Core Viewpoint - The consensus in the AI large model era is that no single large model can dominate the market, leading to challenges for developers in navigating various mainstream models and AI technologies [1][2]. Group 1: MindSpore Overview - Huawei's open-source MindSpore offers a solution for developers to experience mainstream large models within a unified framework [3]. - MindSpore enables "Day0 migration" of large models with minimal code changes while maintaining accuracy and performance [4]. Group 2: Migration and Deployment Features - The inference process is automated for one-click deployment, allowing over 20 mainstream large models to be used out of the box, with loading times for models with billions of parameters under 30 seconds [5][23]. - MindSpore's "translation tool" MSAdapter allows for seamless migration of code from other frameworks, achieving nearly zero loss in performance [8][10]. Group 3: Technical Enhancements - MindSpore employs several unique techniques to accelerate training and debugging, including multi-stage processing, JIT compilation, and automatic strategy optimization, resulting in performance improvements of up to 9.5% [11][13][16]. - The code modification required for distributed task initiation is minimal, with Python script changes being less than 1% [14]. Group 4: Inference Deployment - The vLLM-MindSpore plugin facilitates the deployment of HuggingFace models within half an hour, with significant reductions in loading times and latency [18][23]. - For large models like Pangu Pro MoE 72B, the deployment can achieve a throughput of 1020 tokens per second with a latency of under 100ms [19].