Stanford professor Percy Liang opens Marin 535B model training to the public

Stanford professor and Simile AI founder Percy Liang has kicked off training for Marin 535B-A23B and is making the process public in real time. The model is described as having 535 billion total parameters and 23 billion active parameters, trained on 18.75 trillion tokens across 11 GB200 NVL72 systems, or about 792 GB200 GPUs. Liang said the run is expected to last roughly three months and consume about 2.7e24 FLOPs, followed by a post-training phase. Before starting the main run, the team completed a four-stage scaling ladder ranging from 1.6B-A61M with 48 billion tokens to 27.7B-A1.2B with 926 billion tokens. Liang said those smaller runs were used to surface and debug issues early and to forecast the behavior of the larger “hero run.” The project has drawn attention because it exposes details that are usually hidden in frontier model development, including data mix, processing methods, training configuration, code, experiment design, live loss curves, and model-state tracking. The team has also published links to a data overview page, a Weights & Biases dashboard, and a GitHub issue page where the work can be followed in detail.