생성형 AI는 변형 오토인코더, 생성적 적대 신경망, 트랜스포머 모델을 기반으로 한 딥러닝 기술입니다.
데이터센터 GPU 시장 부문은 현대 데이터센터의 방대한 컴퓨팅 수요를 충족시키기 위해 설계된 특수 그래픽 처리 장비를 말합니다. 이러한 GPU는 고성능 컴퓨팅, DL, ML, 대규모 그래픽 처리 작업 등 다양한 복잡한 워크로드를 가속화하도록 설계되었습니다. 이 시장에는 CPU, 소비자용 GPU 또는 주문형 집적회로(ASIC)에 대한 지출은 포함되지 않습니다. 전용 GPU 서버 랙과 같은 GPU 시스템이 포함됩니다. 이 시장에는 외부 지출만 포함되며, 구글의 TPU나 AWS의 Trainium 또는 Inferentium과 같은 자체 칩 개발에 대한 지출은 포함되지 않습니다.
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생성형 AI 시장에 대해 IoT 분석을 통한 기업 기술 시장을 대상으로 조사했습니다. 이 보고서에 제시된 정보는 2차 조사와 정성적 조사, 즉 이 분야의 전문가들과의 인터뷰 결과를 바탕으로 작성되었습니다. 이 문서의 주요 목적은 독자들이 현재 생성형 AI(생성형 AI)의 현황과 잠재적 이용 사례를 이해할 수 있도록 하는 것입니다.
목차
제1장 개요
제2장 서론
제3장 기술 개요
- 챕터 개요 : 기술의 개요
- 생성형 AI 기술 스택 : 5개의 주요 블록
- 기초 모델
- 생성형 AI 소프트웨어 에코시스템
- 컴퓨팅 하드웨어
제4장 시장 모델과 전망
- 챕터 개요 : 시장 모델과 전망
- 생성형 AI 기업 시장
- 생성형 AI 시장 : 2022-2030년
- 데이터센터 GPU 시장 : 개요
- 데이터센터 GPU 시장 : 고객 그룹별
- 기반 모델과 모델 관리 플랫폼 시장 : 개요
- 기반 모델과 모델 관리 플랫폼 시장 : 업계별
- 기반 모델과 모델 관리 플랫폼 시장 : 지역별
- 기반 모델과 모델 관리 플랫폼 시장 : 국가별
- 생성형 AI 서비스 시장 : 개요
- 생성형 AI 서비스 시장 : 업계별
- 생성형 AI 서비스 시장 : 지역별
- 생성형 AI 서비스 시장 : 국가별
- 시점 : 생성형 AI 지출과 세계의 소프트웨어 및 서비스 지출의 관계
제5장 경쟁 구도
- 챕터 개요 : 경쟁 구도
- 2024년의 경쟁상황 : 시장 점유율의 개요
- 데이터센터 GPU : 경쟁 구도(매출)
- 데이터센터 GPU : 경쟁 구도(시장 점유율)
- 데이터센터 GPU : NVIDIA
- 데이터센터 GPU : AMD
- 데이터센터 GPU : Intel
- 데이터센터 GPU : Cerebras
- 데이터센터 GPU : Groq
- 기반 모델과 모델 관리 플랫폼 : 경쟁 구도
- 기반 모델과 모델 관리 플랫폼(시장 점유율)
- 기초 모델과 모델 관리 플랫폼 : 최고 LLM
- 기반 모델과 모델 관리 플랫폼 : 주요 오픈 모델
- 기반 모델과 모델 관리 플랫폼 : Microsoft
- 기반 모델과 모델 관리 플랫폼 : AWS
- 기반 모델과 모델 관리 플랫폼 : Google
- 기반 모델과 모델 관리 플랫폼 : OpenAI
- 기반 모델과 모델 관리 플랫폼 : Hugging Face
- 기반 모델과 모델 관리 플랫폼 : Mistral AI
- 생성형 AI의 주요 소프트웨어 플랫폼의 개요 : 개발 플랫폼
- 생성형 AI의 주요 소프트웨어 플랫폼의 개요 : 데이터 관리툴
- 생성형 AI의 주요 소프트웨어 플랫폼의 개요 : AI IaaS, GPU-as-a-Service
- 생성형 AI의 주요 소프트웨어 플랫폼의 개요 : 미들웨어와 통합
- 생성형 AI의 주요 소프트웨어 플랫폼의 개요 : MLOps
- CEO가 선택한 LLM과 LLM 프로바이더에 대해 논의하는 방법
- 생성형 AI 서비스 : 경쟁 구도
- 생성형 AI 서비스 : 경쟁 구도(시장 점유율)
- 생성형 AI 서비스 : Accenture
- 생성형 AI 서비스 : Deloitte
- 생성형 AI 서비스 : Capgemini
- 생성형 AI 서비스 : IBM
제6장 최종사용자의 채택
- 챕터 개요 : 최종사용자의 채택
- 530건 생성형 AI프로젝트의 분석
- 주요 사례 연구 : 예-Klarna
- 주요 사례 연구 : 예-Westnet
- 주요 사례 연구 : 예-Covered California
- 제조업 심 파 : HMI 2024에서 20생성형 AI 솔루션의 개요
- 제조업 심 파 : 사례 연구 시에멘스
- 제조업 심 파 : 조사 통계-제조업에서 AI의 주요 사용 사례
- 테크놀러지와 통신 업계의 딥 다이브 : MWC 2024로 주목받는 생성형 AI 솔루션
- 테크놀러지와 통신 업계의 딥 다이브 : 사례 연구 1-Vodafone
- 테크놀러지와 통신 업계의 딥 다이브 : 사례 연구 2-Soracom
- 테크놀러지와 통신 업계의 딥 다이브 : 사례 연구 3-SAP
제7장 생성형 AI의 응용 상황과 비즈니스 모델의 검토
제8장 동향과 과제
제9장 조사 방법
제10장 IoT 애널리틱스 소개
KSA
A 263-page report on the enterprise Generative AI market, incl. market sizing & forecast, competitive landscape, end user adoption, trends, challenges, and more.
The "Generative AI Market Report 2025-2030" is part of IoT Analytics' ongoing coverage of enterprise technology markets. The information presented in this report is based on the results of secondary research and qualitative research, i.e., interviews with experts with experts in the field. The main purpose of this document is to help our readers understand the current Generative AI (GenAI) landscape and potential use cases.
What is Generative AI?
GenAI is a deep-learning technique based on variational autoencoders, generative adversarial networks, and transformer-based models.
SAMPLE VIEW
What is a Data Center GPU?
The market segment for data center GPUs refers to specialized graphics processing units designed to handle the extensive computational demands of modern data centers. These GPUs are engineered to accelerate a variety of complex workloads, including high-performance computing, DL, ML, and large-scale graphics processing tasks. The market does not include spending on CPUs, consumer-grade GPUs, or application-specific integrated circuits (ASICs). It includes GPU systems such as specialized GPU server racks. The market only includes external spending but not spending on developing own chips e.g., Google's TPUs or AWS' Trainium or Inferentium.
SAMPLE VIEW
What are foundational models and model management platforms?
This market segment includes both foundational models and model management platforms.
- 1. Foundational models are large-scale, pre-trained models that can be adapted to a wide variety of tasks without the need for training from scratch, such as language processing, image recognition, and decision-making algorithms.
- 2. Model management platforms are software platforms that enable users to deploy, fine-tune, and call GenAI models. Model management platforms allow the use of different GenAI models and are not limited to one single model vendor. The market does not include chatbots and applications such as ChatGPT.
SAMPLE VIEW
What are Gen AI services?
GenAI services represent a specialized market segment dedicated to consulting, integration, and implementation support for organizations aiming to integrate GenAI capabilities. These services are tailored to help businesses conceptualize, develop, and execute strategies that leverage GenAI technologies for enhanced innovation, efficiency, and value creation. Services includes consulting, integration, and managed services.
Five building blocks make up the Generative AI stack
The GenAI tech stack includes 5 building blocks:
- 1. Applications (e.g., AI-powered software solutions)
- 2. Platform tools for deployment and management
- 3. Foundation models like OpenAI's GPT 4
- 4. Critical backend infrastructure such as data processing and GPUs
- 5. Governance frameworks for security and compliance
The report includes a structured repository of 530 generative AI projects.*
Database structure
| Column name | Description |
| Company | Name of the company that implemented the project. |
Industry
(ISIC classification) | Industry classification (ISIC code) of the customer |
| Project description | A brief description of the project |
| Country | Country that the project took place in |
| Region | Region that the project took place in |
| Vendor | Name of the vendor that has published the case study/project on their website |
| Year | Year that the project was implemented |
| Link | Unique identifier of each case study/project |
Key department and
activities that are
improved by each project | Each project is grouped into one or more of the follogin departments: Sales, Marketing, Operations/mfg, Maintenance/field service, Finance and account, Human resources, IT/technology, Research and development, Customer service/support, Legal and compliance, Procurement, Logistics and supply chain, Corporate strategy/business development, Facility management. A project can touch mulitple departments. Each department is broken down into key activities. |
The database is suited for:
- AI strategy/business case development
- Sector scan+Customer/vendor selection
- Competitive analysis
- Go-to-market/market entry strategy
- And more
Questions answered:
- What is GenAI, and what are its technological components?
- Which GenAI use cases and applications are being prioritized by enterprises right now?
- What is the current market size for GenAI, and what are the market shares of key players ?
- Who is leading the market for GenAI models and platforms?
- Which companies offer AI accelerators beyond NVIDIA?
- Which consulting and professional services companies are selling the most GenAI projects?
- How do the leading GenAI models compare?
- What are some of the important implementation considerations for GenAI?
- What are the current and next trends and challenges around GenAI?
Companies mentioned:
A selection of companies mentioned in the report.
- AMD
- AWS
- Accenture
- Alibaba
- Anthropic
- Baidu
- Capgemini
- Cerebras
- Cognizant
- Cohere
|
- Google
- Groq
- Huawei
- Hugging Face
- IBM
- Infosys
- Microsoft
- Nvidia
- OpenAI
|
Table of Contents
1. Executive Summary
2. Introduction
- Chapter overview: Introduction
- Starting point: Understanding GenAI and its relationship with AI, ML, and DL
- The history of GenAI
- Interest in GenAI
- Investments in GenAI start-ups
- AI advances: (Gen)AI surpasses human capabilities in many tasks
- GenAI models
- GenAI adoption by industry
- GenAI adoption by business function
- Negative consequences of GenAI adoption
- GenAI model building/integration approaches
- Case study: AI at Thomson Reuters
- Beneficiaries of GenAI tech spending
3. Technology overview
- Chapter overview: Technology Overview
- The GenAI tech stack: 5 main blocks
- Foundation models: The transformer architecture
- Foundation models: What are foundation models?
- Foundation models: Type - Language models
- Foundation models: Type - Vision models
- Foundation models: Type - Speech/audio models
- Foundation models: Type - Multimodal models
- Foundation models: Type - Industry-specific models
- Foundation models: Optimization techniques
- Foundation models: Comparing GenAI models
- Foundation models: Best-performing models
- Foundation models: Open models
- GenAI software ecosystem: The five main types of platforms
- GenAI software ecosystem: The foundation model value chain
- GenAI software ecosystem: Databases
- GenAI software ecosystem: IaaS/GPU-as-a-service
- GenAI software ecosystem: Development platforms
- GenAI software ecosystem: Middleware & integration tools
- Computing hardware: AI chips overview
- Computing hardware: Types of AI chips and their capabilities
- Computing hardware: AI chips' power consumption
- Computing hardware: Training vs. Inference
- Computing hardware: NVIDIA vs. AMD chips
- Computing hardware: Emergence of new AI chips
- Computing hardware: GPU types in research papers
- Computing hardware: Data center infrastructure
4. Market model & outlook
- Chapter overview: Market model & outlook
- GenAI enterprise market: What is included and what is not
- GenAI market 2022-2030
- 1. Data center GPU market: Overview
- 1. Data center GPU market: By customer group
- 2. Foundation models & model mgmt. platforms market: Overview
- 2. Foundation models & model mgmt. platforms market: By vertical
- 2. Foundation models & model mgmt. platforms market: By region
- 2. Foundation models & model mgmt. platforms market: By country
- 3. GenAI services market: Overview
- 3. GenAI services market: By vertical
- 3. GenAI services market: By region
- 3. GenAI services market: By country
- Perspective: GenAI spending in relation to global software and services spending
5. Competitive landscape
- Chapter overview: Competitive landscape
- Competitive landscape 2024: Market Share Overview
- Data center GPUs: Competitive landscape (revenue)
- Data center GPUs: Competitive landscape (market share)
- Data center GPUs: NVIDIA
- Data center GPUs: AMD
- Data Center GPUs: Intel
- Data Center GPUs: Cerebras
- Data center GPUs: Groq
- Foundation models & model mgmt. platforms: Competitive landscape
- Foundation models & model mgmt. platforms (market share)
- Foundation models & model mgmt. platforms: Best LLMs
- Foundation models & model mgmt. platforms: Leading open models
- Foundation models & model mgmt. platforms: Microsoft
- Foundation models & model mgmt. platforms: AWS
- Foundation models & model mgmt. platforms: Google
- Foundation models & model mgmt. platforms: OpenAI
- Foundation models & model mgmt. platforms: Hugging Face
- Foundation models & model mgmt. platforms: Mistral AI
- Overview of key software platforms for GenAI: 1. Development Platforms
- Overview of key software platforms for GenAI: 2. Data Management Tools
- Overview of key software platforms for GenAI: 3. AI IaaS, GPU-as-a-Service
- Overview of key software platforms for GenAI: 4. Middleware & Integration
- Overview of key software platforms for GenAI: 5. MLOps
- How CEOs discuss selected LLMs and LLM providers
- GenAI services: Competitive landscape
- GenAI services: Competitive landscape (market share)
- GenAI services: Accenture
- GenAI services: Deloitte
- GenAI services: Capgemini
- GenAI services: IBM
6. End user adoption
- Chapter overview: End user adoption
- Analysis of 530 GenAI projects: Overview
- Analysis of 530 GenAI projects: By department
- Analysis of 530 GenAI projects: By department and activity
- Analysis of 530 GenAI projects: By industry
- Analysis of 530 GenAI projects: By industry and department
- Analysis of 530 GenAI projects: Crossing the chasm
- Key case studies: Example - Klarna
- Key case studies: Example - Westnet
- Key case studies: Example - Covered California
- Manufacturing deep dive: Overview of 20 GenAI solutions at HMI 24
- Manufacturing deep dive: GenAI solutions highlighted at HMI 2024
- Manufacturing deep dive: Case study - Siemens
- Manufacturing deep dive: Survey stats - Top AI use cases in manufacturing
- Tech & TelCo deep dive: GenAI solutions highlighted at MWC 2024
- Tech & TelCo deep dive: Case study 1 - Vodafone
- Tech & TelCo deep dive: Case study 2 - Soracom
- Tech & TelCo deep dive: Case study 3 - SAP
7. GenAI applications landscape & business model considerations
- Chapter overview: GenAI application landscape & business model considerations
- GenAI applications landscape 2024
- Considerations on GenAI business models
- Consideration 1
- Consideration 2
- Consideration 3
- Consideration 4
- Consideration 5
- Consideration 6
- Consideration 7
8. Trends & challenges
- Chapter overview: Trends & challenges
- Trend 1
- Trend 2
- Trend 3
- Trend 4
- Trend 5
- Trend 6
- Trend 7
- Trend 8
- Trend 9
- Challenge 1
- Challenge 2
- Challenge 3
- Challenge 4
- Challenge 5
- Challenge 6
- Challenge 7: Other challenges
9. Methodology
10. About IoT Analytics
