Sparse Neural Network Modeling Infrastructure

A computer system is optimized for implementing a neural network nodal graph that has dense inputs and sparse inputs. The computer system has a local machine that receives user inputs and is optimized for computing power, and has a remote machine that stores embedding matrices and parameters, and is optimized for memory capacity. In accordance with a cost function applied to each node, the neural network nodal graph is divided into graph segments based on its types of inputs and needed computing resources for execution. In accordance with the cost functions, the graph segments are divided between the remote and local machines for execution, and the results of all the graph segments are combined in the local machine.