Networking and GPU machines

Higher network bandwidths can improve the performance of your GPU instances to support distributed workloads that are running on Compute Engine.

The maximum network bandwidth that is available for instances with attached GPUs on Compute Engine is as follows:

For A4 and A3 accelerator-optimized instances, you can get a maximum network bandwidth of up to 3,600 Gbps, based on the machine type.
For A2 and G2 accelerator-optimized instances, you can get a maximum network bandwidth of up to 100 Gbps, based on the machine type.
For N1 general-purpose instances that have P100 and P4 GPUs attached, a maximum network bandwidth of 32 Gbps is available. This is similar to the maximum rate available to N1 instances that don't have GPUs attached. For more information about network bandwidths, see maximum egress data rate.
For N1 general-purpose instances that have T4 and V100 GPUs attached, you can get a maximum network bandwidth of up to 100 Gbps, based on the combination of GPU and vCPU count.

Review network bandwidth and NIC arrangement

Use the following section to review the network arrangement and bandwidth speed for each GPU machine type.

A4 and A3 Ultra machine types

The A4 machine types have NVIDIA B200 GPUs attached and A3 Ultra machine types have NVIDIA H200 GPUs attached.

These machine types provide eight NVIDIA ConnectX-7 (CX7) network interface cards (NICs) and two Google virtual NICs (gVNIC). The eight CX7 NICs deliver a total network bandwidth of 3,200 Gbps. These NICs are dedicated for only high-bandwidth GPU to GPU communication and can't be used for other networking needs such as public internet access. As outlined in the following diagram, each CX7 NIC is aligned with one GPU to optimize non-uniform memory access (NUMA). All eight GPUs can rapidly communicate with each other by using the all to all NVLink bridge that connects them. The two other gVNIC network interface cards are smart NICs that provide an additional 400 Gbps of network bandwidth for general purpose networking requirements. Combined, the network interface cards provide a total maximum network bandwidth of 3,600 Gbps for these machines.

Network architecture for A4 and A3 Ultra. — Figure 1. Network architecture for A4 and A3 Ultra

To use these multiple NICs, you need to create 3 Virtual Private Cloud networks as follows:

2 VPC networks: each gVNIC NIC must attach to a different VPC network
1 VPC network with the RDMA network profile: all eight CX7 NICs share the same VPC network

To set up these networks, see Create VPC networks in the AI Hypercomputer documentation.

A4 VMs

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

^*A vCPU is implemented as a single hardware hyper-thread on one of the available CPU platforms.
^†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.
^‡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 Ultra VMs

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

A3 Mega, High, and Edge machine types

These machine types have H100 GPUs attached. Each of these machine types have a fixed GPU count, vCPU count, and memory size.

Single NIC A3 VMs: For A3 VMs with 1 to 4 GPUs attached, only a single physical network interface card (NIC) is available.
Multi-NIC A3 VMs: For A3 VMs with 8 GPUS attached, multiple physical NICs are available. For these A3 machine types the NICs are arranged as follows on a Peripheral Component Interconnect Express (PCIe) bus:
- For the A3 Mega machine type: a NIC arrangement of 8+1 is available. With this arrangement, 8 NICs share the same PCIe bus, and 1 NIC resides on a separate PCIe bus.
- For the A3 High machine type: a NIC arrangement of 4+1 is available. With this arrangement, 4 NICs share the same PCIe bus, and 1 NIC resides on a separate PCIe bus.
- For the A3 Edge machine type machine type: a NIC arrangement of 4+1 is available. With this arrangement, 4 NICs share the same PCIe bus, and 1 NIC resides on a separate PCIe bus. These 5 NICs provide a total network bandwidth of 400 Gbps for each VM.
NICs that share the same PCIe bus, have a non-uniform memory access (NUMA) alignment of one NIC per two NVIDIA H100 GPUs. These NICs are ideal for dedicated high bandwidth GPU to GPU communication. The physical NIC that resides on a separate PCIe bus is ideal for other networking needs. For instructions on how to setup networking for A3 High and A3 Edge VMs, see set up jumbo frame MTU networks.

A3 Mega

Tip: 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^*	Instance memory (GB)	Attached Local SSD (GiB)	Physical NIC count	Maximum network bandwidth (Gbps)^†	GPU count	GPU memory^‡ (GB HBM3)
`a3-megagpu-8g`	208	1,872	6,000	9	1,800	8	640

A3 High

Tip: When provisioning a3-highgpu-1g, a3-highgpu-2g, or a3-highgpu-4g machine types, you must create instances using Spot VMs or a feature that uses the Dynamic Workload Scheduler (DWS), such as resize requests in a MIG. For detailed instructions on either of these options, review the following:

To create Spot VMs, set the provisioning model to SPOT when you Create an accelerator-optimized VM.
To create a resize request in a MIG, which uses DWS, see Create a MIG with GPU VMs.

						Attached NVIDIA H100 GPUs
Machine type	vCPU count^*	Instance memory (GB)	Attached Local SSD (GiB)	Physical NIC count	Maximum network bandwidth (Gbps)^†	GPU count	GPU memory^‡ (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 count^*	Instance memory (GB)	Attached Local SSD (GiB)	Physical NIC count	Maximum network bandwidth (Gbps)^†	GPU count	GPU memory^‡ (GB HBM3)
`a3-edgegpu-8g`	208	1,872	6,000	5	800: for asia-south1 and northamerica-northeast2 400: for all other A3 Edge regions	8	640

A2 machine types

Each A2 machine type has a fixed number of NVIDIA A100 40GB or NVIDIA A100 80 GB GPUs attached. Each machine type also has a fixed vCPU count and memory size.

A2 machine series are available in two types:

A2 Ultra: these machine types have A100 80GB GPUs and Local SSD disks attached.
A2 Standard: these machine types have A100 40GB GPUs attached.

A2 Ultra

					Attached NVIDIA A100 80GB GPUs
Machine type	vCPU count^*	Instance memory (GB)	Attached Local SSD (GiB)	Maximum network bandwidth (Gbps)^†	GPU count	GPU memory^‡ (GB HBM3)
`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 count^*	Instance memory (GB)	Local SSD supported	Maximum network bandwidth (Gbps)^†	GPU count	GPU memory^‡ (GB HBM3)
`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

G2 machine types

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.

To get the higher network bandwidth rates (50 Gbps or higher) applied to most GPU instances, it is recommended that you use Google Virtual NIC (gVNIC). For more information about creating GPU instances that use gVNIC, see Creating GPU instances that use higher bandwidths.

						Attached NVIDIA L4 GPUs
Machine type	vCPU count^*	Default instance memory (GB)	Custom instance memory range (GB)	Max Local SSD supported (GiB)	Maximum network bandwidth (Gbps)^†	GPU count	GPU memory^‡ (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 + GPU machine types

For N1 general-purpose instances that have T4 and V100 GPUs attached, you can get a maximum network bandwidth of up to 100 Gbps, based on the combination of GPU and vCPU count. For all other N1 GPU instances, see Overview.

Review the following section to calculate the maximum network bandwidth that is available for your T4 and V100 instances based on the GPU model, vCPU, and GPU count.

Less than 5 vCPUs

For T4 and V100 instances that have 5 vCPUs or less, a maximum network bandwidth of 10 Gbps is available.

More than 5 vCPUs

For T4 and V100 instances that have more than 5 vCPUs, maximum network bandwidth is calculated based on the number of vCPUs and GPUs for that VM.

GPU model	Number of GPUs	Maximum network bandwidth calculation
NVIDIA V100	1	`min(vcpu_count * 2, 32)`
	2	`min(vcpu_count * 2, 32)`
	4	`min(vcpu_count * 2, 50)`
	8	`min(vcpu_count * 2, 100)`
NVIDIA T4	1	`min(vcpu_count * 2, 32)`
	2	`min(vcpu_count * 2, 50)`
	4	`min(vcpu_count * 2, 100)`

MTU settings and GPU machine types

To maximize network bandwidth, set a higher maximum transmission unit (MTU) value for your VPC networks. Higher MTU values increase the packet size and reduce the packet-header overhead, which in turn increases payload data throughput.

For GPU machine types, we recommend the following MTU settings for your VPC networks.

GPU machine type	Recommended MTU (in bytes)
	VPC network	VPC network with RDMA profiles
A4X A4 A3 Ultra	8896	8896
A3 Mega A3 High A3 Edge	8244	N/A
A2 Standard A2 Ultra G2 N1 machine types that support GPUs	8896	N/A

When setting the MTU value, note the following:

8192 is two 4 KB pages.
8244 is recommended in A3 Mega, A3 High, and A3 Edge VMs for GPU NICs that have header split enabled.
Use a value of 8896 unless otherwise indicated in the table.

Create high bandwidth GPU machines

To create GPU instances that use higher network bandwidths, use one of the following methods based on the machine type:

To create A2, G2 and N1 instances that use higher network bandwidths, see Use higher network bandwidth for A2, G2, and N1 instances. To test or verify the bandwidth speed for these machines, you can use the benchmarking test. For more information, see Checking network bandwidth.
To create A3 Mega instances that use higher network bandwidths, see Deploy an A3 Mega Slurm cluster for ML training. To test or verify the bandwidth speed for these machines, use a benchmarking test by following the steps in Checking network bandwidth.
For A3 High and A3 Edge instances that use higher network bandwidths, see Create an A3 VM with GPUDirect-TCPX enabled. To test or verify the bandwidth speed for these machines, you can use the benchmarking test. For more information, see Checking network bandwidth.
For other accelerator-optimized machine types, no action is required to use higher network bandwidth; creating an instance as documented already uses high network bandwidth. To learn how to create instances for other accelerator-optimized machine types, see Create a VM that has attached GPUs.

What's next?

Learn more about GPU platforms.
Learn how to create instances with attached GPUs.
Learn about Use higher network bandwidth.
Learn about GPU pricing.