AWS HPC Blog
Tag: AI
Scaling your LLM inference workloads: multi-node deployment with TensorRT-LLM and Triton on Amazon EKS
LLMs are scaling exponentially. Learn how advanced technologies like Triton, TRT-LLM and EKS enable seamless deployment of models like the 405B parameter Llama 3.1. Let’s go large.
Automotive component design at Nifco using generative AI and diffusion models
Combining generative AI with AWS services, Nifco USA is exploring new frontiers in structural design. See how they’re using diffusion models, SageMaker, and Batch to create game-changing lightweight auto parts.
Whisper audio transcription powered by AWS Batch and AWS Inferentia
Transcribe audio files at scale for really low cost using Whisper and AWS Batch with Inferentia. Check out this post to deploy a cost-effective solution in minutes!
Using machine learning to drive faster automotive design cycles
Aerospace and automotive companies are speeding up their product design using AI. In this post we’ll discuss how they’re using machine learning to shift design cycles from hours to seconds using surrogate models.
Protein language model training with NVIDIA BioNeMo framework on AWS ParallelCluster
In this new post, we discuss pre-training ESM-1nv for protein language modeling with NVIDIA BioNeMo on AWS. Learn how you can efficiently deploy and customize generative models like ESM-1nv on GPU clusters with ParallelCluster. Whether you’re studying protein sequences, predicting properties, or discovering new therapeutics, this post has tips to accelerate your protein AI workloads on the cloud.
Using large-language models for ESG sentiment analysis using Databricks on AWS
ESG is now a boardroom issue. See how Databricks’ AI solution helps understand emissions data and meet new regulations.
Launch self-supervised training jobs in the cloud with AWS ParallelCluster
In this post we describe the process to launch large, self-supervised training jobs using AWS ParallelCluster and Facebook’s Vision Self-Supervised Learning (VISSL) library.
Building a Scalable Predictive Modeling Framework in AWS – Part 3
In this final part of this three-part blog series on building predictive models at scale in AWS, we will use the synthetic dataset and the models generated in the previous post to showcase the model updating and sensitivity analysis capabilities of the aws-do-pm framework.
Building a Scalable Predictive Modeling Framework in AWS – Part 2
In the first part of this three-part blog series, we introduced the aws-do-pm framework for building predictive models at scale in AWS. In this blog, we showcase a sample application for predicting the life of batteries in a fleet of electric vehicles, using the aws-do-pm framework.
Building a Scalable Predictive Modeling Framework in AWS – Part 1
Predictive models have powered the design and analysis of real-world systems such as jet engines, automobiles, and powerplants for decades. These models are used to provide insights on system performance and to run simulations, at a fraction of the cost compared to experiments with physical hardware. In this first post of three, we described the motivation and general architecture of the open-source aws-do-pm framework project for building predictive models at scale in AWS.