Create an A4X Max Slurm cluster

This document describes how to deploy an A4X Max Slurm cluster. These clusters provide direct access to the host server's CPU and memory, optimizing your environment for compute-intensive, network-bound machine learning (ML) training and high performance computing (HPC) workloads.

The A4X Max machine series runs on an exascale platform based on NVIDIA's rack-scale architecture and is optimized for compute and memory-intensive, network-bound ML training and HPC workloads. A4X Max offers bare metal instances, which provide direct access to the host server's CPU and memory, without the Compute Engine hypervisor layer.

To learn more about the A4X machine series, see the A4X Max and A4X machine series section in the Compute Engine documentation.

A4X Max limitations

The following limitations apply to A4X Max bare metal instances in your Slurm cluster:

You don't receive sustained use discounts or flexible committed use discounts for instances that use this machine type.
You can only create instances in certain regions and zones.
You can't use Persistent Disk (regional or zonal). You can only use Google Cloud Hyperdisk.
This machine type is only available on the NVIDIA Grace platform.
Machine type changes aren't supported for A4X Max. To switch to or from this machine type, you must create a new instance.
You can't run Windows operating systems on this machine type. For a list of supported Linux operating systems, review the supported operating systems for GPU instances.
A4X Max instances don't support the following:
You can't attach Hyperdisk ML disks created before February 4, 2026 to A4X Max machine types.

Before you begin

Before creating a Slurm cluster, if you haven't already done so, complete the following steps:

Choose a consumption option: your choice of consumption option determines how you get and use GPU resources. To learn more, see Choose a consumption option.
Obtain capacity: the process to obtain capacity differs for each consumption option. To learn about the process to obtain capacity for your chosen consumption option, see Capacity overview.
Note: When you request A4X Max or A4X capacity, you obtain it in the all capacity mode. This mode is the only supported reservation operational mode for A4X Max or A4X machine types. For more information about all capacity mode, see Reservation operational mode.
Enable the Google Cloud Managed Lustre API and verify regional quota and availability: the default Cluster Toolkit blueprint for A4X Max provisions a Google Cloud Managed Lustre (lustre.googleapis.com) instance (default 18,000 GiB capacity at 500 MBps/TiB throughput). Before deploying, ensure that the lustre.googleapis.com and servicenetworking.googleapis.com APIs are enabled and that your target zone supports Managed Lustre (see Managed Lustre locations). Also verify that you have enough Managed Lustre capacity and throughput quota in the target region (see Managed Lustre quotas).
Install Cluster Toolkit: to provision Slurm clusters, you must use Cluster Toolkit version v1.104.0 or later.
To install Cluster Toolkit, see Set up Cluster Toolkit.

In the Google Cloud console, activate Cloud Shell.

Activate Cloud Shell

At the bottom of the Google Cloud console, a Cloud Shell session starts and displays a command-line prompt. Cloud Shell is a shell environment with the Google Cloud CLI already installed and with values already set for your current project. It can take a few seconds for the session to initialize.

Required roles

To get the permissions that you need to deploy a Slurm cluster, ask your administrator to grant you the following IAM roles:

Storage Admin (roles/storage.admin) on your project
Compute Instance Admin (v1) (roles/compute.instanceAdmin.v1) on your project
Service Account User (roles/iam.serviceAccountUser) on the service account itself
To consume a single-project reservation: Compute Instance Admin (v1) (roles/compute.instanceAdmin.v1) on your project
To consume a shared reservation: Compute Instance Admin (v1) (roles/compute.instanceAdmin.v1) on both the owner project and any consumer project where you want to consume the reservation

For more information about granting roles, see Manage access to projects, folders, and organizations.

You might also be able to get the required permissions through custom roles or other predefined roles.

Set up a storage bucket

Cluster blueprints use Terraform modules to provision Cloud infrastructure. A best practice when working with Terraform is to store the state remotely in a version enabled file. On Google Cloud, you can create a Cloud Storage bucket that has versioning enabled.

To create this bucket and enable versioning from the CLI, run the following commands:

gcloud storage buckets create gs://BUCKET_NAME \
    --project=PROJECT_ID \
    --default-storage-class=STANDARD --location=BUCKET_REGION \
    --uniform-bucket-level-access
gcloud storage buckets update gs://BUCKET_NAME --versioning

Replace the following:

BUCKET_NAME: a name for your Cloud Storage bucket that meets the bucket naming requirements.
PROJECT_ID: your project ID.
BUCKET_REGION: any available location.

Open the Cluster Toolkit directory

To use Slurm with Google Cloud, you must install Cluster Toolkit. In your terminal, navigate to the Cluster Toolkit directory:

cd cluster-toolkit

If you cloned the Cluster Toolkit GitHub repository instead of downloading a prebuilt release, then run the make command first to build the gcluster binary.

This cluster deployment requires Cluster Toolkit version v1.104.0 or later. Verify your installed version:

./gcluster --version

Create a deployment file

Create a deployment file that you can use to specify the Cloud Storage bucket, set names for your network and subnetwork, and set deployment variables such as project ID, region, and zone.

To create your deployment file, use a text editor to create a YAML file named a4xmax-bm-slurm-deployment.yaml and add the following content.


terraform_backend_defaults:
  type: gcs
  configuration:
    bucket: BUCKET_NAME
vars:
  deployment_name: DEPLOYMENT_NAME
  project_id: PROJECT_ID
  region: REGION
  zone: ZONE
  a4x_max_cluster_size: NUMBER_OF_INSTANCES
  a4x_max_reservation_name: RESERVATION_NAME

Replace the following:

BUCKET_NAME: the name of your Cloud Storage bucket, which you created in the previous section.
DEPLOYMENT_NAME: a name for your deployment. If creating multiple clusters, ensure that you select a unique name for each one.
PROJECT_ID: your project ID.
REGION: the region that has the reserved machines.
ZONE: the zone where you want to provision the cluster. If you're using a reservation-based consumption option, the region and zone information was provided by your account team when the capacity was delivered.
NUMBER_OF_INSTANCES: the number of A4X Max bare metal instances in your cluster. You can specify any number of instances. However, A4X Max instances are physically interconnected by a multi-node NVLink system in groups of 18 instances (72 GPUs) to form an NVLink domain.
For optimal network performance, we recommend that you specify a value that is a multiple of 18 instances (for example, 18, 36, or 54). When you create an A4X Max cluster, the A4X Max blueprint automatically creates and applies a compact placement policy with a GPU topology of 1x72 for each group of 18 instances.
RESERVATION_NAME: the name of your reservation.

Provision a Slurm cluster

Cluster Toolkit provisions the cluster based on the deployment file that you created in the previous step and the default cluster blueprint. For more information about the software that is installed by the blueprint, including NVIDIA drivers and CUDA, see Slurm custom images.

The A4X Max blueprint integrates Managed Lustre for high-performance home directory storage (/home). The blueprint automatically configures private service access (PSA) to peer your VPC network with Managed Lustre. The blueprint also configures Lustre networking (LNet) on the eth0 primary network interface. For more information, see the Managed Lustre overview.

Deploy the Slurm cluster. This process can take about 20 to 30 minutes: To provision the cluster, use the ./gcluster deploy command from the Cluster Toolkit directory. This process can take about 20 to 30 minutes.

./gcluster deploy -d a4xmax-bm-slurm-deployment.yaml examples/machine-learning/a4x-maxgpu-4g-metal/a4xmax-bm-slurm-blueprint.yaml

Connect to the Slurm cluster

To access your cluster, you must sign in to the Slurm login node. To sign in, you can use either Google Cloud console or Google Cloud CLI.

Console

Go to the Compute Engine > VM instances page.

Go to the VM instances page
Locate the login node. The node has a name with the pattern DEPLOYMENT_NAME-login-001.
In the Connect column of the login node, click SSH.

gcloud

To connect to the login node, complete the following steps:

Identify the login node by using the gcloud compute instances list command.
```
gcloud compute instances list \
  --zones=ZONE \
  --filter="name ~ login" \
  --format="value(name)"
```
If the output lists multiple Slurm clusters, you can identify your login node by the DEPLOYMENT_NAME that you specified.
Use the gcloud compute ssh command to connect to the login node.
```
gcloud compute ssh LOGIN_NODE \
  --zone=ZONE --tunnel-through-iap
```
Replace the following:
- ZONE: the zone where the VMs for your cluster are located.
- LOGIN_NODE: the name of the login node, which you identified in the previous step.

Test network performance on the Slurm cluster

We recommend that you validate the functionality of provisioned clusters. To do so, use NCCL/gIB tests, which are NVIDIA Collective Communications Library (NCCL) tests that are optimized for the Google environment.

Redeploy the Slurm cluster

If you need to increase the number of compute nodes or add new partitions to your cluster, you might need to update configurations for your Slurm cluster by redeploying. You can speed up a redeployment by using an existing image from a previous deployment. To avoid creating new images during a redeploy, specify the --only flag.

To redeploy the cluster by using an existing image, do the following:

Connect to the cluster. For instructions, see Connect to the Slurm cluster.
Use the ./gcluster deploy command for your required machine type:
```
./gcluster deploy \
   -d a4xmax-bm-slurm-deployment.yaml \
   examples/machine-learning/a4x-maxgpu-4g-metal/a4xmax-bm-slurm-blueprint.yaml \
   --only cluster-env,cluster -w
```
This command applies only to redeployments when an image already exists; it redeploys only the cluster and its infrastructure.

Destroy the Slurm cluster

When you destroy the Slurm cluster, all provisioned resources are deleted, including the Managed Lustre instance that the blueprint mounts at /home and all data stored on it. Before you destroy the cluster, back up any data that you want to keep.

If you are connected to the cluster, disconnect from it.
In your terminal, navigate to the Cluster Toolkit directory.
To destroy the cluster, use the ./gcluster destroy command:
```
./gcluster destroy DEPLOYMENT_FOLDER
```
Replace the following:
- DEPLOYMENT_FOLDER: the name of the deployment folder, typically the same as DEPLOYMENT_NAME.
When the destruction completes, the output is similar to the following:
```
Destroy complete! Resources: NUMBER destroyed.
```

To learn how to cleanly destroy infrastructure and for advanced manual deployment instructions, see the deployment folder located at the root of the Cluster Toolkit directory: DEPLOYMENT_FOLDER/instructions.txt