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When you create a Kubernetes cluster in Google Distributed Cloud (GDC) air-gapped, you
create node pools that are responsible for running your container workloads in
the cluster. You provision nodes based on your container workload requirements,
and can update them as your requirements evolve.
GDC provides predefined machine types for your worker
nodes that are selectable when you
add a node pool.
There are also multiple ways to partition separate GPU instances using the
Multi-Instance GPU (MIG) feature.
Reference the following sections for available machine types and GPU support.
Available machine types
GDC defines machine types with some parameters
for a Kubernetes cluster node, which include CPU, memory, and GPU.
GDC has various machine types for different purposes.
For example, clusters use n2-standard-4-gdc for general purpose container
workloads. If you plan to run artificial intelligence (AI) and
machine learning (ML) notebooks, you must provision GPU machines, such as
a2-highgpu-1g-gdc.
The following is a list of all GDC predefined machine
types available for Kubernetes cluster worker nodes:
Name
vCPUs
Memory
GPU
n2-standard-4-gdc
4
16G
N/A
n2-standard-8-gdc
8
32G
N/A
n2-standard-16-gdc
16
64G
N/A
n2-standard-32-gdc
32
128G
N/A
n2-highmem-4-gdc
4
32G
N/A
n2-highmem-8-gdc
8
64G
N/A
n2-highmem-16-gdc
16
128G
N/A
n2-highmem-32-gdc
32
256G
N/A
a2-highgpu-1g-gdc
12
85G
1x A100 40GB
a2-ultragpu-1g-gdc
12
170G
1x A100 80GB
a2-ultragpu-2g-gdc
24
340G
2x A100 80GB
a3-highgpu-1g-gdc
28
240G
1x H100 94GB
a3-highgpu-2g-gdc
56
480G
2x H100 94GB
a3-highgpu-4g-gdc
112
960G
4x H100 94GB
Supported MIG profiles
This section defines the supported partitioning schemes of MIG profiles on
supported GPUs. You can define a partitioning scheme for a node pool in your
Cluster custom resource.
For more information on how to apply a GPU partitioning scheme, see
Add a node pool.
A100 40GB GPU
The following table defines the MIG profiles supported on the A100 40GB NVIDIA
GPU:
Partitioning Scheme
Available Partitions
1g.5gb
7x 1g.5gb
2g.10gb
3x 2g.10gb
3g.20gb
2x 3g.20gb
7g.40gb
1x 7g.40gb
mixed-1
1x 4g.20gb 1x 2g.10gb 1x 1g.5gb
mixed-2
1x 4g.20gb 3x 1g.5gb
mixed-3
1x 3g.20gb 2x 2g.10gb
mixed-4
1x 3g.20gb 1x 2g.10gb 2x 1g.5gb
mixed-5
1x 3g.20gb 4x 1g.5gb
mixed-6
3x 2g.10gb 1x 1g.5b
mixed-7
2x 2g.10gb 3x 1g.5b
mixed-8
1x 2g.10gb 5x 1g.5gb
A100 80GB GPU
The following table defines the MIG profiles supported on the A100 80GB NVIDIA
GPU:
Partitioning Scheme
Available Partitions
1g.10gb
7x 1g.10gb
2g.20gb
3x 2g.20gb
3g.40gb
2x 3g.40gb
7g.80gb
1x 7g.80gb
mixed-1
1x 4g.40gb 1x 2g.20gb 1x 1g.10gb
mixed-2
1x 4g.40gb 3x 1g.10gb
mixed-3
1x 3g.40gb 2x 2g.20gb
mixed-4
1x 3g.40gb 1x 2g.20gb 2x 1g.10gb
mixed-5
1x 3g.40gb 4x 1g.10gb
mixed-6
3x 2g.20gb 1x 1g.10gb
mixed-7
2x 2g.20gb 3x 1g.10gb
mixed-8
1x 2g.20gb 5x 1g.10gb
H100 94GB GPU
The following table defines the MIG profiles supported on the H100 94GB NVIDIA
GPU:
[[["Easy to understand","easyToUnderstand","thumb-up"],["Solved my problem","solvedMyProblem","thumb-up"],["Other","otherUp","thumb-up"]],[["Hard to understand","hardToUnderstand","thumb-down"],["Incorrect information or sample code","incorrectInformationOrSampleCode","thumb-down"],["Missing the information/samples I need","missingTheInformationSamplesINeed","thumb-down"],["Other","otherDown","thumb-down"]],["Last updated 2025-08-29 UTC."],[[["\u003cp\u003eGoogle Distributed Cloud (GDC) air-gapped Kubernetes clusters utilize node pools for running container workloads, allowing for node provisioning and updates based on evolving requirements.\u003c/p\u003e\n"],["\u003cp\u003eGDC offers a variety of predefined machine types for worker nodes, including general-purpose options like \u003ccode\u003en2-standard-4-gdc\u003c/code\u003e and GPU-enabled options like \u003ccode\u003ea2-highgpu-1g-gdc\u003c/code\u003e for AI/ML workloads.\u003c/p\u003e\n"],["\u003cp\u003eThe Multi-Instance GPU (MIG) feature allows for partitioning GPU instances, and applying a chosen partitioning scheme will affect all the GPUs available in a specified node.\u003c/p\u003e\n"],["\u003cp\u003eDifferent NVIDIA GPUs, such as A100 40GB, A100 80GB, and H100 94GB, have different supported MIG profiles, which define the available partitioning schemes and their specific configurations.\u003c/p\u003e\n"],["\u003cp\u003eSome machine types such as the a3-highgpu-1g-gdc and a3-highgpu-2g-gdc are in preview at the moment.\u003c/p\u003e\n"]]],[],null,["# Cluster node machine types\n\nWhen you create a Kubernetes cluster in Google Distributed Cloud (GDC) air-gapped, you\ncreate node pools that are responsible for running your container workloads in\nthe cluster. You provision nodes based on your container workload requirements,\nand can update them as your requirements evolve.\n\nGDC provides predefined machine types for your worker\nnodes that are selectable when you\n[add a node pool](/distributed-cloud/hosted/docs/latest/gdch/platform/pa-user/manage-node-pools#add-a-node-pool).\nThere are also multiple ways to partition separate GPU instances using the\nMulti-Instance GPU (MIG) feature.\n\nReference the following sections for available machine types and GPU support.\n\nAvailable machine types\n-----------------------\n\nGDC defines machine types with some parameters\nfor a Kubernetes cluster node, which include CPU, memory, and GPU.\nGDC has various machine types for different purposes.\nFor example, clusters use `n2-standard-4-gdc` for general purpose container\nworkloads. If you plan to run artificial intelligence (AI) and\nmachine learning (ML) notebooks, you must provision GPU machines, such as\n`a2-highgpu-1g-gdc`.\n\nThe following is a list of all GDC predefined machine\ntypes available for Kubernetes cluster worker nodes:\n\n| **Preview:** The following machine types are in Preview:\n|\n| - a3-highgpu-1g-gdc\n| - a3-highgpu-2g-gdc\n|\n| For more information on Preview features, see [Feature stages](/distributed-cloud/hosted/docs/latest/gdch/resources/feature-stages#preview).\n\nSupported MIG profiles\n----------------------\n\nThis section defines the supported partitioning schemes of MIG profiles on\nsupported GPUs. You can define a partitioning scheme for a node pool in your\n`Cluster` custom resource.\n| **Important:** A partitioning scheme gets applied to all GPUs in a node. For example, the `a3-highgpu-4g-gdc` machine can support four iterations of the `7x 1g.12gb` GPU slicing because there are four GPUs available to the machine type.\n\nFor more information on how to apply a GPU partitioning scheme, see\n[Add a node pool](/distributed-cloud/hosted/docs/latest/gdch/platform/pa-user/manage-node-pools#add-a-node-pool).\n\n### A100 40GB GPU\n\nThe following table defines the MIG profiles supported on the A100 40GB NVIDIA\nGPU:\n\n### A100 80GB GPU\n\nThe following table defines the MIG profiles supported on the A100 80GB NVIDIA\nGPU:\n\n### H100 94GB GPU\n\nThe following table defines the MIG profiles supported on the H100 94GB NVIDIA\nGPU:"]]
