This paper explores the integration of Large Language Models (LLMs) with High-Performance Computing (HPC) and the challenges involved. LLMs, such as GPT-4, have shown great potential in natural language processing and code generation. HPC is crucial for solving complex computational problems and is used in various fields like climate modeling and astrophysics. Recent studies have begun to apply LLMs to HPC tasks, such as parallel code generation, indicating a promising synergy between the two fields. However, there are challenges in integrating LLMs with HPC, including domain-specific knowledge, methodology for leveraging HPC tools, data representation and integration, and performance metrics and evaluation. The paper discusses various pathways for applying LLMs to HPC, including code representation, multimodal learning and fusion, parallel code generation, natural language programming, and reducing development time and errors. It also highlights the advantages of integrating LLMs with HPC, such as improving LLM training efficiency, boosting latency and throughput for real-time applications, and enhancing model size and complexity. The paper also addresses ethical considerations and presents case studies of LLMs enhancing HPC performance and HPC enhancing LLM performance. Finally, it outlines future directions and opportunities for exploring novel machine learning problems with LLMs in HPC and industry applications for innovation in HPC with LLMs.This paper explores the integration of Large Language Models (LLMs) with High-Performance Computing (HPC) and the challenges involved. LLMs, such as GPT-4, have shown great potential in natural language processing and code generation. HPC is crucial for solving complex computational problems and is used in various fields like climate modeling and astrophysics. Recent studies have begun to apply LLMs to HPC tasks, such as parallel code generation, indicating a promising synergy between the two fields. However, there are challenges in integrating LLMs with HPC, including domain-specific knowledge, methodology for leveraging HPC tools, data representation and integration, and performance metrics and evaluation. The paper discusses various pathways for applying LLMs to HPC, including code representation, multimodal learning and fusion, parallel code generation, natural language programming, and reducing development time and errors. It also highlights the advantages of integrating LLMs with HPC, such as improving LLM training efficiency, boosting latency and throughput for real-time applications, and enhancing model size and complexity. The paper also addresses ethical considerations and presents case studies of LLMs enhancing HPC performance and HPC enhancing LLM performance. Finally, it outlines future directions and opportunities for exploring novel machine learning problems with LLMs in HPC and industry applications for innovation in HPC with LLMs.