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description= GPU support for Cloud Run worker pools;
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erview angular ssr next js nuxt js sveltekit deploy a sample function deploy a function using the console deploy a function using gcloud execute a sample job execute a job execute a job from source code go node js python java shell deploy a sample worker pool develop set up your environment plan and prepare your service develop your service containerize your code connect to google cloud services install a system package in your container run gcloud commands within your container plan and prepare your function overview compare cloud run functions write cloud run functions runtimes overview node js overview node js dependencies python overview python dependencies go overview go dependencies java overview java dependencies net ruby php local functions development function triggers tutorials create a function that returns bigquery results create a function that returns spanner results integrate with cloud databases codelabs build and test build sources to containers build functions to containers local testing serve http requests deploy services deploy container images continuous deployment from git deploy from source code deploy from compose use the cloud run remote mcp server deploy functions serve web traffic mapping custom domains serving static assets with cdn serving traffic from multiple regions automate failover with service health enable session affinity frontend proxying using nginx manage services view copy or delete services view or delete revisions traffic migration gradual rollouts rollbacks configure services overview capacity memory limits cpu limits gpu gpu configuration gpu performance best practices request timeout maximum concurrent requests about maximum concurrent requests per instance configure maximum concurrent requests billing optimize service configurations with recommender environment container port and entrypoint environment variables volume mounts cloud storage volumes nfs volumes in memory volumes cifs smb ephemeral disk execution environment sandboxes container health checks http 2 requests secrets service identity scaling about instance autoscaling for services maximum instances about maximum instances for services configure maximum instances minimum instances configure custom scaling controls manual scaling metadata description labels tags source deploy configurations supported language runtimes and base images configure automatic base image updates build environment variables build service account build worker pools invoke and trigger services invoke with https requests host a webhook target stream with websockets overview build a websocket chat service tutorial invoke asynchronously invoke services on a schedule create a workflow invoke services as part of a workflow connect a series of services from cloud functions and cloud run tutorial execute asynchronous tasks call a service from a pub sub push subscription trigger service from pub sub integrate image processing into pub sub sample tutorial trigger from events create triggers with eventarc pub sub triggers create pub sub eventarc triggers trigger functions from pub sub using eventarc trigger functions from routed log entries cloud storage triggers create triggers with cloud storage trigger services from cloud storage using eventarc trigger functions from cloud storage using eventarc firestore triggers create triggers with firestore trigger functions from events in a firestore database connect with other services using grpc best practices general development tips for services cost optimization optimize java services optimize python services optimize node js services load testing best practices understand zonal redundancy functions best practices overview configure event driven function retries execute job tasks to completion create jobs execute jobs execute jobs execute scheduled jobs execute jobs from workflows configure jobs container entrypoint cpu limits memory limits gpu gpu configuration gpu best practices environment variables container health checks volume mounts cloud storage volumes nfs volumes in memory volumes using cifs smb network file systems ephemeral disk labels maximum retries parallelism secrets service identity task timeout tags manage jobs view or delete jobs view or stop job executions best practices jobs retries and checkpoints cost optimization perform continuous background work deploy worker pools deploy worker pools deploy worker pools from source code manage worker pools view or delete worker pools view or delete worker pool revisions instance splits and rollbacks configure worker pools capacity memory limits cpu limits gpu gpu configuration gpu best practices environment container and entrypoint environment variables volume mounts cloud storage volumes nfs volumes in memory volumes using cifs smb network file systems ephemeral disk container health checks secrets service identity instance count metadata description labels scale based on external metrics autoscale worker pools with external metrics kafka autoscaler host github runners with worker pools autoscale worker pools based on prometheus metrics autoscale worker pools with pub sub pull subscriptions automate scaling with workflows cost optimization configure networking best practices for cloud run networking configure private networking send traffic to vpc network overview direct vpc register private ips for worker pools using cloud dns dual stack ipv4 and ipv6 migrate standard vpc connector to direct vpc vpc connectors send traffic to shared vpc network overview direct vpc migrate shared vpc connector to direct vpc connectors in service projects connectors in host project static outbound ip address network security restrict endpoint ingress services use vpc service controls vpc sc cloud service mesh secure security design overview authenticate requests overview allow public access custom audiences authenticate developers service to service authenticate users end user authentication tutorial secure your resources access control with iam configure iap for cloud run introduction to service identity protect services with cloud armor use binary authorization use cloud run threat detection use customer managed encryption keys manage custom constraints for projects view software supply chain security insights secure cloud run services tutorial multi tenant platforms running untrusted code monitor and log monitoring and logging overview view built in metrics write prometheus metrics write opentelemetry metrics log and view logs audit logging error reporting use distributed tracing for services run ai solutions overview explore resources ai agents overview build and deploy a2a agents overview deploy a2a agents build and deploy adk agents build and deploy n8n agents mcp servers overview build and deploy a remote mcp server tools code execution browser automation inference with gpus overview services run llm inference on cloud run gpus with ollama run agents with gemma 4 models on cloud run run opencv on cloud run with gpu acceleration run llm inference on cloud run gpus with hugging face transformers js jobs fine tune llms using gpus with cloud run jobs run batch inference using gpus with cloud run jobs gpu accelerated video transcoding with ffmpeg ai assisted development and vibe coding introduction to cloud run for ai assisted developers cookbook migrate an existing web service from app engine from cloud run functions 1st gen from aws lambda from heroku from cloud foundry migration overview choose an oci compliant strategy migrate to oci containers migrate configuration sample migration spring music from vmware tanzu from a vm using migrate to containers from kubernetes to gke troubleshoot introduction troubleshoot errors local troubleshooting tutorial known issues samples all cloud run code samples all cloud run functions code samples code samples for all products ai and ml application development application hosting compute data analytics and pipelines databases distributed hybrid and multicloud industry solutions migration networking observability and monitoring security storage access and resources management costs and usage management infrastructure as code sdk languages frameworks and tools home documentation application hosting cloud run guides send feedback gpu support for worker pools stay organized with collections save and categorize content based on your preferences this page describes gpu configuration for your cloud run workerpools 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 important if you are using llm with the cloud run gpu feature make sure you also consult best practices cloud run workerpools with gpus 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 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 535 x x 12 2 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 co 2 us central1 iowa low co 2 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 co 2 europe west4 netherlands low co 2 us central1 iowa low co 2 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 is a difference in cost between gpu zonal redundancy and non zonal redundancy see cloud run pricing for gpu pricing details gpu worker pools cannot be autoscaled you are charged for the gpu even if the gpu is not running any process and as long as the worker pool gpu instance is running cpu and memory for worker pools is priced differently than services and jobs however gpu sku is priced the same as services and jobs 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 worker pool 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 worker pools during zonal outages you can configure zonal redundancy specifically for gpus zonal redundancy turned on default cloud run reserves gpu capacity for your worker pool across multiple zones this significantly increases the probability that your worker pool 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 worker pool 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 worker pool use the links provided in the following buttons to request the quota you need important quota increase for gpus with zonal redundancy turned off are more likely to be granted and will be made available more quickly 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 note if you don t plan to keep the resources that you create in this procedure create a project instead of selecting an...
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