Large-scale LLM inference engine

Foundation model benchmarking tool. Run any model on any AWS platform and benchmark for performance across instance type and serving stack options.

This Guidance demonstrates how to deploy a machine learning inference architecture on Amazon Elastic Kubernetes Service (Amazon EKS). It addresses the basic implementation requirements as well as ways you can pack thousands of unique PyTorch deep learning (DL) models into a scalable architecture and evaluate performance

CMP314 Optimizing NLP models with Amazon EC2 Inf1 instances in Amazon Sagemaker

Collection of bet practices, reference architectures, examples, and utilities for foundation model development and deployment on AWS.

This repository provides an easy hands-on way to get started with AWS Inferentia. A demonstration of this hands-on can be seen in the AWS Innovate 2023 - AIML Edition session.

Scalable multimodal AI system combining FSDP, RLHF, and Inferentia optimization for customer insights generation.

Sentence Transformers on EC2 Inf1

Deploy Large Models on AWS Inferentia (Inf2) instances.

End-to-end solution for cold-start recommendations using vLLM, DeepSeek Llama (8B & 70B), and FAISS on AWS Trainium (Trn1) with the Neuron SDK and NeuronX Distributed. Includes LLM-based interest expansion, embedding comparisons (T5 & SentenceTransformers), and scalable retrieval workflows.