AI Infrastructure Market by Offerings (Compute (GPU, CPU, FPGA), Memory (DDR, HBM), Network (NIC/Network Adapters, Interconnect), Storage, Software), Function (Training, Inference), Deployment (On-premises, Cloud, Hybrid) - Global Forecast to 2030
상품코드:1606236
리서치사:MarketsandMarkets
발행일:2024년 12월
페이지 정보:영문 338 Pages
라이선스 & 가격 (부가세 별도)
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한글목차
세계 AI 인프라 시장 규모는 2024년 1,358억 1,000만 달러, 2030년까지 3,944억 6,000만 달러에 달할 것으로 예상되며, 2024-2030년간 연평균 19.4% 성장할 것으로 예상됩니다.
디지털 전환, IoT, 소셜 미디어, 전자상거래로 인한 데이터 생성의 급증으로 인해 AI 및 머신러닝 모델을 위한 방대한 데이터 세트를 관리할 수 있는 고급 컴퓨팅 및 스토리지가 요구되고 있습니다. 또한, 데이터센터 내 클라우드 기반 AI 인프라에 대한 수요가 증가함에 따라 기업들이 복잡한 AI 워크로드를 관리하는 방식이 재편되고 있으며, 주요 클라우드 제공업체들은 증가하는 세계 수요와 산업별 수요에 대응하기 위해 AI 지원 인프라에 많은 투자를 하고 있습니다. 하고 있습니다. 이러한 요인들이 종합적으로 AI 인프라 시장의 주요 성장 동력으로 작용하고 있습니다.
조사 범위
조사 대상년도
2020년-2030년
기준 연도
2023년
예측 기간
2024년-2030년
단위
10억 달러
부문
제공, 메모리, 네트워크, 기능, 배포, 지역
대상 지역
북미, 유럽, 아시아태평양 및 기타 지역
"생성형 AI 부문은 예측 기간 동안 가장 높은 CAGR을 유지할 것입니다."
생성형 AI는 산업 전반에 걸쳐 고급 AI 용도에 대한 수요가 증가함에 따라 높은 성장률을 보일 것으로 예상됩니다. 생성형 AI는 컨텐츠 제작, 언어 모델, 이미지 합성 등의 기능을 향상시키며, 이는 모두 대규모 신경망을 훈련하고 실행하기 위한 방대한 연산 능력에 크게 의존하고 있습니다. 이러한 수요는 집중적인 처리 요구를 지원할 수 있는 고성능 GPU와 DPU의 능력에 중점을 둔 고가의 인프라 투자를 필요로 합니다. 2024년 11월, GMO Internet Group, Inc.(일본)은 NVIDIA Corporation(미국)의 H200 Tensor Core GPU, Spectrum-X Ethernet, Blueprints, Blueprints, H200 Tensor Core GPU를 도입하여 AI 인프라 시장을 확대할 것으로 예상된다, Dell PowerEdge 서버에서 개발된 이 인프라는 낮은 대기시간과 넓은 대역폭을 통해 생산급 AI 생성 및 처리 속도를 향상시켜주는 'GMO GPU 클라우드'를 제공합니다. 기능을 통해 프로덕션급 생성형 AI 용도의 요구 사항을 지원하도록 조정됐습니다. 이러한 혁신은 생성형 AI 워크로드에 최적화된 인프라를 대규모로 구축하는 데 있어 업계의 추진력을 보여줍니다. 또한, 이러한 혁신 기술은 고성능 클라우드 솔루션이 이 부문의 성장을 가속하고 있습니다.
"예측 기간 동안 AI 인프라 시장에서 높은 CAGR로 성장할 것으로 예상됩니다."
기업 부문이 AI 인프라 시장에서 높은 성장세를 기록할 것으로 예상됩니다. 기업들은 AI를 활용하여 디지털 전환을 촉진하고, 업무 효율성을 높이고, 고객 경험을 개선하기 위해 AI 인프라를 도입하고 있습니다. 제조, 금융, 유통 기업들은 AI 기반 예측 분석, 프로세스 자동화, 고객 인사이트 툴에 대한 투자를 늘리고 있으며, 이를 위해서는 강력하고 확장성이 높은 AI 인프라가 필요합니다. 또한 기업들은 프라이빗 클라우드 인프라 및 하이브리드 클라우드 모델에 투자하여 AI 기능을 확장하고 있습니다. 특히 클라우드 기반 AI의 유연성과 혁신의 혜택을 누리면서도 기밀 데이터를 보호하고자 합니다. 2024년 2월, 시스코와 엔비디아는 기업 AI에 필요한 높은 컴퓨팅 파워를 제공하는 동시에 구축 및 관리를 간소화하고, 기존 IT 환경과 통합할 수 있는 적응형 고성능 AI 리소스에 대한 종합적인 지원을 제공한다는 내용의 파트너십을 체결했습니다. 클라우드 기반 및 온프레미스 기업을 위한 유연한 AI 인프라 옵션, 클라우드 기반 및 온프레미스 기업을 위한 유연한 AI 인프라 옵션, 강력한 네트워킹, 강력한 AI 인프라, 강력한 AI 인프라, 강력한 AI 인프라, 강력한 AI 인프라, 강력한 AI 인프라, 강력한 AI 인프라, 강력한 AI 인프라, 강력한 AI 인프라, 강력한 AI 인프라, 강력한 AI 인프라, 강력한 AI 인프라, 강력한 AI 인프라, 강력한 AI 인프라, 강력한 AI 인프라, 강력한 AI 인프라, 강력한 AI 인프라, 강력한 AI 인프라, 강력한 AI 인프라, 강력한 AI 인프라를 제공합니다. 강력한 네트워킹, 보안, 엔드-투-엔드 가시성 기능을 포함합니다. 기업들은 AI 솔루션 구축을 위해 확장 가능하고 안전하며 관리 가능한 인프라를 요구하고 있으며, 이러한 파트너십은 엔터프라이즈 AI 인프라 시장의 성장을 뒷받침할 것으로 기대됩니다.
본 보고서에서는 세계 AI 인프라 시장에 대해 조사 분석했으며, 주요 촉진요인과 억제요인, 경쟁 구도, 향후 동향 등의 정보를 제공합니다.
목차
제1장 서론
제2장 조사 방법
제3장 주요 요약
제4장 프리미엄 인사이트
AI 인프라 시장 기업에 있어서 매력적인 기회
AI 인프라 시장 : 기능별
AI 인프라 시장 : 전개 형태별
AI 인프라 시장 : 용도별
AI 인프라 시장 : 최종사용자별
AI 인프라 시장 : 지역별
AI 인프라 시장 : 국가별
제5장 시장 개요
서론
시장 역학
성장 촉진요인
성장 억제요인
기회
과제
고객의 비즈니스에 영향을 미치는 동향/혼란
가격 분석
주요 기업 참고 가격 : 연산 별
평균 판매 가격 동향 : 지역별
밸류체인 분석
생태계 분석
투자와 자금조달 시나리오
기술 분석
주요 기술
보완 기술
인접 기술
클라우드 서비스 제공업체의 향후 데이터센터 전개
클라우드 서비스 제공업체의 설비 투자
프로세서 벤치마크
NVIDIA의 GPU 벤치마크
NVIDIA의 CPU 벤치마크
특허 분석
무역 분석
수입 시나리오(HS코드 854231)
수출 시나리오(HS코드 854231)
주요 컨퍼런스 및 이벤트(2024년-2025년)
사례 연구 분석
규제 상황
규제기관, 정부기관, 기타 조직
표준
Porter의 Five Forces 분석
주요 이해관계자와 구입 기준
제6장 AI서버 산업 상황
서론
AI 서버 보급과 성장 예측
AI 서버 산업 : 프로세서 유형별
GPU 기반 서버
FPGA 기반 서버
ASIC 기반 서버
AI서버 산업 : 기능별
트레이닝
추론
AI서버 산업 점유율 분석(2023년)
제7장 AI 인프라 시장 : 제공별
서론
연산
GPU
CPU
FPGA
TPU
Dojo/FSD
Trainium·Inferentia
ATHENA
T-HEAD
MTIA
LPU
기타 ASIC
메모리
DDR
HBM
네트워크
스토리지
서버 소프트웨어
제8장 AI 인프라 시장 : 기능별
서론
트레이닝
추론
제9장 AI 인프라 시장 : 전개 형태별
서론
온프레미스
클라우드
하이브리드
제10장 AI 인프라 시장 : 용도별
서론
생성형 AI
룰 기반 모델
통계 모델
딥러닝
적대적 생성 네트워크(GAN)
자동 인코더
CONVOLUTIONAL NEURAL NETWORKS (CNNS)
TRANSFORMER MODELS
머신러닝
자연언어처리
컴퓨터 비전
제11장 AI 인프라 시장 : 최종사용자별
서론
클라우드 서비스 제공업체
기업
의료
은행/금융서비스/보험(BFSI)
자동차
소매 및 E-Commerce
미디어 및 엔터테인먼트
기타 기업
정부기관
제12장 AI 인프라 시장 : 지역별
서론
북미
북미의 거시경제 전망
미국
캐나다
멕시코
유럽
유럽의 거시경제 전망
영국
독일
프랑스
이탈리아
스페인
기타 유럽
아시아태평양
아시아태평양의 거시경제 전망
중국
일본
인도
한국
기타 아시아태평양
기타 지역
기타 지역의 거시경제 전망
중동
아프리카
남미
제13장 경쟁 구도
개요
주요 시장 진출기업의 전략/강점(2019년-2024년)
매출 분석(2021년-2023년)
시장 점유율 분석(2023년)
연산 시장 점유율 분석(2023년)
메모리 시장 점유율 분석(2023년)
기업 평가와 재무 지표(2023년)
브랜드/제품 비교
기업 평가 매트릭스 : 주요 기업(2023년)
기업 평가 매트릭스 : 스타트업/중소기업(2023년)
경쟁 시나리오
제14장 기업 개요
주요 기업
NVIDIA CORPORATION
ADVANCED MICRO DEVICES, INC.
SK HYNIX INC.
SAMSUNG
MICRON TECHNOLOGY, INC.
INTEL CORPORATION
GOOGLE
AMAZON WEB SERVICES, INC.
TESLA
MICROSOFT
META
GRAPHCORE
CEREBRAS
기타 기업
KIOXIA HOLDINGS CORPORATION
WESTERN DIGITAL CORPORATION
MYTHIC
BLAIZE
GROQ, INC.
HAILO TECHNOLOGIES LTD
SIMA TECHNOLOGIES, INC.
KNERON, INC.
RAIN NEUROMORPHICS INC.
TENSTORRENT
SAMBANOVA SYSTEMS, INC.
TAALAS
SAPEON INC.
REBELLIONS INC.
RIVOS INC.
SHANGHAI BIREN TECHNOLOGY CO., LTD.
제15장 부록
LSH
영문 목차
영문목차
The AI Infrastructure market is expected to be worth USD 135.81 billion in 2024 and is estimated to reach USD 394.46 billion by 2030, growing at a CAGR of 19.4% between 2024 and 2030. The AI infrastructure market is being driven by the rapid growth in data generation due to digital transformation, IoT, social media, and e-commerce, which requires advanced computing and storage to manage vast datasets for AI and machine learning models. Additionally, the increasing need for cloud-based AI infrastructure in data centers is reshaping how companies manage complex AI workloads, with major cloud providers investing heavily in AI-ready infrastructure to meet growing global and industry-specific demands. These factors collectively serve as key drivers propelling the growth of the AI infrastructure market.
Scope of the Report
Years Considered for the Study
2020-2030
Base Year
2023
Forecast Period
2024-2030
Units Considered
Value (USD Billion)
Segments
By Offerings, Memory, Network, Function, Deployment and Region
Regions covered
North America, Europe, APAC, RoW
"Generative AI segment will hold highest CAGR in the forecast period."
Generative AI is expected to exhibit high growth rate due to a rise in demand for advanced AI applications across industries. Generative AI powers capabilities like content creation, language models, and image synthesis, all of which depends heavily on substantial computational power to train and run large neural networks. This demand requires high infrastructure investments, with an emphasis on high-performance GPU and DPU capabilities able to support intensive processing requirements. As more enterprises seek to capitalize on generative AI's potential, the market for AI infrastructure will rise. In November 2024, GMO Internet Group, Inc. (Japan) launched the GMO GPU Cloud, powered by NVIDIA Corporation's (US) H200 Tensor Core GPUs, Spectrum-X Ethernet, BlueField-3 DPUs, and NVIDIA AI Enterprise software, exemplifies the kind of specialized infrastructure needed to support generative AI at scale. This infrastructure, developed on Dell PowerEdge servers, is aligned to support the needs of production-grade generative AI applications, with reduced latency and high-bandwidth capability. Such innovations underline industry momentum in wide-scale deployment of infrastructure optimized for generative AI workloads. Further, this makes the local high-performance cloud solutions to be drivers for growth of this segment.
"Enterprises is projected to grow at a high CAGR of AI Infrastructure market during the forecasted timeline"
The enterprise segment is expected to record high growth in AI Infrastructure Market. Enterprises are increasingly adopting AI infrastructure as they use AI to facilitate digital transformation, enhance operational efficiency, and enhance customer experience. Manufacturing, finance, and retail enterprises are increasing their investments in AI powered predictive analysis, process automation, and customer insight tools that require robust and scalable AI infrastructure. Enterprises are also expanding their AI capabilities by investing in private cloud infrastructure and hybrid cloud models, especially as they seek to protect sensitive data while benefiting from cloud-based AI's flexibility and innovation. Companies are providing cloud and on-premises flexibility along with comprehensive support that aligns with enterprise needs for adaptable, high-performance AI resources that can integrate with existing IT environments. In February 2024, Cisco and NVIDIA announced a partnership to bring AI infrastructure solutions tailored for data centers, designed to streamline deployment and management while offering high computing power necessary for enterprise AI. Its joint offering includes flexible AI infrastructure options for cloud-based and on-premises enterprises, as well as robust networking, security, and end-to-end observability features. Such collaborations support the growth of enterprise AI infrastructure market because companies demand scalable, secure, and manageable infrastructure for the deployment of AI solutions.
"Asia Pacific is expected to hold high CAGR in during the forecast period."
AI infrastructure market in Asia Pacific will grow at a high CAGR during the forecast period. Countries like China, Japan, South Korea, and India are at the forefront of AI innovation and governments and private sectors in the region are making high investments in AI research, infrastructure, and talent development. In September 2024 Lenovo (Hong Kong) announced its mass manufacturing operations for high-performance AI servers in India, and it also unveiled a cutting-edge research & development (R&D) lab, adding to the advancement of Lenovo's Infrastructure Solutions. These are the significant efforts that Lenovo has taken towards the significant positioning of India as a hub in innovation and manufacturing, while supporting 'Make in India' and 'AI for All' vision by the Indian government. Initiatives like these will speed up region's leadership in AI technology and will lead to significant growth in AI infrastructure deployment across Asia Pacific industries. Moreover, as enterprises and governments are driving digital transformation and cloud adoption, the requirement for high-performance AI offerings is increasing, making Asia Pacific one of the fastest-growing markets for AI Infrastructure globally.
Extensive primary interviews were conducted with key industry experts in the AI Infrastructure market space to determine and verify the market size for various segments and subsegments gathered through secondary research. The break-up of primary participants for the report has been 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 - 50%, Tier 2 - 20%, and Tier 3 - 30%
By Designation: C-level Executives - 20%, Directors - 30%, and Others - 50%
By Region: North America - 30%, Europe - 20%, Asia Pacific - 40%, and RoW - 10%
The report profiles key players in the AI Infrastructure market with their respective market ranking analysis. Prominent players profiled in this report are NVIDIA Corporation (US), Advanced Micro Devices, Inc. (US), SK HYNIX INC. (South Korea), SAMSUNG (South Korea), Micron Technology, Inc. (US), Intel Corporation (US), Google (US), Amazon Web Services, Inc. (US), Tesla (US), Microsoft (US), Meta (US), Graphcore (UK), and Cerebras (US), among others.
Apart from this, KIOXIA Holdings Corporation (Japan), Western Digital Corporation (US), Mythic (US), Blaize (US), Groq, Inc. (US), HAILO TECHNOLOGIES LTD (Israel), SiMa Technologies, Inc. (US), Kneron, Inc. (US), Rain Neuromorphics Inc. (US), Tenstorrent (Canada), SambaNova Systems, Inc. (US), Taalas (Canada), SAPEON Inc. (US), Rebellions Inc. (South Korea), Rivos Inc. (US), and Shanghai BiRen Technology Co., Ltd. (China) are among a few emerging companies in the AI Infrastructure market.
Research Coverage: This research report categorizes the AI infrastructure market based on offerings, function, deployment, application, end user, and region. The report describes the major drivers, restraints, challenges, and opportunities pertaining to the AI infrastructure market and forecasts the same till 2030. Apart from these, the report also consists of leadership mapping and analysis of all the companies included in the AI infrastructure ecosystem.
Key Benefits of Buying the Report The report will help the market leaders/new entrants in this market with information on the closest approximations of the revenue numbers for the overall AI infrastructure market and the subsegments. This report will help stakeholders understand the competitive landscape and gain more insights to position their businesses better and plan suitable go-to-market strategies. The report also helps stakeholders understand the pulse of the market and provides them with information on key market drivers, restraints, challenges, and opportunities.
The report provides insights on the following pointers:
Analysis of key drivers (Rising demand for high-performance computing in AI workloads, government initiatives and investments in AI research and development, and growing implementation of AI and ML solutions across enterprises) influencing the growth of the AI infrastructure market.
Product Development/Innovation: Detailed insights on upcoming technologies, research & development activities, and new product & service launches in the AI infrastructure market.
Market Development: Comprehensive information about lucrative markets - the report analysis the AI infrastructure market across varied regions
Market Diversification: Exhaustive information about new products & services, untapped geographies, recent developments, and investments in the AI infrastructure market
Competitive Assessment: In-depth assessment of market shares, growth strategies, and service offerings of leading players like NVIDIA Corporation (US), Advanced Micro Devices, Inc. (US), SK HYNIX INC. (South Korea), SAMSUNG (South Korea), Micron Technology, Inc. (US) among others in the AI infrastructure market.
TABLE OF CONTENTS
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 AND PRIMARY RESEARCH
2.1.2 SECONDARY DATA
2.1.2.1 List of key secondary sources
2.1.2.2 Key data from secondary sources
2.1.3 PRIMARY DATA
2.1.3.1 List of primary interview participants
2.1.3.2 Breakdown of primaries
2.1.3.3 Key data from primary sources
2.1.3.4 Key industry insights
2.2 MARKET SIZE ESTIMATION METHODOLOGY
2.2.1 BOTTOM-UP APPROACH
2.2.1.1 Approach to arrive at market size using bottom-up analysis (demand side)
2.2.2 TOP-DOWN APPROACH
2.2.2.1 Approach to arrive at market size using top-down analysis (supply side)
2.3 MARKET BREAKDOWN AND DATA TRIANGULATION
2.4 RESEARCH ASSUMPTIONS
2.5 RISK ANALYSIS
2.6 RESEARCH LIMITATIONS
3 EXECUTIVE SUMMARY
4 PREMIUM INSIGHTS
4.1 ATTRACTIVE OPPORTUNITIES FOR PLAYERS IN AI INFRASTRUCTURE MARKET
4.2 AI INFRASTRUCTURE MARKET, BY FUNCTION
4.3 AI INFRASTRUCTURE MARKET, BY DEPLOYMENT
4.4 AI INFRASTRUCTURE MARKET, BY APPLICATION
4.5 AI INFRASTRUCTURE MARKET, BY END USER
4.6 AI INFRASTRUCTURE MARKET, BY REGION
4.7 AI INFRASTRUCTURE MARKET, BY COUNTRY
5 MARKET OVERVIEW
5.1 INTRODUCTION
5.2 MARKET DYNAMICS
5.2.1 DRIVERS
5.2.1.1 Rising demand for high-performance computing in AI workloads
5.2.1.2 Government-led fundings to boost AI R&D
5.2.1.3 Growing popularity AI and ML solutions among enterprises
5.2.1.4 Massive data generation due to rapid digital transformation
5.2.2 RESTRAINTS
5.2.2.1 Compatibility issues with legacy systems
5.2.2.2 Consumption of large amount of energy
5.2.3 OPPORTUNITIES
5.2.3.1 Rise of AI-as-a-Service platforms
5.2.3.2 Surging demand for cloud-based AI infrastructure
5.2.3.3 Growing adoption of AI-driven decision making systems
5.2.3.4 Advancements in neuromorphic and quantum computing for AI
5.2.3.5 Increasing investments in data centers by cloud service providers
5.2.4 CHALLENGES
5.2.4.1 High initial investments
5.2.4.2 Maintaining data security and integrity in distributed AI systems
5.2.4.3 Complexities associated with integrating AI technologies into existing IT ecosystems
5.3 TRENDS/DISRUPTIONS IMPACTING CUSTOMER BUSINESS
5.4 PRICING ANALYSIS
5.4.1 INDICATIVE PRICING OF KEY PLAYERS, BY COMPUTE
5.4.2 AVERAGE SELLING PRICE TREND, BY REGION
5.5 VALUE CHAIN ANALYSIS
5.6 ECOSYSTEM ANALYSIS
5.7 INVESTMENT AND FUNDING SCENARIO
5.8 TECHNOLOGY ANALYSIS
5.8.1 KEY TECHNOLOGIES
5.8.1.1 Generative AI
5.8.1.2 Conversational AI
5.8.1.3 AI-optimized cloud platforms
5.8.2 COMPLEMENTARY TECHNOLOGIES
5.8.2.1 Blockchain
5.8.2.2 Edge computing
5.8.2.3 Cybersecurity
5.8.3 ADJACENT TECHNOLOGIES
5.8.3.1 Big data
5.8.3.2 Predictive analysis
5.9 UPCOMING DEPLOYMENT OF DATA CENTERS BY CLOUD SERVICE PROVIDERS
5.10 CAPEX OF CLOUD SERVICE PROVIDERS
5.11 PROCESSOR BENCHMARKING
5.11.1 GPU BENCHMARKING BY NVIDIA
5.11.2 CPU BENCHMARKING BY NVIDIA
5.12 PATENT ANALYSIS
5.13 TRADE ANALYSIS
5.13.1 IMPORT SCENARIO (HS CODE 854231)
5.13.2 EXPORT SCENARIO (HS CODE 854231)
5.14 KEY CONFERENCES AND EVENTS, 2024-2025
5.15 CASE STUDY ANALYSIS
5.16 REGULATORY LANDSCAPE
5.16.1 REGULATORY BODIES, GOVERNMENT AGENCIES, AND OTHER ORGANIZATIONS
5.16.2 STANDARDS
5.17 PORTER'S FIVE FORCES ANALYSIS
5.17.1 THREAT OF NEW ENTRANTS
5.17.2 THREAT OF SUBSTITUTES
5.17.3 BARGAINING POWER OF SUPPLIERS
5.17.4 BARGAINING POWER OF BUYERS
5.17.5 INTENSITY OF COMPETITION RIVALRY
5.18 KEY STAKEHOLDERS AND BUYING CRITERIA
5.18.1 KEY STAKEHOLDERS IN BUYING PROCESS
5.18.2 BUYING CRITERIA
6 AI SERVER INDUSTRY LANDSCAPE
6.1 INTRODUCTION
6.2 AI SERVER PENETRATION AND GROWTH FORECAST
6.3 AI SERVER INDUSTRY, BY PROCESSOR TYPE
6.3.1 GPU-BASED SERVERS
6.3.1.1 Ability to process massive datasets and run intricate algorithms efficiently to drive market
6.3.2 FPGA-BASED SERVERS
6.3.2.1 Growing need for flexibility and customization for AI workloads to boost demand
6.3.3 ASIC-BASED SERVERS
6.3.3.1 Increasing demand for high-performance computing and machine learning to foster market growth
6.4 AI SERVER INDUSTRY, BY FUNCTION
6.4.1 TRAINING
6.4.1.1 Surging adoption of deep learning technologies to fuel market growth
6.4.2 INFERENCE
6.4.2.1 Rapid shift toward edge computing to accelerate demand
6.5 AI SERVER INDUSTRY SHARE ANALYSIS, 2023
7 AI INFRASTRUCTURE MARKET, BY OFFERING
7.1 INTRODUCTION
7.2 COMPUTE
7.2.1 GPU
7.2.1.1 Growing demand from hyperscale cloud service providers to fuel market growth
7.2.2 CPU
7.2.2.1 Increasing need for cost-effective and high-performance AI infrastructure to offer lucrative growth opportunities
7.2.3 FPGA
7.2.3.1 Growing need to reconfigure hardware to address growing AI workloads to boost demand
7.2.4 TPU
7.2.4.1 Rising need to accelerate deep learning and neural network processing to foster market growth
7.2.5 DOJO & FSD
7.2.5.1 Surging computational demands of deep learning and neural network training to accelerate demand
7.2.6 TRAINIUM & INFERENTIA
7.2.6.1 Growing demand for cost-effective training and inference to offer lucrative growth opportunities
7.2.7 ATHENA
7.2.7.1 Increasing emphasis on accelerating AI model training and inference capabilities to fuel demand
7.2.8 T-HEAD
7.2.8.1 Growing demand for AI-powered applications across data centers to offer lucrative growth opportunities
7.2.9 MTIA
7.2.9.1 Rising demand to optimize training and inference of ML models to foster market growth
7.2.10 LPU
7.2.10.1 Increasing need to handle demanding computational requirements of NLP to fuel market growth
7.2.11 OTHER ASIC
7.3 MEMORY
7.3.1 DDR
7.3.1.1 Increasing demand among semiconductor manufacturers to fuel market growth
7.3.2 HBM
7.3.2.1 Rising application for real-time image recognition to boost demand
7.4 NETWORK
7.4.1 NIC/NETWORK ADAPTERS
7.4.1.1 Increasing emphasis on advancing network speeds to offer lucrative growth opportunities
7.4.1.2 InfiniBand
7.4.1.2.1 Growing emphasis on reducing latency during large-scale AI model training to foster market growth
7.4.1.3 Ethernet
7.4.1.3.1 Rising need for high-speed solutions to meet next-gen AI model demands to foster market growth
7.4.1.4 Interconnects
7.4.1.4.1 Increasing demand for larger-scale AI models to fuel market growth
7.5 STORAGE
7.5.1 GROWING NEED FOR SUSTAINABLE AND COST-EFFECTIVE STORAGE SOLUTIONS TO OFFER LUCRATIVE GROWTH OPPORTUNITIES
7.6 SERVER SOFTWARE
7.6.1 RISING NEED FOR SECURE AND STABLE COMPUTING ENVIRONMENTS IN AI DATA CENTERS TO FUEL MARKET GROWTH
8 AI INFRASTRUCTURE MARKET, BY FUNCTION
8.1 INTRODUCTION
8.2 TRAINING
8.2.1 INCREASING COMPLEXITIES AND SCALE OF AI MODEL DEVELOPMENT TO DRIVE MARKET
8.3 INFERENCE
8.3.1 RISING POPULARITY OF EDGE COMPUTING TO FUEL MARKET GROWTH
9 AI INFRASTRUCTURE MARKET, BY DEPLOYMENT
9.1 INTRODUCTION
9.2 ON-PREMISES
9.2.1 GROWING CONCERNS OF DATA PRIVACY TO DRIVE MARKET
9.3 CLOUD
9.3.1 ABILITY TO SCALE RESOURCES ON-DEMAND TO FUEL MARKET GROWTH
9.4 HYBRID
9.4.1 INCREASING DEMAND FOR SCALABLE SOLUTIONS TO BALANCE PERFORMANCE AND SECURITY TO FOSTER MARKET GROWTH
10 AI INFRASTRUCTURE MARKET, BY APPLICATION
10.1 INTRODUCTION
10.2 GENERATIVE AI
10.2.1 RULE-BASED MODELS
10.2.1.1 Integration with ML and deep learning to offer lucrative growth opportunities
10.2.2 STATISTICAL MODELS
10.2.2.1 Growing application in finance, economics, and healthcare sectors to predict trends and outcomes to fuel market growth
10.2.3 DEEP LEARNING
10.2.3.1 Surging demand for AI-generated content and automation to offer lucrative growth opportunities
10.2.4 GENERATIVE ADVERSARIAL NETWORKS (GANS)
10.2.4.1 Increasing application to create 3D models in entertainment and gaming sectors to foster market growth
10.2.5 AUTOENCODERS
10.2.5.1 Growing need to reduce dimensionality of data and handle complex datasets to accelerate demand
10.2.6 CONVOLUTIONAL NEURAL NETWORKS (CNNS)
10.2.6.1 Rising number of autonomous vehicles and smart cities to drive market
10.2.7 TRANSFORMER MODELS
10.2.7.1 Growing popularity of GPT models and BERT to offer lucrative growth opportunities
10.3 MACHINE LEARNING
10.3.1 RISING APPLICATION FOR REAL-TIME DECISION-MAKING AND DATA ANALYSIS TO FOSTER MARKET GROWTH
10.4 NATURAL LANGUAGE PROCESSING
10.4.1 GROWING USAGE OF MACHINES FOR SENTIMENT ANALYSIS, LANGUAGE TRANSLATION, AND SPEECH RECOGNITION TO ACCELERATE DEMAND
10.5 COMPUTER VISION
10.5.1 INCREASING DEMAND FOR AUTOMATED VISUAL RECOGNITION SYSTEMS TO FUEL MARKET GROWTH
11 AI INFRASTRUCTURE MARKET, BY END USER
11.1 INTRODUCTION
11.2 CLOUD SERVICE PROVIDERS
11.2.1 RISING EMPHASIS ON OFFERING PRE-BUILT AI MODELS TO OFFER LUCRATIVE GROWTH OPPORTUNITIES
11.3 ENTERPRISES
11.3.1 HEALTHCARE
11.3.1.1 Growing demand for personalized treatment to fuel market growth
11.3.2 BFSI
11.3.2.1 Rising focus on enhancing security and improving customer services to foster market growth
11.3.3 AUTOMOTIVE
11.3.3.1 Increasing popularity of connected vehicles to offer lucrative growth opportunities
11.3.4 RETAIL & E-COMMERCE
11.3.4.1 Rapid shift toward data-centric models to enhance customer engagement to accelerate demand
11.3.5 MEDIA & ENTERTAINMENT
11.3.5.1 Rising demand for content recommendation engines and interactive media experiences to foster market growth
11.3.6 OTHER ENTERPRISES
11.4 GOVERNMENT ORGANIZATIONS
11.4.1 GROWING NEED TO ENHANCE PUBLIC SAFETY AND SECURITY TO OFFER LUCRATIVE GROWTH OPPORTUNITIES
12 AI INFRASTRUCTURE MARKET, BY REGION
12.1 INTRODUCTION
12.2 NORTH AMERICA
12.2.1 MACROECONOMIC OUTLOOK FOR NORTH AMERICA
12.2.2 US
12.2.2.1 Presence of established AI infrastructure manufacturers to drive market
12.2.3 CANADA
12.2.3.1 Growing emphasis on commercializing AI to offer lucrative growth opportunities
12.2.4 MEXICO
12.2.4.1 Rapid digital transformation and surging adoption of cloud computing to fuel market growth
12.3 EUROPE
12.3.1 MACROECONOMIC OUTLOOK FOR EUROPE
12.3.2 UK
12.3.2.1 Growing investments in data center infrastructure to boost demand
12.3.3 GERMANY
12.3.3.1 Rising adoption smart technologies to boost manufacturing to drive market
12.3.4 FRANCE
12.3.4.1 Favorable government initiatives to strengthen AI infrastructure to fuel market growth
12.3.5 ITALY
12.3.5.1 Increasing emphasis on developing digital infrastructure to offer lucrative growth opportunities
12.3.6 SPAIN
12.3.6.1 Rapid adoption of cloud computing to accelerate demand
12.3.7 REST OF EUROPE
12.4 ASIA PACIFIC
12.4.1 MACROECONOMIC OUTLOOK FOR ASIA PACIFIC
12.4.2 CHINA
12.4.2.1 Proliferation of IoT devices to drive market
12.4.3 JAPAN
12.4.3.1 Rising investments to boost cloud infrastructure to foster market growth
12.4.4 INDIA
12.4.4.1 Government-led initiatives to strengthen AI infrastructure to offer lucrative growth opportunities
12.4.5 SOUTH KOREA
12.4.5.1 Thriving semiconductor industry to accelerate demand
12.4.6 REST OF ASIA PACIFIC
12.5 ROW
12.5.1 MACROECONOMIC OUTLOOK FOR ROW
12.5.2 MIDDLE EAST
12.5.2.1 Growing emphasis on digital transformation and technological innovation to drive market
12.5.2.2 GCC
12.5.2.3 Rest of Middle East
12.5.3 AFRICA
12.5.3.1 Rising need for managing advanced data processing requirements to fuel market growth
12.5.4 SOUTH AMERICA
12.5.4.1 Growing demand for flexible and secure cloud storage solutions to foster market growth
13 COMPETITIVE LANDSCAPE
13.1 OVERVIEW
13.2 KEY PLAYER STRATEGIES/RIGHT TO WIN, 2019-2024
13.3 REVENUE ANALYSIS, 2021-2023
13.4 MARKET SHARE ANALYSIS, 2023
13.4.1 COMPUTE MARKET SHARE ANALYSIS, 2023
13.4.2 MEMORY MARKET SHARE ANALYSIS, 2023
13.5 COMPANY VALUATION AND FINANCIAL METRICS, 2023
13.6 BRAND/PRODUCT COMPARISON
13.7 COMPANY EVALUATION MATRIX: KEY PLAYERS, 2023
13.7.1 STARS
13.7.2 EMERGING LEADERS
13.7.3 PERVASIVE PLAYERS
13.7.4 PARTICIPANTS
13.7.5 COMPANY FOOTPRINT: KEY PLAYERS, 2023
13.7.5.1 Company footprint
13.7.5.2 Region footprint
13.7.5.3 Offering footprint
13.7.5.4 Function footprint
13.7.5.5 Deployment footprint
13.7.5.6 Application footprint
13.7.5.7 End user footprint
13.8 COMPANY EVALUATION MATRIX: STARTUPS/SMES, 2023
