Propose an architecture that leverages both data parallelism and model parallelism using ASGD to scale up the training process. Address potential concerns related to framework compatibility and provide a tradeoff analysis.

Implement a hybrid approach where each machine uses ASGD to compute multiple gradient descent steps in parallel, while also distributing different parts of the model across machines for processing. This can be achieved using frameworks like TensorFlow or PyTorch with some modifications to handle asynchronous computations and data partitioning. However, it’s essential to note that this approach requires careful synchronization mechanisms to ensure correct updates to the shared model parameters.