HPE pumps AI cloud lineup with extra Nvidia capabilities

Steven

HPE pumps AI cloud lineup with extra Nvidia capabilities

Summary

HPE is upgrading its Private Cloud AI stack with Nvidia technology and tighter networking and storage integrations to speed enterprise AI rollouts. The company will offer RTX PRO 6000 Blackwell Server Edition GPUs and STIG-hardened Nvidia NIMs across its AI private cloud SKUs, add GPU fractionalisation to boost utilisation, and integrate Juniper routing (MX for edge on-ramps, PTX for datacentre interconnect). HPE also introduced Alletra Storage MP X10000 Data Intelligence Nodes that perform inline data enrichment, metadata tagging and vector generation to streamline data preparation for GPUs. An AI Factory Lab in Grenoble (due Q2 2026) will let customers test and refine workloads. Separately, Carbon3.ai is launching a UK Private AI Lab on HPE’s platform targeting sovereign, renewable-powered infrastructure.

Key Points

  • HPE will make Nvidia RTX PRO 6000 Blackwell Server Edition GPUs and STIG-hardened NIMs available across its Private Cloud AI offerings.
  • GPU fractionalisation/virtualisation support added to improve utilisation and lower costs for GPU-heavy workloads.
  • Juniper integration (post-acquisition): MX routers for edge on-ramp and PTX routers for datacentre interconnect to link AI clusters across distances and clouds.
  • Alletra Storage MP X10000 Data Intelligence Nodes perform inline data prep—metadata tagging, embedded vector generation and formatting—to reduce pre-LLM tooling and speed pipelines.
  • HPE and Nvidia plan an AI Factory Lab in Grenoble (Q2 2026) to help customers ready workloads for production.
  • Carbon3.ai is building a UK Private AI Lab on HPE’s platform to offer sovereign, sustainable AI infrastructure for enterprises.

Context and relevance

This matters because many enterprises find data preparation and networking—not raw GPU capacity—the real bottleneck for production AI. HPE’s combined push (GPUs, virtualisation, integrated networking and storage that enriches data inline) aligns with broader trends: hybrid/sovereign AI, on-prem and private cloud deployments, and hardware-software co-design with Nvidia. If you manage AI infrastructure, data pipelines or cloud strategy, these moves are directly relevant to reducing time-to-production and cost per workload.

Author style

Punchy: HPE isn’t just bolting in faster GPUs — it’s tying compute, storage and networking together and offering hands-on labs. For infra teams and decision-makers, that tight integration could be the difference between pilot projects and scaled production.

Why should I read this?

Short and blunt: if you’re running or planning serious AI projects, this update saves you time. HPE is making it easier to get GPUs, networking and data prep working together — and offering a real-world lab in France to test your workloads. Pop in if you want to move beyond pilots and actually ship.

Source

Source: https://go.theregister.com/feed/www.theregister.com/2025/12/01/hpe_ai_cloud_nvidia/