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生成式人工智慧市场：2025-2030
市场调查报告书
商品编码
1540311

生成式人工智慧市场：2025-2030

Generative AI Market Report 2025-2030
出版日期: | 出版商: IoT Analytics GmbH | 英文 263 Pages | 商品交期: 最快1-2个工作天内

价格
简介目录

生成式人工智慧是一种基于变分自动编码器、生成对抗网路和Transformer模型的深度学习技术。

资料中心 GPU 市场部分是指专门设计用于处理现代资料中心的大量运算需求的图形处理单元。这些 GPU 目的是加速各种复杂的工作负载，包括高效能运算、DL、ML 和大规模图形处理任务。这个市场不包括在 CPU、消费级 GPU 或专用积体电路（ASIC）上的支出。这包括 GPU 系统，例如专用 GPU 伺服器机架。这个市场仅包括外部支出，不包括开发专有晶片（如Google的TPU 或 AWS 的Trainium 或 Inferentium）的支出。

范例视图


范例视图


范例视图

本报告从物联网分析驱动的企业技术市场的角度研究生成式人工智慧市场。本报告中提供的资讯是基于二次研究和定性研究的结果，即对该领域专家的访谈。本文檔的主要目标是帮助读者了解生成式人工智慧的当前状况和潜在用例。

目录

第1章 执行摘要

第2章 简介

第3章 技术概述

  • 章节概述：技术概述
  • 生成式人工智慧技术堆迭：5个关键模组
  • 基础模型
  • 生成式人工智慧软体生态系统
  • 计算硬体

第4章 市场模式及展望

  • 章节概要：市场模式与展望
  • 生成 AI 企业市场
  • 生成式人工智慧市场：2022-2030年
  • 资料中心 GPU 市场概览
  • 依客户群划分的资料中心 GPU 市场
  • 基础模型与模型管理平台市场：概览
  • 基于模型和模型管理平台市场：依垂直行业
  • 基础模型与模型管理平台市场：依地区
  • 基础模型和模型管理平台市场：依国家
  • 生成式人工智慧服务市场：概览
  • 生成式人工智慧服务市场：依行业
  • 生成式人工智慧服务市场：依地区
  • 各国新一代人工智慧服务市场
  • 观点：生成式人工智慧支出与全球软体与服务支出

第5章 竞争格局

  • 章节概要：竞争格局
  • 2024年竞争格局：市场占有率概览
  • 资料中心 GPU：竞争格局（营收）
  • 资料中心 GPU：竞争格局（市场占有率）
  • 资料中心 GPU：NVIDIA
  • 资料中心 GPU：AMD
  • 资料中心 GPU：Intel
  • 资料中心 GPU：Cerebras
  • 资料中心 GPU：Groq
  • 基础模型与模型管理平台：竞争格局
  • 基础模型与模型管理平台（市场占有率）
  • 基础模型与模型管理平台：最佳法学硕士
  • 基础模型与模型管理平台：主要开放模型
  • 基础模型与模型管理平台：Microsoft
  • 基础模型与模型管理平台：AWS
  • 基础模型与模型管理平台：Google
  • 基础模型与模式管理平台：OpenAI
  • 基础模式及模式管理平台：Hugging Face
  • 基础模型与模型管理平台：Mistral AI
  • 生成式人工智慧主要软体平台概述：开发平台
  • 生成式人工智慧关键软体平台概述：资料管理工具
  • 生成式 AI 主要软体平台概述：AI IaaS、GPU 即服务
  • 生成式人工智慧的关键软体平台概述：中间件和整合
  • 领先的生成式 AI 软体平台概要：MLOps
  • 执行长如何讨论他们选择的LLM和LLM提供者
  • 生成式人工智慧服务：竞争格局
  • 生成式人工智慧服务：竞争格局（市场占有率）
  • 生成式人工智慧服务：Accenture
  • 生成式人工智慧服务：Deloitte
  • 生成式人工智慧服务：Capgemini
  • 生成式人工智慧服务：IBM

第6章 最终使用者采用

  • 章节概述：最终使用者采用
  • 530个生成式 AI 专案分析
  • 精选案例研究：范例 - Klarna
  • 精选案例研究：范例 - Westnet
  • 精选案例研究：范例 - Covered California
  • 製造业深度剖析：2024年 HMI 中的20 种生成式 AI 解决方案概述
  • 深入研究製造业：个案研究 - Siemens
  • 深入探究製造业：调查与统计 - 人工智慧在製造业的主要用例
  • 深入探讨科技与通讯：生成式 AI 解决方案成为 MWC2024 的焦点
  • 深入探讨科技与通讯：个案研究 1 - Vodafone
  • 深入探究科技与通讯：个案研究 2 - Soracom
  • 深入探究科技与通讯：个案研究 3 - SAP

第7章 分析生成式人工智慧和商业模式的当前应用状况

第8章 趋势与挑战

第9章 研究方法

第10章 物联网分析

简介目录

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 nameDescription
CompanyName of the company that implemented the project.
Industry
(ISIC classification)		Industry classification (ISIC code) of the customer
Project descriptionA brief description of the project
CountryCountry that the project took place in
RegionRegion that the project took place in
VendorName of the vendor that has published the case study/project on their website
YearYear that the project was implemented
LinkUnique 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