Global Market For Artificial Intelligence Large Language Models Was Estimated To Be Worth Us$ 1591 Million In 2023 And Is Forecast To A Readjusted Size Of Us$ 259840 Million By 2030 With A Cagr Of 79.8% During Forecast Period 2024-2030

The global Artificial Intelligence Large Language Models market is carefully researched in the report while largely concentrating on top players and their business tactics, geographical expansion, market segments, competitive landscape, manufacturing, and pricing and cost structures. Each section of the research study is specially prepared to explore key aspects of the global Artificial Intelligence Large Language Models market. For instance, the market dynamics section digs deep into the drivers, restraints, trends, and opportunities of the global Artificial Intelligence Large Language Models Market. With qualitative and quantitative analysis, we help you with thorough and comprehensive research on the global Artificial Intelligence Large Language Models market. We have also focused on SWOT, PESTLE, and Porter's Five Forces analyses of the global Artificial Intelligence Large Language Models market.

Leading players of the global Artificial Intelligence Large Language Models market are analyzed taking into account their market share, recent developments, new product launches, partnerships, mergers or acquisitions, and markets served. We also provide an exhaustive analysis of their product portfolios to explore the products and applications they concentrate on when operating in the global Artificial Intelligence Large Language Models market. Furthermore, the report offers two separate market forecasts - one for the production side and another for the consumption side of the global Artificial Intelligence Large Language Models market. It also provides useful recommendations for new as well as established players of the global Artificial Intelligence Large Language Models market.

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A large language model (LLM) is a type of language model notable for its ability to achieve general-purpose language understanding and generation. LLMs acquire these abilities by using massive amounts of data to learn billions of parameters during training and consuming large computational resources during their training and operation. LLMs are artificial neural networks (mainly transformers) and are (pre-)trained using self-supervised learning and semi-supervised learning. As autoregressive language models, they work by taking an input text and repeatedly predicting the next token or word. Up to 2020, fine tuning was the only way a model could be adapted to be able to accomplish specific tasks. Larger sized models, such as GPT-4, however, can be prompt-engineered to achieve similar results. They are thought to acquire embodied knowledge about syntax, semantics and "ontology" inherent in human language corpora, but also inaccuracies and biases present in the corpora.

The global market for Artificial Intelligence Large Language Models was estimated to be worth US$ 1591 million in 2023 and is forecast to a readjusted size of US$ 259840 million by 2030 with a CAGR of 79.8% during the forecast period 2024-2030.

The key players of Large Language Model (LLM) include Open AI(ChatGPT), Google(PaLM) and Meta (LLaMA), etc. The top three players hold a share over 86%. North America is the largest market, has a share about 53%. In terms of product type, Below 100 Billion Parameters is the largest segment, occupied for a share of about 70%. In terms of application, Chatbots and Virtual Assistants has a share about 32%.

The Artificial Intelligence Large Language Models market size, estimations, and forecasts are provided in terms of sales revenue ($ millions), considering 2023 as the base year, with history and forecast data for the period from 2019 to 2030. With both quantitative and qualitative analysis, to help readers develop business/growth strategies, assess the market competitive situation, analyze their position in the current marketplace, and make informed business decisions regarding Artificial Intelligence Large Language Models.

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Market Segmentation

By Company

Open AI(ChatGPT)

Google(PaLM)

Meta (LLaMA)

AI21 Labs(Jurassic)

Cohere

Anthropic(Claude)

Microsoft(Turing-NLG, Orca)

Huawei(Pangu)

Naver(HyperCLOVA)

Tencent(Hunyuan)

Yandex(YaLM)

Amazon(Titan, Olympus)

Alibaba(Qwen)

Baidu (Ernie)

Technology Innovation Institute (TII) (Falcon)

Crowdworks

NEC

Segment by Type

Below 100 Billion Parameters

Above 100 Billion Parameters

Segment by Application

Chatbots and Virtual Assistants

Content Generation

Language Translation

Code Development

Sentiment Analysis

Medical Diagnosis and Treatment

Education

Others

By Region

North America

United States

Canada

Asia-Pacific

China

Japan

South Korea

Southeast Asia

India

Australia

Rest of Asia-Pacific

Europe

Germany

France

U.K.

Italy

Netherlands

Nordic Countries

Rest of Europe

Latin America

Mexico

Brazil

Rest of Latin America

Middle East & Africa

Turkey

Saudi Arabia

UAE

Rest of MEA

Chapter Outline

Chapter 1: Introduces the report scope of the report, global total market size. This chapter also provides the market dynamics, latest developments of the market, the driving factors and restrictive factors of the market, the challenges and risks faced by manufacturers in the industry, and the analysis of relevant policies in the industry.

Chapter 2: Detailed analysis of Artificial Intelligence Large Language Models company competitive landscape, revenue market share, latest development plan, merger, and acquisition information, etc.

Chapter 3: Provides the analysis of various market segments by Type, covering the market size and development potential of each market segment, to help readers find the blue ocean market in different market segments.

Chapter 4: Provides the analysis of various market segments by Application, covering the market size and development potential of each market segment, to help readers find the blue ocean market in different downstream markets.

Chapter 5: Revenue of Artificial Intelligence Large Language Models in regional level. It provides a quantitative analysis of the market size and development potential of each region and introduces the market development, future development prospects, market space, and market size of each country in the world.

Chapter 6: Revenue of Artificial Intelligence Large Language Models in country level. It provides sigmate data by Type, and by Application for each country/region.

Chapter 7: Provides profiles of key players, introducing the basic situation of the main companies in the market in detail, including product revenue, gross margin, product introduction, recent development, etc.

Chapter 8: Analysis of industrial chain, including the upstream and downstream of the industry.

Chapter 9: Conclusion.

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Report Objectives

• Analyzing the size of the global Artificial Intelligence Large Language Models market on the basis of value and volume.

• Accurately calculating the market shares, consumption, and other vital factors of different segments of the global Artificial Intelligence Large Language Models market.

• Exploring the key dynamics of the global Artificial Intelligence Large Language Models market.

• Highlighting important trends of the global Artificial Intelligence Large Language Models market in terms of production, revenue, and sales.

• Deeply profiling top players of the global Artificial Intelligence Large Language Models market and showing how they compete in the industry.

• Studying manufacturing processes and costs, product pricing, and various trends related to them.

• Showing the performance of different regions and countries in the global Artificial Intelligence Large Language Models market.

• Forecasting the market size and share of all segments, regions, and the global market.

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