Creative Compute
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HPE Machine Learning Development Environment

SoftwareConfigure to orderManufacturer documented

Software for training models across shared accelerators. It schedules jobs, tracks experiments and handles distributed training, so several people can use the same expensive hardware without treading on each other or losing track of what produced which result.

The problem it solvesYour GPUs are either idle or fought over, and nobody can reproduce last month's best model.

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Representative system image

Where does this sit?

  1. 01Local
  2. 02Workstation
  3. 03Departmental
  4. 04Enterprise
  5. 05Cluster
  6. 06Rack-scale

Enterprise. Four to eight enterprise accelerators in a rack chassis, sized for production load rather than experiments. Typically bought by an enterprise ai or platform team. Datacentre or a well-provisioned rack.

Fit

Is this the right thing for you?

Who it's for

  • Machine learning teams training their own models
  • Research groups sharing a GPU cluster
  • Organisations fine-tuning models on proprietary data

What it runs

  • Distributed training
  • Fine-tuning
  • Hyperparameter search
  • Experiment tracking

When it is the wrong answer

  • Inference-only estates, where a serving layer matters more than a training scheduler
Where it goes
On-premises GPU cluster or hybrid
Complexity
moderate
Cooling
Awaiting verification
Specification

What the numbers mean

Every figure below is explained in plain English. Switch to the technical view for the bare specification.

Capabilities

Purpose

Distributed model training, scheduling and experiment tracking

MattersIt is what stops a shared GPU estate becoming a queue managed over chat.

Origin

Built on the Determined AI platform acquired by HPE

Version and support matrixUnverified

Awaiting verification

Verification

Manufacturer documented — HPE Machine Learning Development Environment product page. Reviewed 2026-08-31.

Manufacturer source
Before you commit

Practical considerations

The things that catch people out after the hardware has already been ordered.

  • The value appears once more than one person shares the hardware. Below that, it is overhead.
Completeness

Nothing arrives working on its own

What else will I need?

  • A GPU estate to schedule
  • Shared storage the training jobs can read at speed
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Goes with

What normally sits alongside it

Relationships documented by the manufacturer, or by us during a deployment.