A new industry research report on the artificial intelligence chip market finds that the global market for AI‑focused microprocessors — specialized chips designed to accelerate machine learning, deep learning, and other AI workloads — continues to grow strongly, driven by widespread adoption of AI across industries. According to the forecast analyzed in the report, the global AI chip market was valued at approximately USD 21.3 billion in 2024 and is expected to expand at a compound annual growth rate (CAGR) of around 33 percent through 2030, potentially reaching over USD 118 billion in annual value by the end of the decade. This reflects accelerating demand for dedicated AI compute in devices, data centers, and real‑world applications requiring real‑time processing.
The report highlights several key drivers of market growth. AI integration into sectors like automotive, healthcare, consumer electronics, and telecommunications is increasing the need for chips optimized for tasks that general‑purpose processors struggle with. Demand for edge computing — where processing happens close to where data is generated — also contributes, as specialized AI chips enable low‑latency inference and decision‑making outside centralized data centers. These trends broaden the market’s reach beyond purely cloud‑based computing.
At the same time, challenges remain for the AI chip industry. High research and development costs and complex manufacturing processes create significant barriers to entry, meaning that only a relatively small number of major players can sustain the investment required to innovate cutting‑edge processor designs. This dynamic contributes to market concentration and can slow the pace of diversification among chip makers.
Emerging market trends include advanced packaging techniques and the integration of modular “chiplets” — smaller functional units combined into a larger system — which help elevate performance and efficiency. These approaches allow designers to mix and match optimized components, improving power efficiency and throughput for complex AI workloads. The report also segments the market across chip types (such as GPUs, ASICs, FPGAs, and others), processing types (edge vs. cloud), technologies, and end users, underscoring the wide array of applications that AI chips support.
