Achieve AI Infrastructure Goodput of up to 96% with 3 Key Strategies

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This post details CoreWeave's strategies for achieving up to 96% AI infrastructure goodput. It emphasizes the importance of GPU reliability and the challenges in training large models due to scaling laws and accelerated model launch times. The post outlines three key strategies: 1. Utilizing high-performance hardware optimized for AI workloads, including NVIDIA GB200 NVL72 systems, NVIDIA Quantum-2 InfiniBand, NVIDIA BlueField-3 DPUs, CoreWeave Kubernetes Service (CKS), and Slurm on Kubernetes (SUNK) for topology-aware scheduling. 2. Proactively identifying and remediating interruptions through CoreWeave Mission Control, which provides advanced cluster validation, health monitoring, proactive node replacement, and deep observability. This includes the Fleet Lifecycle Controller for rigorous AI infrastructure validation and the Node Lifecycle Controller for continuous monitoring and proactive health checks to detect and replace unhealthy nodes. 3. Providing customers with full transparency into cluster health and performance, enabling measurement of detailed hardware metrics (GPU health, fan speed, temperature) and job-level metrics for faster diagnosis and resolution of interruption issues. The post also highlights the 24/7 monitoring by FleetOps and CloudOps teams and close collaboration with customers.