Asia Pacific Artificial Intelligence (AI) Chip Market Size, Development, Key Opportunity, Application & Forecast to 2027

The increase in data generation due to the increasing proliferation of digital services is boosting the growth of the AI chip market. The significant investments in AI chip start-ups are also anticipated to boost the market growth. Moreover, the adoption of cloud-based AI solutions is anticipated to propel growth in the forecast period. Presently, the major applications of AI chip are in the data center/cloud computing segment, and this trend is anticipated to continue over the forecast period. Also, rising adoption of AI in developing regions, evolving architectures of AI chips, and increasing applications across various industry verticals are driving the AI chip market. Owing to this growing trend, companies are anticipated to produce high-quality service by adopting cloud-based artificial intelligence services. The artificial intelligence technology is complex and expensive to build in-house by any enterprise. Not every mid-size or to some extent even the large enterprise finds it feasible to invest in in-house artificial intelligence system. Hence, most enterprises use data centers for their artificial intelligence applications. Each enterprise requires a specific artificial solution, and using cloud service providers for the same are considered as the best business model for artificial intelligence technology providers. Hence, in the coming years, the adoption of cloud-based artificial intelligence solutions is expected to grow at an impressive rate over the forecast period.

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The GPU segment of the AI Chip market led the North America market with maximum share and is expected to continue its dominance over the forecast period. The AI Chip market by type is segmented into CPU, GPU, ASIC, FPGA, and others. The other segment includes SoC and other custom chips used for AI applications. GPUs are good at fetching large amounts of memory, and they offer energy efficient means of managing the complex array of calculations involved in training a neural network. It means that GPUs can train more neural networks with less hardware. However, in artificial intelligence applications, inference applications are as important as the training ones, so companies require chips which can quickly execute neural networks through inference. For this, many big tech companies are building their specific inference solutions such as Google’s TPU. For inference applications, Microsoft uses FPGAs and Baidu uses GPUs; they are more relevant for training but can work for inference also when combined with suitable software.

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AI CHIP MARKET SEGMENTATION

By Segment

Data Center
Edge

By Type

CPU
GPU
ASIC
FPGA
Others

By Industry Vertical

BFSI
Retail
IT & Telecom
Automotive & Transportation
Healthcare
Media & Entertainment
Others

The List of Companies

Advanced Micro Devices, Inc.
Alphabet Inc. (Google)
Huawei Technologies Co., Ltd.
IBM Corporation
Intel Corporation
Micron Technology, Inc.
NVIDIA Corporation
Qualcomm Incorporated
Samsung Electronics Co., Ltd.
Xilinx, Inc.

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