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
( |
|
|
The following GPU models can be attached to N1 general-purpose 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.
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 also offers 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 (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
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.
| 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 |
1A vCPU is implemented as a single hardware hyper-thread on one of
the available CPU platforms.
2Maximum 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.
3GPU 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 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 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.
| 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 |
1A vCPU is implemented as a single hardware hyper-thread on one of
the available CPU platforms.
2Maximum 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.
3GPU 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
A4 accelerator-optimized
machine types have
NVIDIA B200 Blackwell GPUs
(nvidia-b200) attached and are ideal for foundation model
training and serving.
| 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 |
1A vCPU is implemented as a single hardware hyper-thread on one of
the available CPU platforms.
2Maximum 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.
3GPU 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
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.
| 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 |
1A vCPU is implemented as a single hardware hyper-thread on one of
the available CPU platforms.
2Maximum 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.
3GPU 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
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
| Attached NVIDIA H100 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 HBM3) |
a3-megagpu-8g |
208 | 1,872 | 6,000 | 9 | 1,800 | 8 | 640 |
A3 High
| Attached NVIDIA H100 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 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
| Attached NVIDIA H100 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 HBM3) |
a3-edgegpu-8g |
208 | 1,872 | 6,000 | 5 |
|
8 | 640 |
1A vCPU is implemented as a single hardware hyper-thread on one of
the available CPU platforms.
2Maximum 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.
3GPU 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.
A2 machine series
A2 accelerator-optimized machine types have NVIDIA A100 GPUs attached and are ideal for model fine tuning, large model and 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 Local SSD disks when creating an A2 Standard instance. For the number of disks you can attach, see Machine types that require you to choose a number of Local SSD disks.
A2 Ultra
| Attached NVIDIA A100 80GB GPUs | ||||||
|---|---|---|---|---|---|---|
| Machine type | vCPU count1 | Instance memory (GB) | Attached Local SSD (GiB) | Maximum network bandwidth (Gbps)2 | GPU count | GPU memory3 (GB HBM2e) |
a2-ultragpu-1g |
12 | 170 | 375 | 24 | 1 | 80 |
a2-ultragpu-2g |
24 | 340 | 750 | 32 | 2 | 160 |
a2-ultragpu-4g |
48 | 680 | 1,500 | 50 | 4 | 320 |
a2-ultragpu-8g |
96 | 1,360 | 3,000 | 100 | 8 | 640 |
A2 Standard
| Attached NVIDIA A100 40GB GPUs | ||||||
|---|---|---|---|---|---|---|
| Machine type | vCPU count1 | Instance memory (GB) | Local SSD supported | Maximum network bandwidth (Gbps)2 | GPU count | GPU memory3 (GB HBM2) |
a2-highgpu-1g |
12 | 85 | Yes | 24 | 1 | 40 |
a2-highgpu-2g |
24 | 170 | Yes | 32 | 2 | 80 |
a2-highgpu-4g |
48 | 340 | Yes | 50 | 4 | 160 |
a2-highgpu-8g |
96 | 680 | Yes | 100 | 8 | 320 |
a2-megagpu-16g |
96 | 1,360 | Yes | 100 | 16 | 640 |
1A vCPU is implemented as a single hardware hyper-thread on one of
the available CPU platforms.
2Maximum 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.
3GPU 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.
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, see
G4 GPU peer-to-peer communication.
| 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 |
1A vCPU is implemented as a single hardware hyper-thread on one of
the available CPU platforms.
2You can add Titanium SSD disks when creating a G4 instance. For the number of disks
you can attach, see
Machine types that require you to choose a number of Local SSD disks.
3Maximum egress bandwidth cannot exceed the number given. Actual
egress bandwidth depends on the destination IP address and other factors.
See Network bandwidth.
4GPU 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.
G2 machine series
G2 accelerator-optimized machine types have NVIDIA L4 GPUs attached and are ideal for cost-optimized inference, graphics-intensive and high performance computing workloads.
Each G2 machine type also has a default memory and a custom memory range. The custom memory range defines the amount of memory that you can allocate to your instance for each machine type. You can also add Local SSD disks when creating a G2 instance. For the number of disks you can attach, see Machine 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 |
1A vCPU is implemented as a single hardware hyper-thread on one of
the available CPU platforms.
2Maximum 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.
3GPU 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.
N1 machine series
You can attach the following GPU models to an N1 machine type with the exception of the N1 shared-core machine types.
Unlike the machine types in the accelerator-optimized machine series, N1 machine types don't come with a set number of attached GPUs. Instead, you specify the number of GPUs to attach when creating the instance.
N1 instances with fewer GPUs limit the maximum number of vCPUs. In general, a higher number of GPUs lets you create instances with a higher number of vCPUs and memory.
N1+T4 GPUs
You can attach NVIDIA T4 GPUs 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 |
1GPU memory is the memory available on a GPU device that you can use for temporary data storage. It is separate from the instance's memory and is specifically designed to handle the higher bandwidth demands of your graphics-intensive workloads.
N1+P4 GPUs
You can attach NVIDIA P4 GPUs 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 |
1GPU memory is the memory that is available on a GPU device
that you can use for temporary data storage. It is separate from the instance's
memory and is specifically designed to handle the higher bandwidth demands of
your graphics-intensive workloads.
2For instances with attached NVIDIA P4 GPUs, Local SSD disks
are only supported in zones us-central1-c and
northamerica-northeast1-b.
N1+V100 GPUs
You can attach NVIDIA V100 GPUs 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 |
1GPU memory is the memory available on a GPU device that you can use
for temporary data storage. It is separate from the instance's memory and is
specifically designed to handle the higher bandwidth demands of your
graphics-intensive workloads.
2For instances with attached NVIDIA V100 GPUs, Local SSD disks
aren't supported in us-east1-c.
N1+P100 GPUs
You can attach NVIDIA P100 GPUs to N1 general-purpose instances with the following instance configurations.
For some NVIDIA P100 GPUs, the maximum CPU and memory available for some configurations 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 |
1GPU memory is the memory available on a GPU device that you can use for temporary data storage. It is separate from the instance's memory and is specifically designed to handle the higher bandwidth demands of your graphics-intensive workloads.
General comparison chart
The following table describes the GPU memory size, feature availability, and ideal workload types of different GPU models on Compute 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 on Compute Engine, see GPU 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 specialized Tensor Cores achieve. These are dedicated hardware units (often called matrix units) designed specifically to accelerate the large matrix multiply-accumulate operations that form the backbone of deep learning, 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 the same data type.
Vector or standard CUDA cores: Vector performance refers to the throughput standard CUDA cores achieve. These are general-purpose units that 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's Blackwell architecture.
Tensor Core
NVIDIA's Blackwell architecture, used by these machine types, introduces Tensor Core support for FP4 precision and expanded INT4 capabilities for breakthrough performance in large-model inference.
| Machine type (GPU model) | FP64 (TFLOPS) | TF32 (TFLOPS) | Mixed FP16/32 (TFLOPS) | INT8 (TOPS) | INT4 (TOPS) | FP8 (TFLOPS) | FP4 (TFLOPS) |
|---|---|---|---|---|---|---|---|
| A4X Max (GB300) | 1.3 | 2,500 | 5,000 | 330 | 155 | 10,000 | 15,000 |
| A4X (GB200) | 40 | 2,500 | 5,000 | 10,000 | 20,000 | 10,000 | 10,000 |
| A4 (B200) | 40 | 1,100 | 4,500 | 9,000 | - | 9,000 | - |
| G4 (RTX PRO 6000) | 1.8 | 140 | 935.6 | 1,871.2 | - | - | 2,200 |
- For mixed precision training, NVIDIA GB300, GB200, B200, and RTX PRO 6000
also support the
bfloat16data type. - NVIDIA GB300, GB200, B200, and RTX PRO 6000 GPUs support structural sparsity, which can double computational throughput. The performance values in this section assume dense matrix multiplication—if you use structural sparsity, performance is doubled.
Standard CUDA cores
The machine types that use the Blackwell architecture provide high-performance FP64 and FP32 operations for demanding HPC and AI workloads.
For A4X Max, A4X, and A4, FP16 operations are accelerated by Tensor Cores. For G4, FP16 performance on standard CUDA cores is included because graphics workloads, such as rendering and visualization, can benefit from the reduced memory usage and bandwidth requirements of FP16 precision, even when not using Tensor Cores.
| Machine type (GPU model) | FP64 (TFLOPS) | FP32 (TFLOPS) | FP16 (TFLOPS) |
|---|---|---|---|
| A4X Max (GB300) | 1.3 | 80 | - |
| A4X (GB200) | 40 | 80 | - |
| A4 (B200) | 40 | 80 | - |
| G4 (RTX PRO 6000) | 2 | 117 | 117 |
Hopper, Ada Lovelace, and Ampere architectures
The A3 series uses the Hopper architecture, which introduced specialized engines for transformer models. The A2 series uses the Ampere architecture, providing a balanced foundation for high-performance training and inference. The G2 series uses the Ada Lovelace architecture, which provides versatile and energy-efficient acceleration for AI inference, video transcoding, and graphics workloads.
Tensor Core
The Hopper, Ada Lovelace, and Ampere architectures, feature advanced Tensor Cores that accelerate TF32, FP16, FP8, and INT8 data types, providing high throughput for mixed-precision training and inference.
| Machine type (GPU model) | FP64 (TFLOPS) | TF32 (TFLOPS) | Mixed FP16/32 (TFLOPS) | INT8 (TOPS) | INT4 (TOPS) | FP8 (TFLOPS) |
|---|---|---|---|---|---|---|
| A3 Ultra (H200) | 67 | 989 | 1,979 | 3,958 | - | 3,958 |
| A3 Mega/High/Edge (H100) | 67 | 989 | 1,979 | 3,958 | - | 3,958 |
| A2 Ultra (A100 80GB) | 19.5 | 156 | 312 | 624 | 1248 | - |
| A2 Standard (A100 40GB) | 19.5 | 156 | 312 | 624 | 1248 | - |
| G2 (L4) | - | 120 | 242 | 485 | - | 485 |
- For mixed precision training, NVIDIA H200, H100, A100, and L4 also support
the
bfloat16data type. - NVIDIA H200, H100, A100, and L4 GPUs support structural sparsity, which can double computational throughput. The performance values in this section assume dense matrix multiplication—if you use structural sparsity, performance is doubled.
Standard CUDA cores
The machine types that use the Hopper, Ada Lovelace, and Ampere architectures provide high-performance FP64 and FP32 operations for demanding HPC and AI workloads.
| Machine type (GPU model) | FP64 (TFLOPS) | FP32 (TFLOPS) |
|---|---|---|
| A3 Ultra (H200) | 34 | 67 |
| A3 Mega, High, Edge (H100) | 34 | 67 |
| A2 Ultra (A100 80GB) | 9.7 | 19.5 |
| A2 Standard (A100 40GB) | 9.7 | 19.5 |
| G2 (L4) | 0.5 | 30.3 |
Volta, Pascal, and Turing architectures
The N1 machine types use the following GPU architectures:
Tensor Core
NVIDIA's Turing and Volta architectures, available on N1 instances, provide Tensor Core support for mixed-precision, INT8, and INT4 operations, offering foundational acceleration for deep learning inference.
These GPUs introduced the first generations of Tensor Cores, used primarily for FP16 training and INT8 and INT4 quantization in inference. This table doesn't include the N1 (P4) and N1 (P100) machine types because they don't have Tensor cores.
| Machine type (GPU model) | Mixed FP16/32 (TFLOPS) | INT8 (TOPS) | INT4 (TOPS) |
|---|---|---|---|
| N1 (V100) | 125 | - | - |
| N1 (T4) | 65 | 130 | 260 |
Standard CUDA cores
The machine types that use the Volta, Pascal, and Turing architectures are equipped with FP64 and FP32 CUDA cores to accelerate a range of HPC and AI workloads. For P100 and P4 GPUs, FP16 and INT8 performance is also included to support deep learning workloads.
| Machine type (GPU model) | FP64 (TFLOPS) | FP32 (TFLOPS) | Additional metrics |
|---|---|---|---|
| N1 (V100) | 7.8 | 15.7 | - |
| N1 (P100) | 4.7 | 9.3 | FP16: 18.7 TFLOPS |
| N1 (T4) | 0.25 | 8.1 | - |
| N1 (P4) | 0.2 | 5.5 | INT8: 22 TOPS |
What's next?
- Learn more about Compute Engine GPUs.
- Check GPU regions and zones availability.
- Review Network bandwidths and GPUs.
- View GPU pricing details.