We’re looking for our next cohort of graduate fellows. Our 2027–2028 Graduate Fellowship Program is open to Ph.D. students worldwide with awards up to $60,000, plus mentorship and technical support. Apply by October 30: https://nvda.ws/4rGDZb2
About us
Explore the latest breakthroughs made possible with AI. From deep learning model training and large-scale inference to enhancing operational efficiencies and customer experience, discover how AI is driving innovation and redefining the way organizations operate across industries.
- Website
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https://developer.nvidia.com/blog/
External link for NVIDIA AI
- Industry
- Computer Hardware Manufacturing
- Company size
- 10,001+ employees
- Headquarters
- Santa Clara, CA
Updates
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One prompt built and deployed a visual AI agent for a manufacturing line in under 30 minutes, with alerts, video search, and incident reports. Check out our new tutorial that shows you how to build one with the new Build Vision AI skill in the NVIDIA VSS Blueprint 3.3. Tutorial: https://nvda.ws/4AydQzj We’re going live tomorrow at 9 a.m. PT to build a visual AI agent from a single prompt. Bring your questions: https://lnkd.in/ggJqETx2
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Your agent got the right answer. How much work did it take to get there? We worked with Nous Research/Teknium on a hands-on walkthrough of NVIDIA NeMo Relay to collect traces for Hermes Agent. Run two example scenarios, follow the agent’s calls and retries, and see the full trace in Arize Phoenix. The blog also looks at how Nous used traces and task results to evaluate fixes across repeated runs. TechBlog: https://nvda.ws/4xW19M5 📽️ by our own Patrick Moorhead:
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NVIDIA AI reposted this
I’m very excited to share NVIDIA Kumo Tabular, a new family of foundation models for tabular data. Kumo Tabular establishes the new Pareto frontier across the entire accuracy–inference-time tradeoff. Just as importantly, we are releasing it openly: open weights, open-source software, and a permissive license for commercial use. Not that long ago, building a machine learning system meant carefully designing features, choosing a model architecture, training it from scratch, tuning hyperparameters, and repeating this process for every new problem. Then foundation models changed how we think about text, images, and increasingly other modalities: pretrain once, then adapt to new tasks through context. The same transition is now happening for structured data. With Kumo Tabular, you provide a table with labeled examples and rows you want predictions for. The model produces predictions in a single forward pass --- with no task-specific training, no fine-tuning, and no feature engineering. What makes this especially exciting to me is that this is not just a new model, but part of a rapidly growing research ecosystem around tabular foundation models. There is tremendous innovation happening across academia and industry in architectures, synthetic pretraining, in-context learning, evaluation, and efficient inference. NVIDIA wants to be an active part of that ecosystem --- contributing research, releasing models openly, and building infrastructure that helps the community push the field forward. There are several aspects of the work I find particularly interesting. ** The models are pretrained entirely on synthetic tables generated from structural causal models, allowing us to expose them to enormous diversity without training on customer data or benchmark datasets. The largest model sees more than 100 million synthetic tables during pretraining. ** The resulting models are both accurate and efficient. Across major tabular benchmarks, Kumo Tabular improves upon strong existing approaches while requiring no per-dataset training. On TabArena, for example, Kumo Tabular Large sits on the accuracy–speed Pareto frontier and is 17× faster at prediction than LimiX-2. To me, the bigger story is the direction ML is moving: from hand-built models for individual tasks to pretrained models that learn broad representations of a domain and can solve new problems from context. We have seen this transformation in language and vision. It is exciting to see it now reaching the enormous world of structured data. Huge congratulations to the team — and to the broader tabular foundation model research community whose ideas and work are making this new paradigm possible. We’re excited to contribute, learn, and help build this ecosystem together. HuggingFace: https://lnkd.in/dJd4vVBc GitHub: https://lnkd.in/d_eRr_ca
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Congrats to the OpenClaw community on OpenClaw Enterprise! 🦞 Organizations can use NVIDIA OpenShell as an open source option for governing agents with OpenClaw Enterprise. Glad to keep working with the community to make agents safer.
Today we’re announcing OpenClaw Enterprise In collaboration with Red Hat, NVIDIA and OpenAI the OpenClaw Foundation is open sourcing a powerful enterprise control plane for persistent agents OpenClaw Enterprise is built to run on your own infrastructure and will always be free for an organization to use https://lnkd.in/gYyr8z6D
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AI agents need clear limits on what they can do, and those limits need to hold up while they’re working. Jensen Huang was on CNBC this morning to talk about the safety measures we’re building to help make that happen. 🎥 from CNBC's "Squawk Box":
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A live look at how Motif, a small Korean team, achieved a top 5 AA II score using NVIDIA’s technology stack. Discover how Motif, a small Korean AI team, achieved a top-five AA II score using the NVIDIA technology stack. This NVIDIA Nemotron Labs session explores the technical strategies behind Motif’s performance gains, the role NVIDIA technologies played, and the trade-offs the team navigated with limited resources. Attendees will gain practical insights for building world-class AI systems without big-tech scale, and see how Motif’s success contributes to Korea’s broader sovereign AI ambitions. What you'll learn: - The technical approach Motif took to push their AA score higher - How NVIDIA’s stack contributed to that performance gain - Key trade-offs a resource-constrained team had to navigate - Practical takeaways for similarly-sized teams
How a Small Korean Team Hit Top 5 AA II with an Open Model | Nemotron Labs
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H-Company's open-source vision is to scale autonomous computer-use agents using transparent foundation models, including the NVIDIA Holo and Holotron model families, and optimized NVIDIA infrastructure. This technical session explores the open architecture behind those agents and shows you how to deploy Holo and Holotron computer-use agentic workloads using NVIDIA Dynamo on NVIDIA GPUs.
How H-Company Optimizes VLM Serving for Computer Use Agents
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Agents can run for days, calling tools, hitting errors, and trying again. The security policy has to keep working through all of that. NVIDIA OpenShell enforces it while the agent runs. Teams can add NVIDIA Sentry on BlueField-4 for independent monitoring and enforcement.
Today, with over 100 industry partners, we introduced the NVIDIA Open Agent Safety Platform, bringing together OpenShell and Sentry. Artificial intelligence is extraordinary technology that will advance discovery, productivity, security, health, and prosperity for generations to come. But its full promise can only be realized when people have confidence that AI is being built to be safe and deployed with wisdom and responsibility. NVIDIA Open Agent Safety Platform Reference Design combines NVIDIA OpenShell and NVIDIA Sentry. OpenShell is an open-source secure runtime that gives AI agents clear, enforceable boundaries. It traces their actions and enforces policy as they work. NVIDIA Sentry delivers added layer of security with hardware-based enforcement on NVIDIA BlueField, continuously monitoring agent activity through a trusted telemetry and detection pipeline and enabling millisecond-scale containment and quarantine. This is bigger than a single product. It's the beginning of an open ecosystem to build the trust layer for safe agent systems. Together, we are building the foundation of the AI economy. Trust and innovation are not in conflict. Safety is how trust is earned. We must build not only the most capable AI, but the most trusted AI, so that this extraordinary technology can realize its enormous promise for the world. https://nvda.ws/4hOkDx7
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Running AI on local devices is becoming more valuable over time. As models get more capable and hardware gets more powerful, local AI is unlocking new possibilities: sovereignty, privacy, cost efficiency, resilience, and access in regulated or air-gapped environments. In this session, Exo Labs joins Nemotron Labs for a live demo of Nemotron 3.5 Lightning on DGX Spark and a discussion on why local AI and why now. What you'll learn: Why local AI has crossed from interesting to useful and why its value keeps compounding What open models like Nemotron 3.5 Lightning unlock for developers running AI on their own hardware How DGX Spark and DGX Station fit into a local AI stack built for always-on agentic workloads Have questions about local AI or running Nemotron on DGX Spark or DGX Station? Drop them in chat live, and our guests will answer in real time.
Local AI: Running Nemotron on DGX Spark & Station | Nemotron Labs
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