Accelerator-optimized machine family


The accelerator-optimized machine family is designed by Google Cloud to deliver the needed performance and efficiency for GPU accelerated workloads such as artificial intelligence (AI), machine learning (ML), and high performance computing (HPC).

The accelerator-optimized machine family is available in A2 standard and ultra, and G2 standard machine types. Each accelerator-optimized machine type has a specific model and number of NVIDIA GPUs attached. You can also attach some GPU models to N1 general-purpose machine types.

Machine series recommendation by workload type

The following section provide the recommended machine series based on your GPU workloads.

Large AI models

Workload type Best fit
Multiple (distributed) server training A2
Inference A2

Mainstream models

Workload type Best fit Good alternative
Multiple (distributed) server training A2
  • G2
  • N1+V100
Single server training A2
  • G2
  • N1+V100
Inference G2
  • N1+T4
  • N1+V100

Graphics-intensive workloads

Workload type Best fit
Video streaming and transcoding, remote virtual workstations, digital twins
  • G2
  • N1+T4

High performance computing

For high performance computing workloads, any accelerator-optimized machine series works well. The best fit depends on the amount of computation that must be offloaded to the GPU.

Pricing and discount

All accelerator-optimized machine types support the following discount and consumption options:

The accelerator-optimized machine types are billed for their attached GPUs, predefined vCPU, memory, and bundled Local SSD (if applicable). For more pricing information for accelerator-optimized VMs, see Accelerator-optimized machine type family section on the VM instance pricing page.

The A2 machine series

The A2 machine series is available in A2 standard and A2 ultra machine types. These machine types have 12 to 96 vCPUs, and up to 1360 GB of memory.

The A2 machine series also provides the following features:

  • NVIDIA GPUs attached: each A2 machine type has NVIDIA A100 GPUs. These are available in both A100 40GB and A100 80GB options.

  • Industry-leading NVLink scale that provides peak GPU to GPU NVLink bandwidth of 600 GBps. For example, systems with 16 GPUs have an aggregate NVLink bandwidth of up to 9.6 TBps. These 16 GPUs can be used as a single high performance accelerator with unified memory space to deliver up to 10 petaFLOPS of compute power and up to 20 petaFLOPS of inference compute power that can be used for artificial intelligence, deep learning, and machine learning workloads.

  • Improved Computing speed: the attached NVIDIA A100 GPUs offer up to 10x improvements in computing speed when compared to previous generation NVIDIA V100 GPUs.

    With the A2 machine series, you can get up to 100 Gbps network bandwidth.

  • Storage: for fast scratch disks or for feeding data into the GPUs while preventing I/O bottlenecks, the A2 machine types support Local SSD as follows:

    • For the A2 standard machine types, you can add up to 3 TB of Local SSD.
    • For the A2 ultra machine types, Local SSD is automatically attached when you create the VM.

    You can also attach up to 257 TB of persistent disk storage to A2 VMs for applications that require this higher storage performance.

  • Compact placement policy support: provides you with more control over the physical placement of your VMs within data centers. This enables lower-latency and higher bandwidth for VM placement within a single availability zone. For more information, see Reduce latency by using compact placement policies.

Supported disk types for A2

A2 VMs can use the following block storage types:

  • Balanced Persistent Disk (pd-balanced)
  • SSD (performance) Persistent Disk (pd-ssd)
  • Standard Persistent Disk (pd-standard)
  • Local SSD: which is automatically attached to VMs created by using the A2 ultra machine types.

A2 standard machine types

These machine types have a fixed number of A100 40GB GPUs.

Machine types GPU count vCPUs* Memory (GB) Max number of Persistent Disk (PDs) Max total PD size (TB) Local SSD Maximum egress bandwidth (Gbps)
a2-highgpu-1g 1 12 85 128 257 Yes 24
a2-highgpu-2g 2 24 170 128 257 Yes 32
a2-highgpu-4g 4 48 340 128 257 Yes 50
a2-highgpu-8g 8 96 680 128 257 Yes 100
a2-megagpu-16g 16 96 1360 128 257 Yes 100

*A vCPU is implemented as a single hardware hyper-thread on one of the available CPU platforms.
Persistent disk usage is charged separately from machine type pricing.
Maximum egress bandwidth cannot exceed the number given. Actual egress bandwidth depends on the destination IP address and other factors. See Network bandwidth.

A2 standard limitations

  • You don't receive sustained use discounts for VMs that use A2 standard machine types.
  • You can only use A2 standard machine types in certain regions and zones.
  • The A2 standard machine types is only available on the Cascade Lake platform.
  • You can't use regional persistent disks on VMs that use A2 standard machine types.
  • If your VM uses an A2 standard machine type, you can only switch from one A2 standard machine type to another A2 standard machine type. You can't change to any other machine type. For more information, see Modify accelerator-optmized VMs.
  • You can't use the a2-megagpu-16g A2 standard machine type on Windows operating systems. When using Windows operating systems, choose a different A2 standard machine type.
  • You can't do a quick format of the attached Local SSDs on Windows VMs that use A2 standard machine types. To format these Local SSDs, you must do a full format by using the diskpart utility and specifying format fs=ntfs label=tmpfs.

A2 ultra machine types

These machine types have a fixed number of A100 80GB GPUs. Local SSD is automatically attached to VMs created by using the A2 ultra machine types.

Machine types GPU count vCPUs* Memory (GB) Max number of Persistent Disk (PDs) Max total PD size (TB) Bundled Local SSD Maximum egress bandwidth (Gbps)
a2-ultragpu-1g 1 12 170 128 257 375 GB 24
a2-ultragpu-2g 2 24 340 128 257 750 GB 32
a2-ultragpu-4g 4 48 680 128 257 1.5 TB 50
a2-ultragpu-8g 8 96 1360 128 257 3 TB 100

*A vCPU is implemented as a single hardware hyper-thread on one of the available CPU platforms.
Persistent disk usage is charged separately from machine type pricing.
Maximum egress bandwidth cannot exceed the number given. Actual egress bandwidth depends on the destination IP address and other factors. See Network bandwidth.

A2 ultra limitations

  • You don't receive sustained use discounts for VMs that use A2 ultra machine types.
  • You can only use A2 ultra machine types in certain regions and zones.
  • The A2 ultra machine types is only available on the Cascade Lake platform.
  • You can't use regional persistent disks on VMs that use A2 ultra machine types.
  • If your VM uses an A2 ultra machine type, you can't change the machine type. If you need to use a different A2 ultra machine type, or any other machine type, you must create a new VM.
  • You can't change any other machine type to an A2 ultra machine type. If you need to create a VM that uses an A2 ultra machine type, you must create a new VM.
  • You can't do a quick format of the attached Local SSDs on Windows VMs that use A2 ultra machine types. To format these Local SSDs, you must do a full format by using the diskpart utility and specifying format fs=ntfs label=tmpfs.

The G2 machine series

The G2 machine series is available in standard machine types that have 4 to 96 vCPUs, and up to 432 GB of memory. This machine series is optimized for inference and graphics workloads.

The G2 machine series also provides the following features:

  • NVIDIA GPUs attached: each G2 machine type has NVIDIA L4 GPUs.

  • Improved inference rates: the G2 machine types provide support for the FP8 (8-bit floating point) data type which speeds up ML inference rates and reduces memory requirements.

  • Next generation graphics performance: NVIDIA L4 GPUs provide up to 3X improvement in graphics performance by using third-generation RT cores and NVIDIA DLSS 3 (Deep Learning Super Sampling) technology.

  • High performance network bandwidth: with the G2 machine series, you can get up to 100 Gbps network bandwidth.

  • Storage: you can add up to 3 TB of Local SSD to G2 VMs. This can be used for fast scratch disks or for feeding data into the GPUs while preventing I/O bottlenecks.

    You can also attach up to 257 TB of Persistent Disk storage to G2 VMs for applications that require this higher storage performance.

  • Compact placement policy support: provides you with more control over the physical placement of your VMs within data centers. This enables lower-latency and higher bandwidth for VM placement within a single availability zone. For more information, see Reduce latency by using compact placement policies.

Supported disk types for G2

G2 VMs can use the following block storage types:

  • Balanced Persistent Disk (pd-balanced)
  • SSD (performance) Persistent Disk (pd-ssd)
  • Local SSD

G2 standard machine types

Each G2 machine type has a fixed number of NVIDIA L4 GPUs and vCPUs attached. 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 VM for each machine type. You can specify your custom memory during VM creation.

Machine types GPU count vCPUs* Default memory (GB) Custom memory range (GB) Max number of Persistent Disk (PDs) Max total PD size (TB) Max Local SSD supported (GB) Maximum egress bandwidth (Gbps)
g2-standard-4 1 4 16 16 - 32 128 257 375 10
g2-standard-8 1 8 32 32 - 54 128 257 375 16
g2-standard-12 1 12 48 48 - 54 128 257 375 16
g2-standard-16 1 16 64 54 - 64 128 257 375 32
g2-standard-24 2 24 96 96 - 108 128 257 750 32
g2-standard-32 1 32 128 96 - 128 128 257 375 32
g2-standard-48 4 48 192 192 - 216 128 257 1500 50
g2-standard-96 8 96 384 384 - 432 128 257 3000 100

*A vCPU is implemented as a single hardware hyper-thread on one of the available CPU platforms.
Persistent disk usage is charged separately from machine type pricing.
Maximum egress bandwidth cannot exceed the number given. Actual egress bandwidth depends on the destination IP address and other factors. See Network bandwidth.

G2 standard limitations

  • You don't receive sustained use discounts for VMs that use G2 standard machine types.
  • You can only use G2 standard machine types in certain regions and zones.
  • The G2 standard machine types is only available on the Cascade Lake platform.
  • You can't use regional persistent disks on VMs that use G2 standard machine types.
  • Standard persistent disks (pd-standard) are not supported on VMs that use G2 standard machine types. For supported disk types, see Supported disk types for G2.
  • You can't create Multi-Instance GPUs on G2 standard machine types.
  • If you need to change the machine type of a G2 VM, review Modify accelerator-optmized VMs.
  • You can't use Deep Learning VM Images as boot disks for your VMs that use G2 standard machine types.
  • The current default driver for Container-Optimized OS doesn't support L4 GPUs running on G2 machine types. Container-Optimized OS also only support a select set of drivers. If you want to use Container-Optimized OS on G2 machine types, review the following notes:
    • Use a Container-Optimized OS version that supports the minimum recommended NVIDIA driver version 525.60.13 or later. For more information, review the Container-Optimized OS release notes.
    • When you install the driver, specify the latest available version that works for the L4 GPUs. For example, sudo cos-extensions install gpu -- -version=525.60.13.
  • You must use the Google Cloud CLI or REST to create G2 VMs for the following scenarios:
    • You want to specify custom memory values.
    • You want to customize the number of visible CPU cores.

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