They say there’s no such thing as a dumb question. As someone who asks dumb questions for a living, I can tell you that’s a really stupid thing to say.
But the best questions are often the ones where someone smart explains something from the ground up to a total novice (read: me). The beauty of NVIDIA: there are a lot of smart people upon whom I can inflict my very dumbest questions.
Turns out I’m not alone. Month in and month out, tens of thousands of readers ask search engines these very questions. And they get connected to the answers through our blog.
What are they? Smart question. Here are five of our most popular in 2019.
This post is over a decade old, but the answer — thanks to the emergence of deep-learning driven AI, supercomputing, and self-driving cars — is more relevant than ever. That’s why we’ve updated our original post earlier this year, and why more readers are seeing this post than ever.
Visualize the fields of AI, machine learning and deep learning as concentric circles. AI — the idea that came first — is the largest circle. Then comes machine learning, which blossomed later. And finally deep learning — which is driving today’s AI explosion — fitting inside both. Click on the link, above, for more.
This is one of the key questions in AI right now, which is why this post has become one of our most popular. Click on the link for a plain English answer to these questions, and a walk through the kinds of datasets and problems that lend themselves to each kind of learning.
This is another question that’s drawn more readers over time. Training, in short, is the process of running data through a neural network to teach it a task. That’s taught computers to do things that, just a decade ago, most believed could only be done by humans. Inference, by contrast, is the process of putting that trained network to work, in everything from hyperscale data centers to autonomous machines.
Used to be if you wanted to see ray tracing, you went to the movies. If you wanted to see rasterization, you fired up a video game. Ray tracing models the way light moves around the real world beautifully but it’s computationally intensive. Rasterization, by contrast, can be done in a hurry. NVIDIA’s latest Turing architecture GPUs blur these lines, with hardware acceleration for real-time ray tracing, making truly cinematic games possible.
Have a question you want answered? Send us your idea.
Firebird Launches CIS Region’s Largest AI Factory in Armenia
Firebird, NVIDIA, Dell Technologies, CoreWeave and regional leaders mark a milestone in building AI infrastructure to support economic growth, scientific research and technological advancements.
The global buildout of AI infrastructure reached a new milestone today — Firebird, an emerging AI cloud, launched the CIS region’s largest AI factory in Armenia, establishing a new AI computing hub powered by NVIDIA accelerated computing and Dell Technologies high-performance AI infrastructure.
Nikol Pashinyan, prime minister of the Republic of Armenia; Zhaslan Madiyev, deputy prime minister of the Republic of Kazakhstan; and David Allen, U.S. chargé d`affaires, a.i. in Armenia, attended the AI factory opening ceremony.
AI factories are the foundational infrastructure for the AI era, providing the computing capacity needed to train, fine-tune and deploy AI models for every domain at scale.
Building the Infrastructure to Create Intelligence at Home
While AI services are available globally, countries also need the capacity to develop and run AI for their own languages, industries and national priorities. Firebird’s AI factory brings that capacity to Armenia, giving developers, startups, enterprises, universities and public institutions the compute to build and scale AI at home.
Firebird plans to deploy more than 70,000 NVIDIA Rubin and Blackwell GPUs and 300 megawatts of AI infrastructure capacity in Armenia by the end of 2027, accelerating the country’s development as a center for AI research, advanced computing and innovation.
“Our ambition for what we are building in the next 2 years or so is roughly 2 gigawatts of capacity around the world. We’re very focused on merging into frontier markets,” said Alexander Yesayan, co-founder of Firebird.
At this scale, energy efficiency is essential. Built on the NVIDIA DSX platform, this AI factory integrates accelerated computing, networking, power and cooling as one codesigned system. Firebird’s AI factory is designed from the ground up to turn compute into revenue. With DSX, it can run up to 40% more GPUs on the same footprint, producing more tokens per dollar and extracting more value from every megawatt of capacity.
A Magnet for Global AI, a Catalyst for Local Innovation
Firebird’s ambitions extend beyond a single site. With NVIDIA’s support, the company is pursuing an approximately 2-gigawatt AI infrastructure roadmap spanning Armenia, Kazakhstan and additional markets.
Firebird also announced that NVIDIA intends to invest in the company, following an earlier investment by CoreWeave this year. These investments will help Firebird expand its global infrastructure and operational footprint, and support its efforts to establish the largest and most advanced compute clusters across frontier markets.
Delivered in just over six months, the Armenia AI factory demonstrates Firebird’s ability to turn ambitious infrastructure plans into operational AI capacity with exceptional speed.
Schneider Electric provides the power infrastructure supporting Firebird’s AI factory in Hrazdan, helping Firebird meet its accelerated deployment schedule by rapidly delivering and setting up critical systems, including medium- and low-voltage switchgear, three-phase uninterruptible power supply systems and rack enclosures. This keeps the power buildout moving at the pace of the compute and provides a reliable foundation to bring NVIDIA accelerated computing online at scale.
To support the facility’s thermal needs, Vertiv provided a cooling architecture combining chilled-water technology, advanced controls and Vertiv TrimCooler technology for efficient heat rejection. Vertiv’s iCOM CWM Chilled Water Manager centrally coordinates cooling resources, improving visibility, efficiency and responsiveness as demand shifts with AI workloads.
Early demand is coming from AI-native companies including Perplexity, which is working with Firebird to access high-performance AI infrastructure for its AI agent platform and answer engine.
As AI becomes essential infrastructure worldwide, Firebird’s expansion can help make the CIS region a magnet for global companies building and running AI — and a catalyst for local developers, researchers and enterprises.
Powered by NVIDIA’s total AI factory platform — reference architecture, accelerated computing, networking and AI software — and deployed on Dell PowerEdge servers, the new Firebird AI factory will help Armenia’s builders turn energy into intelligence and connect their innovations to the global AI economy.
Editor’s note: This post is part of Into the Omniverse, a series focused on how developers, 3D practitioners and enterprises can transform their workflows using the latest advancements in OpenUSD and NVIDIA Omniverse.
In July, NVIDIA joined more than 200 companies and organizations in signing “Open Weights and American AI Leadership,” an open letter arguing that AI leadership will be measured not by any single frontier model but by whether an open ecosystem reaches every sector.
Open models, which anyone can download, inspect, modify and run on their own infrastructure, are what make that possible. Nowhere is that more crucial than in physical AI, where every deployment is a specialization problem.
Physical AI has to understand and predict consequences, not just appearances.
To make this possible, world models learn how physical environments behave, what may happen next and which following actions make sense. They can generate physically grounded world and action data, simulate future states and provide a foundation that teams can specialize for a robot, autonomous vehicle or vision AI system.
Open world models are already being used to generate training data, test policies and specialize physical AI systems. NVIDIA Cosmos 3 brings these capabilities together in an open model family, with leading benchmark results and adoption across robotics, autonomous vehicles and vision AI.
And NVIDIA Omniverse libraries, part of NVIDIA Agent Toolkit, provides prebuilt capabilities for building simulation-ready worlds that physical AI teams can use to train, test and validate systems before real-world deployment.
World Models Are the Foundation of Physical AI
The data behind physical AI is difficult and expensive to collect at the scale required. Rare events and long-tail scenarios can be especially difficult to reproduce safely and repeatedly.
World models enable:
More useful data by learning physical relationships from large-scale multimodal scenarios.
More diverse environments that vary in weather, lighting, objects and trajectories.
A better foundation to build on and adapt to a particular robot, vehicle, sensor configuration, task or operating environment.
A general model hasn’t seen a team’s particular robot, sensors or operating environment. Closing that gap requires access to model weights, a license that permits adaptation and the tools needed for post-training.
NVIDIA Cosmos world foundation models are available under the Linux Foundation’s OpenMDW 1.1 license, enabling teams to post-train models on their own data and hardware. Specialization is where openness becomes a practical technical requirement.
Specializing a model is only part of the workflow. Teams also need environments to generate data, run simulations and test behavior.
Omniverse libraries help developers build simulation-ready environments, while OpenUSD provides the open framework for composing, reusing and exchanging complex 3D data across digital twins, simulations and synthetic data generation workflows. Together, Omniverse and OpenUSD cut the duplicated work that can otherwise pile up every time assets, sensor configurations or environmental conditions change.
Cosmos 3: The Frontier Model
NVIDIA Cosmos 3 — a frontier open physical AI foundation omni-model built on a mixture-of-transformers architecture — combines vision reasoning, world generation and action prediction, letting developers use one model family to understand scenes, generate synthetic data, simulate future states and build specialized world action models.
Developers can use Cosmos 3 as a vision language model, as a physics-grounded world simulator that predicts future world states and generates large-scale synthetic data, or as the backbone for world action models, instead of assembling and maintaining a separate model for each capability.
The family includes Cosmos 3 Super (64B) for high-fidelity world modeling, Cosmos 3 Nano (16B) for efficient reasoning and post-training, and Cosmos 3 Edge (4B) for on-device vision reasoning and robot policy deployment. Lightweight enough to run on edge GPUs, Cosmos 3 Edge can be deployed across NVIDIA RTX GPUs, NVIDIA DGX systems and NVIDIA Jetson, including Jetson Thor platforms.
Across benchmark evaluations, Cosmos 3 ranks No. 1 on Artificial Analysis for open weights text-to-image and image-to-video generation, on PAI-Bench for world generation and in the image-to-video category of Physics-IQ. For robot policy, it ranks No. 1 on RoboLab. Cosmos 3 Super is also the highest-ranked open model on VANTAGE-Bench for vision understanding.
In addition to Cosmos, NVIDIA’s physical AI stack includes Isaac GR00T for robotics, Alpamayo for autonomous vehicles and Metropolis for vision AI.
How Developers Are Putting Cosmos 3 to Work
Across industries, developers are building on NVIDIA Cosmos for physical AI applications: Doosan Robotics, LG Electronics, Samsung Electronics and Skild AI in robotics; Li Auto, Xiaomi and Afari in autonomous vehicles; and Centific, Fogsphere, Linker Vision, Milestone Systems and Yuan for vision AI agents powering industrial AI and smart spaces applications.
The NVIDIA Cosmos Coalition extends this work by bringing together world model builders, AI developers and physical AI leaders to contribute models, research and evaluation methods. NVIDIA recently expanded the coalition to Japan, where robotics and manufacturing leaders intend to join and develop open world models for factories, logistics, agriculture, construction, healthcare and transportation.
Together, these implementations and collaborations are establishing open world models as an adaptable foundation for physical AI across robots, autonomous vehicles and vision AI systems.
Get Plugged In
Learn more about world models, OpenUSD and physical AI development by exploring these resources:
