⚠️ Warning: may cause hunger...Check out this deliciously stylized BBQ scene built in Blender on an NVIDIA RTX GPU by Rabia Türkoğlu. 🍖🔥Share your digital art creations using #StudioShare for a chance to be featured. 🙌

As AI models evolve and adoption grows, enterprises must perform a delicate balancing act to achieve maximum value. That’s because inference — the process of running data through a model to get an output — offers a different computational challenge than training a model. Pretraining a model — the process of ingesting data, breaking it down into tokens and finding patterns — is essentially a one-time cost. But in inference, every prompt to a model generates tokens, each of which incur a cost. That means that as AI model performance and use increases, so do the amount of tokens generated and their associated computational costs. For companies looking to build AI capabilities, the key is generating as many tokens as possible — with maximum speed, accuracy and quality of service — without sending computational costs skyrocketing. As such, the AI ecosystem has been working to make inference cheaper and more efficient. Inference costs have been trending down for the past year thanks to major leaps in model optimization, leading to increasingly advanced, energy-efficient accelerated computing infrastructure and full-stack solutions. According to the Stanford University Institute for Human-Centered AI’s 2025 AI Index Report, “the inference cost for a system performing at the level of GPT-3.5 dropped over 280-fold between November 2022 and October 2024. At the hardware level, costs have declined by 30% annually, while energy efficiency has improved by 40% each year. Open-weight models are also closing the gap with closed models, reducing the performance difference from 8% to just 1.7% on some benchmarks in a single year. Together, these trends are rapidly lowering the barriers to advanced AI.” As models evolve and generate more demand and create more tokens, enterprises need to scale their accelerated computing resources to deliver the next generation of AI reasoning tools or risk rising costs and energy consumption. What follows is a primer to understand the concepts of the economics of inference, enterprises can position themselves to achieve efficient, cost-effective and profitable AI solutions at scale. Key Terminology for the Economics of AI Inference Knowing key terms of the economics of inference helps set the foundation for understanding its importance. Tokens are the fundamental unit of data in an AI model. They’re derived from data during training as text, images, audio clips and videos. Through a process called tokenization, each piece of data is broken down into smaller constituent units. During training, the model learns the relationships between tokens so it can perform inference and generate an accurate, relevant output. Throughput refers to the amount of data — typically measured in tokens — that the model can output in a specific amount of time, which itself is a function of the infrastructure running the model. Throughput is often measured in tokens per second, with higher throughput meaning greater return on infrastructure. Latency is a measure of the amount of time between inputting a prompt and the start of the model’s response. Lower latency means faster responses. The two main ways of measuring latency are: Time to First Token: A measurement of the initial processing time required by the model to generate its first output token after a user prompt. Time per Output Token: The average time between consecutive tokens — or the time it takes to generate a completion token for each user querying the model at the same time. It’s also known as “inter-token latency” or token-to-token latency. Time to first token and time per output token are helpful benchmarks, but they’re just two pieces of a larger equation. Focusing solely on them can still lead to a deterioration of performance or cost. To account for other interdependencies, IT leaders are starting to measure “goodput,” which is defined as the throughput achieved by a system while maintaining target time to first token and time per output token levels. This metric allows organizations to evaluate performance in a more holistic manner, ensuring that throughput, latency and cost are aligned to support both operational efficiency and an exceptional user experience. Energy efficiency is the measure of how effectively an AI system converts power into computational output, expressed as performance per watt. By using accelerated computing platforms, organizations can maximize tokens per watt while minimizing energy consumption. How the Scaling Laws Apply to Inference Cost The three AI scaling laws are also core to understanding the economics of inference: Pretraining scaling: The original scaling law that demonstrated that by increasing training dataset size, model parameter count and computational resources, models can achieve predictable improvements in intelligence and accuracy. Post-training: A process where models are fine-tuned for accuracy and specificity so they can be applied to application development. Techniques like retrieval-augmented generation can be used to return more relevant answers from an enterprise database. Test-time scaling (aka “long thinking” or “reasoning”): A technique by which models allocate additional computational resources during inference to evaluate multiple possible outcomes before arriving at the best answer. While AI is evolving and post-training and test-time scaling techniques become more sophisticated, pretraining isn’t disappearing and remains an important way to scale models. Pretraining will still be needed to support post-training and test-time scaling. Profitable AI Takes a Full-Stack Approach In comparison to inference from a model that’s only gone through pretraining and post-training, models that harness test-time scaling generate multiple tokens to solve a complex problem. This results in more accurate and relevant model outputs — but is also much more computationally expensive. Smarter AI means generating more tokens to solve a problem. And a quality user experience means generating those tokens as fast as possible. The smarter and faster an AI model is, the more utility it will have to companies and customers. Enterprises need to scale their accelerated computing resources to deliver the next generation of AI reasoning tools that can support complex problem-solving, coding and multistep planning without skyrocketing costs. This requires both advanced hardware and a fully optimized software stack. NVIDIA’s AI factory product roadmap is designed to deliver the computational demand and help solve for the complexity of inference, while achieving greater efficiency. AI factories integrate high-performance AI infrastructure, high-speed networking and optimized software to produce intelligence at scale. These components are designed to be flexible and programmable, allowing businesses to prioritize the areas most critical to their models or inference needs. To further streamline operations when deploying massive AI reasoning models, AI factories run on a high-performance, low-latency inference management system that ensures the speed and throughput required for AI reasoning are met at the lowest possible cost to maximize token revenue generation. Learn more by reading the ebook “AI Inference: Balancing Cost, Latency and Performance.”

Financial services has long been at the forefront of adopting technological innovations. Today, generative AI and agentic systems are redefining the industry, from customer interactions to enterprise operations. Prem Natarajan, executive vice president, chief scientist and head of AI at Capital One, joined the NVIDIA AI Podcast to discuss how his organization is building proprietary AI systems that deliver value to over 100 million customers. “AI is at its best when it transfers cognitive burden from the human to the system,” Natarajan said. “It allows the human to have that much more fun and experience that magic.” Capital One’s strategy centers on a “test, iterate, refine” approach that balances innovation with rigorous risk management. The company’s first agentic AI deployment is a chat concierge that helps customers navigate the car-buying process, such as by scheduling test drives. Rather than simply integrating third-party solutions, Capital One builds proprietary AI technologies that tap into its vast data repositories. “Your data advantage is your AI advantage,” Natarajan emphasized. “Proprietary data allows you to build proprietary AI that provides enduring differentiated services for your customers.” Capital One’s AI architecture combines open-weight foundation models with deep customizations using proprietary data. This approach, Natarajan explained, supports the creation of specialized models that excel at financial services tasks and integrate into multi-agent workflows that can take actions. Natarajan stressed that responsible AI is fundamental to Capital One’s design process. His teams take a “responsibility through design” approach, implementing robust guardrails — both technological and human-in-the-loop — to ensure safe deployment. The concept of an AI factory — where raw data is processed and refined to produce actionable intelligence — aligns naturally with Capital One’s cloud-native technology stack. AI factories incorporate all the components required for financial institutions to generate intelligence, combining hardware, software, networking and development tools for AI applications in financial services. Time Stamps 1:10 – Natarajan’s background and journey to Capital One. 4:50 – Capital One’s approach to generative AI and agentic systems. 15:56 – Challenges in implementing responsible AI in financial services. 28:46 – AI factories and Capital One’s cloud-native advantage. You Might Also Like…  NVIDIA’s Jacob Liberman on Bringing Agentic AI to Enterprises Agentic AI enables developers to create intelligent multi-agent systems that reason, act and execute complex tasks with a degree of autonomy. Jacob Liberman, director of product management at NVIDIA, explains how agentic AI bridges the gap between powerful AI models and practical enterprise applications. Telenor Builds Norway’s First AI Factory, Offering Sustainable and Sovereign Data Processing Telenor opened Norway’s first AI factory in November 2024, enabling organizations to process sensitive data securely on Norwegian soil while prioritizing environmental responsibility. Telenor’s Chief Innovation Officer and Head of the AI Factory Kaaren Hilsen discusses the AI factory’s rapid development, going from concept to reality in under a year. Imbue CEO Kanjun Qiu on Transforming AI Agents Into Personal Collaborators Kanjun Qiu, CEO of Imbue, explores the emerging era where individuals can create and use their own AI agents. Drawing a parallel to the PC revolution of the late 1970s and ‘80s, Qiu discusses how modern AI systems are evolving to work collaboratively with users, enhancing their capabilities rather than just automating tasks.

RT NVIDIA GeForceGeForce RTX 5090 and 5080 laptops are available now! 💻Experience breakthrough performance and next-gen AI built for gamers and creators, all in a thin, portable design. See what press are saying about the all new RTX 50 Series Laptops.Learn More: https://nvda.ws/41Zpwfe

The grand finale is here! 🫧Part 5 of our photorealistic 3D render tutorial hosted by the talented Aleksandr Eskin wraps up with final touches & rendering in Houdini & Octane. 📺 Learn something new: https://nvda.ws/3Yb2yiz

AI is rapidly reshaping what’s possible on a PC — whether for real-time image generation or voice-controlled workflows. As AI capabilities grow, so does their complexity. Tapping into the power of AI can entail navigating a maze of system settings, software and hardware configurations. Enabling users to explore how on-device AI can simplify and enhance the PC experience, Project G-Assist — an AI assistant that helps tune, control and optimize GeForce RTX systems — is now available as an experimental feature in the NVIDIA app. Developers can try out AI-powered voice and text commands for tasks like monitoring performance, adjusting settings and interacting with supporting peripherals. Users can even summon other AIs powered by GeForce RTX AI PCs. And it doesn’t stop there. For those looking to expand Project G-Assist capabilities in creative ways, the AI supports custom plug-ins. With the new ChatGPT-based G-Assist Plug-In Builder, developers and enthusiasts can create and customize G-Assist’s functionality, adding new commands, connecting external tools and building AI workflows tailored to specific needs. With the plug-in builder, users can generate properly formatted code with AI, then integrate the code into G-Assist — enabling quick, AI-assisted functionality that responds to text and voice commands. Teaching PCs New Tricks: Plug-Ins and APIs Explained Plug-ins are lightweight add-ons that give software new capabilities. G-Assist plug-ins can control music, connect with large language models and much more. Under the hood, these plug-ins tap into application programming interfaces (APIs), which allow different software and services to talk to each other. Developers can define functions in simple JSON formats, write logic in Python and quickly integrate new tools or features into G-Assist. With the G-Assist Plug-In Builder, users can: Use a responsive small language model running locally on GeForce RTX GPUs for fast, private inference. Extend G-Assist’s capabilities with custom functionality tailored to specific workflows, games and tools. Interact with G-Assist directly from the NVIDIA overlay, without tabbing out of an application or workflow. Invoke AI-powered GPU and system controls from applications using C++ and Python bindings. Integrate with agentic frameworks using tools like Langflow, letting G-Assist function as a component in larger AI pipelines and multi-agent systems. Built for Builders: Using Free APIs to Expand AI PC Capabilities  NVIDIA’s GitHub repository provides everything needed to get started on developing with G-Assist — including sample plug-ins, step-by-step instructions and documentation for building custom functionalities. Developers can define functions in JSON and drop config files into a designated directory, where G-Assist can automatically load and interpret them. Users can even submit plug-ins for review and potential inclusion in the NVIDIA GitHub repository to make new capabilities available for others. Hundreds of free, developer-friendly APIs are available today to extend G-Assist capabilities — from automating workflows to optimizing PC setups to boosting online shopping. For ideas, find searchable indices of free APIs for use across entertainment, productivity, smart home, hardware and more on publicapis.dev, free-apis.github.io, apilist.fun and APILayer. Available sample plug-ins include Spotify, which enables hands-free music and volume control, and Google Gemini, which allows G-Assist to invoke a much larger cloud-based AI for more complex conversations, brainstorming and web searches using a free Google AI Studio API key. In the clip below, G-Assist asks Gemini for advice on which Legend to pick in the hit game Apex Legends when solo queueing, as well as whether it’s wise to jump into Nightmare mode for level 25 in Diablo IV: And in the following clip, a developer uses the new plug-in builder to create a Twitch plug-in for G-Assist that checks if a streamer is live. After generating the necessary JSON manifest and Python files, the developer simply drops them into the G-Assist directory to enable voice commands like, “Hey, Twitch, is [streamer] live?” In addition, users can customize G-Assist to control select peripherals and software applications with simple commands, such as to benchmark or adjust fan speeds, or to change lighting on supported Logitech G, Corsair, MSI and Nanoleaf devices. Other examples include a Stock Checker plug-in that lets users quickly look up real-time stock prices and performance data, or a Weather plug-in allows users to ask G-Assist for current weather conditions in any city. Details on how to build, share and load plug-ins are available on the NVIDIA GitHub repository. Start Building Today With the G-Assist Plugin Builder and open API support, anyone can extend G-Assist to fit their exact needs. Explore the GitHub repository and submit features for review to help shape the next wave of AI-powered PC experiences. Plug in to NVIDIA AI PC on Facebook, Instagram, TikTok and X — and stay informed by subscribing to the RTX AI PC newsletter. Follow NVIDIA Workstation on LinkedIn and X. See notice regarding software product information.

An AI agent is only as accurate, relevant and timely as the data that powers it. Now generally available, NVIDIA NeMo microservices are helping enterprise IT quickly build AI teammates that tap into data flywheels to scale employee productivity. The microservices provide an end-to-end developer platform for creating state-of-the-art agentic AI systems and continually optimizing them with data flywheels informed by inference and business data, as well as user preferences. With a data flywheel, enterprise IT can onboard AI agents as digital teammates. These agents can tap into user interactions and data generated during AI inference to continuously improve model performance — turning usage into insight and insight into action. Building Powerful Data Flywheels for Agentic AI Without a constant stream of high-quality inputs — from databases, user interactions or real-world signals — an agent’s understanding can weaken, making responses less reliable and agents less productive. Maintaining and improving the models that power AI agents in production requires three types of data: inference data to gather insights and adapt to evolving data patterns, up-to-date business data to provide intelligence, and user feedback data to advise if the model and application are performing as expected. NeMo microservices help developers tap into these three data types. NeMo microservices speed AI agent development with end-to-end tools for curating, customizing, evaluating and guardrailing the models that drive their agents. NVIDIA NeMo microservices — including NeMo Customizer, NeMo Evaluator and NeMo Guardrails — can be used alongside NeMo Retriever and NeMo Curator to ease enterprises’ experiences building, optimizing and scaling AI agents through custom enterprise data flywheels. For example: NeMo Customizer accelerates large language model fine-tuning, delivering up to 1.8x higher training throughput. This high-performance, scalable microservice uses popular post-training techniques including supervised fine-tuning and low-rank adaptation. NeMo Evaluator simplifies the evaluation of AI models and workflows on custom and industry benchmarks with just five application programming interface (API) calls. NeMo Guardrails improves compliance protection by up to 1.4x with only half a second of additional latency, helping organizations implement robust safety and security measures that align with organizational policies and guidelines. With NeMo microservices, developers can build data flywheels that boost AI agent accuracy and efficiency. Deployed through the NVIDIA AI Enterprise software platform, NeMo microservices are easy to operate and can run on any accelerated computing infrastructure, on premises or in the cloud, with enterprise-grade security, stability and support. The microservices have become generally available at a time when enterprises are building large-scale multi-agent systems, where hundreds of specialized agents — with distinct goals and workflows — collaborate to tackle complex tasks as digital teammates, working alongside employees to assist, augment and accelerate work across functions. This enterprise-wide impact positions AI agents as a trillion-dollar opportunity — with applications spanning automated fraud detection, shopping assistants, predictive machine maintenance and document review — and underscores the critical role data flywheels play in transforming business data into actionable insights. Data flywheels built with NVIDIA NeMo microservices constantly curate data, retrain models and evaluate their performance, all with minimal human interactions and maximum autonomy. Industry Pioneers Boost AI Agent Accuracy With NeMo Microservices NVIDIA partners and industry pioneers are using NeMo microservices to build responsive AI agent platforms so that digital teammates can help get more done. Working with Arize and Quantiphi, AT&T has built an advanced AI-powered agent using NVIDIA NeMo, designed to process a knowledge base of nearly 10,000 documents, refreshed weekly. The scalable, high-performance AI agent is fine-tuned for three key business priorities: speed, cost efficiency and accuracy — all increasingly critical as adoption scales. AT&T boosted AI agent accuracy by up to 40% using NeMo Customizer and Evaluator by fine-tuning a Mistral 7B model to help deliver personalized services, prevent fraud and optimize network performance. BlackRock is working with NeMo microservices for agentic AI capabilities in its Aladdin tech platform, which unifies the investment management process through a common data language. Teaming with Galileo, Cisco’s Outshift team is using NVIDIA NeMo microservices to power a coding assistant that delivers 40% fewer tool selection errors and achieves up to 10x faster response times. Nasdaq is accelerating its Nasdaq Gen AI Platform with NeMo Retriever microservices and NVIDIA NIM microservices. NeMo Retriever enhanced the platform’s search capabilities, leading to up to 30% improved accuracy and response times, in addition to cost savings. Broad Model and Partner Ecosystem Support for NeMo Microservices NeMo microservices support a broad range of popular open models, including Llama, the Microsoft Phi family of small language models, Google Gemma, Mistral and Llama Nemotron Ultra, currently the top open model on scientific reasoning, coding and complex math benchmarks. Meta has tapped NVIDIA NeMo microservices through new connectors for Meta Llamastack. Users can access the same capabilities — including Customizer, Evaluator and Guardrails — via APIs, enabling them to run the full suite of agent-building workflows within their environment. “With Llamastack integration, agent builders can implement data flywheels powered by NeMo microservices,” said Raghotham Murthy, software engineer, GenAI, at Meta. “This allows them to continuously optimize models to improve accuracy, boost efficiency and reduce total cost of ownership.” Leading AI software providers such as Cloudera, Datadog, Dataiku, DataRobot, DataStax, SuperAnnotate, Weights & Biases and more have integrated NeMo microservices into their platforms. Developers can use NeMo microservices in popular AI frameworks including CrewAI, Haystack by deepset, LangChain, LlamaIndex and Llamastack. Enterprises can build data flywheels with NeMo Retriever microservices using NVIDIA AI Data Platform offerings from NVIDIA-Certified Storage partners including DDN, Dell Technologies, Hewlett Packard Enterprise, Hitachi Vantara, IBM, NetApp, Nutanix, Pure Storage, VAST Data and WEKA. Leading enterprise platforms including Amdocs, Cadence, Cohesity, SAP, ServiceNow and Synopsys are using NeMo Retriever microservices in their AI agent solutions. Enterprises can run AI agents on NVIDIA-accelerated infrastructure, networking and software from leading system providers including Cisco, Dell, Hewlett Packard Enterprise and Lenovo. Consulting giants including Accenture, Deloitte and EY are building AI agent platforms for enterprises using NeMo microservices. Developers can download NeMo microservices from the NVIDIA NGC catalog. The microservices can be deployed as part of NVIDIA AI Enterprise with extended-life software branches for API stability, proactive security remediation and enterprise-grade support.

Steel meets serenity in this dreamy NVIDIA RTX accelerated render by @_xvni. ⚔️🌻The reflections? The textures? Perfection. 🤌Show us your art with #StudioShare for a chance to be featured! 🖌

What software did you first learn digital art on? 🎨

Cinematic lighting. Moody reflections. A story behind every pixel... ☔ Rendered on a NVIDIA RTX GPU by @naridarbandi. 🙌✨ Share your digital art with #StudioShare to be featured next!

Step into the world of stylized fantasy... ✨Learn how to create a stunning 3D environment from scratch with the talented @bosse_ton_art in our latest Studio Sessions tutorial.🧪🍄 Watch now: https://nvda.ws/43HFV9e

RT ASUS North AmericaTransform any room into a home studio with #NVIDIABroadcast & a @NVIDIAStudio RTX GPU. 🎙️Take your livestreams, voice chats, and video conference calls to the next level with AI-enhanced voice and video - check out the demo at our #NABShow booth! 🙌

What do you think they’re listening to? 🎧🚌This feel-good short created by loek.3d (IG) on an RTX GPU is pure animation joy. 🙌Share your creations with #StudioShare for a chance to be featured!

Whether at sea, land or in the sky — even outer space — NVIDIA technology is helping research scientists and developers alike explore and understand oceans, wildlife, the climate and far out existential risks like asteroids. These increasingly intelligent developments are helping to analyze environmental pollutants, damage to habitats and natural disaster risks at an accelerated pace. This, in turn, enables partnerships with local governments to take climate mitigation steps like pollution prevention and proactive planting. Sailing the Seas of AI Amphitrite, based in France, uses satellite data with AI to simulate and predict ocean currents and weather. Its AI models, driven by the NVIDIA AI and Earth-2 platforms, offer insights for positioning vessels to best harness the power of ocean currents. This helps determine when it’s best to travel, as well as the optimal course, reducing travel times, fuel consumption and carbon emissions. Amphitrite is a member of the NVIDIA Inception program for cutting-edge startups. https://blogs.nvidia.com/wp-content/uploads/2025/04/amphitrite-suez-canal.mp4 Watching Over Wildlife With AI München, Germany-based OroraTech monitors animal poaching and wildfires with NVIDIA CUDA and Jetson. The NVIDIA Inception program member uses the EarthRanger platform to offer a wildfire detection and monitoring service that uses satellite imagery and AI to safeguard the environment and prevent poaching. Keeping AI on the Weather Weather agencies and climate scientists worldwide are using NVIDIA CorrDiff, a generative AI weather model enabling kilometer-scale forecasts of wind, temperature and precipitation type and amount. CorrDiff is part of the NVIDIA Earth-2 platform for simulating weather and climate conditions. It’s available as an easy-to-deploy NVIDIA NIM microservice. In another climate effort, NVIDIA Research announced a new generative AI model, called StormCast, for reliable weather prediction at a scale larger than storms. The model, outlined in a paper, can help with disaster and mitigation planning, saving lives. Avoiding Mass Extinction Events Researchers reported in Nature how a new method was able to spot 10-meter asteroids within the main asteroid belt located between Jupiter and Mars. Such space rocks can range from bus-sized to several Costco stores in width and deliver destruction to cities. It used NASA’s James Webb Space Telescope (JWST), which was tapped for views of these asteroids from previous research and enabled by NVIDIA accelerated computing. Boosting Energy Efficiency With Liquid-Cooled Blackwell NVIDIA GB200 NVL72 rack-scale, liquid-cooled systems, built on the Blackwell platform, offer exceptional performance while balancing energy costs and heat. It delivers 40x higher revenue potential, 30x higher throughput, 25x more energy efficiency and 300x more water efficiency than air-cooled architectures. NVIDIA GB300 NVL72 systems built on the Blackwell Ultra platform offer a 50x higher revenue potential, 35x higher throughput with 30x more energy efficiency. Enroll in the free new NVIDIA Deep Learning Institute course Applying AI Weather Models With NVIDIA Earth-2. Learn more about NVIDIA Earth-2 and NVIDIA Blackwell.

RT Gerald UndoneNEW VIDEO! The 50 Series "Blackwell" GPUs from NVIDIA added decode/encode support for 4:2:2, which means editing mirrorless camera footage just got faster. 👍🤓Link: https://youtu.be/CPL67Kc-0X8

Which do you prefer: creating something entirely from scratch or remixing/enhancing existing work? 🔄

Traditionally, data centers have relied on air cooling — where mechanical chillers circulate chilled air to absorb heat from servers, helping them maintain optimal conditions. But as AI models increase in size, and the use of AI reasoning models rises, maintaining those optimal conditions is not only getting harder and more expensive — but more energy-intensive. While data centers once operated at 20 kW per rack, today’s hyperscale facilities can support over 135 kW per rack, making it an order of magnitude harder to dissipate the heat generated by high-density racks. To keep AI servers running at peak performance, a new approach is needed for efficiency and scalability. One key solution is liquid cooling — by reducing dependence on chillers and enabling more efficient heat rejection, liquid cooling is driving the next generation of high-performance, energy-efficient AI infrastructure. The NVIDIA GB200 NVL72 and the NVIDIA GB300 NVL72 are rack-scale, liquid-cooled systems designed to handle the demanding tasks of trillion-parameter large language model inference. Their architecture is also specifically optimized for test-time scaling accuracy and performance, making it an ideal choice for running AI reasoning models while efficiently managing energy costs and heat. Liquid-cooled NVIDIA Blackwell compute tray. Driving Unprecedented Water Efficiency and Cost Savings in AI Data Centers Historically, cooling alone has accounted for up to 40% of a data center’s electricity consumption, making it one of the most significant areas where efficiency improvements can drive down both operational expenses and energy demands. Liquid cooling helps mitigate costs and energy use by capturing heat directly at the source. Instead of relying on air as an intermediary, direct-to-chip liquid cooling transfers heat in a technology cooling system loop. That heat is then cycled through a coolant distribution unit via liquid-to-liquid heat exchanger, and ultimately transferred to a facility cooling loop. Because of the higher efficiency of this heat transfer, data centers and AI factories can operate effectively with warmer water temperatures — reducing or eliminating the need for mechanical chillers in a wide range of climates. The NVIDIA GB200 NVL72 rack-scale, liquid-cooled system, built on the NVIDIA Blackwell platform, offers exceptional performance while balancing energy costs and heat. It packs unprecedented compute density into each server rack, delivering 40x higher revenue potential, 30x higher throughput, 25x more energy efficiency and 300x more water efficiency than traditional air-cooled architectures. Newer NVIDIA GB300 NVL72 systems built on the Blackwell Ultra platform boast a 50x higher revenue potential and 35x higher throughput with 30x more energy efficiency. Data centers spend an estimated $1.9-2.8M per megawatt (MW) per year, which amounts to nearly $500,000 spent annually on cooling-related energy and water costs. By deploying the liquid-cooled GB200 NVL72 system, hyperscale data centers and AI factories can achieve up to 25x cost savings, leading to over $4 million dollars in annual savings for a 50 MW hyperscale data center. For data center and AI factory operators, this means lower operational costs, enhanced energy efficiency metrics and a future-proof infrastructure that scales AI workloads efficiently — without the unsustainable water footprint of legacy cooling methods. Moving Heat Outside the Data Center As compute density rises and AI workloads drive unprecedented thermal loads, data centers and AI factories must rethink how they remove heat from their infrastructure. The traditional methods of heat rejection that supported predictable CPU-based scaling are no longer sufficient on their own. Today, there are multiple options for moving heat outside the facility, but four major categories dominate current and emerging deployments. Key Cooling Methods in a Changing Landscape Mechanical Chillers: Mechanical chillers use a vapor compression cycle to cool water, which is then circulated through the data center to absorb heat. These systems are typically air-cooled or water-cooled, with the latter often paired with cooling towers to reject heat. While chillers are reliable and effective across diverse climates, they are also highly energy-intensive. In AI-scale facilities where power consumption and sustainability are top priorities, reliance on chillers can significantly impact both operational costs and carbon footprint. Evaporative Cooling: Evaporative cooling uses the evaporation of water to absorb and remove heat. This can be achieved through direct or indirect systems, or hybrid designs. These systems are much more energy-efficient than chillers but come with high water consumption. In large facilities, they can consume millions of gallons of water per megawatt annually. Their performance is also climate-dependent, making them less effective in humid or water-restricted regions. Dry Coolers: Dry coolers remove heat by transferring it from a closed liquid loop to the ambient air using large finned coils, much like an automotive radiator. These systems don’t rely on water and are ideal for facilities aiming to reduce water usage or operate in dry climates. However, their effectiveness depends heavily on the temperature of the surrounding air. In warmer environments, they may struggle to keep up with high-density cooling demands unless paired with liquid-cooled IT systems that can tolerate higher operating temperatures. Pumped Refrigerant Systems: Pumped refrigerant systems use liquid refrigerants to move heat from the data center to outdoor heat exchangers. Unlike chillers, these systems don’t rely on large compressors inside the facility and they operate without the use of water. This method offers a thermodynamically efficient, compact and scalable solution that works especially well for edge deployments and water-constrained environments. Proper refrigerant handling and monitoring are required, but the benefits in power and water savings are significant. Each of these methods offers different advantages depending on factors like climate, rack density, facility design and sustainability goals. As liquid cooling becomes more common and servers are designed to operate with warmer water, the door opens to more efficient and environmentally friendly cooling strategies — reducing both energy and water use while enabling higher compute performance. Optimizing Data Centers for AI Infrastructure As AI workloads grow exponentially, operators are reimagining data center design with infrastructure built specifically for high-performance AI and energy efficiency. Whether they’re transforming their entire setup into dedicated AI factories or upgrading modular components, optimizing inference performance is crucial for managing costs and operational efficiency. To get the best performance, high compute capacity GPUs aren’t enough — they need to be able to communicate with each other at lightning speed. NVIDIA NVLink boosts communication, enabling GPUs to operate as a massive, tightly integrated processing unit for maximum performance with a full-rack power density of 120 kW. This tight, high-speed communication is crucial for today’s AI tasks, where every second saved on transferring data can mean more tokens per second and more efficient AI models. Traditional air cooling struggles at these power levels. To keep up, data center air would need to be either cooled to below-freezing temperatures or flow at near-gale speeds to carry the heat away, making it increasingly impractical to cool dense racks with air alone. At nearly 1,000x the density of air, liquid cooling excels at carrying heat away thanks to its superior heat capacitance and thermal conductivity. By efficiently transferring heat away from high-performance GPUs, liquid cooling reduces reliance on energy-intensive and noisy cooling fans, allowing more power to be allocated to computation rather than cooling overhead. Liquid Cooling in Action Innovators across the industry are leveraging liquid cooling to slash energy costs, improve density and drive AI efficiency: Vertiv’s reference architecture for NVIDIA GB200 NVL72 servers reduces annual energy consumption by 25%, cuts rack space requirements by 75% and shrinks the power footprint by 30%. Schneider Electric’s liquid-cooling infrastructure supports up to 132 kW per rack, improving energy efficiency, scalability and overall performance for GB200 NVL72 AI data centers. CoolIT Systems’ high-density CHx2000 liquid-to-liquid coolant distribution units provide 2MW cooling capacity at 5°C approach temperature, ensuring reliable thermal management for GB300 NVL72 deployments. Also, CoolIT Systems’ OMNI All-Metal Coldplates with patented Split-Flow technology provide targeted cooling of over 4,000W thermal design power while reducing pressure drop. Boyd’s advanced liquid-cooling solutions, incorporating the company’s over two decades of high-performance compute industry experience, include coolant distribution units, liquid-cooling loops and cold plates to further maximize energy efficiency and system reliability for high-density AI workloads. Cloud service providers are also adopting cutting-edge cooling and power innovations. Next-generation AWS data centers, featuring jointly developed liquid cooling solutions, increase compute power by 12% while reducing energy consumption by up to 46% — all while maintaining water efficiency. Cooling the AI Infrastructure of the Future As AI continues to push the limits of computational scale, innovations in cooling will be essential to meeting the thermal management challenges of the post-Moore’s law era. NVIDIA is leading this transformation through initiatives like the COOLERCHIPS program, a U.S. Department of Energy-backed effort to develop modular data centers with next-generation cooling systems that are projected to reduce costs by at least 5% and improve efficiency by 20% over traditional air-cooled designs. Looking ahead, data centers must evolve not only to support AI’s growing demands but do so sustainably — maximizing energy and water efficiency while minimizing environmental impact. By embracing high-density architectures and advanced liquid cooling, the industry is paving the way for a more efficient AI-powered future. Learn more about breakthrough solutions for data center energy and water efficiency presented at NVIDIA GTC 2025 and discover how accelerated computing is driving a more efficient future with NVIDIA Blackwell.

About 15% of the world’s population — over a billion people — are affected by neurological disorders, from commonly known diseases like Alzheimer’s and Parkinson’s to hundreds of lesser-known, rare conditions. BrainStorm Therapeutics, a San Diego-based startup, is accelerating the development of cures for these conditions using AI-powered computational drug discovery paired with lab experiments using organoids: tiny, 3D bundles of brain cells created from patient-derived stem cells. This hybrid, iterative method, where clinical data and AI models inform one another to accelerate drug development, is known as lab in the loop. “The brain is the last frontier in modern biology,” said BrainStorm’s founder and CEO Robert Fremeau, who was previously a scientific director in neuroscience at Amgen and a faculty member at Duke University and the University of California, San Francisco. “By combining our organoid disease models with the power of generative AI, we now have the ability to start to unravel the underlying complex biology of disease networks.” The company aims to lower the failure rate of drug candidates for brain diseases during clinical trials — currently over 93% — and identify therapeutics that can be applied to multiple diseases. Achieving these goals would make it faster and more economically viable to develop treatments for rare and common conditions. “This alarmingly high clinical trial failure rate is mainly due to the inability of traditional preclinical models with rodents or 2D cells to predict human efficacy,” said Jun Yin, cofounder and chief technology officer at BrainStorm. “By integrating human-derived brain organoids with AI-driven analysis, we’re building a platform that better reflects the complexity of human neurobiology and improves the likelihood of clinical success.” Fremeau and Yin believe that BrainStorm’s platform has the potential to accelerate development timelines, reduce research and development costs, and significantly increase the probability of bringing effective therapies to patients. BrainStorm Therapeutics’ AI models, which run on NVIDIA GPUs in the cloud, were developed using the NVIDIA BioNeMo Framework, a set of programming tools, libraries and models for computational drug discovery. The company is a member of NVIDIA Inception, a global network of cutting-edge startups. Clinical Trial in a Dish BrainStorm Therapeutics uses AI models to develop gene maps of brain diseases, which they can use to identify promising targets for potential drugs and clinical biomarkers. Organoids allow them to screen thousands of drug molecules per day directly on human brain cells, enabling them to test the effectiveness of potential therapies before starting clinical trials. “Brains have brain waves that can be picked up in a scan like an EEG, or electroencephalogram, which measures the electrical activity of neurons,” said Maya Gosztyla, the company’s cofounder and chief operating officer. “Our organoids also have spontaneous brain waves, allowing us to model the complex activity that you would see in the human brain in this much smaller system. We treat it like a clinical trial in a dish for studying brain diseases.” BrainStorm Therapeutics is currently using patient-derived organoids for its work on drug discovery for Parkinson’s disease, a condition tied to the loss of neurons that produce dopamine, a neurotransmitter that helps with physical movement and cognition. “In Parkinson’s disease, multiple genetic variants contribute to dysfunction across different cellular pathways, but they converge on a common outcome — the loss of dopamine neurons,” Fremeau said. “By using AI models to map and analyze the biological effects of these variants, we can discover disease-modifying treatments that have the potential to slow, halt or even reverse the progression of Parkinson’s.” The BrainStorm team used single-cell sequencing data from brain organoids to fine-tune foundation models available through the BioNeMo Framework, including the Geneformer model for gene expression analysis. The organoids were derived from patients with mutations in the GBA1 gene, the most common genetic risk factor for Parkinson’s disease. BrainStorm is also collaborating with the NVIDIA BioNeMo team to help optimize open-source access to the Geneformer model. Accelerating Drug Discovery Research With its proprietary platform, BrainStorm can mirror human brain biology and simulate how different treatments might work in a patient’s brain. “This can be done thousands of times, much quicker and much cheaper than can be done in a wet lab — so we can narrow down therapeutic options very quickly,” Gosztyla said. “Then we can go in with organoids and test the subset of drugs the AI model thinks will be effective. Only after it gets through those steps will we actually test these drugs in humans.” View of an organoid using Fluorescence Imaging Plate Reader, or FLIPR — a technique used to study the effect of compounds on cells during drug screening. This technology led to the discovery that Donepezil, a drug prescribed for Alzheimer’s disease, could also be effective in treating Rett syndrome, a rare genetic neurodevelopmental disorder. Within nine months, the BrainStorm team was able to go from organoid screening to applying for a phase 2 clinical trial of the drug in Rett patients. This application was recently cleared by the U.S. Food and Drug Administration. BrainStorm also plans to develop multimodal AI models that integrate data from cell sequencing, cell imaging, EEG scans and more. “You need high-quality, multimodal input data to design the right drugs,” said Yin. “AI models trained on this data will help us understand disease better, find more effective drug candidates and, eventually, find prognostic biomarkers for specific patients that enable the delivery of precision medicine.” The company’s next project is an initiative with the CURE5 Foundation to conduct the most comprehensive repurposed drug screen to date for CDKL5 Deficiency Disorder, another rare genetic neurodevelopmental disorder. “Rare disease research is transforming from a high-risk niche to a dynamic frontier,” said Fremeau. “The integration of BrainStorm’s AI-powered organoid technology with NVIDIA accelerated computing resources and the NVIDIA BioNeMo platform is dramatically accelerating the pace of innovation while reducing the cost — so what once required a decade and billions of dollars can now be investigated with significantly leaner resources in a matter of months.” Get started with NVIDIA BioNeMo for AI-accelerated drug discovery.

The fight is over. But this knight's legacy echoes in every arrow...🏹⚔️This epic Blender render by @blendreams captures the beauty in defeat — or maybe... a final victory?Share your creations with #StudioShare for a chance to be featured.

Morning artist or midnight creator? 🌅🌙

RT TwinmotionTwinmotion's 2025.1.1 release brings support for @NVIDIAStudio DLSS 4, the latest version of NVIDIA's neural rendering tech. DLSS 4's features include Super Resolution, Deep Learning Anti-Aliasing, Frame Generation, and Multi Frame Generation! ▶️ http://twinmotion.com/download

Learn how to turn a concept into a storybook-worthy scene. 🏠🌲@bosse_ton_art walks through his 2D narrative painting workflow in @Photoshop—full of atmosphere, character, and fantasy vibes.🎣 Dive in to the newest Studio Sessions tutorial: https://nvda.ws/3Rle07F

The April #NVIDIAStudio Driver is here. ✅Download now for the latest optimizations ➡️ https://nvda.ws/42x5JTn

What’s the best way to get creative inspiration? 💡

Level up your video editing workflow! 🎬👇80 LEVEL: Discover how NVIDIA's GeForce RTX 50 Series GPUs unlock AI-powered capabilities in video editing workflows.Details: https://80.lv/articles/discover-how-nvidia-s-geforce-rtx-50-series-gpus-unlock-ai-powered-capabilities-in-video-editing-workflows/#ad #sponsored @nvidia

Gold prospecting in Venezuela has led to a malaria resurgence, but researchers have developed AI to take a bite out of the problem. In Venezuela’s Bolivar state, deforestation for gold mining in waters has disturbed mosquito populations, which are biting miners and infecting them with the deadly parasite. Venezuela was certified as malaria-free in 1961 by the World Health Organization. It’s estimated that worldwide there were 263 million cases of malaria and 597,000 deaths in 2023, according to the WHO. In the Venezuelan outbreak, the area affected is rural and has limited access to medical clinics, so detection with microscopy by trained professionals is lacking. But researchers at the intersection of medicine and technology have tapped AI and NVIDIA GPUs to come up with a solution. They recently published a paper in Nature, describing the development of a convolutional neural network (CNN) for automatically detecting malaria parasites in blood samples. “At some point in Venezuela, malaria was almost eradicated,” said 25-year-old Diego Ramos-Briceño, who has a bachelor’s in engineering that he earned while also pursuing a doctorate in medicine. “I believe it was around 135,000 cases last year.” Identifying Malaria Parasites in Blood Samples The researchers — Ramos-Briceño, Alessandro Flammia-D’Aleo, Gerardo Fernández-López, Fhabián Carrión-Nessi and David Forero-Peña — used the CNN to identify Plasmodium falciparum and Plasmodium vivax in thick blood smears, achieving 99.51% accuracy. To develop the model, the team acquired a dataset of 5,941 labeled thick blood smear microscope images from the Chittagong Medical College Hospital, in Bangladesh. They processed this dataset to create nearly 190,000 labeled images. “What we wanted for the neural network to learn is the morphology of the parasite, so from out of the nearly 6,000 microscope level images, we extracted every single parasite, and from all that data augmentation and segmentation, we ended up having almost 190,000 images for model training,” said Ramos-Briceño. The model comes as traditional microscopy methods are also challenged by limitations in accuracy and consistency, according to the research paper. Harnessing Gaming GPUs and CUDA for Model Training, Inference To run model training, the malaria paper’s team tapped into an RTX 3060 GPU from a computer science teacher mentoring their research. “We used PyTorch Lightning with NVIDIA CUDA acceleration that enabled us to do efficient parallel computation that significantly sped up the matrix operations and the preparations of the neural network compared with what a CPU would have done,” said Ramos-Briceño. For inference, malaria determinations from blood samples can be made within several seconds, he said, using such GPUs. Clinics lacking trained microscopists could use the model and introduce their own data for transfer learning so that the model performs optimally with the types of images they submit, handling the lighting conditions and other factors, he said. “For communities that are far away from the urban setting, where there’s more access to resources, this could be a way to approach the malaria problem,” said Ramos-Briceño.

As the days grow longer and the flowers bloom, GFN Thursday brings a fresh lineup of games to brighten the week. Dive into thrilling hunts and dark fantasy adventures with the arrivals of titles like Hunt: Showdown 1896 — now available on Xbox and PC Game Pass — and Mandragora: Whispers of the Witch Tree on GeForce NOW. Whether chasing bounties in the Colorado Rockies or battling chaos in a cursed land, players will gain unforgettable experiences with these games in the cloud. Plus, roll with the punches in Capcom’s MARVEL vs. CAPCOM Fighting Collection: Arcade Classics, part of 11 games GeForce NOW is adding to its cloud gaming library — featuring over 2,000 titles playable with GeForce RTX 4080 performance. Spring Into Gaming Anywhere With the arrivals of Hunt: Showdown 1896 and Mandragora: Whispers of the Witch Tree in the cloud, GeForce NOW members can take their gaming journeys anywhere, from the wild frontiers of the American West to the shadowy forests of a dark fantasy realm. It’s the wild, wild west. Hunt: Showdown 1896 transports players to the untamed Rockies, where danger lurks behind every pine and in every abandoned mine. PC Game Pass members — and those who own the game on Xbox — can stream the action instantly. Whether players are tracking monstrous bounties solo or teaming with friends, the game’s tense player vs. player vs. environment action and new map, Mammon’s Gulch, are ideal for springtime exploration. Jump into the hunt from the living room, in the backyard or even on the go — no high-end PC required with GeForce NOW. Every whisper is a warning. Step into a beautifully hand-painted world teetering on the edge of chaos in Mandragora: Whispers of the Witch Tree. As an Inquisitor, battle nightmarish creatures and uncover secrets beneath the budding canopies of Faelduum. With deep role-playing game mechanics and challenging combat, Mandragora is ideal for players seeking a fresh adventure this season. GeForce NOW members can continue their quest wherever spring takes them — including on their laptops, tablets and smartphones. Time for New Games Everyone’s shouting from the excitement of being in the cloud. Catch MARVEL vs. CAPCOM Fighting Collection: Arcade Classics in the cloud this week. In this legendary collection of arcade classics from the fan-favorite Marvel and Capcom crossover games, dive into an action-packed lineup of seven titles, including heavy hitters X-MEN vs. STREET FIGHTER and MARVEL vs. CAPCOM 2 New Age of Heroes, as well as THE PUNISHER.  Each game in the collection can be played online or in co-op mode. Whether new or returning to the series from their arcade days, players of all levels can together enjoy these timeless classics in the cloud. Look for the following games available to stream in the cloud this week: Forever Skies (New release on Steam, available April 14) Night Is Coming (New release on Steam, available April 14) Hunt: Showdown 1896 (New release on Xbox, available on PC Game Pass April 15) Crime Scene Cleaner (New release on Xbox, available on PC Game Pass April 17) Mandragora: Whispers of the Witch Tree (New release on Steam, available April 17) Tempest Rising (New release on Steam, Advanced Access starts April 17) Aimlabs (Steam) Blue Prince (Steam, Xbox) ContractVille (Steam) Gedonia 2 (Steam)  MARVEL vs. CAPCOM Fighting Collection: Arcade Classics (Steam) Path of Exile 2 (Epic Games Store) What are you planning to play this weekend? Let us know on X or in the comments below.

Isomorphic Labs is reimagining the drug discovery process with an AI-first approach. At the heart of this work is a new way of thinking about biology. Max Jaderberg, chief AI officer, and Sergei Yakneen, chief technology officer at Isomorphic Labs joined the AI Podcast to explain why they look at biology as an information processing system. “We’re building generalizable AI models capable of learning from the entire universe of protein and chemical interactions,” Jaderberg said. “This fundamentally breaks from the target-specific, siloed approach of conventional drug development.” Isomorphic isn’t just working to optimize existing drug design workflows but completely rethinking how drugs are discovered — moving away from traditional methods that have historically been slow and inefficient. By modeling cellular processes with AI, Isomorphic’s teams can predict molecular interactions with exceptional accuracy. Their advanced AI models enable scientists to computationally simulate how potential therapeutics interact with their targets in complex biological systems. Using AI to reduce dependence on wet lab experiments accelerates the drug discovery pipeline and creates possibilities for addressing previously untreatable conditions. And that’s just the beginning. Isomorphic Labs envisions a future of precision medicine, where treatments are tailored to an individual’s unique molecular and genetic makeup. While regulatory hurdles and technical challenges remain, Jaderberg and Yakneen are optimistic and devoted to balancing ambitious innovation with scientific rigor. “We’re committed to proving our technology through real-world pharmaceutical breakthroughs,” said Jaderberg. Time Stamps 1:14 – How AI is boosting the drug discovery process. 17:25 – Biology as a computational system. 19:50 – Applications of AlphaFold 3 in pharmaceutical research. 23:05 – The future of precision and preventative medicine. You Might Also Like…  NVIDIA’s Jacob Liberman on Bringing Agentic AI to Enterprises Agentic AI enables developers to create intelligent multi-agent systems that reason, act and execute complex tasks with a degree of autonomy. Jacob Liberman, director of product management at NVIDIA, joined the NVIDIA AI Podcast to explain how agentic AI bridges the gap between powerful AI models and practical enterprise applications. Roboflow Helps Unlock Computer Vision for Every Kind of AI Builder Roboflow’s mission is to make the world programmable through computer vision. By simplifying computer vision development, the company helps bridge the gap between AI and the people looking to harness it. Cofounder and CEO Joseph Nelson discusses how Roboflow empowers users in manufacturing, healthcare and automotive to solve complex problems with visual AI. How World Foundation Models Will Advance Physical AI With NVIDIA’s Ming-Yu Liu AI models that can accurately simulate and predict outcomes in physical, real-world environments will enable the next generation of physical AI systems. Ming-Yu Liu, vice president of research at NVIDIA and an IEEE Fellow, explains the significance of world foundation models — powerful neural networks that can simulate physical environments.

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 advances in OpenUSD and NVIDIA Omniverse. As industrial and physical AI streamline workflows, businesses are looking for ways to most effectively harness these technologies. Scaling AI in industrial settings — like factories and other manufacturing facilities — presents unique challenges, such as fragmented data pipelines, siloed tools and the need for real-time, high-fidelity simulations. The Mega NVIDIA Omniverse Blueprint — available in preview on build.nvidia.com — helps address these challenges by providing a scalable reference workflow for simulating multi-robot fleets in industrial facility digital twins, including those built with the NVIDIA Omniverse platform. Industrial AI leaders — including Accenture, Foxconn, Kenmec, KION and Pegatron — are now using the blueprint to accelerate physical AI adoption and build autonomous systems that efficiently perform actions in industrial settings. Built on the Universal Scene Description (OpenUSD) framework, the blueprint enables seamless data interoperability, real-time collaboration and AI-driven decision-making by unifying diverse data sources and improving simulation fidelity. Industrial Leaders Adopt the Mega Blueprint At Hannover Messe, the world’s largest industrial trade show that took place in Germany earlier this month, Accenture and Schaeffler, a leading motion technology company, showcased the adoption of the Mega blueprint to simulate Digit, a humanoid robot from Agility Robotics, performing material handling in kitting and commissioning areas. Video courtesy of  Schaeffler, Accenture, Agility Robotics KION, a supply chain solutions company, with Accenture are now using Mega to optimize warehouse and distribution processes. At the NVIDIA GTC global AI conference in March, Accenture and Foxconn representatives discussed the impacts of introducing Mega into their industrial AI workflows. Accelerating Industrial AI With Mega  Mega NVIDIA Omniverse Blueprint architecture diagram With the Mega blueprint, developers can accelerate physical AI workflows through: Robot Fleet Simulation: Test and train diverse robot fleets in a safe, virtual environment to ensure they work seamlessly together. Digital Twins: Use digital twins to simulate and optimize autonomous systems before physical deployment. Sensor Simulation and Synthetic Data Generation: Generate realistic sensor data to ensure robots can accurately perceive and respond to their real-world environment. Facility and Fleet Management Systems Integration: Connect robot fleets with management systems for efficient coordination and optimization. Robot Brains as Containers: Use portable, plug-and-play modules for consistent robot performance and easier management. World Simulator With OpenUSD: Simulate industrial facilities in highly realistic virtual environments using NVIDIA Omniverse and OpenUSD. Omniverse Cloud Sensor RTX APIs: Ensure accurate sensor simulation with NVIDIA Omniverse Cloud application programming interfaces to create detailed virtual replicas of industrial facilities. Scheduler: Manage complex tasks and data dependencies with a built-in scheduler for smooth and efficient operations. Video Analytics AI Agents: Integrate AI agents built with the NVIDIA AI Blueprint for video search and summarization (VSS), leveraging NVIDIA Metropolis, to enhance operational insights. Dive deeper into the Mega blueprint architecture on the NVIDIA Technical Blog. Industrial AI is also being accelerated by the latest Omniverse Kit SDK 107 release, including major updates for robotics application development and enhanced simulation capabilities such as RTX Real-Time 2.0. Get Plugged Into the World of OpenUSD Learn more about OpenUSD and industrial AI by watching sessions from GTC, now available on demand, and by watching how ecosystem partners like Pegatron and others are pushing their industrial automation further, faster. Join NVIDIA at COMPUTEX, running May 19-23 in Taipei, to discover the latest breakthroughs in AI. Watch NVIDIA founder and CEO Jensen Huang’s keynote on Sunday, May 18, at 8:00 p.m. PT. Discover why developers and 3D practitioners are using OpenUSD and learn how to optimize 3D workflows with the new self-paced “Learn OpenUSD” curriculum for 3D developers and practitioners, available for free through the NVIDIA Deep Learning Institute. For more resources on OpenUSD, explore the Alliance for OpenUSD forum and the AOUSD website. Plus, tune in to the “OpenUSD Insiders” livestream taking place today at 11:00 a.m. PT to hear more about the Mega NVIDIA Omniverse Blueprint. Additionally, don’t miss next week’s livestream on April 26 at 11:00 a.m. PT, to hear Accenture discuss how they’re using the blueprint to build Omniverse digital twins for training and testing industrial AI’s robot brains. Stay up to date by subscribing to NVIDIA news, joining the community and following NVIDIA Omniverse on Instagram, LinkedIn, Medium and X. Featured image courtesy of: Left and Top Right: Accenture, KION Group Middle: Accenture, Agility Robotics, Schaeffler Bottom Right: Foxconn

The final days of the AI Mathematical Olympiad’s latest competition were a transcontinental relay for team NVIDIA. Every evening, two team members on opposite ends of the U.S. would submit an AI reasoning model to Kaggle — the online Olympics of data science and machine learning. They’d wait a tense five hours before learning how well the model tackled a sample set of 50 complex math problems. After seeing the results, the U.S. team would pass the baton to teammates waking up in Armenia, Finland, Germany and Northern Ireland, who would spend their day testing, modifying and optimizing different model versions. “Every night I’d be so disappointed in our score, but then I’d wake up and see the messages that came in overnight from teammates in Europe,” said Igor Gitman, senior applied scientist. “My hopes would go up and we’d try again.” While the team was disheartened by their lack of improvement on the public dataset during the competition’s final days, the real test of an AI model is how well it can generalize to unseen data. That’s where their reasoning model leapt to the top of the leaderboard — correctly answering 34 out of 50 Olympiad questions within a five-hour time limit using a cluster of four NVIDIA L4 GPUs. “We got the magic in the end,” said Northern Ireland-based team member Darragh Hanley, a Kaggle grandmaster and senior large language model (LLM) technologist. Building a Winning Equation The NVIDIA team competed under the name NemoSkills — a nod to their use of the NeMo-Skills collection of pipelines for accelerated LLM training, evaluation and inference. The seven members each contributed different areas of expertise, spanning LLM training, model distillation and inference optimization. For the Kaggle challenge, over 2,200 participating teams submitted AI models tasked with solving 50 math questions — complex problems at the National Olympiad level, spanning algebra, geometry, combinatorics and number theory — within five hours. https://blogs.nvidia.com/wp-content/uploads/2025/04/Sample-Reasoning-AI.mp4 The team’s winning model uses a combination of natural language reasoning and Python code execution. To complete this inference challenge on the small cluster of NVIDIA L4 GPUs available via Kaggle, the NemoSkills team had to get creative. Their winning model used Qwen2.5-14B-Base, a foundation model with chain-of-thought reasoning capabilities which the team fine-tuned on millions of synthetically generated solutions to math problems. These synthetic solutions were primarily generated by two larger reasoning models — DeepSeek-R1 and QwQ-32B — and used to teach the team’s foundation model via a form of knowledge distillation. The end result was a smaller, faster, long-thinking model capable of tackling complex problems using a combination of natural language reasoning and Python code execution. To further boost performance, the team’s solution reasons through multiple long-thinking responses in parallel before determining a final answer. To optimize this process and meet the competition’s time limit, the team also used an innovative early-stopping technique. A reasoning model might, for example, be set to answer a math problem 12 different times before picking the most common response. Using the asynchronous processing capabilities of NeMo-Skills and NVIDIA TensorRT-LLM, the team was able to monitor and exit inference early if the model had already converged at the correct answer four or more times. TensorRT-LLM also enabled the team to harness FP8 quantization, a compression method that resulted in a 1.5x speedup over using the more commonly used FP16 format. ReDrafter, a speculative decoding technique developed by Apple, was used for a further 1.8x speedup. The final model performed even better on the competition’s unseen final dataset than it did on the public dataset — a sign that the team successfully built a generalizable model and avoided overfitting their LLM to the sample data. “Even without the Kaggle competition, we’d still be working to improve AI reasoning models for math,” said Gitman. “But Kaggle gives us the opportunity to benchmark and discover how well our models generalize to a third-party dataset.” Sharing the Wealth  The team will soon release a technical report detailing the techniques used in their winning solution — and plans to share their dataset and a series of models on Hugging Face. The advancements and optimizations they made over the course of the competition have been integrated into NeMo-Skills pipelines available on GitHub. Key data, technology, and insights from this pipeline were also used to train the just-released NVIDIA Llama Nemotron Ultra model. “Throughout this collaboration, we used tools across the NVIDIA software stack,” said Christof Henkel, a member of the Kaggle Grandmasters of NVIDIA, known as KGMON. “By working closely with our LLM research and development teams, we’re able to take what we learn from the competition on a day-to-day basis and push those optimizations into NVIDIA’s open-source libraries.” After the competition win, Henkel regained the title of Kaggle World Champion — ranking No. 1 among the platform’s over 23 million users. Another teammate, Finland-based Ivan Sorokin, earned the Kaggle Grandmaster title, held by just over 350 people around the world. For their first-place win, the group also won a $262,144 prize that they’re directing to the NVIDIA Foundation to support charitable organizations. Meet the full team — Igor Gitman, Darragh Hanley, Christof Henkel, Ivan Moshkov, Benedikt Schifferer, Ivan Sorokin and Shubham Toshniwal — in the video below: Sample math questions in the featured visual above are from the 2025 American Invitational Mathematics Examination. Find the full set of questions and solutions on the Art of Problem Solving wiki.