Floating-Point Precision and Its Effects to Model Inference
A model is a collection of numbers. The format used to store and compute with those numbers affects how much…
Govern AI agent access with Okta Cross App Access and Amazon Bedrock AgentCore – Part 1
An AI agent that updates a ticket or reads a customer record acts for a person, inside applications that person…
Govern AI agent access with Okta Cross App Access and Amazon Bedrock AgentCore – Part 2
In Part 1, you built a task agent on Amazon Bedrock AgentCore, a platform to build, connect, and optimize agents…
Serverless MCP server for AI agent data lake access on Amazon ECS Fargate
Organizations building applications powered by artificial intelligence (AI) face a recurring challenge: giving large language model (LLM) agents access to…
Taxonomy of agentic primitives in Amazon Quick
Over the past year, as we built Amazon Quick, our agents went from answering questions to taking actions: scheduling meetings,…
SageMaker Inference now supports Multi-Instance GPU to maximize GPU utilization for model endpoints
When you deploy a model to a GPU endpoint, the smallest unit of GPU you can request is one whole…
How NVIDIA Uses AI Agents to Streamline AI Factory Operations
As GPU clusters scale, operations span compute, network, hardware, and scheduling. Site reliability engineers (SREs) often have to examine multiple…
Five Ways NVIDIA Co-Design Maximizes the Value of Every Byte in the AI Factory
In an AI factory, high bandwidth memory (HBM)is the fastest, most expensive, and most contested memory tier. Every byte spent…
Configure, Certify, and Operate Kubernetes GPU Clusters with NVIDIA DSX OS
One underperforming GPU can slow an entire distributed training job. The node may pass basic health checks and show normal…
Accelerating Reinforcement Learning Weight Refit with the Reshard Collective
During reinforcement learning (RL) post-training, training trajectories are generated on the fly by running inference with the current policy model.…