# Nemotron Published articles for Nemotron. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Salesforce Announces Koa Reasoning Model Built on NVIDIA Nemotron 3 Super DevFeed: [Salesforce Announces Koa Reasoning Model Built on NVIDIA Nemotron 3 Super]() Original publisher: [Read original article]() Author: Brian Caulfield Published: 2026-09-15T22:24:34Z Content type: news Language: en Sources: [NVIDIA Blog]() Topics: [Koa](), [Nemotron](), [Artificial Intelligence](), [Nvidia](), [post-training](), [ai safety]() Tags: [agentic](), [agentic-ai](), [ai](), [cloud](), [corporate](), [events](), [model](), [nemotron](), [nvidia](), [nvidia-nemo](), [post-training](), [safety](), [security](), [software]() ### AI overview At Salesforce Dreamforce, NVIDIA CEO Jensen Huang discussed AI infrastructure, safety, and enterprise adoption alongside Salesforce CEO Marc Benioff. The event coincided with the announcement of Koa, Salesforce's first CRM reasoning model, built by post-training NVIDIA Nemotron 3 Super on a proprietary synthetic dataset derived from nearly three decades of enterprise CRM deployments. ### Source excerpt Know everything. Do anything. That was the message NVIDIA founder and CEO Jensen Huang brought to Salesforce Dreamforce Tuesday, joining CEO Marc Benioff onstage in an appearance that coincided with the announcement of Koa -- Salesforce's first CRM reasoning model, built on NVIDIA Nemotron 3 Super. Huang didn't just take the stage. He walked into [...] ## Dense vs. MoE Models: Active Parameters, Throughput, and When to Choose Each DevFeed: [Dense vs. MoE Models: Active Parameters, Throughput, and When to Choose Each]() Original publisher: [Read original article]() Author: Elizabeth Goodman Published: 2026-09-15T17:00:11Z Content type: article Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [Mixture of Experts (MoE)](), [Nemotron](), [Routing (disambiguation)]() Tags: [agentic-ai-generative-ai](), [ai-inference](), [architecture](), [cost](), [data-center-cloud](), [deployment](), [llms](), [memory](), [mixture-of-experts-moe](), [models](), [moe](), [nemo](), [nemotron](), [performance](), [router](), [routing](), [tokens]() ### AI overview This article explains how dense and Mixture-of-Experts models activate parameters, compares their effects on throughput, memory cost, and serving complexity, and discusses when each architecture fits different deployment constraints. It uses Nemotron 3.5 Lightning as an example of an MoE model. ### Source excerpt How can a 30B-parameter model activate only 3B parameters per token, and still use the capacity of the larger model? Nemotron 3.5 Lightning illustrates the... ## Chinese AI models dominate OpenRouter's US token consumption. It can now guarantee that traffic stays entirely in the US. DevFeed: [Chinese AI models dominate OpenRouter's US token consumption. It can now guarantee that traffic stays entirely in the US.]() Original publisher: [Read original article]() Author: Paul Sawers Published: 2026-09-14T13:59:33Z Content type: news Language: en Sources: [The New Stack]() Topics: [AI Models](), [Routing (disambiguation)](), [Security](), [data](), [Availability](), [hugging face](), [Nvidia](), [Nemotron](), [OpenAI]() Tags: [ai](), [ai-infrastructure](), [ai-models](), [availability](), [data](), [hugging-face](), [large-language-models](), [models](), [nemotron](), [nvidia](), [openai](), [routing](), [security]() ### AI overview OpenRouter has launched US in-region routing for business and enterprise customers. Requests sent through its US endpoint are decrypted, processed, and served entirely inside the United States, or rejected if that cannot be guaranteed. The feature addresses concerns about data location when businesses use open-weight models, including models developed in China. ### Source excerpt Everyone knows the open-weight model pitch by now: companies can download the weights, customize them, run them on infrastructure of The post Chinese AI models dominate OpenRouter's US token consumption. It can now guarantee that traffic stays entirely in the US. appeared first on The New Stack. ## Teaching AI to Reason Through Detection Triage DevFeed: [Teaching AI to Reason Through Detection Triage]() Original publisher: [Read original article]() Author: Amol Khanna - Manu Nandan - Cristian Viorel Popa - Joan Pujol-Roig - Diana Bolocan - Laura Vasilie - Alexandru Apostu - Chase Helwig - Mihaela Gaman - Mickey Brautbar - Edward Raff - Chase Midler - Sv Published: 2026-09-12T11:17:51.295154Z Content type: article Language: en Sources: [Blog]() Topics: [AI Chat]() Tags: [agentic](), [agentic-soc](), [ai](), [automation](), [chain-of-thought](), [classification](), [cybersecurity](), [language-models](), [llm](), [model](), [nemotron](), [nvidia](), [open](), [reasoning](), [research](), [security](), [soc](), [windows]() ### AI overview CrowdStrike describes research on a reasoning-enabled language-model classifier for security detection triage. The model produces a verdict and an auditable rationale, with the stated goals of improving accuracy, transparency, and safe alert automation. ### Source excerpt New CrowdStrike research shows how step-by-step reasoning can improve detection triage accuracy, transparency, and safe automation. ## Palantir and NVIDIA Deploy a Sovereign Nemotron Supply Chain Stack, Starting With the 1.3 Million Parts in Every Vera Rubin Rack DevFeed: [Palantir and NVIDIA Deploy a Sovereign Nemotron Supply Chain Stack, Starting With the 1.3 Million Parts in Every Vera Rubin Rack]() Original publisher: [Read original article]() Author: Harold Fritts Published: 2026-09-10T20:56:11Z Content type: news Language: en Sources: [StorageReview.com]() Topics: [Nemotron](), [Nvidia](), [Artificial Intelligence](), [Vera Rubin](), [AI Infrastructure](), [AI Platform](), [cuOpt](), [Complex Systems](), [Optimization]() Tags: [ai](), [ai-infrastructure](), [artificial-intelligence](), [complex-systems](), [cuopt](), [enterprise](), [models](), [nemotron](), [nvidia](), [open](), [optimization](), [supply-chain](), [systems](), [vera-rubin]() ### AI overview Palantir and NVIDIA have deployed a sovereign AI stack for supply chain operations, initially using NVIDIA's own Vera Rubin supply chain as the first customer. The system combines Nemotron open models with Palantir Foundry and AIP, NVIDIA NeMo Data Libraries, and cuOpt to support materials allocation, scenario planning, optimization, and risk detection while keeping final decisions with supply chain experts. ### Source excerpt Palantir and NVIDIA have built a sovereign AI stack for supply chain operations and are running it first inside NVIDIA's own supply chain, the one that has to line up 1.3 million parts for every Vera Rubin rack. The stack brings NVIDIA Nemotron open models into Palantir Foundry and its Artificial Intelligence Platform (AIP), grounded The post Palantir and NVIDIA Deploy a Sovereign Nemotron Supply Chain Stack, Starting With the 1.3 Million Parts in Every Vera Rubin Rack appeared first on StorageReview.com. ## How Full-Stack NIM Optimizations Deliver 2.5x More Users on Nemotron 3 Ultra DevFeed: [How Full-Stack NIM Optimizations Deliver 2.5x More Users on Nemotron 3 Ultra]() Original publisher: [Read original article]() Author: Elizabeth Goodman Published: 2026-09-10T16:55:32Z Content type: article Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [Inference Performance](), [Language models]() Tags: [agentic-ai-generative-ai](), [benchmark](), [build-ai-agents](), [cache](), [developer-tools-techniques](), [gpu](), [inference](), [inference-performance](), [latency](), [mamba](), [mixture-of-experts](), [nemotron](), [nim](), [nvidia-blackwell](), [optimization]() ### AI overview The article explains how NVIDIA NIM bundles serving optimizations to improve throughput for Nemotron 3 Ultra while meeting latency targets on GPU infrastructure. ### Source excerpt Deploying a large language model is only the first step toward production-ready serving. Production teams also need to serve as many concurrent users as... ## Deploying AI You Control Doesn't Need to be So Hard DevFeed: [Deploying AI You Control Doesn't Need to be So Hard]() Original publisher: [Read original article]() Author: Jeetu Patel Published: 2026-09-10T09:00:50Z Content type: news Language: en Sources: [Cisco Blogs]() Topics: [Artificial Intelligence](), [AI Platforms/Deployment](), [AI Factory](), [Nvidia](), [Critical Infrastructure](), [post-training](), [data](), [Nemotron](), [on-prem](), [Fine-tuning](), [Cloud](), [Benchmark]() Tags: [ai](), [ai-factory](), [ai-infrastructure](), [ai-strategy](), [cisco-cloud-control-framework](), [cisco-secure-ai-factory](), [cloud](), [critical-infrastructure](), [data](), [executive-platform](), [fine-tuning](), [infrastructure](), [model](), [models](), [nemotron](), [nvidia](), [on-prem](), [post-training]() ### AI overview Cisco announces a collaboration with Palantir to deliver Palantir's Ontology for Cybersecurity through Cisco's Secure AI Factory, using NVIDIA as a preferred full-stack foundation for Palantir's Sovereign AI OS. The article argues that enterprise AI decisions should balance intelligence, cost, and control, including custom evaluations, post-training with proprietary data, and deployment in the cloud, at the edge, or on-premises. ### Source excerpt Announcing a collaboration with Palantir to deliver Cisco's Secure AI Factory with NVIDIA as a preferred full-stack foundation for Palantir's Sovereign AI OS. ## Nvidia and Palantir fine-tune a 30B Nemotron model for Nvidia's supply chain. It beats a model 18 times its size. DevFeed: [Nvidia and Palantir fine-tune a 30B Nemotron model for Nvidia's supply chain. It beats a model 18 times its size.]() Original publisher: [Read original article]() Author: Paul Sawers Published: 2026-09-10T09:00:43Z Content type: news Language: en Sources: [The New Stack]() Topics: [AI, ML & Data Engineering]() Tags: [ai](), [ai-infrastructure](), [ai-models](), [ai-operations](), [cuopt](), [deployment](), [nemotron](), [nvidia](), [partnership](), [supply-chain](), [training]() ### AI overview Nvidia and Palantir are fine-tuning Nemotron for Nvidia supply-chain decisions as a sovereign-AI deployment. The companies say the 30B-parameter model outperforms a model 18 times larger and plan to extend lessons from the deployment to other sectors. ### Source excerpt Nvidia and Palantir announced Thursday that they're working together to bring "sovereign AI to critical supply chains," kicking off initially The post Nvidia and Palantir fine-tune a 30B Nemotron model for Nvidia's supply chain. It beats a model 18 times its size. appeared first on The New Stack. ## From Wafer-Out to First Token: Codifying Supply Chain Expertise with Nemotron and Palantir Foundry DevFeed: [From Wafer-Out to First Token: Codifying Supply Chain Expertise with Nemotron and Palantir Foundry]() Original publisher: [Read original article]() Author: Elizabeth Goodman Published: 2026-09-10T09:00:00Z Content type: article Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [datacenter](), [networking](), [Software](), [data]() Tags: [agentic-ai-generative-ai](), [ai-agent](), [blackwell](), [compute](), [cuopt](), [data-center](), [gb200](), [infrastructure](), [llms](), [manufacturing](), [memory](), [nemo](), [nemotron](), [networking](), [nvidia](), [nvidia-blackwell](), [nvl72](), [performance](), [production](), [real-time](), [software](), [supply-chain](), [vera-rubin]() ### AI overview NVIDIA describes how it measures and reduces the time from wafer-out to first token across complex Grace Blackwell and Vera Rubin supply chains. The article focuses on time-to-rack, critical material allocation, real-time visibility, redundancy, reliability, and codifying human expertise. ### Source excerpt NVIDIA has one of the largest and most complex supply chains in the world, and its performance is measured from wafer-out to first token. The interval is in two... ## NVIDIA and CrowdStrike Strengthen Agentic Cybersecurity Frontier DevFeed: [NVIDIA and CrowdStrike Strengthen Agentic Cybersecurity Frontier]() Original publisher: [Read original article]() Author: Brian Caulfield Published: 2026-09-01T21:19:20Z Content type: news Language: en Sources: [NVIDIA Blog]() Topics: [AI Bots]() Tags: [agentic](), [ai](), [ai-safety](), [automation](), [corporate](), [cybersecurity](), [frontier-ai](), [nemotron](), [nvidia](), [open-source](), [software]() ### AI overview NVIDIA and CrowdStrike announced SafeMind, an agentic cybersecurity system that combines CrowdStrike models and harnesses with defensive models built on NVIDIA Nemotron. The system uses an offense-and-defense coevolution loop intended to improve customer-environment security. ### Source excerpt "We're at an inflection point in cybersecurity," Jensen Huang told a sold-out crowd at CrowdStrike's Fal.Con 2026 in Las Vegas Tuesday. Attacks are now automated. Defense has to be, too. The NVIDIA founder and CEO joined CrowdStrike CEO and founder George Kurtz to announce CrowdStrike SafeMind, its agentic cybersecurity system developed by the CrowdStrike Cyber [...] ## Building an Adaptive Agentic Cybersecurity System with NVIDIA Nemotron DevFeed: [Building an Adaptive Agentic Cybersecurity System with NVIDIA Nemotron]() Original publisher: [Read original article]() Author: Michelle Horton Published: 2026-09-01T17:00:04Z Content type: article Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [AI Bots]() Tags: [agentic](), [agentic-ai-generative-ai](), [ai](), [attacks](), [cybersecurity](), [evaluation](), [models](), [nemo](), [nemotron](), [nvidia](), [open](), [orchestration](), [security-for-ai](), [telemetry](), [testing](), [top-stories](), [traces](), [trustworthy-ai-cybersecurity]() ### AI overview The article describes an agentic cybersecurity system that uses red and blue agents to continuously test attacks, analyze telemetry, generate detections, and retest them in an isolated representative environment. It discusses NVIDIA Nemotron models used with CrowdStrike SafeMind for defensive orchestration and detection generation. ### Source excerpt AI is changing the pace of cybersecurity. Agentic systems can coordinate work and pursue complex objectives over long horizons. Security teams are beginning to... ## NVIDIA Vera Rubin and Blackwell Set a New Standard for Agentic AI Performance per Watt DevFeed: [NVIDIA Vera Rubin and Blackwell Set a New Standard for Agentic AI Performance per Watt]() Original publisher: [Read original article]() Author: Elizabeth Goodman Published: 2026-08-24T15:00:05Z Content type: article Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [AI-assisted coding]() Tags: [agentic-ai](), [agentic-ai-generative-ai](), [agentic-coding](), [ai-agent](), [ai-agents](), [ai-factory](), [ai-infrastructure](), [benchmark](), [blackwell](), [cache](), [cloud-networking](), [cloud-services](), [data-center-cloud](), [gb300-nvl72](), [hardware](), [inference](), [long-context](), [mixture-of-experts-moe](), [nemotron](), [nvidia](), [performance](), [software-defined-data-center](), [tools](), [vera-rubin](), [vera-rubin-nvl72]() ### AI overview The article examines the SemiAnalysis AgentX benchmark for measuring infrastructure efficiency on replayed agentic coding sessions. It compares Vera Rubin and Blackwell NVL72 systems by agentic throughput per megawatt and explains why dynamic, stateful sessions require more realistic evaluation than fixed-length inference tests. ### Source excerpt AI agents have expanded inference from single-turn interactions into multi-step workflows that reason, invoke tools, coordinate subagents, and carry growing... ## GPU-Accelerated Clustering for Financial Instruments at Scale DevFeed: [GPU-Accelerated Clustering for Financial Instruments at Scale]() Original publisher: [Read original article]() Author: Elizabeth Goodman Published: 2026-08-21T16:21:04Z Content type: article Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [GPU](), [Algorithm](), [Matrix]() Tags: [batch](), [communication](), [data-analytics-processing](), [data-science](), [financial-services](), [gb200](), [gpu](), [memory](), [nemo](), [nemotron](), [nvidia](), [post](), [scale](), [simulation-modeling-design](), [workflow]() ### AI overview A GPU-accelerated workflow uses rolling correlation and tail-dependence matrices to cluster financial instruments for portfolio construction, risk aggregation, statistical arbitrage, and trade surveillance. Its adaptive SymNMF-based solver supports soft factor loadings and hard cluster labels, while memory-efficient and distributed implementations scale from single GPUs to one million instruments across multiple nodes. ### Source excerpt Use AdaptGrow, a GPU-accelerated matrix factorization algorithm, to turn rolling correlation and tail-dependence matrices into hard clusters, soft factor... ## Developing Nemotron 3.5 Lightning NVFP4 with QAD Using NVIDIA Model Optimizer DevFeed: [Developing Nemotron 3.5 Lightning NVFP4 with QAD Using NVIDIA Model Optimizer]() Original publisher: [Read original article]() Author: Tanya Lenz Published: 2026-08-17T18:12:48Z Content type: tutorial Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [Nemotron](), [NVFP4](), [quantization](), [Post-training optimization](), [Nvidia](), [post-training](), [Mamba]() Tags: [agentic-ai-generative-ai](), [benchmarks](), [compute](), [developer-tools-techniques](), [developers](), [edge-computing](), [evaluation](), [featured](), [latency](), [mamba](), [megatron](), [memory](), [model](), [model-optimizer](), [models](), [nemotron](), [nvfp4](), [nvidia](), [open](), [post-training](), [quantization](), [speed](), [training](), [training-ai-models]() ### AI overview This tutorial explains how quantization-aware distillation (QAD) creates the Nemotron 3.5 Lightning NVFP4 checkpoint using NVIDIA Model Optimizer. It covers post-training quantization, teacher-student distillation, and evaluation, showing how QAD can recover accuracy while reducing memory usage and increasing throughput. ### Source excerpt Teams customize their models to hit their targets for latency, speed, memory, and compute. With the open NVIDIA Nemotron family of models, developers can find... ## State of Open Models: Summer 2026 Observations DevFeed: [State of Open Models: Summer 2026 Observations]() Original publisher: [Read original article]() Author: Adina Yakefu; Apolinário from multimodal AI art; Irene Solaiman Published: 2026-08-14T00:00:00Z Content type: article Language: en Sources: [Hugging Face - Blog]() Topics: [Artificial Intelligence](), [Open Source](), [quantization](), [datasets](), [Nvidia](), [spaces](), [Hardware](), [Nemotron](), [qwen](), [Benchmark](), [API]() Tags: [api](), [artificial-intelligence](), [benchmark](), [community](), [datasets](), [hardware](), [hub](), [nemotron](), [nvidia](), [open-source](), [quantization](), [qwen](), [research](), [spaces]() ### AI overview This article examines the summer 2026 state of open models, highlighting rapid growth in public model repositories, datasets, and Spaces; the dominance of a small number of repositories in downloads; the rising scale of Chinese open models; differing model portfolio strategies; and the strong role of AMD, NVIDIA, and community quantization in making large models accessible. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science. ## NVIDIA Nemotron 3.5 Lightning Delivers Fast, Accurate Specialized Task Execution for Long-Running Agents DevFeed: [NVIDIA Nemotron 3.5 Lightning Delivers Fast, Accurate Specialized Task Execution for Long-Running Agents]() Original publisher: [Read original article]() Author: Tanya Lenz Published: 2026-08-11T13:01:07Z Content type: release Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [Language models](), [AI Bots]() Tags: [agentic-ai-generative-ai](), [agents](), [ai-agent](), [ai-agents](), [dgx-spark](), [featured](), [inference](), [low-latency](), [mixture-of-experts-moe](), [nemoclaw](), [nemotron](), [nvfp4](), [nvidia](), [open-source](), [orchestration](), [routing](), [top-stories]() ### AI overview NVIDIA introduces Nemotron 3.5 Lightning, an open 30B MoE model with 3B active parameters for fast, high-volume execution in long-running AI agents. It also presents NeMo Switchyard for routing tasks to appropriate models. ### Source excerpt Long-running AI agents spend most of their time on high-volume execution: tool calls, result validation, and subagent delegation. Using a frontier reasoning... ## OpenAI sells a $230 keypad to run AI agents. I built one for $28 on Linux. DevFeed: [OpenAI sells a $230 keypad to run AI agents. I built one for $28 on Linux.]() Original publisher: [Read original article]() Author: Ivan Morgillo Published: 2026-07-29T10:00:00Z Content type: tutorial Language: en Sources: [Ivan Morgillo]() Topics: [codex](), [AI-assisted coding](), [OpenAI](), [Linux](), [asr](), [Nemotron]() Tags: [ai-coding](), [ai-coding-agents](), [asr](), [codex](), [gpu](), [hardware](), [linux](), [nemotron](), [openai]() ### AI overview The article explains how the author recreated the core workflow of OpenAI's Codex Micro for $28 on Linux using a low-cost macropad. The setup supports voice dictation through a local speech-to-text server and one-press commands for interacting with coding agents. ### Source excerpt OpenAI's first hardware is a $230 keypad for driving AI coding agents. I rebuilt the part that matters -- voice dictation and one-press agent commands -- for $28, on Linux, with a cheap Chinese macropad. ## NVIDIA Nemotron 3 Ultra Leads Open Models on Accuracy and Efficiency in Agentic RTL Coding DevFeed: [NVIDIA Nemotron 3 Ultra Leads Open Models on Accuracy and Efficiency in Agentic RTL Coding]() Original publisher: [Read original article]() Author: Nirmal Kumar Juluru Published: 2026-07-27T00:45:00Z Content type: article Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [coding](), [Verilog](), [AI-assisted coding](), [Nvidia](), [Artificial Intelligence](), [debugging](), [Benchmark](), [Code generation](), [Hardware](), [long-context]() Tags: [agentic](), [agentic-ai-generative-ai](), [ai](), [ai-agent](), [ai-agents](), [automation](), [benchmark](), [chip-design](), [code](), [code-generation](), [coding](), [debugging](), [developer-tools-techniques](), [efficiency](), [engineering](), [featured](), [hardware-semiconductor](), [llms](), [long-context](), [models](), [nemotron](), [nvidia](), [open](), [reasoning](), [top-stories](), [validation]() ### AI overview NVIDIA's article presents ACE-RTL and Nemotron 3 Ultra as a combined approach to agentic RTL coding. The workflow generates Verilog, runs simulations and other EDA checks, analyzes failures, and iteratively refines designs while maintaining debugging context. The CVDP benchmark evaluates accuracy and efficiency on realistic RTL generation, modification, debugging, and verification tasks. ### Source excerpt Modern chip design is increasingly limited by engineering time. Register transfer level (RTL) development and verification require specialized hardware... ## Start Customizing NVIDIA Nemotron 3 Nano with Prime Intellect Lab in Minutes DevFeed: [Start Customizing NVIDIA Nemotron 3 Nano with Prime Intellect Lab in Minutes]() Original publisher: [Read original article]() Author: Chris Alexiuk Published: 2026-07-23T16:00:00Z Content type: tutorial Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [rlvr](), [Large Language Model](), [Fine-tuning](), [Reinforcement learning](), [Python](), [coding](), [math]() Tags: [agentic-ai-generative-ai](), [blog](), [coding](), [customization](), [developer-tools-techniques](), [developers](), [experiment](), [featured](), [fine-tuning](), [getting-started](), [math](), [nemotron](), [nvidia](), [open](), [python](), [reinforcement-learning](), [rlvr](), [top-stories](), [train](), [training](), [tutorial]() ### AI overview This tutorial shows how to customize NVIDIA Nemotron 3 Nano with Prime Intellect Lab using reinforcement learning with verifiable rewards on a Python Math environment. It covers a baseline-training-reevaluation workflow and produces a downloadable LoRA adapter. ### Source excerpt Customization is what enables developers to take a general model and tailor it to use cases, domains, languages, and more. However, customization comes with a... ## Lessons From the Leaderboard: What 5,000+ Kagglers Taught Us About Improving AI Reasoning DevFeed: [Lessons From the Leaderboard: What 5,000+ Kagglers Taught Us About Improving AI Reasoning]() Original publisher: [Read original article]() Author: Elizabeth Goodman Published: 2026-07-14T18:20:32Z Content type: article Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [Artificial Intelligence](), [Benchmark](), [Chain-of-thought](), [datasets](), [Cloud](), [Google](), [NCCL]() Tags: [agentic-ai-generative-ai](), [ai](), [ai-agent](), [ai-inference](), [benchmark](), [blackwell](), [chain-of-thought](), [cost](), [deep-learning](), [engineering](), [evaluation](), [featured](), [google](), [kaggle](), [lora](), [nemotron](), [nvidia](), [performance](), [pre-trained-foundation-models](), [synthetic](), [techniques]() ### AI overview The article distills lessons from NVIDIA's Nemotron Model Reasoning Challenge, where more than 5,000 Kaggle participants tested ways to improve AI reasoning under shared model, infrastructure, and evaluation constraints. It highlights synthetic chain-of-thought data, trace quality, targeted solvers, validation beyond public leaderboards, and careful training and context-budget management. ### Source excerpt The NVIDIA Nemotron Model Reasoning Challenge invited the Kaggle community to explore a focused question: What techniques can improve reasoning accuracy when... ## Synthetic Data Generation for Financial AI Research with NVIDIA NeMo DevFeed: [Synthetic Data Generation for Financial AI Research with NVIDIA NeMo]() Original publisher: [Read original article]() Author: Elizabeth Goodman Published: 2026-07-09T19:40:37Z Content type: article Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [Synthetic Data Generation](), [synthetic-data](), [Large Language Model](), [Fine-tuning](), [Natural language processing](), [Nemotron](), [vllm](), [datasets]() Tags: [agentic-ai-generative-ai](), [ai](), [ai-agent](), [ai-ready-data](), [cloud-services](), [compute](), [data](), [data-science](), [featured](), [financial-services](), [fine-tuning](), [generation](), [hardware](), [inference](), [llms](), [models](), [natural-language-processing](), [nemo](), [nemotron](), [nvidia](), [research](), [structured-generation](), [synthetic](), [synthetic-data](), [synthetic-data-generation](), [vllm](), [workflow]() ### AI overview This developer article presents an iterative pipeline for generating a diverse synthetic dataset of more than 500,000 financial news headlines. It combines NeMo Data Designer for structured generation, NeMo Curator for semantic deduplication, Nemotron models for synthesis, and a farthest-from-centroid few-shot strategy to reduce repetition and correct category imbalance. ### Source excerpt Fine-tuning LLMs for financial natural language processing (NLP) is constrained by limited, imbalanced data. Real-world financial news overrepresents earnings... ## Create a LangChain Deep Agents Harness Profile for NVIDIA Nemotron 3 Ultra to Improve Performance DevFeed: [Create a LangChain Deep Agents Harness Profile for NVIDIA Nemotron 3 Ultra to Improve Performance]() Original publisher: [Read original article]() Author: Sean Lopp Published: 2026-07-08T18:17:59Z Content type: tutorial Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [AI Bots](), [ai observability]() Tags: [agentic](), [agentic-ai-generative-ai](), [agents](), [ai-agent](), [benchmark](), [customization](), [evaluation](), [featured](), [langchain](), [nemotron](), [nvidia](), [performance](), [profile](), [python](), [top-stories](), [tutorial]() ### AI overview A tutorial on creating and evaluating a LangChain Deep Agents harness profile for NVIDIA Nemotron 3 Ultra. It uses benchmark-driven iteration to tune harness behavior and improve performance without fine-tuning the model. ### Source excerpt Agentic systems often face a trade-off between accuracy and cost. The highest-performing proprietary frontier models and harnesses provide top accuracy but are... ## Building an Analysis AI Agent for Industrial Alarm Management with NVIDIA Nemotron DevFeed: [Building an Analysis AI Agent for Industrial Alarm Management with NVIDIA Nemotron]() Original publisher: [Read original article]() Author: Tanya Lenz Published: 2026-07-07T17:00:00Z Content type: article Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [AI Bots]() Tags: [agentic-ai-generative-ai](), [ai](), [ai-agent](), [build-ai-agents](), [developer-tools-techniques](), [featured](), [gpu](), [industrial-digitalization-digital-twin](), [llms](), [nemo](), [nemo-retriever](), [nemotron](), [nvidia](), [ocr](), [openshell]() ### AI overview The article describes an NVIDIA-based AI agent for analyzing industrial alarms. It gathers historical and playbook context, runs specialist checks such as anomaly detection and OCR, and returns structured recommendations through an HTTP endpoint. ### Source excerpt Industrial machinery generates more alarms than technicians can triage. For each important alarm requiring follow-up, the technician pulls historical context,... ## Mastering Agentic Techniques: AI Agent Reinforcement Learning DevFeed: [Mastering Agentic Techniques: AI Agent Reinforcement Learning]() Original publisher: [Read original article]() Author: Elizabeth Goodman Published: 2026-07-01T17:04:02Z Content type: tutorial Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [Language models](), [AI Bots]() Tags: [agentic](), [agentic-ai-generative-ai](), [ai-agent](), [ai-platforms-deployment](), [featured](), [language-models](), [nemo](), [nemotron](), [post-training](), [rag](), [reinforcement-learning](), [rlhf](), [rlvr](), [tool]() ### AI overview A guide to using reinforcement learning with verifiable rewards to post-train language models for specialized, long-running AI agents. It explains when prompting, RAG, tools, and agent harnesses are insufficient, and describes reward signals based on verifiers, execution, validation, models, and human feedback. ### Source excerpt Reinforcement learning (RL) is central to aligning language models, from reinforcement learning with human feedback (RLHF) within AI assistants to newer... [Next page]()