Category: Artificial Intelligence

This post references AWS ParallelCluster. Check out AWS Parallel Computing Service (AWS PCS), our new managed Slurm service for running HPC and AI workloads on AWS. This post was contributed by Santosh Kumar, Bhagyaraju Kasina, Dr. Sandeep Sovani and Dr. Max Starr Researchers and engineering teams running High Performance Computing (HPC) jobs face a constant […]

This post was contributed by Manu Pillai, Gloria Macia and Natalia Jimenez, PhD Organizations running high-performance computing (HPC) workloads today operate largely as they have for decades: users manually specify required compute specifications for each of their jobs. Users spend valuable time analyzing workload requirements, selecting instance types, and troubleshooting infrastructure issues – time that […]

Agentic AI is revolutionizing computational fluid dynamics (CFD) simulations, enabling experienced engineers to focus on physics, innovation, and engineering judgment rather than tedious coding and debugging. Our latest blog explores how this transformative technology can help your team deliver complex projects more rapidly while maintaining scientific rigor.

Running NVIDIA Cosmos world foundation models on AWS provides powerful physical AI capabilities at scale. This blog covers two production-ready architectures, each optimized for different organizational needs and constraints.

This post references AWS ParallelCluster. Check out AWS Parallel Computing Service (AWS PCS), our new managed Slurm service for running HPC and AI workloads on AWS. We want to empower every scientist and engineer to solve hard problems by giving them access to the compute and analytical tools they need, when they need them. Cloud […]

This post references AWS ParallelCluster. Check out AWS Parallel Computing Service (AWS PCS), our new managed Slurm service for running HPC and AI workloads on AWS. AWS re:Invent 2025 returns to Las Vegas, Nevada on December 1, uniting AWS builders, customers, partners, and IT professionals from across the globe. This year’s event offers you exclusive […]

When seeking to improve machine learning model performance, hyperparameter tuning is often the go-to recommendation. However, this approach faces significant limitations, particularly for complex models requiring extensive training times. In this post, we’ll explore a novel approach that combines gradient norm analysis with Large Language Model (LLM) guidance to intelligently redesign neural network architectures. This […]

In this post, we’ll review the key optimizations and performance gains for our Whisper audio transcription solution powered by AWS Batch and AWS Inferentia.

In this post, we’ll examine how this multi-agent approach works, the architecture behind it, and the efficiency improvements it enables. While the focus is on an engineering use case, the principles apply broadly to any organization facing the challenge of coordinating specialized expertise to deliver faster, more consistent results.

Autonomous robots are increasingly used across industries, from warehouses to space exploration. While developing these robots requires complex simulation and reinforcement learning (RL), setting up training environments can be challenging and time-consuming. AWS Batch multi-node parallel (MNP) infrastructure, combined with NVIDIA Isaac Lab, offers a solution by providing scalable, cost-effective robot training capabilities for sophisticated behaviors and complex tasks.