Enterprises’ biggest AI infrastructure decision next year won’t be GPUs—it will be upgrading the database. Oracle is steering its 19c base toward the long-term support release 26ai, which adds vector search to the same engine that runs transactions. When vector indexes live in the database, the underlying block array and SAN fabric become AI infrastructure, carrying retrieval traffic alongside production workloads.

The Database Changed Underneath AI

Oracle built 26ai around convergence: native VECTOR type, SQL vector search, ONNX in-database execution, and SELECT AI, all in an LTS release with more than 300 new features. DBAs can generate embeddings, store them beside source rows, and run similarity searches in the same transaction scope as order lookups—no separate vector store, sync pipeline, or second regulated copy.

IBM cites IDC: integrated vector databases are becoming the preferred platform for agentic AI because organizations want unified, governed environments. Oracle, MySQL, SQL Server, and PostgreSQL all now support vectors natively or through extensions; IBM chose Oracle for validation due to market leadership, but the storage argument applies broadly.

On-premises block storage remains essential because operational data—patient records, claims, orders, trades—cannot leave secured environments. Copying it to a separate AI platform doubles cost, creates drift, and weakens governance. Keeping retrieval in the database avoids all three.

The Data Already Lives on Block Storage, and Now the AI Comes to It

This does not put all AI on block storage. File and object storage still belong to documents, images, video, training sets, and model checkpoints. In IBM’s healthcare demo, raw X-rays stayed on object storage while only embeddings and metadata went into Oracle. The real change is that arrays now run mixed I/O profiles simultaneously.

OLTP is characterized by small random reads/writes, low tail latency, and redo/undo/checkpoint traffic; RMAN is sequential backup. Vector search on Oracle 26ai behaves more like OLTP than a pure scan—each index hop is roughly a 4KB random read, making it IOPS-bound and latency-sensitive. Bandwidth matters mainly during index builds or re-embedding bursts.

The Validated Architecture

IBM and Broadcom validated the mixed-workload case in Colorado using Oracle AI Database 26ai on IBM FlashSystem 9000 with Brocade Gen 8 switches and Emulex SecureHBAs. VectorDBBench and application tests ran against the same storage backplane. The clinical app tier used a separate GPU server with Llama 3 and NVIDIA cuVS. FlashSystem family arrays share the same Storage Virtualize code, so validated features transfer across models, though measured results are specific to the test system.

I/O QoS: Protecting OLTP from Vector Workload Noise

IBM demonstrated volume-level I/O throttling as the key control. With OLTP alone, volumes held about 20,500 ops/s. When an unconstrained vector generator started, OLTP dropped 37%. A 45,000 IOPS cap per vector volume restored OLTP to around 17,000 ops/s; removing the cap reversed it. In production-style testing with both workloads pre-capped, OLTP stayed stable while vector volumes ran at ceiling, showing that throttling can protect transaction latency without static partitioning.

Ransomware Detection on Live Oracle Volumes

IBM targeted the RMAN backup path while leaving database volumes untouched. FlashCore Modules analyze entropy, compressibility, block size, and rewrite patterns inline; the Storage Virtualize inference engine alerted within 60 seconds. Safeguarded Copy then created an immutable snapshot and extended retention of the last known good copy, while suspect snapshots were marked to prevent restore.

AutoZoning: SAN Onboarding Without Manual Zoning

Built on Brocade Target Driven Zoning, FlashSystem AutoZoning discovers HBAs through FDMI, selects target ports, and pushes zones to both fabrics automatically. After the SAN team enables TDZ on facing ports, hosts can be added without switch login. IBM positions it for Oracle RAC node additions and HBA swaps.

PBHA, Grid Migration, Multi-Tenancy, and Placement

Policy-Based HA replicates storage partitions synchronously at metro distance for zero RPO. FlashSystem Grid federates up to 32 arrays, allowing partition moves for hardware refreshes, firmware drains, or vector tier growth. Storage Insights scores placement and recommends migration. Multi-tenancy isolates DB and AI teams through separate storage partitions and role-based access.

32G vs. 64G Fibre Channel Under Vector Search

Using the same array, host, and dataset, 64G delivered about 95% higher search throughput than 32G—85.0 QPS vs. 43.7 QPS—while host reads scaled from 6.3GB/s to 12.5GB/s. Latency diverged more under load: at 80 threads, 32G p99 reached 4.08s vs. 1.14s on 64G. Load and index build times also improved on 64G with no meaningful change in recall or NDCG.

Planning for Vector Capacity Growth

A 1,000-byte text chunk can become a 4,096-byte 1024-dimension FLOAT32 vector before row overhead. On BioASQ 1M, the source text was 0.90GB, while the Oracle vector table plus IVF_FLAT index reached about 16GB—a 18× amplification before TEMP, UNDO, and REDO. Model width is critical: a 4,096-dimension Mistral model can reach ~32×, while smaller models amplify less. IBM recommends measuring 1–5% of the real corpus with production settings before scaling.

End-to-End AI Inferencing: The Healthcare Application

The RAD-AI demo stores clinical metadata and vectors in Oracle while raw images remain on object storage. A CLIP model runs inside Oracle via ONNX, embeddings stay atomic with case rows, and an approximate nearest-neighbor search supplies context to Llama 3 on the GPU tier. Batch embedding of 112,120 chest X-rays took 9.1 hours on database-host CPUs and 3.6 hours with Oracle Private AI Services offload. HNSW index build dropped from 8.47s to 2.26s using NVIDIA cuVS offload.

What Comes Next

Phase 2 will move the LLM KV cache onto FlashSystem over the same Fibre Channel fabric, keeping more working state accessible while Emulex SecureHBAs and Brocade Gen 8 maintain encryption in flight. IBM is also building a content-aware refresh pipeline and extending validation to other multi-model databases with vector search.

Beijing Qianxing Jietong Technology Co., Ltd.
Sandy Yang/Global Strategy Director
WhatsApp / WeChat: +86 13426366826
Email: yangyd@qianxingdata.com
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