Infrastructure architects, AI platform engineers, and IT leaders evaluating pre-validated hardware and software reference architectures for high-performance on-premises object storage to support enterprise AI training, inference, and data lakehouse workloads.
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The 8-node Supermicro SYS-212H-TN and MinIO AIStor reference architecture sustains 229 GiB/s GET and 118 GiB/s PUT throughput on a 400 GbE fabric, with performance scaling linearly as nodes are added -- confirming that the architecture fully utilizes available bandwidth rather than bottlenecking at storage or metadata tiers.
Enterprise AI storage demands performance across the full object size spectrum. This architecture sustains 68,213 GET obj/s at 4 KiB and 156,000+ STAT operations per second independent of object size, meaning the same foundation that feeds GPU training pipelines also handles the high-IOPS metadata workloads that inference and analytics generate.
Starting at 19.6PB raw capacity across 8 nodes and scaling to exabyte deployments, the pre-validated architecture reduces time to production from months to weeks, giving AI teams a high-performance, predictable TCO foundation without the integration work of assembling components independently.
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