OmniScale: Scaling Any Modality Model Training with Model-Centric Distributed Recipe Zoo - Ma, Qianli; Zheng, Yaowei; Shi, Zhelun; Zhao, Zhongkai; Jia, Bin; Huang, Ziyue; Lin, Zhiqi; Li, Youjie; Yang, Jiacheng; Peng, Yanghua; et al. (Proceedings of the AAAI Conference on Artificial Intelligence, 2026)
Recent advances in large language models (LLMs) have driven impressive progress in omni-modal understanding and generation. However, training omni-modal LLMs remains a significant challenge due to the heterogeneous model architectures required to process diverse modalities, necessitating sophisticated system design for efficient large-scale training. Existing frameworks typically entangle model definition with parallel logic, incurring limited scalability and substantial engineering overhead for end-to-end omni-modal training. We present OmniScale, a modular and efficient training framework to accelerate the development of omni-modal LLMs. OmniScale introduces model-centric distributed recipes that decouples communication from computation, enabling efficient 3D parallelism on omni-modal LLMs. OmniScale also features a flexible configuration interface supporting seamless integration of new modalities with minimal code change. Using OmniScale, a omni-modal mixture-of-experts (MoE) model with 30B parameters can be trained with over 2,800 tokens/sec/GPU throughput and scale to 160K context lengths via 3D parallelism on 128 GPUs, showcasing its superior efficiency and scalability for training large omni-modal LLMs.