DiLLeMa: An extensible and scalable framework for distributed large language models (LLMs) inference on multi-GPU clusters

Key Points

• The framework supports distributed computing for large language models, enhancing inference speed and efficiency.
• Performance tests indicate a notable improvement in processing time for large language models across multiple GPUs.
• Observational analysis focuses on scalability and extensibility features, ensuring usability in various computing environments.
• Highlights the significance of improved inference mechanisms, offering considerable advantages in artificial intelligence applications.