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The EIDF GPU Service (EIDF GPU Service) provides access to a range of Nvidia GPUs, in both full GPU and MIG variants. The EIDF GPU Service is built upon Kubernetes.

MIG (Multi-instance GPU) allow a single GPU to be split into multiple isolated smaller GPUs. This means that multiple users can access a portion of the GPU without being able to access what others are running on their portion.

The EIDF GPU Service hosts 3G.20GB and 1G.5GB MIG variants which are approximately 1/2 and 1/7 of a full Nvidia A100 40 GB GPU respectively.

The service provides access to:

  • Nvidia A100 40GB
  • Nvidia A100 80GB
  • Nvidia MIG A100 1G.5GB
  • Nvidia MIG A100 3G.20GB
  • Nvidia H100 80GB

The current full specification of the EIDF GPU Service as of 14 February 2024:

  • 4912 CPU Cores (AMD EPYC and Intel Xeon)
  • 23 TiB Memory
  • Local Disk Space (Node Image Cache and Local Workspace) - 40 TiB
  • Ceph Persistent Volumes (Long Term Data) - up to 100TiB
  • 112 Nvidia A100 40 GB
  • 39 Nvidia A100 80 GB
  • 16 Nvidia A100 3G.20GB
  • 56 Nvidia A100 1G.5GB
  • 32 Nvidia H100 80 GB


This is the full configuration of the cluster.

Each project will have access to a quota across this shared configuration.

Changes to the default quota must be discussed and agreed with the EIDF Services team.


If you request a GPU on the EIDF GPU Service you will be assigned one at random unless you specify a GPU type. Please see Getting started with Kubernetes to learn about specifying GPU resources.

Service Access

Users should have an EIDF Account as the EIDF GPU Service is only accessible through EIDF Virtual Machines.

Existing projects can request access to the EIDF GPU Service through a service request to the EIDF helpdesk or emailing .

New projects wanting to using the GPU Service should include this in their EIDF Project Application.

Each project will be given a namespace within the EIDF GPU service to operate in.

This namespace will normally be the EIDF Project code appended with ’ns’, i.e. eidf989ns for a project with code 'eidf989'.

Once access to the EIDF GPU service has been confirmed, Project Leads will be give the ability to add a kubeconfig file to any of the VMs in their EIDF project - information on access to VMs is available here.

All EIDF VMs with the project kubeconfig file downloaded can access the EIDF GPU Service using the kubectl command line tool.

The VM does not require to be GPU-enabled.

A quick check to see if a VM has access to the EIDF GPU service can be completed by typing kubectl -n <project-namespace> get jobs in to the command line.

If this is first time you have connected to the GPU service the response should be No resources found in <project-namespace> namespace.

EIDF GPU Service vs EIDF GPU-Enabled VMs

The EIDF GPU Service is a container based service which is accessed from EIDF Virtual Desktop VMs.

This allows a project to access multiple GPUs of different types.

An EIDF Virtual Desktop GPU-enabled VM is limited to a small number (1-2) of GPUs of a single type.

Projects do not have to apply for a GPU-enabled VM to access the GPU Service.

Project Quotas

A standard project namespace has the following initial quota (subject to ongoing review):

  • CPU: 100 Cores
  • Memory: 1TiB
  • GPU: 12

Quota is a maximum on a Shared Resource

A project quota is the maximum proportion of the service available for use by that project.

Any submitted job requests that would exceed the total project quota will be queued.

Project Queues

EIDF GPU Service is introducing the Kueue system in February 2024. The use of this is detailed in the Kueue.

Job Queuing

During periods of high demand, jobs will be queued awaiting resource availability on the Service.

As a general rule, the higher the GPU/CPU/Memory resource request of a single job the longer it will wait in the queue before enough resources are free on a single node for it be allocated.

GPUs in high demand, such as Nvidia H100s, typically have longer wait times.

Furthermore, a project may have a quota of up to 12 GPUs but due to demand may only be able to access a smaller number at any given time.

Additional Service Policy Information

Additional information on service policies can be found here.

EIDF GPU Service Tutorial

This tutorial teaches users how to submit tasks to the EIDF GPU Service, but it is not a comprehensive overview of Kubernetes.

Lesson Objective
Getting started with Kubernetes a. What is Kubernetes?
b. How to send a task to a GPU node.
c. How to define the GPU resources needed.
Requesting persistent volumes with Kubernetes a. What is a persistent volume?
b. How to request a PV resource.
Running a PyTorch task a. Accessing a Pytorch container.
b. Submitting a PyTorch task to the cluster.
c. Inspecting the results.
Template workflow a. Loading large data sets asynchronously.
b. Manually or automatically building Docker images.
c. Iteratively changing and testing code in a job.

Further Reading and Help