Large language models (LLMs) require significant computing resources to generate responses, especially during inference, the stage where a trained model is used to answer questions or perform tasks. As these models grow in size and complexity, the memory system becomes a critical factor in how efficiently they operate. Our technical report examines how high-bandwidth memory (HBM) contributes to the performance of LLMs during inference. Specifically, it looks at how HBM helps reduce delays and manage large volumes of data more effectively when models are run on GPUs with HBM memory.

Understanding the role of HBM is important for anyone studying or working with AI systems. It offers insight into how hardware choices like HBM enable the model to respond quickly to user input and also help the system to perform well even as model size increases. This report provides useful insights into how memory architecture affects the speed and accuracy of model responses.