As artificial intelligence increasingly integrates into enterprise workflows and products, the demand for Machine Learning Operations (MLOps) platforms is rising. These platforms assist companies in creating, testing, and deploying machine learning models more easily. However, despite the presence of several competitors in the market, such as startups like InfuseAIComet and major companies like Google Cloud, Azure, and AWS, South Korea's VESSL AI aims to carve out its niche by focusing on optimizing GPU costs.

The Transformer architecture, a star in the field of artificial intelligence, has led a revolution in natural language processing with its self-attention mechanism at its core. However, when handling long contexts, the resource consumption of self-attention calculations becomes a bottleneck. To address this issue, researchers have proposed the Tree Attention method, which decomposes the calculation tasks through tree reduction to improve efficiency. This method not only reduces communication overhead and memory usage but is also 8 times faster than existing methods in a multi-GPU environment. The introduction of Tree Attention not only enhances performance.