Windows accelerated computing instances - Amazon Elastic Compute Cloud

Windows accelerated computing instances

Accelerated computing instances use hardware accelerators, or co-processors, to perform some functions, such as floating point number calculations, graphics processing, or data pattern matching, more efficiently than is possible in software running on CPUs. These instances enable more parallelism for higher throughput on compute-intensive workloads.

If you require high processing capability, you'll benefit from using accelerated computing instances, which provide access to hardware-based compute accelerators such as Graphics Processing Units (GPUs).

GPU instances

GPU-based instances provide access to NVIDIA GPUs with thousands of compute cores. You can use these instances to accelerate scientific, engineering, and rendering applications by leveraging the CUDA or Open Computing Language (OpenCL) parallel computing frameworks. You can also use them for graphics applications, including game streaming, 3-D application streaming, and other graphics workloads.

If your application needs a small amount of additional graphics acceleration, but is better suited for an instance type with different compute, memory, or storage specifications, use an Elastic Graphics accelerator instead. For more information, see Amazon Elastic Graphics.

G4 instances

G4 instances use NVIDIA Tesla GPUs and provide a cost-effective, high-performance platform for general purpose GPU computing using the CUDA or machine learning frameworks along with graphics applications using DirectX or OpenGL. G4 instances provide high- bandwidth networking, powerful half and single-precision floating-point capabilities, along with INT8 and INT4 precisions. Each GPU has 16 GiB of GDDR6 memory, making G4 instances well-suited for machine learning inference, video transcoding, and graphics applications like remote graphics workstations and game streaming in the cloud.

For more information, see Amazon EC2 G4 Instances.

G4 instances support NVIDIA GRID Virtual Workstation. For more information, see NVIDIA Marketplace offerings.

G3 instances

G3 instances use NVIDIA Tesla M60 GPUs and provide a cost-effective, high-performance platform for graphics applications using DirectX or OpenGL. G3 instances also provide NVIDIA GRID Virtual Workstation features, such as support for four monitors with resolutions up to 4096x2160, and NVIDIA GRID Virtual Applications. G3 instances are well-suited for applications such as 3D visualizations, graphics-intensive remote workstations, 3D rendering, video encoding, virtual reality, and other server-side graphics workloads requiring massively parallel processing power.

For more information, see Amazon EC2 G3 Instances.

G3 instances support NVIDIA GRID Virtual Workstation and NVIDIA GRID Virtual Applications. To activate either of these features, see Activate NVIDIA GRID Virtual Applications.

G2 instances

G2 instances use NVIDIA GRID K520 GPUs and provide a cost-effective, high-performance platform for graphics applications using DirectX or OpenGL. NVIDIA GRID GPUs also support NVIDIA’s fast capture and encode API operations. Example applications include video creation services, 3D visualizations, streaming graphics-intensive applications, and other server-side graphics workloads.

P3 instances

P3 instances use NVIDIA Tesla V100 GPUs and are designed for general purpose GPU computing using the CUDA or OpenCL programming models or through a machine learning framework. P3 instances provide high-bandwidth networking, powerful half, single, and double-precision floating-point capabilities, and up to 32 GiB of memory per GPU, which makes them ideal for deep learning, computational fluid dynamics, computational finance, seismic analysis, molecular modeling, genomics, rendering, and other server-side GPU compute workloads. Tesla V100 GPUs do not support graphics mode.

For more information, see Amazon EC2 P3 Instances.

P3 instances support NVIDIA NVLink peer to peer transfers. For more information, see NVIDIA NVLink.

P2 instances

P2 instances use NVIDIA Tesla K80 GPUs and are designed for general purpose GPU computing using the CUDA or OpenCL programming models. P2 instances provide high-bandwidth networking, powerful single and double precision floating-point capabilities, and 12 GiB of memory per GPU, which makes them ideal for deep learning, graph databases, high-performance databases, computational fluid dynamics, computational finance, seismic analysis, molecular modeling, genomics, rendering, and other server-side GPU compute workloads.

P2 instances support NVIDIA GPUDirect peer to peer transfers. For more information, see NVIDIA GPUDirect.

Instances with AWS Inferentia

These instances are designed to accelerate machine learning using AWS Inferentia, a custom AI/ML chip from Amazon that provides high performance and low latency machine learning inference. These instances are optimized for deploying Deep Learning (DL) models for applications, such as natural language processing, object detection and classification, content personalization and filtering, and speech recognition.

There are a variety of ways that you can get started:

  • Use SageMaker, a fully-managed service that is the easiest way to get started with machine learning models. For more information, see Compile and deploy a TensorFlow model on Inf1 instances on github.

  • Launch an Inf1 instance using the Deep Learning AMI. For more information, see AWS Inferentia with DLAMI in the AWS Deep Learning AMI Developer Guide.

  • Launch an Inf1 instance using your own AMI and install the AWS Neuron SDK, which enables you to compile, run, and profile deep learning models for AWS Inferentia.

  • Launch a container instance using an Inf1 instance and an Amazon ECS-optimized AMI. For more information, see Amazon Linux 2 (Inferentia) AMIs in the Amazon Elastic Container Service Developer Guide.

  • Create an Amazon EKS cluster with nodes running Inf1 instances. For more information, see Inferentia support in the Amazon EKS User Guide.

For more information, see Machine Learning on AWS.

Hardware specifications

The following is a summary of the hardware specifications for accelerated computing instances.

Instance type Default vCPUs Memory (GiB) Accelerators
p2.xlarge 4 61 1
p2.8xlarge 32 488 8
p2.16xlarge 64 732 16
p3.2xlarge 8 61 1
p3.8xlarge 32 244 4
p3.16xlarge 64 488 8
p3dn.24xlarge 96 768 8
g2.2xlarge 8 15 1
g2.8xlarge 32 60 4
g3s.xlarge 4 30.5 1
g3.4xlarge 16 122 1
g3.8xlarge 32 244 2
g3.16xlarge 64 488 4
g4dn.xlarge 4 16 1
g4dn.2xlarge 8 32 1
g4dn.4xlarge 16 64 1
g4dn.8xlarge 32 128 1
g4dn.12xlarge 48 192 4
g4dn.16xlarge 64 256 1
g4dn.metal 96 384 8
f1.2xlarge 8 122 1
f1.4xlarge 16 244 2
f1.16xlarge 64 976 8

For more information about the hardware specifications for each Amazon EC2 instance type, see Amazon EC2 Instance Types.

For more information about specifying CPU options, see Optimizing CPU options.

Instance performance

EBS-optimized instances enable you to get consistently high performance for your EBS volumes by eliminating contention between Amazon EBS I/O and other network traffic from your instance. Some accelerated computing instances are EBS-optimized by default at no additional cost. For more information, see Amazon EBS–optimized instances.

Network performance

You can enable enhanced networking on supported instance types to provide lower latencies, lower network jitter, and higher packet-per-second (PPS) performance. Most applications do not consistently need a high level of network performance, but can benefit from access to increased bandwidth when they send or receive data. For more information, see Enhanced networking on Windows.

The following is a summary of network performance for accelerated computing instances that support enhanced networking.

Instance type Network performance Enhanced networking
f1.2xlarge | f1.4xlarge | g3.4xlarge | p3.2xlarge Up to 10 Gbps † ENA
g3s.xlarge | g3.8xlarge | p2.8xlarge | p3.8xlarge 10 Gbps ENA
g4dn.xlarge | g4dn.2xlarge | g4dn.4xlarge Up to 25 Gbps † ENA
f1.16xlarge | g3.16xlarge | p2.16xlarge | p3.16xlarge 25 Gbps ENA
g4dn.8xlarge | g4dn.12xlarge | g4dn.16xlarge 50 Gbps ENA
g4dn.metal | p3dn.24xlarge 100 Gbps ENA

† These instances use a network I/O credit mechanism to allocate network bandwidth to instances based on average bandwidth utilization. They accrue credits when their bandwidth is below their baseline bandwidth, and can use these credits when they perform network data transfers. For more information, open a support case and ask about baseline bandwidth for the specific instance types that you are interested in.

Instance features

The following is a summary of features for accelerated computing instances.

EBS only NVMe EBS Instance store Placement group

G2

No

No

SSD

Yes

G3

Yes

No

No

Yes

G4

No

Yes

NVMe *

Yes

P2

Yes

No

No

Yes

P3

24xlarge: No

All other sizes: Yes

24xlarge: Yes

All other sizes: No

24xlarge: NVMe *

Yes

F1

No

No

NVMe *

Yes

* The root device volume must be an Amazon EBS volume.

For more information, see the following:

Release notes

  • You must launch the instance using an HVM AMI.

  • Instances built on the Nitro System have the following requirements:

    The current AWS Windows AMIs meet these requirements.

  • GPU-based instances can't access the GPU unless the NVIDIA drivers are installed. For more information, see Installing NVIDIA drivers on Windows instances.

  • Launching a bare metal instance boots the underlying server, which includes verifying all hardware and firmware components. This means that it can take 20 minutes from the time the instance enters the running state until it becomes available over the network.

  • To attach or detach EBS volumes or secondary network interfaces from a bare metal instance requires PCIe native hotplug support.

  • Bare metal instances use a PCI-based serial device rather than an I/O port-based serial device. The upstream Linux kernel and the latest Amazon Linux AMIs support this device. Bare metal instances also provide an ACPI SPCR table to enable the system to automatically use the PCI-based serial device. The latest Windows AMIs automatically use the PCI-based serial device.

  • There is a limit of 100 AFIs per Region.

  • There is a limit on the total number of instances that you can launch in a Region, and there are additional limits on some instance types. For more information, see How many instances can I run in Amazon EC2? in the Amazon EC2 FAQ.

  • If you launch a multi-GPU instance with a Windows AMI that was created on a single-GPU instance, Windows does not automatically install the NVIDIA driver for all GPUs. You must authorize the driver installation for the new GPU hardware. You can correct this manually in the Device Manager by opening the Other device category (the inactive GPUs do not appear under Display Adapters). For each inactive GPU, open the context (right-click) menu, choose Update Driver Software, and then choose the default Automatic Update option.

  • When using Microsoft Remote Desktop Protocol (RDP), GPUs that use the WDDM driver model are replaced with a non-accelerated Remote Desktop display driver. We recommend that you use a different remote access tool to access your GPU, such as Teradici Cloud Access Software, NICE Desktop Cloud Visualization (DCV), or VNC. You can also use one of the GPU AMIs from the AWS Marketplace because they provide remote access tools that support 3D acceleration.