Teaching your AI new tricks.

Santa Clara, CA
Local AI: Running Nemotron on DGX Spark & Station | Nemotron Labs nitter.net/i/broadcasts/1RKZzBOEW…
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How a Small Korean Team Hit a Top 5 AA II Score in open model | Nemotron Labs nitter.net/i/broadcasts/1XxygwaaA…
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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. @JensenHuang was on CNBC this morning to talk about the safety measures we’re building to help make that happen. 🎥 from @SquawkCNBC:
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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. 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. nvda.ws/4hcoq7m
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Engineer-to-engineer collabs are the best 🙌 Congrats to @AIatMeta on Muse Realtime Avatar! We worked with their team to make the model more efficient so the avatar can keep up when you talk to it.
@finkd just unveiled Muse Realtime Avatar, our real-time embodiment technology that turns Muse Realtime Voice into expressive, interactive avatars. Muse Realtime Avatar enables an entirely new range of interactions in @Muse, starting with real-time conversations. 🧵👇 #MetaConnect
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Ten years ago at NVIDIA GTC 2016, our CEO @JensenHuang introduced DGX-1, our first AI supercomputer. Since then, NVIDIA DGX has transformed from a single system into a blueprint for AI factories. What hasn’t changed is the reason why we build DGX: to test the systems and work through the details of interconnects, cooling, and software so our partners and customers have a proven foundation to build on. Charlie Boyle, our VP of DGX Systems, looks back at how DGX got its start, what’s changed, and what comes next. Thanks to everyone who’s been on this journey with us. #DecadeOfDGX Watch the full video: nvda.ws/4yQkRJW
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Love this news. More doctors around the world will be able to use OpenEvidence for free. Congrats to @OpenEvidence and @AnthropicAI 💚
“Medicine is a field where you don't need to squint to see the benefits of AI. It's your mom, it's your dad, it's your sister, it's your brother. And it's the doctors who will care for them in their most vulnerable moments.” Yesterday, during the UN General Assembly, we announced a partnership with @AnthropicAI to make OpenEvidence available free of charge to physicians in low- and middle-income countries, placing life-saving medical AI into the hands of physicians worldwide, regardless of the economics.
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NVIDIA AI retweeted
🚀 New benchmark alert: SWE-Serve (research.nvidia.com/benchmar…) Can AI agents develop inference engine and make it serve real models? Our team at @nvidia built 53 tasks from real @sgl_project engineering work to find out. The chart shows why live-serving tests matter. 🧵
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Our new Nemotron 3 Diarization model + @pollenrobotics Reachi Mini + DGX Spark Great work Andi @huggingface 🙌
Voice agents still don’t understand who’s speaking to them. That’s a huge gap compared with humans, hidden by all the “phone-call” demos. But that changes today! NVIDIA is open-sourcing Nemotron 3 Diarization: a model that can reliably track speakers in live conversations, under a commercial-friendly license! In my tests, the quality is really good with one-second speech chunks. So we can use it for voice agents! I tested it with Reachy Mini and speech-to-speech running on a DGX Spark. It’s super fun to see the robot notice a new voice, ask for a name, and remember it. The model has day-zero integration with Transformers! Kudos to the NVIDIA team for shipping useful tools for the whole community!
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When several people talk at once, a transcript can get messy fast. Our new Nemotron 3 Diarization model tracks who spoke when, even when voices overlap. It handles up to eight speakers, has 100M parameters, and is now available on @huggingface 🤗
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Nemotron 3 Diarization ranked #1 of 12 systems in @voicearena_ai's initial Diarization-Bench results. Its 14.72% error rate was ~24% lower than the runner-up. Here’s a four-speaker comparison from the benchmark. Full results: voicearena.com/diarization-b…
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We also put together a live demo on @huggingface if you want to try it yourself: huggingface.co/spaces/nvidia… And, a blog with more on how it works and how to get started: huggingface.co/blog/nvidia/n…
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Figuring out a data center’s carbon footprint means piecing together a lot of messy data, from parts lists and documents to supplier emails. Sluicebox built an AI supplier agent called Lucy to help. Using NVIDIA Nemotron 3 Ultra, Lucy collects and checks supplier data so engineers can understand the impact of their choices while they’re still designing. In Sluicebox’s tests, Nemotron improved accuracy, cut costs by 51–80% and delivered responses up to 2x faster. See how it works: nvda.ws/4iIQi4e
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Ask the Experts: Evaluating Agent Skills | Nemotron Labs nitter.net/i/broadcasts/1lKQRWXyD…
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NVIDIA AI retweeted
vLLM integrated PyNvVideoCodec to offload video decoding from CPU to GPU's NVDEC. Result: 2x+ throughput at 8×H100, CPU bottleneck gone! This is huge for video captioning at scale (AV training, metadata), and ships with CUDA vLLM releases. 🔗 vllm.ai/blog/2026-09-18-pynv…
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And the winners are .... Congratulations to our #NVIDIAGTC Berlin Golden Ticket winners! Chosen by our panel of judges @asierarranz @choroukmalmoum @SteveNouri @mervenoyann @itsjohnnynunez and @googlecloud, these six winners will join us in Berlin next month: 🏆 Lucas Hudson 🏆 James Kane 🏆 Devin Nicholson 🏆 Aaron Nowak 🏆 Vladimir Shirokun 🏆 Stefan Trauth Thanks to everyone who participated and shared your projects - we loved seeing what this community is building with open models. See you at GTC!
What are you building with open models? Show us what you’re working on and you could be headed to #NVIDIAGTC Berlin. 🎫 Your Golden Ticket includes: → Free conference pass → VIP seating for Jensen’s keynote → Exclusive NVIDIA merch → Access to special events Submissions are open Aug 18 – Sep 10, 2026. Details and how to enter: nvda.ws/4wFdunb
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Your LLM endpoint works. But how does it perform when traffic increases? NVIDIA Dynamo AIPerf helps you measure TTFT, ITL, latency and throughput at scale, then test with realistic traffic patterns you can reliably repeat. Read the blog: nvda.ws/4hdIeGd
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Seattle Spark Hack Winners Livestream Spotlight: LiveKit & Memo nitter.net/i/broadcasts/1qKVmyRlX…
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What happens when you give a world model 32 images of a place we know very well? World Labs put Atlas to the test on Voyager. Atlas brings text, images, video, and 3D into a shared spatial context, enabling one model to generate new views, reconstruct scenes, and simulate worlds. With Voyager, you can now see that in action in real time. Pretrained from scratch on NVIDIA Blackwell GPUs. Take a look around 👇
From 32 input images to real-time flight through @nvidia's Voyager headquarters. Trained on NVIDIA Blackwell GPUs, Atlas uses these images as 3D spatial context to generate new views, letting you explore with pixel-perfect camera control. Take a look around.
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