Using LLM for Scientific Computing

Scientific computing increasingly relies on large language models to bridge the gap between domain expertise and implementation. Researchers routinely use LLMs to generate numerical routines, refactor legacy Fortran or C++ into modern Python, extract structured parameters from PDFs, and orchestrate multi-step simulation workflows. Because scientific tasks often involve long-context inputs, full research papers, extensive codebases, and multi-turn agentic loops, the underlying inference platform must handle lengthy prompts efficiently without