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HPE GPU Systems

HPE accelerator platforms for AI training, inference, and accelerated computing.

Full manufacturer warrantyAuthorized channel48-hour quote

We help you choose, configure, and deliver the right system — no obligation.

HPE GPU Systems — HPE enterprise hardware
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HPE GPU Systems hardware detail 1
HPE GPU Systems hardware detail 2
HPE GPU Systems hardware detail 3

Configuration at a Glance

PlatformsHPE GPU-optimized servers
GPU OptionsData-center GPUs (training / inference)
ScaleSingle-node to multi-node
NetworkingHigh-speed Ethernet / InfiniBand

Tailored per engagement. Full technical overview below.

Configuration Options

Core specifications for this system. Every component is configurable to your workload — request a quote for a tailored build.

GPU / Accelerator

Data-center GPUs (training / inference)

Overview

HPE GPU Systems cover HPE's accelerator-optimized servers for organizations scaling GPU compute on the HPE platform. Nexus Compute specifies and sources the right HPE GPU systems for your training or inference workloads, with HPE management and support throughout.

Who This Solution Is For

Enterprises scaling GPU compute on HPE
Teams deploying HPE accelerator platforms
Organizations requiring HPE support on GPU systems
IT groups extending HPE estates to AI workloads

Business Benefits

GPU compute on HPE

Add accelerated computing while keeping HPE management and support consistency.

Training and inference

Configurations span both model training and production inference workloads.

Unified operations

iLO and OneView extend familiar operations to GPU infrastructure.

Workload-matched specification

We select GPU type and node count for your AI workload.

Typical Business Use Cases

1

GPU-accelerated AI training on HPE

2

Production inference on HPE platforms

3

Accelerated HPC and analytics

4

Scaling GPU capacity within an HPE estate

Industry Applications

AI & Machine LearningEducation & ResearchFinancial ServicesHealthcare & Life Sciences

Technical Overview

HPE accelerator-optimized servers supporting multiple data-center GPUs with appropriate power and cooling, high-speed networking, HPE storage integration, and iLO/OneView management. Specified to your GPU workload and scale.

PlatformsHPE GPU-optimized servers
GPU OptionsData-center GPUs (training / inference)
ScaleSingle-node to multi-node
NetworkingHigh-speed Ethernet / InfiniBand
ManagementiLO + OneView
SupportHPE support
EngagementSpecification, sourcing, delivery

Specifications are indicative and configured to each engagement. Request a quote for a configuration tailored to your requirements.

Warranty, Support & Fulfillment

Every system ships from an authorized channel, configured and tested, with the documentation enterprise buyers need — backed by warranty and a dedicated account team.

Enterprise Warranty

Full manufacturer warranty with optional on-site, next-business-day support and extended coverage.

Authorized Channel

Sourced through Tier-1 distribution and OEM partners — never grey market. Asset & warranty records included.

Lead Time & Deployment

48-hour quotes, then configured, burn-in tested, and delivered on a committed schedule.

Nationwide Fulfillment

Coordinated logistics, rack-and-stack, and delivery wherever your infrastructure lives.

Frequently Asked Questions

Which GPUs do HPE systems support?

A range of current data-center GPUs, subject to allocation. We confirm supported options and timelines in your quote.

Can we scale to multiple nodes?

Yes. We design networking that supports growth from single-node to multi-node GPU configurations.

How does this compare to a dedicated GPU server?

For HPE-standardized organizations valuing unified support, HPE GPU Systems are ideal. For maximum GPU density, our GPU Server line may fit better — we advise on the trade-off.

Hardware Assistance

Configure the HPE GPU Systems with Nexus Compute

Tell us your requirements and a hardware specialist will help you specify, configure, and quote the right system — typically within two business days. No obligation.