This article demonstrates deploying thousands of model ensembles on Amazon SageMaker multi-model endpoints (MMEs) using GPU instances to reduce hosting costs by up to 50%.

• SageMaker MMEs enable hosting multiple models on shared GPU resources with dynamic loading/unloading
• NVIDIA Triton inference server creates model ensembles as directed acyclic graphs (DAGs) for preprocessing and postprocessing
• Example deploys two ensembles: DALI preprocessing + TensorFlow Inception v3 for images, and BERT + Python pre/postprocessing for text
• Models dynamically load from S3 to instance memory; unused models unload to preserve GPU memory
• Single endpoint invocation specifies target model via TargetModel parameter, enabling multi-model routing
• No endpoint updates needed to add/remove models; simply upload/delete from S3 bucket
• Supports auto-scaling policies for handling high traffic across multiple instances

SageMaker MMEs with Triton provide cost-effective deployment of complex ML pipelines while reducing operational overhead through dynamic model management.