By Manuel Nau, Editorial Director at IoT Business News. In 2026, the momentum behind on-device AI—also known as edge inference or tinyML—has moved well beyond experimentation. Driven by new low-power AI accelerators, maturing development toolchains, and the cost of cloud inference, IoT manufacturers are reassessing where intelligence should sit in connected architectures. The question is shifting from “Can we run ...
By Manuel Nau, Editorial Director at IoT Business News. In 2026, the momentum behind on-device AI—also known as edge inference or tinyML—has moved well beyond experimentation. Driven by new low-power AI accelerators, maturing development toolchains, and the cost of cloud inference, IoT manufacturers are reassessing where intelligence should sit in connected architectures. The question is shifting from “Can we run ...
By Manuel Nau, Editorial Director at IoT Business News. In 2026, the momentum behind on-device AI—also known as edge inference or tinyML—has moved well beyond experimentation. Driven by new low-power AI accelerators, maturing development toolchains, and the cost of cloud inference, IoT manufacturers are reassessing where intelligence should sit in connected architectures. The question is shifting from “Can we run ...
By Manuel Nau, Editorial Director at IoT Business News. In 2026, the momentum behind on-device AI—also known as edge inference or tinyML—has moved well beyond experimentation. Driven by new low-power AI accelerators, maturing development toolchains, and the cost of cloud inference, IoT manufacturers are reassessing where intelligence should sit in connected architectures. The question is shifting from “Can we run ...
By Manuel Nau, Editorial Director at IoT Business News. In 2026, the momentum behind on-device AI—also known as edge inference or tinyML—has moved well beyond experimentation. Driven by new low-power AI accelerators, maturing development toolchains, and the cost of cloud inference, IoT manufacturers are reassessing where intelligence should sit in connected architectures. The question is shifting from “Can we run ...
By Manuel Nau, Editorial Director at IoT Business News. In 2026, the momentum behind on-device AI—also known as edge inference or tinyML—has moved well beyond experimentation. Driven by new low-power AI accelerators, maturing development toolchains, and the cost of cloud inference, IoT manufacturers are reassessing where intelligence should sit in connected architectures. The question is shifting from “Can we run ...
By Manuel Nau, Editorial Director at IoT Business News. In 2026, the momentum behind on-device AI—also known as edge inference or tinyML—has moved well beyond experimentation. Driven by new low-power AI accelerators, maturing development toolchains, and the cost of cloud inference, IoT manufacturers are reassessing where intelligence should sit in connected architectures. The question is shifting from “Can we run ...
By Manuel Nau, Editorial Director at IoT Business News. In 2026, the momentum behind on-device AI—also known as edge inference or tinyML—has moved well beyond experimentation. Driven by new low-power AI accelerators, maturing development toolchains, and the cost of cloud inference, IoT manufacturers are reassessing where intelligence should sit in connected architectures. The question is shifting from “Can we run ...
By Manuel Nau, Editorial Director at IoT Business News. In 2026, the momentum behind on-device AI—also known as edge inference or tinyML—has moved well beyond experimentation. Driven by new low-power AI accelerators, maturing development toolchains, and the cost of cloud inference, IoT manufacturers are reassessing where intelligence should sit in connected architectures. The question is shifting from “Can we run ...
By Manuel Nau, Editorial Director at IoT Business News. In 2026, the momentum behind on-device AI—also known as edge inference or tinyML—has moved well beyond experimentation. Driven by new low-power AI accelerators, maturing development toolchains, and the cost of cloud inference, IoT manufacturers are reassessing where intelligence should sit in connected architectures. The question is shifting from “Can we run ...