As models become larger, ML accelerators are a scarce resource whose performance must be continually optimized to improve efficiency. Existing performance analysis tools are coarse grained, fail to capture model performance at the machine-code level and often do not provide specific recommendations for optimizations. In addition, existing methodologies are hard to apply in Google's production environment, as they require hardware changes or recompilation. We present SnR, a fine-grained methodology for analyzing ML models at the machine-code level that provides actionable optimization suggestions. It requires no hardware changes and no recompilation.