MinIO Blog

Moving your Iceberg catalog to MinIO AIStor no longer means re-registering tables by hand. The mc table migrate command reads metadata from your existing catalog, whether that's Nessie, Polaris, Hive Metastore, AWS Glue, or a SQL-backed catalog, and registers your tables directly in AIStor's built-in Iceberg REST catalog. Your Parquet files stay where they are.

MinIO joins the Databricks Software-Defined Storage Ecosystem. Live, zero-copy access to on-premises data.

MinIO AIStor joins ClickHouse House Mates as a validated, trusted storage partner for ClickHouse Private deployments. AIStor gives ClickHouse the high-performance, fully S3-compatible object storage layer it needs to scale without limits.

Solidigm and MinIO put high-density QLC NVMe object storage under real load and disproved the long-held assumption that density costs performance. The tested node is the same building block that scales directly into MinIO's ExaPOD reference architecture, so the path from a single pod to exascale runs on identical hardware and software.

Not every semantic search problem needs a vector database. NVIDIA cuVS runs GPU vector search in memory; MinIO AIStor persists every artifact from raw documents to indexes. Full pipeline, working code.

MinIO MemKV eliminates the recompute tax in GPU inference clusters with shared petabyte-scale context memory.

Until recently, data that stayed on-premises was data that Databricks couldn't reach. If your analytics and AI workloads ran in Databricks, and your most valuable data lived on-prem, you had two options: build and maintain a replication pipeline to copy data into the cloud, or accept that certain datasets simply wouldn't participate in your cloud analytics.Both options carry real costs. But a third option now exists: Databricks querying on-premises data directly, with no copies and no pipelines, through the open Delta Sharing protocol embedded natively in MinIO AIStor.

Databricks needs your on-prem data. AIStor Table Sharing delivers it live — no replication, no pipelines.

Modern AI architecture rests on a comfortable assumption: when AI slows down, the fix is more compute or a better model. Bigger GPUs. Denser clusters. New architectures. That assumption is now costing organizations real money.

Storage, not models or compute, is now AI's biggest bottleneck. The fix is an architectural one, not a bigger GPU.

Learn how to build a vector database lab with Milvus and AIStor

106 million vectors indexed 12x faster. AIStor with NVIDIA cuVS and GPUDirect RDMA rewrites the benchmark.

Legacy storage talks to the AI factory. AIStor lives inside it — running natively on NVIDIA BlueField-4 Vera

Delta Sharing + AIStor enables zero-copy access to on-prem data from Databricks without duplication

Table Sharing connects Databricks directly to on-prem AIStor data via Delta Sharing without copying/syncing

AIStor powers autonomous AI at tactical edge—air-gapped, JADC2-aligned for DDIL combat environments

GPUDirect RDMA bypasses CPU for direct GPU-to-storage S3 data paths, boosting AI factory performance

KV cache offload treats GPU memory as tiered hierarchy using object storage for overflow

Technical comparison detailing 13,000+ commits separating AIStor from unmaintained OSS

Federal agencies need unified data governance and infrastructure before AI algorithms

AIStor Tables GA: First built-in Apache Iceberg V3 data store unifying objects & tables for AI/analytics

Engine-agnostic Iceberg views enable virtual tables across Spark, Trino, and Dremio

First object store supporting Iceberg V3 with deletion vectors and native geospatial types

Multi-protocol storage promised everything, delivered compromise—purpose-built object storage beats retrofitted file/block hybrids