LSTMs and GRUs are both popular choices for overcoming the limitations of traditional recurrent neural networks. In terms of differences, GRU units have two gates (reset and update) as opposed to LSTM units having three (input, forget, and output). This compression of gating components allows GRUs to require fewer parameters and thus require less training data. On the other hand, LSTM units are known for their ability to retain long-term memory, making them suitable for tasks involving long-term dependencies. It's important to experiment and compare performance when choosing between LSTM and GRU units to find the best fit for your specific problem.

The main difference between GRU and LSTM units lies in their internal gating mechanisms. While both are designed to overcome the vanishing gradient problem, GRU units are generally considered to be more computationally efficient as they have fewer gating components. GRU units also combine the forget and input gates into a single update gate, simplifying the architecture. However, LSTM units tend to perform better on tasks that require modeling long-term dependencies due to their explicit memory cell and separate forget and input gates. The choice between GRU and LSTM ultimately depends on the specific requirements of your dataset and task at hand.