This document outlines the NVIDIA GPU models that you can use toaccelerate machine learning (ML), data processing, and graphics-intensiveworkloads on your Compute Engine instances. Thisdocument also details which GPUs come pre-attached to accelerator-optimizedmachine series such as A4X Max, A4X, A4, A3, A2, G4, and G2, and which GPUs youcan attach to N1 general-purpose instances.

Use this document to compare the performance, memory, and features of differentGPU models. For a more detailed overview of the accelerator-optimized machinefamily, including information on CPU platforms, storage options, and networkingcapabilities, and to find the specific machine type that matches your workload,seeAccelerator-optimized machine family.

For more information about GPUs on Compute Engine, seeAbout GPUs.

To view available regions and zones for GPUs on Compute Engine, seeGPUs regions and zone availability.

Overview

Compute Engine offers different machine types to support your variousworkloads.

Some machine types supportNVIDIA 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 pricingfor virtual workstations, see theGPU 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 supportNVIDIA 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 andg1-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) (nvidia-a100-40gb)	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)

You can also use some GPU machine types onAI Hypercomputer. AI Hypercomputer is asupercomputing system that is optimized to support your artificial intelligence(AI) and machine learning (ML) workloads. This option is recommended for creating adensely allocated, performance-optimized infrastructure that has integrationsfor Google Kubernetes Engine (GKE) and Slurm schedulers.

A4X Max and A4X machine series

TheA4X Max and A4X machine seriesruns on an exascale platform based onNVIDIA's rack-scale architectureand is optimized for compute and memory-intensive, network-bound ML training andHPC workloads. A4X Max and A4X differ primarily in their GPU and networkingcomponents. A4X Max also offers bare metal instances, which providedirect access to the host server's CPU and memory, without the Compute Enginehypervisor layer.

A4X Max machine types (bare metal)

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 asbare metal instances.

A4X Max is an exascale platform based onNVIDIA 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: To get started with A4X Max machine types, contact your account team.

						Attached NVIDIA GB300 Grace Blackwell Ultra Superchips
Machine type	vCPU count1	Instance memory (GB)	Attached Local SSD (GiB)	Physical NIC count	Maximum network bandwidth (Gbps)2	GPU count	GPU memory3 (GB HBM3e)
a4x-maxgpu-4g-metal	144	960	12,000	6	3,600	4	1,116

A4X machine types

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 onNVIDIA 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 mustreserve capacity to create instances and cluster. You can then create instances that use the features and services available from AI Hypercomputer. For more information, seeDeployment options overview in the AI Hypercomputer documentation.

						Attached NVIDIA GB200 Grace Blackwell Superchips
Machine type	vCPU count1	Instance memory (GB)	Attached Local SSD (GiB)	Physical NIC count	Maximum network bandwidth (Gbps)2	GPU count	GPU memory3 (GB HBM3e)
a4x-highgpu-4g	140	884	12,000	6	2,000	4	744

A4 machine series

A4 accelerator-optimizedmachine types haveNVIDIA B200 Blackwell GPUs(nvidia-b200) attached and are ideal for foundation modeltraining and serving.

Note: When provisioning A4 machine types, you mustreserve capacity to create instances or clusters, use Spot VMs, useFlex-start VMs, or create a resize request in a MIG. For instructions on how to create A4instances, seeCreate an A3 Ultra or A4 instance. .

						Attached NVIDIA B200 Blackwell GPUs
Machine type	vCPU count1	Instance memory (GB)	Attached Local SSD (GiB)	Physical NIC count	Maximum network bandwidth (Gbps)2	GPU count	GPU memory3 (GB HBM3e)
a4-highgpu-8g	224	3,968	12,000	10	3,600	8	1,440

A3 machine series

A3 accelerator-optimizedmachine types have NVIDIA H100 SXM or NVIDIA H200 SXM GPUs attached.

A3 Ultra machine type

A3 Ultramachine types haveNVIDIA H200 SXM GPUs(nvidia-h200-141gb) attached and provides the highest networkperformance in the A3 series. A3 Ultra machine types are ideal for foundation model training andserving.

Note: When provisioning A3 Ultra machinetypes, you must reserve capacity to create instances or clusters, use Spot VMs, useFlex-start VMs, or create a resize request in a MIG. For more information about theparameters to set when creating an A3 Ultra instance, seeCreate an A3 Ultra or A4 instance.

						Attached NVIDIA H200 GPUs
Machine type	vCPU count1	Instance memory (GB)	Attached Local SSD (GiB)	Physical NIC count	Maximum network bandwidth (Gbps)2	GPU count	GPU memory3 (GB HBM3e)
a3-ultragpu-8g	224	2,952	12,000	10	3,600	8	1128

A3 Mega, High, and Edge machine types

To useNVIDIA 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 alimited set of regions.

A2 machine series

A2 accelerator-optimizedmachine types haveNVIDIA A100 GPUsattached and are ideal for model fine tuning, large modeland cost optimized inference.

The A2 machine series offers two types:

• A2 Ultra: these machine types have A100 80GB GPUs(nvidia-a100-80gb) and Local SSD disks attached.
• A2 Standard: these machine types have A100 40GB GPUs(nvidia-tesla-a100) attached. You can also add LocalSSD disks when creating an A2 Standard instance. For the number of disksyou can attach, seeMachine types that require you to choose a number of Local SSD disks.

G4 machine series

G4 accelerator-optimized machine types use NVIDIA RTX PRO 6000 Blackwell Server Edition GPUs (nvidia-rtx-pro-6000) and are suitable for NVIDIA Omniverse simulation workloads, graphics-intensive applications, video transcoding, and virtual desktops. G4 machine types also provide a low-cost solution for performing single host inference and model tuning compared with A series machine types.

A key feature of the G4 series is support for direct GPU peer-to-peer (P2P) communication on multi-GPU machine types (g4-standard-96,g4-standard-192,g4-standard-384). This allows GPUs within the same instance to exchange data directly over the PCIe bus, without involving the CPU host. For more information about G4 GPU peer-to-peer communication, seeG4 GPU peer-to-peer communication.

Note: To get started with G4 instances, seeCreate a G4 instance.

						Attached NVIDIA RTX PRO 6000 GPUs
Machine type	vCPU count1	Instance memory (GB)	Maximum Titanium SSD supported (GiB)2	Physical NIC count	Maximum network bandwidth (Gbps)3	GPU count	GPU memory4 (GB GDDR7)
g4-standard-48	48	180	1,500	1	50	1	96
g4-standard-96	96	360	3,000	1	100	2	192
g4-standard-192	192	720	6,000	1	200	4	384
g4-standard-384	384	1,440	12,000	2	400	8	768

G2 machine series

G2 accelerator-optimizedmachine types haveNVIDIA L4 GPUsattached and are ideal for cost-optimized inference, graphics-intensive andhigh performance computing workloads.

Each G2 machine type also has a default memory and a custommemory range. The custom memory range defines the amount of memory thatyou can allocate to your instance for each machine type. You can also add LocalSSD disks when creating a G2 instance. For the number of disksyou can attach, seeMachine types that require you to choose a number of Local SSD disks.

						Attached NVIDIA L4 GPUs
Machine type	vCPU count1	Default instance memory (GB)	Custom instance memory range (GB)	Max Local SSD supported (GiB)	Maximum network bandwidth (Gbps)2	GPU count	GPU memory3 (GB GDDR6)
g2-standard-4	4	16	16 to 32	375	10	1	24
g2-standard-8	8	32	32 to 54	375	16	1	24
g2-standard-12	12	48	48 to 54	375	16	1	24
g2-standard-16	16	64	54 to 64	375	32	1	24
g2-standard-24	24	96	96 to 108	750	32	2	48
g2-standard-32	32	128	96 to 128	375	32	1	24
g2-standard-48	48	192	192 to 216	1,500	50	4	96
g2-standard-96	96	384	384 to 432	3,000	100	8	192

N1 machine series

You can attach the following GPU models to anN1 machine typewith the exception of theN1 shared-core machine types.

Unlike the machine types in the accelerator-optimized machine series, N1 machinetypes don't come with a set number of attached GPUs. Instead, you specify thenumber of GPUs to attach when creating the instance.

N1 instances with fewer GPUs limit the maximum number of vCPUs. In general, ahigher number of GPUs lets you create instances with a higher number of vCPUsand memory.

N1+T4 GPUs

You can attachNVIDIA T4GPUs to N1 general-purpose instances with the following instance configurations.

Accelerator type	GPU count	GPU memory1 (GB GDDR6)	vCPU count	Instance memory (GB)	Local SSD supported
nvidia-tesla-t4 or nvidia-tesla-t4-vws	1	16	1 to 48	1 to 312	Yes
	2	32	1 to 48	1 to 312	Yes
	4	64	1 to 96	1 to 624	Yes

N1+P4 GPUs

You can attachNVIDIA P4GPUs to N1 general-purpose instances with the following instance configurations.

Accelerator type	GPU count	GPU memory1 (GB GDDR5)	vCPU count	Instance memory (GB)	Local SSD supported2
nvidia-tesla-p4 or nvidia-tesla-p4-vws	1	8	1 to 24	1 to 156	Yes
	2	16	1 to 48	1 to 312	Yes
	4	32	1 to 96	1 to 624	Yes

N1+V100 GPUs

You can attachNVIDIA V100GPUs to N1 general-purpose instances with the following instance configurations.

Accelerator type	GPU count	GPU memory1 (GB HBM2)	vCPU count	Instance memory (GB)	Local SSD supported2
nvidia-tesla-v100	1	16	1 to 12	1 to 78	Yes
	2	32	1 to 24	1 to 156	Yes
	4	64	1 to 48	1 to 312	Yes
	8	128	1 to 96	1 to 624	Yes

N1+P100 GPUs

You can attachNVIDIA P100 GPUsto N1 general-purpose instances with the following instance configurations.

For some NVIDIA P100 GPUs, the maximum CPU and memory available for someconfigurations depends on the zone in which the GPU resource runs.

Accelerator type	GPU count	GPU memory1 (GB HBM2)	Zone	vCPU count	Instance memory (GB)	Local SSD supported
nvidia-tesla-p100 or nvidia-tesla-p100-vws	1	16	All P100 zones	1 to 16	1 to 104	Yes
	2	32	All P100 zones	1 to 32	1 to 208	Yes
	4	64	us-east1-c, europe-west1-d, europe-west1-b	1 to 64	1 to 208	Yes
			All other P100 zones	1 to 96	1 to 624	Yes

General comparison chart

The following table describes the GPU memory size, feature availability, andideal workload types of different GPU models onCompute Engine.

Machine type (GPU model)	GPU memory	Interconnect	NVIDIA RTX Virtual Workstation (vWS) support	Best used for
A4X Max (GB300)	279 GB HBM3e @ 8 TBps	NVLink Full Mesh @ 1,800 GBps		Large-scale distributed training and inference of MoE LLMs, Recommenders, HPC
A4X (GB200)	186 GB HBM3e @ 8 TBps	NVLink Full Mesh @ 1,800 GBps		Large-scale distributed training and inference of LLMs, Recommenders, HPC
A4 (B200)	180 GB HBM3e @ 8 TBps	NVLink Full Mesh @ 1,800 GBps		Large-scale distributed training and inference of LLMs, Recommenders, HPC
A3 Ultra (H200)	141 GB HBM3e @ 4.8 TBps	NVLink Full Mesh @ 900 GBps		Large models with massive data tables for ML Training, Inference, HPC, BERT, DLRM
A3 Mega, A3 High, A3 Edge (H100)	80 GB HBM3 @ 3.35 TBps	NVLink Full Mesh @ 900 GBps		Large models with massive data tables for ML Training, Inference, HPC, BERT, DLRM
A2 Ultra (A100 80GB)	80 GB HBM2e @ 1.9 TBps	NVLink Full Mesh @ 600 GBps		Large models with massive data tables for ML Training, Inference, HPC, BERT, DLRM
A2 Standard (A100 40GB)	40 GB HBM2 @ 1.6 TBps	NVLink Full Mesh @ 600 GBps		ML Training, Inference, HPC
G4 (RTX PRO 6000)	96 GB GDDR7 with ECC @ 1597 GBps	N/A		ML Inference, Training, Remote Visualization Workstations,Video Transcoding, HPC
G2 (L4)	24 GB GDDR6 @ 300 GBps	N/A		ML Inference, Training, Remote Visualization Workstations,Video Transcoding, HPC
N1 (T4)	16 GB GDDR6 @ 320 GBps	N/A		ML Inference, Training, Remote Visualization Workstations, Video Transcoding
N1 (P4)	8 GB GDDR5 @ 192 GBps	N/A		Remote Visualization Workstations, ML Inference, and Video Transcoding
N1 (V100)	16 GB HBM2 @ 900 GBps	NVLink Ring @ 300 GBps		ML Training, Inference, HPC
N1 (P100)	16 GB HBM2 @ 732 GBps	N/A		ML Training, Inference, HPC, Remote Visualization Workstations

To compare GPU pricing for the different GPU models and regions available onCompute Engine, seeGPU pricing.

Tensor Core and standard CUDA core performance

The following sections provide performance metrics for each GPU architecture,separated into vector or standard CUDA cores and Tensor Core performance.

• Tensor Cores: Tensor performance refers to the throughput specializedTensor Cores achieve. These are dedicated hardware units (often calledmatrix units) designed specifically to accelerate the largematrix multiply-accumulate operations that form the backbone of deeplearning, training, and inference.

  This type of performance is best for deep learning, large language models(LLMs), and any workload that can be expressed as dense matrix operations.Tensor Cores provide significantly higher throughput than CUDA cores for thesame data type.

• Vector or standard CUDA cores: Vector performance refers to thethroughput standard CUDA cores achieve. These are general-purpose unitsthat operate using a single instruction, multiple threads (SIMT) model,typically performing operations on individual data elements or vectors.

  This type of performance is best for general compute, graphics rendering,and workloads that don't involve dense matrix math.

Blackwell architecture

The A4X Max, A4X, A4, and G4 machine types run on NVIDIA'sBlackwell architecture.

Hopper, Ada Lovelace, and Ampere architectures

The A3 series uses theHopper architecture,which introduced specialized engines for transformer models. The A2 series usestheAmpere architecture,providing a balanced foundation for high-performance training and inference.The G2 series uses theAda Lovelace architecture,which provides versatile and energy-efficient acceleration for AI inference,video transcoding, and graphics workloads.

Volta, Pascal, and Turing architectures

The N1 machine types use the following GPU architectures:

• Volta (V100)
• Pascal (P100 and P4)
• Turing (T4)

What's next?

• Learn more aboutCompute Engine GPUs.
• CheckGPU regions and zones availability.
• ReviewNetwork bandwidths and GPUs.
• ViewGPU pricing details.