Artificial Intelligence (AI) Chipsets Market Size, Share, Growth Analysis, By Chipset Type(CPU, GPU, FPGA, ASIC), By Workload Domain(Training, Inference), By Computing Technology(Cloud AI Computing, Edge AI Computing), By Industry Vertical(Healthcare, Manufacturing, Automotive, Retail & E-Commerce), By Region - Industry Forecast 2024-2031

Artificial Intelligence (AI) Chipset Market Drivers 

Growing Demand for Autonomous Vehicles is Expanding the Market:

• The continuously rising demand for self driving vehicles is driving the expansion of the market. These chipsets are important for autonomous vehicles and offers crucial processing power, machine learning capacity, and real time decision making which helps them in certainly a secure and organized self driving experience. For instance, Tesla in August 2023, activated an immense supercomputing array which comprises of around 10,000 Nvidia H100 AI processors for speeding up their advancements in fully autonomous driving technology. 

5G Technology Market Stimulates the Growth of AI Chipset Market:

• Increasing 5G implementation is foreseen to fuel the development of AI (artificial intelligence) chipsets going forward. 5G technology meant to provide high-speed, low latency, and better connection for wireless devices and applications. The incorporation of AI into 5G networks would help in providing seamless network management and increasing communication efficiency. For instance, in September 2023 Qualcomm technologies launched application specific semiconductors and 5G chipsets along with the entities expertise in private 5G for fortifying the 5G environment, enhancing AI process capabilities at the network edge, and catalyzing innovation across various industries. Consequently, 5G technology expansion is the major factor for AI chipset market growth. 

Restraints 

Shortage of AI hardware professionals and skilled workforce:

• AI is a complex system; companies need specialized expertise and a skilled staff to build, administer, and employ AI systems. In examples, people working with artificial intelligence might include ML (Machine Learning), cognitive computing, deep learning, machine intelligence, and image recognition technologies. Also, putting AI technology into the existing systems is not an easy way as it requires fully supported in-house research and patent filing with heavy funding. For instance, seemingly minor mistakes can be revealed in the systematic failure or erroneous fix to a problem, which can greatly affect the desired output. 

High cost of AI chipsets 

• The excessive cost of AI chips is a major restraint in the market. These two facets are complex and involve sophisticated manufacturing techniques, making them costly for their production. The steep price of these chipsets is obstructing their adoption across different sectors, particularly in emerging markets, developing economies and among small and medium sized enterprises. Moreover, this expense can pose a daunting barrier for startups and researchers, restricting their capacity to develop and experiment with AI-driven applications.