Austin, Oct. 28, 2024 (GLOBE NEWSWIRE) -- Market Size & Growth Outlook:
According to the S&S Insider, “The AI Chip Market Size was valued at USD 61.45 Billion in 2023 and is expected to reach USD 621.15 Billion by 2032 and grow at a CAGR of 29.4% over the forecast period 2024-2032.”
Artificial Intelligence (AI) Chip Market Growth Fueled by AI Revolution
The AI chip market is witnessing rapid expansion, largely driven by the growing adoption of artificial intelligence across diverse sectors such as healthcare, automotive, and consumer electronics. Companies are leveraging AI chips to boost efficiency, enabling faster data processing, decision-making, and automation. The rise of AI in cloud computing, edge computing, and the integration of machine learning and deep learning models into business operations is transforming industries globally. Additionally, governments and enterprises are investing heavily in AI research, further propelling market demand. The convergence of AI and the Internet of Things (IoT) is another significant driver, particularly in applications such as autonomous vehicles, smart cities, and robotics.
Surging Demand for AI Chips: A USD 21 Billion Market Revolution Driven by Generative AI and Hyperscalers
The increasing consumption of AI servers by hyperscalers and the rising demand for Generative AI technologies, including GenAI and AIoT, in sectors like BFSI, healthcare, retail & e-commerce, and media & entertainment are key drivers for the AI chip market. By 2024, AI chips in data centers are expected to generate USD 21 billion in revenue, growing at nearly 12% annually to reach USD 33 billion by 2028. According to the fifth annual Global Technology Report, the AI hardware market will continue to expand at a rate of 40-55%, potentially generating revenues between USD 780 billion and USD 990 billion, supported by strong government investments in AI infrastructure, particularly in the Asia-Pacific region.
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Leading Market Players with their Product Listed in this Report are:
- NVIDIA Corporation (NVIDIA A100 Tensor Core GPU, NVIDIA Jetson AGX Xavier)
- Intel Corporation (Intel Xeon Scalable Processors with AI Acceleration, Intel Nervana Neural Network Processor)
- Xilinx Inc. (Versal AI Core, Kria SOMs)
- Samsung Electronics Co., Ltd. (Exynos AI Processor, Samsung AI Accelerator)
- Micron Technology (Micron AI Accelerator, Micron AI Memory)
- Qualcomm Technologies (Snapdragon 8 Series Processors, Qualcomm AI Engine)
- IBM Corporation (IBM Power Systems with AI, IBM TrueNorth)
- Google Inc. (Tensor Processing Unit (TPU), Edge TPU)
- Microsoft Corporation (Azure Machine Learning, Project Brainwave)
- Apple Inc. (Apple Neural Engine, Apple Silicon)
- Amazon Web Services (AWS) (AWS Inferentia, AWS Trainium)
- Advanced Micro Devices, Inc. (AMD Radeon Instinct GPUs, AMD EPYC Processors with AI Acceleration)
- Graphcore (Graphcore IPU, Graphcore Poplar SDK)
- General Vision (GV1, GV2)
- Mellanox Technologies (ConnectX-6, BlueField-2)
- Huawei Technologies Co. Ltd. (Ascend AI Processor, MindSphere)
- Fujitsu (Fujitsu A64FX, Fujitsu PRIMEHPC FX100)
- Wave Computing (Wave Processor, Wave Computing Cloud)
- Mythic Inc. (Mythic MCU, Mythic AI Platform)
- Adapteva (Parallel Processing Unit (PPU), Adapteva Epiphany)
- Koniku (Koniku Labs Neural Processor, Koniku Labs Neuro-AI Platform)
- Tenstorrent (Tenstorrent Processor, Tenstorrent AI Platform)
AI Chip Market Report Scope:
| Report Attributes | Details |
| Market Size in 2023 | USD 61.45 Billion |
| Market Size by 2032 | USD 621.15 Billion |
| CAGR | CAGR of 29.4% From 2024 to 2032 |
| Report Scope & Coverage | Market Size, Segments Analysis, Competitive Landscape, Regional Analysis, DROC & SWOT Analysis, Forecast Outlook |
| Key Segments | •By Technology (Generative AI, Machine Learning, Natural Language Processing, Computer Vision) •By Chip Type (CPU, GPU, ASIC, FPGA, Others) •By Function (Training, Inference) •By End-User (Consumer, Data Center, Government Organizations) |
| Key Drivers | • Increasing adoption of AI servers by hyperscalers |
Surging Demand for Inference and GPU Segments Drives Growth in the AI Chip Market
In 2023, the inference segment led the AI chip market and is projected to grow at a remarkable CAGR of 30.38% during the forecast period. Inference leverages pre-trained AI models to make predictive decisions based on new data, driving the need for enhanced capabilities within data centers as businesses prioritize AI integration to boost production efficiency and customer experience. The growing demand for energy-efficient and high-performing inference chips further supports market growth. For instance, SEMIFIVE recently launched a 14 nm AI Inference SoC Platform in collaboration with Mobilint, featuring a quad-core 64-bit CPU and PCIe Gen4 interfaces, catering to custom AI applications in data centers.
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The GPU segment is projected to capture the largest market share of 34% during the forecast period. GPUs are essential for handling the significant computational demands of training and running deep learning models through complex matrix multiplications, making them crucial in data centers and AI research. Major manufacturers like NVIDIA, Intel, and AMD are continuously developing new GPUs to enhance AI capabilities both in data centers and at the edge. For instance, in November 2023, NVIDIA launched its upgraded HGX H200 platform featuring the H200 Tensor core GPU, which provides 141 GB of HBM3e memory at a speed of 4.8 terabytes per second. Leading cloud service providers, including AWS, Google Cloud, Microsoft Azure, and Oracle Cloud, are committed to integrating H200-based GPUs, further driving market growth through advancements in GPU memory capabilities
Growth Drivers of the AI Chip Market: Insights from North America and Asia-Pacific
The North American AI chip market is anticipated to grow at a robust CAGR of 31.78% during the forecast period, largely due to the dominance of numerous U.S.-based technology giants and emerging AI startups in the region. Key factors propelling this growth include rising demand for AI technologies across various industries such as healthcare, automotive, retail, agriculture, manufacturing, marketing, law, and fintech. Additionally, a substantial increase in consumer purchasing power, ongoing investments in infrastructure, and a focus on developing in-house AI applications will further contribute to the expansion of the artificial intelligence chipset market in North America in the coming years.
In 2023, the Asia-Pacific region led the AI chip market with a share of approximately 33%. The rising demand for artificial intelligence across industries such as healthcare, manufacturing, and automotive in countries like China, Japan, South Korea, and Australia is fueling this growth. India is expected to play a significant role soon, exemplified by Yotta Data Services in Mumbai, which has ordered 16,000 GPUs from Nvidia to meet the growing demand for cloud-based AI applications.
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Recent Development
- June 2024: Advanced Micro Devices, Inc. (AMD) launched the Ryzen AI 300 Series processors, featuring 50 TOPS AI-processing power with 12 high-performance CPU cores based on the new Zen5 architecture, enhancing AI capabilities for gaming and productivity.
- May 2024: Google unveiled its sixth-generation TPU, named Trillium, improving training and serving times for AI workloads with accelerated clock speeds and larger matrix multiply units, aimed at driving the next wave of AI models
- April 2024: Micron Technology, Inc. partnered with Silvaco Group, Inc. to develop the Fab Technology Co-Optimization (FTCO) offering, enabling machine learning simulations using manufacturing data. Micron invested USD 5 million in FTCO development.
Table of Contents - Major Points
1. Introduction
2. Executive Summary
3. Research Methodology
4. Market Dynamics Impact Analysis
5. Statistical Insights and Trends Reporting
5.1 Wafer Production Volumes, by Region (2023)
5.2 Chip Design Trends (Historic and Future)
5.3 Fab Capacity Utilization (2023)
5.4 Supply Chain Metrics
6. Competitive Landscape
7. AI Chip Market Segmentation, by Chip Type
9. AI Chip Market Segmentation, by Technology
10. AI Chip Market Segmentation, by Function
11. AI Chip Market Segmentation, by End-User
12. Regional Analysis
13. Company Profiles
14. Use Cases and Best Practices
15. Conclusion
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