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Meta accelerates AI infrastructure, targeting a 1GW 'Prometheus' supercomputer with 1.3M NVIDIA H100 GPUs (2 exaflops) by 2026, plus 5GW 'Hyperion' cluster. Plans $60-65B investment by 2025 for AI/data centers, competing with OpenAI/xAI. Commits to open-source and privacy despite environmental concerns.....

AWS upgraded SageMaker with model observability and local IDE integration. HyperPod now monitors training performance and connects local dev environments to cloud. GPU cluster management was optimized for flexible resource allocation.....

AI infrastructure company CoreWeave plans to acquire data center operator Core Scientific for $9 billion in stock, with the transaction expected to be completed by Q4 2025. The acquisition will help CoreWeave reduce operational costs, decrease external dependencies, and enhance its AI computing capabilities. Core Scientific currently has 1.3 gigawatts of power capacity and 1 gigawatt of expansion potential, which will support CoreWeave's growing AI computing demand. Previously, OpenAI had invested in Core

Recently, Andreas Schierenbeck, CEO of Hitachi Energy, the world's largest transformer manufacturer, stated in an interview with the Financial Times that as large technology companies see a surge in power demand when training artificial intelligence models, governments need to take measures to limit these fluctuations to ensure the stability of the power supply. Image source note: The image is generated by AI, and the image licensing service provider is Midjourney. Schierenbeck said that the power demand fluctuations in AI data centers are extremely severe,

Recently, NVIDIA and CoreWeave announced that NVIDIA's latest artificial intelligence graphics processor, Blackwell Ultra, has been commercially deployed on CoreWeave. This news undoubtedly injects new vitality into AI cloud computing services. Dell also stated that CoreWeave has received customized devices based on the NVIDIA GB300NVL72AI system, marking CoreWeave as the first to install a Blackwe

Google's latest sustainability report reveals a startling fact: within just four years, the company's data center power consumption more than doubled, rising from 14.4 million megawatt-hours in 2020 to 30.8 million megawatt-hours in 2024. If the timeline is extended to ten years, compared to an estimated 4 million megawatt-hours in 2014, Google's data center power consumption has increased sevenfold. Growing electricity demand: data centers are major energy consumers, efficiency improvements face bottlenecks. Data shows that Google's power issues are almost entirely concentrated in data centers.

On June 30th, the world's first trillion-parameter large model for the power generation industry, Qinyuan, developed independently by the State Energy Group, was officially released. This innovative large model, with its unique full-stack independent and controllable characteristics, marks an important step forward for the power generation industry into a new era of intelligent decision-making. The Qinyuan large model aims to integrate the diverse scenarios, high complexity, and strong professionalism of the power generation industry, fully utilizing the State Energy Group's globally largest installed capacity and massive data resources. The launch of this model not only creates a new engine for multi-energy coordination and dynamic optimization, but also establishes an electricity

Recently, AI security company XBOW announced that its self-developed AI tool XBOW has outperformed others on the globally renowned vulnerability testing platform HackerOne, ranking first in the United States. This is the first time an AI tool has surpassed human security researchers to top the HackerOne vulnerability disclosure ranking, marking a milestone breakthrough for AI in the field of vulnerability detection. XBOW AI: Pioneer of Fully Automated Penetration Testing XBOW's AI tool is a completely autonomous penetration testing (pentest) system that requires no human intervention.

French AI startup Mistral AI announced on Wednesday its full entry into the AI infrastructure sector, positioning itself as a strong response to American cloud giants from Europe. At the same time, the company also launched a new inference model that can rival the most advanced systems of OpenAI. This Paris-based company released Mistral Compute, an integrated AI infrastructure platform developed in collaboration with NVIDIA (Nvidia), designed to provide European enterprises and governments with alternative solutions to摆脱对

At the recent VivaTech科技Summit held in Paris, NVIDIA CEO Jensen Huang stated that European artificial intelligence (AI) computing power is expected to increase tenfold within the next two years. This prediction has drawn significant attention from the industry, especially given the increasing worldwide focus on AI technology. Huang pointed out that Europe is actively building more than 20 "AI superfactories", which will provide strong infrastructure support for AI development. He emphasized that countries in Europe have already realized

Gongbu Group recently announced a comprehensive and in-depth full-stack AI cooperation with Alibaba Cloud. The two parties will jointly create products based on Qwen large model and AI computing power and other advanced technologies to explore innovative end-side intelligent solutions. This move not only marks another significant breakthrough for Gongbu in the smart home field, but also indicates that its future will accelerate its overseas strategy by relying on Alibaba Cloud's global infrastructure. Gongbu and Alibaba Cloud have a long history of deep cooperation, establishing a close partnership since 2017. Currently, Gongbu's IoT system has been fully running on the Alibaba Cloud platform.

Tomorrow, the 2025FORCE Original Power Conference will be held in grand style. During the conference, the capability upgrade of the BeanPod Large Model family will be revealed. At the same time, the highly anticipated new BeanPod Video Generation Model will also be officially released. It is reported that this new model has multiple excellent features. In terms of multi-camera narration, through efficient model structure design, as well as multimodal positional encoding and multitask unified modeling technology, the model can seamlessly support unique and stable multi-camera expression, making video narration more smooth and natural.