Video Captioning at Scale: 600 TB in 95 Minutes | CoreWeave Blog

This post details the successful deployment and scaling of a video captioning pipeline using Ray Data on CoreWeave's bare-metal Kubernetes infrastructure. It highlights the efficiency gains of using Ray Data over Ray Core for data processing and inference, achieving 3.7x more captions per GPU-hour. The post quantifies the performance of CoreWeave AI Object Storage (CAIOS) and its Local Object Transport Accelerator (LOTA) in handling large datasets (600 TB) with high throughput (40 GB/s writes, 110-120 GB/s reads) and low latency. It also showcases the rapid setup time (under 24 hours) from account signup to a production job running on 1,600 GPUs, emphasizing the synergy between Anyscale's workload orchestration and CoreWeave's managed infrastructure.