This paper introduces a novel persona-driven data synthesis methodology that leverages diverse personas to generate synthetic data at scale. The methodology is supported by Persona Hub, a collection of 1 billion diverse personas automatically curated from web data. These personas, representing a significant portion of the world's population, can tap into various perspectives within large language models (LLMs), enabling the creation of diverse synthetic data across multiple scenarios. The paper demonstrates the versatility and scalability of persona-driven data synthesis, showing its potential to revolutionize synthetic data creation and applications. It also highlights the use cases of Persona Hub in generating high-quality mathematical and logical reasoning problems, instructions, knowledge-rich texts, game NPCs, and tools. The methodology is flexible and easy to use, making it applicable to a wide range of data synthesis tasks. The paper also discusses the ethical concerns and potential risks associated with the use of Persona Hub, including the possibility of replicating the knowledge of powerful LLMs. The authors emphasize the importance of responsible use and ethical considerations in the application of this technology. The paper concludes with a discussion on the future directions of research and the potential impact of persona-driven data synthesis on LLM research and development.This paper introduces a novel persona-driven data synthesis methodology that leverages diverse personas to generate synthetic data at scale. The methodology is supported by Persona Hub, a collection of 1 billion diverse personas automatically curated from web data. These personas, representing a significant portion of the world's population, can tap into various perspectives within large language models (LLMs), enabling the creation of diverse synthetic data across multiple scenarios. The paper demonstrates the versatility and scalability of persona-driven data synthesis, showing its potential to revolutionize synthetic data creation and applications. It also highlights the use cases of Persona Hub in generating high-quality mathematical and logical reasoning problems, instructions, knowledge-rich texts, game NPCs, and tools. The methodology is flexible and easy to use, making it applicable to a wide range of data synthesis tasks. The paper also discusses the ethical concerns and potential risks associated with the use of Persona Hub, including the possibility of replicating the knowledge of powerful LLMs. The authors emphasize the importance of responsible use and ethical considerations in the application of this technology. The paper concludes with a discussion on the future directions of research and the potential impact of persona-driven data synthesis on LLM research and development.