This paper details the development of capability boundaries to predict the downstream performance of language models based on pre-training compute budgets. Researchers move beyond standard scaling laws by using high-quantile pinball loss and monotone splines to track the upper envelope of achievable results rather than average trends. This methodology addresses benchmark saturation, where static tests lose their ability to distinguish between top-tier models as capabilities grow. By focusing on a 98th percentile frontier, the framework creates a stable, probabilistic estimate of what competitive training pipelines can reliably achieve. Ultimately, the work offers a principled way for practitioners to manage resource allocation and evaluate model performance relative to an evolving global capability ceiling.