Accelerating hyperparameter optimization with training performance estimation. Because model hyperparameters, including learning rates and batch sizes, are essential for achieving optimal model performance and are non-transferable between different domains and model/dataset sizes , we need efficient strategies for scalable HPO in deep chemical models. We adapt Training Speed Estimation (TSE) , a simple technique for ranking computer vision architectures during neural architecture searches, to accelerate HPO and model selection for ChemGPT and GNNs. We call this method "Training Performance Estimation" (TPE), as it uses training speed to more generally enable performance estimation across a wide range of applications. TPE generalizes TSE to HPO for new deep learning domains (Large Language Models [LLMs], GNNs) and can be used to directly predict converged loss, in addition to rank ordering different architectures. HPO typically involves training tens or hundreds of networks and using random search and/or Bayesian optimization to identify optimal hyperparameters. For optimal performance, the process must be repeated when considering new datasets or distribution shift.