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description= View the Hyperdisk size, performance, and VM attachment limits for each supported Compute Engine machine type.;
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Text of the page (random words):
rformance on windows vms monitor disks monitor disk health monitor the replica states 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 performance limits for machine series that support hyperdisk stay organized with collections save and categorize content based on your preferences to meet your workload s specific iops and throughput requirements both the google cloud hyperdisk type and the machine type that your workload uses must support that level of performance or higher this document lists performance limits by machine series and google cloud hyperdisk type for a detailed discussion of how hyperdisk performance works see about hyperdisk performance for persistent disk performance limits see performance limits by machine type for persistent disk determine which limits apply the performance limit that applies to your scenario depends on the instance s machine type and the number and type of hyperdisk volumes you attached to the instance choose from the following scenarios to determine which limits to consult you re using multiple hyperdisk types with the same instance and you want to know the maximum performance for all the disks attached to the instance for example suppose you have one hyperdisk balanced volume and two hyperdisk throughput volumes attached to a c3 virtual machine vm instance that has 22 vcpus the applicable performance limits are listed in the performance limits by machine type section of this document you attached one or more hyperdisk volumes of the same type to an instance and you want to know the maximum performance all the volumes can reach for example suppose you attached three hyperdisk balanced volumes to a c3 instance that has 8 vcpus the applicable performance limits for the three volumes are listed in the performance limits by hyperdisk type section of this document terminology in this document machine types are denoted in this format m n where m is the machine series for example c3 represents all memory and cpu configurations for example standard or highcpu n is the number of vcpus cpus or the gpu designation for example 22 for an instance with 22 vcpus or 8g for an instance with 8 gpus this format includes the following machine types wherever available bare metal machine types metal machine types that come with attached local ssd disks lssd standardlssd or highlssd for example c3 176 refers to all c3 machine types with 176 vcpus namely c3 standard 176 c3 highcpu 176 c3 highmem 176 and c3 standard 176 lssd for more information see compute engine terminology performance limits by machine type if you use different hyperdisk types with a single instance the following table lists the maximum performance you can expect this limit is shared among all hyperdisk and if supported persistent disk volumes attached to the instance to learn more about how these limits work see how instance performance limits affect performance the performance limits also apply to custom n2 n2d n4 n4a and n4d machine types select a machine series to display the performance limits for its machine types a2 a3 h100 a3 h200 a4 a4x a4x max c3 c3d c4 c4a c4d c4n g2 g4 h3 h4d m1 m2 m3 m4 m4n n2 n2d n4 n4a n4d t2d tpu v6e tpu7x x4 z3 instance machine type maximum iops maximum throughput mib s a2 a2 ultragpu 1g 30 000 1 800 a2 ultragpu 2g 60 000 2 400 a2 ultragpu 4g 120 000 2 400 a2 ultragpu 8g 160 000 4 800 a3 h100 a3 edgegpu 8g 400 000 8 000 a3 highgpu 1g 120 000 1 800 a3 highgpu 2g 160 000 2 400 a3 highgpu 4g 350 000 5 000 a3 highgpu 8g 400 000 8 000 a3 megagpu 8g 400 000 8 000 a3 h200 1 a3 ultragpu 8g 1 500 000 10 000 a4 1 a4 highgpu 8g 500 000 10 000 a4x 1 a4x highgpu 4g 1 500 000 10 000 a4x max 1 a4x maxgpu 4g metal 1 500 000 12 500 c3 c3 4 25 000 400 c3 8 50 000 800 c3 22 120 000 1 800 c3 44 160 000 2 400 c3 88 350 000 5 000 c3 176 500 000 10 000 c3 192 500 000 10 000 c3d c3d 4 25 000 400 c3d 8 50 000 800 c3d 16 75 000 1 200 c3d 30 93 750 1 200 c3d 60 350 000 5 000 c3d 90 350 000 5 000 c3d 180 350 000 5 000 c3d 360 350 000 5 000 c4 1 c4 2 1 50 000 400 c4 4 1 50 000 400 c4 8 1 50 000 800 c4 16 1 100 000 1 600 c4 24 1 100 000 1 600 c4 32 1 100 000 1 600 c4 48 1 160 000 2 400 c4 96 1 350 000 5 000 c4 144 1 350 000 5 000 c4 192 1 500 000 10 000 c4 288 1 500 000 12 500 c4 288 lssd metal 1 400 000 12 500 c4a c4a 1 25 000 400 c4a 2 50 000 800 c4a 4 50 000 800 c4a 8 50 000 1 000 c4a 16 80 000 1 600 c4a 32 120 000 2 400 c4a 48 160 000 3 300 c4a 64 350 000 5 000 c4a 72 350 000 5 000 c4a 96 metal 350 000 5 000 c4d 1 c4d 2 1 22 500 400 c4d 4 1 50 000 400 c4d 8 1 50 000 800 c4d 16 1 75 000 1 200 c4d 32 1 75 000 1 600 c4d 48 1 75 000 1 600 c4d 64 1 350 000 5 000 c4d 96 1 350 000 5 000 c4d 192 1 350 000 5 000 c4d 384 1 500 000 10 000 c4n 1 c4n 2 80 000 1 000 c4n 4 100 000 1 000 c4n 8 120 000 1 800 c4n 16 160 000 3 000 c4n 24 240 000 4 000 c4n 48 380 000 8 000 c4n 96 500 000 12 500 c4n 192 1 000 000 25 000 g2 g2 standard 4 15 000 800 g2 standard 8 19 200 1 200 g2 standard 12 28 800 1 800 g2 standard 16 38 400 2 400 g2 standard 24 38 400 2 400 g2 standard 32 60 000 2 400 g2 standard 48 60 000 2 400 g2 standard 96 100 000 2 400 g4 g4 standard 6 50 000 400 g4 standard 12 50 000 800 g4 standard 24 75 000 1 200 g4 standard 48 75 000 1 600 g4 standard 96 350 000 5 000 g4 standard 192 350 000 5 000 g4 standard 384 500 000 10 000 h3 h3 standard 88 15 000 240 h4d 1 h4d 192 1 15 000 240 m1 m1 ultramem 40 60 000 1 200 m1 ultramem 80 100 000 2 400 m1 megamem 96 100 000 4 000 m1 ultramem 160 100 000 4 000 m2 m2 208 100 000 2 400 m2 416 100 000 4 000 m3 m3 32 160 000 2 400 m3 64 350 000 5 000 m3 128 450 000 6 870 m4 1 m4 16 1 100 000 1 600 m4 28 1 100 000 1 600 m4 32 1 120 000 1 800 m4 56 1 160 000 2 400 m4 64 1 200 000 3 000 m4 112 1 350 000 5 000 m4 224 1 500 000 10 000 m4n 1 m4n 16 160 000 3 000 m4n 28 280 000 5 000 m4n 32 320 000 6 000 m4n 56 400 000 10 000 m4n 64 420 000 11 500 m4n 112 500 000 12 500 m4n 224 1 000 000 25 000 n2 n2 2 15 000 240 n2 4 15 000 240 custom n2 types with 6 vcpus 15 000 240 n2 8 30 000 800 custom n2 types with 10 to 14 vcpus 30 000 800 n2 16 60 000 1 200 custom n2 types with 18 to 30 vcpus 60 000 1 200 n2 32 120 000 2 400 custom n2 types with 36 to 44 vcpus 120 000 2 400 n2 48 120 000 2 400 custom n2 types with 52 to 60 vcpus 120 000 2 400 n2 64 160 000 4 800 custom n2 types with 68 to 76 vcpus 160 000 4 800 n2 80 160 000 5 000 n2 96 160 000 5 000 n2 128 160 000 5 000 n2d n2d 2 15 000 240 n2d 4 15 000 240 n2d 8 30 000 800 n2d 16 60 000 1 200 n2d 32 120 000 2 400 n2d 48 120 000 2 400 n2d 64 160 000 4 800 n2d 80 160 000 4 800 n2d 96 160 000 4 800 n2d 128 160 000 4 800 n2d 224 160 000 4 800 n4 n4 2 30 000 240 n4 4 30 000 240 custom n4 types with 6 vcpus 30 000 240 n4 8 30 000 480 custom n4 types with 10 to 14 vcpus 30 000 480 n4 16 80 000 1 200 custom n4 types with 18 to 30 vcpus 80 000 1 200 n4 32 100 000 1 600 custom n4 types with 34 to 46 vcpus 100 000 1 600 n4 48 160 000 2 400 custom n4 types with 50 to 62 vcpus 160 000 2 400 n4 64 160 000 2 400 custom n4 types with 66 to 78 vcpus 160 000 2 400 n4 80 160 000 2 400 n4a n4a 1 15 000 240 n4a 2 30 000 240 custom n4a types with 3 vcpus 30 000 240 n4a 4 30 000 240 custom n4a types with 5 to 7 vcpus 30 000 240 n4a 8 30 000 480 custom n4a types with 9 to 15 vcpus 30 000 480 n4a 16 80 000 1 200 custom n4a types with 17 to 31 vcpus 80 000 1 200 n4a 32 100 000 1 600 custom n4a types with 33 to 47 vcpus 100 000 1 600 n4a 48 160 000 2 400 custom n4a types with 49 to 63 vcpus 160 000 2 400 n4a 64 160 000 2 400 n4d n4d 2 30 000 240 n4d 4 30 000 240 n4d 8 30 000 480 n4d 16 80 000 1 200 n4d 32 100 000 1 600 n4d 48 160 000 2 400 n4d 64 160 000 2 400 n4d 80 160 000 2 400 n4d 96 160 000 2 400 t2d t2d 1 2 15 000 240 t2d 2 2 15 000 240 t2d 4 2 15 000 240 t2d 8 15 000 800 t2d 16 25 000 1 200 t2d 32 60 000 1 200 t2d 48 60 000 1 200 t2d 60 60 000 1 200 tpu v6e ct6e standard 1t 75 000 1 200 ct6e standard 4t 160 000 2 400 ct6e standard 8t 160 000 2 400 tpu7x 1 tpu7x standard 4t 320 000 6 000 x4 x4 480 160 000 4 800 x4 960 400 000 10 000 x4 1440 400 000 10 000 x4 1920 400 000 10 000 z3 z3 14 100 000 1 600 z3 16 100 000 1 600 z3 22 120 000 1 800 z3 32 160 000 2 400 z3 44 160 000 2 400 z3 88 350 000 5 000 z3 176 350 000 5 000 z3 192 500 000 10 000 1 also offers steady state performance for these limits see performance limits for machine types with ste...
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