AI Chip Market to Hit $400B by 2030

AI Chip Market Growth

The increasing deployment of AI data centers and AI commercialization, coupled with the rising performance demands from large AI models, are projected to drive the AI chip market to over $400 billion within five years. According to a recent IDTechEx report, the AI chip market is expected to reach $453 billion by 2030, exhibiting a CAGR of 14 percent between 2025 and 2030.

However, the report cautions that the underlying technology must advance to stay competitive, meeting demands for more efficient computation, lower costs, higher performance, massively scalable systems, faster inference, and domain-specific computation. Frontier AI continues to attract global investment as governments and hyperscalers compete in areas like drug discovery and autonomous infrastructure. Graphics processing units (GPUs) and other AI chips have been vital in enhancing the performance of top AI systems, providing the necessary compute for deep learning in data centers and cloud infrastructure.

Challenges and Alternatives

With the expansion of global data centers and investments reaching hundreds of billions of dollars, concerns regarding the energy efficiency and costs of current hardware are growing. Hyperscaler AI data centers and supercomputers, which can provide on-premise or distributed network performance, represent the largest AI systems.

While high-performance GPUs have been essential for training AI models, they have limitations, including a high total cost of ownership (TCO), vendor lock-in risks, low utilization for AI-specific operations, and being overkill for certain inference workloads. Consequently, hyperscalers are increasingly adopting custom AI ASICs from ASIC designers like Broadcom and Marvell. These custom AI ASICs feature purpose-built cores for AI workloads, offering lower costs per operation, specialization for particular systems, and energy-efficient inference. They also enable hyperscalers and CSPs to achieve full-stack control and differentiation without compromising performance.

Market Innovation

The market is expanding as major vendors and AI chip startups introduce alternative AI chips designed with similar and novel AI chip architectures that provide benefits over existing GPU technologies. These chips aim to be more suitable for AI workloads, reducing costs and improving AI computations. Large chip vendors like Intel, Huawei, and Qualcomm have developed AI accelerators using heterogeneous arrays of compute units (similar to GPUs) that are purpose-built to accelerate AI workloads, balancing performance, power efficiency, and flexibility for specific applications.

AI chip-focused startups are pursuing different strategies, utilizing cutting-edge architectures and fabrication techniques, such as dataflow-controlled processors, wafer-scale packaging, spatial AI accelerators, processing-in-memory (PIM) technologies, and coarse-grained reconfigurable arrays (CGRAs). These diverse technologies in design and manufacturing offer significant potential for future technological innovation across the semiconductor industry supply chain.

Government and Investment Impact

The report concludes that government policy and substantial public and private investment highlight the strong interest in advancing frontier AI, necessitating large volumes of AI chips within AI data centers to meet the growing demand.