Resource requests in Autopilot


This page describes the maximum, minimum, and default resource requests that you can specify for your Google Kubernetes Engine (GKE) Autopilot workloads, and how Autopilot automatically modifies those requests to maintain workload stability.

Overview of resource requests in Autopilot

Autopilot uses the resource requests that you specify in your workload configuration to configure the nodes that run your workloads. Autopilot enforces minimum and maximum resource requests based on the compute class or the hardware configuration that your workloads use. If you don't specify requests for some containers, Autopilot assigns default values to let those containers run correctly.

When you deploy a workload in an Autopilot cluster, GKE validates the workload configuration against the allowed minimum and maximum values for the selected compute class or hardware configuration (such as GPUs). If your requests are less than the minimum, Autopilot automatically modifies your workload configuration to bring your requests within the allowed range. If your requests are greater than the maximum, Autopilot rejects your workload and displays an error message.

The following list summarizes the categories of resource requests:

  • Default resource requests: Autopilot adds these if you don't specify your own requests for workloads
  • Minimum and maximum resource requests: Autopilot validates your specified requests to ensure that they're within these limits. If your requests are outside the limits, Autopilot modifies your workload requests.
  • Workload separation and extended duration requests: Autopilot has different default values and different minimum values for workloads that you separate from each other, or for Pods that get extended protection from GKE-initiated eviction.
  • Resource requests for DaemonSets: Autopilot has different default, minimum, and maximum values for containers in DaemonSets.

How to request resources

In Autopilot, you request resources in your Pod specification. The supported minimum and maximum resources that you can request change based on the hardware configuration of the node on which the Pods run. To learn how to request specific hardware configurations, refer to the following pages:

Default resource requests

If you don't specify resource requests for some containers in a Pod, Autopilot applies default values. These defaults are suitable for many smaller workloads.

Additionally, Autopilot applies the following default resource requests regardless of the selected compute class or hardware configuration:

  • Containers in DaemonSets

    • CPU: 50 mCPU
    • Memory: 100 MiB
    • Ephemeral storage: 100 MiB
  • All other containers

    • Ephemeral storage: 1 GiB

For more information about Autopilot cluster limits, see Quotas and limits.

Default requests for compute classes

Autopilot applies the following default values to resources that are not defined in the Pod specification for Pods that run on compute classes:

Compute class Resource Default request
General-purpose CPU 0.5 vCPU
Memory 2 GiB
Balanced CPU 0.5 vCPU
Memory 2 GiB
Performance CPU
  • C3 machine series: 2 vCPU
  • C3 machine series with Local SSD: 2 vCPU
  • C3D machine series: 2 vCPU
  • C3D machine series with Local SSD: 4 vCPU
  • H3 machine series: 80 vCPU
  • C2 machine series: 2 vCPU
  • C2D machine series: 2 vCPU
  • T2A machine series: 2 vCPU
  • T2D machine series: 2 vCPU
Memory
  • C3 machine series: 8 GiB
  • C3 machine series with Local SSD: 8 GiB
  • C3D machine series: 8 GiB
  • C3D machine series with Local SSD: 16 GiB
  • H3 machine series: 320 GiB
  • C2 machine series: 8 GiB
  • C2D machine series: 8 GiB
  • T2A machine series: 8 GiB
  • T2D machine series: 8 GiB
Ephemeral storage
  • C3 machine series: 1 GiB
  • C3 machine series with Local SSD: 1 GiB
  • C3D machine series: 1 GiB
  • C3D machine series with Local SSD: 1 GiB
  • H3 machine series: 1 GiB
  • C2 machine series: 1 GiB
  • C2D machine series: 1 GiB
  • T2A machine series: 1 GiB
  • T2D machine series: 1 GiB
Scale-Out CPU 0.5 vCPU
Memory 2 GiB

Default requests for other hardware configurations

Autopilot applies the following default values to resources that are not defined in the Pod specification for Pods that run on nodes with specialized hardware, such as GPUs:

Hardware Resource Total default request
A100 (40GB) GPUs
nvidia-tesla-a100
CPU
  • 1 GPU: 9 vCPU
  • 2 GPUs: 20 vCPU
  • 4 GPUs: 44 vCPU
  • 8 GPUs: 92 vCPU
  • 16 GPUs: 92 vCPU
Memory
  • 1 GPU: 60 GiB
  • 2 GPUs: 134 GiB
  • 4 GPUs: 296 GiB
  • 8 GPUs: 618 GiB
  • 16 GPUs: 1250 GiB
A100 (80GB) GPUs
nvidia-a100-80gb
CPU
  • 1 GPU: 9 vCPU
  • 2 GPUs: 20 vCPU
  • 4 GPUs: 44 vCPU
  • 8 GPUs: 92 vCPU
Memory
  • 1 GPU: 134 GiB
  • 2 GPUs: 296 GiB
  • 4 GPUs: 618 GiB
  • 8 GPUs: 1250 GiB
Ephemeral storage
  • 1 GPU: 1 GiB
  • 2 GPUs: 1 GiB
  • 4 GPUs: 1 GiB
  • 8 GPUs: 1 GiB
L4 GPUs
nvidia-l4
CPU
  • 1 GPU: 2 vCPU
  • 2 GPUs: 21 vCPU
  • 4 GPUs: 45 vCPU
  • 8 GPUs: 93 vCPU
Memory
  • 1 GPU: 7 GiB
  • 2 GPUs: 78 GiB
  • 4 GPUs: 170 GiB
  • 8 GPUs: 355 GiB
T4 GPUs
nvidia-tesla-t4
CPU 0.5 vCPU
Memory 2 GiB

Minimum and maximum resource requests

The total resources requested by your deployment configuration should be within the supported minimum and maximum values that Autopilot allows. The following conditions apply:

  • The ephemeral storage request must be between 10 MiB and 10 GiB for all compute classes and hardware configurations unless otherwise specified. For larger volumes, it is recommended to use generic ephemeral volumes which provide equivalent functionality and performance to ephemeral storage but with significantly more flexibility as they can be used with any GKE storage option. For example, the maximum size for a generic ephemeral volume using pd-balanced is 64 TiB.
  • The CPU:memory ratio must be within the allowed range for the selected compute class or hardware configuration. If your CPU:memory ratio is outside the allowed range, Autopilot automatically increases the smaller resource. For example, if you request 1 vCPU and 16 GiB of memory (1:16 ratio) for Pods running on the Scale-Out class, Autopilot increases the CPU request to 4 vCPUs, which changes the ratio to 1:4.

Minimums and maximums for compute classes

The following table describes the minimum, maximum, and allowed CPU:memory ratio for each compute class that Autopilot supports:

Compute class CPU:memory ratio (vCPU:GiB) Resource Minimum Maximum
General-purpose Between 1:1 and 1:6.5 CPU 0.25 vCPU 30 vCPU
Memory 0.5 GiB 110 GiB
Balanced Between 1:1 and 1:8 CPU 0.25 vCPU

222 vCPU

If minimum CPU platform selected:

  • Intel platforms: 126 vCPU
  • AMD platforms: 222 vCPU
Memory 0.5 GiB

851 GiB

If minimum CPU platform selected:

  • Intel platforms: 823 GiB
  • AMD platforms: 851 GiB
Performance Not enforced CPU 0.001 vCPU
  • C3 machine series: 174 vCPU
  • C3 machine series with Local SSD: 174 vCPU
  • C3D machine series: 358 vCPU
  • C3D machine series with Local SSD: 358 vCPU
  • H3 machine series: 86 vCPU
  • C2 machine series: 58 vCPU
  • C2D machine series: 110 vCPU
  • T2A machine series: 46 vCPU
  • T2D machine series: 58 vCPU
Memory 1 MiB
  • C3 machine series: 1,345 GiB
  • C3 machine series with Local SSD: 670 GiB
  • C3D machine series: 2750 GiB
  • C3D machine series with Local SSD: 1,375 GiB
  • H3 machine series: 330 GiB
  • C2 machine series: 218 GiB
  • C2D machine series: 835 GiB
  • T2A machine series: 172 GiB
  • T2D machine series: 218 GiB
Ephemeral storage 10 MiB
  • C3 machine series: 250 GiB
  • C3 machine series with Local SSD: 10,000 GiB
  • C3D machine series: 250 GiB
  • C3D machine series with Local SSD: 10,000 GiB
  • H3 machine series: 250 GiB
  • C2 machine series: 250 GiB
  • C2D machine series: 250 GiB
  • T2A machine series: 250 GiB
  • T2D machine series: 250 GiB
Scale-Out 1:4 CPU 0.25 vCPU
  • arm64: 43 vCPU
  • amd64: 54 vCPU
Memory 1 GiB
  • arm64: 172 GiB
  • amd64: 216 GiB

To learn how to request compute classes in your Autopilot Pods, refer to Choose compute classes for Autopilot Pods.

Minimums and maximums for other hardware configurations

The following table describes the minimum, maximum, and allowed CPU:memory ratio for Pods that run on nodes with specific hardware such as GPUs:

Hardware CPU:memory ratio (vCPU:GiB) Resource Minimum Maximum
A100 (40GB) GPUs
nvidia-tesla-a100
Not enforced CPU
  • 1 GPU: 9 vCPU
  • 2 GPUs: 20 vCPU
  • 4 GPUs: 44 vCPU
  • 8 GPUs: 92 vCPU
  • 16 GPUs: 92 vCPU
  • 1 GPU: 11 vCPU
  • 2 GPUs: 22 vCPU
  • 4 GPUs: 46 vCPU
  • 8 GPUs: 94 vCPU
  • 16 GPUs: 94 vCPU

The sum of CPU requests of all DaemonSets that run on an A100 GPU node must not exceed 2 vCPU.

Memory
  • 1 GPU: 60 GiB
  • 2 GPUs: 134 GiB
  • 4 GPUs: 296 GiB
  • 8 GPUs: 618 GiB
  • 16 GPUs: 1250 GiB
  • 1 GPU: 74 GiB
  • 2 GPUs: 148 GiB
  • 4 GPUs: 310 GiB
  • 8 GPUs: 632 GiB
  • 16 GPUs: 1264 GiB

The sum of memory requests of all DaemonSets that run on an A100 GPU node must not exceed 14 GiB.

A100 (80GB) GPUs
nvidia-a100-80gb
Not enforced CPU
  • 1 GPU: 9 vCPU
  • 2 GPUs: 20 vCPU
  • 4 GPUs: 44 vCPU
  • 8 GPUs: 92 vCPU
  • 1 GPU: 11 vCPU
  • 2 GPUs: 22 vCPU
  • 4 GPUs: 46 vCPU
  • 8 GPUs: 94 vCPU

The sum of CPU requests of all DaemonSets that run on an A100 (80GB) GPU node must not exceed 2 vCPU.

Memory
  • 1 GPU: 134 GiB
  • 2 GPUs: 296 GiB
  • 4 GPUs: 618 GiB
  • 8 GPUs: 1250 GiB
  • 1 GPU: 148 GiB
  • 2 GPUs: 310 GiB
  • 4 GPUs: 632 GiB
  • 8 GPUs: 1264 GiB

The sum of memory requests of all DaemonSets that run on an A100 (80GB) GPU node must not exceed 14 GiB.

Ephemeral storage
  • 1 GPU: 512 MiB
  • 2 GPUs: 512 MiB
  • 4 GPUs: 512 MiB
  • 8 GPUs: 512 MiB
  • 1 GPU: 280 GiB
  • 2 GPUs: 585 GiB
  • 4 GPUs: 1220 GiB
  • 8 GPUs: 2540 GiB
L4 GPUs
nvidia-l4
  • 1 GPU: Between 1:3.5 and 1:4
  • 2, 4, and 8 GPUs: Not enforced
CPU
  • 1 GPU: 2 vCPU
  • 2 GPUs: 21 vCPU
  • 4 GPUs: 45 vCPU
  • 8 GPUs: 93 vCPU
  • 1 GPU: 31 vCPU
  • 2 GPUs: 23 vCPU
  • 4 GPUs: 47 vCPU
  • 8 GPUs: 95 vCPU

The sum of CPU requests of all DaemonSets that run on an L4 GPU node must not exceed 2 vCPU.

Memory
  • 1 GPU: 7 GiB
  • 2 GPUs: 78 GiB
  • 4 GPUs: 170 GiB
  • 8 GPUs: 355 GiB
  • 1 GPU: 115 GiB
  • 2 GPUs: 86 GiB
  • 4 GPUs: 177 GiB
  • 8 GPUs: 363 GiB

The sum of memory requests of all DaemonSets that run on an L4 GPU node must not exceed 14 GiB.

Ephemeral storage All GPU quantities: 512 MiB All GPU quantities: 10 GiB
T4 GPUs
nvidia-tesla-t4
Between 1:1 and 1:6.25 CPU 0.5 vCPU
  • 1 GPU: 46 vCPU
  • 2 GPUs: 46 vCPU
  • 4 GPUs: 94 vCPU
Memory 0.5 GiB
  • 1 GPU: 287.5 GiB
  • 2 GPUs: 287.5 GiB
  • 4 GPUs: 587.5 GiB

To learn how to request GPUs in your Autopilot Pods, refer to Deploy GPU workloads in Autopilot.

Resource requests for workload separation and extended duration

Autopilot lets you manipulate Kubernetes scheduling and eviction behavior using methods such as the following:

If your specified requests are less than the minimums, the behavior of Autopilot changes based on the method that you used, as follows:

  • Taints, tolerations, selectors, and extended duration Pods: Autopilot modifies your Pods to increase the requests when scheduling the Pods.
  • Pod anti-affinity: Autopilot rejects the Pod and displays an error message.

The following table describes the default requests and the minimum resource requests that you can specify. If a configuration or compute class isn't in this table, Autopilot doesn't enforce special minimum or default values.

Compute class Resource Default Minimum
General-purpose CPU 0.5 vCPU 0.5 vCPU
Memory 2 GiB 0.5 GiB
Balanced CPU 2 vCPU 1 vCPU
Memory 8 GiB 4 GiB
Scale-Out CPU 0.5 vCPU 0.5 vCPU
Memory 2 GiB 2 GiB

Init containers

Init containers run in serial and must complete before the application containers start. If you don't specify resource requests for your Autopilot init containers, GKE allocates the total resources available to the Pod to each init container. This behavior is different than in GKE Standard, where each init container can use any unallocated resources available on the node on which the Pod is scheduled.

Unlike application containers, GKE recommends that you don't specify resource requests for Autopilot init containers, so that each container gets the full resources available to the Pod. If you request less resources than the defaults, you constrain your init container. If you request more resources than the Autopilot defaults, you might increase your bill for the lifetime of the Pod.

Setting resource limits in Autopilot

Kubernetes lets you set both requests and limits for resources in your Pod specification. The behavior of your Pods changes depending on whether your limits are different than your requests, as described in the following table:

cpu: "400m" limits: cpu: "400m"
Values set Autopilot behavior Example
requests equal to limits Pods use the Guaranteed QoS class.
requests set, limits not set Pods can burst into available resource capacity.
requests not set, limits set Autopilot sets requests to the value of limits, which is the default Kubernetes behavior.

Before:

resources:
  limits:
    cpu: "400m"

After:

resources:
  requests:
    cpu: "250m"
  limits:
    cpu: "250m"
requests less than limits Pods can burst up to the value specified in limits
requests greater than limits Autopilot sets requests to the value of limits.

Before:

resources:
  requests:
    cpu: "450m"
  limits:
    cpu: "400m"

After:

resources:
  requests:
    cpu: "400m"
  limits:
    cpu: "250m"
requests not set, limits not set Autopilot sets requests to the default values for the compute class or hardware configuration. Autopilot doesn't set limits.

In most situations, set adequate resource requests and equal limits for your workloads.

Automatic resource management in Autopilot

If your specified resource requests for your workloads are outside of the allowed ranges, or if you don't request resources for some containers, Autopilot modifies your workload configuration to comply with the allowed limits. Autopilot calculates resource ratios, increments, and the resource scale up requirements after applying default values to containers with no request specified.

  • Missing requests: If you don't request resources in some containers, Autopilot applies the default requests for the compute class or hardware configuration.
  • CPU:memory ratio: Autopilot scales up the smaller resource to bring the ratio within the allowed range.
  • Ephemeral storage: Autopilot modifies your ephemeral storage requests to meet the minimum amount required by each container. The cumulative value of storage requests across all containers cannot be more than the maximum allowed value. Autopilot scales the request down if the value exceeds the maximum.
  • Requests below minimums: If you request fewer resources than the allowed minimum for the selected hardware configuration, Autopilot automatically modifies the Pod to request at least the minimum resource value.

By default, when Autopilot automatically scales a resource up to meet a minimum or default resource value, GKE allocates the extra capacity to the first container in the Pod manifest. In GKE version 1.27.2-gke.2200 and later, you can tell GKE to allocate the extra resources to a specific container by adding the following to the annotations field in your Pod manifest:

autopilot.gke.io/primary-container: "CONTAINER_NAME"

Replace CONTAINER_NAME with the name of the container.

Resource modification examples

The following example scenario shows how Autopilot modifies your workload configuration to meet the requirements of your running Pods and containers.

Single container with < 0.25 vCPU

Container number Original request Modified request
1 CPU: 180 mCPU
Memory: 0.5 GiB
Ephemeral storage: 10 MiB
CPU: 250 mCPU
Memory: 0.5 GiB
Ephemeral storage: 10 MiB

Multiple containers with total CPU < 0.25 vCPU

Container number Original requests Modified requests
1 CPU: 0.07 vCPU
Memory: 0.5 GiB
Ephemeral storage: 10 MiB
CPU: 0.11 vCPU
Memory: 0.5 GiB
Ephemeral storage: 10 MiB
2 CPU: 0.07 vCPU
Memory: 0.5 GiB
Ephemeral storage: 10 MiB
CPU: 0.07 vCPU
Memory: 0.5 GiB
Ephemeral storage: 10 MiB
3 CPU: 0.07 vCPU
Memory: 0.5 GiB
Ephemeral storage: 10 MiB
CPU: 0.07 vCPU
Memory: 0.5 GiB
Ephemeral storage: 10 MiB
Total Pod resources CPU: 0.25 vCPU
Memory: 1.5 GiB
Ephemeral storage: 30 MiB

Multiple containers with more than 0.25 vCPU total

For multiple containers with total resources >= 0.25 vCPU, the CPU is rounded to multiples of 0.25 vCPU and the extra CPU is added to the first container. In this example, the original cumulative CPU is 0.32 vCPU and is modified to a total of 0.5 vCPU.

Container number Original requests Modified requests
1 CPU: 0.17 vCPU
Memory: 0.5 GiB
Ephemeral storage: 10 MiB
CPU: 0.35 vCPU
Memory: 0.5 GiB
Ephemeral storage: 10 MiB
2 CPU: 0.08 vCPU
Memory: 0.5 GiB
Ephemeral storage: 10 MiB
CPU: 0.08 vCPU
Memory: 0.5 GiB
Ephemeral storage: 10 MiB
3 CPU: 0.07 vCPU
Memory: 0.5 GiB
Ephemeral storage: 10 MiB
CPU: 0.07 vCPU
Memory: 0.5 GiB
Ephemeral storage: 10 MiB
4 Init container, resources not defined Will receive Pod resources
Total Pod resources CPU: 0.5 vCPU
Memory: 1.5 GiB
Ephemeral storage: 30 MiB

Single container with memory too low for requested CPU

In this example, the memory is too low for the amount of CPU (1 vCPU:1 GiB minimum). The minimum allowed ratio for CPU to memory is 1:1. If the ratio is lower than that, the memory request is increased.

Container number Original request Modified request
1 CPU: 4 vCPU
Memory: 1 GiB
Ephemeral storage: 10 MiB
CPU: 4 vCPU
Memory: 4 GiB
Ephemeral storage: 10 MiB
Total Pod resources CPU: 4 vCPU
Memory: 4 GiB
Ephemeral storage: 10 MiB

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