Category: Artificial Intelligence

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.

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 HPC can be a real human progress catalyst. If you run large scale simulations, tune complex models, or support researchers who consistently need more compute, the […]

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 access to compelling customer stories and insights from AWS leadership as they tackle today’s most critical challenges in high-performance computing, from accelerating scientific discovery to optimizing […]

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.

Developing robust investment strategies requires thorough testing, but relying solely on historical data can introduce biases and limit your insights. Learn how synthetic data from agent-based models can provide an unbiased testbed to systematically evaluate your strategies and prepare for future market scenarios. Part 1 of 2 covers the theoretical foundations of the approach.

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.