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description= Understand instance options available to support GPU-accelerated workloads such as machine learning, data processing, and graphics workloads on Compute Engine.;
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igure 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 gpu machine types stay organized with collections save and categorize content based on your preferences this document outlines the nvidia gpu models that you can use to accelerate machine learning ml data processing and graphics intensive workloads on your compute engine instances this document also details which gpus come pre attached to accelerator optimized machine series such as a4x max a4x a4 a3 a2 g4 and g2 and which gpus you can attach to n1 general purpose instances use this document to compare the performance memory and features of different gpu models for a more detailed overview of the accelerator optimized machine family including information on cpu platforms storage options and networking capabilities and to find the specific machine type that matches your workload see accelerator optimized machine family for more information about gpus on compute engine see about gpus to view available regions and zones for gpus on compute engine see gpus regions and zone availability overview 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 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 a4x max and a4x machine series the a4x max and a4x machine series runs on an exascale platform based on nvidia s rack scale architecture and is optimized for compute and memory intensive network bound ml training and hpc workloads a4x max and a4x differ primarily in their gpu and networking components a4x max is available only as bare metal instances which provide direct access to the host server s cpu and memory without the compute engine hypervisor layer a4x max machine types nvidia gb300 a4x max accelerator optimized machine types use nvidia gb300 grace blackwell ultra superchips nvidia gb300 and are ideal for foundation model training and serving a4x max machine types are available as bare metal instances a4x max is an exascale platform based on nvidia gb300 nvl72 each machine has two sockets with nvidia grace cpus with arm neoverse v2 cores these cpus are connected to four nvidia b300 blackwell gpus with fast chip to chip nvlink c2c communication note when provisioning a4x max instances you must reserve capacity to create instances and clusters you can then create instances that use the features and services available from ai hypercomputer for more information see deployment options overview in the ai hypercomputer documentation attached nvidia gb300 grace blackwell ultra superchips machine type vcpu count 1 instance memory gb attached local ssd gib physical nic count maximum network bandwidth gbps 2 gpu count gpu memory 3 gb hbm3e a4x maxgpu 4g metal 144 960 12 000 6 3 600 4 1 116 1 a vcpu is implemented as a single hardware hyper thread on one of the available cpu platforms 2 maximum egress bandwidth cannot exceed the number given actual egress bandwidth depends on the destination ip address and other factors for more information about network bandwidth see network bandwidth 3 gpu memory is the memory on a gpu device that can be used for temporary storage of data it is separate from the instance s memory and is specifically designed to handle the higher bandwidth demands of your graphics intensive workloads a4x machine type nvidia gb200 a4x accelerator optimized machine types use nvidia gb200 grace blackwell superchips nvidia gb200 and are ideal for foundation model training and serving a4x is an exascale platform based on nvidia gb200 nvl72 each machine has two sockets with nvidia grace cpus with arm neoverse v2 cores these cpus are connected to four nvidia b200 blackwell gpus with fast chip to chip nvlink c2c communication note when provisioning a4x instances you must reserve capacity to create instances and clusters you can then create instances that use the features and services available from ai hypercomputer for more information see deployment options overview in the ai hypercomputer documentation attached nvidia gb200 grace blackwell superchips machine type vcpu count 1 instance memory gb attached local ssd gib physical nic count maximum network bandwidth gbps 2 gpu count gpu memory 3 gb hbm3e a4x highgpu 4g 140 884 12 000 6 2 000 4 744 1 a vcpu is implemented as a single hardware hyper thread on one of the available cpu platforms 2 maximum egress bandwidth cannot exceed the number given actual egress bandwidth depends on the destination ip address and other factors for more information about network bandwidth see network bandwidth 3 gpu memory is the memory on a gpu device that can be used for temporary storage of data it is separate from the instance s memory and is specifically designed to handle the higher bandwidth demands of your graphics intensive workloads a4 machine series nvidia b200 a4 accelerator optimized machine types have nvidia b200 blackwell gpus nvidia b200 attached and are ideal for foundation model training and serving note when provisioning a4 machine types you must reserve capacity to create instances or clusters use spot vms use flex start vms or create a resize request in a mig for instructions on how to create a4 instances see create an a3 ultra or a4 instance attached nvidia b200 blackwell gpus machine type vcpu count 1 instance memory gb attached local ssd gib physical nic count maximum network bandwidth gbps 2 gpu count gpu memory 3 gb hbm3e a4 highgpu 8g 224 3 968 12 000 10 3 600 8 1 440 1 a vcpu is implemented as a single hardware hyper thread on one of the available cpu platforms 2 maximum egress bandwidth cannot exceed the number given actual egress bandwidth depends on the destination ip address and other factors for more information about network bandwidth see network bandwidth 3 gpu memory is the memory on a gpu device that can be used for temporary storage of data it is separate from the instance s memory and is specifically designed to handle the higher bandwidth demands of your graphics intensive workloads a3 machine series a3 accelerator optimized machine types have nvidia h100 sxm or nvidia h200 sxm gpus attached a3 ultra machine type nvidia h200 a3 ultra machine types have nvidia h200 sxm gpus nvidia h200 141gb attached and provides the highest network performance in the a3 series a3 ultra machine types are ideal for foundation model training and serving note when provisioning a3 ultra machine types you must reserve capacity to create instances or clusters use spot vms use flex start vms or create a resize request in a mig for more information about the parameters to set when creating an a3 ultra instance see create an a3 ultra or a4 instance attached nvidia h200 gpus machine type vcpu count 1 instance memory gb attached local ssd gib physical nic count maximum network bandwidth gbps 2 gpu count gpu memory 3 gb hbm3e a3 ultragpu 8g 224 2 952 12 000 10 3 600 8 1128 1 a vcpu is implemented as a single hardware hyper thread on one of the available cpu platforms 2 maximum egress bandwidth cannot exceed the number given actual egress bandwidth depends on the destination ip address and other factors for more information about network bandwidth see network bandwidth 3 gpu memory is the memory on a gpu device that can be used for temporary storage of data it is separate from the instance s memory and is specifically designed to handle the higher bandwidth demands of your graphics intensive workloads a3 mega high and edge machine types nvidia h100 to use nvidia h100 sxm gpus you have the following options a3 mega these machine types have h100 sxm gpus nvidia h100 mega 80gb and are ideal for large scale training and serving workloads a3 high these machine types have h100 sxm gpus nvidia h100 80gb and are well suited for both training and serving tasks a3 edge these machine types have h100 sxm gpus nvidia h100 80gb are designed specifically for serving and are available in a limited set of regions a3 mega note when provisioning a3 megagpu 8g machine types we recommend using a cluster of these instances and deploying with a scheduler such as google kubernetes engine gke or slurm for detailed instructions on either of these options review the following to create google kubernetes engine cluster see deploy an a3 mega cluster with gke to create a slurm cluster see deploy an a3 mega slurm cluster attached nvidia h100 gpus machine type vcpu count 1 instance memory gb attached local ssd gib physical nic count maximum network bandwidth gbps 2 gpu count gpu memory 3 gb hbm3 a3 megagpu 8g 208 1 872 6 000 9 1 800 8 640 a3 high note when provisioning a3 highgpu 1g a3 highgpu 2g or a3 highgpu 4g machine types you must create instances by using spot vms or flex start vms for detailed instructions on these options review the following to create spot vms set the provisioning model to spot when you create an accelerator optimized vm to create flex start vms you can use one of the following methods create a standalone vm and set the provisioning model to flex_start when you create an accelerator optimized vm create a resize request in a managed instance group mig for instructions see create a mig with gpu vms attached nvidia h100 gpus machine type vcpu count 1 instance memory gb attached local ssd gib physical nic count maximum network bandwidth gbps 2 gpu count gpu memory 3 gb hbm3 a3 highgpu 1g 26 234 750 1 25 1 80 a3 highgpu 2g 52 468 1 500 1 50 2 160 a3 highgpu 4g 104 936 3 000 1 100 4 320 a3 highgpu 8g 208 1 872 6 000 5 1 000 8 640 a3 edge note to get started with a3 edge instances see create an a3 vm with gpudirect tcpx enabled attached...
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