text: The paper provides methods for training deep networks using half-precision floating point numbers without losing model accuracy or changing the model hyper-parameters.  The main ideas are to use a master copy of weights when updating the weights, scaling the loss before back-prop and using full precision variables to store products.  Experiments are performed on a large number of state-of-art deep networks, tasks and datasets  which show that the proposed mixed precision training does provide the same accuracy at half the memory. Positives - The experimental evaluation is fairly exhaustive on a large number of deep networks, tasks and datasets  and the proposed training preserves the accuracy of all the tested networks at half the memory cost. Negatives - The overall technical contribution is fairly small and are ideas that are regularly implemented when optimizing systems. - The overall advantage is only a 2x reduction in memory which can be gained by using smaller batches at the cost of extra compute.