2025年，随着普惠算力需求爆发，九章云极旗下九章智算云(Alaya NeW Cloud)发展迅猛。6月，公司宣 布全面升级Alaya NeW Cloud2.0，并发布全球首个强化学习平台"AgentiCTRL"及智算生态基金。市场数 据显示，九章云极的普惠算力战略成效卓越：截至目前，其智能算力纳管规模已突破万PFLOP，以 13.1%的市场份额稳居华南地区第三方普惠智算云市场首位；算力服务版图同步扩张，覆盖国内多 数"双一流"高校AI研究团队及QS前100高校，并以68%的占比成为中小企业首选；在已部署智算云的百 人规模企业中，其占比与AWS、Lambda、Azure等国际巨头形成有力竞争。凭借"普惠AI"战略，九章云 极已跻身亚太最具潜力创新企业，领跑全球AI基础设施云赛道。 随着模型训练与推理需求持续攀升，智算云正逐步显现为算力供给的终局形态，其在弹性调度、跨区域 部署和算力利用效率上的优势愈发凸显：Forrester数据显示，62%的企业决策者计划2026年布局AI原生 智算云以提升训练灵活性，76%的管理者认定AI训练工作负载云端部署"至关重要"，智算云已成为企业 在AI时代构建核心竞争力的关键基础 ...

Core Viewpoint - The article discusses the paradigm shift in AI from passive language models to autonomous decision-making agents, highlighting the importance of reinforcement learning (RL) as a key technology driving this transition towards general artificial intelligence (AGI) [1][2]. Summary by Sections Reinforcement Learning and Its Challenges - Reinforcement learning is becoming central to achieving a closed-loop system of perception, decision-making, and action in AI [2]. - Current RL methods face challenges such as the need for high-frequency data interaction and large-scale computing resources, which traditional cloud platforms struggle to accommodate [2][8]. AgentiCTRL Platform Launch - In June 2025, the company launched AgentiCTRL, the first industrial-grade RL cloud platform capable of supporting heterogeneous computing resource scheduling at scale [3]. - AgentiCTRL enhances model inference capabilities and improves end-to-end training efficiency by 500%, while reducing overall costs by 60% compared to traditional RL solutions [4][22]. Systematic Reconstruction for RL - The company has restructured the RL training process from the ground up, moving beyond simple GPU scaling to a more complex system design that includes resource scheduling and fault tolerance [9][8]. - AgentiCTRL simplifies the RL training process, allowing users to initiate training with minimal code, significantly improving development efficiency [11][12]. Serverless Architecture and Resource Management - AgentiCTRL integrates a serverless architecture that allows for elastic resource allocation, maximizing resource utilization and reducing training costs [15][16]. - The platform is the first to support "ten-thousand card" level RL training, addressing communication bottlenecks and synchronization challenges in distributed systems [17]. Performance Validation and Cost Efficiency - The platform has demonstrated significant performance improvements, such as a 37% reduction in training time and a 25% increase in GPU utilization, with a 90% decrease in manual intervention [19]. - Overall costs can decrease by up to 60%, making RL more accessible and cost-effective [22][39]. Strategic Vision and Ecosystem Development - The company aims to build a comprehensive native cloud infrastructure for intelligent agents, positioning RL as a core capability rather than a mere cloud service module [27][28]. - The strategic direction includes the establishment of the "AI-STAR Enterprise Ecosystem Alliance" to foster collaboration and investment in RL applications across various industries [33]. Future Implications - The successful implementation of AgentiCTRL signifies a shift in the AI infrastructure landscape, where RL becomes a standard component of AI systems rather than a specialized tool [41]. - The company is poised to lead in the next generation of AI ecosystems by mastering the training-feedback-deployment loop for intelligent agents [33][41].