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

This post details three strategies CoreWeave employs to achieve high AI infrastructure goodput (up to 96%): 1. Utilizing high-performance hardware optimized for AI workloads, including the latest NVIDIA GPUs (GB200 NVL72), NVIDIA Quantum-2 InfiniBand, and NVIDIA BlueField-3 DPUs, along with a purpose-built software stack like CoreWeave Kubernetes Service (CKS) and Slurm on Kubernetes (SUNK) for topology-aware scheduling. 2. Proactively identifying and remediating interruptions through rigorous health checking and monitoring via CoreWeave Mission Control, Fleet Lifecycle Controller, and Node Lifecycle Controller, which detect subtle issues like silent data corruption and proactively swap unhealthy nodes. 3. Optimizing resource utilization through efficient scheduling and monitoring, providing customers with transparency into hardware metrics and detailed job-level metrics. The post emphasizes the importance of minimizing interruptions and enabling quick recovery for faster time-to-market and cost-effective model training and deployment.