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  4. Mistral Advances AI for Engineering with NVIDIA
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Hardware & Compute

Mistral Advances AI for Engineering with NVIDIA

Mistral is collaborating with NVIDIA to develop a new generation of AI-native engineering workflows powered by NVIDIA accelerated computing.

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Two companies are collaborating to develop AI tools designed to speed up engineering work by combining AI models with physics simulation capabilities and accelerated computing. The partnership aims to create shared public benchmarks and datasets so that engineers and researchers can evaluate how well these AI tools perform on real engineering tasks across industries. This matters because engineering has traditionally been limited by how fast simulations can run, and this collaboration is intended to address that constraint. The companies are building on previous work together and expanding the computational infrastructure needed to support these new engineering-focused AI capabilities.

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Google Cloud and NVIDIA Announce General Availability of NVIDIA Vera Rubin Infrastructure

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

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European Leaders Turn to NVIDIA Vera CPUs to Build Local Infrastructure

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

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Hugging Face ❤️ TorchTPU: TPUs, Natively

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

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Google Cloud Ships llm-d 1.0 With Cache-Aware Routing for TPU and GPU Fleets

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

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Google has joined Amazon in attacking Nvidia’s stronghold with AI app developers

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

Hardware & ComputeOpen story →

CoreWeave’s debt is becoming the whole AI industry’s problem

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