Nebius

Black Forrest Labs x NEBIUS: The European Computing & AI Powerhouse!! 🌲🖤 [Anzeige] Meet Black Forest Labs × Nebius! At NVIDIA GTC Berlin, I met Stephen, Developer Advocate at Black Forest Labs, one of Germany’s most exciting AI startups! And there’s a pretty interesting collaboration behind this picture! Black Forest Labs develops cutting-edge generative AI models, including the FLUX family for visual and Physical AI. But building these models requires ENORMOUS computing power! That’s where Nebius comes in! ☑️ GERMAN AI MODELS: Black Forest Labs develops frontier visual intelligence models. ☑️ NVIDIA BLACKWELL GPUs: Black Forest Labs used Nebius’s NVIDIA B200 infrastructure for research behind Self-Flow, a training method foundational to FLUX 3. ☑️ EUROPEAN AI INFRASTRUCTURE: Nebius provides the computing infrastructure needed to develop and scale AI models! Besides all the AI applications we build, Europe needs more companies building the models & infrastructure behind it. And I LOVE seeing these companies doing this by working together!! 🖤 P.S. Stephen, it was great meeting you! Maybe I’ll take you all on a little trip to the German BLACK FOREST soon to explore Black Forest Labs behind the scenes! 🌲 #Nebius #BlackForestLabs #NVIDIA #AI

JUST IN: Nebius just launched Physical AI Workbench, an open-source framework built in collaboration with NVIDIA that gives robotics teams one place to run the entire physical AI development cycle. Data generation. Simulation. Training. Evaluation. All connected. All agent-operable. The problem it solves is real. Building physical AI means stitching together tools that each have their own dependencies, data formats and infrastructure requirements. Moving outputs between stages takes custom scripts. Repeating an experiment requires careful tracking of every scene, dataset and checkpoint. When one tool changes, the whole pipeline breaks. Workbench replaces all of that with a single CLI — npa — that a coding agent can operate directly. The results customers are already reporting: → Up to 40% reduction in engineering time → Iteration cycles cut from weeks to days → 14-stage sim-to-real reference workflow, from task definition to a checkpoint ready for hardware testing The NVIDIA integration is deep. Workbench now includes NVIDIA Cosmos Data Factory built on Cosmos 3, runs on Isaac Sim and Isaac Lab for simulation, Isaac GR00T for robot policy models, and NVIDIA SONIC for humanoid behavior. The first real-world proof point is with Sereact, the German AI robotics company already deployed with Zalando, IKEA, BMW, DHL and Mercedes-Benz. Together they built a sim-to-real pipeline for returns handling at Zalando, one of the hardest warehouse processes to automate given the variability of returned items. Available now on GitHub under Apache 2.0. The infrastructure layer of physical AI is being built in Europe. 🇪🇺📊 Learn more here: https://lnkd.in/d4XgSjFN ~~ ♻️ Join the weekly robotics newsletter, and never miss any news → ziegler.substack.com

NEW open-source framework for robotics. Cut engineering time by up to 40%. (📌 GitHub link below 👇) Infrastructure setup and management, simulation, robot policy training, synthetic data, and evaluation... Anyone building robots knows the headache of connecting different tools, managing dependencies, and moving data between environments. Repeating an experiment often means tracking datasets, scenes, checkpoints, and custom scripts. Nebius just released Physical AI Workbench, developed in collaboration with NVIDIA. ㅤ It brings data generation, simulation, training, and evaluation into one workflow that AI coding agents can operate directly. Workbench aims to simplify that through a single CLI called npa. ㅤ What's inside? ㅤ → 14-stage sim-to-real reference workflow → NVIDIA Cosmos Data Factory, including Cosmos 3 → Isaac Sim and Isaac Lab for simulation → Isaac GR00T for robot policies → NVIDIA SONIC for humanoid behavior ㅤ Early customers report up to 40% less engineering time, with iteration cycles dropping from weeks to days. ㅤ One interesting example comes from Germany's Sereact. ㅤ Together with Nebius, the company developed a sim-to-real pipeline for returns handling at Zalando. ㅤ Returned items vary in packaging, condition, and orientation, making this one of the more difficult warehouse tasks to automate. ㅤ Sereact is already deploying robotics software with companies including IKEA, DHL, BMW, and Mercedes-Benz. ㅤ I'm curious how much engineering work AI agents will eventually take over as these tools become more integrated. ㅤ The framework is available under Apache 2.0. 📌 GitHub: https://lnkd.in/enDHvQtd Blog: https://lnkd.in/e48dhu7S Video: Nebius Physical AI Workbench repo —— Weekly robotics and AI insights. Subscribe free: 22astronauts.com

You run Terraform, Kubernetes, and IAM. GPU clusters add Slurm, InfiniBand, and node health. The Nebius AI CloudOps Engineer exam covers that layer. 60 minutes online, Credly badge valid 3 years. Early-bird pricing until December 31.

LIVE from NVIDIA GTC Berlin with Nebius!! 🎬✌🏼 I had the honor of joining Nebius Live to share my takeaways from Jensen’s keynote, together with the wonderful Deirdre Bosa, former CNBC journalist. One thing I was particularly excited about: Europe has something the AI revolution desperately needs: INDUSTRIAL EXPERTISE. What I loved most about Jensen Huang’s keynote today? Europe has a seat at the table. And Jensen clearly sees its potential! Looking at the stage today, I saw some of Germany’s biggest technology and industrial players: Deutsche Telekom, Siemens, Bosch, Agile Robots, NEURA Robotics, and our automotive industry. We have decades of industrial knowledge. We have the data. We understand manufacturing, engineering, automation and complex physical systems. And that’s exactly what we need to take AI to the next level! NVIDIA has committed to invest nearly $10 billion across Europe’s tech ecosystem and is working with more than 12,000 partners here. That’s a pretty strong signal! And there’s something else I always love about Jensen’s keynotes: He makes incredibly complex technology understandable. Today, he broke down the AI economy again into five layers: 1. Energy 2. Compute 3. AI Factories 4. AI Models 5. Applications He also explained what it actually takes to build autonomous AI agents: a model that reasons and plans, a harness that coordinates its actions, a secure sandbox that controls what it can access, and the knowledge and tools it needs to get things done. Because the more autonomous AI becomes, the more important security becomes! Then there was Vera Rubin, NVIDIA’s next-generation AI computing platform, connecting 72 GPUs through NVLink into one massive, liquid-cooled system. And of course, PHYSICAL AI! One of my favorite moments was actually at the very end, when Jensen brought out the tiny Hugging Face robots and started explaining to them what he had just presented on stage. It was such a fun way to show something incredibly important: Robots don’t just learn in the real world anymore. They can be trained in simulated environments using synthetic data, combined with real-world data and feedback, before transferring those capabilities into physical machines. And that brings me right back to Europe. Because when AI moves from our screens into factories, machines and robots, our industrial expertise becomes a huge advantage. I’m really proud to be working with Nebius, one of NVIDIA’s major neocloud partners, and to have been part of this conversation. And a little women power at the table with Deirdre made it even better!! If you’re still at NVIDIA GTC Berlin tonight, you might find me at Nebius House! Later I’ll be at the creator evening & the Deloitte SOHO house party. Looking forward to a great evening here in Berlin and more exciting collaborations with the Nebius team! 💚 How did YOU like Jensen’s keynote today? What was your biggest takeaway?

You already wire LLMs into apps. The Nebius Agentic AI Builder exam tests the harder parts: fine-tuning vs RAG, rate limits, cost per task. 60 minutes online, Credly badge valid 3 years. Early-bird pricing until December 31.