TinyML AI Chipset Shipments to Top 4.1 Billion by 2031 as Embedded AI Scales Across Industrial IoT

Global technology intelligence firm ABI Research forecasts that TinyML AI chipset shipments excluding personal and work devices will grow at a 37% CAGR through 2031, surpassing 4.1 billion units, while related revenue will exceed US$7.8 billion. The finding underscores how embedded AI is moving from experimentation to scaled deployment, particularly in industrial IoT and other far-edge environments.

“The AI chipset market is fragmented but maturing at the same time,” said Paul Schell, Senior Analyst at ABI Research. “TinyML is gaining real traction as enterprises push intelligence closer to sensors and endpoints, while cloud and premium device segments continue to absorb the most advanced AI workloads. Vendors that can balance performance, power efficiency, and developer accessibility will be best positioned to win.”

ABI Research’s latest market data also shows that the TinyML segment remains led by MCUs through the decade, while NPUs post the fastest growth at a 90% CAGR. In edge AI, Europe and North America are projected to grow at 17% and 16% CAGRs, respectively, through 2031, while Asia-Pacific will expand at an 18% CAGR and exceed 721 million AI chipset shipments by the end of the decade. In cloud AI, training demand remains strong as cluster sizes increase, while inference grows with rising token generation, multimodal generative output, reasoning models, and agentic AI workloads.

The report highlights that market momentum will not be uniform across device categories. In personal and work devices, medium- to low-priced smartphones face near-term pressure from higher DRAM prices, prompting major manufacturers such as Xiaomi, vivo, and OPPO to reduce 2026 sales forecasts, while premium smartphones remain more resilient. Meanwhile, heterogeneous SoC architectures are gaining share across devices as vendors including Qualcomm, MediaTek, Apple, AMD, and Intel optimize AI workloads across CPUs, GPUs, and NPUs for greater efficiency and broader framework support. ‌

“Over the next several years, competitive advantage in AI semiconductors will come from architectural fit, not just raw compute,” Schell said. “The strongest suppliers will be those that align silicon roadmaps with deployment realities, whether that means ultra-low-power inference at the far edge, premium on-device AI experiences, or scalable cloud platforms for training and orchestration. That shift will create new openings for both established chipset leaders and specialist challengers.” ‌

These findings are from ABI Research’s Artificial Intelligence & Machine Learning Market Data Overview: 2Q 2026 market data report, part of the company’s AI & Machine Learning research service, which includes research, data, and ABI Insights.