Core Insights - Huawei is set to release a groundbreaking technology in the AI field on November 21, aimed at improving the efficiency of computing resource utilization [1] - The new technology is expected to increase the utilization rate of GPU and NPU resources from the industry average of 30%-40% to 70%, significantly unlocking the potential of computing hardware [1] - The technology will enable unified resource management and utilization of computing power from Nvidia, Ascend, and other third-party sources through software innovation, enhancing resource support for AI training and inference [1] - Huawei's upcoming technology shares commonalities with the core technology route of Israeli AI startup Run:ai, which was acquired by Nvidia for $700 million at the end of 2024 [1] - Run:ai has focused on GPU scheduling technology since its establishment in 2018, aiming to create a platform that allows AI models to run in parallel, regardless of whether the hardware is on-premises, in the cloud, or at the edge [1][2] - Managing workloads for generative AI, recommendation systems, and search engines requires complex scheduling to optimize system and underlying hardware performance [1] Technology Overview - Run:ai's core product is a software platform built on Kubernetes, designed for scheduling GPU computing resources [2] - The platform optimizes GPU resource utilization through dynamic scheduling, pooling, and sharding techniques, enabling efficient execution of deep learning training and inference tasks in enterprise environments [2]