华为发布AI推理新技术 中国银联大模型效率提高125倍

Core Viewpoint - Huawei has launched the Unified Cache Manager (UCM), an AI inference memory data management technology aimed at optimizing inference speed, efficiency, and cost in large model inference processes [1][3]. Group 1: UCM Technology Overview - UCM is a KV Cache-centered inference acceleration suite that integrates various caching acceleration algorithms to manage KV Cache memory data generated during inference, thereby expanding the context window for inference [1][3]. - The technology aims to enhance the AI inference experience, improve cost-effectiveness, and accelerate the commercial cycle of AI applications [1][4]. - UCM features a hierarchical adaptive global prefix caching technology that can reduce the latency of the first token by up to 90% [3][6]. Group 2: Industry Application and Impact - In a pilot application with China UnionPay, UCM technology improved large model inference speed by 125 times, allowing for precise identification of customer queries in just 10 seconds [4]. - The financial sector is the first to adopt this technology due to its digital nature and high demands for speed, efficiency, and reliability, making it an ideal testing ground for new AI technologies [4][6]. Group 3: Differentiation and Competitive Advantage - UCM's differentiation lies in its integration of professional storage capabilities, offering a comprehensive lifecycle management mechanism for KV Cache, including preheating, tiering, and elimination [6][7]. - Unlike existing solutions that primarily focus on prefix caching, UCM incorporates a broader range of algorithms, including sparse full-process algorithms and suffix retrieval algorithms, enhancing its reliability and effectiveness [6][7]. - UCM is designed to adapt to various inference scenarios, allowing for smooth optimization across different input and output conditions [6][7]. Group 4: Open Source Initiative and Industry Collaboration - Huawei plans to open source UCM in September, providing a unified interface that can adapt to various inference engines, computing power, and storage systems, promoting collaboration across the industry [7]. - The company aims to address efficiency and cost issues in the AI industry by fostering a collaborative ecosystem among framework vendors, storage providers, and computing power suppliers [7].