Ionospheric models are required in satellite-based positioning to compensate for the fluctuating ionospheric delay er-rors in the receiver's pseudoranges, mainly when single-frequency receivers are used. The emerging Precise Point positioning (PPP) single-frequency receivers offer low-complexity mass-market so-lutions for an extended range of devices. Ionospheric models have been widely studied for Global Positioning System (GPS), Galileo, and Beidou systems. Most of the existing ionospheric models, such as Klobuchar's model, rely on correction parameters broadcast by the satellites. Alternative models that do not rely on real-time parameters have also been proposed in recent literature, and the Interpolated and Averaged Memory Model (IAMM), previously proposed by the authors, is one of these models. While the accuracy of an ionospheric model is of utmost importance, less attention has been paid to the complexity of the models and their ability to be adapted, in a generic way, to multiple satellite constellations (e.g., GNSS, LEO, etc.). IAMM is one such generic model that does not require parameters to be broadcast by satellites and relies on pre-processed data that can be stored in the receiver's memory. This paper focuses on solutions for reducing the complexity of IAMM by decreasing the size of the data stored at the receiver. These solutions are tested with measurements from dynamic Android smartphones and fixed GNSS stations. The resulting positioning errors from each scenario indicated that reducing the complexity of IAMM while maintaining positioning accuracy is possible, where it was able to provide promising results with as little as 185–278 kB memory requirements at the receiver, which is a decrease of 8–12 times compared to the original IAMM with averaging and 243–365 times lower than the original IAMM without averaging.