PyTorch Tutorial: FlagOS Stack for Portable Large Models on Heterogeneous Accelerators
A tutorial by M. Lyu, H. Zhang & Y. Lyu introduces the FlagOS open-source stack to simplify porting large models across diverse AI accelerators, covering Triton kernels, FlagGems operator coverage, and unified plugins for vLLM and Megatron.
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This resource is a tutorial presented by M. Lyu, H. Zhang & Y. Lyu on the PyTorch official channel, published on September 24, 2026. It focuses on the challenge of running large language models efficiently across fragmented hardware ecosystems.
The session demonstrates how the FlagOS open-source stack bridges the gap between high-level frameworks like PyTorch, vLLM, and SGLang and underlying vendor-specific libraries. Key components discussed include Triton for portable kernel programming, FlagGems for PyTorch operator coverage, and KernelGen for auto-coding on different accelerators.
Developers seeking to reduce porting effort when deploying open models such as Qwen, MiniMax, or DeepSeek on non-NVIDIA hardware may find this overview relevant. The content covers training and inference support via unified plugins for vLLM, Megatron, and VeRL.