MinIO AIStor Blog: AI Storage & Infrastructure Insights

MinIO AIStor can run natively on NVIDIA BlueField-4 Vera DPUs. This architecture allows object data to move at wire speed by placing the storage layer directly inside the AI factory infrastructure, optimizing inference context memory.

MinIO provides a three-layer GPU storage stack consisting of AIStor, MemKV, and a secure data fabric. This architecture is designed to reduce inference latency. MemKV specifically serves as a purpose-built context store for scale-out inference, eliminating the 'recompute tax' in GPU clusters via shared petabyte-scale context memory.

AIStor Memory provides an enterprise memory foundation for agentic AI, enabling agents to maintain a bounded relationship with learned history, active workspaces, and specific credential scopes. It allows for state persistence and handoffs between agents; for example, if one worker is deleted mid-task, a second worker can verify the last accepted boundary in a shared AIStor Memory Workspace and resume work without restarting the entire process.

MinIO MemKV is a purpose-built context store designed for GPU inference clusters at scale. It eliminates the 'recompute tax' by providing a shared, petabyte-scale NVMe-backed memory tier for KV (Key-Value) state. This allows prompt caching and prefix reuse to persist across GPU replicas, HBM (High Bandwidth Memory) evictions, worker restarts, and concurrency, rather than being limited to a single process's local memory.

MinIO AIStor provides the object data stores required for the NVIDIA STX reference architecture, supporting the GPU storage stack for AI factories.

MinIO AIStor allows Databricks to query on-premises data directly without the need for replication pipelines or data copying. This is achieved through the open Delta Sharing protocol, which is embedded natively in MinIO AIStor.

You can migrate an entire Iceberg catalog to MinIO AIStor without moving the underlying Parquet files. The migration process reads metadata from the existing catalog (supporting Nessie, Polaris, Hive Metastore, AWS Glue, or SQL-backed catalogs) and registers the tables directly into the built-in Iceberg REST catalog in AIStor.

mc table migrate

Using AIStor Table Sharing and Delta Sharing, organizations can provide Databricks with secure, zero-copy access to on-premises data. This eliminates the need for data duplication when accessing on-prem datasets from the Databricks platform.

MinIO AIStor can be used in conjunction with Milvus to build a vector database lab for Retrieval-Augmented Generation (RAG) applications.

By combining MinIO AIStor with NVIDIA cuVS and GPUDirect RDMA, developers can significantly accelerate vector index creation. Benchmarks show 106 million vectors indexed up to 12x faster than standard configurations.

MinLZ enables fast, selective searches of compressed data without requiring full decompression. This reduces I/O overhead and accelerates queries on object storage.