谢旭璋表示，为了满足多元数据处理需求，爱诗科技通过阿里云大数据开发治理平台DataWorks将 MySQL数据集成至Hologres，有效解决了MySQL访问性能瓶颈问题。 每经记者｜叶晓丹 每经编辑｜张海妮 5月22日，在"2025阿里云中企出海大会"上，爱诗科技表示，基于阿里云大数据技术和智算算力进行数 据处理和模型训练，公司旗下产品PixVerse正加速全球化布局。 爱诗科技是一家专注于AI（人工智能）视频生成技术研发与应用的公司，目前其旗下AI视频生成平台 PixVerse的全球用户量已超过6000万，近期更新的V4.5版本一度冲上美国iOS应用商店总榜第四。 爱诗科技联合创始人谢旭璋说："AI技术带来了视频创作门槛的降低，全球更多用户都能够参与到这一 浪潮。" 而在走向全球化的过程中，如何把分散在全球各地的数据归集一处，如何处理和分析海量的元数据，成 为爱诗科技的一大挑战。 封面图片来源：每日经济新闻（资料图） 在底层视频模型上，爱诗科技采取Diffusion+Transformer（DiT）架构，在模型训练和推理过程中需要处 理大量视频、文本和元数据，对数据库的实时分析、多模态数据处理和高并发的查 ...

Core Insights - Chinese companies are increasingly recognized for their technological innovation and quality, moving beyond the perception of being merely cost-effective [1] - Alibaba Cloud is enhancing its support for Chinese enterprises going global by investing in a unified global cloud computing network and accelerating AI capabilities [3][4] - The integration of cloud computing and AI is becoming a core competitive advantage for cloud service providers, as highlighted by market research [7] Group 1: Alibaba Cloud's Global Strategy - Alibaba Cloud is building a global cloud computing network to support Chinese enterprises in international markets, with 87 available zones across 29 regions and offering 394 cloud and AI products [4] - The "Global One Network" enables standardized cloud operations, automated services, and resource management, ensuring consistent cloud experiences for businesses worldwide [6] - Alibaba Cloud's infrastructure has supported major events like the Tokyo and Beijing Olympics, demonstrating its capability in handling high concurrency and low latency [6] Group 2: Investment and AI Development - Alibaba Cloud plans to invest over 380 billion RMB in the next three years to upgrade its cloud and AI infrastructure, focusing on AI as a core element [8] - The PAI platform achieves over 90% utilization of AI computing power, covering the entire process from data management to model training [8] - Alibaba Cloud has developed a large open-source model family, with over 200 models available, making it the largest open-source model group globally [8] Group 3: Compliance and Security - Alibaba Cloud has obtained over 150 international compliance certifications, positioning itself as one of the most qualified cloud service providers in Asia [11] - The company has established a multi-layered security defense system for clients like XPeng Motors and Beiqi Foton, ensuring compliance with local regulations [15] - A 24/7 multilingual support network has been created, with multiple service centers and local teams to provide efficient technical support [15] Group 4: Market Presence and Clientele - Alibaba Cloud has served 250,000 Chinese enterprises in their global expansion efforts, covering various industries [16] - Notable clients include NetEase, MiHoYo, and Xiaomi, indicating a strong market presence and trust in Alibaba Cloud's capabilities [16] - The company is positioned to support Chinese enterprises in seizing opportunities in the global market [16]

以下文章来源于傅里叶的猫 ，作者CC 傅里叶的猫 . 芯片EDA大厂资深工程师，曾在中科院造卫星，代码还在天上飞。 半导体高质量发展创新成果征集 文章内容来自国盛证券的研报，里面分析了目前GPU云的行业趋势、各个大厂的竞争格局以及目前的 GPU租赁市场行情。 行业趋势总览 当前AI与云计算产业的协同发展已形成紧密的飞轮效应，其核心逻辑在于技术迭代、应用扩展和算力 需求三者的正反馈循环。AI大模型能力的快速提升（如Qwen3、Llama4的多模态升级与逻辑推理优化） 正推动AI从辅助工具向核心生产力渗透，这一过程高度依赖云服务商在算力、存储和运维等底层能力 的持续升级。 以阿里云为例，其第九代ECS实例算力提升20%而价格下降5%，通过硬件性能优化和规模效应摊薄成 本，为企业降低AI开发门槛，进而刺激更多应用场景的落地，例如谷歌Gemini 2.5 Pro在复杂推理任务 中超越人类的表现，以及阿里Qwen2.5-Omni以轻量化模型实现手机端全模态交互，均显示出AI应用正 向企业级和消费级市场双向渗透。 与此同时，模型效率提升（如GPT-4o响应速度优化）虽降低单次推理的算力消耗，但用户规模与调用 频次的指数级 ...

Industry Trends Overview - The synergy between AI and cloud computing has created a tight feedback loop driven by technological iteration, application expansion, and computing power demand [3] - The rapid enhancement of AI large model capabilities is pushing AI from being an auxiliary tool to a core productivity driver, heavily relying on cloud service providers for continuous upgrades in computing power, storage, and operations [3] - For instance, Alibaba Cloud's ninth-generation ECS instance has seen a 20% increase in computing power while prices have decreased by 5%, lowering the AI development threshold for enterprises [3] Cloud Service Providers' Technological Upgrades and Competitive Landscape - Cloud service providers are engaged in intense competition centered around AI computing power demands, with leading firms building competitive advantages through differentiated technological paths [5] - Alibaba Cloud focuses on end-to-end optimization, achieving a 20% improvement in AI preprocessing efficiency and a 92% reduction in response time for its PAI platform [5][6] - Huawei Cloud emphasizes architectural innovation, with its CloudMatrix 384 super node achieving three times the GPU density of traditional servers, addressing enterprise needs for customized AI solutions [6] AI Model Progress and Multimodal Breakthroughs - The current phase of AI model iteration is driven by "multimodal + deep thinking," with significant breakthroughs transitioning from laboratories to commercial applications [7] - Upcoming releases like Qwen3 and Llama4 are expected to enhance logical reasoning and voice interaction capabilities, while Alibaba's Qwen2.5-Omni demonstrates end-to-end processing across four modalities [7][8] - The competition among AI models is intensifying, with Google’s Gemini 2.5 Pro showcasing its potential in complex reasoning tasks, while GPT-4o aims to improve image generation precision for enterprise needs [7] Computing Power Demand Surge and Price Transmission in the Industry Chain - The explosive growth of AI technology is leading to a significant surge in computing power demand, creating a structural shortage on the supply side [9] - For example, the price of H100 calls has jumped 22% within two weeks, reflecting the scarcity of computing resources [11] - In North America, IDC rents have increased by over 60% due to high demand and limited supply, while in China, the upgrade of AI-specific data centers has raised unit cabinet costs [15][16] Rise of Computing Power Leasing Models - The emergence of computing power leasing models is becoming a new variable to balance supply and demand contradictions, with companies like CoreWeave reducing marginal costs [17] - However, the sustainability of this business model depends on the downstream application side's ability to pay, as some startups face losses due to high inference costs [17] - Overall, the price transmission in the computing power industry chain is shifting from short-term spikes to long-term structural inflation, reinforcing the barriers for leading firms while posing risks for smaller players [17]