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# NVIDIA Run:ai

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  1. NVIDIA Puts Its Self-Healing Recovery Engine on Kubernetes: Faulty Nodes Get Isolated While Training Jobs Restart Without Waiting for Root Cause

    NVIDIA Mission Control 2.5 extends an autonomous recovery engine that previously served only Slurm high-performance computing workloads to Kubernetes workloads managed by NVIDIA Run:ai. Such AI factories had relied on manual intervention, static checkpointing and reactive monitoring, which does not scale to dozens of concurrent multi-node training jobs. The release splits recovery into three coordinated layers: workload placement, job recovery that uses failure history and job topology to choose restart, retry or requeue, and hardware recovery that isolates suspect nodes and runs diagnostics, so recovery need not wait for root-cause analysis.

  2. NVIDIA Open-Sources the Full Run:ai Platform: GPU Orchestration Leaves the Subscription, Four Components Stand Alone

    NVIDIA has released the complete Run:ai platform as open source software within its DSX AI Factory platform, giving customers, developers and ecosystem partners access to production-grade AI workload and GPU orchestration. The platform previously shipped as a commercial product that dynamically allocated GPU resources on Kubernetes and managed workloads across the full lifecycle, from data processing and training to inference. The release includes four components that can also be adopted independently: KAI Scheduler, GPU Fractioning Operator, KAI Resource Management and Workload Map, so partners can take the whole platform, build on it, or integrate single pieces.

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