HPE’s turnkey AI data-center offering with NVIDIA is HPE Private Cloud AI: a configured private AI platform that combines HPE servers, storage, management software and services with NVIDIA GPUs, networking and AI software. It is designed to help organizations deploy workloads such as inference, fine-tuning and retrieval-augmented generation (RAG) without assembling and validating every layer themselves. It is a platform—not a single server—and pricing depends on the configuration.
What is HPE Private Cloud AI?
HPE Private Cloud AI is part of the NVIDIA AI Computing by HPE portfolio. HPE and NVIDIA describe it as a co-engineered private AI factory: infrastructure, software, model tools and management are brought together in a validated design for enterprise use.
HPE supplies elements including ProLiant compute, storage, GreenLake cloud management, AI Essentials and lifecycle services. NVIDIA contributes accelerated computing, networking, NVIDIA AI Enterprise software, NIM inference microservices and validated blueprints. The aim is to simplify deployment and ongoing operation compared with sourcing, integrating and supporting each component independently.
“Turnkey” does not mean that every customer receives the same fixed appliance. HPE offers configurations and server options intended for different scales and workloads; the precise hardware, services and availability depend on the configuration and region.
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- PLEASE NOTE: Exporting an NVIDIA RTX Pro 6000 GPU outside the US requires strict adherence to the U.S. Export Administration Regulations (EAR) and issuance of an export license from the Bureau of Industry and Security (BIS). Compliance and Know Your Customer (KYC) screening may be required as a condition of order acceptance. [NVIDIA Blackwell Streaming Multiprocessor] The new SM features increased processing throughput, and new neural shaders that integrate neural networks inside of programmable shaders | DLSS 4: Multi Frame Generation ensures ultra-smooth frame pacing for lifelike simulations.
- [Double-Flow-Through Design] The RTX PRO 6000 Blackwell features a double-flow-through cooling design, optimizing efficiency and airflow to sustain peak performance under 600W power loads. | [5th Gen Tensor Cores] Deliver up to 3X the performance of the previous generation and support for FP4 precision for faster AI model processing times with reduced memory usage, enabling local fine-tuning of LLMs and generative AI | [4th Gen Ray Tracing Cores] Double the ray-triangle intersection rate of the previous generation to create photoreal, physically accurate scenes and immersive 3D designs with RTX Mega Geometry, which enables up to 100X more ray-traced triangles.
- [PCIe Gen 5] Support for PCIe Gen 5 provides double the bandwidth of PCIe Gen 4, improving data-transfer speeds from CPU memory and unlocking faster performance for data-intensive tasks like AI, data science, and 3D modeling. | [GDDR7 Memory] With 96 GB of GPU memory and 1.8 TB ps bandwidth, it can tackle massive 3D and AI projects, fine-tune AI models locally, explore large-scale VR environments, and drive larger multi-app workflows.
- [DisplayPort 2.1] Achieve unparalleled visual clarity and performance, driving high resolution displays at up to 8K at 240 Hz and 16K at 60 Hz. Increased bandwidth enables seamless multi-monitor setups while HDR and higher color depth support ensures superior color accuracy for precision work, such as video editing, 3D design, and live broadcasting.
- [Universal MIG] Divide a single RTX PRO 6000 Blackwell into multiple isolated instances, each with dedicated resources, allowing for concurrent execution of multiple workloads, optimized GPU utilization, and secure isolation of different applications or users. [WARRANTY] 3 YR Manufacturer's Warranty. Bulk OEM Packaging. Retail Packaging is NOT included.
What workloads can it run?
HPE and NVIDIA position the platform for production enterprise AI, including applications that use an organization’s proprietary data. Announced workload categories include:
- Inference: serving a trained model to answer prompts or make predictions. NVIDIA NIM microservices are among the software components named for this purpose.
- Retrieval-augmented generation: grounding model responses in information retrieved from an organization’s data sources.
- Fine-tuning: adapting a model to a task or domain using additional training.
- Agentic and physical AI: categories HPE cites for the platform, alongside other enterprise AI applications.
These are stated workload targets, not a guarantee that every model or application will run on every configuration. Buyers should validate model, software, storage, performance and capacity requirements against the proposed system.
How the platform has developed
HPE’s announcements describe an evolving set of configurations and capabilities rather than one unchanged hardware specification. The milestones below distinguish the original launch from later additions.
| Announcement | What HPE described |
|---|---|
| 2024 launch | Four right-sized configurations, a self-service cloud experience and lifecycle management. HPE described support for inference, fine-tuning and RAG with proprietary data, using NVIDIA AI Enterprise and NIM alongside HPE AI Essentials, GreenLake, ProLiant servers and storage. |
| March 2025 update | A developer system, NVIDIA AI Data Platform integration, HPE Data Fabric, pre-validated blueprints including multimodal PDF extraction and digital twins, and GPU optimization through HPE OpsRamp. Announced server options included GB300 NVL72, HGX B300, GB200 NVL4 and RTX PRO 6000 Blackwell Server Edition. HPE also described an AI Mod POD modular data-center design supporting up to 1.5 MW per module. |
| June 2025 update | Blackwell support, air-gapped management, multi-tenancy and federated resource pooling. HPE also cited integration with NVIDIA Spectrum-X, BlueField-3 and AI Enterprise, and a try-and-buy program through Equinix. |
| March 2026 update | Network expansion racks were announced to scale deployments to 128 GPUs. HPE said the large system was available in an air-gapped configuration and that RTX PRO 6000 Blackwell Server Edition GPUs were supported across configurations. HPE also said Fortanix Confidential AI certification work was underway for selected systems. |
The 1.5 MW figure is HPE’s stated capacity for an AI Mod POD module; it is not a power requirement for every Private Cloud AI deployment. Likewise, the 128-GPU figure describes the scaling enabled by the announced network expansion racks, not the GPU count of a standard configuration.
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- The speed of FP32 calculation is twice as fast as previous generations, which greatly improves the complex 3D processing and graphics simulation workflow
- Up to 2X the throughput compared to previous generations and significantly faster workloads such as video content rendering, architectural design assessments, and virtual prototypes of product design
- Achieve more than twice the previous generation AI performance improvement, support faster FP8 precision data and accelerate the execution of mixed flotation decimal and whole numbers
- It has a large capacity of memory necessary for working with a vast array of data sets and workloads such as rendering, data science, and simulation
What hardware is available?
The announced hardware spans multiple NVIDIA GPU generations and system designs. HPE materials list NVIDIA H200 NVL, RTX PRO 6000 Blackwell Server Edition and Blackwell systems, among other options. The March 2025 update named GB300 NVL72, HGX B300 and GB200 NVL4; these are specific announced server options, not interchangeable names for one standard system.
For developers, HPE’s developer portal describes a configuration with two NVIDIA H100 NVL 96GB GPUs and 32 TB of integrated storage. The portal characterizes deployment as possible in days rather than months and calls it private AI “in a box”; those timing statements are vendor positioning, not independently established deployment results. The developer configuration should not be assumed to represent the hardware or capacity of larger production deployments.
How private and governable is it?
Private data control and enterprise governance are central to HPE’s positioning. The platform includes management and lifecycle capabilities, and HPE has described multi-tenancy and federated resource pooling for sharing infrastructure across teams or workloads. An air-gapped option is intended for deployments isolated from external networks; the March 2026 update specifically cited an air-gapped configuration for the large system.
Air-gapped operation is a deployment choice, not an automatic property of every configuration. Buyers should confirm which management, update, support and data flows are available in the proposed isolated setup, along with the governance controls their organization requires. HPE said Fortanix Confidential AI certification work was underway for selected systems in March 2026; that statement does not establish that certification is complete.
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How does it compare with building an AI cluster?
A self-built cluster can give an organization direct control over component selection and integration. A turnkey platform instead offers a vendor-validated combination of infrastructure, software and management, with lifecycle support. The trade-off is not simply convenience versus performance: it also involves operational ownership, capacity planning, flexibility and cost.
| Decision area | What to compare |
|---|---|
| Integration and deployment | Which hardware and software layers arrive validated together, what deployment work remains for your team, and what support HPE provides through the lifecycle. |
| Data control and isolation | Whether the system can meet your data-residency and security requirements, including whether the required configuration supports air-gapped operation. |
| Workload fit | Support for your target inference, RAG, fine-tuning or agentic workloads, including the specific models, data pipelines and software versions you intend to use. |
| Capacity and expansion | GPU type and count, memory, networking, storage, expansion path and whether the announced 128-GPU scale is relevant to your deployment. |
| Operations and governance | Multi-tenancy, resource allocation, monitoring, lifecycle management, observability and the division of responsibility between your staff and the vendor. |
| Facility and total cost | Power and cooling needs, space, networking, support and software costs, as well as the system purchase or consumption model. |
HPE’s reviewed announcements do not provide an apples-to-apples performance benchmark against competing AI factories or self-built clusters. They also do not establish a complete-system list price, so a technical and financial comparison requires a configuration-specific proposal.
Availability and pricing
In June 2025, HPE said DL380a Gen12 servers with RTX PRO 6000 were available to order, the next-generation Private Cloud AI with those GPUs was planned for the second half of 2025, new AI factory solutions were available immediately, and Compute XD690 was planned for October 2025. HPE’s March 2026 update reported current air-gapped and RTX PRO 6000 availability, while network expansion racks were planned for July. Because these are time-sensitive vendor statements, check with HPE or an authorized channel partner for current regional availability and the exact configuration being quoted.
The reviewed announcements do not publish a complete-system price. Expect enterprise pricing to depend on GPU and server selection, storage and networking, software, support and deployment requirements; request a quote that itemizes those elements rather than treating an individual server price as the cost of the platform.
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Product prices and availability are accurate as of the date/time indicated and are subject to change. Any price and availability information displayed on Amazon at the time of purchase will apply.
