How id Software Used Neural Rendering and Path Tracing in DOOM: The Dark Ages
DOOM: The Dark Ages pushes real-time graphics to new limits by integrating RTX neural rendering and path tracing, setting a new standard for how modern games...
Unlock GPU Performance: Global Memory Access in CUDA
Managing memory is one of the most important performance characteristics to consider when writing a GPU kernel. This post walks you through the important...
3 Easy Ways to Supercharge Your Robotics Development Using OpenUSD
The increasing demand for robotics is driving the need for physics-accurate simulation at an unprecedented scale. Universal Scene Description (OpenUSD) is key...
How to Integrate Computer Vision Pipelines with Generative AI and Reasoning
Generative AI is opening new possibilities for analyzing existing video streams. Video analytics are evolving from counting objects to turning raw video content...
How to GPU-Accelerate Model Training with CUDA-X Data Science
In previous posts on AI in manufacturing and operations, we covered the unique data challenges in the supply chain and how smart feature engineering can...
How to Accelerate Community Detection in Python Using GPU-Powered Leiden
Community detection algorithms play an important role in understanding data by identifying hidden groups of related entities in networks. Social network...
Build a Real-Time Visual Inspection Pipeline with NVIDIA TAO 6 and NVIDIA DeepStream 8
Building a robust visual inspection pipeline for defect detection and quality control is not easy. Manufacturers and developers often face challenges such as...
Build a Retrieval-Augmented Generation (RAG) Agent with NVIDIA Nemotron
Unlike traditional LLM-based systems that are limited by their training data, retrieval-augmented generation (RAG) improves text generation by incorporating...
The Kaggle Grandmasters Playbook: 7 Battle-Tested Modeling Techniques for Tabular Data
Over hundreds of Kaggle competitions, we've refined a playbook that consistently lands us near the top of the leaderboard—no matter if we’re working with...
How to Reduce KV Cache Bottlenecks with NVIDIA Dynamo
As AI models grow larger and more sophisticated, inference, the process by which a model generates responses, is becoming a major challenge. Large language...
An Introduction to Speculative Decoding for Reducing Latency in AI Inference
Generating text with large language models (LLMs) often involves running into a fundamental bottleneck. GPUs offer massive compute, yet much of that power sits...
Autodesk Research Brings Warp Speed to Computational Fluid Dynamics on NVIDIA GH200
Computer-aided engineering (CAE) forms the backbone for modern product development across industries, from designing safer aircraft to optimizing renewable...
Build a Report Generator AI Agent with NVIDIA Nemotron on OpenRouter
Unlike traditional systems that follow predefined paths, AI agents are autonomous systems that use large language models (LLMs) to make decisions, adapt to...
Build High-Performance Vision AI Pipelines with NVIDIA CUDA-Accelerated VC-6
The constantly increasing compute throughput of NVIDIA GPUs presents a new opportunity for optimizing vision AI workloads: keeping the hardware fed with data....
How Quantization Aware Training Enables Low-Precision Accuracy Recovery
After training AI models, a variety of compression techniques can be used to optimize them for deployment. The most common is post-training quantization (PTQ),...
Accelerate Large-Scale LLM Inference and KV Cache Offload with CPU-GPU Memory Sharing
Large Language Models (LLMs) are at the forefront of AI innovation, but their massive size can complicate inference efficiency. Models such as Llama 3 70B and...
