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1382525

全球人工智慧訓練晶片市場 - 2023-2030

Global AI Training Chip Market - 2023-2030

出版日期: 2023年11月17日 | 出版商:

DataM Intelligence | 英文 201 Pages | 商品交期: 最快1-2個工作天內

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簡介目錄

概述

全球人工智慧訓練晶片市場在2022年達到153億美元，預計到2030年將達到1327億美元，2023-2030年預測期間CAGR為29.2%。

由於各行各業對人工智慧應用和服務的需求不斷成長，全球人工智慧訓練晶片市場正在快速成長。人工智慧晶片是專門的積體電路，旨在加速人工智慧模型的訓練和推理。它通常用於資料中心和其他高效能運算環境。

人工智慧訓練晶片市場在廣泛的行業和應用中提供了實用性。它負責在自動駕駛車輛領域檢測物體、結合感測器資料和做出判斷等任務，從而提高安全性並實現自動駕駛功能。 AI 晶片在醫療保健領域非常有用，可以評估醫學圖片並透過 X 光、MRI 和 CT 掃描輔助診斷。人工智慧晶片提供與語言相關的人工智慧任務，例如語音辨識和語言翻譯，從而推動虛擬助理和即時語言翻譯工具的進步。

CPU晶片類型佔據最高的市場佔有率。同樣，亞太地區在人工智慧訓練晶片市場佔據主導地位，佔據最大市場佔有率，超過 55%。該地區一直是人工智慧訓練晶片開發和製造的主要中心。中國佔亞太地區人工智慧訓練晶片市場總量的最大佔有率，超過60%，其次是日本和韓國。

動力學

深度學習演算法日益流行

深度學習演算法是一種使用人工神經網路從資料中學習的機器學習演算法。它被廣泛應用於圖像識別、自然語言處理和語音識別等領域。深度學習演算法的計算量非常大，這意味著它們需要大量的處理能力來訓練。這就是人工智慧訓練晶片的用武之地。人工智慧訓練晶片是專門為加速深度學習演算法的訓練而設計的。它通常配備大量核心和高效能內存，這使得它們能夠快速有效地處理大量資料。

深度學習演算法的日益普及正在推動人工智慧訓練晶片的需求。各行業擴大採用深度學習技術以及更強大、更有效率的新型人工智慧訓練晶片的開發將推動市場的成長。隨著越來越多的企業和組織採用深度學習技術，人工智慧訓練晶片的需求預計將持續成長。

各行各業對人工智慧應用的需求不斷成長

人工智慧驅動的應用程式正在應用於各個行業，包括醫療保健、製造、汽車、零售和金融。在醫療保健領域，人工智慧被用於開發新藥、診斷疾病和提供個人化治療方案。此外，在汽車領域，人工智慧正被用於開發自動駕駛汽車、改善交通管理和個人化駕駛體驗。

人工智慧應用程式的開發和部署需要大量的運算能力。這就是人工智慧訓練晶片的用武之地。人工智慧訓練晶片是專門為加速人工智慧模型的訓練而設計的。它通常配備大量核心和高效能內存，這使得它們能夠快速有效地處理大量資料。隨著越來越多的企業和組織採用人工智慧技術，人工智慧訓練晶片的需求預計將持續成長。

熟練勞動力短缺

人工智慧訓練晶片的開發和部署需要熟練的勞動力。然而，半導體行業技術工人短缺。這是因為半導體產業是一個高度專業化的領域，需要大量的培訓和經驗。

熟練勞動力的短缺在許多方面限制了人工智慧訓練晶片市場的成長。首先，這使得企業開發和部署新的人工智慧應用變得更加困難。其次，它增加了開發和部署人工智慧應用程式的成本。第三，AI訓練晶片市場創新步伐放緩。

許多國家都在尋求吸引外國人才，以幫助解決技術工人短缺的問題。這可以透過提供有吸引力的簽證和移民政策以及提供經濟誘因來實現。透過解決熟練勞動力短缺的問題，AI訓練晶片市場可以持續成長並支援新的AI應用的開發。

Overview

Global AI Training Chip Market reached US$ 15.3 billion in 2022 and is expected to reach US$ 132.7 billion by 2030, growing with a CAGR of 29.2% during the forecast period 2023-2030.

The global AI training chip market is growing rapidly due to the increasing demand for AI-powered applications and services across a wide range of industries. AI chips are specialized integrated circuits that are designed to accelerate the training and inference of AI models. It is typically used in data centers and other high-performance computing environments.

The AI training chip market provides usefulness in a wide range of industries and applications. It drives duties like as detecting objects, combining sensor data and making judgments in the area of autonomous vehicles, hence enhancing safety and enabling self-driving capabilities. AI chips are useful in healthcare for evaluating medical pictures and aiding diagnosis from X-rays, MRIs and CT scans. AI chips provide language-related AI tasks such as speech recognition and language translation, leading to advancements in virtual assistants and instantaneous language translation tools.

The CPU chip type accounts for the highest market share. Similarly, the Asia-Pacific dominates the AI training chip market, capturing the largest market share of over 55%. The region has been a major hub for the development and manufacturing of AI training chips. China accounted for the largest share of over 60% of the total AI training chip market in Asia-Pacific, followed by Japan and South Korea.

Dynamics

Growing popularity of deep learning algorithms

Deep learning algorithms are a type of machine learning algorithm that uses artificial neural networks to learn from data. It is used in a wide variety of applications, such as image recognition, natural language processing and speech recognition. Deep learning algorithms are very computationally intensive, which means that they require a lot of processing power to train. The is where AI training chips come in. AI training chips are specifically designed to accelerate the training of deep learning algorithms. It is typically equipped with a large number of cores and high-performance memory, which allows them to process large amounts of data quickly and efficiently.

The growing popularity of deep learning algorithms is driving the demand for AI training chips. The growth of the market will be driven by the increasing adoption of deep learning technologies in various industries and the development of new AI training chips that are more powerful and efficient. As more and more businesses and organizations adopt deep learning technologies, the demand for AI training chips is expected to continue to grow.

Increasing demand for AI-powered applications in a wide range of industries

AI-powered applications are being used in a variety of industries, including healthcare, manufacturing, automotive, retail and finance. In the healthcare sector, AI is being used to develop new drugs, diagnose diseases and provide personalized treatment plans. Furthermore, in the automotive sector, AI is being used to develop self-driving cars, improve traffic management and personalized driving experiences.

The development and deployment of AI-powered applications require a lot of computing power. The is where AI training chips come in. AI training chips are specifically designed to accelerate the training of AI models. It is typically equipped with a large number of cores and high-performance memory, which allows them to process large amounts of data quickly and efficiently. As more and more businesses and organizations adopt AI technologies, the demand for AI training chips is expected to continue to grow.

Shortage of skilled labor workforce

The development and deployment of AI training chips require a skilled workforce. However, there is a shortage of skilled workers in the semiconductor industry. The is due to the fact that the semiconductor industry is a highly specialized field and requires a lot of training and experience.

The shortage of skilled labor is restraining the growth of the AI training chip market in a number of ways. First, it is making it more difficult for companies to develop and deploy new AI applications. Second, it is increasing the cost of developing and deploying AI applications. Third, it is slowing down the pace of innovation in the AI training chip market.

Many countries are looking to attract foreign talent to help address the shortage of skilled workers. It can be done by offering attractive visa and immigration policies, as well as by providing financial incentives. By addressing the shortage of skilled labor, the AI training chip market can continue to grow and support the development of new AI applications.

Segment Analysis

The global AI training chip market is segmented based on hardware, chip type, technology, application, end-user and region.

Inexpensive, Easy to find and well-supported by Software Developers

CPUs are general-purpose processors that are designed to perform a variety of tasks. However, they are not specifically designed for AI applications. Despite this, CPUs are becoming increasingly popular for AI training because they are relatively inexpensive and easy to find. It is also well-supported by software developers.

CPUs are relatively inexpensive compared to other types of AI training chips, such as GPUs and ASICs. The makes them a good option for businesses and organizations that are on a budget. It is readily available from a variety of vendors. The makes it easy for businesses and organizations to get their hands on the chips they need. There are a wide variety of software tools available for developing and deploying AI applications on CPUs. The makes it easy for businesses and organizations to get started with AI training.

Geographical Penetration

Growing number of startups and continuous government support

Asia-Pacific has been a dominant force in the global AI training chip market. The region is home to some of the leading players in the AI training chip market, such as Intel, NVIDIA and Qualcomm. Asia-Pacific is a major hub for the adoption of AI technologies. The region is home to some of the world's largest economies, such as China, India and Japan. The economies are investing heavily in AI technologies to improve their competitiveness.

Asia-Pacific is home to a growing number of startups that are developing AI applications. The startups are driving the demand for AI training chips. For example, MediaTek is a Taiwanese multinational semiconductor company that offers a range of AI training chips. The company's AI training chips are used in a variety of applications, including smartphones and tablets. The region has a large pool of skilled labor in the semiconductor industry. The makes it a good place to develop and manufacture AI training chips. Governments in Asia-Pacific are supporting the development of AI technologies. The is helping to create a favorable environment for the growth of the AI training chip market.

COVID-19 Impact Analysis

The COVID-19 pandemic has had a mixed impact on the AI training chip market. On the one hand, the pandemic has led to an increase in demand for AI training chips, as businesses and organizations have turned to AI to automate tasks and improve efficiency. On the other hand, the pandemic has also caused disruptions to the supply chain, making it more difficult to obtain AI training chips.

The pandemic has led to an increased demand for AI training chips, as businesses and organizations have turned to AI to automate tasks and improve efficiency. The is because AI can be used to perform tasks such as facial recognition, contact tracing and fraud detection, which are all important in the fight against the COVID-19 outbreak. The pandemic has accelerated innovation in the AI training chip market. Chipmakers are developing new AI training chips that are more powerful and efficient. The is because businesses and organizations are willing to pay more for chips that can help them automate tasks and improve efficiency.

Russia-Ukraine War Impact Analysis

The Russia-Ukraine war is having a significant impact on the AI training chip market. The war has disrupted the supply chain for AI training chips, as many of the components used to make these chips are manufactured in Russia and Ukraine. The has led to shortages and price increases for AI training chips. The shortages of AI training chips have led to price increases. The is making it more expensive for businesses and organizations to develop and deploy AI applications.

In addition, the war has increased uncertainty in the global economy, which is making businesses and organizations hesitant to invest in new AI projects. The is also having a negative impact on the demand for AI training chips. The war is also delaying the development of new AI training chips. The is because many of the companies that are developing these chips have operations in Russia and Ukraine.

Businesses and organizations should work with their suppliers to develop contingency plans in case of further disruptions. The Russia-Ukraine war is a major challenge for the AI training chip market. However, by taking steps to mitigate the impact of the war, businesses and organizations can continue to develop and deploy AI applications.

By Hardware

Processor
Memory
Network
Others

By Chip Type

GPU
CPU
ASIC
FPGA
Others

By Technology

System on Chip
System in Package
Multi-chip Module
Others

By Application

Natural Language Processing
Robotics
Computer Vision
Network Security
Others

By End-User

BFSI
Healthcare
Automotive and Transportation
IT and Telecommunications
Others

By Region

North America
- U.S.
- Canada
- Mexico
Europe
- Germany
- UK
- France
- Italy
- Russia
- Rest of Europe
South America
- Brazil
- Argentina
- Rest of South America
Asia-Pacific
- China
- India
- Japan
- Australia
- Rest of Asia-Pacific
Middle East and Africa

Key Developments

On July 2o, 2023, Tesla starts production of Dojo supercomputer to train driverless cars. It uses Tesla-designed chips and the entire infrastructure, as well as video data from the Tesla fleet, to train the neural network that is critical to supporting Tesla's machine vision technology for autonomous driving.
On May 28, 2023, NVIDIA announced a new class of large-memory AI supercomputer - an NVIDIA DGX supercomputer powered by NVIDIA GH200 Grace Hopper Superchips and the NVIDIA NVLink Switch System - created to enable the development of giant, next-generation models for generative AI language applications, recommender systems and data analytics workloads.
On August 30, 2023, Google made its artificial intelligence-powered tools available to enterprise customers at a monthly price of US$30 per user. Google's new tools include "Duet AI in Workspace", which will assist customers across its apps with writing in Docs, drafting emails in Gmail and generating custom visuals in Slides, among others.

Competitive Landscape

major global players in the market include: Tesla, Inc., NVIDIA Corporation, Intel Corporation, Graphcore Limited, Google Corporation, Qualcomm Technologies, Inc., Shanghai Enflame Technology Co Ltd, Kunlun Core (Beijing) Technology Co., Ltd., T-Head (Hangzhou) Semiconductor Co., Ltd. and MetaX Integrated Circuits (Shanghai) Co., Ltd.

Why Purchase the Report?

To visualize the global AI training chip market segmentation based on hardware, chip type, technology, application, end-user and region, as well as understand key commercial assets and players.
Identify commercial opportunities by analyzing trends and co-development.
Excel data sheet with numerous data points of AI training chip market-level with all segments.
PDF report consists of a comprehensive analysis after exhaustive qualitative interviews and an in-depth study.
Product mapping available as excel consisting of key products of all the major players.

The global AI training chip market report would provide approximately 77 tables, 85 figures and 201 Pages.

Target Audience 2023

Manufacturers/ Buyers
Industry Investors/Investment Bankers
Research Professionals
Emerging Companies

1. Methodology and Scope

1.1. Research Methodology
1.2. Research Objective and Scope of the Report

2. Definition and Overview

3. Executive Summary

3.1. Snippet by Hardware
3.2. Snippet by Chip Type
3.3. Snippet by Technology
3.4. Snippet by Application
3.5. Snippet by End-User
3.6. Snippet by Region

4. Dynamics

4.1. Impacting Factors
- 4.1.1. Drivers
  - 4.1.1.1. Growing Popularity of Deep Learning Algorithms
  - 4.1.1.2. Increasing Demand for AI-powered Applications in a Wide Range of Industries
- 4.1.2. Restraints
  - 4.1.2.1. Shortage of Skilled Labor Workforce
- 4.1.3. Opportunity
- 4.1.4. Impact Analysis

5. Industry Analysis

5.1. Porter's Five Force Analysis
5.2. Supply Chain Analysis
5.3. Pricing Analysis
5.4. Regulatory Analysis
5.5. Russia-Ukraine War Impact Analysis
5.6. DMI Opinion

6. COVID-19 Analysis

6.1. Analysis of COVID-19
- 6.1.1. Scenario Before COVID
- 6.1.2. Scenario During COVID
- 6.1.3. Scenario Post COVID
6.2. Pricing Dynamics Amid COVID-19
6.3. Demand-Supply Spectrum
6.4. Government Initiatives Related to the Market During Pandemic
6.5. Manufacturers Strategic Initiatives
6.6. Conclusion

7. By Hardware

7.1. Introduction
- 7.1.1. Market Size Analysis and Y-o-Y Growth Analysis (%), By Hardware
- 7.1.2. Market Attractiveness Index, By Hardware
7.2. Processor*
- 7.2.1. Introduction
- 7.2.2. Market Size Analysis and Y-o-Y Growth Analysis (%)
7.3. Memory
7.4. Network
7.5. Others

8. By Chip Type

8.1. Introduction
- 8.1.1. Market Size Analysis and Y-o-Y Growth Analysis (%), By Chip Type
- 8.1.2. Market Attractiveness Index, By Chip Type
8.2. GPU*
- 8.2.1. Introduction
- 8.2.2. Market Size Analysis and Y-o-Y Growth Analysis (%)
8.3. CPU
8.4. ASIC
8.5. FPGA
8.6. Others

9. By Technology

9.1. Introduction
- 9.1.1. Market Size Analysis and Y-o-Y Growth Analysis (%), By Technology
- 9.1.2. Market Attractiveness Index, By Technology
9.2. System on Chip*
- 9.2.1. Introduction
- 9.2.2. Market Size Analysis and Y-o-Y Growth Analysis (%)
9.3. System in Package
9.4. Multi-chip Module
9.5. Others

10. By Application

10.1. Introduction
- 10.1.1. Market Size Analysis and Y-o-Y Growth Analysis (%), By Application
- 10.1.2. Market Attractiveness Index, By Application
10.2. Natural Language Processing*
- 10.2.1. Introduction
- 10.2.2. Market Size Analysis and Y-o-Y Growth Analysis (%)
10.3. Robotics
10.4. Computer Vision
10.5. Network Security
10.6. Others

11. By End-User

11.1. Introduction
- 11.1.1. Market Size Analysis and Y-o-Y Growth Analysis (%), By End-User
- 11.1.2. Market Attractiveness Index, By End-User
11.2. BFSI*
- 11.2.1. Introduction
- 11.2.2. Market Size Analysis and Y-o-Y Growth Analysis (%)
11.3. Healthcare
11.4. Automotive and Transportation
11.5. IT and Telecommunications
11.6. Others

12. By Region

12.1. Introduction
- 12.1.1. Market Size Analysis and Y-o-Y Growth Analysis (%), By Region
- 12.1.2. Market Attractiveness Index, By Region
12.2. North America
- 12.2.1. Introduction
- 12.2.2. Key Region-Specific Dynamics
- 12.2.3. Market Size Analysis and Y-o-Y Growth Analysis (%), By Hardware
- 12.2.4. Market Size Analysis and Y-o-Y Growth Analysis (%), By Chip Type
- 12.2.5. Market Size Analysis and Y-o-Y Growth Analysis (%), By Technology
- 12.2.6. Market Size Analysis and Y-o-Y Growth Analysis (%), By Application
- 12.2.7. Market Size Analysis and Y-o-Y Growth Analysis (%), By End-User
- 12.2.8. Market Size Analysis and Y-o-Y Growth Analysis (%), By Country
  - 12.2.8.1. U.S.
  - 12.2.8.2. Canada
  - 12.2.8.3. Mexico
12.3. Europe
- 12.3.1. Introduction
- 12.3.2. Key Region-Specific Dynamics
- 12.3.3. Market Size Analysis and Y-o-Y Growth Analysis (%), By Hardware
- 12.3.4. Market Size Analysis and Y-o-Y Growth Analysis (%), By Chip Type
- 12.3.5. Market Size Analysis and Y-o-Y Growth Analysis (%), By Technology
- 12.3.6. Market Size Analysis and Y-o-Y Growth Analysis (%), By Application
- 12.3.7. Market Size Analysis and Y-o-Y Growth Analysis (%), By End-User
- 12.3.8. Market Size Analysis and Y-o-Y Growth Analysis (%), By Country
  - 12.3.8.1. Germany
  - 12.3.8.2. UK
  - 12.3.8.3. France
  - 12.3.8.4. Italy
  - 12.3.8.5. Russia
  - 12.3.8.6. Rest of Europe
12.4. South America
- 12.4.1. Introduction
- 12.4.2. Key Region-Specific Dynamics
- 12.4.3. Market Size Analysis and Y-o-Y Growth Analysis (%), By Hardware
- 12.4.4. Market Size Analysis and Y-o-Y Growth Analysis (%), By Chip Type
- 12.4.5. Market Size Analysis and Y-o-Y Growth Analysis (%), By Technology
- 12.4.6. Market Size Analysis and Y-o-Y Growth Analysis (%), By Application
- 12.4.7. Market Size Analysis and Y-o-Y Growth Analysis (%), By End-User
- 12.4.8. Market Size Analysis and Y-o-Y Growth Analysis (%), By Country
  - 12.4.8.1. Brazil
  - 12.4.8.2. Argentina
  - 12.4.8.3. Rest of South America
12.5. Asia-Pacific
- 12.5.1. Introduction
- 12.5.2. Key Region-Specific Dynamics
- 12.5.3. Market Size Analysis and Y-o-Y Growth Analysis (%), By Hardware
- 12.5.4. Market Size Analysis and Y-o-Y Growth Analysis (%), By Chip Type
- 12.5.5. Market Size Analysis and Y-o-Y Growth Analysis (%), By Technology
- 12.5.6. Market Size Analysis and Y-o-Y Growth Analysis (%), By Application
- 12.5.7. Market Size Analysis and Y-o-Y Growth Analysis (%), By End-User
- 12.5.8. Market Size Analysis and Y-o-Y Growth Analysis (%), By Country
  - 12.5.8.1. China
  - 12.5.8.2. India
  - 12.5.8.3. Japan
  - 12.5.8.4. Australia
  - 12.5.8.5. Rest of Asia-Pacific
12.6. Middle East and Africa
- 12.6.1. Introduction
- 12.6.2. Key Region-Specific Dynamics
- 12.6.3. Market Size Analysis and Y-o-Y Growth Analysis (%), By Hardware
- 12.6.4. Market Size Analysis and Y-o-Y Growth Analysis (%), By Chip Type
- 12.6.5. Market Size Analysis and Y-o-Y Growth Analysis (%), By Technology
- 12.6.6. Market Size Analysis and Y-o-Y Growth Analysis (%), By Application
- 12.6.7. Market Size Analysis and Y-o-Y Growth Analysis (%), By End-User

13. Competitive Landscape

13.1. Competitive Scenario
13.2. Market Positioning/Share Analysis
13.3. Mergers and Acquisitions Analysis

14. Company Profiles

14.1. Tesla, Inc.*
- 14.1.1. Company Overview
- 14.1.2. Product Portfolio and Description
- 14.1.3. Financial Overview
- 14.1.4. Key Developments
14.2. NVIDIA Corporation
14.3. Intel Corporation
14.4. Graphcore Limited
14.5. Google Corporation
14.6. Qualcomm Technologies, Inc.
14.7. Shanghai Enflame Technology Co Ltd
14.8. Kunlun Core (Beijing) Technology Co., Ltd.
14.9. T-Head (Hangzhou) Semiconductor Co., Ltd.
14.10. MetaX Integrated Circuits (Shanghai) Co., Ltd.

LIST NOT EXHAUSTIVE