Core Viewpoint - Huawei has launched the AI container technology Flex:ai to address the issue of computing resource waste in the AI industry, which is exacerbated by the rapid growth in AI workloads and low utilization rates of global computing resources [1][3][20]. Group 1: Flex:ai Technology Overview - Flex:ai integrates GPU and NPU resources into a unified system, allowing for dynamic allocation and scheduling of computing resources [1][3]. - The technology is built on the Kubernetes platform and aims to enhance the precision of AI workload matching with computing resources, significantly improving utilization rates [3][19]. Group 2: Key Technological Innovations - The XPU pooling framework developed in collaboration with Shanghai Jiao Tong University allows a single GPU or NPU to be divided into multiple virtual computing units, improving average utilization by 30% while keeping virtualization performance loss below 5% [9]. - The cross-node virtualization technology, developed with Xiamen University, aggregates idle computing resources from various nodes into a shared pool, enabling general servers to offload AI workloads to remote GPU/NPU resources [12]. - Context separation technology designed by Xiamen University reduces external fragmentation by 74% and increases high-priority job throughput by 67% [13]. Group 3: Intelligent Scheduling and Resource Management - The Hi Scheduler, developed with Xi'an Jiaotong University, optimally schedules heterogeneous computing resources across the cluster, ensuring efficient resource utilization even under fluctuating loads [17]. - The increasing demand for AI computing resources highlights the need for improved resource management efficiency, with Flex:ai positioned as a competitive solution against existing technologies like Run:ai [19]. Group 4: Open Source Initiative - Flex:ai will be fully open-sourced to the "Magic Engine Community," contributing to the ModelEngine open-source ecosystem alongside other tools [5]. - The open architecture of Flex:ai is expected to promote the standardization of domestic computing ecosystems and enhance collaboration among global innovators [19][20].