MLPerf® Storage v3.0 results: Leading RetinaNet training on Nebius Object Storage

Nebius submitted MLPerf Storage v3.0 Closed division results for its Enhanced Object Storage service, using standard CPU-only virtual machines and the open-source mlpstorage harness to simulate AI-training I/O over the S3 API. The company reported feeding 768 simulated NVIDIA B200 accelerators for RetinaNet, the highest accelerator count in the round, with 88.4% utilization, 102.83 GiB/s read bandwidth, and 341,881 samples per second; it also achieved the highest Unet3D accelerator count among object-storage submissions, sustaining 114.45 GiB/s and 92.5% utilization across 21 simulated accelerators. For Llama 3.1 checkpointing, aggregate write bandwidth rose from 2.81 GiB/s on one node for an 8B model to 19.46 GiB/s across eight nodes for a 70B model, while read bandwidth increased from 7.14 GiB/s to 38.27 GiB/s. MLPerf Storage evaluates whether storage can maintain minimum accelerator utilization under realistic training workloads, with RetinaNet emphasizing small-file latency and Unet3D emphasizing large-file bandwidth, and Nebius presents its results as evidence that a single production object-storage service can support training and checkpointing workloads at scale.

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