Data that keeps up with reasoning
CoreWeave addresses data bottlenecks in large-scale AI training and inference by offering a specialized storage portfolio. The core solution, CoreWeave AI Object Storage, is designed for massive parallel throughput, achieving up to 7 GB/s per GPU. Key differentiators include S3 compatibility, no egress/ingress/request fees, and the patented Local Object Transport Accelerator (LOTA) which provides global, high-speed caching directly to GPU nodes, ensuring maximum GPU utilization and lowering total cost of ownership (TCO).
Key takeaways
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High-Performance, Scalable Object Storage
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CoreWeave AI Object Storage provides massive parallel throughput, scaling with the addition of GPU nodes, and supports up to 7 GB/s per GPU of throughput. It is S3-compatible and designed specifically for AI workloads.
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Global Data Accessibility and Cost Control
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The service has no egress, ingress, or request fees, making it ideal for multicloud environments. Furthermore, it uses automated, usage-based billing (Hot, Warm, Cold tiers) to reward users for inactive data without requiring manual tiering or data movement.
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Local Object Transport Accelerator (LOTA)
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LOTA is a proxy running on GPU nodes that creates a global cache across all nodes' NVMe drives. This accelerates data access by serving reads and writes from the local cache, significantly boosting performance and enabling fast cross-region data access.
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Optimized GPU Utilization
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By ensuring data is fed to the GPUs as quickly as possible, CoreWeave AI Object Storage maximizes GPU utilization, which is critical for lowering the overall total cost of ownership for large compute clusters.
