flagos-ai/FlagScale

What it solves

FlagScale addresses the high cost and complexity of migrating AI models across different hardware chips. It eliminates the need to rewrite software stacks for every new chip vendor by providing a unified interface for the entire lifecycle of large-scale models.

How it works

It acts as the central toolkit for the FlagOS ecosystem, integrating multiple open-source backend engines (such as Megatron-LM, veRL, and vLLM) under a single configuration and CLI interface. It uses a plugin-based architecture where hardware-specific adaptations are handled by separate plugins, allowing users to "develop once, migrate across various chips."

Who it’s for

It is designed for AI researchers and engineers who need to train, perform reinforcement learning on, or serve large language models (LLMs), multimodal models, and embodied AI models across diverse hardware environments.

Highlights

• Unified Interface: Single CLI and configuration for training, RL, and inference.
• Cross-Chip Compatibility: Reduces migration costs between different AI chip vendors.
• Broad Model Support: Compatible with major models including DeepSeek-V3/R1, Qwen series, LLaMA 2/3, and Mixtral.
• Ecosystem Integration: Works with FlagCX for cross-chip communication and FlagOS-Robo for embodied AI.