Run CircleCI Arm Native Workflows on a SUSE Arm GCP VM
Introduction
Get started with CircleCI on Google Axion C4A
Create a Google Axion C4A Arm virtual machine on GCP
Install CircleCI
Create a resource class
Install CircleCI Machine Runner on SUSE Arm
Build and test an Arm64 Node.js app using CircleCI
Next Steps

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About this Learning Path

Skill level:  Introductory
Reading time:  45 min
Last updated:  31 Jul 2026
Skill level:
  Introductory
Reading time:
  45 min
Last updated:
  31 Jul 2026
Author:Pareena Verma, Arm
Arm IP:Neoverse
Tags: CI-CD Google Cloud Linux CircleCI Node.js npm Docker
Author:
  • Pareena Verma, Arm
Arm IP:
Neoverse
Tags:
CI-CD Google Cloud Linux CircleCI Node.js npm Docker

Who is this for?

This is an introductory topic for developers and DevOps engineers looking to set up and run CircleCI Arm native workflows on SUSE Linux Arm64 virtual machines (VMs), specifically on Google Cloud C4A with Axion processors, using self-hosted runners.

What will you learn?

Upon completion of this Learning Path, you will be able to:

  • Provision a SUSE Arm64 virtual machine on Google Cloud (C4A with Axion processors)
  • Install and configure CircleCI self-hosted machine runners on Arm64
  • Create a cloud-native Node.js demo app to run on the self-hosted Arm runner
  • Write and execute a CircleCI workflow using a custom Arm resource class
  • Test CircleCI workflows locally and understand job execution on Arm64 runners

Prerequisites

Before starting, you will need the following:

Summary

AI-assisted

This summary was drafted with an approved AI-assisted workflow and reviewed by Arm contributors before publication. Human technical review remains part of the process so the final page reflects engineering rigor, accuracy, and Arm editorial standards.

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You’ll provision a SUSE Linux Google Cloud C4A virtual machine (VM) powered by Google Axion, install the CircleCI CLI, create a custom resource class, and deploy the CircleCI Machine Runner so CI/CD jobs execute natively on Arm. After configuring a self-hosted runner linked to a CircleCI namespace, you’ll author and dispatch a workflow that targets the resource class and runs a small Node.js demo app on the Arm VM. You’ll then verify its execution in the CircleCI dashboard and on the VM.

Frequently asked questions

AI-assisted

These FAQs were drafted with an approved AI-assisted workflow and reviewed by Arm contributors before publication. Human technical review remains part of the process so the final page reflects engineering rigor, accuracy, and Arm editorial standards.

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How do I know I created the correct C4A Arm VM in Google Cloud?
During creation, select the c4a-standard-4 machine type in the Google Cloud Console. After launch, open the VM details in Compute Engine and verify the machine type shows c4a-standard-4.
What should I verify after installing the CircleCI CLI on SUSE?
The CLI should be available to validate configuration files, run jobs locally, and manage runners. If it can’t be installed or used, recheck that the required repositories were added to the SUSE environment as shown in the steps.
Which namespace should I use when creating the CircleCI resource class?
Use the CircleCI organization (namespace) where your project workflows run. The resource class links your self-hosted runner to that namespace so only authorized jobs can target the VM.
How can I confirm the machine runner is registered and ready to accept jobs?
Check the CircleCI web dashboard for the resource class and runner status. On the VM, the runner setup completes with a connection to CircleCI and then waits for jobs that reference its resource class.
How do I target the self-hosted Arm runner from my workflow, and what result should I expect?
Configure the job to use the custom resource class created for the Arm runner. When triggered, the job runs on the SUSE arm64 VM and the CircleCI UI shows the job associated with that resource class.
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