Core Insights - The rapid development of the AI industry is creating a massive demand for computing power, but the low utilization rate of global computing resources is becoming a significant bottleneck for industry growth [1] - Huawei's new AI container technology, Flex:ai, aims to address the issue of computing resource waste by allowing a single GPU/NPU card to be divided into multiple virtual computing units, improving resource utilization by 30% [1][2] - Flex:ai is positioned to compete with Nvidia's Run:ai, focusing on software innovation to unify management and scheduling of various computing resources without hardware limitations [2] Group 1 - Flex:ai technology can split a single GPU/NPU card into virtual computing units with a precision of 10%, enabling multiple AI workloads to run simultaneously [1] - The technology has been validated in real-world applications, such as the RuiPath model developed in collaboration with Ruijin Hospital, which improved resource utilization from 40% to 70% [3] - Gartner predicts that by 2027, over 75% of AI workloads will be deployed and run using container technology, indicating a shift towards more efficient resource management [3] Group 2 - Flex:ai will be open-sourced in the Magic Engine community, contributing to Huawei's comprehensive ModelEngine open-source ecosystem for AI training and deployment [3] - Unlike Run:ai, which primarily serves the Nvidia GPU ecosystem, Flex:ai supports a broader range of computing resources, including both Nvidia GPUs and Huawei's Ascend NPUs [2]