MARKET IN NORTH AMERICA, BY COUNTRY, 2021–2026 (USD MILLION)
9.2.1 US
9.2.1.1 Large-scale deployment of modular and colocation facilities drives the demand for data centers in the US
9.2.2 CANADA
9.2.2.1 Growing requirement for R&D creates the need for high processing data, thereby boosting market growth
9.2.3 MEXICO
9.2.3.1 Tech companies are considering Mexico a potential market for data centers
9.3 EUROPE
FIGURE 34 SEGMENTATION: EUROPE, BY COUNTRY
FIGURE 35 EUROPE: DATA CENTER ACCELERATOR MARKET SNAPSHOT
TABLE 66 DATA CENTER ACCELERATOR MARKET FOR HPC IN EUROPE, BY COUNTRY, 2018–2020 (USD MILLION)
TABLE 67 DATA CENTER ACCELERATOR MARKET FOR HPC IN EUROPE, BY COUNTRY, 2021–2026 (USD MILLION)
TABLE 68 DATA CENTER ACCELERATOR MARKET FOR CLOUD IN EUROPE, BY COUNTRY, 2018–2020 (USD MILLION)
TABLE 69 DATA CENTER ACCELERATOR MARKET FOR CLOUD IN EUROPE, BY COUNTRY, 2021–2026 (USD MILLION)
TABLE 70 DATA CENTER ACCELERATOR MARKET IN EUROPE, BY PROCESSOR TYPE, 2018–2020 (USD MILLION)
TABLE 71 DATA CENTER ACCELERATOR MARKET IN EUROPE, BY PROCESSOR TYPE, 2021–2026 (USD MILLION)
TABLE 72 DATA CENTER ACCELERATOR MARKET IN EUROPE, BY APPLICATION, 2018–2020 (USD MILLION)
TABLE 73 DATA CENTER ACCELERATOR MARKET IN EUROPE, BY APPLICATION, 2021–2026 (USD MILLION)
TABLE 74 DATA CENTER ACCELERATOR MARKET IN EUROPE, BY TYPE, 2018–2020 (USD MILLION)
TABLE 75 DATA CENTER ACCELERATOR MARKET IN EUROPE, BY TYPE, 2021–2026 (USD MILLION)
TABLE 76 DATA CENTER ACCELERATOR MARKET IN EUROPE, BY COUNTRY, 2018–2020 (USD MILLION)
TABLE 77 DATA CENTER ACCELERATOR MARKET IN EUROPE, BY COUNTRY, 2021–2026 (USD MILLION)
9.3.1 UK
9.3.1.1 Increasing demand for AI-based solutions to drive the data center accelerator market
9.3.2 GERMANY
9.3.2.1 Cloud computing and Industry 4.0 have led to increased demand for data centers and co-location operators
9.3.3 NETHERLANDS
9.3.3.1 The country is witnessing improvement in the digital infrastructure
9.3.4 REST OF EUROPE
9.3.4.1 Growing investment in Rest of Europe
9.4 APAC
FIGURE 36 MARKET SEGMENTATION: APAC, BY COUNTRY
FIGURE 37 APAC: DATA CENTER ACCELERATOR MARKET SNAPSHOT
TABLE 78 DATA CENTER ACCELERATOR MARKET FOR HPC IN APAC, BY COUNTRY, 2018–2020 (USD MILLION)
TABLE 79 DATA CENTER ACCELERATOR MARKET FOR HPC IN APAC, BY COUNTRY, 2021–2026 (USD MILLION)
TABLE 80 DATA CENTER ACCELERATOR MARKET FOR CLOUD IN APAC, BY COUNTRY, 2018–2020 (USD MILLION)
TABLE 81 DATA CENTER ACCELERATOR MARKET FOR CLOUD IN APAC, BY COUNTRY, 2021–2026 (USD MILLION)
TABLE 82 DATA CENTER ACCELERATOR MARKET IN APAC, BY PROCESSOR TYPE, 2018–2020 (USD MILLION)
TABLE 83 DATA CENTER ACCELERATOR MARKET IN APAC, BY PROCESSOR TYPE, 2021–2026 (USD MILLION)
TABLE 84 DATA CENTER ACCELERATOR MARKET IN APAC, BY APPLICATION, 2018–2020 (USD MILLION)
TABLE 85 DATA CENTER ACCELERATOR MARKET IN APAC, BY APPLICATION, 2021–2026 (USD MILLION)
TABLE 86 DATA CENTER ACCELERATOR MARKET IN APAC, BY TYPE, 2018–2020 (USD MILLION)
TABLE 87 DATA CENTER ACCELERATOR MARKET IN APAC, BY TYPE, 2021–2026 (USD MILLION)
TABLE 88 DATA CENTER ACCELERATOR MARKET IN APAC, BY COUNTRY, 2018–2020 (USD MILLION)
TABLE 89 DATA CENTER ACCELERATOR MARKET IN APAC, BY COUNTRY, 2021–2026 (USD MILLION)
9.4.1 CHINA
9.4.1.1 The data center accelerator market is growing rapidly in the country
9.4.2 JAPAN
9.4.2.1 Several multinational companies across different end-use industries in the country are implementing AI technology
9.4.3 SINGAPORE
9.4.3.1 Small and mid-sized companies are expected to increase their data center cloud services in the country
9.4.4 INDIA
9.4.4.1 India is witnessing growth in data center operations, owing to the strong demand for IT services from both domestic as well as international markets
9.4.5 AUSTRALIA
9.4.5.1 Cloud computing and increasing demand for data are driving the growth of the colocation sector in the country
9.4.6 REST OF APAC
9.4.6.1 Alibaba Group (China) announced the setting up of a data center in Indonesia
9.5 ROW
FIGURE 38 SEGMENTATION: ROW, BY COUNTRY
TABLE 90 DATA CENTER ACCELERATOR MARKET FOR HPC IN ROW, BY COUNTRY, 2018–2020 (USD MILLION)
TABLE 91 DATA CENTER ACCELERATOR MARKET FOR HPC IN ROW, BY COUNTRY, 2021–2026 (USD MILLION)
TABLE 92 DATA CENTER ACCELERATOR MARKET FOR CLOUD IN ROW, BY COUNTRY, 2018–2020 (USD MILLION)
TABLE 93 DATA CENTER ACCELERATOR MARKET FOR CLOUD IN ROW, BY COUNTRY, 2021–2026 (USD MILLION)
TABLE 94 DATA CENTER ACCELERATOR MARKET IN ROW, BY PROCESSOR TYPE, 2018–2020 (USD MILLION)
TABLE 95 DATA CENTER ACCELERATOR MARKET IN ROW, BY PROCESSOR TYPE, 2021–2026 (USD MILLION)
TABLE 96 DATA CENTER ACCELERATOR MARKET IN ROW, BY APPLICATION, 2018–2020 (USD MILLION)
TABLE 97 DATA CENTER ACCELERATOR MARKET IN ROW, BY APPLICATION, 2021–2026 (USD MILLION)
TABLE 98 DATA CENTER ACCELERATOR MARKET IN ROW, BY TYPE, 2018–2020 (USD MILLION)
TABLE 99 DATA CENTER ACCELERATOR MARKET IN ROW, BY TYPE, 2021–2026 (USD MILLION)
TABLE 100 DATA CENTER ACCELERATOR MARKET IN ROW, BY COUNTRY, 2018–2020 (USD MILLION)
TABLE 101 DATA CENTER ACCELERATOR MARKET IN ROW, BY COUNTRY, 2021–2026 (USD MILLION)
9.5.1 BRAZIL
9.5.1.1 Brazil has the largest computing services market in South America, followed by Chile and Argentina
9.5.2 OTHERS
9.5.2.1 Growing demand for AI in healthcare to boost the demand for data center accelerator
10 COMPETITIVE LANDSCAPE (Page No. - 134)
10.1 INTRODUCTION
10.2 REVENUE ANALYSIS OF TOP THREE COMPANIES
FIGURE 39 DATA CENTER ACCELERATOR MARKET: REVENUE ANALYSIS (2020) (USD BILLION)
10.3 MARKET SHARE ANALYSIS, 2020
TABLE 102 DATA CENTER ACCELERATOR MARKET: MARKET SHARE ANALYSIS (2020)
10.4 COMPETITIVE LEADERSHIP MAPPING
10.4.1 STARS
10.4.2 EMERGING LEADERS
10.4.3 PERVASIVE
10.4.4 PARTICIPANTS
FIGURE 40 DATA CENTER ACCELERATOR MARKET: COMPETITIVE LEADERSHIP MAPPING, 2020
10.4.5 DATA CENTER ACCELERATOR MARKET: PRODUCT FOOTPRINT
TABLE 103 COMPANY FOOTPRINT
TABLE 104 APPLICATION FOOTPRINT OF COMPANIES
TABLE 105 REGIONAL FOOTPRINT OF COMPANIES
10.5 SMALL AND MEDIUM ENTERPRISES (SME) EVALUATION QUADRANT, 2020
10.5.1 PROGRESSIVE COMPANIES
10.5.2 RESPONSIVE COMPANIES
10.5.3 DYNAMIC COMPANIES
10.5.4 STARTING BLOCKS
FIGURE 41 DATA CENTER ACCELERATOR MARKET (GLOBAL), SME EVALUATION QUADRANT, 2020
10.6 COMPETITIVE SITUATIONS AND TRENDS
10.6.1 DATA CENTER ACCELERATOR MARKET: PRODUCT LAUNCHES, JANUARY 2018– APRIL 2021
10.6.2 DATA CENTER ACCELERATOR MARKET: DEALS, JANUARY 2018– APRIL 2021
11 COMPANY PROFILES (Page No. - 145)
11.1 INTRODUCTION
11.2 KEY PLAYERS
(Business overview, Products offered, Recent developments, Product Launches, Deals, MNM view, Key strengths/Right to win, Strategic choices made, and Weaknesses and competitive threats)*
11.2.1 INTEL
TABLE 106 INTEL: BUSINESS OVERVIEW
FIGURE 42 INTEL: COMPANY SNAPSHOT
11.2.2 NVIDIA
TABLE 107 NVIDIA: BUSINESS OVERVIEW
FIGURE 43 NVIDIA: COMPANY SNAPSHOT
11.2.3 XILINX
TABLE 108 XILINX: BUSINESS OVERVIEW
FIGURE 44 XILINX: COMPANY SNAPSHOT
11.2.4 MICRON
TABLE 109 MICRON TECHNOLOGY: BUSINESS OVERVIEW
FIGURE 45 MICRON TECHNOLOGY: COMPANY SNAPSHOT
11.2.5 AMD
TABLE 110 AMD: BUSINESS OVERVIEW
FIGURE 46 AMD: COMPANY SNAPSHOT
11.2.6 QUALCOMM
TABLE 111 QUALCOMM: BUSINESS OVERVIEW
FIGURE 47 QUALCOMM: COMPANY SNAPSHOT
11.2.7 IBM
TABLE 112 IBM: BUSINESS OVERVIEW
FIGURE 48 IBM: COMPANY SNAPSHOT
11.2.8 GOOGLE
TABLE 113 GOOGLE: BUSINESS OVERVIEW
FIGURE 49 GOOGLE: COMPANY SNAPSHOT
11.2.9 MARVELL
TABLE 114 MARVELL: BUSINESS OVERVIEW
FIGURE 50 MARVELL: COMPANY SNAPSHOT
11.2.10 ACHRONIX SEMICONDUCTOR
TABLE 115 ACHRONIX SEMICONDUCTOR: BUSINESS OVERVIEW
11.3 OTHER COMPANIES
11.3.1 GRAPHCORE
11.3.2 HUAWEI TECHNOLOGIES
11.3.3 FUJITSU
11.3.4 WAVE COMPUTING
11.3.5 SAMBANOVA
11.3.6 GYRFALCON TECHNOLOGY INC.
11.3.7 NEC
11.3.8 LATTICE SEMICONDUCTOR
11.3.9 ENFLAME TECHNOLOGY
11.3.10 MICROCHIP TECHNOLOGY
11.3.11 LEAP MIND INC.
11.3.12 QNAP SYSTEM INC.
11.3.13 ADVANTECH CO., LTD
11.3.14 SEMPTIAN
11.3.15 BITTWARE
*Details on Business overview, Products offered, Recent developments, Product Launches, Deals, MNM view, Key strengths/Right to win, Strategic choices made, and Weaknesses and competitive threats might not be captured in case of unlisted companies.
12 APPENDIX (Page No. - 202)
12.1 INSIGHTS OF INDUSTRY EXPERTS
12.2 DISCUSSION GUIDE
12.3 KNOWLEDGE STORE: MARKETSANDMARKETS’ SUBSCRIPTION PORTAL
12.4 AVAILABLE CUSTOMIZATIONS
12.5 RELATED REPORTS
12.6 AUTHOR DETAILS
The study involved four major activities in estimating the size of the data center accelerator market. Exhaustive secondary research has been done to collect information on the market, peer market, and parent market. Validation of these findings, assumptions, and sizing with industry experts across the value chain through primary research has been the next step. Both top-down and bottom-up approaches have been employed to estimate the global market size. After that, market breakdown and data triangulation have been used to estimate the market sizes of segments and subsegments.
Secondary Research
The secondary sources referred to for this research study includes TechCrunch News, OLED Data center accelerator, Fraunhofer Data, and Photonics Articles.
In the data center accelerator market report, both top-down and bottom-up approaches have been used to estimate and validate the size of the data center accelerator market, along with other dependent submarkets. The key players in the data center accelerator market have been identified through secondary research, and their market presence has been determined through primary and secondary research. All percentage shares, splits, and breakdowns have been determined using secondary sources and verified through primary sources.
Primary Research
Extensive primary research has been conducted after acquiring an understanding of the data center accelerator market scenario through secondary research. Several primary interviews have been conducted with market experts from both the demand- (consumers, industries, healthcare) and supply-side (data center accelerator product manufacturers) players across four major regions, namely, Americas, Europe, Asia Pacific, and Rest of the World (the Middle East & Africa). Approximately 70% and 30% of primary interviews have been conducted from the supply and demand side, respectively. Primary data has been collected through questionnaires, emails, and telephonic interviews. In the canvassing of primaries, various departments within organizations, such as sales, operations, and administration, were covered to provide a holistic viewpoint in our report.
After interacting with industry experts, brief sessions were conducted with highly experienced independent consultants to reinforce the findings from our primaries. This, along with the in-house subject matter experts’ opinions, has led us to the findings as described in the remainder of this report.
To know about the assumptions considered for the study, download the pdf brochure
Market Size Estimation
Both top-down and bottom-up approaches have been used to estimate and validate the total size of the data center accelerator market. These methods have also been extensively used to estimate the sizes of various market subsegments. The research methodology used to estimate the market sizes includes the following:
- Identifying market for data center accelerator-based processors (GPU, CPU, ASIC, and FPGA) in each country
- Identifying the major applications of data center accelerator-related products
- Estimating the size of the market in each region by adding the sizes of country-wise markets
- Tracking the ongoing and upcoming implementation of data center accelerator projects by various companies in each region and forecasting the size of the data center accelerator market based on these developments and other critical parameters, including COVID-19 related impacts
- Arriving at the size of the global market by adding the sizes of region-wise markets
To know about the assumptions considered for the study, Request for Free Sample Report
Data Triangulation
After arriving at the overall market size—using the market size estimation processes explained above—the market has been split into several segments and subsegments. To complete the overall market engineering process and arrive at the exact statistics of each market segment and subsegment, data triangulation, and market breakdown procedures have been employed, wherever applicable. The data has been triangulated by studying various factors and trends from both the demand and supply sides.
The main objectives of this study are as follows:
- To define, describe, and forecast the data center accelerator market, in terms of value and volume, by processor, application, type, and region
- To forecast the market, for various segments with respect to four main regions—the North Americas, Europe, Asia Pacific (APAC), and Rest of the World (RoW), in terms of value
- To strategically analyze micromarkets1 with respect to individual growth trends, prospects, and contribution to the overall data center accelerator market
- To provide detailed information regarding the major factors (drivers, restraints, opportunities, and challenges) influencing the data center accelerator market growth
- To analyze opportunities in the market for various stakeholders by identifying the high-growth segments of the data center accelerator market
- To study the complete value chain and allied industry segments, and perform a value chain analysis of the data center accelerator market landscape
- To map competitive intelligence based on company profiles, key player strategies, and key developments
- To strategically profile key players and comprehensively analyze their market ranking and core competencies2
- To track and analyze competitive developments such as joint ventures, mergers and acquisitions, product developments, and research and development (R&D) in the data center accelerator market
Available Customizations:
MarketsandMarkets offers the following customizations for this market report:
- Additional country-level analysis of data center accelerator market
- Profiling of additional market players (up to 5)
- Country-level analysis of component, and system type Segment
Get in-depth analysis of the COVID-19 impact on the Data Center Accelerator Market
Benchmarking the rapid strategy shifts of the Top 100 companies in the Data Center Accelerator Market
Request For Special Pricing
- Triangulate with your Own Data
- Get Data as per your Format and Definition
- Gain a Deeper Dive on a Specific Application, Geography, Customer or Competitor
- Any level of Personalization
- What are the Known and Unknown Adjacencies Impacting the Data Center Accelerator Market
- What will your New Revenue Sources be?
- Who will be your Top Customer; what will make them switch?
- Defend your Market Share or Win Competitors
- Get a Scorecard for Target Partners
Growth opportunities and latent adjacency in Data Center Accelerator Market