Using AI Physics for Technology Computer-Aided Design Simulations
Technology Computer-Aided Design (TCAD) simulations, encompassing both process and device simulations, are crucial for modern semiconductor manufacturing. They...
How to Scale Fast Fourier Transforms to Exascale on Modern NVIDIA GPU Architectures
Fast Fourier Transforms (FFTs) are widely used across scientific computing, from molecular dynamics and signal processing to computational fluid dynamics (CFD),...
Improve AI-Native 6G Design with the NVIDIA Aerial Omniverse Digital Twin
AI-native 6G networks will serve billions of intelligent devices, agents, and machines. As the industry moves into new spectrums like FR3 (7–24 GHz), radio...
Optimizing Inference for Long Context and Large Batch Sizes with NVFP4 KV Cache
Quantization is one of the strongest levers for large-scale inference. By reducing the precision of weights, activations, and KV cache, we can reduce the memory...
Optimize Data Center Efficiency for AI and HPC Workloads with Power Profiles
Exponentially growing computational demand is driving power usage higher and pushing data centers to their limits. With facilities power constrained, extracting...
Accelerating Real-Time Financial Decisions with Quantitative Portfolio Optimization
Financial portfolio optimization is a difficult yet essential task that has been consistently challenged by a trade-off between computational speed and model...
NVIDIA-Accelerated Mistral 3 Open Models Deliver Efficiency, Accuracy at Any Scale
The new Mistral 3 open model family delivers industry-leading accuracy, efficiency, and customization capabilities for developers and enterprises. Optimized...
Model Quantization: Concepts, Methods, and Why It Matters
AI models are becoming increasingly complex, often exceeding the capabilities of available hardware. Quantization has emerged as a crucial technique to address...
Breaking Through Reinforcement Learning Training Limits with Scaling Rollouts in BroRL
When training large language models (LLMs) with reinforcement learning from verifiable rewards (RLVR), one of the most compelling questions is how to overcome...
Building Scalable AI on Enterprise Data with NVIDIA Nemotron RAG and Microsoft SQL Server 2025
At Microsoft Ignite 2025, the vision for an AI-ready enterprise database becomes a reality with the announcement of Microsoft SQL Server 2025, giving developers...
Pioneering AI Co-Scientists for Fusion Research and Cancer Treatment
AI is reshaping scientific research and innovation. Scientists can leverage AI to generate, summarize, combine, and analyze scientific data. AI models can find...
Achieve CUTLASS C++ Performance with Python APIs Using CuTe DSL
CuTe, a core component of CUTLASS 3.x, provides a unified algebra for describing data layouts and thread mappings, and abstracts complex memory access patterns...
Gen AI Super-Resolution Accelerates Weather Prediction with Scalable, Low-Compute Models
As AI weather and climate prediction models rapidly gain adoption, the NVIDIA Earth-2 platform provides libraries and tools for accelerating solutions using a...
Building an Interactive AI Agent for Lightning-Fast Machine Learning Tasks
Data scientists spend a lot of time cleaning and preparing large, unstructured datasets before analysis can begin, often requiring strong programming and...
Enhancing GPU-Accelerated Vector Search in Faiss with NVIDIA cuVS
As companies collect more unstructured data and increasingly use large language models (LLMs), they need faster and more scalable systems. Advanced tools for...
