MoE那么大，几段代码就能稳稳推理

Core Viewpoint - The article discusses the launch of Huawei's Omni-Infer project, which aims to enhance the performance and efficiency of large-scale MoE (Mixture of Experts) models on Ascend hardware, providing a comprehensive open-source solution for enterprises and developers [3][9][27]. Group 1: Omni-Infer Project Overview - Omni-Infer is a new open-source project by Huawei that provides a framework and acceleration suite for large-scale MoE model inference, making it easier for enterprises to deploy and maintain these models [3][12]. - The project includes a separation of deployment solutions and system-level optimizations, which are beneficial for enterprise users [4][9]. - The community around Omni-Infer is designed to be open and collaborative, allowing developers to participate actively in its growth and development [27][28]. Group 2: Technical Features - The Pangu Pro MoE model, which is part of the Omni-Infer project, has a total parameter count of 72 billion and an active parameter count of 16 billion, optimized for Ascend hardware [1]. - The inference performance on the Ascend 800I A2 can reach 1148 tokens/s, with speculative acceleration techniques boosting it to 1528 tokens/s, outperforming similar dense models [2]. - Omni-Infer is compatible with mainstream open-source large model inference frameworks like vLLM, allowing for easy integration and reduced maintenance costs [16][18]. Group 3: Community and Ecosystem - The Omni-Infer community emphasizes proactive collaboration with domestic AI open-source projects, aiming for a win-win ecosystem [27]. - The community governance structure includes a Project Management Committee and Special Interest Groups to ensure transparent decision-making [27]. - The project has already engaged with major open-source foundations, indicating a commitment to building a robust open-source ecosystem [28].