Hitachi Vantara and Supermicro team up to enable enterprise AI at scale
Hitachi Vantara and Supermicro are finalising a partnership to deliver an integrated stack for AI infrastructure, mission-critical apps, and data-heavy workloads. The plan: combine Supermicro's GPU and AI compute systems with Hitachi Vantara's Virtual Storage Platform One (VSP One) so enterprises can run advanced models, analytics, and core business operations on a single foundation. Supermicro will offer VSP One to its customers, while Hitachi Vantara will sell Supermicro servers, storage, GPUs, and related hardware-expanding access through both companies' channels.
Why this matters for Operations
Consolidation reduces integration overhead, procurement friction, and operational risk. A unified platform with enterprise support gives teams one stack to plan, deploy, monitor, and scale-across AI training, inferencing, analytics, and transactional systems.
For Ops, this means clearer SLAs, fewer moving parts, and faster time-to-value for AI programs.
Enterprise data pressures you're already feeling
Data growth isn't slowing. The companies cite survey findings showing 85% of organisations already use data lakehouses to develop AI models; 55% say most analytics run on lakehouses today, rising to 67% within three years. If your teams are wrangling fragmented systems, slow data movement, and rising costs, you're not alone.
A converged compute-and-data approach can reduce bottlenecks and help you extract more value from existing AI investments. If you need a primer on what a lakehouse architecture entails, this overview is helpful: What is a data lakehouse?
How the stack fits together
VSP One unifies block, file, object, and software-defined storage in a single environment. Pairing VSP One Block with Supermicro servers brings an all-flash architecture aimed at high throughput, low latency, and high IOPS-suited to mission-critical applications and AI training.
VSP One SDS extends these capabilities into hybrid cloud through software-defined storage. VSP One Object adds data intelligence services, including native support for Amazon S3 Tables, enabling analytics on open-format data without extra data handling. For teams standardising on open table formats, see Apache Iceberg.
"The convergence of Supermicro's leadership in AI compute with the scale and resiliency of Hitachi Vantara's VSP One platform marks an important step in building the foundation that will guide the future of enterprise AI," said Sheila Rohra, Chief Executive Officer, Hitachi Vantara. "As data volumes explode, bringing compute and data closer together will enable scalable workload support without disruption. Coupled with enterprise-class service and support, we are helping organisations gain better control over their data so they can unlock new sources of value and lead in the era of AI-driven business."
VSP One is also the base for Hitachi iQ, the AI and data orchestration portfolio that streamlines workflows for analytics and machine learning. Integrating VSP One with Supermicro's GPU-accelerated hardware through Hitachi iQ is aimed at improving data processing, governance, and protection, while keeping compute aligned with data management.
"Supermicro and Hitachi are collaborating to help enterprises accelerate their adoption and use of AI," said Vik Malyala, President and Managing Director, EMEA and Senior Vice President, Technology and AI, Supermicro. "Our AI-optimised, compute, and storage servers leverage advanced GPUs, CPUs, and NVME's. Combined with Hitachi Vantara's enterprise data management platform and Hitachi iQ portfolio options will support compute-intensive workloads for dynamic vertical applications yielding better performance and efficiency for customers."
Distribution and management
The companies plan global distribution of VSP One Block, VSP One SDS, and VSP One Object. Management is integrated into the VSP 360 unified control plane, giving Ops a single pane to provision, monitor, and govern.
Channel partners gain more options: Supermicro partners get enterprise block storage choices, while Hitachi Vantara partners get timely access to GPU servers inside the Hitachi iQ portfolio.
What Operations leaders should do next
- Map workloads: classify AI training, inferencing, analytics, and transactional systems; define storage tiers and performance baselines (throughput, latency, IOPS).
- Validate data paths: confirm how data lands in object storage, moves to block/file for training, and returns to object for archival/governance.
- Plan hybrid: identify which datasets stay on-prem vs. cloud; align with VSP One SDS policies and your network fabric (RDMA/RoCE considerations).
- Frame SLAs and SLOs: set targets for RPO/RTO, snapshot cadence, and encryption/key management; verify integration with your SIEM and IAM.
- Pilot with a reference stack: run a POC on a representative AI workload; record throughput, GPU utilisation, and job completion times vs. cost.
- Lock in lifecycle ops: firmware cadence, non-disruptive upgrades, observability hooks, capacity forecasting, and spare strategy.
- Align procurement: use the expanded channels to secure lead times for GPUs and flash; model 3-year TCO across compute, storage, power, and support.
- Upskill the team: close gaps in data lakehouse, GPU scheduling, and storage policies. Curated options by job role: AI courses by job.
Bottom line
If you're running AI and analytics alongside core business systems, a single, enterprise-backed stack simplifies planning and daily operations. This partnership gives Ops teams clearer procurement paths, unified management, and a practical way to scale AI without adding complexity.
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