# Nemotron Nemotron is a family of open NVIDIA models with open weights, training data, and recipes for building specialized AI agents. 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... ## 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. ## 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. ## 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. ## 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... ## Nemotron 3.5 Content Safety: Customizable Multimodal Safety for Global Enterprise AI DevFeed: [Nemotron 3.5 Content Safety: Customizable Multimodal Safety for Global Enterprise AI]() Original publisher: [Read original article]() Author: Varun Singh; Isabel Hulseman; Anuj Doshi; Shyamala Prayaga Published: 2026-06-04T18:57:45Z Content type: article Language: en Sources: [Hugging Face - Blog]() Topics: [Nemotron](), [multimodal](), [Artificial Intelligence](), [Open Source Models & Datasets](), [long-context]() Tags: [ai](), [context-window](), [data](), [multimodal](), [nemotron](), [production]() ### AI overview Nemotron 3.5 Content Safety adds deeper multimodal safety analysis by evaluating a user prompt, optional image, and optional assistant response together in one context window. It supports custom policy specifications, multilingual transfer, optional reasoning traces, low-latency verdicts, and the release of its safety dataset for production safety pipelines. ### Source excerpt This post covers what changes in 3.5, the design decisions behind each new capability, and how to integrate the model into production safety pipelines. Nemotron 3 introduced image understanding; Nemotron 3.5 deepens the multimodal integration. The model takes a user prompt, an optional image, and an optional assistant response as a single context window and produces a coherent safety verdict over the combined input. ## Nemotron 3 Ultra now available on AI Gateway DevFeed: [Nemotron 3 Ultra now available on AI Gateway]() Original publisher: [Read original article]() Author: Jerilyn Zheng Published: 2026-06-04T07:00:00Z Content type: release Language: en Sources: [Vercel News]() Topics: [Nemotron](), [Mixture of Experts (MoE)](), [API](), [context window](), [Nvidia](), [Vercel](), [Inference](), [SDKs](), [vercel ai sdk](), [Latency]() Tags: [ai](), [api](), [context-window](), [inference](), [latency](), [mixture-of-experts](), [nemotron](), [nvidia](), [performance](), [sdk](), [tokens](), [tool](), [vercel]() ### AI overview Nemotron 3 Ultra from Nvidia is now available through Vercel AI Gateway. The open Mixture-of-Experts reasoning model supports long-running, multi-turn agent workflows with a 1M token context window, tool use, delegation, and error recovery. The gateway provides model access, usage and cost tracking, retries, failover, provider sorting, and performance optimizations. ### Source excerpt Nemotron 3 Ultra from Nvidia is now available on Vercel AI Gateway. Nemotron 3 Ultra is an open Mixture-of-Experts reasoning model built for orchestrating long-running agent workflows, with a 1M token context window. The model targets multi-turn agent workflows: planning, tool use, sub-agent delegation, and error recovery. Throughput reaches up to 350 tokens per second, with up to 30% lower cost on agentic tasks. To use Nemotron 3 Ultra, set model to nvidia/nemotron-3-ultra-550b-a55b in the AI SDK. AI Gateway provides a unified API for calling models, tracking usage and cost, and configuring retries, failover, and performance optimizations for higher-than-provider uptime. It includes built-in custom reporting, Zero Data Retention support, dynamic provider sorting by latency and cost, and more. AI Gateway reflects provider pricing with no markup and does not charge a platform fee on inference, including on Bring Your Own Key (BYOK) requests. Learn more about AI Gateway, view the AI Gateway model leaderboard or try it in our model playground. Read more ## DigitalOcean and NVIDIA Discuss Open-Source AI and Agentic AI Development at Deploy 2026 DevFeed: [DigitalOcean and NVIDIA Discuss Open-Source AI and Agentic AI Development at Deploy 2026]() Original publisher: [Read original article]() Author: Jess Lulka Published: 2026-06-02T18:29:57Z Content type: article Language: en Sources: [DigitalOcean]() Topics: [Digital Ocean](), [Nvidia](), [Artificial Intelligence](), [Generative AI](), [Open Source](), [Development](), [Nemotron](), [Large Language Model](), [GPU]() Tags: [2026](), [ai](), [community](), [design](), [developers](), [digitalocean](), [generative-ai](), [gpu](), [llms](), [nemotron](), [nvidia](), [open-source]() ### AI overview The article covers a DigitalOcean Deploy 2026 session about open-source AI, agentic AI development, and the infrastructure and model support needed to move open models into production. It discusses NVIDIA Nemotron, software libraries, and running open-weight large language models locally for performance, privacy, and customization. ### Source excerpt The growth of generative AI isn't driven solely by AI companies with proprietary models. Open-source AI is reshaping the developer ecosystem, fueled by a growing community of builders. But what does it take to go from open models to production-ready agentic AI, and what do developers need to know to get there? This question was the focus of the DigitalOcean Deploy session, "Open by Design: How NVIDIA and DigitalOcean Are Building the Stack for the Always-On Agentic Era." During this 30-minute chat, Kari Briski, VP Gen AI at NVIDIA, and Salman Paracha, SVP AI at DigitalOcean, discuss why AI-native teams are demanding openness, model flexibility, and infrastructure built for agents that never sleep--and what NVIDIA and DigitalOcean are doing to build support for this next generation of AI development. Watch the full recorded session from Deploy 2026: View YouTube video Open-Source Models Need Commitment, Not Just a Launch There are many open models in the ecosystem, but having great models doesn't guarantee they will be consistently improved or regularly updated. NVIDIA noticed a potential gap in this space for its enterprise customers, who regularly wanted access to open-source models that are launched and then left untouched. This spurred the development of open models such as NVIDIA Nemotron. Released in March 2026, it serves as a family of multi-modal models designed for agentic AI. Having access to these open models enables developers to create agentic applications that require advanced reasoning, high compute efficiency, and open source standards. With Nemotron models and NVIDIA software libraries, developers can evolve their projects over time and receive regular updates and expanded support. Running open-weight LLMs locally gives you more control over performance, privacy, and customization. This NVIDIA Nemotron 3 tutorial walks through deploying NVIDIA's Nemotron 3 Nano on a DigitalOcean GPU Droplet, helping you experiment with efficient open models on dedicat ## Building FOFOCA: An Open-Source AI Robot with ESP32, ESP32-C3, and Edge AI DevFeed: [Building FOFOCA: An Open-Source AI Robot with ESP32, ESP32-C3, and Edge AI]() Original publisher: [Read original article]() Author: John Lee Published: 2026-05-15T00:00:00Z Content type: tutorial Language: en Sources: [Blog on Developer Portal]() Topics: [ESP32](), [Embedded Software Dev](), [ESP32-C3](), [Espressif](), [Raspberry Pi](), [Hardware](), [MQTT](), [Open Source](), [Nemotron](), [Cloud]() Tags: [ai](), [api](), [architecture](), [ble](), [blog](), [bluetooth](), [building](), [computer-vision](), [edge](), [edge-ai](), [esp32](), [esp32-c3](), [espressif](), [firmware](), [hardware](), [microcontrollers](), [mqtt](), [nemotron](), [nvidia](), [open-source](), [pwm](), [raspberry-pi](), [real-time](), [risc-v](), [robotics]() ### AI overview This article presents FOFOCA, an open-source household robot reference design using an ESP32 for real-time motor and sensor control, an ESP32-C3 for an MQTT-connected OLED display, and a Raspberry Pi 5 as the processing brain. A local Dell R210 server runs NVIDIA Nemotron Nano 8B and supporting services, while ThinkNEO provides AI governance and audit routing. ### Source excerpt FOFOCA is an open-source reference design for an AI-governed household robot, built around a Raspberry Pi 5 brain, an ESP32 for real-time motor control and sensor polling, and an ESP32-C3 driving an OLED status display over MQTT. This article walks through the hardware architecture, the firmware running on each Espressif chip, and how all of it connects to a local edge AI server running NVIDIA Nemotron Nano 8B for inference -- no cloud dependency required. ## Introducing NVIDIA Nemotron 3 Nano Omni: Long-Context Multimodal Intelligence for Documents, Audio and Video Agents DevFeed: [Introducing NVIDIA Nemotron 3 Nano Omni: Long-Context Multimodal Intelligence for Documents, Audio and Video Agents]() Original publisher: [Read original article]() Author: Tuomas Rintamaki; Amala Sanjay Deshmukh; Nabin Mulepati; Collin McCarthy; Pritam Biswas; Arushi Goel; Alexandre Milesi; Danial Mohseni Taheri; Kateryna Chumachenko; Isabel Hulseman; Zhehuai Chen; Kara Published: 2026-04-28T15:58:57Z Content type: article Language: en Sources: [Hugging Face - Blog]() Topics: [multimodal](), [Nemotron](), [long-context](), [asr](), [computer-use](), [Mamba](), [model architecture](), [moe](), [Transformer](), [Nvidia](), [Reinforcement learning](), [NVFP4]() Tags: [alternatives](), [analysis](), [architecture](), [benchmarks](), [computer-use](), [efficiency](), [long-context](), [models](), [multimodal](), [nemotron](), [nvidia](), [optimization](), [reasoning](), [reinforcement-learning](), [speed]() ### AI overview NVIDIA introduces Nemotron 3 Nano Omni, an omni-modal model for document analysis, image reasoning, speech recognition, long audio-video understanding, computer use, and general reasoning. It combines a hybrid Mamba-Transformer Mixture-of-Experts backbone with vision and audio encoders, supports long multimodal contexts, and reports strong benchmark accuracy, throughput, reasoning speed, and system efficiency. ### Source excerpt Introducing NVIDIA Nemotron 3 Nano Omni: Long-Context Multimodal Intelligence for Documents, Audio and Video Agents - NVIDIA Nemotron 3 Nano Omni is a new omni-modal understanding model built for real-world document analysis, multiple image reasoning, automatic speech recognition, long audio-video understanding, agentic computer use, and general reasoning. - It extends the Nemotron multimodal line from a strong vision-language system to a broader text + image + video + audio model. ## Build a Domain-Specific Embedding Model in Under a Day DevFeed: [Build a Domain-Specific Embedding Model in Under a Day]() Original publisher: [Read original article]() Author: Steve Han; Rucha Apte; Sean Sodha; Oliver Holworthy Published: 2026-03-20T19:38:16Z Content type: tutorial Language: en Sources: [Hugging Face - Blog]() Topics: [Fine-tuning](), [synthetic-data](), [Synthetic Data Generation](), [Model Development](), [Inference](), [model-deployment](), [NeMo](), [Nemotron](), [NVIDIA NIM](), [TensorRT](), [Claude](), [API keys]() Tags: [api](), [fine-tuning](), [gpu](), [inference](), [nemo](), [nemotron](), [nim](), [nvidia](), [nvidia-nim](), [synthetic](), [synthetic-data](), [synthetic-data-generation]() ### AI overview A tutorial showing how to fine-tune a general-purpose embedding model for a specific domain in less than a day using synthetic question-answer pairs generated from domain documents. It covers data generation, contrastive training, retrieval evaluation, and deployment, using NVIDIA NeMo components and a Llama-Nemotron embedding model. ### Source excerpt With a single GPU and less than a day of training time, you can transform a general-purpose embedding model into one that truly understands your domain, no manual labeling required. To help you hit the ground running, we are also releasing a ready-to-use synthetic training dataset generated from NVIDIA's public documentation using this exact pipeline. ## Introducing Mistral Small 4 DevFeed: [Introducing Mistral Small 4]() Original publisher: [Read original article]() Published: 2026-03-16T21:00:00Z Content type: release Language: en Sources: [Mistral AI Blog]() Topics: [Artificial Intelligence](), [multimodal](), [Mixture of Experts (MoE)](), [Open Source Models & Datasets](), [agentic-coding](), [AI Development](), [Nemotron](), [long-context](), [Low Latency](), [Nvidia]() Tags: [agentic-coding](), [ai](), [context-window](), [latency](), [mixture-of-experts-moe](), [models](), [multimodal](), [nemotron](), [net](), [nvidia](), [open](), [open-source]() ### AI overview Mistral announces Mistral Small 4, a hybrid open model that combines instruction following, reasoning, multimodal understanding, and agentic coding in one system. It accepts text and image inputs, uses a Mixture-of-Experts architecture with a 256k context window, offers configurable reasoning effort, and reports lower latency and higher throughput than Mistral Small 3. ### Source excerpt The most powerful AI platform for enterprises. Customize, fine-tune, and deploy AI assistants, autonomous agents, and multimodal AI with open models. ## Mistral AI partners with NVIDIA to accelerate open frontier models DevFeed: [Mistral AI partners with NVIDIA to accelerate open frontier models]() Original publisher: [Read original article]() Published: 2026-03-16T20:00:00Z Content type: news Language: en Sources: [Mistral AI Blog]() Topics: [Artificial Intelligence](), [Nvidia](), [foundation-models](), [Open Source](), [Nemotron](), [Fine-tuning](), [Model Development](), [NVIDIA DGX](), [DGX Cloud](), [Synthetic Data Generation](), [model architecture](), [NeMo]() Tags: [ai](), [dgx-cloud](), [fine-tuning](), [foundation-models](), [models](), [multimodal](), [nemo](), [nemotron](), [nvidia](), [open](), [optimization](), [post-training](), [synthetic](), [synthetic-data-generation]() ### AI overview Mistral AI announces a partnership with NVIDIA and its founding membership in the NVIDIA Nemotron Coalition. The collaboration will develop open frontier AI models using Mistral AI's model expertise and NVIDIA's compute, development tools, and synthetic-data pipelines. The coalition's first initiative will support the NVIDIA Nemotron 4 family, while Mistral AI also releases Mistral Small 4 for developers, researchers, and organizations. ### Source excerpt The most powerful AI platform for enterprises. Customize, fine-tune, and deploy AI assistants, autonomous agents, and multimodal AI with open models. ## NVIDIA brings agents to life with DGX Spark and Reachy Mini DevFeed: [NVIDIA brings agents to life with DGX Spark and Reachy Mini]() Original publisher: [Read original article]() Author: Jeff Boudier; Nader Khalil; Alec Fong Published: 2026-01-05T00:00:00Z Content type: tutorial Language: en Sources: [Hugging Face - Blog]() Topics: [DGX Spark](), [Nvidia](), [reachy](), [Artificial Intelligence](), [Nemotron](), [Deployment](), [inference-endpoints](), [Orchestration](), [Python](), [Open Source](), [Cloud](), [Serverless]() Tags: [agents](), [ai-agent](), [artificial-intelligence](), [code](), [deployment](), [dgx-spark](), [hardware](), [inference-endpoints](), [nvidia](), [open-source](), [orchestration](), [partnerships](), [python](), [reachy](), [robotics](), [serverless]() ### AI overview A step-by-step guide to building a desk-based AI companion with NVIDIA DGX Spark and Reachy Mini. It combines open reasoning and vision models, text-to-speech, Python, agent orchestration, and tool handlers, with options for local, cloud, and serverless deployment. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science. ## The Open Evaluation Standard: Benchmarking NVIDIA Nemotron 3 Nano with NeMo Evaluator DevFeed: [The Open Evaluation Standard: Benchmarking NVIDIA Nemotron 3 Nano with NeMo Evaluator]() Original publisher: [Read original article]() Author: Seph Mard; Isabel Hulseman; Besmira Nushi; Piotr Januszewski; Grzegorz Chlebus; VivienneZhang; Wojciech Prazuch; Pablo Ribalta; Nik Spirin; Ferenc Galko Published: 2025-12-17T13:22:18Z Content type: article Language: en Sources: [Hugging Face - Blog]() Topics: [LLM evaluation / benchmarking](), [Benchmark](), [NeMo](), [Nemotron](), [Artificial Intelligence](), [Nvidia](), [configuration](), [Library](), [Inference]() Tags: [ai](), [benchmark](), [benchmarking](), [benchmarks](), [build](), [configuration](), [evaluation](), [inference](), [library](), [model](), [models](), [nemo](), [nemotron](), [nvidia](), [open](), [pipeline](), [pipelines](), [reuse](), [validation](), [verify]() ### AI overview This article presents an open, reproducible evaluation recipe for NVIDIA Nemotron 3 Nano 30B A3B using the NeMo Evaluator library. It explains how complete configurations, prompts, runtime settings, harness versions, logs, and artifacts enable independent verification and consistent model comparisons across inference backends and infrastructure. ### Source excerpt NVIDIA released Nemotron 3 Nano 30B A3B with an explicitly open evaluation approach to make that distinction clear. Alongside the model card, we are publishing the complete evaluation recipe used to generate the results, built with the NVIDIA NeMo Evaluator library, so anyone can rerun the evaluation pipeline, inspect the artifacts, and analyze the outcomes independently. We believe that open innovation is the foundation of AI progress. ## NVIDIA Nemotron Nano 2 VL 12B: Architecture and Improvements for Multimodal Reasoning DevFeed: [NVIDIA Nemotron Nano 2 VL 12B: Architecture and Improvements for Multimodal Reasoning]() Original publisher: [Read original article]() Author: Alex Razvant Published: 2025-11-22T14:58:39Z Content type: article Language: en Sources: [Neural Bits]() Topics: [Nemotron](), [multimodal](), [Nvidia](), [context window](), [long-context](), [Benchmark](), [Mamba](), [Transformer](), [AI Models]() Tags: [ai-ml](), [architecture](), [benchmark](), [context-window](), [long-context](), [mamba](), [ml-engineering](), [multimodal](), [nemotron](), [nvidia]() ### AI overview This article examines NVIDIA's Nemotron Nano 2 VL 12B, an open small vision-language model designed for document understanding, long-video comprehension, and multimodal reasoning. It discusses the model's architecture, encoders, training and inference improvements, reasoning modes, and reported benchmark performance, including a 128k-token context window. ### Source excerpt What makes NVIDIA Nemotron Nano 2 VL a breakthrough in small, fast, long-context visual reasoning. ## NVIDIA Nemotron Nano v2: Long-Context Reasoning and Efficient Inference in Smaller Models DevFeed: [NVIDIA Nemotron Nano v2: Long-Context Reasoning and Efficient Inference in Smaller Models]() Original publisher: [Read original article]() Author: Alex Razvant Published: 2025-11-15T14:47:33Z Content type: article Language: en Sources: [Neural Bits]() Topics: [Nemotron](), [Artificial Intelligence](), [Nvidia](), [long-context](), [model architecture](), [Inference](), [Open Source](), [Transformer](), [Large Language Model]() Tags: [ai-ml](), [inference](), [long-context](), [model-architecture](), [moe](), [nemotron](), [nvidia](), [open-source](), [reasoning]() ### AI overview This technical article examines NVIDIA's Nemotron Nano v2 Transformer-Hybrid models, focusing on their long-context reasoning, fast inference, and reported performance against larger open models. It also describes the broader Nemotron family's open models, datasets, and fine-tuning recipes for agentic AI systems. ### Source excerpt How NVIDIA's Nemotron Nano V2 SLM, built for long-context reasoning, fast inference, can compete with models 4-5x its size. ## Nemotron-Personas-India: Synthesized Data for Sovereign AI DevFeed: [Nemotron-Personas-India: Synthesized Data for Sovereign AI]() Original publisher: [Read original article]() Author: Kiran Praveen; Utkarsh Vaidya; Evan A; Lipika Ramaswamy; Dhruv Nathawani; Dane Corneil; Yev Meyer Published: 2025-10-13T23:00:42Z Content type: article Language: en Sources: [Hugging Face - Blog]() Topics: [Artificial Intelligence](), [Synthetic Data Generation](), [synthetic-data](), [datasets](), [Sovereign AI](), [NeMo](), [Nemotron](), [LLMs]() Tags: [ai](), [ai-adoption](), [data](), [datasets](), [evaluation](), [generation](), [india](), [language](), [llms](), [model](), [models](), [nemo](), [nemotron](), [nvidia](), [open-source](), [synthetic](), [synthetic-data](), [synthetic-data-generation]() ### AI overview NVIDIA releases Nemotron-Personas-India, an open synthetic dataset of Indic personas designed to address the lack of multilingual and culturally representative data for Indian AI systems. Built with NeMo Data Designer and licensed under CC BY 4.0, it contains 21 million personas across English and Hindi in Devanagari and Latin scripts, with demographic, geographic, occupational, and cultural attributes. ### Source excerpt India represents one of the world's largest AI opportunities -- with over 700 million internet users, a multitude of languages, and a rapidly growing developer ecosystem. Yet, most open datasets reflect Western norms and English-only contexts, creating a data gap that limits AI adoption in India's multilingual, multi-script environment. ## A Partnership with NVIDIA and a Technical Walkthrough of Nemotron Models DevFeed: [A Partnership with NVIDIA and a Technical Walkthrough of Nemotron Models]() Original publisher: [Read original article]() Author: Alex Razvant Published: 2025-10-04T14:09:01Z Content type: article Language: en Sources: [Neural Bits]() Topics: [Nvidia](), [Nemotron](), [AI Models](), [Deep learning]() Tags: [ai-models](), [deep-learning](), [nemotron](), [nvidia](), [post-training](), [rag]() ### AI overview The author describes a partnership with NVIDIA and explains that it followed earlier coverage of NVIDIA technologies and interactions with NVIDIA teams. The article also includes a technical walkthrough of NVIDIA Nemotron models, discussing post-training techniques, compute kernels, and neural network layers. ### Source excerpt This means I can distill direct insights from NVIDIA experts! ## Nemotron-Personas-Japan: ソブリン AI のための合成データセット DevFeed: [Nemotron-Personas-Japan: ソブリン AI のための合成データセット]() Original publisher: [Read original article]() Author: Atsunori Fujita; Masaya Ogushi; Vincent Gong; Kotaro Yamamoto; Yoshi Suhara; Dane Corneil; Yev Meyer Published: 2025-09-26T06:25:50Z Content type: article Language: ja Sources: [Hugging Face - Blog]() Topics: [datasets](), [Nemotron](), [Artificial Intelligence](), [data](), [Large Language Model](), [NeMo](), [Nvidia](), [gpt-oss](), [pii](), [Pydantic]() Tags: [ai](), [apache](), [data](), [gpt-oss](), [japan](), [llm](), [nemo](), [nemotron](), [nvidia](), [oss](), [pii]() ### AI overview NVIDIA has released Nemotron-Personas-Japan, an open synthetic dataset of Japanese personas designed to support culturally grounded and privacy-preserving AI development. Built with NeMo Data Designer, it contains six million Japanese-language personas aligned with Japanese demographic, geographic, cultural, and labor statistics, without including personally identifiable information. ### Source excerpt 高品質で多様なトレーニングデータなしに、日本文化を真に理解するAIを構築することはこれまでほぼ不可能でした。これを変えるため、NVIDIAは、日本の人口統計、地理的分布、文化的特性に沿ったペルソナを含む初のオープン合成データセット、Nemotron-Personas-Japan を公開しました。CC BY 4.0 ライセンスのもと提供される本データセットは、機微な個人データに依存することなく日本社会を反映した AI システム構築のための、プライバシー保護と規制対応を両立した基盤を提供します。 NVIDIA のエンタープライズ向け合成データ生成システム、NeMo Data Designer を用いて作成されたNemotron-Personas-Japan は、すでに広く利用されている US Personas データセットの成功を機に日本版として開発されました。本リリースは、各国・地域におけるソブリン AI 開発を支援する合成ペルソナデータセットとデータ構築方法のグローバルコレクションの第一弾です。 ## NVIDIA Releases 6 Million Multi-Lingual Reasoning Dataset DevFeed: [NVIDIA Releases 6 Million Multi-Lingual Reasoning Dataset]() Original publisher: [Read original article]() Author: Jane Polak Scowcroft; Dhruv Nathawani; Shuoyang Ding; Oleksii Kuchaiev; Vitaly Lavrukhin Published: 2025-08-20T22:13:18Z Content type: article Language: en Sources: [Hugging Face - Blog]() Topics: [datasets](), [Nemotron](), [post-training](), [Open Source Models & Datasets](), [Mamba](), [Transformer](), [Large Language Model](), [Nvidia]() Tags: [datasets](), [japanese](), [llama](), [mamba](), [model-development](), [nemotron](), [nvidia](), [open](), [post-training](), [reasoning]() ### AI overview NVIDIA announces a 6-million-example multilingual reasoning dataset translated into French, Spanish, German, Italian, and Japanese. The article also presents Nemotron Nano 2 9B, an edge-oriented model using a hybrid Transformer-Mamba architecture, configurable thinking budgets, and open model weights and training resources. ### Source excerpt NVIDIA continues releasing permissive datasets in support of the open ecosystem with 6 Million Multilingual Reasoning Dataset. Continuing the success of the recent Nemotron Post-Training Dataset v1 release used in Llama Nemotron Super model, and our Llama Nemotron Post-Training Dataset release earlier this year, we're excited to release the reasoning dataset translated into five target languages: French, Spanish, German, Italian, and Japanese. ## Measuring Open-Source Llama Nemotron Models on DeepResearch Bench DevFeed: [Measuring Open-Source Llama Nemotron Models on DeepResearch Bench]() Original publisher: [Read original article]() Author: Jay Rodge Published: 2025-08-04T19:51:50Z Content type: article Language: en Sources: [Hugging Face - Blog]() Topics: [Nvidia](), [Open Source](), [hugging face](), [llama](), [Nemotron](), [Large Language Model](), [Open Source Models & Datasets](), [long-context](), [Inference](), [post-training](), [GPU](), [Reinforcement learning]() Tags: [agentic](), [ai](), [gpu](), [hallucination-detection](), [hugging-face](), [inference](), [llama](), [llm](), [long-context](), [nemotron](), [nvidia](), [open-source](), [post-training](), [reinforcement-learning]() ### AI overview The article presents NVIDIA's AI-Q Blueprint as a portable, open deep research agent that reached the top of Hugging Face's "LLM with Search" leaderboard on DeepResearch Bench. It combines Llama 3.3-70B Instruct with Llama-3.3-Nemotron-Super-49B-v1.5 for retrieval, reasoning, synthesis, query planning, and tool use, and incorporates NVIDIA NeMo Retriever and the NVIDIA NeMo Agent toolkit. The architecture emphasizes long-context, multimodal, low-latency search over local and web data, privacy, compliance, on-premise deployment, transparent model lineage, and efficient inference on GPUs. ### Source excerpt NVIDIA's AI-Q Blueprint--the leading portable, open deep research agent--recently climbed to the top of the Hugging Face "LLM with Search" leaderboard on DeepResearch Bench. This is a significant step forward for the open-source AI stack, proving that developer-accessible models can power advanced agentic workflows that rival or surpass closed alternatives. What sets AI-Q apart? [Next page]()