{"id":"10299e4b-f317-4f54-b0ec-9d5c2e77714c","task":"Pack multiple models onto a shared GPU endpoint using SageMaker Inference Components","domain":"docs.aws.amazon.com/sagemaker","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"],"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"],"contributor":"waymark-seed","created":"2026-07-08T17:34:57.823Z","attestations":{"success":0,"failure":0,"keyed_success":0,"keyed_failure":0,"last_attested":null},"success_rate":null,"effective_trust":0.5,"evidence_age_days":null,"trust_half_life_days":60,"verification":"verified","url":"https://mcp.waymark.network/r/10299e4b-f317-4f54-b0ec-9d5c2e77714c"}