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description= Discover more about the machine families, series, and types you can choose from when creating a compute instance with Google Cloud s resource guide.;
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ates of regional disks monitor disks list of metrics for pools monitor pools monitor reservations organize resources using labels scale autoscale groups of vms about autoscaling groups of vms create and manage autoscalers scale based on cpu utilization scale based on predictions scale based on load balancing serving capacity scale based on monitoring metrics scale based on schedules use an autoscaling policy with multiple signals manage autoscalers understand autoscaler decisions view autoscaler logs autoscale node groups reserve vm capacity choose a reservation type sharing reservations best practices for shared reservations allow a project to share reservations on demand reservations about on demand reservations create an on demand reservation for a single project for multiple projects combine an on demand reservation with a cud modify an on demand reservation delete an on demand reservation future reservations about future reservations create a reservation request for a single project for multiple projects modify a reservation request delete a reservation request future reservations in calendar mode about future reservations in calendar mode create a reservation request in calendar mode delete a reservation request in calendar mode view reservations or reservation requests consume a reservation prevent vms from consuming reservations load balancing about load balancing and scaling add an instance group to a load balancer request routing to a multi region external https load balancer cross region load balancing for microsoft iis backends set up internal tcp udp load balancing build reliable and scalable applications use autohealing for highly available applications use load balancing for highly available applications use autoscaling for highly scalable applications globally autoscale a web service on compute engine patterns for scalable and resilient applications patterns for using floating ip addresses on compute engine optimize resource utilization use recommendations to manage resources apply machine type recommendations to vms configure machine type recommendations apply machine type recommendations to migs view and apply idle resources recommendations view and understand vm insights view and understand mig insights manage idle vm recommendations idle vm recommendations overview view and apply idle vm recommendations configure idle vm recommendations manage reservation recommendations reservation recommendations overview view and apply idle reservation recommendations view and apply underutilized reservation recommendations configure idle reservation recommendations configure underutilized reservation recommendations overcommit cpus on sole tenant vms manual live migration about manual live migration manually live migrate vms share sole tenant node groups next generation dynamic resource management cost savings get discounts for committed usage about commitments and committed use discounts cuds resource based 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 machine families resource and comparison guide stay organized with collections save and categorize content based on your preferences this document describes the machine families machine series and machine types that you can choose from to create a virtual machine vm instance or bare metal instance with the resources that you need for accelerator optimized machines this document describes only graphics processing unit gpu accelerators for information about machine types that contain google s custom developed tensor processing units tpus see tpu machines there are several machine families you can choose from each machine family is further organized into machine series and predefined machine types within each series for example within the n2 machine series in the general purpose machine family you can select the n2 standard 4 machine type when you create a compute instance you select a machine type from a machine family and series the machine type determines the resources that compute engine allocates to the instance for example the n2 standard 4 machine type creates a vm with 4 vcpus and 16 gb of memory for information about machine series that support spot vms and preemptible vms see compute engine instances provisioning models note this is a list of compute engine machine families for a detailed explanation of each machine family see the following pages general purpose best price performance ratio for a variety of workloads storage optimized best for workloads that are low in core usage and high in storage density compute optimized designed for high performance computing hpc solutions and compute intensive workloads offers high performance per core on compute engine network optimized purpose built for io intensive workloads that require high network bandwidth superior packet processing performance and superior block storage throughput and iops per vcpu memory optimized ideal for memory intensive workloads offering more memory per core than other machine families with up to 12 tb of memory accelerator optimized ideal for massively parallelized compute unified device architecture cuda compute workloads such as machine learning ml and high performance computing hpc this family is the best option for workloads that require accelerators gpus or tpus compute engine terminology this documentation uses the following terms machine family a curated set of processor and hardware configurations optimized for specific workloads for example general purpose accelerator optimized or memory optimized machine series machine families are further classified by series generation and processor type each series focuses on a different aspect of computing power or performance for example the e series offers efficient vms at a low cost while the c series offers better performance the generation is denoted by an ascending number for example the n1 series within the general purpose machine family is the older version of the n2 series a higher generation or series number usually indicates newer underlying cpu platforms or technologies for example the m3 series which runs on intel xeon scalable processor 3rd generation ice lake is a newer generation than the m2 series which runs on intel xeon scalable processor 2nd generation cascade lake generation intel amd arm 4th generation machine series n4 c4 c4n x4 m4 m4n a4 c4d g4 n4d h4d n4a c4a a4x max a4x 3rd generation machine series c3 h3 z3 m3 a3 c3d n a 2nd generation machine series n2 e2 c2 m2 a2 g2 n2d c2d t2d e2 t2a machine type every machine series offers at least one machine type each machine type provides a set of resources for your compute instance such as vcpus memory disks and gpus if a predefined machine type does not meet your needs you can also create a custom machine type for some machine series the following sections describe the different machine types predefined machine types predefined machine types come with a non modifiable amount of memory and vcpus predefined machine types use a variety of vcpu to memory ratios highcpu from 1 to 3 gb memory per vcpu typically 2 gb memory per vcpu standard from 3 to 7 gb memory per vcpu typically 4 gb memory per vcpu highmem from 7 to 12 gb memory per vcpu typically 8 gb memory per vcpu megamem from 12 to 15 gb memory per vcpu typically 14 gb memory per vcpu ultramem from 24 to 31 gb memory per vcpu hypermem from 15 to 24 gb memory per vcpu typically 16 gb memory per vcpu for example a c3 standard 22 machine type has 22 vcpus and as a standard machine type it also has 88 gb of memory local ssd machine types local ssd machine types are special predefined machine types the machine type names include lssd when you create a compute instance using one of the following machine types titanium ssd or local ssd disks are automatically attached to the instance lssd available with the c4 c4a c4d c4n preview c3 c3d and h4d machine series these machine types attach a predetermined number of 375 gib titanium ssd or local ssd disks to the instance examples of this machine type include c4a standard 4 lssd c3 standard 88 lssd and c3d highmem 360 lssd standardlssd available with the storage optimized z3 machine series these machine types provide up to 350 gib of titanium ssd disk capacity per vcpu these machine types are recommended for high performance search and data analysis for medium sized data sets an example of this machine type is z3 highmem 22 standardlssd highlssd available with the z3 machine series these machine types provide between 350 gib and 600 gib of titanium ssd disk capacity per vcpu these machine types offer high performance and are recommended for storage intensive streaming and data analysis for large sized data sets an example of this machine type is z3 highmem 88 highlssd other machine series also support local ssd disks but don t use a machine type name that includes lssd for a list of all the machine types that you can use with titanium ssd or local ssd disks see choose a valid number of local ssd disks bare metal machine types bare metal machine types are special predefined machine types the machine type name includes metal when you create a compute instance using one of these machine types there is no hypervisor installed on the instance you can attach disks to a bare metal instance just as you would with a vm instance bare metal instances can be used in vpc networks and subnetworks in the same way as vm instances note compute engine bare metal instances aren t related to bare metal solution for more information see bare metal instances on compute engine custom machine types if none of the predefined machine types match your workload needs you can create a vm instance with a custom machine type for the n and e machine series in the general purpose machine family custom machine types cost slightly more to use compared to an equivalent predefined machine type also there are limitations in the amount of memory and vcpus that you can select for a custom machine type the on demand prices for custom machine types include a 5 premium over the on demand and commitment prices for predefined machine types when creating a custom machine type you can use the extended memory feature instead of using the default memory size based on the number of vcpus you select you can specify an amount of memory up to the limit for the machine series for more information see create a vm with a custom machine type shared core machine types the e2 and n1 series contain shared core machine types these machine types timeshare a physical core which can be a cost effective method for running small non resource intensive apps e2 offers e2 micro e2 small and e2 medium shared core machine types with 2 vcpus for short periods of bursting n1 offers f1 micro and g1 small shared core machine types which have up to 1 vcpu available for short periods of bursting for more information see cpu bursting machine family and series recommendations the following tables provide recommendations for different workloads to get ai powered assistance when you evaluate which machine family best fits your workload requirements compare pricing trade offs and generate deployment code snippets you can prompt gemini in the google cloud console for more information see design your compute infrastruc...
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