Linux Accelerated Computing Instances - Amazon Elastic Compute Cloud
Accelerated Computing Instance FamiliesHardware SpecificationsInstance PerformanceNetwork PerformanceInstance FeaturesRelease Notes

Linux Accelerated Computing Instances

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) or Field Programmable Gate Arrays (FPGAs). Accelerated computing instances enable more parallelism for higher throughput on compute-intensive workloads.

GPU-based instances provide access to NVIDIA GPUs with thousands of compute cores. You can use GPU-based accelerated computing 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.

FPGA-based instances provide access to large FPGAs with millions of parallel system logic cells. You can use FPGA-based accelerated computing instances to accelerate workloads such as genomics, financial analysis, real-time video processing, big data analysis, and security workloads by leveraging custom hardware accelerations. You can develop these accelerations using hardware description languages such as Verilog or VHDL, or by using higher-level languages such as OpenCL parallel computing frameworks. You can either develop your own hardware acceleration code or purchase hardware accelerations through the AWS Marketplace.

Important

FPGA-based instances do not support Microsoft Windows.

You can cluster accelerated computing instances into a cluster placement group. Cluster placement groups provide low latency and high-bandwidth connectivity between the instances within a single Availability Zone. For more information, see Placement Groups.

Contents

For information about Windows accelerated computing instances, see Windows Accelerated Computing Instances in the Amazon EC2 User Guide for Windows Instances.

Accelerated Computing Instance Families

Accelerated computing instance families 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. The following accelerated computing instance families are available for you to launch in Amazon EC2.

F1 Instances

F1 instances use Xilinx UltraScale+ VU9P FPGAs and are designed to accelerate computationally intensive algorithms, such as data-flow or highly parallel operations not suited to general purpose CPUs. Each FPGA in an F1 instance contains approximately 2.5 million logic elements and approximately 6,800 Digital Signal Processing (DSP) engines, along with 64 GiB of local DDR ECC protected memory, connected to the instance by a dedicated PCIe Gen3 x16 connection. F1 instances provide local NVMe SSD volumes.

Developers can use the FPGA Developer AMI and AWS Hardware Developer Kit to create custom hardware accelerations for use on F1 instances. The FPGA Developer AMI includes development tools for full-cycle FPGA development in the cloud. Using these tools, developers can create and share Amazon FPGA Images (AFIs) that can be loaded onto the FPGA of an F1 instance.

For more information, see Amazon EC2 F1 Instances.

Inf1 Instances

Inf1 instances use AWS Inferentia machine learning inference chips. Inferentia was developed to enable highly cost-effective low latency inference performance at any scale.

Developers can use the AWS Deep Learning AMI which comes with prepackaged and optimized software for AWS Inferentia.

For more information, see Amazon EC2 Inf1 Instances.

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.

To view topology information about the system, run the following command:

nvidia-smi topo -m

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.

To view topology information about the system, run the following command:

nvidia-smi topo -m

For more information, see NVIDIA GPUDirect.

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.

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.

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.

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
f1.2xlarge 8 122 1
f1.4xlarge 16 244 2
f1.16xlarge 64 976 8
inf1.xlarge 4 8 1
inf1.2xlarge 8 16 1
inf1.6xlarge 24 48 4
inf1.24xlarge 96 192 16

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

There are several GPU setting optimizations that you can perform to achieve the best performance on your instances. For more information, see Optimizing GPU Settings.

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.

Some accelerated computing instance types provide the ability to control processor C-states and P-states on Linux. C-states control the sleep levels that a core can enter when it is inactive, while P-states control the desired performance (in CPU frequency) from a core. For more information, see Processor State Control for Your EC2 Instance.

Network Performance

You can enable enhanced networking capabilities on supported instance types. Enhanced networking provides significantly higher packet-per-second (PPS) performance, lower network jitter, and lower latencies. For more information, see Enhanced Networking on Linux.

Instance types that use the Elastic Network Adapter (ENA) for enhanced networking deliver high packet per second performance with consistently low latencies. Most applications do not consistently need a high level of network performance, but can benefit from having access to increased bandwidth when they send or receive data. Instance sizes that use the ENA and are documented with network performance of "Up to 10 Gbps" or "Up to 25 Gbps" use a network I/O credit mechanism to allocate network bandwidth to instances based on average bandwidth utilization. These instances accrue credits when their network bandwidth is below their baseline limits, and can use these credits when they perform network data transfers.

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 | inf1.xlarge | inf1.2xlarge

Up to 25 Gbps

ENA

f1.16xlarge | g3.16xlarge | inf1.6xlarge | p2.16xlarge | p3.16xlarge

25 Gbps

ENA

g4dn.8xlarge | g4dn.12xlarge | g4dn.16xlarge

50 Gbps

ENA

inf1.24xlarge | p3dn.24xlarge

100 Gbps

ENA

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

Inf1

Yes

No

No

Yes

P2

Yes

No

No

Yes

P3

p3dn.24xlarge: No

All other sizes: Yes

p3dn.24xlarge: Yes

All other sizes: No

p3dn.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.

  • The following are requirements for G4 instances:

    The following Linux AMIs meet these requirements:

    • Amazon Linux 2

    • Amazon Linux AMI 2018.03

    • Ubuntu 14.04 (with linux-aws kernel) or later

    • Red Hat Enterprise Linux 7.4 or later

    • SUSE Linux Enterprise Server 12 SP2 or later

    • CentOS 7.4.1708 or later

    • FreeBSD 11.1 or later

    • Debian GNU/Linux 9 or later

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

  • There is a limit of 100 AFIs per region.

  • There is a limit on the number of instances that you can run. For more information, see How many instances can I run in Amazon EC2? in the Amazon EC2 FAQ. To request an increase in these limits, use the following form: Request to Increase Amazon EC2 Instance Limit.