AI Chip Market Size, Share & Industry Trends Growth Analysis Report by Offerings (GPU, CPU, FPGA, NPU, TPU, Trainium, Inferentia, T-head, Athena ASIC, MTIA, LPU, Memory (DRAM (HBM, DDR)), Network (NIC/Network Adapters, Interconnects)), Function (Training, Inference) & Region – Global Forecast to 2029
Updated on : Oct 22, 2024
AI Chip Market Size & Share
The global AI chip market size is projected to grow from USD 123.16 billion in 2024 to USD 311.58 billion by 2029, growing at a CAGR of 20.4% during the forecast period from 2024 to 2029.
The AI chip market is driven by the increasing adoption of AI servers by hyperscalers and the growing use of Generative AI technologies and applications, such as GenAI and AIoT, across various industries, including BFSI, healthcare, retail & e-commerce, and media & entertainment.
AI chips help achieve high-speed parallel processing in AI servers, offering high performance and efficiently handling AI workloads in the cloud data center ecosystem. Moreover, the surging adoption of edge AI computing and the rising focus on real-time data processing, coupled with robust government-led investments in AI infrastructure development, especially in economies across the Asia Pacific region, further contribute to the AI chip industry growth.
AI Chip Market Forecast to 2029
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AI Chips Market Trends
Driver: Increasing adoption of AI servers by hyperscalers
There is a spike in demand for AI chips with the rising deployment of AI servers in diversified AI-powered applications across several industries, including BFSI, healthcare, retail & e-commerce, media & entertainment, and automotive. Data center owners and cloud service providers are upgrading their infrastructure to enable AI applications.
According to MarketsandMarkets analysis, AI server penetration represented 8.8% of all servers in 2023 and is anticipated to reach 30% by 2029. The rising inclination toward using chatbots, Artificial Intelligence of Things (AIoT), predictive analytics, and natural language processing drives the need for AI servers to support these applications. These applications require powerful hardware platforms to perform complex computations and process large data volumes.
AI servers have advanced computational capabilities and are designed to handle large datasets. They can also process data in real time and play a crucial role in training AI models. Owing to the growing demand for faster processing speeds and greater energy efficiency, AI servers are primarily used by cloud service providers, enterprises, academic institutions, and commercial end users.
Increasing investments and a growing trend of AI-enhanced infrastructure set the base for the high demand for AI chips.
Restraint: Adverse impact of high-power consuming graphics processing units (GPUs) and application-specific integrated circuits (ASICs) on environment
Data centers and other infrastructure supporting AI workloads use GPUs and ASICs with parallel processing features. This makes them suitable for handling complex AI workloads; however, parallel processing in GPUs results in high power consumption. This increases energy costs for data centers and organizations deploying AI infrastructure. AI systems can handle large-scale AI operations; however, they also consume significant power to carry out these functions.
As AI models become more complex and the volume of data increases, there is a surge in power demands for AI chips. Excessive power consumption results in excessive heating, which can only be handled by more advanced cooling systems. This adds to the complexity and cost of infrastructure.
GPUs and ASICs work in parallel with thousands of cores. This requires immense computational power to carry out advanced AI workloads, including deep learning training and large-scale simulations. Hence, companies adopt network components with higher thermal design power (TDP) values. GPUs with higher TDP are in demand due to their better performance.
Therefore, AI chip manufacturers are focused on developing GPUs with a high TDP range. For example, in August 2022, Intel Corporation (US) launched the Flex140 data center GPU, followed by the Max 1450 GPU in October 2023, both with a TDP rating of around 600 watts compared to their older versions, such as Flex 140 GPU and Flex 170 GPU, both having TDP 150 watts. As data-intensive computing requirements continue to rise, manufacturers are developing chips with high processing power. However, the high energy consumption of GPUs and ASICs raises concerns about the environmental impact, particularly in terms of carbon emissions and sustainability. As governments push for greener practices, the environmental footprint of AI hardware could become a critical factor in decision-making, limiting the adoption of high-power-consuming chips.
Opportunity: Planned investments in data centers by cloud service providers
Cloud service providers (CSPs) are making massive investments in scaling and upgrading data center infrastructures to support accelerating demand for AI-based applications and services. Most investments that CSPs make in data centers aim to attain scalability and operational efficiency.
As they increase their cloud services, demand for AI chips is likely to increase, creating growth opportunities for AI chip providers. For instance, AWS (US) declared an investment of USD 5.30 billion into constructing cloud data centers in Saudi Arabia. Similarly, in November 2023, Microsoft (US) declared its plan to build several new data centers in Quebec, expanding across Canada. In the next two years, it will invest USD 500 million to build up its cloud computing and AI infrastructure in Quebec. It needs state-of-the-art AI chips powered by GPUs, TPUs, and AI accelerators to take control of the ever-increasing computational requirements in AI training and inference.
Challenge: Addressing delivery delays due to supply chain disruptions
Supply chain disruption is one of the major challenges faced by players in the AI chip market. It affects the production quantity, delivery time, and, ultimately, the cost of processors. Component shortages result from either the lack of sufficient semiconductor material or limited production capacity, which creates significant production delays. Production delays may also occur due to equipment breakdown or the complexity of processing cutting-edge AI chips. There is a greater demand for high-performance GPUs with faster real-time large language model (LLM) training and inference capabilities. This can further increase the time to market. Thus, supply chain disruptions significantly impact the entire AI chipset market.
Hardware manufacturers face challenges in meeting production schedules due to delays in the availability of AI chips. System integrators that are dependent on the timely delivery of components to set up and configure AI infrastructure face project delays, which prevent the delivery of solutions to clients on time. Setbacks are suffered by cloud service providers scaling up their data center operations to match the surging demand for AI-driven services. For instance, the demand for NVIDIA H1OO and A1OO GPUs is considerably high; therefore, the lead time of GPU servers extends up to 52 weeks. This prolonged lead time creates big problems for organizations deploying high-performance GPUs in their AI infrastructure. Apart from affecting the deployment timeline, this also causes delays and increases costs; for instance, organizations may need to wait longer or find other options at higher prices.
AI Chip Industry Ecosystem
AI Chip Market Segment
GPU segment is expected to record largest market share during forecast period
The GPU segment is projected to witness the largest market share during the forecast period. GPUs can effectively handle huge computational loads required to train and run deep learning models using complex matrix multiplications. This makes them vital in data centers and AI research, where the fast growth of AI applications calls for efficient hardware solutions.
New GPUs, which enhance AI capabilities not only for data centers but also at the edge, are constantly developed and released by major manufacturers such as NVIDIA Corporation (US), Intel Corporation (US), and Advanced Micro Devices, Inc. (US). For example, in November 2023, NVIDIA Corporation released an upgraded HGX H200 platform based on Hopper architecture featuring the H200 Tensor core GPU. The first GPU to pack HBM3e memory provides 141 GB of memory at a blazing speed of 4.8 terabytes per second.
Leading cloud service providers, including Amazon Web Services, Inc.; Google Cloud; Microsoft Azure; and Oracle Cloud Infrastructure, are committed to deploying H200-based GPUs to prove that GPUs are one of the critical components of the cloud computing ecosystem. Improvements in GPU memory capabilities and the growing adoption of highly advanced GPUs by cloud service providers will further accelerate market growth.
Inference segment to account for largest share of AI chip market throughout forecast period
The AI chipset market for inference functions accounted for the largest market share in 2023 and is projected to grow at the highest rate during the forecast period. Inference leverages pre-trained AI models to make accurate predictions or timely decisions based on new data. With businesses shifting toward AI integration to improve production efficiency, enhance customer experience, and drive innovation, there is a growing need for robust inference capabilities in the data center.
Data centers are rapidly scaling up their AI capabilities, highlighting the importance of efficiency and performance in inference processing. A critical factor fostering the growth of the AI chipset market is the elevating requirement for more energy-efficient and high-performing inference chips. For example, SEMIFIVE has unveiled its 14 nm AI Inference SoC Platform, developed in collaboration with Mobilint, Inc. of South Korea. This platform is designed explicitly for inference tasks and features a quad-core high-performance 64-bit CPU, PCIe Gen4 interfaces, and LPDDR4 memory channels.
It is suitable for custom AI chips, including ASICs. Such chips are designed to power data center accelerators, AI vision processors, and big data analytics tools implemented for image and video recognition. All these tools rely highly on efficient and scalable inference processing. Developing AI inference SoC platforms underpins the increasing demand for special-purpose hardware solutions, which can help optimize inference workload performance within data centers.
Generative AI segment to account for majority of market share throughout forecast period
Generative AI technology is likely to dominate the AI chips market throughout the forecast period. There is an exponential increase in the demand for AI models that can generate high-quality content, including text, images, and codes.
As GenAI models are becoming more complex, there is a high requirement for AI chips with higher processing capabilities and memory bandwidth from data center service providers. GenAI applications are also adopted at a significantly high rate across various enterprises, including retail & e-commerce, BFSI, healthcare, media & entertainment, in dynamic applications, such as NLP, content generation, and automated design generation and process. The rising demand for GenAI solutions across these industries is expected to fuel the AI chip market growth in the coming years.
Cloud service providers segment to capture largest share of AI chip market during forecast period
The cloud service providers (CSPs) segment is likely to hold the largest share of the AI chips market during the forecast period. Cloud service providers are increasingly implementing high-end AI chips in their data centers to stay competitive in the market.
For instance, in July 2024, Northern Data Group (Germany) unveiled Europe's pioneering cloud services featuring NVIDIA's H200 GPUs. Leveraging 2,000 NVIDIA H200 GPUs, the company is set to deliver a remarkable 32 petaFLOPS of performance. Such significant investments by CSPs will propel the growth of the AI chip market during the forecast period.
AI Chip Market Regional Analysis
Asia Pacific to be fastest-growing market during forecast period
The AI chip market in Asia Pacific is poised to grow at the highest CAGR during the forecast period. The escalating adoption of AI technologies in countries such as China, South Korea, India, and Japan will stimulate market growth.
AI research and development (R&D) activities receive significant funding from regional government entities, fostering a favorable environment for Al developments. Additionally, the presence of high-bandwidth memory (HBM) tech giants, such as Samsung (South Korea), Micron Technology Inc. (US), and SK Hynix Inc (South Korea), which have dedicated HBM manufacturing facilities in South Korea, Taiwan, and China, will further boost the AI chip industry growth in Asia Pacific in the next few years.
AI Chip Market by Region
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Top AI Chip Companies - Key Market Players
Major vendors in the AI chip companies are
- NVIDIA Corporation (US),
- Advanced Micro Devices, Inc. (US),
- Intel Corporation (US),
- Micron Technology, Inc. (US),
- Google (US),
- SK HYNIX INC. (South Korea),
- Qualcomm Technologies, Inc. (US),
- Samsung (South Korea),
- Huawei Technologies Co., Ltd. (China),
- Apple Inc. (US),
- Imagination Technologies (UK),
- Graphcore (UK), and
- Cerebras (US).
Apart from this, Mythic (US), Kalray (France), Blaize (US), Groq, Inc. (US), HAILO TECHNOLOGIES LTD (Israel), GreenWaves Technologies (France), SiMa Technologies, Inc. (US), Kneron, Inc. (US), Rain Neuromorphics Inc. (US), Tenstorrent (Canada), SambaNova Systems, Inc. (US), Taalas (Canada), SAPEON Inc. (US), Rebellions Inc. (South Korea), Rivos Inc. (US), and Shanghai BiRen Technology Co., Ltd. (China) are among a few emerging companies in the AI chip industry.
AI Chip Market Report Scope
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Report Metric |
Details |
| Estimated Market Size | USD 123.16 billion in 2024 |
| Projected Market Size | USD 311.58 billion by 2029 |
| Growth Rate | CAGR of 20.4%. |
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AI Chip Market size available for years |
2020–2029 |
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Base year |
2023 |
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Forecast period |
2024–2029 |
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Segments covered |
Offering, Technology, Function, End User, and Region |
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Geographic regions covered |
North America, Europe, Asia Pacific, and RoW |
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Companies covered |
A total of 28 players have been covered. The major players include NVIDIA Corporation (US), Intel Corporation (US), Advanced Micro Devices, Inc. (US), Micron Technology, Inc. (US), Google (US), Samsung (South Korea), SK HYNIX INC. (South Korea), Qualcomm Technologies, Inc. (US), Huawei Technologies Co., Ltd. (China), Apple Inc. (US), Imagination Technologies (UK), Graphcore (UK), and Cerebras (US), among others. |
AI Chip Market Highlights
This research report categorizes the AI Chip Market by Offerings, Function, Technology, End User, and Region.
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Segment |
Subsegment |
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By Offerings: |
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By Technology: |
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By Function: |
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By End-User: |
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By Region: |
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Recent Developments in AI Chip Industry
- In June 2024, Advanced Micro Devices, Inc. (US) introduced AMD Ryzen AI 300 Series processors with powerful NPUs offering 50 TOPS AI-processing power for next-generation AI PCs. These processors are powered by the new Zen5 architecture with 12 high-performance CPU cores and feature advanced AI architecture for gaming and productivity.
- In May 2024, Google (US) introduced Trillium, a sixth-generation TPU with improved training and serving times for AI workloads. It also has increased clock speed and the size of matrix multiply units. Trillium TPU powers the next wave of AI models.
- In April 2024, Micron Technology, Inc. (US) and Silvaco Group, Inc. (US) extended their partnership to develop an AI-based solution: Fab Technology Co-Optimization (FTCO). This solution enables customers to use manufacturing data to perform machine learning software simulations and create a computer model to simulate the wafer fabrication process. Micron Technology, Inc. (US) invested USD 5 million in the development of FTCO.
- In March 2024, NVIDIA Corporation (US) introduced the NVIDIA Blackwell platform to enable organizations to build and run real-time GenAI featuring six transformative technologies for accelerated computing. The platform allows AI training and real-time LLM inference for models with up to 10 trillion parameters.
- In February 2024, Intel Corporation (US) and Cadence Design Systems, Inc. (US) expanded their strategic partnership through a multiyear agreement to develop advanced system-on-chip (SoC) designs. The partnership aims to meet the rising demand from the fast-growing markets, including AI, ML, HPC, and premium mobile computing.
Key Questions Addressed in Report:
At what CAGR is the AI chip market expected to grow from 2024 to 2029?
The global AI chip market is expected to grow at a CAGR of 20.4% from 2024 to 2029.
Which regions are expected to witness significant demand for AI chips during the forecast period?
Asia Pacific and North America are anticipated to exhibit significant demand for AI chips during the forecast period. Economies such as the US, Canada, China, Japan, India, and South Korea are likely to experience high market growth in the near future.
Which factors will create opportunities for the players in the AI chip market?
Surging demand for AI-based field programmable gate array (FPGA) technology, the rising integration of AI-powered solutions into defense systems, the growing potential of AI-based tools in the healthcare sector, planned investments by cloud service providers in upgrading data center infrastructure, and the elevating adoption of AI-powered ASICs by data center owners are projected to create lucrative opportunities for the players in the AI chip market during the forecast period.
Who are the key players in the AI chip market?
The key players in the AI chip market are NVIDIA Corporation (US), Advanced Micro Devices, Inc. (US), Intel Corporation (US), Micron Technology, Inc. (US), and Google (US).
Who are the major end users of AI chips?
The major end users for AI chips are cloud service providers, enterprises (healthcare, BFSI, automotive, retail & e-commerce, and media & entertainment), and government organizations.
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The study involved four major activities in estimating the size for AI Chip market. Exhaustive secondary research was done to collect information on the market, peer market, and parent market. The next step was to validate these findings, assumptions, and sizing with industry experts across value chains through primary research. The bottom-up approach was employed to estimate the overall market size. After that, market breakdown and data triangulation were used to estimate the market size of segments and subsegments.
Secondary Research
Various secondary sources have been referred to in the secondary research process for identifying and collecting information important for this study. The secondary sources include annual reports, press releases, and investor presentations of companies; white papers; journals and certified publications; and articles from recognized authors, websites, directories, and databases. Secondary research has been conducted to obtain key information about the industry’s supply chain, market’s value chain, the total pool of key players, market segmentation according to the industry trends (to the bottom-most level), geographic markets, and key developments from both market- and technology-oriented perspectives. The secondary data has been collected and analyzed to determine the overall market size, further validated by primary research.
Primary Research
Extensive primary research has been conducted after acquiring knowledge about the AI Chip market scenario through secondary research. Several primary interviews have been conducted with market experts from both the demand and supply sides across four major geographies: North America, Europe, Asia Pacific, and RoW (the Middle East, Africa, and South America). Approximately 60% and 40% of primary interviews were conducted with both the supply and demand sides, respectively. This primary data has been collected through emails, questionnaires, and telephonic interviews.
To know about the assumptions considered for the study, download the pdf brochure
Market Size Estimation
In the complete market engineering process, both top-down and bottom-up approaches, along with several data triangulation methods, have been used to perform the market size estimation and forecasting for the overall market segments and subsegments listed in this report. Extensive qualitative and quantitative analyses have been conducted on the complete market engineering process to list the key information/insights throughout the report.
The key players in the market such as Intel Corporation (US), NVIDIA Corporation (US), Google (US), Advanced Micro Devices, Inc. (US), and Micron Technology, Inc. (US) have been identified through secondary research, and their market shares in the respective regions have been determined through primary and secondary research. This entire procedure includes the study of annual and financial reports of the top players as well as extensive interviews with industry experts (such as CEOs, VPs, directors, and marketing executives) for key insights (both quantitative and qualitative) on the AI Chip market. All percentage shares, splits, and breakdowns have been determined using secondary sources and verified through primary sources. All the possible parameters that affect the markets covered in this research study have been accounted for, viewed in extensive detail, verified through primary research, and analyzed to get the final quantitative and qualitative data. This data has been consolidated and supplemented with detailed inputs and analysis from MarketsandMarkets and presented in this report.
AI Chip Market: Bottom-Up Approach
The bottom-up approach has been employed to arrive at the overall size of the AI chip market. The bottom-up methodology for AI chip market calculations begins with determining AI chip demand by multiplying AI server numbers by chip attach-rates per server. Average Selling Prices for each AI chip offering type are then identified. Revenues are calculated for each region or country by multiplying the local AI chip demand by the corresponding ASP, considering product billing locations. These regional revenues are summed to provide a global figure. The total market size is derived by combining all calculated revenues.
AI Chip Market: Top-Down Approach
In the top-down approach, the overall market size has been used to estimate the size of the individual markets (mentioned in the market segmentation) through percentage splits from secondary and primary research. The top-down methodology for the AI chip market analysis starts with the total market size as the foundation. This overall figure is then broken down into percentage splits for key segments such as Function, Technology, and End User. Each of these segments is further divided into geographic regions, providing a clear picture of market distribution across different areas. The analysis then delves deeper, offering region and country-wise splits for each sub-segment. This approach allows for a comprehensive view of the market, starting from the broadest perspective and progressively narrowing down to specific details. This privides an effectively mapped out entire AI chip market landscape, identifying trends, opportunities, and potential growth areas across various dimensions and geographical locations.
Data Triangulation
After arriving at the overall market size from the market size estimation process explained earlier, the total market was split into several segments and subsegments. Data triangulation and market breakdown procedures have been employed to complete the overall market engineering process and arrive at the exact statistics for all segments and subsegments, wherever applicable. The data has been triangulated by studying various factors and trends from both the demand and supply sides. Along with this, the AI chip market has been validated using both top-down and bottom-up approaches.
Definition
An AI chip, is a type of specialized AI processor which is designed to efficiently perform the artificial intelligence tasks, particularly in the machine learning, natural language processing, generative AI, computer vision, and neural network computations. These chips are capable of conducting parallel processing in complex AI operations including AI training and inference, allowing for faster executions of AI workloads compard to the general-purpose processors.
Key Stakeholders
- Government and financial institutions and investment communities
- Analysts and strategic business planners
- Semiconductor product designers and fabricators
- Application providers
- Al solution providers
- Al platform providers
- Business providers
- Professional service/solution providers
- Research organizations
- Technology standard organizations, forums, alliances, and associations
- Technology investors
Report Objectives
- To define, describe, and forecast the Al chip market based on offerings, function, technology, and end-user
- To forecast the size of the market segments for four major regions-North America, Europe, Asia Pacific, and the Rest of the World (ROW)
- To forecast the size and market segments of the Al chip market by volume based on offerings
- To provide detailed information regarding drivers, restraints, opportunities, and challenges influencing the growth of the market
- To provide an ecosystem analysis, case study analysis, patent analysis, technology analysis, pricing analysis, Porter's five forces analysis, investment and funding scenario, and regulations pertaining to the market
- To provide a detailed overview of the value chain analysis of the AI chip ecosystem
- To strategically analyze micro markets with regard to individual growth trends, prospects, and contributions to the total market
- To analyze opportunities for stakeholders by identifying high-growth segments of the market
- To strategically profile the key players, comprehensively analyze their market positions in terms of ranking and core competencies, and provide a competitive landscape of the market
- To analyze strategic approaches such as product launches, acquisitions, agreements, and partnerships in the AI chip market
Available Customizations
With the given market data, MarketsandMarkets offers customizations according to the company’s specific needs. The following customization options are available for the report:
Company Information
- Detailed analysis and profiling of additional market players (up to 7)
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Growth opportunities and latent adjacency in AI Chip Market