Small AI Models Gain Traction Around the World
IEEE Spectrum AI David Berreby
Small AI models that run on low-power devices without internet connections are gaining adoption in developing countries for applications like counterfeit drug detection, crop disease identification, and malaria mosquito detection, with advocates arguing they are more sustainable and accessible than large language models. By the end of 2026, more than half of all smartphones worldwide will be capable of running small AI models, driven by improvements in hardware and the release of open-weight models like Google DeepMind's Gemma 4 and Alibaba's Qwen 3.5. The shift toward small AI deployment could reach millions of people without access to data centers or reliable electricity, though long-term success depends on countries investing in supporting infrastructure like power grids and digital supply chains.
Why it matters
One morning in 2019, Adebayo Alonge was in a Cape Town hotel room, preparing to demonstrate his startup’s AI answer to a serious problem in African health care: counterfeit medication, which kills thousands of people across the continent every year.The RxScanner is a handheld spectrometer that scans a pill with infrared light, then sends the item’s molecular profile to an AI model equipped with a pharmaceutical database. In seconds, the AI identifies the medication from its molecular profile—or reports that it’s phony.Pharmacies were using the system in more than a dozen countries, including Ghana, Kenya, Myanmar, and Alonge’s native Nigeria. But that morning in South Africa, it didn’t work. “I was shocked,” Alonge says.The spectrometer connected to the AI model—but the data center was 14,000 kilometers away and bandwidth was limited. “Our server was in the United States, and just to get the result of a single scan was taking me over 5 minutes.”So Alonge immediately asked his engineers
