AI agents are quickly moving beyond chat. They inspect code, run tests, read documents, search knowledge bases, query internal systems, and operate for hours on...

AI agents are quickly moving beyond chat. They inspect code, run tests, read documents, search knowledge bases, query internal systems, and operate for hours on behalf of a user. This unlocks productivity, but can also give agents access to sensitive enterprise data and the ability to complete tasks and take action across business systems, making a secure, governed environment essential.

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AI agents have changed a lot in the last two years. The first could only answer one question at a time. Then came multi-turn chat, where the model could keep...

AI agents have changed a lot in the last two years. The first could only answer one question at a time. Then came multi-turn chat, where the model could keep some context across a session. Today, we have long-horizon agents. Systems that plan many steps, split work between sub-agents, keep context across a long task, and run tools in a safe sandbox. The NVIDIA AI-Q Blueprint is an open source…

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As context windows grow longer, moving large model weights efficiently becomes critical to performance. A common way to address this is quantization, an...

As context windows grow longer, moving large model weights efficiently becomes critical to performance. A common way to address this is quantization, an optimization technique that compresses model weights into a smaller data format. One quantization format is NVFP4, an innovative 4-bit floating point introduced with NVIDIA Blackwell architecture. That’s the approach behind our new Nemotron 3…

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Generative AI workloads are rapidly outgrowing the memory and compute budget of single GPUs. For inference developers building media generation pipelines, the...

Generative AI workloads are rapidly outgrowing the memory and compute budget of single GPUs. For inference developers building media generation pipelines, the challenge is scaling across multiple devices without sacrificing the critical optimizations—like kernel fusions, memory planning, and quantization—that NVIDIA TensorRT delivers for production deployments. Multi-device inference support…

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As AI systems move from single-turn interactions to coordinated multiagent workflows, low-latency inference becomes increasingly important. Autoregressive LLMs...

As AI systems move from single-turn interactions to coordinated multiagent workflows, low-latency inference becomes increasingly important. Autoregressive LLMs generate tokens sequentially, which can limit GPU utilization and constrain throughput in latency-sensitive serving scenarios. Speculative decoding helps mitigate this bottleneck by using a lightweight model to draft future tokens…

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Telecom operators are adopting AI across network operations, customer care, and back-office workflows, but most are still early in the journey to autonomy. In...

Telecom operators are adopting AI across network operations, customer care, and back-office workflows, but most are still early in the journey to autonomy. In network operations, for example, automation typically sits in the Level 2–3 band of TM Forum’s autonomous networks levels taxonomy, streamlining execution of predefined solutions in selective network domains. Reaching Level 4–5 autonomy…

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When AlphaFold2 revolutionized drug discovery in 2020, its success relied entirely on the roughly 170,000 protein structures collected by scientists since 1971...

When AlphaFold2 revolutionized drug discovery in 2020, its success relied entirely on the roughly 170,000 protein structures collected by scientists since 1971 and preserved in the Protein Data Bank. Measured data is the backbone for all AI models and workflows that process data as it’s created, act on what matters in real time, and analyzes data for deep insights. With the current rise of modern…

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Physical AI—robots working autonomously alongside people in factories, warehouses, hospitals, and homes—is arriving faster than most expected. Traditional...

Physical AI—robots working autonomously alongside people in factories, warehouses, hospitals, and homes—is arriving faster than most expected. Traditional safety which was built for structured environments can not work anymore as the spaces become more unstructured and robots move out of cages. AI-driven safety is the key. Marking a major milestone in the arrival of physical AI…

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Developers building for AR glasses and wearable devices face an infrastructure gap. The hardware is ready, but creating AI experiences requires integrating live...

Developers building for AR glasses and wearable devices face an infrastructure gap. The hardware is ready, but creating AI experiences requires integrating live camera and microphone streams, multimodal AI models, enterprise data, tool use, deployment infrastructure, and device-specific runtimes. NVIDIA XR AI is designed to address this challenge by providing a reusable foundation for…

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NVIDIA delivered a clean sweep in MLPerf Training v6.0, the latest edition of industry-standard AI training benchmarks developed by the MLCommons consortium....

NVIDIA delivered a clean sweep in MLPerf Training v6.0, the latest edition of industry-standard AI training benchmarks developed by the MLCommons consortium. NVIDIA achieved the fastest time to train at scale, and also delivered the highest performance when normalized on a per-accelerator basis on every benchmark. It was also the only platform to submit on every test.

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Mixture-of-experts (MoE) models have quickly become a foundational component of modern, large-scale AI systems. They are widely adopted because they enable...

Mixture-of-experts (MoE) models have quickly become a foundational component of modern, large-scale AI systems. They are widely adopted because they enable substantially larger model capacity while activating only a subset of parameters for each token, offering an unparalleled approach for scaling performance within a practical compute budget. As model scales continue to grow…

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Quick glossary for readers new to VLA/WAM terminology VLA Vision-Language-Action model: a robot policy that starts from a pretrained VLM backbone and adapts it...

Quick glossary for readers new to VLA/WAM terminology VLA Vision-Language-Action model: a robot policy that starts from a pretrained VLM backbone and adapts it to generate actions from visual observations and language instructions. Large-scale VLM pretraining is a core part of the recipe. See Pi-0 and GR00T N1. WAM World-Action Model: a policy that starts from a pretrained world-model or video…

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AI agents have fundamentally changed the complexity of inference workloads. Until now, the industry has struggled to define a standard for measuring how...

AI agents have fundamentally changed the complexity of inference workloads. Until now, the industry has struggled to define a standard for measuring how inference systems perform under these conditions. Artificial Analysis AgentPerf (AA-AgentPerf) offers the industry’s first multi-vendor open benchmarks profiling trajectories that are representative of real-world AI agent coding tasks.

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As enterprise AI adoption scales, developers are increasingly forced to stitch together fragmented pipelines—separate models for text, vision, and...

As enterprise AI adoption scales, developers are increasingly forced to stitch together fragmented pipelines—separate models for text, vision, and code—leading to added complexity, higher costs, and slower iteration. MiniMax M3—available on NVIDIA accelerated infrastructure, including NVIDIA Blackwell—changes this by enabling a single multimodal system capable of long-context reasoning…

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NVIDIA Quantum InfiniBand now offers intent-based security profiles in Unified Fabric Manager (UFM) that enable multi-tenant fabric security in a single...

NVIDIA Quantum InfiniBand now offers intent-based security profiles in Unified Fabric Manager (UFM) that enable multi-tenant fabric security in a single click. NVIDIA Quantum InfiniBand supports three profiles: General, Bare Metal Cloud, and Secured Bare Metal Cloud. Network administrators can now auto-configure: This cuts deployment time to minutes from hours or days…

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Developers building real-time AI—such as chat assistants, copilots, and agentic workflows—are often constrained by token-by-token generation speed. This...

Developers building real-time AI—such as chat assistants, copilots, and agentic workflows—are often constrained by token-by-token generation speed. This limits responsiveness, increases serving costs, and makes fluid, interactive experiences difficult to achieve. DiffusionGemma, created by Google DeepMind and optimized to run efficiently across NVIDIA platforms, introduces a new approach to…

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As AI infrastructure scales, enterprise expectations for operational maturity are increasing. Organizations expect these systems to be provisionable,...

As AI infrastructure scales, enterprise expectations for operational maturity are increasing. Organizations expect these systems to be provisionable, observable, secure, and manageable at scale—the same standard applied to all critical infrastructure. The moment an AI system moves from development into enterprise deployment, that operational foundation is essential. NVIDIA DGX Spark and…

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This post is the third of a three-part series. See also Model Quantization: Concepts, Methods, and Why It Matters and Model Quantization: Post-Training...

This post is the third of a three-part series. See also Model Quantization: Concepts, Methods, and Why It Matters and Model Quantization: Post-Training Quantization Using NVIDIA Model Optimizer. Converting a quantized checkpoint into an NVIDIA TensorRT engine bridges the gap between model optimization and production deployment, enabling faster inference, higher throughput…

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Federated learning (FL) research often begins with a deceptively simple question: What should we try next? A new aggregation rule, a FedProx coefficient, a...

Federated learning (FL) research often begins with a deceptively simple question: What should we try next? A new aggregation rule, a FedProx coefficient, a server optimizer setting, a SCAFFOLD variant, or a model architecture tweak may all look promising before an experiment starts. After the run finishes, the harder questions begin: Did the change actually improve the metric?

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Training a speech AI model to correctly recognize or synthesize clinical terminology is surprisingly difficult. Drug names like Acetaminophen, Amlodipine,...

Training a speech AI model to correctly recognize or synthesize clinical terminology is surprisingly difficult. Drug names like Acetaminophen, Amlodipine, Cefazolin, and Biktarvy are not part of everyday vocabulary. Procedure names, anatomy terms, and specialty-specific diagnoses introduce the same problem in a different form. Off-the-shelf speech systems can sound fluent and still miss the words…

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