With the advent of artificial intelligence, graph embedding techniques are more and more used to mine the information from graphs. However, graphs in real world are usually large and distributed graph embedding is needed. There are two main challenges in distributed graph embedding. (1) There exist many graph embedding methods and there is not a general framework for most of the embedding algorithms. (2) Existing distributed implementations of graph embedding suffer from poor scalability and perform bad on large graphs. To tackle the above two challenges, a general framework is firstly presented for distributed graph embedding. In detail, the process of sampling and training is separated in graph embedding such that the framework can describe different graph embedding methods. Second, a parameter server-based model partitioning strategy is proposed—the model is partitioned to both workers and servers and shuffling is used to ensure that there is no model exchange among workers. A prototype system is implemented on parameter server and solid experiments are conducted to show that partitioning-based strategy can get better performance than all baseline systems without loss of accuracy.