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Transform Video Into Instantly Searchable, Actionable Intelligence with AI Agents and Skills 

13 May 2026 at 18:00
In today’s data-driven world, organizations increasingly rely on video to capture critical information, yet extracting meaningful, real-time insights from...

In today’s data-driven world, organizations increasingly rely on video to capture critical information, yet extracting meaningful, real-time insights from massive amounts of footage remains a challenge. NVIDIA Metropolis Blueprint for video search and summarization (VSS) overcomes this hurdle by transforming millions of live video streams or hours of recorded video into instantly searchable…

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How to Eliminate Pipeline Friction in AI Model Serving

12 May 2026 at 18:00
The path from a trained AI model to production should be smooth, but rarely is. Many teams invest weeks fine-tuning models, only to discover that exporting to a...

The path from a trained AI model to production should be smooth, but rarely is. Many teams invest weeks fine-tuning models, only to discover that exporting to a deployment format breaks layers, input shapes cause runtime failures, or version mismatches silently degrade performance. These issues are collectively known as pipeline friction, and they cost organizations time, money…

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Improving Bash Generation in Small Language Models with Grammar-Constrained Decoding

8 May 2026 at 17:13
Bash is one of the most flexible and powerful interfaces exposed to AI agents. In the right system, a model that emits grep, curl, tar, or a shell pipeline is...

Bash is one of the most flexible and powerful interfaces exposed to AI agents. In the right system, a model that emits , , , or a shell pipeline is producing an executable action that can read files, mutate a workspace, open network connections, and chain tools together. For the NVIDIA AI Red Team, this makes command generation a useful research target. If smaller language models can be guided…

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

8 May 2026 at 15:59
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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Model Quantization: Post-Training Quantization Using NVIDIA Model Optimizer

7 May 2026 at 21:18
This post is the second of a three-part series. See also Model Quantization: Concepts, Methods, and Why It Matters and Model Quantization: Turn FP8 Checkpoints...

This post is the second of a three-part series. See also Model Quantization: Concepts, Methods, and Why It Matters and Model Quantization: Turn FP8 Checkpoints into High-Performance Inference Engines with NVIDIA TensorRT. Model quantization is an effective method to reduce VRAM usage and improve inference performance on consumer devices such as NVIDIA GeForce RTX GPUs.

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How to Build In-Vehicle AI Agents with NVIDIA: From Cloud to Car 

5 May 2026 at 16:00
The automotive cockpit is undergoing a fundamental shift from rule-based interfaces to agentic, multimodal AI systems capable of reasoning, planning, and...

The automotive cockpit is undergoing a fundamental shift from rule-based interfaces to agentic, multimodal AI systems capable of reasoning, planning, and acting. In most vehicles on the road today, in-vehicle assistants still rely on fixed command-response patterns: interpret a phrase, trigger an action, reset. While effective for well-defined tasks, this approach doesn’t scale to modern…

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Building for the Rising Complexity of Agentic Systems with Extreme Co-Design

5 May 2026 at 15:52
Generative AI’s explosive first chapter was defined by humans sending requests and models responding. The agentic chapter is different.  Agents don't...

Generative AI’s explosive first chapter was defined by humans sending requests and models responding. The agentic chapter is different. Agents don’t follow a pre-determined sequence of actions. They call tools, spawn sub-agents with different tasks and models, retain information in memory, manage their own context window, and decide for themselves when they’re finished. In doing so…

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Optimize Supply Chain Decision Systems Using NVIDIA cuOpt Agent Skills

4 May 2026 at 20:55
Decorative image.Modern supply chains operate under the constant pressures of fluctuating demand, volatile costs, constrained capacity, and interdependent decision-making....Decorative image.

Modern supply chains operate under the constant pressures of fluctuating demand, volatile costs, constrained capacity, and interdependent decision-making. Traditionally, specialized operations research (OR) teams solved these problems by translating business questions into mathematical models. This process can take weeks and often produces fragile solutions that struggle to adapt when conditions…

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Speed Up Unreal Engine NNE Inference with NVIDIA TensorRT for RTX Runtime

30 April 2026 at 17:00
Decorative image.Neural network techniques are increasingly used in computer graphics to boost image quality, improve performance, and streamline content creation. Approaches...Decorative image.

Neural network techniques are increasingly used in computer graphics to boost image quality, improve performance, and streamline content creation. Approaches like super resolution, denoising, and neural rendering help real-time engines work more efficiently, offering new creative possibilities while keeping performance in mind. Unreal Engine 5 (UE5) has taken several steps in this direction…

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How to Build, Run, and Scale High-Quality Creator Workflows in ComfyUI

30 April 2026 at 16:16
Creative and visualization teams today produce more assets, in more formats, with leaner teams. Generative AI can accelerate that work – compressing tasks...

Creative and visualization teams today produce more assets, in more formats, with leaner teams. Generative AI can accelerate that work – compressing tasks that once took hours of manual effort into automated, repeatable pipelines. ComfyUI is an open-source, node-based creative tool that runs locally on NVIDIA RTX GPUs. It connects image generation, video synthesis, and language models into…

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NVIDIA Nemotron 3 Nano Omni Powers Multimodal Agent Reasoning in a Single Efficient Open Model

28 April 2026 at 16:01
Decoratove image showing mult-modal processing.Agentic systems often reason across screens, documents, audio, video, and text within a single perception‑to‑action loop. However, they still rely on...Decoratove image showing mult-modal processing.

Agentic systems often reason across screens, documents, audio, video, and text within a single perception‑to‑action loop. However, they still rely on fragmented model chains—separate stacks for vision, audio, and text. This increases inference hops and orchestration complexity, driving up inference costs while weakening cross-modal context consistency. NVIDIA Nemotron 3 Nano Omni…

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