
PCE measures how much provisioned power is available for compute rather than support systems, helping operators free up capacity for AI demand without new grid power.
Power compute effectiveness (PCE) is a proposed metric that measures how much of a data center's total provisioned power is actually available for computing equipment versus support systems like cooling and electrical losses. Unlike the industry standard metric PUE, which measures operational efficiency after a facility is running, PCE looks at power allocation before servers are turned on. The metric matters because data centers face pressure to support growing AI demand within fixed power budgets, and PCE can help operators identify whether infrastructure upgrades like cooling retrofits could free up additional power for compute without requiring new grid capacity. PCE appears most valuable for retrofit projects and capital planning, where it can reveal whether investment in infrastructure changes could reallocate existing power from support systems to IT equipment.
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General availability of the instances is coming next month, with planned expansion of availability to Europe — bringing an integrated platform for training, inference and large-scale reinforcement learning to cloud customers. A5X instances bring together NVIDIA Vera Rubin NVL72, NVIDIA ConnectX-9 SuperNICs, Google Cloud Virgo Network and Google Cloud’s fourth-generation advanced liquid-cooling technology for demanding agentic inference and reinforcement learning workloads at scale. Google Cloud

Nebius and Nscale are among global technology partners looking to NVIDIA Vera CPUs to build the next wave of infrastructure. Their momentum reflects rapid advancements in agentic computing. Agents do more than generate answers: they retrieve information, run software tools, execute code and evaluate results. Each step depends on fast CPU processing. Vera accelerates that work, helping agents respond faster and enabling data centers to make better use of computing capacity. Vera is designed to
Tighter integration between the popular open-source platform and Google's specialized processors could lower barriers for researchers training large models.
Also reported by MarkTechPost and SiliconANGLE

Google team have released llm-d 1.0, the first stable version of its distributed inference stack. This open-source, Kubernetes-native layer turns vLLM or SGLang model servers into one coordinated pool. They released it at the PyTorch Conference North America. It targets self-hosted agent and RL workloads, where round-robin load balancing breaks down. TL;DR A serving stack: Google reports a published benchmark on a 17,000-node multi-region GKE deployment. Runs on: Kubernetes with NVID

Google has joined Amazon in releasing a developer tool that makes it easy for PyTorch users to move their apps off Nvidia GPUs.

CoreWeave had a bad week last week. Its bonds slipped into distressed territory by a common definition - when investors demand 1,000 basis points more in yield than they'd get from government bonds. Last Tuesady Chamath "SPAC King" Palihapitiya noticed the distressed debt, and also asked whether the AI buildout was a private credit bubble. Its shares are down more than 20 percent since Tuesday's close, which is kind of a lot! There even seemed to be some collateral damage in th