Tech2Wild
141 posts

Tech2Wild
@Tech2Wild
🎮 Tech, gaming, AI, and everything in between. 🤖 Building with it, not just talking about it. 🔥 From the mind of @ToNYD2WiLD











Today we’re introducing Gemma 4 12B — our latest open model that brings advanced agentic reasoning, vision and audio directly to your laptop. It delivers performance nearing our larger Gemma models with a much smaller total memory footprint, while being small enough to run locally with just 16GB of VRAM. It’s open and accessible for everyone to use under a permissive Apache 2.0 license. This is all made possible by our new, unified architecture that removes separate multimodal encoders. Here’s how we did it 🧵

@KariDaniels What folks don’t realize is China will be the number one economy in 5-10 years due to US negligence and the ramping up of MAGA economics




Introducing NVIDIA Cosmos 3 We released NVIDIA Cosmos 3 last night. And today, seeing it take the top spots across 8+ open model leaderboards feels surreal. We spent months working towards this moment. Here’s the breakdown: The Leaderboard Wins World Reasoning 🏆 #1 open model on VANTAGE-Bench for vision AI 🏆 #1 overall on Traffic Anomaly Reasoning (TAR) World Generation 🏆 #1 open model on Artificial Analysis Image-to-Video leaderboard 🏆 #1 open model on Artificial Analysis Text-to-Image leaderboard 🏆 #1 open model on PAI-Bench for physical AI synthetic data generation 🏆 #1 open model on Physics-IQ, which measures accuracy on physical laws 🏆 #1 open model on R-Bench for world generation quality World Action 🏆 #1 on RoboArena for specialized policy 🏆 #1 on RoboLab for action generation But the leaderboards are only part of the story. The real story is why we built Cosmos 3 in the first place. The Problem Training robots and autonomous systems in the real world is painfully hard. Robots need to try the same thing numerous times before they succeed reliably. Self-driving cars need rare edge cases that may never happen naturally. Smart machines need to understand physics, motion, contact, failure, and surprise. And real-world data is slow, expensive, and sometimes dangerous to collect. At some point, the answer cannot just be “collect more data.” You can’t collect your way out of an infinite physical world. You have to generate it. That… was the question behind Cosmos: Can one model understand the physical world deeply enough to reason about it, simulate it, and generate actions inside it? What We Built Cosmos 3 is the first omni-model for physical AI. It can understand and generate across: language · images · video · audio · action sequences It is not just a VLM. Not just a video generator. Not just a robot policy model. It is all of them, in one single model. That matters because physical AI has been fragmented for a long time. Cosmos 3 is our attempt to collapse that fragmentation. Depending on how you configure the inputs and outputs, the same model can act as a vision-language model, a video/world generator, a world simulator, or a world-action model. No separate architecture required. The Architecture Under the hood, Cosmos 3 uses a dual-tower Mixture-of-Transformers architecture. One tower is autoregressive for reasoning. It handles next-token prediction for language and discrete understanding. The other tower is diffusion-based- for generation. It denoises images, video, audio, and action trajectories. Two towers. Dual-stream joint attention. One shared world representation. Each modality gets its own tools: visual encoders, video VAEs, audio VAEs, and action projectors that can map different embodiments into a unified action space. Action is a first-class modality in Cosmos 3. That’s what makes it more than a video model. It doesn’t just predict and generate what the world might look like. It can connect reasoning and world modeling to physically grounded action. Why This Matters One of the most interesting findings from the ablation work is that training action domains together creates positive transfer. That means adding more embodiments does not just add more use cases. It can actually make the model better. This is the heart of why omnimodal training matters. A shared world representation is not just convenient. It can make each individual task stronger. That’s the part that feels like the beginning of something much bigger. The part I’m most excited about is that Cosmos 3 is fully open. Developers get the models, scripts, optimization, inference endpoints, post-training recipes, datasets, and benchmarks. Everything is available under the Linux Foundation’s OpenMDW 1.1 License. You can use Cosmos 3 out of the box. You can use the VLM, world model, or world-action pieces separately. You can post-train it for your own domain, embodiment, or accuracy target. That’s what makes this feel different. Cosmos 3 is not just a model release. It is the foundation for building intelligence for autonomous machines. For me, Cosmos 3 feels like a step toward a world where physical AI development becomes much more scalable and accessible - to a new age of developers and agents. That’s what we built Cosmos 3 for. I cannot wait to see what you build with it. Download Models on Hugging Face huggingface.co/collections/nv… Customize Models on GitHub github.com/NVIDIA/cosmos Read the Tech Blog to Learn More developer.nvidia.com/blog/develop-p…








