Pune, May 14, 2025 (GLOBE NEWSWIRE) -- Generative AI Chipset Market Size Analysis:
“The Generative AI Chipset Market size was USD 37.29 billion in 2023 and is expected to reach USD 454.50 billion by 2032 and grow at a CAGR of 32.3% over the forecast period of 2024–2032.”
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Major Players Analysis Listed in this Report are:
- Advanced Micro Devices, Inc. (AMD Instinct MI300, Ryzen AI)
- Apple Inc. (Apple M3 Chip, Apple Neural Engine)
- Arm Holdings plc (Arm Cortex-A78AE, Arm Neoverse V2)
- Broadcom Inc. (Broadcom AI Accelerator, Tomahawk 5 Chipset)
- Cerebras Systems (Cerebras CS-2, Wafer-Scale Engine 2)
- Google Inc. (Tensor Processing Unit (TPU) v5, Edge TPU)
- Graphcore (Graphcore IPU-M2000, Bow IPU)
- Intel Corporation (Intel Gaudi 2, Intel Xeon CPU Max Series)
- Micron Technology, Inc. (Micron HBM3 Memory, Micron LPDDR5X)
- Mythic AI (Mythic M1076 AMP, Mythic Analog Matrix Processor)
- NVIDIA Corporation (NVIDIA H100 Tensor Core GPU, NVIDIA Grace Hopper Superchip)
- Qualcomm Technologies, Inc. (Qualcomm AI Engine, Snapdragon X Elite)
- Xilinx Inc. (Xilinx Versal AI Core, Xilinx Alveo U280)
Generative AI Chipset Market Report Scope:
| Report Attributes | Details |
| Market Size in 2023 | US$ 37.29 Billion |
| Market Size by 2032 | US$ 454.50 Billion |
| CAGR | CAGR of 32.3 % From 2024 to 2032 |
| Base Year | 2023 |
| Forecast Period | 2024-2032 |
| Historical Data | 2020-2022 |
| Key Regional Coverage | North America (US, Canada, Mexico), Europe (Eastern Europe [Poland, Romania, Hungary, Turkey, Rest of Eastern Europe] Western Europe [Germany, France, UK, Italy, Spain, Netherlands, Switzerland, Austria, Rest of Western Europe]). Asia Pacific (China, India, Japan, South Korea, Vietnam, Singapore, Australia, Rest of Asia Pacific), Middle East & Africa (Middle East [UAE, Egypt, Saudi Arabia, Qatar, Rest of Middle East], Africa [Nigeria, South Africa, Rest of Africa], Latin America (Brazil, Argentina, Colombia Rest of Latin America) |
| Key Growth Drivers | Rising Adoption of Generative AI Across Industries is Driving Demand for High-Performance AI Chipsets |
Generative AI Chipset Market Accelerates Amid Soaring AI Integration
The world is seeing rapid developments in the Generative AI Chipset Market as AI becomes more pervasive across various industry sectors, including cloud computing, healthcare, automotive, as well as finance. But the growing use of AI models in these areas creates the need for special hardware to process large amounts of data quickly and in real time. This means there is an increased need for powerful chipsets, such as GPUs, TPUs, and customized AI accelerators.
The U.S. Generative AI Chipset Market was valued at USD 10.70 billion in 2023 and is projected to reach USD 120.11 billion by 2032, expanding at a CAGR of 30.96% during the forecast period from 2024 to 2032. This exponential expansion is driven by the increasing implementation of AI-based applications in major industries, including cloud computing, healthcare, and autonomous systems. Concurrent with increased processing power and energy efficiency, advances in chipset design, specifically GPUs, TPUs, and AI-dedicated accelerators, are shaping current-gen chips. Together with strong investment in AI infrastructure and fast development of data centers, the U.S. market is expected to grow considerably in the future.
By Chipset Type, GPUs Dominate Generative AI Chipset Market in 2023 with 43% Share, While ASICs are set to Lead Growth with the Highest CAGR Through 2032
GPUs dominated the market in 2023 with a revenue share of 43%. They reign because they can efficiently deal with an immense quantity in parallel, which is critical to training and running generative AI models. With the help of leading companies, as well as NVIDIA, AMD, and Intel, the GPU has become a staple in AI, especially in the cloud and when it comes to autonomous machines.
The ASICs (Application-Specific Integrated Circuits) are anticipated to exhibit the fastest CAGR of 33.4% during the forecast period till 2032. These are not monolithic processor chips, but finely-tuned custom chips for particular kinds of AI workloads that are orders of magnitude faster and more power efficient. ASIC use is scaling, specifically in edge AI and real-time inference applications, driven by their performance advantage over general-purpose processors.
By Application, Resource Management Leads in 2023, Traffic Prediction Set for Fastest Growth
Resource Management and Network Adoption Lead 2023 with 36% market share. This market space is capitalizing on the growing demand for AI-centric networks, cloud infrastructure, and data center efficiency. Niche trends such as NVIDIA’s BlueField-3 DPU and Intel’s IPUs, are also critical developments in this area as they provide enhanced telemetry, workload optimization.
The traffic prediction and classification segment is estimated to witness the fastest growth, 33.9% CAGR, propelled by the increase in smart cities, autonomous driving, and AI-based network security. Using tools like Arm’s Neoverse V2, real-time traffic analytics are now possible, changing both urban infrastructure and telecom networks.
By End Use, Cloud Providers Dominate AI Chipset Market with 32% Share in 2023
In 2023, Cloud Service Providers will hold a 32% share of the market, as large AI model training and AI-as-a-Service gained traction. Generative AI infrastructure is a significant investment for these providers, and companies such as Google, Microsoft, and Amazon are currently in a race to roll out the next-generation AI chipsets in their data centers. This market segment is predicted to continue dominating as the utilization of enterprise cloud grows.
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Generative AI Chipset Market Segmentation:
By Chipset Type
- CPU
- GPU
- FPGA
- ASIC
- Others
By Application
- Information Cognition
- Traffic Prediction and Classification
- Resource Management and Network Adoption
- Performance Prediction and Configuration Extrapolation
By End Use
- Telecom Service Provider
- Cloud Service Provider
- Managed Network Service Provider
- Other Enterprises
North America Leads AI Chipset Market in 2023, Asia Pacific Poised for Fastest Growth
North America is the dominating region in the Generative AI Chipset Market in 2023, with a significant market share in North America around 42% of the world. This leadership is driven by the strength of the local AI ecosystem, the of the art semicon industry, and the region's key players such as NVIDIA, Intel, AMD, and Google. It's hard to believe that these tech heavyweights are also leading on AI chip innovation with the introductions of beefier processors designed around generative AI setups.
Asia Pacific is anticipated to witness the fastest CAGR of 33.3% during the forecast period. China, Japan, South Korea, and India are expanding the integration of the AI industry-wide through active public-private collaboration and compelling AI strategies. Semiconductor giants, such as TSMC, Samsung, and Huawei, all have aggressive plans for investing in AI chipset development, especially in edge computing and autonomous devices.
Recent Developments
- October 2024: AMD revealed the mass production of its upcoming next-generation AI chip, MI325X, for release sometime in Q4. With better external memory and a major new architecture, the chip is expected to make the speedier MI200X smash the MI300X and the MI250X, which in turn made a big dent in NVIDIA’s revenue stream.
- March 2025: Apple made one of the biggest strategic shifts and ventured into the AI data center market after it invested around USD 1 billion in NVIDIA’s GB300 NVL72 systems. It will be working with Dell Technologies and Super Micro Computer to build the server cluster to support large language models.
Table of Contents – Major Key Points
1. Introduction
2. Executive Summary
3. Research Methodology
4. Market Dynamics Impact Analysis
5. Statistical Insights and Trends Reporting
6. Competitive Landscape
7. Generative AI Chipset Market Segmentation By Chipset Type
8. Generative AI Chipset Market Segmentation By Application
9. Generative AI Chipset Market Segmentation By End-Use
10. Regional Analysis
11. Company Profiles
12. Use Cases and Best Practices
13. Conclusion
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