From Operator Coverage to Performance: How FlagOS Optimizes LLM Inference

LLM inference is transitioning from single-hardware optimization to multi-chip collaborative adaptation. To support this shift, the system software stack must address key challenges such as rapid model deployment, full operator coverage, and performance portability. FlagOS 2.0 builds a unified, open-source system software stack for various AI chips. Through the FlagGems multi-chip operator library and the FlagTree unified compiler, it connects models, operators, compilers, and hardware backends. Focusing on frontier models such as DeepSeek V4, the project explores Day-0 adaptation on diverse hardware and full Triton operator coverage. We aim to steadily improve key operators and end-to-end inference performance through algorithm-level operator reconstruction, FlagOSTune auto-tuning, and FlagTree deep compiler optimizations. Moving forward, FlagGems and FlagTree will continue to expand coverage across models, operators, and chips, contributing to an open, collaborative ecosystem for multi-chip AI infrastructure software. ● Website: https://flagos.io ● GitHub: https://github.com/flagos-ai ● GitCode: https://gitcode.com/flagos-ai ● SkillHub: https://skillhub.flagos.io