Storage Vendors Orbit the Nvidia Sun at GTC

Why It Matters

By aligning storage stacks with Nvidia’s GPU and DPU roadmap, vendors dramatically cut AI latency and cost, accelerating enterprise adoption of large‑model workloads.

Key Takeaways

• Hitachi iQ now supports Blackwell and RTX PRO GPUs.
• IBM cut query time 83% using Nvidia cuDF integration.
• Nutanix Agentic AI adds GPU‑optimized hypervisor and Kubernetes tools.
• Seagate hybrid KV‑Cache merges SSD speed with HDD capacity.
• Nvidia DPUs enable automated tiering across all vendor solutions.

Pulse Analysis

Nvidia’s GPU and DPU strategy has become a de‑facto standard for AI‑first data centers, and GTC 2026 highlighted how storage vendors are embedding that standard into their product roadmaps. Hitachi Vantara’s iQ platform now ships with Blackwell and RTX PRO GPUs, offering pre‑validated stacks that combine compute, networking and storage under a single management layer. The integration of the Model Context Protocol and Hammerspace further simplifies data governance for generative AI, positioning Hitachi as a turnkey AI infrastructure provider.

IBM’s collaboration with Nvidia showcases the performance upside of GPU‑accelerated analytics, slashing a Nestlé order‑to‑cash query from fifteen minutes to three. By allocating 10 petabytes of IBM Storage Scale for GPU‑native workloads and pairing Blackwell Ultra GPUs with Red Hat AI Factory, IBM is building an end‑to‑end AI stack that addresses both regulated and high‑throughput use cases. Nutanix’s Agentic AI extends this vision, embedding Nvidia AI Enterprise into its hypervisor, Kubernetes platform and unified storage, thereby delivering a cloud‑operating model that lets enterprises spin up LLM‑driven services with minimal friction.

Seagate’s hybrid KV‑Cache demo underscores a pragmatic approach to cost‑effective AI storage. By marrying a flash tier of NVMe SSDs with a massive HDD tier and orchestrating data placement through BlueField‑4 DPUs, Seagate achieves near‑NVMe latency while leveraging the low cost‑per‑gigabyte of hard drives. This tiered architecture could become a template for enterprises seeking to balance inference performance with storage economics as model sizes and context windows grow. Together, these vendor moves illustrate a broader market trend: AI‑ready storage is no longer an add‑on but a core component of modern data center strategy, and organizations that adopt these integrated solutions will gain a competitive edge in speed, scalability and total cost of ownership.