GPU support for services

This page describes GPU configuration for your Cloud Run services. GPUs work well for AI inference workloads, such as large language models (LLMs) or other compute intensive non-AI use cases such as video transcoding and 3D rendering. Google provides NVIDIA RTX PRO 6000 Blackwell GPU with 96 GB of GPU memory (VRAM) and NVIDIA L4 GPUs with 24 GB of GPU memory (VRAM), which is separate from the instance memory.

GPU on Cloud Run is fully managed, with no extra drivers or libraries needed. The GPU feature offers on-demand availability with no reservations needed, similar to the way on-demand CPU and on-demand memory work in Cloud Run. Instances of a Cloud Run service that has been configured to use GPU can scale down to zero for cost savings when not in use.

Cloud Run instances with an attached NVIDIA RTX PRO 6000 Blackwell GPU or L4 GPU with drivers pre-installed start in approximately 5 seconds, at which point the processes running in your container can start to use the GPU.

You can configure one GPU per Cloud Run instance. If you use sidecar containers, note that the GPU can only be attached to one container.

Supported GPU types

Cloud Run supports two types of GPUs:

• NVIDIA RTX PRO 6000 Blackwell GPU with the current NVIDIA driver version: 580.x.x (13.0). For NVIDIA RTX PRO 6000 Blackwell GPU, you must use a minimum of 20 CPU and 80 GiB of memory.
• L4 GPU with the current NVIDIA driver version: 580.x.x (13.0). For L4 GPUs, you must use a minimum of 4 CPU and 16 GiB of memory.

Supported regions

The following regions are supported by the NVIDIA RTX PRO 6000 Blackwell GPU:

• asia-southeast1 (Singapore).
• asia-south2 (Delhi, India).
• europe-west4 (Netherlands) Low CO₂
• us-central1 (Iowa) Low CO₂

The following regions are supported by the L4 GPU:

• asia-southeast1 (Singapore)
• asia-south1 (Mumbai) . This region is available by invitation only. Contact your Google Account team if you are interested in this region.
• europe-west1 (Belgium) Low CO₂
• europe-west4 (Netherlands) Low CO₂
• us-central1 (Iowa) Low CO₂ . Additional resource scaling in this region might require a quota increase request. Contact your Google Account team if you are interested in this region.
• us-east4 (Northern Virginia) . Additional resource scaling in this region might require a quota increase request. Contact your Google Account team if you are interested in this region.

Pricing impact

See Cloud Run pricing for GPU pricing details. Note the following requirements and considerations:

• There are no per request fees. You must use instance-based billing to use the GPU feature, minimum instances are charged at the full rate even when idle.
• There is a difference in cost between GPU zonal redundancy and non-zonal redundancy. See Cloud Run pricing for GPU pricing details.
• When you deploy a Cloud Run service or function from source code with GPUs enabled, Cloud Run uses the e2-highcpu-8 machine type, instead of the default e2-standard-2 machine type to build your source code. The larger machine type provides higher CPU support, and higher network bandwidth which results in faster build times.
• The CPU and memory configurations of your resource.
• GPU is billed for the entire duration of the instance lifecycle.

GPU zonal redundancy options

By default, Cloud Run deploys your service across multiple zones within a region. This architecture provides inherent resilience: if a zone experiences an outage, Cloud Run automatically routes traffic away from the affected zone to healthy zones within the same region.

When working with GPU resources, keep in mind GPU resources have specific capacity constraints. During a zonal outage, the standard failover mechanism for GPU workloads relies on sufficient unused GPU capacity being available in the remaining healthy zones. Due to the constrained nature of GPUs, this capacity might not always be available.

To increase the availability of your GPU-accelerated services during zonal outages, you can configure zonal redundancy specifically for GPUs:

• Zonal Redundancy Turned On (default): Cloud Run reserves GPU capacity for your service across multiple zones. This significantly increases the probability that your service can successfully handle traffic rerouted from an affected zone, offering higher reliability during zonal failures with additional cost per GPU second.

• Zonal Redundancy Turned Off: Cloud Run attempts failover for GPU workloads on a best-effort basis. Traffic is routed to other zones only if sufficient GPU capacity is available at that moment. This option does not guarantee reserved capacity for failover scenarios but results in a lower cost per GPU second.

SLA

The SLA for Cloud Run GPU depends on whether the service uses the zonal redundancy or non-zonal redundancy option. Refer to the SLA page for details.

Request a quota increase

Quota for Cloud Run nvidia-rtx-pro-6000 GPU is granted in milliGPUs. Projects using nvidia-rtx-pro-6000 GPU in a region for the first time will be automatically granted 3,000 milliGPU quota (zonal redundancy off) when the first deployment is created. This is equivalent to 3 GPUs. Projects using Cloud Run nvidia-l4 GPUs in a region for the first time are automatically granted 3 GPU quota (zonal redundancy off) when the first deployment is created.

If you need additional Cloud Run GPUs, you must request a quota increase for your Cloud Run service. Use the links provided in the following buttons to request the quota you need.

Quota needed	Quota link
NVIDIA RTX PRO 6000 Blackwell GPU with zonal redundancy turned off (lower price)	Request GPU quota without zonal redundancy
NVIDIA RTX PRO 6000 Blackwell GPU with zonal redundancy turned on (higher price)	Request GPU quota with zonal redundancy
L4 GPU with zonal redundancy turned off (lower price)	Request GPU quota without zonal redundancy
L4 GPU with zonal redundancy turned on (higher price)	Request GPU quota with zonal redundancy

For more information on requesting quota increases, see How to increase quota.

Before you begin

The following list describes requirements and limitations when using GPUs in Cloud Run:

Sign in to your Google Cloud account. If you're new to Google Cloud, create an account to evaluate how our products perform in real-world scenarios. New customers also get $300 in free credits to run, test, and deploy workloads.

In the Google Cloud console, on the project selector page, select or create a Google Cloud project.

Roles required to select or create a project

• Select a project: Selecting a project doesn't require a specific IAM role—you can select any project that you've been granted a role on.
• Create a project: To create a project, you need the Project Creator role (roles/resourcemanager.projectCreator), which contains the resourcemanager.projects.create permission. Learn how to grant roles.

Go to project selector

Verify that billing is enabled for your Google Cloud project.

In the Google Cloud console, on the project selector page, select or create a Google Cloud project.

Roles required to select or create a project

• Select a project: Selecting a project doesn't require a specific IAM role—you can select any project that you've been granted a role on.
• Create a project: To create a project, you need the Project Creator role (roles/resourcemanager.projectCreator), which contains the resourcemanager.projects.create permission. Learn how to grant roles.

Go to project selector

Verify that billing is enabled for your Google Cloud project.

1. Enable the Cloud Run API.

   Roles required to enable APIs

   To enable APIs, you need the serviceusage.services.enable permission. If you created the project, then you likely already have this permission through the Owner role (roles/owner). Otherwise, you can get this permission through the Service Usage Admin role (roles/serviceusage.serviceUsageAdmin). Learn how to grant roles.

   Enable the API

2. Request required quota.
3. Consult Best practices: AI inference on Cloud Run with GPUs for recommendations on building your container image and loading large models.
4. Make sure your Cloud Run service has the following configurations:
• Configure the billing settings to instance-based billing. Note that services that are set to instance-based billing can still scale to zero.
• For NVIDIA RTX PRO 6000 Blackwell GPU, configure a minimum of 20 CPU and a minimum of 80 GiB of memory.
• For L4 GPU, configure a minimum of 4 CPU for your service, with 8 CPU recommended, and a minimum of 16 GiB of memory, with 32 GiB recommended.
• Determine and set an optimal maximum concurrency for your GPU usage.
• Maximum instances must be set to a number that is below the allowed quota per project per region for GPU. See About GPUs and maximum instances.

Required roles

To get the permissions that you need to configure and deploy Cloud Run services, ask your administrator to grant you the following IAM roles on services:

• Cloud Run Developer (roles/run.developer) - the Cloud Run service
• Service Account User (roles/iam.serviceAccountUser) - the service identity

If you are deploying a service or function from source code, you must also have additional roles granted to you on your project and Cloud Build service account.

For a list of IAM roles and permissions that are associated with Cloud Run, see Cloud Run IAM roles and Cloud Run IAM permissions. If your Cloud Run service interfaces with Google Cloud APIs, such as Cloud Client Libraries, see the service identity configuration guide. For more information about granting roles, see deployment permissions and manage access.

Configure a Cloud Run service with GPU

Any configuration change leads to the creation of a new revision. Subsequent revisions will also automatically get this configuration setting unless you make explicit updates to change it.

You can use the Google Cloud console, Google Cloud CLI or YAML to configure GPU.