A company, restricted by information security policies, plans to deploy large language models on-premise to support its technical requirements. The needs span C/C++ code assistance for embedded development, chip-level driver and protocol stack development, log fault diagnosis, code assistance in Android middleware development, system performance optimization, compatibility testing analysis, technical documentation generation, and internal knowledge Q&A. The company seeks to understand which open-source large models are better suited for these coding and debugging needs to enhance R&D efficiency and solve technical challenges. This discussion provides practical insights into AI applications in the chip and embedded systems domains.
