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Blog
In this blog, Greg Lindstrom, Vice President of ML Trading at Blackout Power Trading, an electricity trading firm in North American power markets, shares how they leverage Alluxio to power their offline feature store. This approach delivers multi-join query performance in the double-digit millisecond range, while maintaining the cost and durability benefits of Amazon S3 for persistent storage. As a result, they achieved a 22 to 37x reduction in large-join query latency for training and a 37 to 83x reduction in large-join query latency for inference.
In the latest MLPerf Storage v2.0 benchmarks, Alluxio demonstrated how distributed caching accelerates I/O for AI training and checkpointing workloads, achieving up to 99.57% GPU utilization across multiple workloads that typically suffer from underutilized GPU resources caused by I/O bottlenecks.