Enterprise AI data volumes are scaling from petabytes to multi-exabytes, driven by continuous model training, real-time inference, and fine-tuned feedback loops from deployed AI agents and autonomous systems. MinIO ExaPOD is the reference architecture purpose-built for this era. It extends MinIO's production-hardened DataPOD design — originally optimized for 100 PB-scale deployments — into a horizontally scalable 1 EiB architecture powered by MinIO AIStor. ExaPOD combines high-density NVMe flash with 400GbE ethernet fabrics to deliver consistent low-latency throughput across the full AI/ML pipeline: GPU preprocessing, checkpointing, fine-tuning, and inference workloads. This reference architecture document provides the hardware specifications, network topology, deployment guidance, and performance characteristics for organizations building exabyte-scale AI infrastructure.
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Enterprise AI data is scaling from petabytes to multi-exabytes — driven by continuous model training, inference, and AI agent feedback loops — requiring infrastructure designed for exabyte scale from the ground up.
MinIO ExaPOD combines high-density NVMe flash with 400GbE ethernet fabric to deliver consistent low-latency throughput for the full AI/ML pipeline, from GPU preprocessing to fine-tuning and inference.
ExaPOD extends MinIO's proven DataPOD design into a horizontally scalable 1 EiB architecture — providing a production-ready reference blueprint for organizations building next-generation AI infrastructure.
AI infrastructure architects, data center engineers, and enterprise IT leaders responsible for designing and deploying exabyte-scale storage systems for large-scale AI training and inference workloads.
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