AI Chip Market

[210 Pages Report] The global AI chip market is projected to grow from USD 123.16 billion in 2024 to USD 311.58 billion by 2029, at a CAGR of 20.4%.

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 market growth.

AI Chip Market Forecast to 2029

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Market Dynamics

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 chip 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 Ecosystem

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 chip 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 chip 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 chip 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 chip 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.

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 market growth in Asia Pacific in the next few years.

AI Chip Market by Region

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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 market.

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Scope of the Report

AI Chip Market Highlights

This research report categorizes the AI Chip Market by Offerings, Function, Technology, End User, and Region.

Recent Developments

• 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:

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