For most glaciated areas, detailed mountain glacier evolution since the last interglacial is largely unknown. Due to limitations of traditional numerical modelling, previous studies have typically operated at a coarse spatial resolution, limited study area size, or focused on major climate events. Here we address these limitations by applying the Instructed Glacier Model (IGM), a deep-learning ice-flow emulator enabling efficient GPU-accelerated transient simulations. We model mountain glacier evolution since 130 ka and up to the present-day at 500 m resolution across eight broad mountain ranges in North America, South America, Eurasia, and Africa. Paleoclimate variables are approximated and regionalised using a combination of global and regional climate proxy datasets. We perform 617 parameter-calibration experiments varying paleoclimate and ice-dynamic parameters, with an average runtime of 21 hours per experiment. Model performance is assessed using combined areal and volumetric validation at two known glacial states; the last glacial maximum and the present-day. We also introduce a reproducible probabilistic model-evaluation framework combining confusion matrix validation score and simulation rank to identify sets of acceptable model parameters rather than a single best-fit solution. Our results show that IGM can model realistic ice-flow patterns, glacier geometries, and transient evolution across full glacial-interglacial cycles, demonstrating that machine-learning models of ice dynamics generalise to new domains and conditions, although performance can decline at coarser spatial resolutions. Together, these results demonstrate the feasibility of global-scale, high-resolution, transient glacier modelling over orbital timescales using a deep learning instructed model, while providing a 100-year interval dataset including glacier extent, ice thickness, and ice flow patterns for the last glacial cycle.