Text-attributed graphs (TAGs) require jointly modeling relational structure and node-level text. Existing GNN-LLM approaches perform by incorporating large language models at inference time for processing the text attributes, resulting in costly deployment. More fundamentally, LLM knowledge is typically used in a sample-wise manner, leading to inefficient utilization across graph instances. In this work, we study how interactions with LLM embedding spaces affect graph representations, and show that projecting into the LLM space can learn better GNNs. That is to say, the knowledge encoded in LLM embeddings can be compressed into graph representations. Based on this insight, we propose a framework that internalizes LLM knowledge within graph models and supports inference-efficient TAG learning. Our framework employs a hierarchical Proxy-Purifier module with distribution-level regularization, using LLM embeddings only as training-time guidance. With this module, the model operates TAGs without invoking LLMs, achieving high efficiency as standard GNNs without LLMs. Notably, experiments on five popular TAG tasks further demonstrate that our method can also achieve consistent performance gains, in comparison to existing GNN-LLM approaches.