Infra & Hardware

An AI-native framework refers to systems or processes designed from the ground up with AI as a core component, with AI c…

Amazon Bedrock is an AWS fully managed service that provides access to high-performing foundation models from multiple p…

cuDNN is a GPU-accelerated library from NVIDIA that provides highly optimized implementations of core deep learning oper…

Edge deployment means running AI models or apps close to where data is created — for example on factory lines, inside re…

FlashAttention-4 is a highly optimized GPU kernel for computing 'attention' operations in large-scale AI models, deliver…

A GPU (Graphics Processing Unit) is a processor built with thousands of small cores to execute many operations in parall…

A GPU cluster is a system where multiple GPUs (Graphics Processing Units) are networked together to function as a single…

Inference cost is the ongoing cost of running data through a trained AI model to produce an output—like a prediction, ge…

Inference latency is the actual time it takes for an AI model to process an input and return an output. It typically ref…

Model parallelism is a technique where a large AI model is partitioned across multiple devices (such as GPUs), with each…

Nvidia is a technology company best known for its graphics processing units (GPUs) and a full-stack AI platform that inc…

NVIDIA Blackwell is a GPU architecture designed for next-generation AI performance, serving as a core technology for AI …

NVIDIA DGX Cloud is a cloud-based AI supercomputer designed for large-scale AI development. It provides a comprehensive …

On-device AI means running artificial intelligence directly on your own device—like a phone, laptop, or tablet—instead o…

Real-time inference refers to the process where a trained machine learning model accepts live input data and generates p…

Trainium is Amazon Web Services (AWS)’s custom AI training chip designed to train large deep learning models—especially …