徐直军：华为对为人工智能发展提供充裕算力充满信心

Core Insights - The article emphasizes the critical role of computing power in the development of artificial intelligence (AI) and highlights Huawei's advancements in this area through the launch of powerful supernodes and clusters [1][6]. Group 1: Huawei's Supernodes and Clusters - Huawei introduced the Atlas 950 SuperPoD and Atlas 960 SuperPoD, which support 8192 and 15488 Ascend cards respectively, claiming to be the world's strongest supernodes in terms of scale, total computing power, memory capacity, and interconnect bandwidth [1][2]. - The Atlas 950 SuperCluster and Atlas 960 SuperCluster were also launched, with computing power exceeding 500,000 cards and reaching one million cards, respectively, positioning them as the strongest computing clusters globally [1][2]. Group 2: Ascend Chips and AI Strategy - The Ascend chips are foundational to Huawei's AI computing strategy, with a roadmap that includes the Ascend 950 series, Ascend 960, and Ascend 970 series, with the Ascend 950PR chip expected to launch in Q1 2026 [2][6]. - The article notes that the Ascend 910C chip, part of the Atlas 900 supernode, can achieve a maximum computing power of 300 PFLOPS, maintaining its status as the largest supernode globally [2]. Group 3: Innovations in Interconnect Technology - Huawei faced significant challenges in designing the Atlas 950 and Atlas 960 supernodes, particularly in achieving long-distance, high-reliability connections and high bandwidth with low latency [4][5]. - The company implemented innovative solutions to enhance interconnect reliability by introducing high-reliability mechanisms at various protocol layers and achieving a 100-fold increase in optical interconnect reliability [5]. Group 4: Future of AI Infrastructure - The concept of supernodes is redefining AI infrastructure, enabling a new paradigm where multiple machines function as a single entity capable of learning, reasoning, and inference [3]. - Huawei's mixed supernode architecture is positioned as a solution for next-generation generative recommendation systems, supporting high-dimensional user features and low-latency inference [3].