Explore a groundbreaking PyTorch native framework for large language model (LLM) training in this 24-minute conference talk by Hongyu Zhu from ByteDance. Learn about VeScale, a novel solution that combines PyTorch nativeness with automatic parallelism to address the challenges of distributed training for giant LLMs. Discover how this framework prioritizes ease of use, allowing developers to write single-device PyTorch code while automatically parallelizing it into nD parallelism. Gain insights into the importance of PyTorch ecosystem dominance and the necessity of complex nD parallelism for training massive models. Understand the limitations of existing industry-level frameworks and how VeScale aims to overcome them by offering a user-friendly approach to scaling LLM training.