Data Center GPU Market by Deployment (Cloud, On-premises), Function (Training, Inference), Application (Generative AI, Machine Learning, Natural Language Processing, Computer Vision), End User (CSP, Enterprises) & Region - Global Forecast to 2030
The global data center GPU market was valued at USD 87.32 billion in 2024. It is projected to reach USD 228.04 billion by 2030, at a CAGR of 13.7% during the forecast period of 2025 to 2030.
The data center GPU market is growing rapidly due to several key factors, including the widespread adoption of artificial intelligence (AI) and machine learning (ML), increased demand for high-performance computing, and expanding cloud services. Enterprises are utilizing GPUs to improve deep learning, large language models, and data analytics. The rise of generative AI applications and real-time inference systems further boosts the need for robust GPU infrastructure. Investments in hyperscale data centers and government initiatives to support national AI capabilities also play a role in this growth. Major cloud providers like Amazon Web Services, Google Cloud, and Microsoft Azure are enhancing their GPU offerings, while companies like NVIDIA and AMD are launching advanced GPUs tailored for training and inference workloads.
“On-premises segment is expected to hold the highest CAGR during the forecast period.”
On-premises solutions are expected to have the highest CAGR due to the increasing needs for data protection, low latency, and regulatory compliance in sectors like banking, automotive, retail, and healthcare. Organizations prefer to manage sensitive data with in-house GPU hardware for better control, rather than relying on third-party cloud services. On-premises data centers also allow for customized infrastructure, optimizing workloads for AI tasks that require low latency, which is crucial for real-time applications such as autonomous systems and high-frequency trading. As GPU servers become more affordable, mid-sized enterprises can invest in dedicated infrastructure. On-premises deployment is often preferred in regions with limited cloud connectivity or data sovereignty concerns, such as Asia Pacific, Europe, and the Middle East.
“Training segment is projected to record the second-highest CAGR during the forecast period.”
The training segment is expected to see the highest growth in the data center GPU market, driven by businesses developing and optimizing large-scale machine learning and AI models. Training deep neural networks for applications like generative AI, computer vision, and natural language processing requires substantial computing power, which GPUs provide effectively. The rise of large language models, including OpenAI's GPT, Meta's LLaMA, and Google's Gemini, is increasing demand for powerful GPUs in technology, finance, and healthcare sectors. These models require extensive training over weeks and large datasets, leading to a need for dedicated GPU clusters. Companies are also creating proprietary AI models for competitive advantage. Cloud providers such as AWS, Microsoft Azure, and Google Cloud are enhancing their GPU-based training infrastructure. With AI at the forefront of business transformation, the demand for training infrastructure is set to grow significantly.
“Cloud service providers (CSPs) are expected to hold the highest share of the end-user market in 2030”
The Cloud Service Providers (CSPs) segment is expected to command the largest market share in the data center GPU market due to their scale, increasing AI infrastructure spending, and ability to meet the needs of enterprises and developers. Major CSPs like Amazon Web Services, Microsoft Azure, and Google Cloud are rapidly expanding their GPU data centers to meet the rising demand for AI training, inference, data analytics, and cloud gaming. They offer GPU-as-a-Service solutions, allowing companies to access advanced GPU technology without significant upfront investments. Additionally, the rise of foundation models and generative AI drives CSPs to create specialized AI supercomputers with thousands of GPUs. With their global infrastructure and robust developer ecosystems, CSPs are well-positioned to lead the data center GPU market in both revenue and volume.
“North America will likely register the second-highest market share in 2030.”
North America is expected to lead the data center GPU market due to its advanced technological ecosystem and established cloud infrastructure. Major cloud computing companies like Amazon Web Services, Microsoft Azure, and Google Cloud are creating GPU-based data centers to support AI workloads, high-performance computing, and data analysis. North America also has a strong enterprise customer base across industries like healthcare, finance, automotive, and government, increasingly relying on AI-driven solutions that need GPU acceleration. Significant R&D investments, favorable government policies, and early technology adoption support this leadership.
Extensive primary interviews were conducted with key industry experts in the data center GPU market space to determine and verify the market size for various segments and subsegments gathered through secondary research. The breakdown of primary participants for the report is shown below.
The study contains insights from various industry experts, from component suppliers to Tier 1 companies and OEMs. The break-up of the primaries is as follows:
The study includes an in-depth competitive analysis of these key players in the data center GPU market, with their company profiles, recent developments, and key market strategies.
Research Coverage:
The report segments the data center GPU market and forecasts its size by deployment (cloud, on-premises), function (training, inference), application (generative AI, machine learning, natural language processing, computer vision), and end user (cloud service providers, enterprises, and government organizations). It also discusses the market’s drivers, restraints, opportunities, and challenges. It gives a detailed view of the market across four main regions (North America, Europe, Asia Pacific, and RoW). The report includes an ecosystem analysis of the key players.
Key Benefits of Buying the Report:
The data center GPU market is growing rapidly due to several key factors, including the widespread adoption of artificial intelligence (AI) and machine learning (ML), increased demand for high-performance computing, and expanding cloud services. Enterprises are utilizing GPUs to improve deep learning, large language models, and data analytics. The rise of generative AI applications and real-time inference systems further boosts the need for robust GPU infrastructure. Investments in hyperscale data centers and government initiatives to support national AI capabilities also play a role in this growth. Major cloud providers like Amazon Web Services, Google Cloud, and Microsoft Azure are enhancing their GPU offerings, while companies like NVIDIA and AMD are launching advanced GPUs tailored for training and inference workloads.
“On-premises segment is expected to hold the highest CAGR during the forecast period.”
On-premises solutions are expected to have the highest CAGR due to the increasing needs for data protection, low latency, and regulatory compliance in sectors like banking, automotive, retail, and healthcare. Organizations prefer to manage sensitive data with in-house GPU hardware for better control, rather than relying on third-party cloud services. On-premises data centers also allow for customized infrastructure, optimizing workloads for AI tasks that require low latency, which is crucial for real-time applications such as autonomous systems and high-frequency trading. As GPU servers become more affordable, mid-sized enterprises can invest in dedicated infrastructure. On-premises deployment is often preferred in regions with limited cloud connectivity or data sovereignty concerns, such as Asia Pacific, Europe, and the Middle East.
“Training segment is projected to record the second-highest CAGR during the forecast period.”
The training segment is expected to see the highest growth in the data center GPU market, driven by businesses developing and optimizing large-scale machine learning and AI models. Training deep neural networks for applications like generative AI, computer vision, and natural language processing requires substantial computing power, which GPUs provide effectively. The rise of large language models, including OpenAI's GPT, Meta's LLaMA, and Google's Gemini, is increasing demand for powerful GPUs in technology, finance, and healthcare sectors. These models require extensive training over weeks and large datasets, leading to a need for dedicated GPU clusters. Companies are also creating proprietary AI models for competitive advantage. Cloud providers such as AWS, Microsoft Azure, and Google Cloud are enhancing their GPU-based training infrastructure. With AI at the forefront of business transformation, the demand for training infrastructure is set to grow significantly.
“Cloud service providers (CSPs) are expected to hold the highest share of the end-user market in 2030”
The Cloud Service Providers (CSPs) segment is expected to command the largest market share in the data center GPU market due to their scale, increasing AI infrastructure spending, and ability to meet the needs of enterprises and developers. Major CSPs like Amazon Web Services, Microsoft Azure, and Google Cloud are rapidly expanding their GPU data centers to meet the rising demand for AI training, inference, data analytics, and cloud gaming. They offer GPU-as-a-Service solutions, allowing companies to access advanced GPU technology without significant upfront investments. Additionally, the rise of foundation models and generative AI drives CSPs to create specialized AI supercomputers with thousands of GPUs. With their global infrastructure and robust developer ecosystems, CSPs are well-positioned to lead the data center GPU market in both revenue and volume.
“North America will likely register the second-highest market share in 2030.”
North America is expected to lead the data center GPU market due to its advanced technological ecosystem and established cloud infrastructure. Major cloud computing companies like Amazon Web Services, Microsoft Azure, and Google Cloud are creating GPU-based data centers to support AI workloads, high-performance computing, and data analysis. North America also has a strong enterprise customer base across industries like healthcare, finance, automotive, and government, increasingly relying on AI-driven solutions that need GPU acceleration. Significant R&D investments, favorable government policies, and early technology adoption support this leadership.
Extensive primary interviews were conducted with key industry experts in the data center GPU market space to determine and verify the market size for various segments and subsegments gathered through secondary research. The breakdown of primary participants for the report is shown below.
The study contains insights from various industry experts, from component suppliers to Tier 1 companies and OEMs. The break-up of the primaries is as follows:
- By Company Type: Tier 1–60%, Tier 2–10%, and Tier 3–30%
- By Designation: C-level executives–10%, Directors–30%, and Others–60%
- By Region: Europe–20%, North America–70%, Asia Pacific–5%, and RoW–5%
The study includes an in-depth competitive analysis of these key players in the data center GPU market, with their company profiles, recent developments, and key market strategies.
Research Coverage:
The report segments the data center GPU market and forecasts its size by deployment (cloud, on-premises), function (training, inference), application (generative AI, machine learning, natural language processing, computer vision), and end user (cloud service providers, enterprises, and government organizations). It also discusses the market’s drivers, restraints, opportunities, and challenges. It gives a detailed view of the market across four main regions (North America, Europe, Asia Pacific, and RoW). The report includes an ecosystem analysis of the key players.
Key Benefits of Buying the Report:
- Analysis of key drivers (growing adoption of AI and machine learning, demand for high-performance computing, cloud computing expansion, restraints (high costs of GPUs and infrastructure, short product lifecycle), opportunities (growth in autonomous systems, emergence of edge computing, advancements in quantum computing synergy), and challenges (existence of alternative technologies, stringent regulatory framework, supply chain disruptions)
- Service Development/Innovation: Detailed insights on upcoming technologies, research and development activities, and new product launches in the data center GPU market
- Market Development: Comprehensive information about lucrative markets – the report analyses the data center GPU market across varied regions
- Market Diversification: Exhaustive information about new products and services, untapped geographies, recent developments, and investments in the data center GPU market.
- Competitive Assessment: In-depth assessment of market shares, growth strategies, and service offerings of leading players, such as NVIDIA Corporation (US), Advanced Micro Devices, Inc. (US), Intel Corporation (US), Google Cloud (US), Microsoft (US), and Amazon Web Services, Inc. (US)
1 INTRODUCTION
1.1 STUDY OBJECTIVES
1.2 MARKET DEFINITION
1.3 STUDY SCOPE
1.3.1 MARKETS COVERED
1.3.2 INCLUSIONS AND EXCLUSIONS
1.3.3 YEARS CONSIDERED
1.4 CURRENCY CONSIDERED
1.5 UNIT CONSIDERED
1.6 LIMITATIONS
1.7 STAKEHOLDERS
1.8 SUMMARY OF CHANGES
2 RESEARCH METHODOLOGY
2.1 RESEARCH DATA
2.1.1 SECONDARY DATA
2.1.1.1 List of major secondary sources
2.1.1.2 Key data from secondary sources
2.1.2 PRIMARY DATA
2.1.2.1 List of primary interview participants
2.1.2.2 Breakdown of primaries
2.1.2.3 Key data from primary sources
2.1.2.4 Key industry insights
2.1.3 SECONDARY AND PRIMARY RESEARCH
2.2 MARKET SIZE ESTIMATION
2.2.1 BOTTOM-UP APPROACH
2.2.1.1 Approach to estimate market size using bottom-up analysis
(demand side)
2.2.2 TOP-DOWN APPROACH
2.2.2.1 Approach to estimate market size using top-down analysis
(supply side)
2.3 MARKET BREAKDOWN AND DATA TRIANGULATION
2.4 RESEARCH ASSUMPTIONS
2.5 RISK ASSESSMENT
2.6 RESEARCH LIMITATIONS
3 EXECUTIVE SUMMARY
4 PREMIUM INSIGHTS
4.1 ATTRACTIVE OPPORTUNITIES FOR PLAYERS IN DATA CENTER GPU MARKET
4.2 DATA CENTER GPU MARKET, BY DEPLOYMENT
4.3 DATA CENTER GPU MARKET, BY FUNCTION
4.4 DATA CENTER GPU MARKET, BY APPLICATION
4.5 DATA CENTER GPU MARKET, BY END USER
4.6 DATA CENTER GPU MARKET, BY COUNTRY
5 MARKET OVERVIEW
5.1 INTRODUCTION
5.2 MARKET DYNAMICS
5.2.1 DRIVERS
5.2.1.1 Growing adoption of AI and machine learning
5.2.1.2 Growing demand for high performance computing (HPC)
5.2.1.3 Cloud computing expansion
5.2.2 RESTRAINTS
5.2.2.1 High costs of GPUs and infrastructure
5.2.2.2 Short product lifecycle
5.2.3 OPPORTUNITIES
5.2.3.1 Growth in autonomous systems
5.2.3.2 Emergence of edge computing
5.2.3.3 Advancements in quantum computing synergy
5.2.4 CHALLENGES
5.2.4.1 Existence of alternative technologies
5.2.4.2 Stringent regulatory framework
5.2.4.3 Supply chain disruptions
5.3 PORTER'S FIVE FORCES ANALYSIS
5.4 ECOSYSTEM ANALYSIS
5.5 VALUE CHAIN ANALYSIS
5.6 REGULATORY LANDSCAPE
5.6.1 REGULATORY BODIES, GOVERNMENT AGENCIES,
AND OTHER ORGANIZATIONS
5.6.2 STANDARDS
5.6.3 REGULATIONS
5.6.3.1 North America
5.6.3.1.1 US
5.6.3.1.2 Canada
5.6.3.2 Europe
5.6.3.2.1 Germany
5.6.3.2.2 France
5.6.3.3 Asia Pacific
5.6.3.3.1 Japan
5.6.3.3.2 China
5.6.3.4 RoW
5.6.3.4.1 Brazil
5.6.3.4.2 South Africa
5.7 TRADE ANALYSIS
5.7.1 IMPORT DATA (HS CODE 847330)
5.7.2 EXPORT SCENARIO (HS CODE 847330)
5.8 PRICING ANALYSIS
5.8.1 INDICATIVE PRICING TREND OF DATA CENTER GPU OFFERED
BY KEY PLAYERS, BY FUNCTION, 2024 (USD)
5.8.2 INDICATIVE PRICING TREND OF DATA CENTER GPUS, BY KEY PLAYER, 2024
5.8.3 AVERAGE SELLING PRICE OF DATA CENTER GPUS, BY REGION,
2021–2024 (USD)
5.9 TECHNOLOGY ANALYSIS
5.9.1 KEY TECHNOLOGIES
5.9.1.1 Parallel processing architectures
5.9.1.2 High bandwidth memory (HBM)
5.9.2 ADJACENT TECHNOLOGIES
5.9.2.1 Application-specific integrated circuits (ASIC)
5.9.2.2 Field-programmable gate arrays (FPGA)
5.9.3 COMPLEMENTARY TECHNOLOGIES
5.9.3.1 Non-volatile memory express (NVMe)
5.9.3.2 Infiniband
5.10 PATENT ANALYSIS
5.11 CASE STUDY ANALYSIS
5.11.1 DECENTRALIZED DIGITAL WORLD OF MEDIA AND ENTERTAINMENT
5.11.2 TERRAY THERAPEUTICS – LEVERAGING GENERATIVE AI FOR
SMALL-MOLECULE DRUG DISCOVERY
5.11.3 SIEMENS HEALTHINEERS – STREAMLINING CANCER RADIATION
THERAPY WITH AI
5.11.4 GAC R&D CENTER – BOOSTING VEHICLE AERODYNAMICS WITH NVIDIA GPUS
5.11.5 STONE RIDGE TECHNOLOGY – REDUCING COMPOSITIONAL MODEL RUNTIMES WITH ECHELON 2.0
5.12 KEY STAKEHOLDERS AND BUYING CRITERIA
5.12.1 KEY STAKEHOLDERS IN BUYING PROCESS
5.12.2 BUYING CRITERIA
5.13 KEY CONFERENCES AND EVENTS, 2025–2026
5.14 INVESTMENT AND FUNDING SCENARIO, 2023 Q1–2024 Q2
5.15 TRENDS/DISRUPTIONS IMPACTING CUSTOMER BUSINESS
5.16 TRUMP IMPACT OVERVIEW
5.17 KEY TARIFF RATES
5.18 KEY IMPACTS ON VARIOUS REGIONS
5.18.1 US
5.18.2 EUROPE
5.18.3 ASIA PACIFIC
5.19 IMPACT ON SUPPLY CHAIN IN ASIA PACIFIC
5.20 EXEMPTIONS AND LOOPHOLES FOR GPUS IN TRUMP TARIFFS UNDER
USMCA AGREEMENT
5.21 END-USE INDUSTRY-LEVEL IMPACT
6 GPU-AS-A-SERVICE (GPUAAS) LANDSCAPE
6.1 INTRODUCTION
6.2 SERVICE MODEL
6.2.1 IAAS
6.2.1.1 Rise in edge computing and real-time data processing to boost segmental growth
6.2.2 PAAS
6.2.2.1 Cost efficiency, scalability, and operational simplicity to contribute to segmental growth
6.3 DEPLOYMENT
6.3.1 PUBLIC CLOUD
6.3.1.1 Scalability and high-performance computing capabilities to augment segmental growth
6.3.2 PRIVATE CLOUD
6.3.2.1 Enhanced control, security, and customization to foster
segmental growth
6.3.3 HYBRID CLOUD
6.3.3.1 Ability to handle dynamic workloads and data security to accelerate segmental growth
7 DATA CENTER GPU MARKET, BY DEPLOYMENT
7.1 INTRODUCTION
7.2 CLOUD
7.2.1 INCREASING FLEXIBILITY, SCALABILITY, AND COST EFFICIENCY TO
DRIVE GROWTH
7.3 ON-PREMISES
7.3.1 GROWING DEMAND FOR CONTROL AND PERFORMANCE DRIVES
ON-PREMISE GPU DEPLOYMENTS
8 DATA CENTER GPU MARKET, BY FUNCTION
8.1 INTRODUCTION
8.2 TRAINING
8.2.1 GPU-DRIVEN PARALLEL PROCESSING ACCELERATES MACHINE LEARNING MODEL DEVELOPMENT IN DATA CENTERS
8.3 INFERENCE
8.3.1 REAL-TIME DECISION-MAKING DRIVES DEMAND FOR LOW-LATENCY
GPU INFERENCE IN DATA CENTERS
9 DATA CENTER GPU MARKET, BY APPLICATION
9.1 INTRODUCTION
9.2 GENERATIVE AI
9.2.1 GENERATIVE AI UNLEASHES UNPRECEDENTED GPU DEMAND
IN DATA CENTERS
9.2.2 RULE-BASED MODELS
9.2.3 STATISTICAL MODELS
9.2.4 DEEP LEARNING
9.2.5 GENERATIVE ADVERSARIAL NETWORKS (GANS)
9.2.6 AUTOENCODERS
9.2.7 CONVOLUTIONAL NEURAL NETWORKS (CNNS)
9.2.8 TRANSFORMER MODELS
9.3 MACHINE LEARNING
9.3.1 MACHINE LEARNING’S EXPANDING FOOTPRINT DRIVES
DATA CENTER GPU GROWTH
9.4 NATURAL LANGUAGE PROCESSING
9.4.1 GPU ACCELERATION DRIVES NLP’S DATA CENTER DOMINANCE
9.5 COMPUTER VISION
9.5.1 GPU-POWERED COMPUTER VISION DRIVES DATA CENTER GROWTH
10 DATA CENTER GPU MARKET, BY END USER
10.1 INTRODUCTION
10.2 CLOUD SERVICE PROVIDERS
10.2.1 RISING USE OF DATA CENTER GPUS FOR AI AND MACHINE LEARNING APPLICATIONS TO DRIVE MARKET
10.3 ENTERPRISES
10.3.1 ENTERPRISE AI ADOPTION FUELS ROBUST GROWTH IN
DATA CENTER GPU DEMAND
10.3.2 HEALTHCARE
10.3.2.1 Growing use of machine learning (ML) and deep learning (DL) models in medical field to propel market
10.3.3 BFSI
10.3.3.1 Increased use of HPC by BFSI enterprises to drive market
10.3.4 AUTOMOTIVE
10.3.4.1 Rising popularity of autonomous cars to fuel adoption of GPUs
10.3.5 RETAIL & E-COMMERCE
10.3.5.1 Rising need to handle massive amounts of retail and e-commerce data to accelerate adoption of GPUs
10.3.6 MEDIA & ENTERTAINMENT
10.3.6.1 Increasing use of AI for content creation and recommendation to drive market
10.3.7 OTHERS
10.4 GOVERNMENT ORGANIZATIONS
10.4.1 RISING ADOPTION OF AI BY GOVERNMENT ORGANIZATIONS FOR
NATIONAL SECURITY TO DRIVE MARKET
11 DATA CENTER GPU MARKET, BY REGION
11.1 INTRODUCTION
11.2 NORTH AMERICA
11.2.1 MACROECONOMIC OUTLOOK FOR NORTH AMERICA
11.2.2 US
11.2.2.1 High demand for GPUs from AI workloads to drive market
11.2.3 CANADA
11.2.3.1 Strategic government initiatives to boost market growth
11.2.4 MEXICO
11.2.4.1 Increasing investments in Mexico by hyperscalers to support
market growth
11.3 EUROPE
11.3.1 MACROECONOMIC OUTLOOK FOR EUROPE
11.3.2 GERMANY
11.3.2.1 Increasing adoption of automation solutions in automotive industry to drive market
11.3.3 UK
11.3.3.1 Strong demand from essential IT services and advent for new startups to drive market
11.3.4 FRANCE
11.3.4.1 Significant AI investments to drive market
11.3.5 ITALY
11.3.5.1 Partnerships between technology providers and government incentives drive market
11.3.6 SPAIN
11.3.6.1 Surging investments by hyperscalers and other companies to
drive market
11.3.7 POLAND
11.3.7.1 Growing cloud adoption and AI investments to boost
market opportunities
11.3.8 NORDICS
11.3.8.1 Rising adoption of accelerated computing technologies in
data center to drive market
11.3.9 REST OF EUROPE
11.4 ASIA PACIFIC
11.4.1 MACROECONOMIC OUTLOOK FOR ASIA PACIFIC
11.4.2 CHINA
11.4.2.1 Rapid government funding and initiatives to drive market
11.4.3 SOUTH KOREA
11.4.3.1 Rising investment and need for real-time data processing
to drive market
11.4.4 JAPAN
11.4.4.1 Increasing hyperscaler investments to drive market
11.4.5 INDIA
11.4.5.1 Government initiatives and incentives to drive market
11.4.6 AUSTRALIA
11.4.6.1 Domestic HPC push signals Australia’s commitment to
AI advancement
11.4.7 INDONESIA
11.4.7.1 Indonesia’s digital ambition drives significant investment
11.4.8 MALAYSIA
11.4.8.1 Global cloud leaders drive massive data center GPU expansion
in Malaysia
11.4.9 THAILAND
11.4.9.1 Strategic location and policies position Thailand for HPC leadership
11.4.10 VIETNAM
11.4.10.1 NVIDIA’s strategic partnerships catalyze market
11.4.11 REST OF ASIA PACIFIC
11.5 ROW
11.5.1 MACROECONOMIC OUTLOOK FOR ROW
11.5.2 SOUTH AMERICA
11.5.2.1 Global players investing in region for data center infrastructure to drive demand
11.5.3 AFRICA
11.5.3.1 Rising focus of manufacturing firms on streamlining workflow and improving product quality to create opportunities
11.5.4 MIDDLE EAST
11.5.4.1 Booming AI initiatives to drive demand
11.5.4.2 GCC
11.5.4.3 Bahrain
11.5.4.3.1 Increased government initiatives to drive market
11.5.4.4 Kuwait
11.5.4.4.1 Kuwait accelerates GPU-driven digital transformation with national cloud and AI initiatives
11.5.4.5 Oman
11.5.4.5.1 Regional HPC hub with GPU-backed data center growth
11.5.4.6 Qatar
11.5.4.6.1 Qatar scales AI infrastructure with GPU investments ahead of smart city and research expansion
11.5.4.7 Saudi Arabia
11.5.4.7.1 Leads Gulf GPU market with hyperscale AI data center mega projects
11.5.4.8 United Arab Emirates (UAE)
11.5.4.8.1 UAE advances AI supercomputing ambitions through massive GPU-powered data center investments
11.5.4.9 Rest of Middle East
12 COMPETITIVE LANDSCAPE
12.1 OVERVIEW
12.2 KEY PLAYER STRATEGIES/RIGHT TO WIN, 2022–2025
12.3 REVENUE ANALYSIS, 2018–2022
12.4 MARKET SHARE ANALYSIS, 2024
12.5 COMPANY VALUATION AND FINANCIAL METRICS
12.6 BRAND/PRODUCT COMPARISON
12.7 COMPANY EVALUATION MATRIX FOR DATA CENTER GPUS: KEY PLAYERS, 2024
12.7.1 STARS
12.7.2 EMERGING LEADERS
12.7.3 PERVASIVE PLAYERS
12.7.4 PARTICIPANTS
12.8 COMPANY EVALUATION MATRIX FOR GPU-AS-A-SERVICE (GPUAAS):
KEY PLAYERS, 2024
12.8.1 STARS
12.8.2 EMERGING LEADERS
12.8.3 PERVASIVE PLAYERS
12.8.4 PARTICIPANTS
12.8.5 COMPANY FOOTPRINT: KEY PLAYERS, 2024
12.8.5.1 Company footprint
12.8.5.2 Regional footprint
12.8.5.3 Deployment footprint
12.8.5.4 Function footprint
12.8.5.5 End user footprint
12.9 COMPANY EVALUATION MATRIX FOR GPU-AS-A-SERVICE (GPUAAS):
STARTUPS/SMES, 2024
12.9.1 PROGRESSIVE COMPANIES
12.9.2 RESPONSIVE COMPANIES
12.9.3 DYNAMIC COMPANIES
12.9.4 STARTING BLOCKS
12.9.5 COMPETITIVE BENCHMARKING: STARTUPS/SMES, 2024
12.9.5.1 Detailed list of key startups/SMEs
12.9.5.2 Detailed list of key startups/SMEs
12.10 COMPETITIVE SCENARIO AND TRENDS
12.10.1 PRODUCT LAUNCHES
12.10.2 DEALS
13 COMPANY PROFILES
13.1 KEY PLAYERS
13.1.1 NVIDIA CORPORATION
13.1.1.1 Business overview
13.1.1.2 Products/Solutions/Services offered
13.1.1.3 Recent developments
13.1.1.3.1 Product launches
13.1.1.3.2 Deals
13.1.1.4 MnM view
13.1.1.4.1 Key strengths
13.1.1.4.2 Strategic choices
13.1.1.4.3 Weaknesses and competitive threats
13.1.2 ADVANCED MICRO DEVICES, INC.
13.1.2.1 Business overview
13.1.2.2 Products/Solutions/Services offered
13.1.2.3 Recent developments
13.1.2.3.1 Product launches
13.1.2.3.2 Deals
13.1.2.4 MnM view
13.1.2.4.1 Key strengths
13.1.2.4.2 Strategic choices
13.1.2.4.3 Weaknesses and competitive threats
13.1.3 INTEL CORPORATION
13.1.3.1 Business overview
13.1.3.2 Products/Solutions/Services offered
13.1.3.3 Recent developments
13.1.3.3.1 Product launches
13.1.3.3.2 Deals
13.1.3.4 MnM view
13.1.3.4.1 Key strengths
13.1.3.4.2 Strategic choices
13.1.3.4.3 Weaknesses and competitive threats
13.1.4 GOOGLE
13.1.4.1 Business overview
13.1.4.2 Recent developments
13.1.4.2.1 Product launches
13.1.4.2.2 Deals
13.1.4.3 MnM view
13.1.4.3.1 Key strengths
13.1.4.3.2 Strategic choices
13.1.4.3.3 Weaknesses and competitive threats
13.1.5 MICROSOFT
13.1.5.1 Business overview
13.1.5.2 Products/Solutions/Services offered
13.1.5.3 Recent developments
13.1.5.3.1 Deals
13.1.5.4 MnM view
13.1.5.4.1 Key strengths
13.1.5.4.2 Strategic choices
13.1.5.4.3 Weaknesses and competitive threats
13.1.6 AMAZON WEB SERVICES, INC.
13.1.6.1 Business overview
13.1.6.2 Products/Solutions/Services offered
13.1.6.3 Recent developments
13.1.6.3.1 Product launches
13.1.6.3.2 Deals
13.1.7 IBM
13.1.7.1 Business overview
13.1.7.2 Products/Solutions/Services offered
13.1.7.3 Recent developments
13.1.7.3.1 Product launches
13.1.7.3.2 Deals
13.1.8 ALIBABA CLOUD
13.1.8.1 Business overview
13.1.8.2 Products/Solutions/Services offered
13.1.8.3 Recent developments
13.1.8.3.1 Product launches
13.1.8.3.2 Deals
13.1.9 ORACLE
13.1.9.1 Business overview
13.1.9.2 Products/Solutions/Services offered
13.1.9.3 Recent developments
13.1.9.3.1 Product launches
13.1.9.3.2 Deals
13.1.10 COREWEAVE.
13.1.10.1 Business overview
13.1.10.2 Products/Solutions/Services offered
13.1.10.3 Recent developments
13.1.10.3.1 Deals
13.1.11 TENCENT CLOUD
13.1.11.1 Business overview
13.1.11.2 Products/Solutions/Services offered
13.1.11.3 Recent developments
13.1.11.3.1 Expansions
13.1.12 LAMBDA
13.1.12.1 Business overview
13.1.12.2 Products/Solutions/Services offered
13.1.12.3 Recent developments
13.1.12.3.1 Deals
13.2 OTHER PLAYERS
13.2.1 VAST.AI
13.2.2 RUNPOD
13.2.3 SCALEMATRIX HOLDINGS, INC.
13.2.4 DIGITALOCEAN
13.2.5 JARVISLABS.AI
13.2.6 FLUIDSTACK
13.2.7 OVH SAS
13.2.8 E2E NETWORKS LIMITED
13.2.9 ACE CLOUD
13.2.10 SNOWCELL
13.2.11 LINODE LLC
13.2.12 YOTTA DATA SERVICES PVT LTD.
13.2.13 VULTR
13.2.14 RACKSPACE TECHNOLOGY
13.2.15 GCORE
13.2.16 NEBIUS B.V.
14 APPENDIX
14.1 INSIGHTS FROM INDUSTRY EXPERTS
14.2 DISCUSSION GUIDE
14.3 KNOWLEDGESTORE: MARKETSANDMARKETS’ SUBSCRIPTION PORTAL
14.4 CUSTOMIZATION OPTIONS
14.5 RELATED REPORTS
14.6 AUTHOR DETAILS
1.1 STUDY OBJECTIVES
1.2 MARKET DEFINITION
1.3 STUDY SCOPE
1.3.1 MARKETS COVERED
1.3.2 INCLUSIONS AND EXCLUSIONS
1.3.3 YEARS CONSIDERED
1.4 CURRENCY CONSIDERED
1.5 UNIT CONSIDERED
1.6 LIMITATIONS
1.7 STAKEHOLDERS
1.8 SUMMARY OF CHANGES
2 RESEARCH METHODOLOGY
2.1 RESEARCH DATA
2.1.1 SECONDARY DATA
2.1.1.1 List of major secondary sources
2.1.1.2 Key data from secondary sources
2.1.2 PRIMARY DATA
2.1.2.1 List of primary interview participants
2.1.2.2 Breakdown of primaries
2.1.2.3 Key data from primary sources
2.1.2.4 Key industry insights
2.1.3 SECONDARY AND PRIMARY RESEARCH
2.2 MARKET SIZE ESTIMATION
2.2.1 BOTTOM-UP APPROACH
2.2.1.1 Approach to estimate market size using bottom-up analysis
(demand side)
2.2.2 TOP-DOWN APPROACH
2.2.2.1 Approach to estimate market size using top-down analysis
(supply side)
2.3 MARKET BREAKDOWN AND DATA TRIANGULATION
2.4 RESEARCH ASSUMPTIONS
2.5 RISK ASSESSMENT
2.6 RESEARCH LIMITATIONS
3 EXECUTIVE SUMMARY
4 PREMIUM INSIGHTS
4.1 ATTRACTIVE OPPORTUNITIES FOR PLAYERS IN DATA CENTER GPU MARKET
4.2 DATA CENTER GPU MARKET, BY DEPLOYMENT
4.3 DATA CENTER GPU MARKET, BY FUNCTION
4.4 DATA CENTER GPU MARKET, BY APPLICATION
4.5 DATA CENTER GPU MARKET, BY END USER
4.6 DATA CENTER GPU MARKET, BY COUNTRY
5 MARKET OVERVIEW
5.1 INTRODUCTION
5.2 MARKET DYNAMICS
5.2.1 DRIVERS
5.2.1.1 Growing adoption of AI and machine learning
5.2.1.2 Growing demand for high performance computing (HPC)
5.2.1.3 Cloud computing expansion
5.2.2 RESTRAINTS
5.2.2.1 High costs of GPUs and infrastructure
5.2.2.2 Short product lifecycle
5.2.3 OPPORTUNITIES
5.2.3.1 Growth in autonomous systems
5.2.3.2 Emergence of edge computing
5.2.3.3 Advancements in quantum computing synergy
5.2.4 CHALLENGES
5.2.4.1 Existence of alternative technologies
5.2.4.2 Stringent regulatory framework
5.2.4.3 Supply chain disruptions
5.3 PORTER'S FIVE FORCES ANALYSIS
5.4 ECOSYSTEM ANALYSIS
5.5 VALUE CHAIN ANALYSIS
5.6 REGULATORY LANDSCAPE
5.6.1 REGULATORY BODIES, GOVERNMENT AGENCIES,
AND OTHER ORGANIZATIONS
5.6.2 STANDARDS
5.6.3 REGULATIONS
5.6.3.1 North America
5.6.3.1.1 US
5.6.3.1.2 Canada
5.6.3.2 Europe
5.6.3.2.1 Germany
5.6.3.2.2 France
5.6.3.3 Asia Pacific
5.6.3.3.1 Japan
5.6.3.3.2 China
5.6.3.4 RoW
5.6.3.4.1 Brazil
5.6.3.4.2 South Africa
5.7 TRADE ANALYSIS
5.7.1 IMPORT DATA (HS CODE 847330)
5.7.2 EXPORT SCENARIO (HS CODE 847330)
5.8 PRICING ANALYSIS
5.8.1 INDICATIVE PRICING TREND OF DATA CENTER GPU OFFERED
BY KEY PLAYERS, BY FUNCTION, 2024 (USD)
5.8.2 INDICATIVE PRICING TREND OF DATA CENTER GPUS, BY KEY PLAYER, 2024
5.8.3 AVERAGE SELLING PRICE OF DATA CENTER GPUS, BY REGION,
2021–2024 (USD)
5.9 TECHNOLOGY ANALYSIS
5.9.1 KEY TECHNOLOGIES
5.9.1.1 Parallel processing architectures
5.9.1.2 High bandwidth memory (HBM)
5.9.2 ADJACENT TECHNOLOGIES
5.9.2.1 Application-specific integrated circuits (ASIC)
5.9.2.2 Field-programmable gate arrays (FPGA)
5.9.3 COMPLEMENTARY TECHNOLOGIES
5.9.3.1 Non-volatile memory express (NVMe)
5.9.3.2 Infiniband
5.10 PATENT ANALYSIS
5.11 CASE STUDY ANALYSIS
5.11.1 DECENTRALIZED DIGITAL WORLD OF MEDIA AND ENTERTAINMENT
5.11.2 TERRAY THERAPEUTICS – LEVERAGING GENERATIVE AI FOR
SMALL-MOLECULE DRUG DISCOVERY
5.11.3 SIEMENS HEALTHINEERS – STREAMLINING CANCER RADIATION
THERAPY WITH AI
5.11.4 GAC R&D CENTER – BOOSTING VEHICLE AERODYNAMICS WITH NVIDIA GPUS
5.11.5 STONE RIDGE TECHNOLOGY – REDUCING COMPOSITIONAL MODEL RUNTIMES WITH ECHELON 2.0
5.12 KEY STAKEHOLDERS AND BUYING CRITERIA
5.12.1 KEY STAKEHOLDERS IN BUYING PROCESS
5.12.2 BUYING CRITERIA
5.13 KEY CONFERENCES AND EVENTS, 2025–2026
5.14 INVESTMENT AND FUNDING SCENARIO, 2023 Q1–2024 Q2
5.15 TRENDS/DISRUPTIONS IMPACTING CUSTOMER BUSINESS
5.16 TRUMP IMPACT OVERVIEW
5.17 KEY TARIFF RATES
5.18 KEY IMPACTS ON VARIOUS REGIONS
5.18.1 US
5.18.2 EUROPE
5.18.3 ASIA PACIFIC
5.19 IMPACT ON SUPPLY CHAIN IN ASIA PACIFIC
5.20 EXEMPTIONS AND LOOPHOLES FOR GPUS IN TRUMP TARIFFS UNDER
USMCA AGREEMENT
5.21 END-USE INDUSTRY-LEVEL IMPACT
6 GPU-AS-A-SERVICE (GPUAAS) LANDSCAPE
6.1 INTRODUCTION
6.2 SERVICE MODEL
6.2.1 IAAS
6.2.1.1 Rise in edge computing and real-time data processing to boost segmental growth
6.2.2 PAAS
6.2.2.1 Cost efficiency, scalability, and operational simplicity to contribute to segmental growth
6.3 DEPLOYMENT
6.3.1 PUBLIC CLOUD
6.3.1.1 Scalability and high-performance computing capabilities to augment segmental growth
6.3.2 PRIVATE CLOUD
6.3.2.1 Enhanced control, security, and customization to foster
segmental growth
6.3.3 HYBRID CLOUD
6.3.3.1 Ability to handle dynamic workloads and data security to accelerate segmental growth
7 DATA CENTER GPU MARKET, BY DEPLOYMENT
7.1 INTRODUCTION
7.2 CLOUD
7.2.1 INCREASING FLEXIBILITY, SCALABILITY, AND COST EFFICIENCY TO
DRIVE GROWTH
7.3 ON-PREMISES
7.3.1 GROWING DEMAND FOR CONTROL AND PERFORMANCE DRIVES
ON-PREMISE GPU DEPLOYMENTS
8 DATA CENTER GPU MARKET, BY FUNCTION
8.1 INTRODUCTION
8.2 TRAINING
8.2.1 GPU-DRIVEN PARALLEL PROCESSING ACCELERATES MACHINE LEARNING MODEL DEVELOPMENT IN DATA CENTERS
8.3 INFERENCE
8.3.1 REAL-TIME DECISION-MAKING DRIVES DEMAND FOR LOW-LATENCY
GPU INFERENCE IN DATA CENTERS
9 DATA CENTER GPU MARKET, BY APPLICATION
9.1 INTRODUCTION
9.2 GENERATIVE AI
9.2.1 GENERATIVE AI UNLEASHES UNPRECEDENTED GPU DEMAND
IN DATA CENTERS
9.2.2 RULE-BASED MODELS
9.2.3 STATISTICAL MODELS
9.2.4 DEEP LEARNING
9.2.5 GENERATIVE ADVERSARIAL NETWORKS (GANS)
9.2.6 AUTOENCODERS
9.2.7 CONVOLUTIONAL NEURAL NETWORKS (CNNS)
9.2.8 TRANSFORMER MODELS
9.3 MACHINE LEARNING
9.3.1 MACHINE LEARNING’S EXPANDING FOOTPRINT DRIVES
DATA CENTER GPU GROWTH
9.4 NATURAL LANGUAGE PROCESSING
9.4.1 GPU ACCELERATION DRIVES NLP’S DATA CENTER DOMINANCE
9.5 COMPUTER VISION
9.5.1 GPU-POWERED COMPUTER VISION DRIVES DATA CENTER GROWTH
10 DATA CENTER GPU MARKET, BY END USER
10.1 INTRODUCTION
10.2 CLOUD SERVICE PROVIDERS
10.2.1 RISING USE OF DATA CENTER GPUS FOR AI AND MACHINE LEARNING APPLICATIONS TO DRIVE MARKET
10.3 ENTERPRISES
10.3.1 ENTERPRISE AI ADOPTION FUELS ROBUST GROWTH IN
DATA CENTER GPU DEMAND
10.3.2 HEALTHCARE
10.3.2.1 Growing use of machine learning (ML) and deep learning (DL) models in medical field to propel market
10.3.3 BFSI
10.3.3.1 Increased use of HPC by BFSI enterprises to drive market
10.3.4 AUTOMOTIVE
10.3.4.1 Rising popularity of autonomous cars to fuel adoption of GPUs
10.3.5 RETAIL & E-COMMERCE
10.3.5.1 Rising need to handle massive amounts of retail and e-commerce data to accelerate adoption of GPUs
10.3.6 MEDIA & ENTERTAINMENT
10.3.6.1 Increasing use of AI for content creation and recommendation to drive market
10.3.7 OTHERS
10.4 GOVERNMENT ORGANIZATIONS
10.4.1 RISING ADOPTION OF AI BY GOVERNMENT ORGANIZATIONS FOR
NATIONAL SECURITY TO DRIVE MARKET
11 DATA CENTER GPU MARKET, BY REGION
11.1 INTRODUCTION
11.2 NORTH AMERICA
11.2.1 MACROECONOMIC OUTLOOK FOR NORTH AMERICA
11.2.2 US
11.2.2.1 High demand for GPUs from AI workloads to drive market
11.2.3 CANADA
11.2.3.1 Strategic government initiatives to boost market growth
11.2.4 MEXICO
11.2.4.1 Increasing investments in Mexico by hyperscalers to support
market growth
11.3 EUROPE
11.3.1 MACROECONOMIC OUTLOOK FOR EUROPE
11.3.2 GERMANY
11.3.2.1 Increasing adoption of automation solutions in automotive industry to drive market
11.3.3 UK
11.3.3.1 Strong demand from essential IT services and advent for new startups to drive market
11.3.4 FRANCE
11.3.4.1 Significant AI investments to drive market
11.3.5 ITALY
11.3.5.1 Partnerships between technology providers and government incentives drive market
11.3.6 SPAIN
11.3.6.1 Surging investments by hyperscalers and other companies to
drive market
11.3.7 POLAND
11.3.7.1 Growing cloud adoption and AI investments to boost
market opportunities
11.3.8 NORDICS
11.3.8.1 Rising adoption of accelerated computing technologies in
data center to drive market
11.3.9 REST OF EUROPE
11.4 ASIA PACIFIC
11.4.1 MACROECONOMIC OUTLOOK FOR ASIA PACIFIC
11.4.2 CHINA
11.4.2.1 Rapid government funding and initiatives to drive market
11.4.3 SOUTH KOREA
11.4.3.1 Rising investment and need for real-time data processing
to drive market
11.4.4 JAPAN
11.4.4.1 Increasing hyperscaler investments to drive market
11.4.5 INDIA
11.4.5.1 Government initiatives and incentives to drive market
11.4.6 AUSTRALIA
11.4.6.1 Domestic HPC push signals Australia’s commitment to
AI advancement
11.4.7 INDONESIA
11.4.7.1 Indonesia’s digital ambition drives significant investment
11.4.8 MALAYSIA
11.4.8.1 Global cloud leaders drive massive data center GPU expansion
in Malaysia
11.4.9 THAILAND
11.4.9.1 Strategic location and policies position Thailand for HPC leadership
11.4.10 VIETNAM
11.4.10.1 NVIDIA’s strategic partnerships catalyze market
11.4.11 REST OF ASIA PACIFIC
11.5 ROW
11.5.1 MACROECONOMIC OUTLOOK FOR ROW
11.5.2 SOUTH AMERICA
11.5.2.1 Global players investing in region for data center infrastructure to drive demand
11.5.3 AFRICA
11.5.3.1 Rising focus of manufacturing firms on streamlining workflow and improving product quality to create opportunities
11.5.4 MIDDLE EAST
11.5.4.1 Booming AI initiatives to drive demand
11.5.4.2 GCC
11.5.4.3 Bahrain
11.5.4.3.1 Increased government initiatives to drive market
11.5.4.4 Kuwait
11.5.4.4.1 Kuwait accelerates GPU-driven digital transformation with national cloud and AI initiatives
11.5.4.5 Oman
11.5.4.5.1 Regional HPC hub with GPU-backed data center growth
11.5.4.6 Qatar
11.5.4.6.1 Qatar scales AI infrastructure with GPU investments ahead of smart city and research expansion
11.5.4.7 Saudi Arabia
11.5.4.7.1 Leads Gulf GPU market with hyperscale AI data center mega projects
11.5.4.8 United Arab Emirates (UAE)
11.5.4.8.1 UAE advances AI supercomputing ambitions through massive GPU-powered data center investments
11.5.4.9 Rest of Middle East
12 COMPETITIVE LANDSCAPE
12.1 OVERVIEW
12.2 KEY PLAYER STRATEGIES/RIGHT TO WIN, 2022–2025
12.3 REVENUE ANALYSIS, 2018–2022
12.4 MARKET SHARE ANALYSIS, 2024
12.5 COMPANY VALUATION AND FINANCIAL METRICS
12.6 BRAND/PRODUCT COMPARISON
12.7 COMPANY EVALUATION MATRIX FOR DATA CENTER GPUS: KEY PLAYERS, 2024
12.7.1 STARS
12.7.2 EMERGING LEADERS
12.7.3 PERVASIVE PLAYERS
12.7.4 PARTICIPANTS
12.8 COMPANY EVALUATION MATRIX FOR GPU-AS-A-SERVICE (GPUAAS):
KEY PLAYERS, 2024
12.8.1 STARS
12.8.2 EMERGING LEADERS
12.8.3 PERVASIVE PLAYERS
12.8.4 PARTICIPANTS
12.8.5 COMPANY FOOTPRINT: KEY PLAYERS, 2024
12.8.5.1 Company footprint
12.8.5.2 Regional footprint
12.8.5.3 Deployment footprint
12.8.5.4 Function footprint
12.8.5.5 End user footprint
12.9 COMPANY EVALUATION MATRIX FOR GPU-AS-A-SERVICE (GPUAAS):
STARTUPS/SMES, 2024
12.9.1 PROGRESSIVE COMPANIES
12.9.2 RESPONSIVE COMPANIES
12.9.3 DYNAMIC COMPANIES
12.9.4 STARTING BLOCKS
12.9.5 COMPETITIVE BENCHMARKING: STARTUPS/SMES, 2024
12.9.5.1 Detailed list of key startups/SMEs
12.9.5.2 Detailed list of key startups/SMEs
12.10 COMPETITIVE SCENARIO AND TRENDS
12.10.1 PRODUCT LAUNCHES
12.10.2 DEALS
13 COMPANY PROFILES
13.1 KEY PLAYERS
13.1.1 NVIDIA CORPORATION
13.1.1.1 Business overview
13.1.1.2 Products/Solutions/Services offered
13.1.1.3 Recent developments
13.1.1.3.1 Product launches
13.1.1.3.2 Deals
13.1.1.4 MnM view
13.1.1.4.1 Key strengths
13.1.1.4.2 Strategic choices
13.1.1.4.3 Weaknesses and competitive threats
13.1.2 ADVANCED MICRO DEVICES, INC.
13.1.2.1 Business overview
13.1.2.2 Products/Solutions/Services offered
13.1.2.3 Recent developments
13.1.2.3.1 Product launches
13.1.2.3.2 Deals
13.1.2.4 MnM view
13.1.2.4.1 Key strengths
13.1.2.4.2 Strategic choices
13.1.2.4.3 Weaknesses and competitive threats
13.1.3 INTEL CORPORATION
13.1.3.1 Business overview
13.1.3.2 Products/Solutions/Services offered
13.1.3.3 Recent developments
13.1.3.3.1 Product launches
13.1.3.3.2 Deals
13.1.3.4 MnM view
13.1.3.4.1 Key strengths
13.1.3.4.2 Strategic choices
13.1.3.4.3 Weaknesses and competitive threats
13.1.4 GOOGLE
13.1.4.1 Business overview
13.1.4.2 Recent developments
13.1.4.2.1 Product launches
13.1.4.2.2 Deals
13.1.4.3 MnM view
13.1.4.3.1 Key strengths
13.1.4.3.2 Strategic choices
13.1.4.3.3 Weaknesses and competitive threats
13.1.5 MICROSOFT
13.1.5.1 Business overview
13.1.5.2 Products/Solutions/Services offered
13.1.5.3 Recent developments
13.1.5.3.1 Deals
13.1.5.4 MnM view
13.1.5.4.1 Key strengths
13.1.5.4.2 Strategic choices
13.1.5.4.3 Weaknesses and competitive threats
13.1.6 AMAZON WEB SERVICES, INC.
13.1.6.1 Business overview
13.1.6.2 Products/Solutions/Services offered
13.1.6.3 Recent developments
13.1.6.3.1 Product launches
13.1.6.3.2 Deals
13.1.7 IBM
13.1.7.1 Business overview
13.1.7.2 Products/Solutions/Services offered
13.1.7.3 Recent developments
13.1.7.3.1 Product launches
13.1.7.3.2 Deals
13.1.8 ALIBABA CLOUD
13.1.8.1 Business overview
13.1.8.2 Products/Solutions/Services offered
13.1.8.3 Recent developments
13.1.8.3.1 Product launches
13.1.8.3.2 Deals
13.1.9 ORACLE
13.1.9.1 Business overview
13.1.9.2 Products/Solutions/Services offered
13.1.9.3 Recent developments
13.1.9.3.1 Product launches
13.1.9.3.2 Deals
13.1.10 COREWEAVE.
13.1.10.1 Business overview
13.1.10.2 Products/Solutions/Services offered
13.1.10.3 Recent developments
13.1.10.3.1 Deals
13.1.11 TENCENT CLOUD
13.1.11.1 Business overview
13.1.11.2 Products/Solutions/Services offered
13.1.11.3 Recent developments
13.1.11.3.1 Expansions
13.1.12 LAMBDA
13.1.12.1 Business overview
13.1.12.2 Products/Solutions/Services offered
13.1.12.3 Recent developments
13.1.12.3.1 Deals
13.2 OTHER PLAYERS
13.2.1 VAST.AI
13.2.2 RUNPOD
13.2.3 SCALEMATRIX HOLDINGS, INC.
13.2.4 DIGITALOCEAN
13.2.5 JARVISLABS.AI
13.2.6 FLUIDSTACK
13.2.7 OVH SAS
13.2.8 E2E NETWORKS LIMITED
13.2.9 ACE CLOUD
13.2.10 SNOWCELL
13.2.11 LINODE LLC
13.2.12 YOTTA DATA SERVICES PVT LTD.
13.2.13 VULTR
13.2.14 RACKSPACE TECHNOLOGY
13.2.15 GCORE
13.2.16 NEBIUS B.V.
14 APPENDIX
14.1 INSIGHTS FROM INDUSTRY EXPERTS
14.2 DISCUSSION GUIDE
14.3 KNOWLEDGESTORE: MARKETSANDMARKETS’ SUBSCRIPTION PORTAL
14.4 CUSTOMIZATION OPTIONS
14.5 RELATED REPORTS
14.6 AUTHOR DETAILS
