AI Lab
Official NVIDIA announcements — model releases, product launches and research, each one summarized with every source covering it, by AIBriefs. RSS
The library provides Python bindings to CUDA-X math libraries, enabling GPU-accelerated math operations at scale. It bridges Python scientific computing with NVIDIA's high-performance math libraries.
NVIDIA blog post outlines four key security practices for deploying AI agents in enterprise workflows, including AI red teaming and continuous monitoring.
Identical NVIDIA H100, GB200 NVL72, or GB300 NVL72 systems can deliver materially different training throughput. The blog discusses lessons for unlocking performance.
NVIDIA's tutorial walks through deploying a validated AI coding assistant with NeMo Guardrails, addressing challenges in regulated or source-sensitive environments. It covers source provenance, code security, and usage policy enforcement.
NVIDIA introduced GPU-native medical physics simulation for healthcare robotics training and Cosmos-H-Dreams, a generative real-time simulator for surgical robotics. These tools address the data scarcity challenge in healthcare robotics by enabling realistic simulation without relying on real-world data collection.
NVIDIA released Ising Calibration, an open-source VLM that automates quantum computer calibration by interpreting diagnostic outputs from quantum processors with enhanced in-context learning.
The blog post explains how agent harness architecture—context rendering, execution planning, tool integration, and more—affects model performance. It covers six key capabilities to build better AI agents.
NVIDIA highlights how its CUDA-X and simulation platforms address growing compute demands in semiconductor manufacturing. The post covers materials engineering, digital twins, and computational chemistry to accelerate innovation.
ModelExpress targets the costly movement of model checkpoints, which can reach hundreds of gigabytes or a terabyte. The tool optimizes distribution to reduce time and expense.
Nvidia will invest $1 billion in Naver to finance an AI data center in South Korea, and expanded its partnership with SK Group to build over 2 gigawatts of AI data centers, with the two companies expecting to do more than $500 billion in business. South Korea plans to inject 20 trillion won ($13.9 billion) into its sovereign wealth fund for AI investments.
NVIDIA and Prime Intellect Lab release a guide for customizing Nemotron 3 Nano using reinforcement learning with verifiable rewards (RLVR) and LoRA adapters. The tutorial covers setup in a math-python environment and training steps to tailor the model for specific use cases.
Jensen Huang commissioned the DGX GB300 at NPS in Monterey, providing on-premises AI for weather, cybersecurity, and disaster resilience. The system serves over 1,500 students and 600 faculty for training and inference.
TensorRT engine builds can now be made observable and cancelable in Python or C++, addressing long-running builds that can take minutes. The feature supports progress callbacks and cancellation for large strongly typed models.
Wistron opened its first U.S. manufacturing facility in Fort Worth, a 324,000-square-foot plant producing NVIDIA GB300 Grace Blackwell Ultra Superchips.
The blog post reviews current simulation platforms and techniques for training and testing physical AI systems, including robotics and autonomous vehicles. It covers key simulators, challenges in sim-to-real transfer, and the role of digital twins. The overview is published on Hugging Face as part of a collaboration between NVIDIA and the AI community.
Built for the Vera Rubin architecture, Spectrum-6 connects hundreds of thousands of GPUs in AI factories to multiply computing power at unprecedented scale.
NVIDIA achieved a world record for mixture-of-experts (MoE) pre-training using the GB300 NVL72 platform. The record demonstrates the scalability of the Megatron framework for large-scale MoE training.
NVIDIA's Rubin GPU architecture targets always-on AI factories with new NVFP4 precision, optimized for mixture-of-experts (MoE) models and inference workloads.
NVIDIA's SIGGRAPH 2026 announcements span open AI models, real-time simulation, and agentic and physical AI. The company says the breakthroughs are transforming media, content creation, and robotics.
New integration lets developers add RTX-accelerated sensor simulation to existing 3D, robotics, and industrial digital twin apps. Built for physical AI and autonomous vehicle workloads, it uses OpenUSD.
BMS announced it is deploying its second NVIDIA-based AI cluster, dubbed 'SuperDuperPOD', one of the largest in life sciences. The cluster, built on NVIDIA Vera Rubin, builds on existing AI infrastructure that has delivered results.
Bristol Myers Squibb (BMS) is deploying its second NVIDIA-powered AI cluster, called the 'SuperDuperPOD', on the new NVIDIA Vera Rubin platform. BMS already runs one of the largest AI clusters in life sciences, achieving significant results in drug discovery.
NVIDIA NemoClaw, a collection of open blueprints for autonomous agents, enables context-aware video AI agents to integrate with enterprise systems like content management, messaging, and databases. The approach moves from analysis to action by orchestrating blueprints for structured reports and multistep workflows.
NVIDIA announced the Jetson T3000 (865 FP4 TFLOPS, 32GB memory) and T2000 (400 FP4 TFLOPS, 16GB) modules based on the Thor architecture for robotics and edge AI. Companies including 1X, Amazon Robotics, and Boston Dynamics are building on the platform.
DeepStream 9.1 adds Multi-View 3D Tracking (MV3DT) and 13 agentic AI skills for real-time multi-sensor video analytics. It eliminates the need for manual camera calibration across large spaces.
NVIDIA's blog post details using AI agents to automate tasks in creating lightweight USD runtimes, reducing development time. It includes practical examples and code for integrating agents into the USD pipeline.
NVIDIA and Noetra Corp. will build an AI factory with 13,750 Vera CPUs and 27,500 Rubin GPUs, delivering 140 MW capacity. Supported by Japan's METI, it will create open multimodal foundation models for physical AI in manufacturing, logistics, and healthcare.
Over 5,000 participants across 4,000 teams competed in the NVIDIA Nemotron Model Reasoning Challenge on Kaggle. Winning approaches treated reasoning as a full engineering workflow, using LoRA adapters (rank ≤32) and synthetic chain-of-thought data to improve accuracy on the Nemotron-3-Nano-30B model.
Nvidia's Nemotron Labs blog argues open models enable enterprises and nations to build specialized, trustworthy AI systems. The post highlights how an open stack delivers real-world value while maintaining control.
NVIDIA claims using TAO 7 agent skills, vision models can exceed 90% accuracy with minimal manual effort. Cosmos 3 is a new open world reasoning VLM, and TAO 7 provides a suite of tools for fine-tuning with coding agents and natural language prompts.
NVIDIA's blog post demonstrates building an autonomous RL research workflow using Codex with GPT 5.5 and NeMo. It covers three capabilities: full-stack autonomy, goal-driven autoresearch, and paper-to-code.
Nvidia argues that performance per watt is the key metric for AI infrastructure, as tokens generated within a fixed power budget determine revenue and profitability. The metric cannot be gamed, only earned through real-world results.
Nvidia released a 1-billion-parameter embedding model with NVFP4 quantization on HuggingFace. The model is part of the Nemotron-3 series and is designed for efficient text embedding.
NVIDIA released Nemotron-3-Embed-8B-BF16, an 8B-parameter embedding model in BF16 precision, available on HuggingFace. It is part of the Nemotron-3 series designed for text embedding tasks.
NVIDIA released Nemotron-3-Embed-1B-BF16 on HuggingFace, a 1B parameter embedding model. The model has garnered 50 likes and over 31,000 downloads since release.
NVIDIA presents guided generative models to efficiently estimate probabilities of rare, high-impact events across science, engineering, and finance. The approach improves sampling efficiency for extreme events critical for risk assessment.
NVIDIA published a blog post detailing RoboLab, its simulation benchmarking platform for evaluating robot foundation models. The guide covers real-world deployment challenges and best practices for testing general-purpose robot policies.
The post explains how kernel fusion improves memory bandwidth and reduces kernel launch overhead in CUDA. It covers strategies for fusing kernels to optimize GPU performance.
Blog post discusses balancing accuracy, throughput, and latency in LLM design. Key dimensions: accuracy, throughput, and deployment latency must be optimized together.
NVIDIA NeMo provides a pipeline for generating synthetic financial news data to fine-tune LLMs, addressing data scarcity and imbalance. The method uses domain-specific prompts to produce diverse, balanced datasets for sentiment analysis.
Step-by-step guide to configuring a LangChain Deep Agents Harness profile for Nemotron 3 Ultra, balancing accuracy and cost in agentic systems.
NVIDIA introduces a new end-to-end workflow for humanoid robot policy development using Isaac GR00T. The platform enables repeatable pipelines from robot bring-up to task-specific skill development.
A guide walks through constructing an AI agent using NVIDIA Nemotron to analyze and triage industrial alarms. It covers agent architecture, retrieval-augmented generation, and integration with existing systems.
US telecom operators have spent over $240B on spectrum in 30 years. NVIDIA's AI Aerial platform uses AI-native RAN to improve spectral efficiency, reducing the need for additional spectrum.
NVIDIA and Hugging Face are collaborating to bring new robot foundation models, simulation tools, and datasets to the open-source LeRobot platform. The effort aims to accelerate physical AI development by providing shared resources to the community.
The technique improves goodput by enabling efficient training across heterogeneous GPU configurations. It addresses the challenge of resource imbalance in large-scale LLM training jobs spanning thousands of GPUs.
Audex is a 30B-A3B MoE model built on Nemotron-Cascade-2, handling both audio understanding and generation. It retains the text intelligence of its backbone; a smaller 2B variant is also available under a noncommercial license.
The 2026 International Conference on Machine Learning (ICML) in Seoul showcased a growing research focus on open frontier models and open AI infrastructure. Industry participants, including NVIDIA and Together AI, presented research on topics ranging from latent planning to inference optimization.
Nations are investing in domestic AI infrastructure to advance economies, protect data, and seize opportunities in transportation, healthcare, and other sectors. The blog from NVIDIA's Calista Redmond highlights AI as a key technology for national priorities.
NVIDIA's blog post describes using Blackwell hardware features to secure AI inference without performance degradation. The solution integrates with TensorRT-LLM and Dynamo for runtime verification and attestation.
NVIDIA introduces a revenue-sharing model enabling AI clouds to procure GPUs with credit support. Sharon AI is among the first partners, deploying up to 40,000 GB300 GPUs. NVIDIA earns standard product revenue plus a share of cloud revenue on supported capacity.
NVIDIA's blog post explores reinforcement learning (RL) methods for AI agents, including RLHF and newer techniques. It provides a practical guide for mastering agentic RL using NeMo and Nemotron.