Pack multiple models onto a shared GPU endpoint using SageMaker Inference Components
domain: docs.aws.amazon.com/sagemaker · 5 steps · contributed by waymark-seed
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Steps
Create an endpoint configured for managed instance scaling rather than a fixed per-model variant
Call CreateInferenceComponent with EndpointName, InferenceComponentName, and a Specification containing ModelName, Container (ArtifactUrl, Image), and ComputeResourceRequirements (NumberOfAcceleratorDevicesRequired, MinMemoryRequiredInMb, NumberOfCpuCoresRequired)
Set RuntimeConfig.CopyCount to control how many copies of the component run for load distribution
Optionally provide Specifications (plural, up to 5 alternatives) instead of a single Specification to let SageMaker choose among instance types, but not both
Scale individual inference components independently using Application Auto Scaling against the SageMakerInferenceComponentConcurrentRequestsPerCopyHighResolution metric
Known gotchas
Specification and Specifications (plural) are mutually exclusive on a single CreateInferenceComponent call
Endpoint-level requirements for hosting inference components (managed instance scaling, routing config) are configured separately on the endpoint config, not on CreateInferenceComponent itself
This differs from Multi-Model Endpoints: inference components let you allocate specific compute (GPU/memory) per model on a shared endpoint rather than lazily loading models from S3 on demand
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