Model distillation: Training a smaller, faster model to mimic the behavior of the original model, which requires retraining and thus is not applicable here.

Dynamic range quantization: Reduces the precision of the model's weights from floating point to integer, significantly decreasing latency with minimal accuracy loss, without the need for retraining.

Weight pruning: Eliminates unnecessary weights in the model to reduce size and latency, but may require fine-tuning to maintain accuracy, which involves retraining.

Dimensionality reduction: Reduces the number of features in the input data, which is not applicable as it doesn't directly optimize the model's inference latency without retraining.