GPU machines in accelerator-optimized machine family

This document describes the Compute Engine instances that have pre-attached NVIDIA GPUs in the accelerator-optimized machine family. These instances are designed specifically for artificial intelligence (AI), machine learning (ML), high performance computing (HPC), and graphics-intensive applications.

The accelerator-optimized machine family includes the following machine series: A4X Max, A4X, A4, A3, A2, G4, and G2. Each machine type within a series has a specific model and number of NVIDIA GPUs attached. You can also attach some GPU models to N1 general-purpose machine types.

For information about accelerator-optimized instances with attached TPUs, see TPU machines in accelerator-optimized machine family.

GPU models and machine series

The following table summarizes the hardware specifications, machine series mappings, networking bandwidth, and storage capabilities of GPU models available on Compute Engine:

GPU model Machine series Architecture GPU memory and bandwidth GPUs per compute instance Max network bandwidth Local SSD Interconnect NVIDIA RTX Virtual Workstation (vWS) support
NVIDIA GB300 A4X Max Blackwell Ultra 279 GB HBM3e (8 TBps) 4 (NVL72) 3,600 Gbps 12,000 GiB NVLink Full Mesh (1,800 GBps) No
NVIDIA GB200 A4X Blackwell 186 GB HBM3e (8 TBps) 4 (NVL72) 2,000 Gbps 12,000 GiB NVLink Full Mesh (1,800 GBps) No
NVIDIA B200 A4 Blackwell 180 GB HBM3e (8 TBps) 8 3,600 Gbps 12,000 GiB NVLink Full Mesh (1,800 GBps) No
NVIDIA H200 A3 Ultra Hopper 141 GB HBM3e (4.8 TBps) 8 3,600 Gbps 12,000 GiB NVLink Full Mesh (900 GBps) No
NVIDIA H100 A3 Mega, High, Edge Hopper 80 GB HBM3 (3.35 TBps) 1, 2, 4, 8 Up to 1,000 Gbps Up to 6,000 GiB NVLink Full Mesh (900 GBps) No
NVIDIA A100 (80GB) A2 Ultra Ampere 80 GB HBM2e (1.9 TBps) 1, 2, 4, 8 100 Gbps Up to 3,000 GiB NVLink Full Mesh (600 GBps) No
NVIDIA A100 (40GB) A2 Standard Ampere 40 GB HBM2 (1.6 TBps) 1, 2, 4, 8, 16 100 Gbps Up to 3,000 GiB NVLink Full Mesh (600 GBps) No
NVIDIA RTX PRO 6000 G4 Blackwell 96 GB GDDR7 (1.597 TBps) 1/8, 1/4, 1/2, 1, 2, 4, 8 200 Gbps Up to 3,000 GiB PCIe Gen 5 Yes
NVIDIA L4 G2 Ada Lovelace 24 GB GDDR6 (300 GBps) 1, 2, 4, 8 100 Gbps Up to 3,000 GiB PCIe Gen 4 Yes
NVIDIA V100 N1+V100 Volta 16 GB HBM2 (900 GBps) 1, 2, 4, 8 100 Gbps Supported NVLink Ring (300 GBps) No
NVIDIA T4 (approaching end of support) N1+T4 Turing 16 GB GDDR6 (320 GBps) 1, 2, 4 100 Gbps Supported PCIe Gen 3 Yes
NVIDIA P4 (approaching end of support) N1+P4 Pascal 8 GB GDDR5 (192 GBps) 1, 2, 4 100 Gbps Supported PCIe Gen 3 Yes

The following section provides the recommended machine series based on your GPU workloads. For more information about how to choose a compute strategy for AI/ML and HPC workloads, see Choose a compute strategy in the AI Hypercomputer documentation.

AI/ML training

AI/ML inference

  • Large model inference (more than 70 billion parameters): serving large language models (LLMs) benefits from high High Bandwidth Memory (HBM3e) capacity (141–186 GB per GPU) to fit model weights across fewer GPUs, reducing inter-chip communication overhead. Recommended machine series:

  • High-throughput and real-time serving: low-latency interactive queries, streaming responses, and image generation use hardware-accelerated quantized numeric formats, such as 8-bit floating-point (FP8), 8-bit integer (INT8), and 4-bit integer (INT4), to maximize request throughput per dollar. Recommended machine series:

    For more information, see Recommendations for serving inference in the AI Hypercomputer documentation.

Graphics and visual

HPC and simulation

The 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 Compute Engine's hypervisor in the middle.

All machine types in the A4X Max and A4X series have two sockets with NVIDIA Grace™ CPUs with Arm® Neoverse™ V2 cores. These CPUs connect to four GPUs with fast chip-to-chip NVLink-C2 communication.

Both A4X Max and A4X machine series are built on NVIDIA's NVL72 rack-scale architecture, which uses NVLink domains to enable large-scale, high-performance GPU computing. An NVLink Domain is a group of interconnected NVIDIA NVSwitch chips and the GPUs that connect to them, forming a high-speed network fabric that allows for direct and fast communication between GPUs. For A4X Max and A4X machine types, a single NVL72 (NVLink) Domain is composed of 18 instances and 72 GPUs.

A4X Max and A4X comparison

The following table compares the features that differ between the A4X Max and A4X machine types:

Feature A4X Max (NVIDIA GB300) A4X (NVIDIA GB200)
GPU acceleration A4X Max instances have NVIDIA GB300 Ultra Superchips automatically attached. These Superchips feature NVIDIA B300 GPUs, offering up to 20 TB of total GPU memory per NVL72 domain, which provides roughly 279 GB per GPU. A4X instances have NVIDIA GB200 Superchips automatically attached. These Superchips feature NVIDIA B200 GPUs and offer 186 GB of memory per GPU.
Enhanced networking with RoCE

For A4X Max instances, RDMA over Converged Ethernet (RoCE) increases network performance by combining NVIDIA ConnectX-8 (CX-8) SuperNICs and Google's datacenter-wide network, which features eight-way rail-alignment. This configuration delivers up to 3,200 Gbps of bandwidth, which is optimal for demanding large-scale training and HPC tasks.

For general-purpose networking, each instance also has up to 400 Gbps of bandwidth.

For A4X instances, RDMA over Converged Ethernet (RoCE) increases network performance by combining NVIDIA ConnectX-7 (CX-7) NICs and Google's datacenter-wide network, which features four-way rail-alignment. This architecture provides up to 1,600 Gbps of bandwidth, enabling high-throughput, low-latency communication for large-scale distributed workloads.

For general-purpose networking, each instance also has up to 400 Gbps of bandwidth.
Performance The NVIDIA GB300 Ultra Superchips provide 15 PetaFLOPS of dense FP4 performance. For large-scale FP4 inference, the GB300 Ultra Superchips are expected to deliver 20–40% higher performance over the GB200 Superchips. The NVIDIA GB200 Superchips provide 10 PetaFLOPS of dense FP4 performance.
Bare metal and VM support Bare metal instances only VM instances only
OS support A4X Max instances support a range of Linux OS images. However, because bare metal instances use the Infrastructure Data Path Function (IDPF) network driver, your OS image must support IDPF. To use an OS image that is available on Compute Engine, see OS images that support IDPF. A4X instances support a range of Linux OS images. For a complete list of supported operating systems on Compute Engine, see OS support for GPUs.

Both A4X Max and A4X machine types share the following features:

  • CPU platform: Both A4X Max and A4X machine types use the NVIDIA Grace CPU platform with Arm® Neoverse™ V2 cores. For more details about the platform, see CPU platforms.

  • NVLink scalability: Multi-node NVLink scales up to 72 GPUs in a single domain and provides bidirectional GPU NVLink bandwidth of 1,800 GBps per GPU.

  • Disk support: A4X Max and A4X instances support Local SSD for fast scratch disks, which is useful for feeding data into GPUs while preventing I/O bottlenecks. 12,000 GiB of Local SSD is automatically added to A4X Max and A4X instances.

    For durable storage, you can attach up to 512 TiB of Hyperdisk storage. For more information about disk types, see Choose a disk type.

  • Dense allocation and topology-aware scheduling support: Both A4X Max and A4X machine types support requesting blocks of densely allocated capacity. Your host machines are allocated physically close to each other, provisioned as blocks of resources, and interconnected with a dynamic ML network fabric to minimize network hops and optimize for low latency. Additionally, for A4X Max and A4X instances, you can get topology information at the node and cluster level that can be used for job placement.

Pricing and consumption options for A4X Max and A4X instances

A4X Max and A4X instances support the following consumption and discount options:

Cloud de Confiance bills A4X Max and A4X instances for their attached GPUs, predefined vCPUs, memory, and bundled Local SSD disks. Hyperdisk and network usage are billed separately from machine type pricing. For more information, see Pricing and consumption options, Accelerator-optimized machine type family pricing, and GPU pricing.

A4X Max machine type (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. For information about machine type limitations, see A4X Max and A4X limitations.

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

A4X Max machine type notes

The following notes apply to the preceding table:

  • vCPU count: a vCPU is implemented as a single hardware hyper-thread on one of the available CPU platforms.
  • Maximum network bandwidth: maximum egress bandwidth can't 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: 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. For information about machine type limitations, see A4X Max and A4X limitations.

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

A4X machine type notes

The following notes apply to the preceding table:

  • vCPU count: a vCPU is implemented as a single hardware hyper-thread on one of the available CPU platforms.
  • Maximum network bandwidth: maximum egress bandwidth can't 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: 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 Max and A4X limitations

The following limitations apply to A4X Max and A4X instances:

A4X Max instances

A4X instances

Supported disk types for A4X Max and A4X instances

A4X Max

A4X Max instances can use the following block storage types:

  • Hyperdisk Balanced (hyperdisk-balanced): this is the only disk type that is supported for the boot disk
  • Hyperdisk Throughput (hyperdisk-throughput)
  • Hyperdisk ML (hyperdisk-ml)
  • Hyperdisk Extreme (hyperdisk-extreme)
  • Local SSD: which is automatically added to instances that are created by using any of the A4X Max machine types

The following table lists the maximum number of disks supported per instance:

Machine types All Hyperdisk Hyperdisk Balanced Hyperdisk Throughput Hyperdisk ML Hyperdisk Extreme Attached Local SSD
a4x-maxgpu-4g-metal 32 32 32 32 8 4

A4X

A4X instances can use the following block storage types:

  • Hyperdisk Balanced (hyperdisk-balanced): this is the only disk type that is supported for the boot disk
  • Hyperdisk Extreme (hyperdisk-extreme)
  • Hyperdisk ML (hyperdisk-ml)
  • Local SSD: which is automatically added to instances that are created by using any of the A4X machine types

The following table lists the maximum number of disks supported per instance:

Machine types All Hyperdisk Hyperdisk Balanced Hyperdisk Balanced High Availability Hyperdisk Throughput Hyperdisk ML Hyperdisk Extreme Attached Local SSD
a4x-highgpu-4g 128 128 0 0 128 8 4

The following note applies to the preceding table:

Disk and capacity limits

You can attach a mixture of different Hyperdisk types to an instance, but the maximum total disk capacity (in TiB) across all disk types can't exceed 512 TiB for all Hyperdisks.

For details about the capacity limits, see Hyperdisk size and attachment limits.

Maintenance experience for A4X Max and A4X instances

During the lifecycle of a Compute Engine instance, the host machine that your instance runs on undergoes multiple host events. A host event can include the regular maintenance of Compute Engine infrastructure, or in rare cases, a host error. Compute Engine also applies some non-disruptive lightweight upgrades for the hypervisor and network in the background.

The following table describes the host maintenance features for A4X Max and A4X machine types:

Machine type Number of GPUs Typical scheduled maintenance event frequency Maintenance behavior Advanced notification for scheduled maintenance On-demand maintenance Simulate maintenance
A4X Max and A4X 4 Minimum of 90 days Terminates with Local SSD data persistence 90 days Yes No

The following notes apply to the preceding table:

  • Typical scheduled maintenance event frequency:

    The maintenance frequencies shown in the previous table are approximations, not guarantees. Compute Engine might occasionally perform maintenance more frequently.

For more information about host maintenance in AI Hypercomputer, see Understand host maintenance in the AI Hypercomputer documentation.

The A4 machine series

The A4 machine series offers machine types with up to 224 vCPUs, and 3,968 GB of memory. A4 instances provide up to 3x performance of previous GPU instance types for most GPU accelerated workloads. A4 is recommended for ML training workloads especially at large scales—for example, hundreds or thousands of GPUs. The A4 machine series is available in a single machine type.

VM instances created by using the A4 machine type provide the following features:

  • GPU acceleration with NVIDIA B200 GPUs: NVIDIA B200 GPUs are automatically attached to A4 instances, which offer 180 GB GPU memory per GPU.

  • 5th Generation Intel Xeon Scalable Processor (Emerald Rapids): offers up to 4.0 GHz sustained single-core max turbo frequency. For more information about this processor, see CPU platform.

  • NVLink scalability: NVIDIA B200 GPUs provide GPU NVLink bandwidth of 1,800 GBps, bidirectionally per GPU.

    With all-to-all NVLink topology between 8 GPUs in a system, the aggregate NVLink Bandwidth is up to 14.4 TBps.

  • Enhanced networking with RoCE: RDMA over Converged Ethernet (RoCE) increases the network performance by combining NVIDIA ConnectX-7 network interface cards (NICs) with Google's datacenter-wide four-way rail-aligned network. By leveraging RDMA over Converged Ethernet (RoCE), A4 instances achieve much higher throughput between instances in a cluster compared to most A3 instances, except those running on the A3 Ultra machine type.

  • Increased network speeds: Offers up to 4x networking speeds when compared to the previous generation A2 instances.

    For more information about networking, see Network bandwidths and GPUs.

  • Virtualization optimizations for data transfers and recovery: the Peripheral Component Interconnect Express (PCIe) topology of A4 instances provides more accurate locality information that workloads can use to optimize data transfers.

    The GPUs also expose Function Level Reset (FLR) for graceful recovery from failures and atomic operations support for concurrency improvements in certain scenarios.

  • Disk support: A4 instances support Local SSD for fast scratch disks, which is useful for feeding data into GPUs while preventing I/O bottlenecks. For durable storage, you can attach Hyperdisk volumes.

    12,000 GiB of Local SSD is automatically added to A4 instances. For workloads that require durable block storage, you can also attach up to 512 TiB of Hyperdisk to A4 instances. For more information about disk types, see Choose a disk type.

  • Dense allocation and topology aware scheduling support: When you provision A4 instances, you can request blocks of densely allocated capacity. Your host machines are allocated physically close to each other, provisioned as blocks of resources, and are interconnected with a dynamic ML network fabric to minimize network hops and optimize for the lowest latency. Additionally, you can get topology information at node and cluster level that can be used for job placement.

Pricing and consumption options for A4 instances

A4 instances support the following consumption and discount options:

Cloud de Confiance bills A4 instances for their attached GPUs, predefined vCPUs, memory, and bundled Local SSD disks. Hyperdisk and network usage are billed separately from machine type pricing. For more information, see Pricing and consumption options, Accelerator-optimized machine type family pricing, and GPU pricing.

A4 machine type (NVIDIA B200)

A4 accelerator-optimized machine types have NVIDIA B200 Blackwell GPUs (nvidia-b200) attached and are ideal for foundation model training and serving. For information about machine type limitations, see A4 limitations.

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

A4 machine type notes

The following notes apply to the preceding table:

  • vCPU count: a vCPU is implemented as a single hardware hyper-thread on one of the available CPU platforms.
  • Maximum network bandwidth: maximum egress bandwidth can't 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: 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 limitations

  • You can only request capacity by using the supported consumption options for an A4 machine type.
  • You don't receive sustained use discounts and flexible committed use discounts for instances that use an A4 machine type.
  • You can only use an A4 machine type in certain regions and zones.
  • You can't use Persistent Disk (regional or zonal). You can only use Google Cloud Hyperdisk.
  • The A4 machine type is only available on the Emerald Rapids CPU platform.
  • You can't change the machine type of an instance to or from A4 machine type. You must create a new instance with this machine type.
  • A4 machine types don't support sole-tenancy.
  • You can't run Windows operating systems on an A4 machine type.
  • For A4 instances, when you use ethtool -S to monitor GPU networking, physical port counters that end in _phy don't update. This is expected behavior for instances that use the MRDMA Virtual Function (VF) architecture. For more information, see MRDMA functions and network monitoring tools.
  • You can't attach Hyperdisk ML disks that were created before February 4, 2026 to A4 machine types.

Supported disk types for A4 instances

A4 instances can use the following block storage types:

  • Hyperdisk Balanced (hyperdisk-balanced): this is the only disk type that is supported for the boot disk
  • Hyperdisk Extreme (hyperdisk-extreme)
  • Hyperdisk ML (hyperdisk-ml)
  • Local SSD: which is automatically added to instances that are created by using any of the A4 machine types

The following table lists the maximum number of disks supported per instance:

Machine types All Hyperdisk Hyperdisk Balanced Hyperdisk Throughput Hyperdisk ML Hyperdisk Extreme Attached Local SSD
a4-highgpu-8g 128 128 N/A 128 8 32

The following note applies to the preceding table:

Disk and capacity limits

You can attach a mixture of different Hyperdisk types to an instance, but the maximum total disk capacity (in TiB) across all disk types can't exceed 512 TiB for all Hyperdisks.

For details about the capacity limits, see Hyperdisk size and attachment limits.

Maintenance experience for A4 instances

During the lifecycle of a Compute Engine instance, the host machine that your instance runs on undergoes multiple host events. A host event can include the regular maintenance of Compute Engine infrastructure, or in rare cases, a host error. Compute Engine also applies some non-disruptive lightweight upgrades for the hypervisor and network in the background.

The following table describes the host maintenance features for A4 machine types:

Machine type Number of GPUs Typical scheduled maintenance event frequency Maintenance behavior Advanced notification for scheduled maintenance On-demand maintenance Simulate maintenance
A4 8 Minimum of 90 days Terminates with Local SSD data persistence 90 days Yes No

The following notes apply to the preceding table:

  • Typical scheduled maintenance event frequency:

    The maintenance frequencies shown in the previous table are approximations, not guarantees. Compute Engine might occasionally perform maintenance more frequently.

For more information about host maintenance in AI Hypercomputer, see Understand host maintenance in the AI Hypercomputer documentation.

The A3 machine series

The A3 machine series has up to 224 vCPUs, and 2,944 GB of memory. This machine series is optimized for compute and memory intensive, network bound ML training, and HPC workloads. The A3 machine series is available in A3 Ultra, A3 Mega, A3 High, and A3 Edge machine types.

The following table compares the features that differ between the A3 machine types:

Feature A3 Ultra (NVIDIA H200) A3 Mega, High, and Edge (NVIDIA H100)
GPU acceleration

NVIDIA H200 SXM GPUs are attached, offering 141 GB of GPU memory per GPU and providing larger and faster memory to support large language models and HPC workloads.

NVIDIA H100 SXM GPUs are attached, offering 80 GB of GPU memory per GPU, which is ideal for large transformer-based language models, databases, and HPC.
Intel Xeon Scalable processors

5th Generation Intel Xeon Scalable processor (Emerald Rapids), which offers up to 4.0 GHz sustained single-core max turbo frequency. For more information about this processor, see CPU platforms.

4th Generation Intel Xeon Scalable processor (Sapphire Rapids), which offers up to 3.3 GHz sustained single-core max turbo frequency. For more information about this processor, see CPU platforms.
Enhanced networking

For this machine type, RDMA over Converged Ethernet (RoCE) increases the network performance by combining NVIDIA ConnectX-7 network interface cards (NICs) with Google's datacenter-wide four-way rail-aligned network. By using RDMA over Converged Ethernet (RoCE), the a3-ultragpu-8g machine type achieves much higher throughput between instances in a cluster when compared to other A3 machine types.
  • For the A3 Mega machine types, GPUDirect-TCPXO further improves on GPUDirect-TCPX by offloading the TCP protocol. By using GPUDirect-TCPXO, the a3-megagpu-8g machine type doubles the network bandwidth when compared to the A3 High and A3 Edge machine types.
  • For the A3 Edge (a3-edgegpu-8g) and A3 High (a3-highgpu-8g) machine types, GPUDirect-TCPX increases the network performance by allowing data packet payloads to transfer directly from GPU memory to the network interface. By using GPUDirect-TCPX, these machine types achieve much higher throughput between instances in a cluster when compared to the A2 or G2 accelerator-optimized machine types.
Improved networking speeds

Offers up to 4x networking speeds when compared to the previous generation A2 machine series.

For more information about networking, see Network bandwidths and GPUs.

Offers up to 2.5x networking speeds when compared to the previous generation A2 machine series.

For more information about networking, see Network bandwidths and GPUs.

All A3 machine types share the following features:

  • NVLink scalability: NVIDIA H200 and H100 GPUs provide peak GPU NVLink bandwidth of 450 GBps, unidirectionally. By using an all-to-all NVLink topology, an 8-GPU system achieves an aggregate bandwidth of up to 7.2 TBps.

  • Virtualization optimizations: The Peripheral Component Interconnect Express (PCIe) topology of A3 instances provides more accurate locality information that workloads can use to optimize data transfers. The GPUs also expose Function Level Reset (FLR) for graceful recovery from failures and atomic operations support for concurrency improvements in certain scenarios.

  • Disk support: A3 instances support Local SSD for fast scratch disks, which is useful for feeding data into GPUs while preventing I/O bottlenecks. For durable storage, you can attach Persistent Disk and Hyperdisk volumes.

    Local SSD is attached as follows:

    • 12,000 GiB of Local SSD is automatically added to A3 Ultra instances.
    • 6,000 GiB of Local SSD is automatically added to A3 Mega, High, and Edge instances.

    For workloads that require durable block storage, you can also attach up to 512 TiB of Persistent Disk and Hyperdisk to machine types in these series. For select machine types, up to 257 TiB of Persistent Disk is also supported. For more information about disk types, see Choose a disk type.

  • Compact placement policy support: Provides you with more control over the physical placement of your instances within data centers. This placement enables lower latency and higher bandwidth for instances that are located within a single availability zone. For more information, see About compact placement policies.

Pricing and consumption options for A3 instances

A3 instances support the following consumption and discount options:

Cloud de Confiance bills A3 instances for their attached GPUs, predefined vCPUs, memory, and bundled Local SSD disks. Persistent Disk, Hyperdisk, and network usage are billed separately from machine type pricing. For more information, see Pricing and consumption options, Accelerator-optimized machine type family pricing, and GPU pricing.

A3 Ultra machine type (NVIDIA H200)

A3 Ultra machine types have NVIDIA H200 SXM GPUs (nvidia-h200-141gb) attached and provide the highest network performance in the A3 series. A3 Ultra machine types are ideal for foundation model training and serving. For information about machine type limitations, see A3 Ultra limitations.

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

A3 Ultra machine type notes

The following notes apply to the preceding table:

  • vCPU count: a vCPU is implemented as a single hardware hyper-thread on one of the available CPU platforms.
  • Maximum network bandwidth: maximum egress bandwidth can't 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: 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 limitations

  • You can only request capacity by using the supported consumption options for an A3 Ultra machine type.
  • You don't receive sustained use discounts and flexible committed use discounts for instances that use an A3 Ultra machine type.
  • You can only use an A3 Ultra machine type in certain regions and zones.
  • You can't use Persistent Disk (regional or zonal). You can only use Google Cloud Hyperdisk.
  • The A3 Ultra machine type is only available on the Emerald Rapids CPU platform.
  • Machine type changes aren't supported for A3 Ultra machine type. To switch to or from this machine type, you must create a new instance.
  • You can't run Windows operating systems on an A3 Ultra machine type.
  • A3 Ultra machine types don't support sole-tenancy.
  • For A3 Ultra instances, when you use ethtool -S to monitor GPU networking, physical port counters that end in _phy don't update. This is expected behavior for instances that use the MRDMA Virtual Function (VF) architecture. For more information, see MRDMA functions and network monitoring tools.

A3 Mega machine type (NVIDIA H100)

A3 Mega machine types have NVIDIA H100 SXM GPUs and are ideal for large model training and multi-host inference. For information about machine type limitations, see A3 Mega limitations.
Machine type vCPU count Instance memory (GB) Attached Local SSD (GiB) Physical NIC count Maximum network bandwidth (Gbps) NVIDIA H100 GPU count GPU memory (GB HBM3)
a3-megagpu-8g 208 1,872 6,000 9 1,800 8 640

A3 Mega machine type notes

The following notes apply to the preceding table:

  • vCPU count: a vCPU is implemented as a single hardware hyper-thread on one of the available CPU platforms.
  • Maximum network bandwidth: maximum egress bandwidth can't 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: 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 limitations (NVIDIA H100)

A3 High machine type (NVIDIA H100)

A3 High machine types have NVIDIA H100 SXM GPUs and are well-suited for both large model inference and model fine tuning. For information about machine type limitations, see A3 High limitations.
Machine type vCPU count Instance memory (GB) Attached Local SSD (GiB) Physical NIC count Maximum network bandwidth (Gbps) NVIDIA H100 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 High machine type notes

The following notes apply to the preceding table:

  • vCPU count: a vCPU is implemented as a single hardware hyper-thread on one of the available CPU platforms.
  • Maximum network bandwidth: maximum egress bandwidth can't 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: 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 High limitations

A3 Edge machine type (NVIDIA H100)

A3 Edge machine types have NVIDIA H100 SXM GPUs and are designed specifically for serving and are available in a limited set of regions. For information about machine type limitations, see A3 Edge limitations.
Machine type vCPU count Instance memory (GB) Attached Local SSD (GiB) Physical NIC count Maximum network bandwidth (Gbps) NVIDIA H100 GPU count GPU memory (GB HBM3)
a3-edgegpu-8g 208 1,872 6,000 5 600 (asia-south1 and northamerica-northeast2) or 400 (all other A3 Edge regions) 8 640

A3 Edge machine type notes

The following notes apply to the preceding table:

  • vCPU count: a vCPU is implemented as a single hardware hyper-thread on one of the available CPU platforms.
  • Maximum network bandwidth: maximum egress bandwidth can't 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: 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 Edge limitations

Supported disk types for A3 instances

A3 Ultra

A3 Ultra instances can use the following block storage types:

  • Hyperdisk Balanced (hyperdisk-balanced): this is the only disk type that is supported for the boot disk
  • Hyperdisk Balanced High Availability (hyperdisk-balanced-high-availability)
  • Hyperdisk Extreme (hyperdisk-extreme)
  • Hyperdisk ML (hyperdisk-ml)
  • Local SSD: which is automatically added to instances that are created by using any of the A3 machine types

The following table lists the maximum number of disks supported per instance:

Machine types All Hyperdisk Hyperdisk Balanced Hyperdisk Balanced High Availability Hyperdisk Throughput Hyperdisk ML Hyperdisk Extreme Attached Local SSD disks
a3-ultragpu-8g 128 128 128 N/A 128 8 32

The following note applies to the preceding table:

A3 Mega

A3 Mega instances can use the following block storage types:

  • Balanced Persistent Disk (pd-balanced)
  • SSD (performance) Persistent Disk (pd-ssd)
  • Hyperdisk Balanced (hyperdisk-balanced)
  • Hyperdisk Balanced High Availability (hyperdisk-balanced-high-availability)
  • Hyperdisk ML (hyperdisk-ml)
  • Hyperdisk Extreme (hyperdisk-extreme)
  • Hyperdisk Throughput (hyperdisk-throughput)
  • Local SSD: which is automatically added to instances that are created by using any of the A3 machine types

The following table lists the maximum number of disks supported per instance:

Machine types All Hyperdisk Hyperdisk Balanced Hyperdisk Balanced High Availability Hyperdisk Throughput Hyperdisk ML Hyperdisk Extreme Attached Local SSD disks
a3-megagpu-8g 128 32 32 64 64 8 16

The following note applies to the preceding table:

A3 High

A3 High instances can use the following block storage types:

  • Balanced Persistent Disk (pd-balanced)
  • SSD (performance) Persistent Disk (pd-ssd)
  • Hyperdisk Balanced (hyperdisk-balanced)
  • Hyperdisk Balanced High Availability (hyperdisk-balanced-high-availability)
  • Hyperdisk ML (hyperdisk-ml)
  • Hyperdisk Extreme (hyperdisk-extreme)
  • Hyperdisk Throughput (hyperdisk-throughput)
  • Local SSD: which is automatically added to instances that are created by using any of the A3 machine types

The following table lists the maximum number of disks supported per instance:

Machine types All Hyperdisk Hyperdisk Balanced Hyperdisk Balanced High Availability Hyperdisk Throughput Hyperdisk ML Hyperdisk Extreme Attached Local SSD disks
a3-highgpu-1g 128 32 32 64 64 N/A 2
a3-highgpu-2g 128 32 32 64 64 N/A 4
a3-highgpu-4g 128 32 32 64 64 8 8
a3-highgpu-8g 128 32 32 64 64 8 16

The following note applies to the preceding table:

A3 Edge

A3 Edge instances can use the following block storage types:

  • Balanced Persistent Disk (pd-balanced)
  • SSD (performance) Persistent Disk (pd-ssd)
  • Hyperdisk Balanced (hyperdisk-balanced)
  • Hyperdisk Balanced High Availability (hyperdisk-balanced-high-availability)
  • Hyperdisk ML (hyperdisk-ml)
  • Hyperdisk Extreme (hyperdisk-extreme)
  • Hyperdisk Throughput (hyperdisk-throughput)
  • Local SSD: which is automatically added to instances that are created by using any of the A3 machine types

The following table lists the maximum number of disks supported per instance:

Machine types All Hyperdisk Hyperdisk Balanced Hyperdisk Balanced High Availability Hyperdisk Throughput Hyperdisk ML Hyperdisk Extreme Attached Local SSD
a3-edgegpu-8g 128 32 32 64 64 8 16

The following note applies to the preceding table:

Disk and capacity limits

If supported by the machine type, you can attach a mixture of Hyperdisk and Persistent Disk volumes to an instance, but the following restrictions apply:

  • The combined number of both Hyperdisk and Persistent Disk volumes can't exceed 128 per instance.

  • The maximum total disk capacity (in TiB) across all disk types can't exceed:

    • For machine types with less than 32 vCPUs:

      • 257 TiB for all Hyperdisk or all Persistent Disk
      • 257 TiB for a mixture of Hyperdisk and Persistent Disk

    • For machine types with 32 or more vCPUs:

      • 512 TiB for all Hyperdisk
      • 512 TiB for a mixture of Hyperdisk and Persistent Disk
      • 257 TiB for all Persistent Disk

For details about the capacity limits, see Hyperdisk size and attachment limits and Persistent Disk maximum capacity.

Maintenance experience for A3 instances

During the lifecycle of a Compute Engine instance, the host machine that your instance runs on undergoes multiple host events. A host event can include the regular maintenance of Compute Engine infrastructure, or in rare cases, a host error. Compute Engine also applies some non-disruptive lightweight upgrades for the hypervisor and network in the background.

The following table describes the host maintenance features for A3 machine types:

Machine type Number of GPUs Typical scheduled maintenance event frequency Maintenance behavior Advanced notification for scheduled maintenance On-demand maintenance Simulate maintenance
A3 Ultra 8 Minimum of 90 days Terminates with Local SSD data persistence 90 days Yes No
A3 Mega and A3 High 8 Minimum of 30 days Terminate and restart 7 days Yes Yes
A3 High 1, 2, 4 Minimum of 30 days Terminate and restart 7 days No Yes
A3 Edge 8 Minimum of 30 days Terminate and restart 7 days Yes Yes

The following notes apply to the preceding table:

  • Typical scheduled maintenance event frequency:

    The maintenance frequencies shown in the previous table are approximations, not guarantees. Compute Engine might occasionally perform maintenance more frequently.

  • A3 Mega and A3 High: the scheduled maintenance frequencies and notification windows exclude instances covered by specific customer maintenance agreements.

For more information about host maintenance in AI Hypercomputer, see Understand host maintenance in the AI Hypercomputer documentation.

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 1,360 GB of memory.

VM instances created by using the A2 machine types provide the following features:

  • GPU acceleration: each A2 instance has NVIDIA A100 GPUs. These are available in both A100 40GB and A100 80GB options.

  • NVLink scalability: NVIDIA A100 GPUs provide 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 speeds: 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.

  • Disk support: A2 instances support Local SSD for fast scratch disks, which is useful for feeding data into GPUs while preventing I/O bottlenecks. For durable storage, you can attach Persistent Disk and Hyperdisk volumes.

    Local SSD is supported as follows:

    • For A2 Standard machine types, you can add up to 3,000 GiB of Local SSD when you create an instance.
    • For A2 Ultra machine types, Local SSD is automatically attached when you create an instance.

    For workloads that require durable block storage, you can attach up to 257 TiB of Persistent Disk and 512 TiB of Hyperdisk volumes to A2 instances. For more information about disk types, see Choose a disk type.

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

Pricing and consumption options for A2 instances

A2 instances support the following consumption and discount options:

Cloud de Confiance bills A2 instances for their attached GPUs, predefined vCPUs, memory, and bundled Local SSD disks (for A2 Ultra). Persistent Disk, Hyperdisk, additional Local SSD disks (for A2 Standard), and network usage are billed separately from machine type pricing. For more information, see Pricing and consumption options, Accelerator-optimized machine type family pricing, and GPU pricing.

The following machine types are available for the A2 machine series.

A2 Ultra machine types (NVIDIA A100 80GB)

These machine types have a fixed number of A100 80GB GPUs. Local SSD is automatically attached to instances created by using the A2 Ultra machine types. For information about machine type limitations, see A2 Ultra limitations.

Machine type vCPU count Instance memory (GB) Attached Local SSD (GiB) Maximum network bandwidth (Gbps) NVIDIA A100 80GB GPU count GPU memory (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 Ultra machine type notes

The following notes apply to the preceding table:

  • vCPU count: a vCPU is implemented as a single hardware hyper-thread on one of the available CPU platforms.
  • Maximum network bandwidth: maximum egress bandwidth can't 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: 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.

A2 Ultra limitations

  • You can only request capacity by using the supported consumption options for an A2 Ultra machine type.
  • You don't receive sustained use discounts and flexible committed use discounts for instances that use an A2 Ultra machine type.
  • You can only use an A2 Ultra machine type in certain regions and zones.
  • The A2 Ultra machine type is only available on the Cascade Lake platform.
  • If your instance 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 instance.
  • You can't change any other machine type to an A2 Ultra machine type. If you need a instance that uses an A2 Ultra machine type, you must create a new instance.
  • You can't do a quick format of the attached Local SSDs on Windows instances 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.

A2 Standard machine types (NVIDIA A100 40GB)

These machine types have a fixed number of A100 40GB GPUs. 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. For information about machine type limitations, see A2 Standard limitations.

Machine type vCPU count Instance memory (GB) Local SSD supported Maximum network bandwidth (Gbps) NVIDIA A100 40GB GPU count GPU memory (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

A2 Standard machine type notes

The following notes apply to the preceding table:

  • vCPU count: a vCPU is implemented as a single hardware hyper-thread on one of the available CPU platforms.
  • Maximum network bandwidth: maximum egress bandwidth can't 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: 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.

A2 Standard limitations

  • You can only request capacity by using the supported consumption options for an A2 Standard machine type.
  • You don't receive sustained use discounts and flexible committed use discounts for instances that use an A2 Standard machine type.
  • You can only use an A2 Standard machine type in certain regions and zones.
  • The A2 Standard machine type is only available on the Cascade Lake platform.
  • If your instance uses an A2 Standard machine type, you can only switch from one A2 Standard machine type type to another A2 Standard machine type. You can't change to any other machine type. For more information, see Modify accelerator-optimized instances.
  • You can't use the Windows operating system with the a2-megagpu-16g machine type. When using a Windows operating system, choose a different A2 Standard machine type.
  • You can't do a quick format of the attached Local SSDs on Windows instances 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.

Supported disk types for A2 instances

A2 instances can use the following block storage types:

  • Hyperdisk ML (hyperdisk-ml)
  • Balanced Persistent Disk (pd-balanced)
  • SSD (performance) Persistent Disk (pd-ssd)
  • Standard Persistent Disk (pd-standard)
  • Local SSD: which is automatically attached to instances created by using the A2 Ultra machine types.

A2 Ultra

The following table lists the maximum number of disks supported per instance:

Machine types All disks Hyperdisk ML Attached Local SSD
a2-ultragpu-1g 128 32 1
a2-ultragpu-2g 128 48 2
a2-ultragpu-4g 128 64 4
a2-ultragpu-8g 128 64 8

The following notes apply to the preceding table:

A2 Standard

The following table lists the maximum number of disks supported per instance:

Machine types All disks Hyperdisk ML Local SSD
a2-highgpu-1g 128 32 8
a2-highgpu-2g 128 48 8
a2-highgpu-4g 128 64 8
a2-highgpu-8g 128 64 8
a2-megagpu-16g 128 64 8

The following notes apply to the preceding table:

If supported by the machine type, you can attach a mixture of Hyperdisk and Persistent Disk volumes to an instance, but the following restrictions apply:

  • The combined number of both Hyperdisk and Persistent Disk volumes can't exceed 128 per instance.

  • The maximum total disk capacity (in TiB) across all disk types can't exceed:

    • For machine types with less than 32 vCPUs:

      • 257 TiB for all Hyperdisk or all Persistent Disk
      • 257 TiB for a mixture of Hyperdisk and Persistent Disk

    • For machine types with 32 or more vCPUs:

      • 512 TiB for all Hyperdisk
      • 512 TiB for a mixture of Hyperdisk and Persistent Disk
      • 257 TiB for all Persistent Disk

For details about the capacity limits, see Hyperdisk size and attachment limits and Persistent Disk maximum capacity.

Maintenance experience for A2 instances

During the lifecycle of a Compute Engine instance, the host machine that your instance runs on undergoes multiple host events. A host event can include the regular maintenance of Compute Engine infrastructure, or in rare cases, a host error. Compute Engine also applies some non-disruptive lightweight upgrades for the hypervisor and network in the background.

The following table describes the host maintenance features for A2 machine types:

Machine type Number of GPUs Typical scheduled maintenance event frequency Maintenance behavior Advanced notification for scheduled maintenance On-demand maintenance Simulate maintenance
A2 Ultra 1, 2, 4, 8 Minimum of 30 days Terminate and restart 7 days Yes (8 GPUs only) Yes
A2 Standard 1, 2, 4, 8, or 16 Minimum of 30 days Terminate and restart 7 days Yes (8 and 16 GPUs only) Yes

The following notes apply to the preceding table:

  • Typical scheduled maintenance event frequency:

    The maintenance frequencies shown in the previous table are approximations, not guarantees. Compute Engine might occasionally perform maintenance more frequently.

The G4 machine series

The G4 machine series uses the AMD EPYC Turin CPU platform and features NVIDIA RTX PRO 6000 Blackwell Server Edition GPUs. This machine series offers significant improvements over the previous-generation G2 machine series, with considerably more GPU memory, increased GPU memory bandwidth, and higher networking bandwidth.

G4 instances have up to 384 vCPUs, 1,440 GB of memory, and 12 TiB of Titanium SSD disks attached. G4 instances also provide up to 400 Gbps of standard network performance.

This machine series is particularly intended for workloads such as NVIDIA Omniverse simulation workloads, graphics-intensive applications, video transcoding, and virtual desktops. The G4 machine series also provide a low-cost solution for performing single host inference and model tuning compared with A series machine types.

Instances that use the G4 machine type provide the following features:

  • GPU acceleration with NVIDIA RTX PRO 6000 Blackwell Server Edition GPUs: G4 instances automatically attach NVIDIA RTX PRO 6000 Blackwell Server Edition GPUs, which offer 96 GB GPU memory per GPU.

  • 5th Generation AMD EPYC Turin CPU Platform: this platform offers up to 4.1 GHz sustained max boost frequency. For more information about this processor, see CPU platform.

  • Next generation graphics performance: the NVIDIA RTX PRO 6000 GPUs provide significant performance and feature upgrades over the NVIDIA L4 GPUs that are attached to the G2 machine series. Thesed upgrades are as follows:

    • 5th-Generation Tensor Cores: these cores introduce support for FP4 precision and DLSS 4 Multi Frame Generation. By using these 5th Generation tensor cores, NVIDIA RTX PRO 6000 GPUs offers improved performance to accelerate tasks like local LLM development and content creation, compared to NVIDIA L4 GPUs.
    • 4th-Generation RT Cores: these cores deliver up to twice the ray-tracing performance of the previous generation NVIDIA L4 GPUs, accelerating rendering for design and manufacturing workloads.
    • Core count: the NVIDIA RTX PRO 6000 GPU includes 24,064 CUDA cores, 752 5th-gen Tensor cores, and 188 4th-gen RT cores. This update represents a substantial increase over prior generations like the L4 GPU, which has 7,680 CUDA cores and 240 Tensor cores.

  • GPU sharing: there are a number of options that you can use to allow multiple workloads to access a single physical GPU. GPU sharing is useful for workloads that don't require the resources of a full GPU, helping you optimize costs. The following GPU sharing options are available for G4 instances:

    • Fractional GPU (vGPU) support: this feature allows a single physical GPU to be shared by multiple virtual machine (VM) instances. vGPUs provide multi-tenant security isolation because each vGPU is a separate VM instance. To use vGPUs, Compute Engine provides the following VM instance shapes: g4-standard-6 (1/8 GPU), g4-standard-12 (1/4 GPU), and g4-standard-24 (1/2 GPU).
    • Multi-Instance GPU (MIG): this feature allows a single GPU to be partitioned into up to four fully isolated instances on a single virtual machine. This option doesn't offer multi-tenant security isolation as all partitions belong to a single VM.

  • Peripheral Component Interconnect Express (PCIe) Gen 5 support: G4 instances supports PCI Express Gen 5, which improves the data transfer speed from CPU memory to GPU compared to PCIe Gen 3 used by G2 instances.

  • Disk support: G4 instances support Titanium SSD for fast scratch disks, which is useful for feeding data into GPUs while preventing I/O bottlenecks. For durable storage, you can attach Hyperdisk volumes.

    G4 instances support attaching up to 12,000 GiB of Titanium SSD. For workloads that require durable block storage, G4 instances also support attaching up to 512 TiB of Hyperdisk. For more information about disk types, see Choose a disk type.

  • GPU Peer-to-Peer (P2P) communication: G4 instances support GPU P2P communication, enabling direct data transfer between GPUs within the same instance. This can significantly improve performance for multi-GPU workloads by reducing data transfer latency and freeing up CPU resources. For more information, see G4 GPU peer-to-peer (P2P) communication.

  • Compact placement policy support: this feature gives you control over the physical location of your instances within data centers. Compact placement policies help you collocate instances close together to reduce network latency, which is useful for latency-sensitive workloads like high performance computing (HPC) or machine learning (ML). For more information, see Reduce latency by using compact placement policies.

Pricing and consumption options for G4 instances

G4 instances support the following consumption and discount options:

Cloud de Confiance bills G4 instances for their attached GPUs, predefined vCPUs, and memory. Hyperdisk, Titanium SSD disks, and network usage are billed separately from machine type pricing. For more information, see Pricing and consumption options, Accelerator-optimized machine type family pricing, and GPU pricing.

G4 machine types (NVIDIA RTX PRO 6000)

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.

You can also add Titanium SSD disks when you create 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. For information about machine type limitations, see G4 limitations.

Machine type vCPU count Instance memory (GB) Maximum Titanium SSD supported (GiB) Physical NIC count Maximum network bandwidth (Gbps) NVIDIA RTX PRO 6000 GPU count GPU memory (GB GDDR7)
g4-standard-6 6 22 0 1 20 1/8 12
g4-standard-12 12 45 375 1 20 1/4 24
g4-standard-24 24 90 750 1 20 1/2 48
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

G4 machine type notes

The following notes apply to the preceding table:

  • vCPU count: a vCPU is implemented as a single hardware hyper-thread on one of the available CPU platforms.
  • Maximum network bandwidth: maximum egress bandwidth can't 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: 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.

G4 limitations

Supported disk types for G4 instances

G4 instances can use the following block storage types:

  • Hyperdisk Balanced (hyperdisk-balanced): this is the only disk type that is supported for the boot disk
  • Hyperdisk Balanced High Availability (hyperdisk-balanced-high-availability)
  • Hyperdisk Extreme (hyperdisk-extreme): this disk type is only supported on G4 instances that have two or more GPUs attached
  • Hyperdisk ML (hyperdisk-ml)
  • Hyperdisk Throughput (hyperdisk-throughput)
  • Titanium SSD: you can add Titanium SSD to instances created by using the G4 machine types.

The following table lists the maximum number of disks supported per instance:

Machine types All Hyperdisk Hyperdisk Balanced Hyperdisk Balanced High Availability Hyperdisk Extreme Hyperdisk ML Hyperdisk Throughput Titanium SSD
g4-standard-6 8 8 8 0 8 8 0
g4-standard-12 16 16 16 0 16 16 1
g4-standard-24 32 32 32 0 32 32 2
g4-standard-48 32 32 32 0 32 32 4
g4-standard-96 32 32 32 8 32 32 8
g4-standard-192 64 64 64 8 64 64 16
g4-standard-384 128 128 128 8 128 128 32

The following note applies to the preceding table:

You can attach a mixture of different Hyperdisk types to an instance, but the maximum total disk capacity (in TiB) across all disk types can't exceed 512 TiB for all Hyperdisks.

For details about the capacity limits, see Hyperdisk size and attachment limits.

GPU peer-to-peer (P2P) communication

G4 instances enhance multi-GPU workload performance by using direct GPU peer-to-peer (P2P) communication, which is supported only on machine types with two or more GPUs. This approach allows GPUs that attach to the same G4 instance to exchange data directly over the PCIe bus, bypassing the need to transfer data through the CPU's main memory. This direct path reduces latency, lowers CPU utilization, and increases the effective bandwidth between GPUs. P2P communication significantly accelerates multi-GPU applications such as machine learning (ML) training and high performance computing (HPC).

Because G4 instances use GPU P2P communication by default, you receive the following benefits without modifying your application code. However, for applications that use the NVIDIA Collective Communication Library (NCCL), to receive the full performance improvements from P2P communication, you must configure NCCL to use P2P.

Key benefits and performance

  • Accelerates multi-GPU workloads on G4 instances with two or more GPUs: provides faster runtimes for applications running on g4-standard-96, g4-standard-192, and g4-standard-384 machine types.
  • Provides high-bandwidth communication: enables high data transfer speeds between GPUs.

  • Improves NCCL performance: provides significant performance improvements for applications that use the NVIDIA Collective Communication Library (NCCL) when compared to communication that doesn't use P2P. Google's hypervisor securely isolates this P2P communication within your instances.

    • On four GPU instances (g4-standard-192), all GPUs are on a single NUMA node, allowing for the most efficient P2P communication. This can lead to performance improvements of up to 2.04x for collectives such as Allgather, Allreduce, and ReduceScatter.
    • On eight GPU instances (g4-standard-384), GPUs are distributed across two NUMA nodes. P2P communication is accelerated for traffic both within and between these nodes, with performance improvements of up to 2.19x for the same collectives.

Configure NCCL to use P2P

To configure NCCL to use P2P, before you run your workloads, set the NCCL_P2P_LEVEL environment variable on your G4 instance based on the machine type:

  • For G4 instances with 2 or 4 GPUs (g4-standard-96, g4-standard-192): set NCCL_P2P_LEVEL=PHB
  • For G4 instances with 8 GPUs (g4-standard-384): set NCCL_P2P_LEVEL=SYS

Set the environment variable by using one of the following options:

  • On the command line, run the appropriate export command, such as export NCCL_P2P_LEVEL=SYS, in the shell session where you plan to run your app. To make this setting persistent, add this command to your shell's startup script, such as ~/.bashrc.
  • Add the appropriate setting, such as NCCL_P2P_LEVEL=SYS, to the NCCL configuration file located at /etc/nccl.conf.

Maintenance experience for G4 instances

During the lifecycle of a Compute Engine instance, the host machine that your instance runs on undergoes multiple host events. A host event can include the regular maintenance of Compute Engine infrastructure, or in rare cases, a host error. Compute Engine also applies some non-disruptive lightweight upgrades for the hypervisor and network in the background.

The following table describes the host maintenance features for G4 machine types:

Machine type Number of GPUs Typical scheduled maintenance event frequency Maintenance behavior Advanced notification for scheduled maintenance On-demand maintenance Simulate maintenance
G4 1, 2, or 4 Minimum of 30 days Terminate and restart. If Local SSD disks are attached, the instance terminates with Local SSD data persistence. 7 days No Yes
G4 8 Minimum of 90 days Terminate and restart. If Local SSD disks are attached, the instance terminates with Local SSD data persistence. 30 days Yes Yes

The following notes apply to the preceding table:

  • Typical scheduled maintenance event frequency:

    The maintenance frequencies shown in the previous table are approximations, not guarantees. Compute Engine might occasionally perform maintenance more frequently.

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 is available in a single standard machine type with multiple configurations.

Instances created by using the G2 machine types provide the following features:

  • GPU acceleration: each G2 machine type has NVIDIA L4 GPUs.

  • Improved inference rates: the G2 machine type provides 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 types, you can get up to 100 Gbps network bandwidth.

  • Disk support: G2 instances support Local SSD for fast scratch disks, which is useful for feeding data into GPUs while preventing I/O bottlenecks. For durable storage, you can attach Persistent Disk and Hyperdisk volumes.

    You can add up to 3,000 GiB of Local SSD to G2 instances. For workloads that require durable block storage, you can attach Hyperdisk and Persistent Disk volumes to G2 instances. The maximum storage capacity depends on the number of vCPUs the instance has. For more information about disk types, see Choose a disk type.

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

Pricing and consumption options for G2 instances

G2 instances support the following consumption and discount options:

Cloud de Confiance bills G2 instances for their attached GPUs, predefined vCPUs, and memory. Persistent Disk, Hyperdisk, Local SSD disks, and network usage are billed separately from machine type pricing. For more information, see Pricing and consumption options, Accelerator-optimized machine type family pricing, and GPU pricing.

G2 machine types (NVIDIA L4)

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.

For information about machine type limitations, see G2 limitations.

Machine type vCPU count Default instance memory (GB) Custom instance memory range (GB) Max Local SSD supported (GiB) Maximum network bandwidth (Gbps) NVIDIA L4 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

G2 machine type notes

The following notes apply to the preceding table:

  • vCPU count: a vCPU is implemented as a single hardware hyper-thread on one of the available CPU platforms.
  • Maximum network bandwidth: maximum egress bandwidth can't 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: 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.

G2 limitations

  • You can only request capacity by using the supported consumption options for a G2 machine type.
  • You don't receive sustained use discounts and flexible committed use discounts for instances that use a G2 machine type.
  • You can only use a G2 machine type in certain regions and zones.
  • The G2 machine type is only available on the Cascade Lake platform.
  • Standard Persistent Disk (pd-standard) isn't supported on instances that use the G2 machine type. For supported disk types, see Supported disk types for G2.
  • You can't create Multi-Instance GPUs on an instance that uses a G2 machine type.
  • If you need to change the machine type of a G2 instance, review Modify accelerator-optmized instances.
  • You can't use Deep Learning VM Images as boot disks for instances that use the G2 machine type.
  • The current default driver for Container-Optimized OS doesn't support L4 GPUs running on G2 machine types. Also, Container-Optimized OS only supports 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 instances for the following scenarios:
    • You want to specify custom memory values.
    • You want to customize the number of visible CPU cores.

Supported disk types for G2 instances

G2 instances can use the following block storage types:

  • Balanced Persistent Disk (pd-balanced)
  • SSD (performance) Persistent Disk (pd-ssd)
  • Hyperdisk ML (hyperdisk-ml)
  • Hyperdisk Throughput (hyperdisk-throughput)
  • Local SSD: you can add Local SSD to instances created by using the G2 machine types.

The following table lists the maximum number of disks supported per instance:

Machine types All disks Hyperdisk ML Hyperdisk Throughput Local SSD
g2-standard-4 128 24 24 1
g2-standard-8 128 32 32 1
g2-standard-12 128 32 32 1
g2-standard-16 128 48 48 1
g2-standard-24 128 48 48 2
g2-standard-32 128 64 64 1
g2-standard-48 128 64 64 4
g2-standard-96 128 64 64 8

The following notes apply to the preceding table:

If supported by the machine type, you can attach a mixture of Hyperdisk and Persistent Disk volumes to an instance, but the following restrictions apply:

  • The combined number of both Hyperdisk and Persistent Disk volumes can't exceed 128 per instance.

  • The maximum total disk capacity (in TiB) across all disk types can't exceed:

    • For machine types with less than 32 vCPUs:

      • 257 TiB for all Hyperdisk or all Persistent Disk
      • 257 TiB for a mixture of Hyperdisk and Persistent Disk

    • For machine types with 32 or more vCPUs:

      • 512 TiB for all Hyperdisk
      • 512 TiB for a mixture of Hyperdisk and Persistent Disk
      • 257 TiB for all Persistent Disk

For details about the capacity limits, see Hyperdisk size and attachment limits and Persistent Disk maximum capacity.

Maintenance experience for G2 instances

During the lifecycle of a Compute Engine instance, the host machine that your instance runs on undergoes multiple host events. A host event can include the regular maintenance of Compute Engine infrastructure, or in rare cases, a host error. Compute Engine also applies some non-disruptive lightweight upgrades for the hypervisor and network in the background.

The following table describes the host maintenance features for G2 machine types:

Machine type Number of GPUs Typical scheduled maintenance event frequency Maintenance behavior Advanced notification for scheduled maintenance On-demand maintenance Simulate maintenance
G2 1, 2, 4, or 8 Minimum of 30 days Terminate and restart 7 days Yes (8 GPUs only) Yes

The following notes apply to the preceding table:

  • Typical scheduled maintenance event frequency:

    The maintenance frequencies shown in the previous table are approximations, not guarantees. Compute Engine might occasionally perform maintenance more frequently.

Pricing and consumption options

Consumption options refers to the ways to get and use compute resources. Cloud de Confiance by S3NS bills accelerator-optimized machine types for their attached GPUs, predefined vCPU, memory, and bundled Local SSD (if applicable). Discounts for accelerator-optimized instances can vary depending on the resource type and consumption option that you use. At any given point, a resource is eligible for only one type of discount.

Depending on the resource type and consumption option that you choose, discounts apply to accelerator-optimized machine types as follows:

  • On-demand: With this consumption option, you can receive compute flexible committed use discounts (CUDs) only for eligible spend on N1, G2, and G4 instance usage, as follows:

    • For N1 machine types that have attached GPUs, you can receive compute flexible CUDs only for vCPUs, memory, and Local SSD disks.
    • For G2 and G4 machine types, you can receive compute flexible CUDs for vCPUs, memory, GPUs, and Local SSD disks.

    To receive resource-based CUDs for other accelerator-optimized machine series, including G2 and G4, use one of the reservation options instead.

  • Spot: Spot VMs automatically receive discounts through Spot VMs pricing.

  • Flex-start: Instances provisioned by using the Flex-start consumption option automatically receive discounts through Dynamic Workload Scheduler pricing.

  • Reservations: You can receive resource-based CUDs for your accelerator-optimized machine type resources by purchasing resource-based commitments. Resource-based commitments for all GPUs and most Local SSD disks require attached reservations for those resources.

For more pricing information for accelerator-optimized instances, see the Accelerator-optimized machine type family section on the VM instance pricing page.

Consumption option availability by machine type

The following table summarizes the availability of each consumption option by machine types. For more information about how to choose a consumption option, see Choose a consumption model in the AI Hypercomputer documentation.

Machine type (GPU model) On-demand Spot Flex-start On-demand reservations Future reservations Future reservations in calendar mode Future reservations in AI Hypercomputer
A4X Max (GB300) and A4X (GB200) No No No No No No Yes
A4 (B200) No Yes Yes No No Yes Yes
A3 Ultra (H200) No Yes Yes No No Yes Yes
A3 Mega (H100) Yes Yes Yes Yes Yes Yes Yes
A3 High with 8 GPUs (H100) Yes Yes Yes Yes Yes Yes Yes
A3 High with less than 8 GPUs (H100) No Yes Yes No No No No
A3 Edge (H100) Yes Yes Yes Yes Yes Yes Yes
A2 (A100) Yes Yes Yes Yes Yes No No
G4 (RTX PRO 6000) Yes Yes Yes Yes Yes No No
G2 (L4) Yes Yes Yes Yes Yes No No
N1 with GPUs (T4/P4/V100) Yes Yes Yes Yes Yes No No

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