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Train Models Faster with JAX and MaxText Using NVFP4 on NVIDIA Blackwell

Decorative image.Pre-training frontier LLMs comes down to throughput. When training spans trillions of tokens across thousands of accelerators, every percentage point of step...Decorative image.

Pre-training frontier LLMs comes down to throughput. When training spans trillions of tokens across thousands of accelerators, every percentage point of step time can add up to days of training and substantial compute costs. Numerical precision is one of the highest-leverage knobs available, but low- bit mixed-precision pretraining is hard to get right. To address this…

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NVIDIA Nemotron 3 Ultra Powers Faster, More Efficient Reasoning for Long-Running Agents

Illustration showing Nemtron 3 Ultra.Single-turn chatbots are evolving into long-running agents that can reason, maintain context, use tools, and run efficiently across many turns to complete...Illustration showing Nemtron 3 Ultra.

Single-turn chatbots are evolving into long-running agents that can reason, maintain context, use tools, and run efficiently across many turns to complete complex workflows. However, these multi-agent workflows cause token counts to grow quickly. Agents plan, call tools, invoke sub-agents, receive information, and then pass history, outputs, and reasoning steps back into the model…

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Build Personal AI Agents on Windows PCs with New Tools from Microsoft and NVIDIA

AI agents are changing how you interact with your PC. Creators, developers, and AI enthusiasts are already using these agents extensively to assist with...

AI agents are changing how you interact with your PC. Creators, developers, and AI enthusiasts are already using these agents extensively to assist with day-to-day tasks such as coding, video editing, and content management. NVIDIA and Microsoft are teaming up to enable the next generation of developers to build on-device agents on the Windows platform, with easier setup, native security…

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Deploy Self-Evolving Agents for Faster, More Secure Research with a Hermes Agent and NVIDIA NemoClaw

Decorative image.AI agents are a powerful tool for synthesizing data to accelerate research, summarize information, and help teams make decisions faster. But combining internal...Decorative image.

AI agents are a powerful tool for synthesizing data to accelerate research, summarize information, and help teams make decisions faster. But combining internal data with public sources poses security challenges. This post shares an open source example using Hermes Agent with NVIDIA NemoClaw for product research across Outlook, Slack, and GitHub. NVIDIA OpenShell enforces a security-approved…

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How to Post-Train Autonomous Vehicle Models in Closed-Loop with NVIDIA Alpamayo

Developing autonomous vehicle (AV) policies requires bridging an important gap between training and deployment. Vision-language-action (VLA) models that can...

Developing autonomous vehicle (AV) policies requires bridging an important gap between training and deployment. Vision-language-action (VLA) models that can reason over more complex driving scenes and produce richer intermediate reasoning are predominantly trained in open-loop, where model outputs are directly compared to ground-truth behaviors without considering their effect on the environment.

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NVIDIA DSX OS Delivers Open, Modular Software for Operating AI Factories at Scale

AI is now essential infrastructure, powered by AI factories that generate intelligence in the form of tokens. As demand grows, these factories must scale...

AI is now essential infrastructure, powered by AI factories that generate intelligence in the form of tokens. As demand grows, these factories must scale faster, operate more efficiently, and lower the cost of intelligence across the five-layer stack: energy, chips, infrastructure, models, and applications. NVIDIA DSX platform provides the complete playbook for designing, simulating, building…

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NVIDIA CUDA 13.3 Enhances GPU Development with Tile Programming in C++, Compiler Autotuning, and Python Updates

Decorative image.NVIDIA CUDA 13.3 brings new capabilities and performance optimizations to developers across the CUDA ecosystem. The launch of NVIDIA CUDA Tile programming in...Decorative image.

NVIDIA CUDA 13.3 brings new capabilities and performance optimizations to developers across the CUDA ecosystem. The launch of NVIDIA CUDA Tile programming in C++, enables high-level, tile-based kernel development that automatically manages complex low-level GPU details for optimal performance and portability. Additionally, CUDA Tile programming is now supported on Compute Capability 9.0…

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Extract More Kernel Performance with NVIDIA CompileIQ Auto-Tuning 

Image of two men working at a computer.NVIDIA CompileIQ tackles one of the hardest problems in performance engineering: finding the compiler options that unlock the best performance for a specific...Image of two men working at a computer.

NVIDIA CompileIQ tackles one of the hardest problems in performance engineering: finding the compiler options that unlock the best performance for a specific workload. Consider a team that has spent weeks optimizing an LLM inference pipeline on GPUs, tuning batch sizes, quantizing to FP8, adopting flash attention, fusing every kernel they can. The profiler says there’s nothing left to squeeze.

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Run Key Genomics and Protein Folding Workloads Faster with NVIDIA RTX PRO 4500 Blackwell 

Precision medicine depends on two fundamental capabilities: understanding disease at the genomic level and identifying treatments at the molecular level. ...

Precision medicine depends on two fundamental capabilities: understanding disease at the genomic level and identifying treatments at the molecular level. NVIDIA’s contributions to precision medicine extend far beyond accelerated computing, delivering a full-stack platform that translates hardware and software advancements directly into healthcare outcomes. Sequencing the human genome…

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Synthesize Realistic 3D Medical Images at Scale to Ship Pre‑Trained Models

High‑quality 3D medical imaging data is the foundation of modern radiology AI, but access to it is often constrained by data scarcity, privacy restrictions,...

High‑quality 3D medical imaging data is the foundation of modern radiology AI, but access to it is often constrained by data scarcity, privacy restrictions, and the high cost of expert annotation. As a result, training reliable 3D medical imaging models is frequently bottlenecked by small, narrow, and hard‑to‑share datasets, limiting model robustness and generalization. To help teams overcome…

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Get Real-Time Visibility into GPU Usage Across Kubernetes Clusters

Maximizing the value of AI infrastructure demands deep visibility into GPU utilization. Yet many platform teams running AI workloads on Kubernetes operate with...

Maximizing the value of AI infrastructure demands deep visibility into GPU utilization. Yet many platform teams running AI workloads on Kubernetes operate with limited visibility into how their GPUs are used. Most don’t know who’s consuming them, how much memory is in use, and whether Kubernetes pods are pending or silently idle. Without a signal, GPU fleets are routinely underutilized and slow to…

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Building Token‑Metered AI Services on Telco AI Factories

Telcos around the world are building sovereign AI factories based on the NVIDIA Cloud Partner (NCP) reference architecture, giving governments, enterprises, and...

Telcos around the world are building sovereign AI factories based on the NVIDIA Cloud Partner (NCP) reference architecture, giving governments, enterprises, and startups access to in‑country AI infrastructure with the right controls, trust, and performance. But infrastructure alone doesn’t get you to high-margin, production-ready enterprise AI services. Model sizes and reasoning workloads…

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NVIDIA-Verified Agent Skills Provide Capability Governance for AI Agents

Autonomous AI agents are becoming more capable. Open models, Model Context Protocol (MCP)-connected tools, and portable skills are also making agents easier to...

Autonomous AI agents are becoming more capable. Open models, Model Context Protocol (MCP)-connected tools, and portable skills are also making agents easier to extend.But scaling agent use with structural transparency and operational integrity requires more than runtime guardrails. Organizations and teams need to understand and trust the skills, or instructions, an agent is using.

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Mastering Agentic Techniques: AI Agent Evaluation

Evaluating an AI model and evaluating an AI agent are related—but they answer fundamentally different questions. A model benchmark tests the capability of a...

Evaluating an AI model and evaluating an AI agent are related—but they answer fundamentally different questions. A model benchmark tests the capability of a foundation model (how well it understands language, follows instructions, or solves problems on static tasks). An agent evaluation tests the behavior of a system operating end-to-end—planning, calling tools, handling uncertainty…

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Accelerated X-Ray Analysis for Nanoscale Imaging (XANI) of Novel Materials

A massive-scale X-ray free-electron laser (XFEL) enables tracking structural and electron dynamics in novel systems, including fusion materials, semiconductors,...

A massive-scale X-ray free-electron laser (XFEL) enables tracking structural and electron dynamics in novel systems, including fusion materials, semiconductors, batteries, and catalysis. It produces ultrashort X-ray pulses that can record the movements of atoms and electrons. These instruments can detect the smallest change in material structure caused by defects and other influences.

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Streaming Tokens and Tools: Multi-Turn Agentic Harness Support in NVIDIA Dynamo 

An agentic exchange must preserve a structured interaction: assistant turns interleave reasoning with one or more tool calls, and subsequent user turns return...

An agentic exchange must preserve a structured interaction: assistant turns interleave reasoning with one or more tool calls, and subsequent user turns return the corresponding tool results to the model context. Reasoning replay is model- and turn-dependent: some reasoning should be retained, while some should be dropped. The inference engine is responsible for supporting this more expressive…

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