大規模人工智慧模型市場報告：趨勢、預測和競爭分析（至 2031 年）

全球大規模人工智慧模型市場前景光明，教育、能源、汽車和醫療保健市場都存在機會。預計全球大規模人工智慧模型市場在 2025 年至 2031 年期間的複合年成長率將達到 28.5%。該市場的主要驅動力是對用於複雜任務的強大大規模語言模型的需求不斷成長、人工智慧工程師和資料科學家的崛起，以及這些模型在醫療保健、金融和汽車等行業中的日益普及。

• Lucintel 預測，按類型分類，基於自然語言處理的模型將在預測期內實現最高的成長。
• 在應用方面，預計教育領域將出現最高的成長。
• 根據地區來看，預計亞太地區將在預測期內實現最高成長。

大規模人工智慧模型市場的策略性成長機會

大規模人工智慧模型市場為各種應用提供了許多成長機會。識別並利用這些機會可以推動創新和競爭優勢。以下是人工智慧模型市場的五個關鍵成長機會，每個機會都有可能影響不同的產業。

• 醫學診斷和個人化醫療：大規模人工智慧模式為醫療保健領域的診斷和個人化醫療提供了巨大潛力。透過分析大量醫療資料，這些模型可以幫助早期發現疾病、制定個人化治療方案和藥物發現。成長機會在於將人工智慧融入醫療保健系統，以提高醫療程序的準確性和效率。
• 自動駕駛汽車和智慧交通：人工智慧模型對於自動駕駛汽車和智慧交通系統的發展至關重要。先進的模型改善了車輛的感知、導航和決策流程。我們有機會利用人工智慧來提高安全性、最佳化交通管理並推動交通基礎設施的創新，為未來的交通出行做出貢獻。
• 金融服務與風險管理：在金融領域，大規模人工智慧模型正在改變風險管理、詐欺偵測和客戶服務。透過分析金融交易和市場資料，人工智慧可以識別模式和異常，從而實現更準確的風險評估和個人化的金融服務。機會在於採用人工智慧來提高決策和業務效率。
• 零售和客戶體驗：人工智慧模型透過個人化客戶體驗和最佳化庫存管理來增強零售業。大規模模型可以分析消費者的行為和偏好，以推動有針對性的行銷並改善產品推薦。成長機會在於利用人工智慧創造更具吸引力的購物體驗並簡化零售業務。
• 工業自動化和預測性維護：人工智慧模型透過分析設備資料來預測故障和最佳化維護計劃，從而推動工業自動化和預測性維護。該應用程式提高了業務效率並減少了停機時間。將人工智慧融入工業流程可以提高生產力並延長設備的使用壽命。

這些策略成長機會凸顯了大規模人工智慧模型改變一系列產業的潛力。透過專注於醫療保健、交通、金融、零售和工業自動化等應用，公司可以推動創新並獲得顯著的競爭優勢。人工智慧模型的持續開發和部署可能會塑造這些行業的未來並開闢新的成長途徑。

大規模人工智慧模型市場促進因素與挑戰

大規模人工智慧模型市場受到各種促進因素​​和挑戰的影響，包括技術進步、經濟因素和監管考量。了解這些因素對於駕馭市場和有效利用機會至關重要。這裡我們重點介紹影響市場的關鍵促進因素和挑戰。

推動大規模人工智慧模型市場的因素：

• 技術進步：運算能力和模型架構的進步正在推動大規模人工智慧模型的成長。變壓器網路和分散式學習技術等創新使得更複雜、更高效能的模式成為可能。這些技術改進將增強人工智慧的能力並擴大其潛在應用，刺激市場成長並吸引投資。
• 資料可用性的提高：資料可用性的大幅提升為訓練大規模人工智慧模型提供了豐富的資源。存取多樣化和廣泛的資料可以提高模型的準確性和能力。這項催化劑將支持更有效的人工智慧解決方案的開發，並加速醫療保健、金融和零售等領域的創新。
• 自動化需求不斷成長：製造業、金融業和物流業等行業對自動化的需求不斷成長是採用人工智慧模型的主要驅動力。自動化可以提高效率、降低成本並提高各種流程的準確性。大規模人工智慧模型在實現先進的自動化解決方案、促進市場擴展和應用方面發揮關鍵作用。
• 投資和資金籌措：來自公共和私營部門的大量投資和資金籌措正在推動大規模人工智慧模型的進步。創業投資、政府津貼和企業投資正在支持研發，加速尖端人工智慧技術的開發和部署。這種財政支持是市場成長的主要動力。
• 全球競爭格局：人工智慧市場的競爭格局推動創新和發展。公司和國家正在投資大規模人工智慧模型，以保持競爭力並引領技術進步。這種壓力推動著持續改進和創造更先進、更強大的人工智慧解決方案。

大規模AI模型市場面臨的挑戰如下：

• 監管和道德問題：監管和道德問題對大規模人工智慧模型市場構成了重大挑戰。資料隱私、演算法偏見和透明度等問題正受到嚴格審查，從而導致嚴格的法規和道德準則的製定。遵守這些規定會影響創新速度並增加營運複雜性。
• 計算成本高：開發和部署大規模人工智慧模型需要大量的運算資源，這意味著高成本。它需要強大的硬體並消耗大量能源，這帶來了財務和環境挑戰。解決這些成本對於確保永續和可擴展的人工智慧解決方案至關重要。
• 人才短缺：缺乏熟練的人工智慧專業人員是一個市場挑戰。開發大規模人工智慧模型的複雜性需要機器學習、資料科學和工程的專業知識。這種人才缺口可能會限制創新的步伐，並阻礙組織充分利用人工智慧技術的能力。

概述的市場促進因素和挑戰凸顯了大規模人工智慧模型市場的動態性質。雖然技術進步、資料可用性和不斷成長的自動化需求正在推動成長，但監管問題、高成本和人才短缺構成了重大障礙。了解這些因素對於駕馭市場和抓住新機會至關重要。

目錄

第1章執行摘要

第2章 全球大規模人工智慧模型市場：市場動態

• 簡介、背景和分類
• 供應鏈
• 產業驅動力與挑戰

第3章市場趨勢與預測分析（2019-2031）

• 宏觀經濟趨勢（2019-2024）及預測（2025-2031）
• 全球大規模AI模型市場趨勢（2019-2024）及預測（2025-2031）
• 全球大規模人工智慧模型市場（按類型）
  • 自然語言處理基礎模型
  • 電腦視覺基礎模型
  • 多模態基礎模型
  • 其他
• 全球大規模人工智慧模型市場（按應用）
  • 教育
  • 能源
  • 車
  • 醫療保健
  • 其他

第4章區域市場趨勢與預測分析（2019-2031）

• 全球大規模人工智慧模型市場（按地區）
• 北美大規模AI模型市場
• 歐洲大規模人工智慧模型市場
• 亞太大規模人工智慧模型市場
• 其他地區大規模人工智慧模型市場

第5章 競爭分析

• 產品系列分析
• 營運整合
• 波特五力分析

第6章 成長機會與策略分析

• 成長機會分析
  • 全球大規模人工智慧模型市場成長機會（按類型）
  • 全球大規模人工智慧模型市場成長機會（按應用）
  • 全球大規模人工智慧模型市場成長機會（按地區）
• 全球大規模AI模型市場新趨勢
• 戰略分析
  • 新產品開發
  • 擴大全球大規模AI模型市場的產能
  • 全球大規模人工智慧模式市場的企業合併
  • 認證和許可

第7章主要企業簡介

• OpenAI
• Microsoft
• Google
• NVIDIA
• Alibaba
• Baidu
• Tencent
• Huawei
• Naver
• Anthropic

The future of the global large AI model market looks promising with opportunities in the education, energy, automotive, and medical markets. The global large AI model market is expected to grow with a CAGR of 28.5% from 2025 to 2031. The major drivers for this market are the increasing demand for powerful large language models for complex tasks, a growing pool of AI engineers & data scientists, and the rising use of this model in industries like healthcare, finance, and automotive.

• Lucintel forecasts that, within the type category, the natural language processing foundation model is expected to witness the highest growth over the forecast period.
• Within the application category, education is expected to witness the highest growth.
• In terms of regions, APAC is expected to witness the highest growth over the forecast period.

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Emerging Trends in the Large AI Model Market

The large AI model market is experiencing transformative shifts driven by several emerging trends. These trends reflect advancements in technology, changes in regulatory landscapes, and evolving market needs. Understanding these trends is crucial for stakeholders aiming to navigate the dynamic AI landscape and capitalize on emerging opportunities.

• Increased Multimodal Capabilities: Large AI models are increasingly incorporating multimodal capabilities, enabling them to process and integrate diverse data types such as text, images, and audio. This trend is driven by advancements in model architectures and training techniques, allowing for more sophisticated and context-aware AI systems. The result is enhanced performance in applications like autonomous vehicles, virtual assistants, and creative content generation.
• Focus on Ethical AI and Regulation: There is a growing emphasis on developing ethical AI frameworks and regulatory standards to address concerns about bias, transparency, and accountability. Governments and organizations are working to establish guidelines that ensure responsible AI use. This trend is reshaping the market by fostering trust and ensuring compliance, which is becoming a competitive differentiator for AI developers.
• Expansion of AI in Industry-Specific Applications: AI models are being increasingly tailored for specific industries such as healthcare, finance, and manufacturing. Industry-specific applications are driving demand for customized models that address unique challenges and requirements. This specialization allows for more effective solutions and drives growth in sectors where AI can provide significant operational improvements and innovations.
• Advancements in Model Efficiency and Accessibility: Innovations in model efficiency are making large AI models more accessible and cost-effective. Techniques such as model compression, pruning, and distributed training are reducing the computational resources required. This trend is democratizing access to advanced AI technologies, enabling smaller organizations and developing countries to leverage powerful AI solutions.
• Integration of AI with Edge Computing: The integration of AI with edge computing is enhancing real-time processing and reducing latency. By deploying AI models on edge devices, organizations can achieve faster data analysis and decision-making. This trend is particularly impactful for applications requiring immediate responses, such as autonomous systems and IoT devices.

These emerging trends are reshaping the large AI model market by enhancing capabilities, fostering ethical practices, and expanding applications across various industries. As AI technology continues to evolve, these trends will drive innovation and influence market dynamics, creating new opportunities and challenges for stakeholders.

Recent Developments in the Large AI Model Market

Recent developments in the large AI model market reflect rapid technological advancements and shifting market dynamics. These developments are shaping the future of AI by introducing new capabilities, addressing regulatory challenges, and influencing global competition. Here are five key developments impacting the market.

• Advancements in Transformer Architectures: Transformer architectures, such as GPT-4 and its successors, have significantly advanced the capabilities of large AI models in natural language understanding and generation. These models are setting new benchmarks in performance, enabling more nuanced and context-aware interactions. The advancements are driving improvements in applications like chatbots, content creation, and language translation.
• Growth of AI-as-a-Service (AIaaS): The rise of AI-as-a-Service platforms is transforming how organizations access and utilize large AI models. Providers like Microsoft Azure and Amazon Web Services offer scalable AI solutions without the need for extensive in-house infrastructure. This development is democratizing access to advanced AI technologies, allowing businesses of all sizes to leverage AI for various applications.
• Increased Focus on AI Ethics and Governance: The market is witnessing a heightened focus on AI ethics and governance, with organizations and governments developing frameworks to address issues related to bias, transparency, and accountability. Initiatives such as the EU's AI Act and industry-specific guidelines are shaping how large AI models are developed and deployed, ensuring responsible use and building public trust.
• Expansion into Emerging Markets: Large AI models are expanding into emerging markets, with significant investments in regions like Asia-Pacific and Latin America. This expansion is driven by growing digital infrastructure and increasing demand for AI solutions in sectors such as finance, healthcare, and retail. The market dynamics are shifting as companies adapt their strategies to cater to diverse regional needs.
• Innovations in Model Training and Deployment: New techniques in model training and deployment, such as federated learning and decentralized AI, are enhancing the efficiency and scalability of large AI models. These innovations allow for more secure and collaborative training processes while reducing the need for centralized data storage. They are enabling more personalized and adaptive AI solutions.

These key developments are driving significant changes in the large AI model market, influencing technology, accessibility, and governance. As the market evolves, these developments will continue to impact how AI is utilized and integrated into various sectors, shaping the future of AI technology and its applications.

Strategic Growth Opportunities for Large AI Model Market

The large AI model market presents numerous growth opportunities across various applications. Identifying and leveraging these opportunities can drive innovation and competitive advantage. Here are five key growth opportunities in the AI model market, each with the potential to impact various sectors.

• Healthcare Diagnostics and Personalized Medicine: Large AI models offer significant potential in healthcare for diagnostics and personalized medicine. By analyzing vast amounts of medical data, these models can assist in early disease detection, personalized treatment plans, and drug discovery. The growth opportunity lies in integrating AI with healthcare systems to enhance accuracy and efficiency in medical practices.
• Autonomous Vehicles and Smart Transportation: AI models are crucial for the development of autonomous vehicles and smart transportation systems. Advanced models improve vehicle perception, navigation, and decision-making processes. The opportunity is in leveraging AI to enhance safety, optimize traffic management, and drive innovations in transportation infrastructure, contributing to the future of mobility.
• Financial Services and Risk Management: In the financial sector, large AI models are transforming risk management, fraud detection, and customer service. By analyzing financial transactions and market data, AI can identify patterns and anomalies, enabling more accurate risk assessments and personalized financial services. The opportunity lies in deploying AI to improve decision-making and operational efficiency.
• Retail and Customer Experience: AI models are enhancing the retail industry by personalizing customer experiences and optimizing inventory management. Large models can analyze consumer behavior and preferences, driving targeted marketing and improving product recommendations. The growth opportunity is in using AI to create more engaging shopping experiences and streamline retail operations.
• Industrial Automation and Predictive Maintenance: AI models are advancing industrial automation and predictive maintenance by analyzing equipment data to predict failures and optimize maintenance schedules. This application improves operational efficiency and reduces downtime. The opportunity is in integrating AI with industrial processes to enhance productivity and extend equipment lifespan.

These strategic growth opportunities highlight the potential of large AI models to transform various industries. By focusing on applications such as healthcare, transportation, finance, retail, and industrial automation, organizations can drive innovation and achieve significant competitive advantages. The continued development and deployment of AI models will shape the future of these sectors and create new avenues for growth.

Large AI Model Market Driver and Challenges

The large AI model market is influenced by a range of drivers and challenges, encompassing technological advancements, economic factors, and regulatory considerations. Understanding these elements is crucial for navigating the market and leveraging opportunities effectively. Here are the major drivers and challenges impacting the market.

The factors responsible for driving the large AI model market include:

• Technological Advancements: Advances in computational power and model architectures are driving the growth of large AI models. Innovations like transformer networks and distributed training techniques enable more sophisticated and capable models. These technological improvements enhance performance and expand the potential applications of AI, fueling market growth and attracting investments.
• Increased Data Availability: The exponential growth in data availability provides a rich resource for training large AI models. Access to diverse and extensive datasets improves model accuracy and capabilities. This driver supports the development of more effective AI solutions and accelerates innovation across various sectors, including healthcare, finance, and retail.
• Rising Demand for Automation: The increasing demand for automation in industries such as manufacturing, finance, and logistics is a key driver for AI model adoption. Automation enhances efficiency, reduces costs, and improves accuracy in various processes. Large AI models play a crucial role in enabling advanced automation solutions, driving market expansion and application.
• Investment and Funding: Significant investment and funding from both public and private sectors are fueling advancements in large AI models. Venture capital, government grants, and corporate investments support research and development, accelerating the development and deployment of cutting-edge AI technologies. This financial backing is a major driver of market growth.
• Global Competitive Pressure: The competitive landscape in the AI market drives innovation and development. Companies and countries are investing in large AI models to maintain a competitive edge and lead in technological advancements. This pressure encourages continuous improvement and the creation of more advanced and capable AI solutions.

Challenges in the large AI model market include:

• Regulatory and Ethical Concerns: Regulatory and ethical concerns pose significant challenges for the large AI model market. Issues such as data privacy, algorithmic bias, and transparency are under scrutiny, leading to the development of stringent regulations and ethical guidelines. Compliance with these regulations can impact the speed of innovation and increase operational complexities.
• High Computational Costs: Developing and deploying large AI models requires substantial computational resources, which translates to high costs. The need for powerful hardware and extensive energy consumption poses financial and environmental challenges. Addressing these costs is essential for ensuring sustainable and scalable AI solutions.
• Talent Shortages: The shortage of skilled AI professionals is a challenge for the market. The complexity of developing large AI models requires expertise in machine learning, data science, and engineering. This talent gap can limit the pace of innovation and hinder the ability of organizations to fully leverage AI technologies.

The drivers and challenges outlined highlight the dynamic nature of the large AI model market. Technological advancements, data availability, and rising demand for automation are propelling growth, while regulatory concerns, high costs, and talent shortages present significant hurdles. Understanding these factors is crucial for navigating the market and capitalizing on emerging opportunities.

List of Large AI Model Companies

Companies in the market compete on the basis of product quality offered. Major players in this market focus on expanding their manufacturing facilities, R&D investments, infrastructural development, and leverage integration opportunities across the value chain. Through these strategies large AI model companies cater increasing demand, ensure competitive effectiveness, develop innovative products & technologies, reduce production costs, and expand their customer base. Some of the large AI model companies profiled in this report include-

• OpenAI
• Microsoft
• Google
• NVIDIA
• Alibaba
• Baidu
• Tencent
• Huawei
• Naver
• Anthropic

Large AI Model by Segment

The study includes a forecast for the global large AI model market by type, application, and region.

Large AI Model Market by Type [Analysis by Value from 2019 to 2031]:

• Natural Language Processing Foundation Model
• Computer Vision Foundation Model
• Multimodal Foundation Model
• Others

Large AI Model Market by Application [Analysis by Value from 2019 to 2031]:

• Education
• Energy
• Automotive
• Medical
• Others

Large AI Model Market by Region [Analysis by Value from 2019 to 2031]:

• North America
• Europe
• Asia Pacific
• The Rest of the World

Country Wise Outlook for the Large AI Model Market

The landscape of the large AI model market is evolving rapidly, driven by advancements in technology, shifting geopolitical dynamics, and varying regulatory environments. As AI models grow in complexity and application, countries like the United States, China, Germany, India, and Japan are at the forefront of significant developments. Each country is advancing in different ways, influencing global trends and competition. This overview highlights the latest advancements and strategic movements in these key markets, providing a snapshot of their unique contributions and challenges.

• United States: The U.S. continues to lead in AI innovation with significant investments from both private and public sectors. Companies like OpenAI and Google are pushing the boundaries with new models that integrate multimodal capabilities, combining text, images, and other data forms. The U.S. also benefits from a robust ecosystem of AI startups and research institutions, fostering rapid development. Regulatory discussions around ethical AI and data privacy are becoming more pronounced, aiming to balance innovation with responsible use.
• China: China is aggressively advancing its AI capabilities, with state-backed initiatives driving the development of large models for various applications, including natural language processing and computer vision. The government's support includes substantial funding and strategic planning through initiatives like the New Generation Artificial Intelligence Development Plan. Chinese tech giants like Baidu and Alibaba are making significant strides, though the market faces challenges related to data privacy regulations and geopolitical tensions impacting international collaborations.
• Germany: Germany is positioning itself as a leader in ethical AI and industry-specific applications. With strong government backing and significant investment in research, German companies are focusing on integrating AI models into manufacturing and automotive sectors. Initiatives like the AI4EU project aim to enhance collaboration across Europe. Germany is also leading discussions on ethical AI standards, ensuring that developments align with European values and regulations, which impacts its competitive positioning on the global stage.
• India: India is emerging as a key player in the AI market with a focus on affordable and scalable AI solutions. The country is leveraging its vast talent pool and growing tech ecosystem to develop models suited for diverse applications, from healthcare to agriculture. Government initiatives such as the National AI Strategy are promoting AI research and development. However, India faces challenges related to infrastructure and data privacy, which could influence the pace of its AI advancements.
• Japan: Japan is known for its innovation in robotics and AI integration into various sectors. Companies like SoftBank and NEC are developing advanced AI models that enhance automation and human-machine interaction. The Japanese government is fostering AI research through initiatives like the Society 5.0 framework, which aims to integrate AI into daily life and industry. Japan's focus on human-centric AI and collaboration between technology and traditional industries is shaping its competitive edge in the global market.

Features of the Global Large AI Model Market

Market Size Estimates: Large AI model market size estimation in terms of value ($B).

Trend and Forecast Analysis: Market trends (2019 to 2024) and forecast (2025 to 2031) by various segments and regions.

Segmentation Analysis: Large AI model market size by type, application, and region in terms of value ($B).

Regional Analysis: Large AI model market breakdown by North America, Europe, Asia Pacific, and Rest of the World.

Growth Opportunities: Analysis of growth opportunities in different types, applications, and regions for the large AI model market.

Strategic Analysis: This includes M&A, new product development, and competitive landscape of the large AI model market.

Analysis of competitive intensity of the industry based on Porter's Five Forces model.

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This report answers following 11 key questions:

• Q.1. What are some of the most promising, high-growth opportunities for the large AI model market by type (natural language processing foundation model, computer vision foundation model, multimodal foundation model, and others), application (education, energy, automotive, medical, and others), and region (North America, Europe, Asia Pacific, and the Rest of the World)?
• Q.2. Which segments will grow at a faster pace and why?
• Q.3. Which region will grow at a faster pace and why?
• Q.4. What are the key factors affecting market dynamics? What are the key challenges and business risks in this market?
• Q.5. What are the business risks and competitive threats in this market?
• Q.6. What are the emerging trends in this market and the reasons behind them?
• Q.7. What are some of the changing demands of customers in the market?
• Q.8. What are the new developments in the market? Which companies are leading these developments?
• Q.9. Who are the major players in this market? What strategic initiatives are key players pursuing for business growth?
• Q.10. What are some of the competing products in this market and how big of a threat do they pose for loss of market share by material or product substitution?
• Q.11. What M&A activity has occurred in the last 5 years and what has its impact been on the industry?

Table of Contents

1. Executive Summary

2. Global Large AI Model Market : Market Dynamics

• 2.1: Introduction, Background, and Classifications
• 2.2: Supply Chain
• 2.3: Industry Drivers and Challenges

3. Market Trends and Forecast Analysis from 2019 to 2031

• 3.1. Macroeconomic Trends (2019-2024) and Forecast (2025-2031)
• 3.2. Global Large AI Model Market Trends (2019-2024) and Forecast (2025-2031)
• 3.3: Global Large AI Model Market by Type
  • 3.3.1: Natural Language Processing Foundation Model
  • 3.3.2: Computer Vision Foundation Model
  • 3.3.3: Multimodal Foundation Model
  • 3.3.4: Others
• 3.4: Global Large AI Model Market by Application
  • 3.4.1: Education
  • 3.4.2: Energy
  • 3.4.3: Automotive
  • 3.4.4: Medical
  • 3.4.5: Others

4. Market Trends and Forecast Analysis by Region from 2019 to 2031

• 4.1: Global Large AI Model Market by Region
• 4.2: North American Large AI Model Market
  • 4.2.1: North American Market by Type: Natural Language Processing Foundation Model, Computer Vision Foundation Model, Multimodal Foundation Model, and Others
  • 4.2.2: North American Market by Application: Education, Energy, Automotive, Medical, and Others
• 4.3: European Large AI Model Market
  • 4.3.1: European Market by Type: Natural Language Processing Foundation Model, Computer Vision Foundation Model, Multimodal Foundation Model, and Others
  • 4.3.2: European Market by Application: Education, Energy, Automotive, Medical, and Others
• 4.4: APAC Large AI Model Market
  • 4.4.1: APAC Market by Type: Natural Language Processing Foundation Model, Computer Vision Foundation Model, Multimodal Foundation Model, and Others
  • 4.4.2: APAC Market by Application: Education, Energy, Automotive, Medical, and Others
• 4.5: ROW Large AI Model Market
  • 4.5.1: ROW Market by Type: Natural Language Processing Foundation Model, Computer Vision Foundation Model, Multimodal Foundation Model, and Others
  • 4.5.2: ROW Market by Application: Education, Energy, Automotive, Medical, and Others

5. Competitor Analysis

• 5.1: Product Portfolio Analysis
• 5.2: Operational Integration
• 5.3: Porter's Five Forces Analysis

6. Growth Opportunities and Strategic Analysis

• 6.1: Growth Opportunity Analysis
  • 6.1.1: Growth Opportunities for the Global Large AI Model Market by Type
  • 6.1.2: Growth Opportunities for the Global Large AI Model Market by Application
  • 6.1.3: Growth Opportunities for the Global Large AI Model Market by Region
• 6.2: Emerging Trends in the Global Large AI Model Market
• 6.3: Strategic Analysis
  • 6.3.1: New Product Development
  • 6.3.2: Capacity Expansion of the Global Large AI Model Market
  • 6.3.3: Mergers, Acquisitions, and Joint Ventures in the Global Large AI Model Market
  • 6.3.4: Certification and Licensing

7. Company Profiles of Leading Players

• 7.1: OpenAI
• 7.2: Microsoft
• 7.3: Google
• 7.4: NVIDIA
• 7.5: Alibaba
• 7.6: Baidu
• 7.7: Tencent
• 7.8: Huawei
• 7.9: Naver
• 7.10: Anthropic