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市場調查報告書

商品編碼

1540311

生成式人工智慧市場：2025-2030

Generative AI Market Report 2025-2030

出版日期: 2025年01月31日 | 出版商:

IoT Analytics GmbH | 英文 263 Pages | 商品交期: 最快1-2個工作天內

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簡介目錄

生成式人工智慧是一種基於變分自動編碼器、生成對抗網路和Transformer模型的深度學習技術。

資料中心 GPU 市場部分是指專門設計用於處理現代資料中心的大量運算需求的圖形處理單元。這些 GPU 目的是加速各種複雜的工作負載，包括高效能運算、DL、ML 和大規模圖形處理任務。這個市場不包括在 CPU、消費級 GPU 或專用積體電路（ASIC）上的支出。這包括 GPU 系統，例如專用 GPU 伺服器機架。這個市場僅包括外部支出，不包括開發專有晶片（如Google的TPU 或 AWS 的Trainium 或 Inferentium）的支出。

範例視圖

本報告從物聯網分析驅動的企業技術市場的角度研究生成式人工智慧市場。本報告中提供的資訊是基於二次研究和定性研究的結果，即對該領域專家的訪談。本文檔的主要目標是幫助讀者了解生成式人工智慧的當前狀況和潛在用例。

章節概要：市場模式與展望
生成 AI 企業市場
生成式人工智慧市場：2022-2030年
資料中心 GPU 市場概覽
依客戶群劃分的資料中心 GPU 市場
基礎模型與模型管理平台市場：概覽
基於模型和模型管理平台市場：依垂直行業
基礎模型與模型管理平台市場：依地區
基礎模型和模型管理平台市場：依國家
生成式人工智慧服務市場：概覽
生成式人工智慧服務市場：依行業
生成式人工智慧服務市場：依地區
各國新一代人工智慧服務市場
觀點：生成式人工智慧支出與全球軟體與服務支出

第5章競爭格局

章節概要：競爭格局
2024年競爭格局：市場佔有率概覽
資料中心 GPU：競爭格局（營收）
資料中心 GPU：競爭格局（市場佔有率）
資料中心 GPU：NVIDIA
資料中心 GPU：AMD
資料中心 GPU：Intel
資料中心 GPU：Cerebras
資料中心 GPU：Groq
基礎模型與模型管理平台：競爭格局
基礎模型與模型管理平台（市場佔有率）
基礎模型與模型管理平台：最佳法學碩士
基礎模型與模型管理平台：主要開放模型
基礎模型與模型管理平台：Microsoft
基礎模型與模型管理平台：AWS
基礎模型與模型管理平台：Google
基礎模型與模式管理平台：OpenAI
基礎模式及模式管理平台：Hugging Face
基礎模型與模型管理平台：Mistral AI
生成式人工智慧主要軟體平台概述：開發平台
生成式人工智慧關鍵軟體平台概述：資料管理工具
生成式 AI 主要軟體平台概述：AI IaaS、GPU 即服務
生成式人工智慧的關鍵軟體平台概述：中間件和整合
領先的生成式 AI 軟體平台概要：MLOps
執行長如何討論他們選擇的LLM和LLM提供者
生成式人工智慧服務：競爭格局
生成式人工智慧服務：競爭格局（市場佔有率）
生成式人工智慧服務：Accenture
生成式人工智慧服務：Deloitte
生成式人工智慧服務：Capgemini
生成式人工智慧服務：IBM

第6章最終使用者採用

章節概述：最終使用者採用
530個生成式 AI 專案分析
精選案例研究：範例 - Klarna
精選案例研究：範例 - Westnet
精選案例研究：範例 - Covered California
製造業深度剖析：2024年 HMI 中的20 種生成式 AI 解決方案概述
深入研究製造業：個案研究 - Siemens
深入探究製造業：調查與統計 - 人工智慧在製造業的主要用例
深入探討科技與通訊：生成式 AI 解決方案成為 MWC2024 的焦點
深入探討科技與通訊：個案研究 1 - Vodafone
深入探究科技與通訊：個案研究 2 - Soracom
深入探究科技與通訊：個案研究 3 - SAP

第7章分析生成式人工智慧和商業模式的當前應用狀況

第8章趨勢與挑戰

第9章研究方法

第10章物聯網分析

簡介目錄

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

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