|
Forecast Period
|
2025-2029
|
|
Market Size
(2023)
|
USD 1.08 Billion
|
|
CAGR (2024-2029)
|
18.19%
|
|
Fastest Growing
Segment
|
BFSI
|
|
Largest Market
|
North America
|
Market Overview
Global Graphics Processing
Units Database Market was valued at USD 1.08 billion in 2023 and is anticipated
to project robust growth in the forecast period with a CAGR of 18.19% through
2029.
The graphics processing units
(GPU) database market refers to the evolving landscape where databases leverage
the parallel processing capabilities of GPUs to enhance data processing speed
and performance. In this market, traditional databases are augmented or
replaced by solutions that harness the computational power of GPUs, originally
designed for graphics rendering. This adaptation allows for accelerated
processing of complex and large datasets, making GPU databases particularly
well-suited for applications such as high-performance computing, artificial
intelligence, machine learning, and data analytics. As organizations across
various industries increasingly demand real-time data insights and efficient
handling of massive datasets, the GPU database market plays a pivotal role in
meeting these requirements. The market encompasses the development, deployment,
and utilization of database systems that leverage GPUs, shaping the
technological landscape to cater to the growing demand for faster and more powerful
data processing solutions.
Key Market Drivers
Increasing
Demand for High-Performance Computing (HPC) Workloads
The global
graphics processing units (GPUs) database market is experiencing a significant
surge due to the escalating demand for high-performance computing (HPC)
workloads across various industries. As organizations strive to process complex
data sets, conduct simulations, and run sophisticated applications, the need
for powerful computing capabilities has intensified. GPUs, with their parallel
processing architecture, have emerged as a key enabler for HPC, driving the
growth of the database market.
Industries such
as healthcare, finance, and scientific research are witnessing a rapid increase
in data-intensive tasks, including genomic analysis, financial modeling, and
simulations. Traditional central processing units (CPUs) often struggle to meet
the computational demands of these workloads. GPUs, on the other hand, excel in
parallel processing, enabling faster and more efficient data handling.
Consequently, enterprises are increasingly adopting GPU-accelerated databases
to enhance the speed and performance of their HPC applications.
The continuous
expansion of artificial intelligence (AI) and machine learning (ML)
applications further amplifies the demand for GPU databases, as these
technologies heavily rely on parallel processing for tasks such as deep
learning and neural network training. As the global HPC market continues to
grow, fueled by advancements in research and technology, the demand for GPU
databases is expected to remain a prominent driver of market expansion.
Proliferation of
Data-Intensive Applications and Big Data Analytics
The
proliferation of data-intensive applications and the widespread adoption of big
data analytics are key drivers propelling the growth of the global GPU database
market. In the digital era, businesses are generating vast amounts of data
daily, necessitating advanced analytics tools to derive valuable insights.
Traditional databases often face challenges in handling the scale and
complexity of big data, leading organizations to explore alternative solutions.
GPUs excel in
processing large datasets in parallel, making them well-suited for accelerating
data analytics tasks. Whether it's real-time analytics, predictive modeling, or
data visualization, GPU databases offer a compelling solution to address the
performance bottlenecks associated with conventional databases. The ability to
perform parallel data processing enables faster query execution and improved
overall responsiveness, enhancing the efficiency of big data analytics
workflows.
Moreover,
industries such as e-commerce, telecommunications, and online services heavily
rely on real-time data processing to deliver personalized services and optimize
user experiences. GPU databases play a pivotal role in meeting the demands of
these dynamic environments by providing high-throughput data processing
capabilities. As organizations strive to extract actionable insights from their
data assets, the adoption of GPU databases as a core component of big data
analytics infrastructure is poised to grow, driving market expansion.
Surge in Demand
for Real-Time Data Processing and Analytics
The growing
emphasis on real-time data processing and analytics is a significant driver
influencing the global GPU database market. In today's fast-paced business
environment, organizations increasingly rely on up-to-the-minute insights to
make informed decisions and gain a competitive edge. Real-time analytics has
become crucial across various sectors, including finance, retail, and
telecommunications, where timely decision-making can have a direct impact on
business outcomes.
Traditional
databases often face challenges in delivering the speed and responsiveness
required for real-time data processing. GPU databases, with their parallel
processing capabilities, excel in handling concurrent tasks, making them
well-suited for real-time analytics applications. The ability to quickly
analyze and respond to streaming data is a key advantage offered by GPU
databases, contributing to their adoption in mission-critical environments.
The surge in IoT
(Internet of Things) devices, which generate a continuous stream of real-time
data, further underscores the importance of GPU databases. These databases can
efficiently handle the high volume and velocity of data generated by IoT
devices, enabling organizations to extract actionable insights in real time. As
the demand for real-time analytics continues to grow, GPU databases are
expected to play a pivotal role in shaping the future of data processing and
analytics.
Evolving Gaming
and Entertainment Industry
The gaming and
entertainment industry is undergoing a transformative evolution, marked by the
increasing complexity of graphics rendering, virtual reality (VR), and
augmented reality (AR) applications. This evolution is a significant driver
fueling the growth of the global GPU database market. Modern video games demand
realistic graphics, immersive experiences, and intricate simulations, pushing
the boundaries of what traditional CPUs can achieve.
GPUs, with their
parallel processing architecture and specialized graphics rendering
capabilities, have become indispensable for delivering high-quality visuals and
optimal gaming performance. As gaming experiences become more sophisticated,
the demand for advanced GPU databases to handle complex graphics rendering and
data-intensive tasks is on the rise. Game developers and studios are leveraging
GPU databases to enhance the efficiency of in-game data processing, enabling
seamless gameplay and immersive graphics.
Additionally,
the rise of VR and AR technologies has further accelerated the demand for
powerful GPUs. These immersive technologies rely heavily on real-time rendering
and complex spatial data processing, making GPUs a critical component for
delivering a compelling user experience. The integration of GPU databases into
gaming and entertainment workflows not only ensures smoother graphics rendering
but also supports data-intensive applications such as content creation and
video editing within the industry.
Adoption of GPU
Databases in Autonomous Vehicles and Robotics
The adoption of
GPU databases in autonomous vehicles and robotics is a key driver propelling
the growth of the global market. As the automotive industry transitions towards
autonomous driving and robotics applications become more prevalent, the demand
for high-performance computing solutions has skyrocketed. GPUs, with their
parallel processing capabilities, are well-suited for handling the complex
computational tasks required for real-time decision-making in these domains.
Autonomous
vehicles rely on a multitude of sensors and cameras to perceive and navigate
their environment. Processing the vast amount of data generated by these
sensors in real time is a critical challenge that traditional databases may
struggle to address. GPU databases, optimized for parallel processing, excel in
handling the simultaneous computation of multiple data streams, making them an
ideal choice for powering the brains of autonomous vehicles.
In the field of
robotics, whether it's industrial automation, drones, or humanoid robots, GPUs
play a crucial role in enabling quick and efficient data processing. Tasks such
as object recognition, path planning, and motion control benefit significantly from
the parallel computing capabilities of GPUs. As the autonomous vehicles and
robotics industries continue to advance, the adoption of GPU databases is
expected to grow, driven by the need for high-performance computing solutions
to support real-time decision-making and enhance overall system efficiency.
Advancements in
GPU Technology and Architecture
The continuous
advancements in GPU technology and architecture serve as a fundamental driver
shaping the landscape of the global GPU database market. GPU manufacturers are
consistently pushing the boundaries of innovation, introducing GPUs with
increased processing power, improved memory bandwidth, and enhanced energy
efficiency. These advancements translate into higher performance and
capabilities for GPU databases, making them increasingly attractive to a
diverse range of industries and applications.
One of the
notable trends in GPU technology is the evolution of AI-specific GPUs, designed
to accelerate machine learning and deep learning workloads. These GPUs feature
dedicated hardware for matrix operations and neural network processing,
unlocking unprecedented speeds for AI applications. As AI continues to permeate
various sectors, the demand for AI-optimized GPU databases is on the rise,
fueled by the need for efficient data processing in machine learning workflows.
Parallel to AI
advancements, the gaming industry also benefits from improved GPU
architectures, enabling developers to create visually stunning and immersive
gaming experiences. The continuous enhancement of graphics rendering
capabilities contributes to the demand for GPU databases in gaming
applications, as developers seek to leverage the latest GPU technologies for
optimal performance.
Moreover, the
energy efficiency improvements in newer GPU architectures make them more
environmentally sustainable, aligning with the increasing focus on green
computing practices. As organizations prioritize sustainability in their
technology infrastructure, the adoption of energy-efficient GPU databases
becomes a strategic choice.
Government Policies are Likely to
Propel the Market
Investment
Incentives for Research and Development in GPU Technology
Governments
worldwide recognize the critical role that innovation plays in fostering
economic growth and competitiveness. In the context of the global graphics
processing units (GPU) database market, policymakers are implementing
investment incentives to encourage research and development (R&D) in GPU
technology. These incentives are designed to stimulate private-sector
investments in cutting-edge technologies, ultimately driving advancements in
GPU capabilities and performance.
Governments
often offer tax credits, grants, and subsidies to companies engaged in R&D
activities related to GPU technology. These financial incentives aim to reduce
the financial burden associated with high-risk research projects, making it
more attractive for businesses to invest in developing innovative GPU
solutions. By fostering a conducive environment for R&D, governments aim to
position their countries at the forefront of GPU technology, attracting talent,
and stimulating economic growth in the technology sector.
The positive
spillover effects of R&D investments in GPU technology extend beyond the
immediate industry, influencing various sectors such as healthcare, finance,
and autonomous vehicles. Government policies supporting R&D in GPU
technology contribute to the creation of a robust ecosystem that fuels
innovation and maintains a competitive edge in the global GPU database market.
Data Privacy and
Security Regulations for GPU Databases
As the reliance
on GPU databases grows across industries, governments are enacting stringent
data privacy and security regulations to safeguard sensitive information. In
response to the increasing concerns about data breaches and cyber threats,
policymakers are implementing measures to ensure the responsible and secure use
of GPU databases in handling personal, financial, and other confidential data.
Government
regulations often mandate data encryption standards, access controls, and
auditing mechanisms to protect the integrity and confidentiality of data stored
and processed by GPU databases. Compliance with these regulations is not only a
legal requirement but also serves to build trust among businesses and
consumers, fostering a secure environment for the adoption of GPU database
technologies.
Moreover,
governments may establish regulatory bodies or agencies responsible for
overseeing data protection and cybersecurity in the context of GPU databases.
These entities work collaboratively with industry stakeholders to develop and
update standards that align with technological advancements and emerging
threats, ensuring a proactive approach to safeguarding sensitive information.
By enforcing
robust data privacy and security regulations, governments contribute to the
establishment of a secure and trustworthy foundation for the global GPU
database market. This, in turn, encourages organizations to embrace GPU
technology for their data processing needs, knowing that stringent measures are
in place to protect against potential risks and vulnerabilities.
Standardization
and Interoperability Guidelines for GPU Databases
To facilitate
seamless integration and interoperability within the global GPU database
market, governments are increasingly focusing on the establishment of
standardization and interoperability guidelines. Standardization ensures that
GPU databases from different vendors adhere to common specifications, enabling
compatibility and ease of use for end-users across various industries.
Government
bodies may collaborate with industry stakeholders to develop and promote
standards for GPU databases, covering aspects such as data formats, query
languages, and communication protocols. The aim is to create a unified
framework that fosters interoperability, allowing organizations to deploy GPU
databases without encountering compatibility issues with other systems and
applications.
By promoting
standardization, governments contribute to a more competitive and dynamic GPU
database market. Organizations can confidently adopt GPU technologies, knowing
that standardized solutions facilitate smoother integration with existing
infrastructure and support interoperability with other software and hardware
components.
Incentives for
Green Computing in GPU Data Centers
Recognizing the
environmental impact of data centers, governments are implementing policies to
encourage green computing practices within the GPU database market. The energy
consumption of GPU data centers, which play a vital role in processing and
managing large datasets, has prompted policymakers to focus on incentivizing
energy-efficient practices and sustainable technologies.
Government
incentives may include tax credits, grants, or regulatory frameworks that
reward businesses adopting energy-efficient GPU technologies and implementing
environmentally friendly data center practices. These policies aim to drive the
development and adoption of GPUs with improved energy efficiency, promoting
responsible and sustainable growth within the GPU database market.
Governments may
also collaborate with industry stakeholders to establish best practices for
energy-efficient data center design and operations, emphasizing the use of
renewable energy sources, efficient cooling systems, and optimized hardware
configurations. By aligning incentives with green computing initiatives,
governments contribute to the reduction of the environmental footprint of GPU
data centers, addressing concerns related to energy consumption and climate
change.
Support for
Skills Development in GPU Technology
To ensure a
skilled workforce capable of contributing to the growth of the global GPU
database market, governments are implementing policies focused on supporting
education and skills development in GPU technology. As GPU databases become
integral to various industries, there is a growing demand for professionals
with expertise in GPU programming, parallel processing, and data analytics.
Government
initiatives may include funding for educational programs, scholarships, and
partnerships with industry leaders to establish training centers and research
institutions focused on GPU technology. By investing in skills development,
governments aim to create a pool of talented individuals equipped with the
knowledge and capabilities to drive innovation and competitiveness in the GPU
database market.
In addition to
formal education, governments may support continuous learning and upskilling
programs for existing professionals, ensuring that the workforce remains
adaptive to the evolving landscape of GPU technology. By fostering a skilled
workforce, governments contribute to the overall resilience and sustainability
of the GPU database market, supporting its continued growth and global
competitiveness.
Trade and Export
Policies to Promote GPU Industry Growth
Governments play
a pivotal role in shaping international trade and export policies that impact
the growth of the GPU industry and its related markets. Policies aimed at
promoting the global expansion of GPU technologies involve creating favorable
trade conditions, reducing trade barriers, and supporting export-oriented
initiatives.
Governments may
negotiate trade agreements and partnerships that facilitate the cross-border
movement of GPU hardware, software, and related technologies. By removing or
reducing tariffs and trade restrictions, policymakers encourage the
international exchange of GPU products, fostering collaboration between
countries and promoting a more interconnected GPU database market.
Furthermore,
export incentives and financial support for GPU industry players engaged in
international trade activities contribute to the competitiveness of domestic
businesses on the global stage. Governments may offer financial assistance,
export credits, or other incentives to promote the export of GPUs and related
technologies, strengthening the position of their country's GPU industry in the
global marketplace.
In conclusion,
government policies play a crucial role in shaping the trajectory of the global
GPU database market. From fostering innovation through R&D incentives to
ensuring data security and promoting international trade, these policies
collectively contribute to a conducive environment for the growth and
sustainability of the GPU database industry on a global scale.
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Key Market Challenges
Scalability and
Performance Optimization in GPU Databases
While graphics
processing units (GPUs) have demonstrated remarkable capabilities in
accelerating parallel processing tasks, the challenge of scalability and
performance optimization remains a significant hurdle in the global GPU
database market. As organizations increasingly adopt GPU databases to handle
large and complex datasets, ensuring that these databases scale efficiently and
deliver consistent high performance becomes paramount.
One of the
primary challenges lies in designing GPU databases that can seamlessly scale
across multiple GPUs or GPU clusters. While GPUs excel in parallel processing,
harnessing the power of multiple GPUs requires careful consideration of
communication overhead, data partitioning, and load balancing. Developing
algorithms and architectures that efficiently distribute computational
workloads across GPUs without sacrificing performance is a complex task that
demands specialized expertise.
Moreover, GPU
databases face the challenge of optimizing performance for diverse workloads.
While GPUs are exceptionally well-suited for certain types of computations, not
all database operations benefit equally from GPU acceleration. Balancing the
workload between GPU and CPU, identifying bottlenecks, and optimizing
algorithms to leverage the strengths of both processing units pose substantial
challenges.
Furthermore,
data movement between the main memory, GPU memory, and storage introduces
latency that can impact overall performance. Efficient data transfer and
management are critical to ensuring that the processing power of GPUs is fully
utilized. This requires innovative approaches to data storage, retrieval, and
caching that can minimize latency and streamline data access.
Addressing the
scalability and performance optimization challenges in GPU databases demands
collaborative efforts from researchers, database developers, and hardware
manufacturers. Additionally, advancements in GPU architecture, interconnect
technologies, and software optimizations are essential to overcoming these
challenges. The industry needs to invest in research and development to create
scalable and high-performance GPU databases that can meet the evolving demands
of modern data-intensive applications.
Interoperability
and Integration with Existing Infrastructure
Another
significant challenge facing the global GPU database market is the seamless
interoperability and integration of GPU databases with existing infrastructure,
including traditional databases, storage systems, and data processing
frameworks. Organizations often grapple with the complexities of integrating
GPU databases into their established data ecosystems, hindering the widespread
adoption of GPU technology.
One key aspect
of this challenge is the diverse range of data processing frameworks and query
languages used across different industries. GPU databases must be compatible
with existing tools and frameworks to ensure a smooth transition for
organizations looking to leverage GPU acceleration. Achieving interoperability
requires standardization in data formats, query languages, and communication
protocols, which is a complex task given the varied nature of data processing
environments.
Moreover,
organizations frequently rely on hybrid data architectures, combining
traditional databases with specialized systems for specific tasks. Integrating
GPU databases into these hybrid architectures requires careful planning and
coordination. Ensuring that data flows seamlessly between different components,
while maintaining data consistency and reliability, poses a significant
challenge that demands collaborative efforts from database vendors and industry
stakeholders.
Data migration
is another aspect of the interoperability challenge. Organizations with large datasets
stored in traditional databases may face difficulties in transitioning to GPU
databases without disrupting their operations. Efficient tools and strategies
for migrating data from legacy systems to GPU-accelerated databases are crucial
for minimizing downtime and ensuring a smooth transition.
The
heterogeneity of hardware architectures within data centers also contributes to
the interoperability challenge. GPU databases must be designed to work
seamlessly with a variety of hardware configurations, including different GPU
models, CPUs, and storage systems. Achieving optimal performance across diverse
hardware environments requires sophisticated optimization techniques and a deep
understanding of the intricacies of each architecture.
To overcome the
challenges of interoperability and integration, industry collaboration and
standardization efforts are essential. Establishing common frameworks,
interfaces, and protocols for GPU databases will facilitate their integration
into existing data ecosystems. Additionally, database vendors should prioritize
providing comprehensive documentation, tools, and support to help organizations
seamlessly adopt GPU-accelerated solutions without disruptions to their
existing workflows.
In conclusion,
addressing the challenges of scalability, performance optimization,
interoperability, and integration is essential for unlocking the full potential
of GPU databases in the global market. As the demand for high-performance
computing and data-intensive applications continues to rise, overcoming these
challenges will be crucial for ensuring the successful adoption and integration
of GPU database technologies across diverse industries.
Segmental Insights
Deployment Insights
The cloud segment held the
largest Market share in 2023. Cloud platforms provide unparalleled scalability,
allowing organizations to easily scale their GPU database resources based on
demand. This flexibility is crucial for handling variable workloads and
managing large datasets efficiently.
Cloud services often operate
on a pay-as-you-go model, enabling organizations to optimize costs by paying
only for the resources they use. This is particularly beneficial in the GPU
database market, where computational needs can vary, and efficient cost management
is essential.
Cloud services offer global
accessibility, allowing organizations to deploy GPU databases and applications
across diverse geographical locations. This is vital for businesses with
distributed teams or customers worldwide, ensuring low-latency access to data
and applications.
Cloud providers offer managed
services that simplify the deployment and management of GPU databases.
Automated tools and services reduce the operational burden on organizations,
enabling them to focus on innovation and application development rather than
infrastructure management.
Cloud platforms provide an
ecosystem of services that seamlessly integrate with GPU databases. This
includes data storage, analytics, machine learning, and other complementary
services, creating a comprehensive solution for organizations with diverse computing
needs.
Cloud services facilitate
rapid deployment of GPU database solutions. Organizations can quickly provision
resources, reducing time-to-market for new applications and services. This
agility is crucial in dynamic business environments.
GPU databases often
experience peak workloads, especially in scenarios such as real-time analytics
or AI model training. Cloud platforms allow for elastic scaling, ensuring that
organizations can meet peak demands without over-provisioning resources during
normal usage periods.
Cloud providers regularly
introduce new GPU instances and hardware, enabling organizations to leverage
the latest technologies without the need for significant capital investments.
This continuous innovation supports businesses in staying competitive and
up-to-date with the latest GPU advancements.
Cloud providers invest
heavily in security measures and compliance certifications. This can be
particularly appealing to organizations in regulated industries, such as
finance or healthcare, where meeting stringent security and compliance
standards is essential.
Cloud services offer robust
disaster recovery and redundancy options. Data is distributed across multiple
data centers, reducing the risk of data loss due to hardware failures or
disasters, and ensuring high availability of GPU database solutions.
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Regional Insights
In 2023, North
America emerged as the primary market leader in the Graphics Processing Units
(GPUs) Database sector. North America, particularly the United States, stands
as a global hub for technological advancement, boasting numerous prominent GPU
manufacturers and technology firms. These entities heavily invest in research
and development, spearheading innovations in GPU technology and its
wide-ranging applications across industries, notably in database management.
Housing major
technology giants like NVIDIA and AMD, North America hosts leading GPU
manufacturers that wield substantial influence over the global GPU market.
Renowned for their prowess in crafting high-performance GPUs tailored for
data-intensive tasks, including database processing and analytics, these
companies significantly shape industry trends. North America boasts a robust
infrastructure of data centers and cloud computing providers, pivotal users of
GPUs for expediting database operations and analytics. With the escalating
adoption of cloud-based database solutions, North American enterprises are
well-equipped to meet burgeoning market demands.
Given the
indispensable role of GPUs in accelerating artificial intelligence (AI) and
machine learning (ML) workloads, which are increasingly integrated into
database systems for advanced analytics, North American firms lead the charge
in developing GPU-accelerated database solutions leveraging AI and ML
technologies. Strategic collaborations between North American companies and
database vendors, cloud providers, and technology integrators are common,
resulting in the delivery of comprehensive database solutions powered by GPUs.
These partnerships foster innovation, extend market reach, and address evolving
customer needs across various industries.
North America fosters a vibrant ecosystem comprising
universities, research institutions, and industry consortia that collaborate on
GPU-centric research and development endeavors. Such cooperative efforts fuel
innovation and knowledge exchange, driving progress in GPU technology and its
applications within database management. Benefiting from a business-friendly
regulatory environment, North America incentivizes innovation and investment in
technology sectors like GPUs and database management. Regulatory frameworks
promote competition, safeguard intellectual property rights, and facilitate
market expansion, allowing North American firms to maintain their leadership
status
Key Market Players
- NVIDIA Corporation
- Intel Corporation
- Samsung Electronics Co. Ltd.
- Microsoft Corporation
- SAP SE
- Kinetica Inc.
- OmniSci Inc.
- SQream Technologies Inc
- Neo4j Inc
- BlazingDB Inc
|
By Component
|
By Deployment
|
By Application
|
By End User
|
By Region
|
- Hardware
- Software
- Services
|
|
- Governance
- Risk, and Compliance
- Threat Intelligence
- Customer Experience
Management
- Fraud Detection and
Prevention
- Supply Chain
Management
- Others
|
- BFSI
- Retail and
E-Commerce
- Telecommunications
and IT
- Transportation and
Logistics
- Healthcare and Pharmaceuticals
- Government and
Defence
- Others
|
- North America
- Europe
- Asia Pacific
- South America
- Middle East &
Africa
|
Report Scope:
In this report, the Global Graphics Processing
Units Database Market has been segmented into the following categories, in
addition to the industry trends which have also been detailed below:
- Graphics
Processing Units Database Market, By Component:
o Hardware
o Software
o Services
- Graphics
Processing Units Database Market, By Deployment:
o Cloud
o On-Premises
- Graphics
Processing Units Database Market, By Application:
o Governance
o Risk, and Compliance
o Threat Intelligence
o Customer Experience Management
o Fraud Detection and Prevention
o Supply Chain Management
o Others
- Graphics
Processing Units Database Market, By End User:
o BFSI
o Retail and E-Commerce
o Telecommunications and IT
o Transportation and Logistics
o Healthcare and Pharmaceuticals
o Government and Defence
o Others
- Graphics
Processing Units Database Market, By Region:
o North America
§ United States
§ Canada
§ Mexico
o Europe
§ France
§ United Kingdom
§ Italy
§ Germany
§ Spain
o Asia-Pacific
§ China
§ India
§ Japan
§ Australia
§ South Korea
o South America
§ Brazil
§ Argentina
§ Colombia
o Middle East & Africa
§ South Africa
§ Saudi Arabia
§ UAE
§ Kuwait
§ Turkey
Competitive Landscape
Company Profiles: Detailed analysis of the major companies
present in the Global Graphics Processing Units Database Market.
Available Customizations:
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