This article presents Render-of-Thought (RoT), a framework that converts textual reasoning steps into images to clarify the reasoning process of Large Language Models. By using existing Vision Language Models as anchors, RoT achieves significant token compression and faster inference without needing extra pre-training. Experiments show it performs competitively in reasoning tasks.