“存力中国行”探讨AI推理挑战，华为开源UCM技术为破局关键

Core Insights - The "Storage Power China Tour" event held in Beijing on November 4 attracted nearly 20 industry representatives, focusing on how advanced storage can reduce costs and improve efficiency for AI inference [1] - Key challenges in AI inference include the upgrade of KVCache storage demands, multi-modal data collaboration, insufficient bandwidth for computing-storage collaboration, load variability, and cost control [1] - Huawei's open-source UCM (Unified Cache Manager) technology is viewed as a critical solution to address these industry pain points, focusing on multi-level caching and inference memory management [1] Industry Developments - UCM technology has recently been open-sourced in the Magic Engine community, featuring four key capabilities: sparse attention, prefix caching, pre-fill offloading, and heterogeneous PD decoupling [2] - The implementation of UCM can reduce the first-round token latency by up to 90%, increase system throughput by a maximum of 22 times, and achieve a tenfold context window expansion, significantly enhancing AI inference performance [2] - The foundational framework and toolchain of UCM are available in the ModelEngine community, allowing developers to access source code and technical documentation to collaboratively improve the technology architecture and industry ecosystem [2] - The open-sourcing of UCM is seen as a move beyond mere technical sharing, enabling developers and enterprises to access leading AI inference acceleration capabilities at lower costs and with greater convenience, promoting the large-scale and inclusive implementation of AI inference technology [2]