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Large language models and multimodal foundational models find widespread application across various domains, including natural language processing, computer vision, and code generation. Nevertheless, the efficiency of their inference processes and the scalability of their deployment have emerged as significant hurdles for their integration into industrial settings. To tackle these challenges, the open-source community and the industry are intensifying their research efforts in optimization technologies tailored for large models. This includes areas such as inference acceleration, memory compression techniques, adaptation to heterogeneous hardware, and distributed deployment strategies. Simultaneously, they are offering reusable and scalable open-source implementations to facilitate broader adoption.
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