训推大模型，为何应该先彩排？

Core Viewpoint - The article discusses the challenges and strategies in achieving breakthroughs in artificial intelligence (AI) and the importance of system engineering over single-point technology advancements [1][3]. Group 1: Challenges - The article identifies three main challenges in AI model training and inference systems: dynamic load demands leading to hardware-software conflicts, the utilization black hole of large-scale training clusters, and the need for stable operation of complex clusters [4][5][6]. - Over 60% of computing power is wasted due to hardware resource mismatches and system coupling, highlighting the limitations of traditional optimization methods in addressing the "triangle contradiction" of computing power, bandwidth, and capacity [3][4]. Group 2: Solutions - The concept of a "digital wind tunnel" is introduced, allowing for pre-simulation of complex AI models in a virtual environment to identify bottlenecks and optimize resource allocation before real-world implementation [7][8]. - The "Sim2Train" framework is designed to optimize the architecture of training clusters, achieving a 41% efficiency improvement through automated optimization of resource allocation and communication strategies [8][10]. - The "Sim2Infer" framework enhances inference performance by over 30% through dynamic optimization and load balancing, ensuring high throughput and low latency for various tasks [12][13]. Group 3: Reliability and Availability - The "Sim2Availability" framework focuses on ensuring high availability of computing systems, achieving a 98% uptime through rapid recovery and fault management strategies [15][16]. - The article emphasizes the importance of continuous innovation in system architecture to support the evolving demands of AI applications and the need for advanced modeling and simulation techniques to enhance the reliability of computing infrastructure [18][20].