In this paper, we developed and applied strategies for scaling large chemical language models and GNN interatomic potentials. To enable the efficient scaling of deep chemical models under computational resource constraints, we introduced Training Performance Estimation (TPE), a generalization of Training Speed Estimation that significantly reduces the computational costs of hyperparameter optimization and model selection for both chemical models trained on large datasets and on GNN interatomic potentials. The use of TPE enabled large-scale experiments, training GPT-style chemical models with over one billion non-embedding parameters on nearly ten million molecules. It also made training tractable for invariant and equivariant GNNs with a wide range of model capacities on up to 100 thousand 3D molecular geometries ( 4.5 million force labels). We discovered empirical power law "neural scaling" behavior that quantifies how converged model loss depends on the scale of model and dataset size over many orders of magnitude. These results enable optimal allocation of computational and data budgets for maximally efficient model performance improvements, and make scalable scientific deep learning more accessible to a broader community of researchers. Finally, we showed that the outputs of large chemical generative models are tunable via prompt engineering and sampling strategies, and that the model embeddings can be used for unsupervised representation learning and similarity search.