enum AcceleratorType

Language	Type name
.NET	Amazon.CDK.AWS.EC2.AcceleratorType
Go	github.com/aws/aws-cdk-go/awscdk/v2/awsec2#AcceleratorType
Java	software.amazon.awscdk.services.ec2.AcceleratorType
Python	aws_cdk.aws_ec2.AcceleratorType
TypeScript (source)	aws-cdk-lib » aws_ec2 » AcceleratorType

Hardware accelerator categories available for EC2 instances.

Defines the general type of hardware accelerator that can be attached to an instance, typically used in instance requirement specifications (e.g., GPUs for compute-intensive tasks, FPGAs for custom logic, or inference chips for ML workloads).

Example

declare const infrastructureRole: iam.Role;
declare const instanceProfile: iam.InstanceProfile;
declare const vpc: ec2.Vpc;

const miCapacityProvider = new ecs.ManagedInstancesCapacityProvider(this, 'MICapacityProvider', {
  infrastructureRole,
  ec2InstanceProfile: instanceProfile,
  subnets: vpc.privateSubnets,
  instanceRequirements: {
    // Required: CPU and memory constraints
    vCpuCountMin: 2,
    vCpuCountMax: 8,
    memoryMin: Size.gibibytes(4),
    memoryMax: Size.gibibytes(32),

    // CPU preferences
    cpuManufacturers: [ec2.CpuManufacturer.INTEL, ec2.CpuManufacturer.AMD],
    instanceGenerations: [ec2.InstanceGeneration.CURRENT],

    // Instance type filtering
    allowedInstanceTypes: ['m5.*', 'c5.*'],

    // Performance characteristics
    burstablePerformance: ec2.BurstablePerformance.EXCLUDED,
    bareMetal: ec2.BareMetal.EXCLUDED,

    // Accelerator requirements (for ML/AI workloads)
    acceleratorTypes: [ec2.AcceleratorType.GPU],
    acceleratorManufacturers: [ec2.AcceleratorManufacturer.NVIDIA],
    acceleratorNames: [ec2.AcceleratorName.T4, ec2.AcceleratorName.V100],
    acceleratorCountMin: 1,

    // Storage requirements
    localStorage: ec2.LocalStorage.REQUIRED,
    localStorageTypes: [ec2.LocalStorageType.SSD],
    totalLocalStorageGBMin: 100,

    // Network requirements
    networkInterfaceCountMin: 2,
    networkBandwidthGbpsMin: 10,

    // Cost optimization
    onDemandMaxPricePercentageOverLowestPrice: 10,
  },
});

Members

Name	Description
GPU	Graphics Processing Unit accelerators, such as NVIDIA GPUs.
FPGA	Field Programmable Gate Array accelerators, such as Xilinx FPGAs.
INFERENCE	Inference accelerators, such as AWS Inferentia.

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GPU

Graphics Processing Unit accelerators, such as NVIDIA GPUs.

Commonly used for machine learning training, graphics rendering, or high-performance parallel computing.

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FPGA

Field Programmable Gate Array accelerators, such as Xilinx FPGAs.

Used for hardware-level customization and specialized workloads.

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INFERENCE

Inference accelerators, such as AWS Inferentia.

Purpose-built for efficient machine learning inference.