The company plans to build and pay for dedicated energy infrastructure to reduce the risk of AI data centers straining local grids and slowing expansion.
The multibillion-dollar deal shows how the growing importance of inference is changing the way AI data centers are designed and operated.
A new top-level effort unifies data center and network oversight as the company targets multi-gigawatt AI scale.
Lenovo’s “AI cloud gigafactory” model promises speed and scalability for enterprises, but experts caution that utility, cooling, and fiber bottlenecks could slow adoption.
Announcements included the MI440X GPU for on-prem enterprise deployments and a longer-term roadmap that includes rack-scale AI systems and next-generation accelerators.
The unpatched flaw affects AsyncOS-based Secure Email appliances, with Cisco investigating scope and urging rebuilds in confirmed compromise cases.
By acquiring the developer of Slurm, Nvidia is strengthening its influence over how AI workloads are scheduled across GPUs and data center networks.
The open-source tool tracks power, temperature, airflow and interconnect health across thousands of GPUs, helping operators spot issues early and prevent throttling.
The move introduces fresh uncertainty into an already constrained GPU market, forcing buyers to rethink both their timelines and their broader procurement plans for upcoming data-center upgrades.
Their exits may strain Microsoft’s push to expand AI capacity as energy constraints and infrastructure bottlenecks increasingly dictate the pace of cloud growth.
