Anyscale and NVIDIA cuDF have integrated GPU-native operators into Ray Data, enabling up to 3x better total cost of ownership (TCO) on select data curation workloads compared to CPU-based solutions. T…
Anyscale's Ray team reports that after a round of Ray Core improvements, batch inference on 500 nodes is 23% faster end to end, Ray Data shuffle is 24% faster, and placement groups are 62× faster to r…
Ray Data 2.56 introduces memory-aware execution and improved prefetching to reduce out-of-memory failures and unnecessary object spilling in AI data pipelines. The update includes automatic batch size…
Ray Data LLM, a library for large-scale batch inference, achieves 2x throughput over vLLM's synchronous LLM engine in production-scale workloads by optimizing hardware utilization and providing fault …
A benchmark by Alluxio and Anyscale shows that using Alluxio as a distributed NVMe cache for Ray Data reduces cross-region training data read times from 4,241 seconds to 208 seconds, a 20x speedup, by…