A high-throughput and memory-efficient inference and serving engine for LLMs
-
Updated
Nov 14, 2024 - Python
A high-throughput and memory-efficient inference and serving engine for LLMs
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.
Deploy Large Models on AWS Inferentia (Inf2) instances.
Add a description, image, and links to the inferentia topic page so that developers can more easily learn about it.
To associate your repository with the inferentia topic, visit your repo's landing page and select "manage topics."