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d cuds purchase resource based commitments without attached reservations with attached reservations for os licenses manage resource based commitments renew commitments automatically extend commitment terms merge and split commitments upgrade commitments share resource based cuds across projects get discounts for sustained usage disk performance optimize hyperdisk performance optimize persistent disk performance optimize local ssd performance workload performance set the number of threads per core customize the number of visible cpu cores analyze the cpu performance using the pmu pmu overview enable the pmu in vms manage the pmu in vms network performance network bandwidth use google virtual nic use irdma network driver use idpf network interface configure a vm with higher bandwidth reduce latency by using compact placement policies optimize tcp network communication optimize tcp network performance optimize tcp network resiliency benchmark higher bandwidth vms optimize app latency with load balancing use dpdk to improve network performance network performance and gpu vms networking and gpu machines use higher network bandwidth patterns for using multiple host nics troubleshoot general tips troubleshoot connectivity troubleshoot rdp troubleshoot ssh troubleshoot os login troubleshoot vms troubleshoot vm operations troubleshoot vm creation troubleshoot resource availability errors troubleshoot bulk api vm creation troubleshoot vm reboots and shutdowns troubleshoot vm suspension troubleshoot vm updates troubleshoot unresponsive vms troubleshoot vm startup troubleshoot fstab errors troubleshoot kernel panic collecting core dumps rescue an inaccessible vm troubleshoot cpu bus locks troubleshoot cpu soft lockups troubleshoot vm configurations troubleshoot arm vms troubleshoot gpu vms troubleshoot nvidia gpu errors generate a nvidia bug report for blackwell gpus troubleshoot nested virtualization troubleshoot using vm screenshots troubleshoot sole tenant nodes troubleshoot vm performance issues troubleshoot sudoers files troubleshoot windows vms troubleshoot windows vms collecting diagnostic information troubleshoot using the serial console troubleshoot using the serial console viewing serial port output troubleshoot instance groups troubleshoot managed instance groups migs troubleshoot os management troubleshoot licenses troubleshoot image import and export troubleshooting sles pay as you go registration troubleshooting ubuntu pro registration troubleshoot metadata server troubleshoot metadata server troubleshoot networking issues troubleshoot common networking issues troubleshoot network drivers troubleshoot vm performance issues troubleshoot storage troubleshoot disk creation troubleshoot full disks and disk resizing troubleshoot disk encryption troubleshoot nvme disks troubleshoot instant snapshots troubleshoot standard snapshots troubleshoot reservations and commitments troubleshoot reservation creation troubleshoot reservation consumption troubleshooting reservation monitoring troubleshoot reservation updates troubleshoot future reservation creation and updates troubleshoot automatic commitment renewal troubleshoot quota errors troubleshoot concurrent operation quota errors troubleshoot workload authentication troubleshoot default service accounts troubleshoot workload to workload authentication 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 compute compute engine guides send feedback about gpu instances stay organized with collections save and categorize content based on your preferences this document describes the features and limitations of compute engine instances that have attached gpus to accelerate specific workloads on compute engine you can either deploy an accelerator optimized instance that has attached gpus or attach gpus to an n1 general purpose instance for most workloads compute engine provides gpus for your instances in pass through mode which provides your instances with direct control over gpus and their memory however for workloads that are more graphics intensive and run on g4 g2 or n1 gpus you can use nvidia rtx virtual workstations vws on g4 instances nvidia rtx vws enables the use of fractional gpu machine types with these machine types a single physical gpu can be shared by multiple virtual workstations by assigning a virtual gpu vgpu to each instance g2 and n1 instances support vws but don t support fractional vgpu machine types you can also use some gpu machine types on ai hypercomputer ai hypercomputer is a supercomputing system that is optimized to support your artificial intelligence ai and machine learning ml workloads this option is recommended for creating a densely allocated performance optimized infrastructure that has integrations for google kubernetes engine gke and slurm schedulers supported machine types compute engine offers different machine types to support your various workloads some machine types support nvidia rtx virtual workstations vws when you create an instance that uses nvidia rtx virtual workstation compute engine automatically adds a vws license for information about pricing for virtual workstations see the gpu pricing page gpu machine types ai and ml workloads graphics and visualization other gpu workloads accelerator optimized a series machine types are designed for high performance computing hpc artificial intelligence ai and machine learning ml workloads the later generation a series are ideal for pre training and fine tuning foundation models that involves large clusters of accelerators while the a2 series can be used for training smaller models and single host inference for these machine types the gpu model is automatically attached to the instance accelerator optimized g series machine types are designed for workloads such as nvidia omniverse simulation workloads graphics intensive applications video transcoding and virtual desktops these machine types support nvidia rtx virtual workstations vws the g series can also be used for training smaller models and for single host inference for these machine types the gpu model is automatically attached to the instance for n1 general purpose machine types except for the n1 shared core f1 micro and g1 small you can attach a select set of gpu models some of these gpu models also support nvidia rtx virtual workstations vws a4x max nvidia gb300 ultra superchips nvidia gb300 a4x nvidia gb200 superchips nvidia gb200 a4 nvidia b200 nvidia b200 a3 ultra nvidia h200 nvidia h200 141gb a3 mega nvidia h100 nvidia h100 mega 80gb a3 high nvidia h100 nvidia h100 80gb a3 edge nvidia h100 nvidia h100 80gb a2 ultra nvidia a100 80gb nvidia a100 80gb a2 standard nvidia a100 4ogb nvidia tesla a100 g4 nvidia rtx pro 6000 nvidia rtx pro 6000 nvidia rtx pro 6000 vws g2 nvidia l4 nvidia l4 nvidia l4 vws the following gpu models can be attached to n1 general purpose machine types nvidia t4 nvidia tesla t4 nvidia tesla t4 vws nvidia p4 nvidia tesla p4 nvidia tesla p4 vws nvidia v100 nvidia tesla v100 nvidia p100 nvidia tesla p100 nvidia tesla p100 vws nvidia p100 is approaching end of support see nvidia p100 end of support gpus on spot vms you can add gpus to your spot vms at lower spot prices for the gpus gpus attached to spot vms work like normal gpus but persist only for the life of the vm spot vms with gpus follow the same preemption process as all spot vms consider requesting dedicated preemptible gpu quota to use for gpus on spot vms for more information see quotas for spot vms during maintenance events spot vms with gpus are preempted by default and cannot be automatically restarted if you want to recreate your vms after they have been preempted use a managed instance group managed instance groups recreate your vm instances if the vcpu memory and gpu resources are available if you want a warning before your vms are preempted or want to configure your vms to automatically restart after a maintenance event use standard vms with a gpu for standard vms with gpus compute engine provides one hour advance notice before preemption compute engine does not charge you for gpus if their vms are preempted in the first minute after they start running to learn how to create spot vms with gpus attached read create a vm with attached gpus and creating spot vms for example see create an a3 ultra or a4 instance using spot vms gpus on instances with predefined run times instances that use the standard provisioning model typically can t use preemptible allocation quotas preemptible quotas are for temporary workloads and are usually more available if your project doesn t have preemptible quota and you have never requested it then all instances in your project consume standard allocation quotas if you request preemptible allocation quota then instances that use the standard provisioning model must meet all of the following criteria to consume preemptible allocation quota the instances have gpus attached the instances are configured to be automatically deleted after a predefined run time through the maxrunduration or terminationtime field for more information see the following limit the run time of an instance limit the run time of instances in a mig the instance isn t allowed to consume reservations for more information see prevent compute instances from consuming reservations when you consume preemptible allocation for time bound gpu workloads you can benefit from both uninterrupted run time and the high obtainability of preemptible allocation quota for more information see preemptible quotas gpus and confidential vm you can use a gpu with a confidential vm instance that uses intel tdx on the a3 high machine type and amd sev on the g4 machine type preview for more information see confidential vm supported configurations to learn how to create a confidential vm instance with gpus see create a confidential vm instance with gpu gpus and block storage when you create an instance by using a gpu machine type you can add persistent or temporary block storage to the instance to store non transient data use persistent block storage like hyperdisk or persistent disk because these disks are independent of the instance s lifecycle data on persistent storage can be retained even after you delete the instance for temporary scratch storage or caches use temporary block storage by adding local ssd disks when you create the instance persistent block storage with persistent disk and hyperdisk volumes you can attach persistent disk and select hyperdisk volumes to gpu enabled instances for machine learning ml and serving workloads use hyperdisk ml volumes which offer high throughput and shorter data load times hyperdisk ml is a more cost effective option for ml workloads because it offers lower gpu idle times hyperdisk ml volumes provide read only multi attach support so you can attach the same disk to multiple instances giving each instance access to the same data for more information about the supported disk types for machine series that support gpus see the n1 and accelerator optimized machine series pages local ssd disks local ssd disks provide fast temporary storage for caching data processing or other transient data local ssd disks provide fast storage because they are physically attached to the server that hosts your instance local ssd disks provide temporary storage because the instance loses data if it restarts avoid storing data with strong persistency requirements on local ssd disks to store non transient data use persistent storage instead warning for instances with gpus compute engine cannot recover data on any local ssd disks attached to the instance if compute engine restarts the instance for host maintenance events if you manually stop an instance with a gpu you can preserve the local ssd data with certain restrictions see the local ssd documentation for more details for regional support for local ssd with gpu types see local ssd availability gpus and host maintenance compute engine always stops instances with attached gpus when it performs maintenance events on the host server if the instance has attached local ssd disks the instance loses the local ssd data after it stops for information on handling maintenance events see handling gpu host maintenance events reserve gpu capacity reservations provide high assurance of capacity for zone specific resources including gpus you can use reservations to ensure that you have gpus available when you need to use them for performance intensive applications for the different methods to reserve zone specific resources in compute engine see choose a reservation type reservations are also required when you want to receive committed use discounts cuds for your gpus gpu pricing if you request compute engine to provision gpus using the spot flex start or reservation bound provisioning model then you get the gpus at discounted prices depending on the gpu type you can also receive committed use discounts or sustained use discounts only with n1 vms for your gpu usage for hourly and monthly pricing for gpus see gpu pricing page committed use discounts for gpus resource based commitments provide deep discounts for compute engine resources in return for committing to using the resources in a specific region for at least one year you typically purchase commitments for resources such as vcpus memory gpus and local ssd disks for use with a specific machine series when you use your resources you receive qualifying resource usage at discounted prices to learn more about these discounts see resource based committed use discounts to purchase a commitment with gpus you must also reserve the gpus and attach the reservations to your commitment for more information about attaching reservations to commitments see attach reservations to resource based commitments sustained use discounts for gpus instances that use n1 machine types with attached gpus receive sustained use discounts suds similar to vcpus when you select a gpu for a virtual workstation compute engine automatically adds an nvidia rtx virtual workstation license to your instance gpu restrictions and limitations for instances with attached gpus the following restrictions and limitations apply only accelerator optimized a4x max a4x a4 a3 a2 g4 and g2 and general purpose n1 machine types support gpus to protect compute engine systems and users new projects have a global gpu quota that limits the total number of gpus you can create in any supported zone when you request a gpu quota you must request a quota for the gpu models that you want to create in each region and an additional global quota for the total number of gpus of all types in all zones instances with one or more gpus ha...
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