This document describes the machine families, machine series, and machine types that you can choose from to create a virtual machine (VM) instance or bare metal instance with the resources that you need. For accelerator-optimized machines, this document describes only Graphics Processing Unit (GPU) accelerators. For information about machine types that contain Google's custom-developed Tensor Processing Units (TPUs), see TPU machines.
There are several machine families you can choose from. Each machine family is
further organized into machine series and predefined machine types within each
series. For example, within the N2 machine series in the general-purpose
machine family, you can select the n2-standard-4 machine type.
When you create a compute instance, you select a machine type from a machine family
and series. The machine type determines the resources that Compute Engine
allocates to the instance. For example, the n2-standard-4 machine type creates a
VM with 4 vCPUs and 16 GB of memory.
For information about machine series that support Spot VMs (and preemptible VMs), see Compute Engine instances provisioning models.
This documentation uses the following terms:
Machine series: Machine families are further classified by series, generation, and processor type.
Each series focuses on a different aspect of computing power or performance. For example, the E series offers efficient VMs at a low cost, while the C series offer better performance.
The generation is denoted by an ascending number. For example, the N1 series within the general-purpose machine family is the older version of the N2 series. A higher generation or series number usually indicates newer underlying CPU platforms or technologies. For example, the M3 series, which runs on Intel Xeon Scalable Processor 3rd Generation (Ice Lake), is a newer generation than the M2 series, which runs on Intel Xeon Scalable Processor 2nd Generation (Cascade Lake).
| Generation | Intel | AMD | Arm |
|---|---|---|---|
| 4th generation machine series | N4, C4, X4, M4, A4 | C4D, G4, N4D, H4D | N4A, C4A, A4X Max, A4X |
| 3rd generation machine series | C3, H3, Z3, M3, A3 | C3D | N/A |
| 2nd generation machine series | N2, E2, C2, M2, A2, G2 | N2D, C2D, T2D, E2 | T2A |
The following sections describe the different machine types.
Predefined machine types come with a non-configurable amount of memory and vCPUs. Predefined machine types use a variety of vCPU to memory ratios:
highcpu — from 1 to 3 GB memory per vCPU; typically,
2 GB memory per vCPU.standard — from 3 to 7 GB memory per vCPU; typically,
4 GB memory per vCPU.highmem — from 7 to 12 GB memory per vCPU; typically,
8 GB memory per vCPU.megamem — from 12 to 15 GB memory per vCPU; typically,
14 GB memory per vCPU.ultramem — from 24 to 31 GB memory per vCPU.
hypermem — from 15 to 24 GB memory per vCPU; typically,
16 GB memory per vCPU.
For example, a c3-standard-22 machine type has 22 vCPUs, and as a
standard machine type, it also has 88 GB of memory.
Local SSD machine types are special predefined machine types. The machine type
names include lssd. When you create a compute instance using one of the
following machine types,
Titanium SSD or Local SSD disks
are automatically attached to the instance:
-lssd: Available with the C4, C4A, C4D, C3, C3D, and H4D
machine series, these machine types attach a predetermined number of
375 GiB Titanium SSD or Local
SSD disks to the instance. Examples of this machine type include
c4a-standard-4-lssd, c3-standard-88-lssd, and c3d-highmem-360-lssd.-standardlssd: Available with the storage-optimized Z3 machine series, these
machine types provide up to 350 GiB of Titanium SSD disk capacity per
vCPU. These machine types are recommended for high performance search and data
analysis for medium-sized data sets. An example of this machine type is
z3-highmem-22-standardlssd.-highlssd: Available with the Z3 machine series, these machine types provide
between 350 GiB and 600 GiB of Titanium SSD disk capacity
per vCPU. These machine types offer high performance and are recommended for
storage-intensive streaming and data analysis for large-sized data sets. An
example of this machine type is z3-highmem-88-highlssd.Other machine series also support Local SSD disks but don't use a machine type
name that includes lssd. For a list of all the machine types that you
can use with Titanium SSD or Local SSD disks, see
Choose a valid number of Local SSD disks.
Bare metal machine types are a special predefined machine type. The machine type
name includes -metal. When you create a compute instance using one of these
machine types, there is no hypervisor installed on the instance. You can attach
disks to a bare metal instance, just as you would with a VM
instance. Bare metal instances can be used in VPC networks and subnetworks in
the same way as VM instances.
For more information, see Bare metal instances on Compute Engine.
If none of the predefined machine types match your workload needs, you can create a VM instance with a custom machine type for the N and E machine series in the general-purpose machine family.
Custom machine types cost slightly more to use compared to an equivalent predefined machine type. Also, there are limitations in the amount of memory and vCPUs that you can select for a custom machine type. The on-demand prices for custom machine types include a 5% premium over the on-demand and commitment prices for predefined machine types.
When creating a custom machine type, you can use the extended memory feature. Instead of using the default memory size based on the number of vCPUs you select, you can specify an amount of memory, up to the limit for the machine series.
For more information, see Create a VM with a custom machine type.
The E2 and N1 series contain shared-core machine types. These machine types timeshare a physical core which can be a cost-effective method for running small, non-resource intensive apps.
E2: offers
e2-micro, e2-small, and e2-medium shared-core machine types with
2 vCPUs for short periods of bursting.
N1: offers
f1-micro and g1-small shared-core machine types
which have up to 1 vCPU available for short periods of bursting.
For more information, see CPU bursting.
The following tables provide recommendations for different workloads.
| General-purpose workloads | |||
|---|---|---|---|
| N4, N4A, N4D, N2, N2D, N1 | C4, C4A, C4D, C3, C3D | E2 | Tau T2D, Tau T2A |
| Balanced price/performance across a wide range of machine types | Consistently high performance for a variety of workloads | Day-to-day computing at a lower cost | Best per-core performance/cost for scale-out workloads |
|
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|
Optimized workloads |
||||
|---|---|---|---|---|
| Storage-optimized | Compute-optimized | Memory-optimized | Accelerator-optimized* (GPUs) | |
| Z3 | H4D, H3, C2 and C2D | X4, M4, M3, M2, M1 | A4X Max, A4X, A4, A3, A2, G4, G2 | |
| Highest block storage to compute ratios for storage-intensive workloads | Highest performance and lower cost for high performance computing (HPC), multi-node and compute-bound workloads | Highest memory to compute ratios for memory-intensive workloads | Optimized for accelerated high performance computing workloads | |
|
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|
* For accelerator-optimized machines containing TPUs, see TPU machines.
After you create a compute instance, you can use rightsizing recommendations to optimize resource utilization based on your workload. For more information, see Applying machine type recommendations for VMs.
The general-purpose machine family offers several machine series with the best price-performance ratio for a variety of workloads.
Compute Engine offers general-purpose machine series that run on either x86 or Arm architecture.
highcpu
(2 GB memory per vCPU), standard (3.75 GB memory per vCPU), and
highmem (7.75 GB memory per vCPU) configurations. C4 instances are
aligned with the underlying
non-uniform memory access (NUMA)
architecture to offer optimal, reliable, and consistent performance.highcpu (1.875 GB memory per vCPU), standard
(3.875 GB memory per vCPU), and highmem (7.875 GB memory per
vCPU) configurations. C4D instances are aligned with the underlying
NUMA architecture to offer optimal, reliable, and consistent performance.highcpu (2 GB per vCPU), standard (4 GB per vCPU), and
highmem (8 GB per vCPU) configurations.highcpu (2 GB per vCPU), standard
(4 GB per vCPU), and highmem (8 GB per vCPU) configurations.The N4A machine series is powered by Google's custom-designed Axion processor. The Axion process is built on Arm Neoverse N3 compute core, which supports Arm V9.2 architecture. The N4A machine series uses Titanium for CPU offloading. N4A instances provide up to 64 vCPUs with up to 8 GB of memory per vCPU with Uniform Memory Access (UMA) domain. N4A instances don't use simultaneous multithreading (SMT). A vCPU in a N4A instance is equivalent to an entire physical core.
The N4A machine series is engineered to be our most efficient and flexible Arm-based series, delivering exceptional price-performance for a wide range of general-purpose and scale-out workloads. Ideal use cases include web and application servers, microservices, containerized applications using Google Kubernetes Engine (GKE), open-source databases, and development and testing environments.
The C4A machine series is powered by Google Axion, and built on Arm Neoverse V2 compute core, which supports Arm V9 architecture. C4A instances are powered by Titanium IPU with disk and network offloads, this improves instance performance by reducing on-host processing.
C4A instances provide up to 72 vCPUs with up to 8 GB of memory per vCPU
in a single UMA domain.
C4A offers -lssd machine types that come with up to 6 TiB of
Titanium SSD capacity.
C4A bare metal instances have 96 vCPUs and up to 768 GB of memory.
C4A instances don't use simultaneous multithreading (SMT). A vCPU in a
C4A instance is equivalent to an entire physical core.
The Tau T2A machine series is the first machine series in Google Cloud built on Arm Neoverse N1 core compute. Tau T2A machines are optimized to deliver compelling price for performance. Each VM can have up to 48 vCPUs with 4 GB of memory per vCPU. The Tau T2A machine series runs on a 64 core Ampere Altra processor with an Arm instruction set and an all-core frequency of 3 GHz. Tau T2A machine types support a single NUMA node and a vCPU is equivalent to an entire core.
The storage-optimized machine family is best suited for high-performance and flash-optimized workloads such as SQL, NoSQL, and vector databases, scale-out data analytics, data warehouses and search, and distributed file systems that need fast access to large amounts of data stored in local storage. The storage-optimized machine family is designed to provide high local storage throughput and IOPS at sub-millisecond latency.
standardlssd instances can have up to 176 vCPUs,
1,408 GB of memory, and 36 TiB of Titanium SSD.highlssd instances can have up to 88 vCPUs, 704 GB of memory,
and 36 TiB of Titanium SSD.Z3 runs on the Intel Xeon Scalable processor (code name Sapphire Rapids) with DDR5 memory and Titanium offload processors. Z3 brings together compute, networking, and storage innovations into one platform. Z3 instances are aligned with the underlying NUMA architecture to offer optimal, reliable, and consistent performance.
The compute-optimized machine family is optimized for running high performance computing (HPC), multi-node, and compute-bound applications by providing high performance per core.
The memory-optimized machine family has machine series that are ideal for OLAP and OLTP SAP workloads, genomic modeling, electronic design automation, and memory intensive HPC workloads. This family offers more memory per core than any other machine family, with up to 32 TB of memory.
The accelerator-optimized machine family is ideal for massively parallelized Compute Unified Device Architecture (CUDA) compute workloads, such as machine learning (ML) and high performance computing (HPC). This machine family is the optimal choice for workloads that require accelerators (GPUs or TPUs). This section provides information about GPU machines. For information about machines containing TPUs, see TPU machines.
Google also offers AI Hypercomputer for creating clusters of accelerator-optimized VMs with inter-GPU communication, which are designed for running very intensive AI and ML workloads. For more information, see AI Hypercomputer overview.
a3-edgegpu-8g-nolssd), which offers 208 vCPUs, 1,872 GB of memory,
and 8 NVIDIA H100 GPUs, on the Intel Sapphire Rapids CPU platform and
Titanium.
Use the following table to compare each machine family and determine which one is appropriate for your workload. For a comparison of TPU versions, see TPU machines.
If, after reviewing this section, you are still unsure which family is best for your workload, start with the general-purpose machine family. For details about all supported processors, see CPU platforms.
To learn how your selection affects the performance of disk volumes attached to your compute instances, see:
Compare the characteristics of different machine series, from C4 to G2. You can select specific properties in the Choose instance properties to compare field to compare those properties across all machine series in the following table.
| General-purpose | General-purpose | General-purpose | General-purpose | General-purpose | General-purpose | General-purpose | General-purpose | General-purpose | General-purpose | General-purpose | General-purpose | General-purpose | Cost optimized | Storage optimized | Compute optimized | Compute optimized | Compute optimized | Compute optimized | Memory optimized | Memory optimized | Memory optimized | Memory optimized | Memory optimized | Accelerator optimized | Accelerator optimized | Accelerator optimized | Accelerator optimized | Accelerator optimized | Accelerator optimized | Accelerator optimized | Accelerator optimized | Accelerator optimized |
| VM and bare metal | VM and bare metal | VM and bare metal | VM and bare metal | VM | VM | VM | VM | VM | VM | VM | VM | VM | VM | VM and bare metal | VM | VM | VM | VM | Bare metal | VM | VM | VM | VM | VM | Bare metal | VM | VM | VM | VM | VM | VM | VM |
| Intel Emerald Rapids and Granite Rapids | Google Axion | AMD EPYC Turin | Intel Sapphire Rapids | AMD EPYC Genoa | Intel Emerald Rapids | Google Axion | AMD EPYC Turin | Intel Cascade Lake and Ice Lake | AMD EPYC Rome and EPYC Milan | Intel Skylake, Broadwell, Haswell, Sandy Bridge, and Ivy Bridge | AMD EPYC Milan | Ampere Altra | Intel Skylake, Broadwell, and Haswell, AMD EPYC Rome and EPYC Milan | Intel Sapphire Rapids | AMD EPYC Turin | Intel Sapphire Rapids | Intel Cascade Lake | AMD EPYC Milan | Intel Sapphire Rapids | Intel Emerald Rapids | Intel Ice Lake | Intel Cascade Lake | Intel Skylake and Broadwell | Intel Skylake, Broadwell, Haswell, Sandy Bridge, and Ivy Bridge | NVIDIA Grace | NVIDIA Grace | Intel Emerald Rapids | Intel Emerald Rapids | Intel Sapphire Rapids | Intel Cascade Lake | AMD EPYC Turin | Intel Cascade Lake |
| x86 | Arm | x86 | x86 | x86 | x86 | Arm | x86 | x86 | x86 | x86 | x86 | Arm | x86 | x86 | x86 | x86 | x86 | x86 | x86 | x86 | x86 | x86 | x86 | x86 | Arm | Arm | x86 | x86 | x86 | x86 | x86 | x86 |
| 2 to 288 | 1 to 96 | 2 to 384 | 4 to 176 | 4 to 360 | 2 to 80 | 1 to 64 | 2 to 96 | 2 to 128 | 2 to 224 | 1 to 96 | 1 to 60 | 1 to 48 | 0.25 to 32 | 8 to 192 | 192 | 88 | 4 to 60 | 2 to 112 | 480 to 1,920 | 16 to 224 | 32 to 128 | 208 to 416 | 40 to 160 | 1 to 96 | 144 | 140 | 224 | 224 | 208 | 12 to 96 | 6 to 384 | 4 to 96 |
| Thread | Core | Thread | Thread | Thread | Thread | Core | Thread | Thread | Thread | Thread | Core | Core | Thread | Thread | Core | Core | Thread | Thread | Thread | Thread | Thread | Thread | Thread | Thread | Core | Core | Thread | Thread | Thread | Thread | Thread | Thread |
| 2 to 2,232 GB | 1 to 768 GB | 3 to 3,072 GB | 8 to 1,408 GB | 8 to 2,880 GB | 2 to 640 GB | 2 to 512 GB | 2 to 768 GB | 2 to 864 GB | 2 to 896 GB | 1.8 to 624 GB | 4 to 240 GB | 4 to 192 GB | 1 to 128 GB | 64 to 1,536 GB | 720 to 1,488 GB | 352 GB | 16 to 240 GB | 4 to 896 GB | 6,144 to 32,768 GB | 248 to 5,952 GB | 976 to 3,904 GB | 5,888 to 11,776 GB | 961 to 3,844 GB | 3.75 to 624 GB | 960 GB | 884 GB | 3,968 GB | 2,952 GB | 1,872 GB | 85 to 1,360 GB | 22 to 1,440 GB | 16 to 432 GB |
| NUMA | UMA | NUMA | NUMA | NUMA | — | UMA | — | — | — | — | — | NUMA | — | NUMA | NUMA | NUMA | NUMA | NUMA | — | — | — | — | — | — | — | — | — | — | — | — | — | — |
| — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | |||||||||
| — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | |||||||
| — | — | — | — | — | — | — | — | — | — | — | ||||||||||||||||||||||
| — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | ||||||||||||
| — | — | AMD SEV | Intel TDX | AMD SEV | — | — | — | — | AMD SEV, AMD SEV-SNP | — | — | — | — | — | — | — | — | AMD SEV | — | — | — | — | — | — | — | — | — | — | Intel TDX, NVIDIA Confidential Computing | — | — | — |
| NVMe | NVMe | NVMe | NVMe | NVMe | NVMe | NVMe | NVMe |
SCSI (PD and Local SSD) NVMe (Local SSD) |
SCSI (PD and Local SSD) NVMe (Local SSD) |
SCSI (PD and Local SSD) NVMe (Local SSD) |
SCSI (PD and Local SSD) NVMe (Local SSD) |
NVMe | SCSI | NVMe | NVMe | NVMe |
SCSI (PD and Local SSD) NVMe (Local SSD) |
SCSI (PD and Local SSD) NVMe (Local SSD) |
NVMe | NVMe | NVMe | SCSI |
SCSI (PD and Local SSD) NVMe (Local SSD) |
SCSI (PD and Local SSD) NVMe (Local SSD) |
NVMe | NVMe | NVMe | NVMe | NVMe |
SCSI (PD and Local SSD) NVMe (Local SSD) |
NVMe | NVMe |
| — | — | — | — | — | — | — | — | — | — | — | ||||||||||||||||||||||
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| 18 TiB | 6 TiB | 12 TiB | 12 TiB | 12 TiB | 0 | 0 | 0 | 9 TiB | 9 TiB | 9 TiB | 0 | 0 | 0 | 36 TiB (VM), 72 TiB (Metal) | 3 TiB | 0 | 3 TiB | 3 TiB | 0 | 0 | 3 TiB | 0 | 3 TiB | 9 TiB | 12 TiB | 12 TiB | 12 TiB | 12 TiB | 6 TiB | 3 TiB | 12 TiB | 3 TiB |
| — | — | — | — | — | — | — | — | Zonal and Regional | Zonal and Regional | Zonal and Regional | Zonal | Zonal | Zonal and Regional | — | — | — | Zonal | Zonal | — | — | — | Zonal | Zonal | Zonal and Regional | — | — | — | — | — | Zonal | — | — |
| — | — | — | Zonal | Zonal | — | — | — | Zonal and Regional | Zonal and Regional | Zonal and Regional | Zonal | Zonal | Zonal and Regional | Zonal | — | Zonal | Zonal | Zonal | — | — | Zonal | Zonal | Zonal | Zonal and Regional | — | — | — | — | Zonal | Zonal | — | Zonal |
| — | — | — | Zonal | Zonal | — | — | — | Zonal and Regional | Zonal and Regional | Zonal and Regional | Zonal | Zonal | Zonal and Regional | Zonal | — | — | Zonal | Zonal | — | — | Zonal | Zonal | Zonal | Zonal and Regional | — | — | — | — | Zonal | Zonal | — | Zonal |
| — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | ||||
| gVNIC and IDPF | gVNIC and IDPF | gVNIC and IDPF | gVNIC and IDPF | gVNIC | gVNIC | gVNIC | gVNIC | gVNIC and VirtIO-Net | gVNIC and VirtIO-Net | gVNIC and VirtIO-Net | gVNIC and VirtIO-Net | gVNIC | gVNIC and VirtIO-Net | gVNIC and IDPF | gVNIC, IRDMA | gVNIC | gVNIC and VirtIO-Net | gVNIC and VirtIO-Net | IDPF | gVNIC | gVNIC | gVNIC and VirtIO-Net | gVNIC and VirtIO-Net | gVNIC and VirtIO-Net | IDPF and MRDMA | gVNIC and MRDMA | gVNIC and MRDMA | gVNIC and MRDMA | gVNIC | gVNIC and VirtIO-Net | gVNIC | gVNIC and VirtIO-Net |
| 10 to 100 Gbps | 10 to 50 Gbps | 10 to 100 Gbps | 23 to 100 Gbps | 20 to 100 Gbps | 10 to 50 Gbps | Up to 50 Gbps | 10 to 50 Gbps | 10 to 32 Gbps | 10 to 32 Gbps | 2 to 32 Gbps | 10 to 32 Gbps | 10 to 32 Gbps | 1 to 16 Gbps | 23 to 100 Gbps | up to 200 Gbps | up to 200 Gbps | 10 to 32 Gbps | 10 to 32 Gbps | up to 100 Gbps | 16 to 100 Gbps | up to 32 Gbps | up to 32 Gbps | up to 32 Gbps | 2 to 32 Gbps | up to 3,600 Gbps | up to 2,000 Gbps | up to 3,600 Gbps | up to 3,200 Gbps | up to 1,800 Gbps | 24 to 100 Gbps | 20 to 400 Gbps | 10 to 100 Gbps |
| 50 to 200 Gbps | 50 to 100 Gbps | 50 to 200 Gbps | 50 to 200 Gbps | 50 to 200 Gbps | — | — | — | 50 to 100 Gbps | 50 to 100 Gbps | — | — | — | — | 50 to 200 Gbps | — | — | 50 to 100 Gbps | 50 to 100 Gbps | — | — | 50 to 100 Gbps | — | — | 50 to 100 Gbps | — | — | — | — | — | — | — | — |
| 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 8 | 4 | 4 | 8 | 8 | 8 | 16 | 8 | 8 |
| — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | |||||||
| — | Only at GA | |||||||||||||||||||||||||||||||
| — | — | Only at GA and for the new CUD model | Only for the new CUD model | — | Only for the new CUD model | Only for the new CUD model | Only for the new CUD model | Only for the new CUD model | — | — | — | — | — | — | — | — | — | |||||||||||||||
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GPUs are used to accelerate workloads, and are supported for A4X Max, A4X, A4, A3, A2, G4, G2, and N1 instances. For instances that use A4X Max, A4X, A4, A3, A2, G4, or G2 machine types, the GPUs are automatically attached when you create the instance. For instances that use N1 machine types, you can attach GPUs to the instance during or after instance creation. GPUs can't be used with any other machine series.
Accelerator-optimized instances have a fixed number of GPUs, vCPUs and memory per machine type, with the exception of G2 machines that offer a custom memory range. N1 instances with fewer GPUs attached are limited to a maximum number of vCPUs. In general, a higher number of GPUs lets you create instances with a higher number of vCPUs and memory.
For more information, see GPUs on Compute Engine.
Learn how to create and start a VM.
Learn how to create a VM with a custom machine type.
Complete the Quickstart using a Linux VM.
Complete the Quickstart using a Windows VM.
Learn more about attaching block storage to your VMs.
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Last updated 2026-06-09 UTC.