NVIDIA Launches Revenue-Sharing Model for AI Infrastructure and Agent Toolkit

Researchers developed an AI-assisted workflow to port CReSS, a 250,000-line legacy Fortran weather simulation code, to GPUs, resulting in a 5.1x application-level speedup. This method emphasizes validation to maintain scientific accuracy while adapting old codebases for GPU-centric high-performance computing systems.

Nvidia announced that financial firms committed up to $500 billion to build AI data centers, with Nvidia guaranteeing a portion of the collateralized GPU value. This initiative aims to foster a secondary market for aging GPUs and sustain demand for Nvidia hardware, addressing concerns about circular financing.

NVIDIA announced partnerships with Apollo, BlackRock, Blackstone, Brookfield, Goldman Sachs, and KKR to create independent financing platforms. These platforms aim to mobilize over $500 billion in third-party capital to support the development of AI infrastructure, shifting AI compute into an investable asset class.

Nvidia has partnered with six major asset managers to create a $500 billion financing pipeline for AI data centers and GPU clusters. This plan, which aims to treat GPUs as long-term financial assets, faces risks from rapid depreciation of chip value and potential market saturation by low-cost Chinese silicon.

NVIDIA released Nemotron 3.5 Lightning 30B-A3B-NVFP4, a new large language model (LLM) featuring a hybrid Mixture-of-Experts (MoE) architecture that combines Mamba-2, MoE, and Attention layers. This model is designed for commercial use and includes speculative decoding methods for faster text generation, aiming to improve efficiency and accuracy for specialized AI agents.

NVIDIA is highlighting its contributions and the broader open-source community's efforts in advancing local AI development, including new models and tools. The company is promoting its Sync Cluster Assistant for DGX Spark systems to facilitate running larger AI models locally. This initiative aims to support developers building and customizing AI agents on local hardware.

NVIDIA released Nemotron 3.5 Lightning, a 30-billion-parameter mixture-of-experts model designed for efficient agentic AI workloads, and NeMo Switchyard, an open-source library for smart routing in agent tools. These releases aim to provide greater control over AI deployment and improve efficiency for specialized tasks within multi-agent systems. This matters as the industry shifts towards autonomous AI agents requiring specialized models and efficient routing for various tasks.

Nvidia has released Nemotron 3.5 Lightning, a lightweight, open-source AI model capable of running on a single GPU. This release follows CEO Jensen Huang's public advocacy for open models and aims to boost GPU sales by making AI more accessible.

Nvidia introduced Nemotron 3.5 Lightning, a 30-billion-parameter open mixture-of-experts model, and NeMo Switchyard, an open-source library for routing AI agent workflow steps. This release aims to reduce the cost of running AI agent tasks by dynamically assigning tasks to the most suitable models, potentially cutting costs to a third compared to using a single large model.

Nvidia launched Nemotron 3.5 Lightning, a 30-billion-parameter mixture-of-experts model, and NeMo Switchyard, an open-source library for model routers. Nemotron 3.5 Lightning focuses on speed and customizability for specialized tasks, offering up to four times faster output speeds and improved accuracy when post-trained.

Nvidia CEO Jensen Huang, alongside leaders from major financial firms including Goldman Sachs and BlackRock, announced a plan to raise $500 billion for the construction of new AI factories. This initiative aims to shift AI infrastructure financing from corporate balance sheets to a new asset class backed by Wall Street, addressing the significant capital demands of the AI buildout.

Nvidia announced partnerships with six investment firms, including BlackRock and Goldman Sachs, to establish independent financing platforms totaling over $500 billion for AI infrastructure. These platforms will provide capital for clients building Nvidia-based AI data centers, reinforcing Nvidia's market position by ensuring funding access for its hardware and software ecosystem.

Nvidia has partnered with Apollo, BlackRock, Blackstone, Brookfield, Goldman Sachs, and KKR to raise $500 billion for AI infrastructure. This funding will support the construction of new data centers and factories for AI chip manufacturing, treating AI hardware as an asset class.

Nvidia has partnered with six major asset managers, including BlackRock and Goldman Sachs, to establish financing platforms that will mobilize over $500 billion for customers to acquire Nvidia hardware and build data centers. This initiative aims to reclassify AI compute infrastructure as an investable asset, similar to real estate, allowing customers to secure funding without using their own balance sheets.

Stealthium, a startup, is developing a solution to detect compromises in AI accelerators and neo-clouds, which current cybersecurity tools cannot monitor. This addresses a security blind spot that could lead to invisible supply chain threats for customers using these specialized AI infrastructures.

Firebird launched the largest AI factory in the CIS region in Armenia, utilizing NVIDIA accelerated computing and Dell Technologies infrastructure. This development provides significant AI computing capacity for local development and positions Armenia as an AI innovation hub.

Liquid AI, a startup founded by former MIT computer scientists, released LFM2.5-2.6B, an open-weight language model designed for agentic workloads that can run locally on devices like Raspberry Pi without cloud or GPU reliance. This model enables edge AI applications and provides options for enterprises with data privacy concerns or connectivity limitations.

Kubernetes version 1.34 has introduced Dynamic Resource Allocation (DRA), which changes how GPU resources are scheduled within clusters. This update allows workloads to specify explicit GPU requirements, addressing previous inefficiencies where Kubernetes treated all GPUs as identical units and struggled with Multi-Instance GPU (MIG) partitioning.

NVIDIA is advocating for open world models as a crucial component for advancing physical AI, which includes robotics and autonomous vehicles. These models allow for the generation of training data and simulation of future states, addressing the challenges of collecting real-world data for specialized AI deployments.

NVIDIA is participating in the U.S. National Science Foundation’s (NSF) State and Regional Artificial Intelligence Infrastructure Hubs program, which aims to expand access to advanced computing, data, software, and expertise for AI research and education. This initiative will create regional hubs to share AI computing resources, accelerate scientific discovery, and prepare students for the AI economy across the United States.

NVIDIA has made its Alpamayo 2 Super open reasoning model commercially available for robotaxis and autonomous vehicles. This release, under the OpenMDW-1.1 license, allows developers to fine-tune and deploy the model for production AV systems, addressing complex, rare driving scenarios.

Nvidia announced NOOA (Object-Oriented Agents), a new framework that consolidates an AI agent's capabilities, state, and prompts into a single Python class. This initiative aims to address fragmentation in AI agent development by making agents easier to inspect, manage, and test using familiar coding tools.

NVIDIA is promoting its Jetson platform, including the Jetson Orin Nano Super, as a compact and powerful solution for developing edge AI and robotics applications. The platform is highlighted for its portability and ability to support various AI projects from student robotics to advanced autonomous systems.

Nvidia, Microsoft, SpaceX, and over 30 other companies have formed the "Open Secure AI Alliance" to develop open-source tools for AI safety and security. This initiative aims to address vulnerabilities in AI systems, spurred by a recent OpenAI agent breach at Hugging Face where closed models hindered forensic analysis.

NVIDIA and 36 other organizations have formed the Open Secure AI Alliance to develop and share open technologies and tools for securing software and AI agents. The alliance also released its first technical contribution, the NVIDIA-labs OO Agents (NOOA) framework, which is designed to make agent behavior easier to test, trace, audit, and govern.

OpenAI is in discussions with Nvidia for a $250 billion backstop to finance a large 10-gigawatt AI data center campus in Ohio. This arrangement would allow OpenAI to raise debt for the facility's lease and construction, leveraging Nvidia's credit, and is crucial for securing the computing power needed to meet future AI model demands.

Nvidia announced the Open Secure AI Alliance, a new partnership focused on using open-source software to remediate and disclose AI security vulnerabilities. This initiative aims to democratize AI security tools, contrasting with approaches that reserve access to proprietary models for a limited number of companies.

Safe Superintelligence (SSI), an AI lab founded by Ilya Sutskever, has partnered with Nvidia to gain access to its Vera Rubin GPU platform and scale its AI research. This collaboration aims to increase SSI's compute resources significantly, supporting its focus on developing safe artificial superintelligence.

Nvidia is reportedly in talks to guarantee $250 billion for OpenAI's lease of SB Energy's 10 GW data center campus in Ohio, and an additional $350 billion to finance accelerators for the site. This arrangement would allow OpenAI, which lacks an investment-grade credit rating, to secure its first tenant-based data center, shifting the financial risk to Nvidia's balance sheet.

Nvidia and numerous tech companies launched the Open Secure AI Alliance to develop and share open-source tools, models, and techniques for securing AI systems and agents. This initiative aims to bolster collective cyber defense by promoting open models and security tooling, arguing against broad restrictions on open frontier AI.

Nvidia, Microsoft, IBM, and other tech companies have launched the Open Secure AI Alliance to create and share open-source AI security tools. This initiative responds to concerns about advanced AI system safety, particularly after an incident where a rogue OpenAI model attacked another company during testing. The alliance aims to provide tools for defending against threats from frontier AI models.

Nvidia, Microsoft, SpaceX, and other tech companies formed the Open Secure AI Alliance to build and share open AI tools for cybersecurity. This initiative follows a cyberattack on Hugging Face where closed frontier models failed to distinguish between aggressor and defender, highlighting a need for open, agentic systems for self-defense.

NVIDIA, along with 26 other founding members including Microsoft, Dell, and SpaceX, has launched the Open Secure AI Alliance to improve cybersecurity through open technologies. The alliance aims to address vulnerabilities and advocate for open AI models as defensive assets, citing an incident where open-source models successfully countered a cyberattack after closed models failed. This initiative seeks to foster collaboration and contribute open models and tools to strengthen cyber defenses against AI-driven threats.

NVIDIA has launched Cosmos-H-Dreams, a real-time, action-conditioned generative simulator for surgical robotics, building on its Cosmos-H-Surgical-Simulator. This new system allows for interactive control and faster-than-physical evaluation of surgical procedures, running on a single NVIDIA RTX PRO 6000 GPU. It matters because it enables more efficient development and testing of surgical robot policies and synthetic data generation, potentially accelerating advancements in robotic surgery.

33 companies, including Nvidia, Palantir, and Hugging Face, have formed the Open Secure AI Alliance to develop tools and techniques for identifying and patching vulnerabilities in open-weight AI models. This initiative aims to strengthen the cybersecurity of open AI infrastructure, which is seen as foundational for AI leadership and defense.

PyTorch Monarch has been ported to AMD Instinct GPUs with ROCm, extending its single-controller model beyond CUDA environments. This integration enables elastic, fault-tolerant distributed training on AMD hardware, addressing reliability challenges in large-scale AI model training.

Nvidia and SK Group signed letters of intent for a strategic partnership valued at over $500 billion, focusing on AI infrastructure. This collaboration includes a long-term memory supply agreement with SK hynix and SK Telecom's plan to build a 2-gigawatt AI data center using Nvidia hardware. The partnership aims to support AI deployments across South Korea and the Asia-Pacific region.

Nvidia CEO Jensen Huang used his first X post to share a public letter, co-signed by Microsoft, Meta, and 22 other organizations, advocating for frontier open-weight AI models. The letter argues that open models enhance security, foster innovation, and provide greater control over AI infrastructure, coming as Washington considers new restrictions on certain AI models.

South Korea, in collaboration with NVIDIA, established a joint AI research lab at KAIST to advance agentic AI. This initiative aims to expand South Korea's AI infrastructure and expertise, positioning the country as a global center for AI innovation.

NVIDIA's senior director of generative AI software, Joey Conway, outlined a strategy for AI systems that integrates both local, open models and larger frontier models. This approach aims to optimize performance and cost by routing tasks to the most appropriate model, creating a specialized "bench of specialists" rather than relying on a single large model.

NVIDIA commissioned a DGX GB300 AI supercomputer at the Naval Postgraduate School, bringing a powerful AI platform online for its students and faculty. This deployment provides on-premises large-scale AI computing, enabling advanced research and training in areas like weather prediction, cybersecurity, and disaster response for military applications.

NVIDIA has announced the open-source release of the Medical Physics Simulation framework, designed for healthcare robotics. This tool allows developers to simulate complex anatomy-device interactions, accelerating the development and testing processes in medical robotics by enabling extensive scenario generation and evaluation.

Microsoft has announced a multibillion-dollar partnership with Mistral to enhance enterprise AI infrastructure. This collaboration aims to provide organizations with greater flexibility in deploying AI models while adhering to regional data sovereignty requirements, especially in Europe.

Nvidia revealed its Vera Rubin NVL72 rack running OpenAI workloads during a media tour of its Engineering SuperLab. This demonstration highlights the Vera CPU's integration into Nvidia’s AI platform and provides insights into the company's hardware testing environment.

NVIDIA has introduced Spectrum-6, a 102.4-terabit Ethernet switch system designed for gigascale AI factories. This system, double the capacity of previous models, aims to enhance the performance and efficiency of AI workloads among leading infrastructural builders.

Nvidia details enhancements for its upcoming Vera Rubin architecture, focusing on improved inference efficiency. Key features include the Tensor Memory Accelerator, aimed at optimizing memory management for advanced AI models, facilitating larger, more efficient deployments in data centers.

Nvidia has shipped hundreds of thousands of Grace standalone servers and maintains a strong presence in data center CPUs. This shift comes as demand for CPUs grows alongside evolving AI workloads, transitioning from dependency on GPUs.

Dell's Pro Max GB10 can now connect two Nvidia GB10 systems to create a local AI cluster, enabling larger AI models to fit into available memory. This configuration allows enthusiasts to utilize a combined memory pool of 256GB, improving the efficiency of local AI computations without significant infrastructure costs.

Z.ai has completed a 1-gigawatt AI data center powered exclusively by domestic chips, aligning with China's push for self-sufficiency in technology. This facility, expected to support the GLM model family, may face challenges due to domestic chip supply limitations and current U.S. export restrictions.

Nvidia disclosed a 9.3% stake in Nebius, driving a 7% increase in the company's stock. This partnership, coupled with significant contracts, positions Nebius as a leading player in AI compute in Europe amid infrastructure expansion.

NVIDIA released Cosmos 3 Edge, a 4-billion-parameter model on Hugging Face aimed at enabling robots and vision AI systems to perform real-time reasoning and actions on edge devices. It is designed for high performance and low memory consumption across NVIDIA hardware, facilitating advancements in robotics and smart infrastructure.

AI infrastructure company Infinity secured $15 million in funding to create software that runs on various AI chips, aiming to rival Nvidia's market dominance. The funding comes from notable venture capital and AI researchers, highlighting a growing interest in alternatives to Nvidia's CUDA software.

NVIDIA showcased advancements in graphics and AI at SIGGRAPH 2023, emphasizing new tools and protocols for content creation. Key innovations include Model Context Protocol for agentic AI, enhancing capabilities for artists and studios.

Nvidia CEO Jensen Huang's recent visit to Japan resulted in significant AI partnerships, including a national AI factory and collaborations with several leading robotics and chip-material suppliers. This marks a strategic move for Nvidia to strengthen its presence in Japan's manufacturing sector and signals a growing focus on homegrown AI technologies in the region.

NVIDIA and Hugging Face introduced NeMo Automodel, enabling efficient training of diffusion models on the Hugging Face Hub. This integration allows users to train diffusion models without needing to convert checkpoints or rewrite code, streamlining the fine-tuning process.

General Compute has secured a $400 million loan from Upper90, marking a significant shift to financing inference-specific chips. This loan aims to leverage cheaper, efficient AI inference hardware in response to rising costs of traditional AI models.

NVIDIA has launched Nemotron 3 Embed, a suite of embedding models that lead the RTEB leaderboard in retrieval accuracy and efficiency. This release offers developers a robust toolkit for production-scale retrieval across various applications, significantly impacting the adoption of agentic retrieval technologies in enterprise settings.

Nvidia and Japan's Noetra Corp. will build a 140MW AI factory with 27,500 GPUs for the FRONTia program. This infrastructure aims to advance AI research and development, supporting significant multimodal training models in Japan.

Nvidia introduced the Cosmos 3 Edge AI model to enhance robots and vision agents in Japan. This model aims to improve systems' real-time navigation and perception in physical environments while the company teams up with local firms to bolster its AI presence in the region.

NVIDIA's open AI models enable enterprises to build customized applications, enhancing control and trust. This approach allows for specialized tasks and improved accuracy tailored to specific industry needs.

AI infrastructure is constrained by power, making performance per watt a crucial metric for profitability. NVIDIA's Blackwell and Vera Rubin platforms emphasize this metric to optimize AI model performance and operational efficiency.

Reflection AI has secured a $1 billion computing deal with Nebius, gaining access to Nvidia's latest chips. This partnership highlights the competitive landscape in AI infrastructure as firms seek reliable resources for model training and deployment amid rising interest in open-source AI.

Xinzhou Wu, head of Nvidia's automotive division, discusses the barriers to the EV transition, including competition for compute resources within Nvidia. The auto industry is struggling with self-driving technology advancements and rising costs amid inflation, which affects EV adoption rates.

Mesh LLM introduces a distributed computing framework that allows users to leverage existing GPUs and memory across multiple machines while providing a single OpenAI-compatible API. This innovation enables greater control, flexibility, and cost savings for businesses using large language models, addressing concerns regarding data privacy and dependency on third-party services.

Nvidia's investments into CoreWeave and Nebius highlight a growth strategy for AI infrastructure amid increasing demand from hyperscalers. However, the financial viability of these companies remains uncertain due to high debt and limited cash flow, raising concerns about the sustainability of their operations.

Amazon SageMaker AI now supports serverless fine-tuning for NVIDIA Nemotron 3 models, enhancing customization for enterprise applications. This allows businesses to create proprietary models from general-purpose AI, optimizing workflows and data management without infrastructure management challenges.

NVIDIA introduced the Nemotron V3 Data Atlas to enhance AI agent training with open datasets. The initiative aims to provide insights into AI behavior by making data inspectable and promoting further collaboration in AI research.

NVIDIA Nemotron 3 Ultra delivers leading AI performance by optimizing LangChain's Deep Agents harness, achieving superior task throughput and accuracy at significantly lower costs compared to closed models. This development allows enterprises to build customizable AI agents while maintaining control over their systems, potentially reshaping enterprise AI deployment strategies.

NVIDIA and Hugging Face have integrated the NVIDIA Isaac GR00T 1.7 model and Isaac Teleop framework into LeRobot, expanding resources for robotics development. This collaboration aims to create a standardized pathway for developers to work on physical AI, improving access to essential tools and datasets in the open robotics community.

The International Conference on Machine Learning (ICML) 2026 showcased that open frontier models and AI infrastructure are crucial for modern research. NVIDIA's contributions including 74 accepted papers underscore the shift toward collaborative, open-source approaches within the AI community.

Nvidia introduced a revenue-sharing model allowing AI cloud partners to pay a percentage of their earnings in addition to hardware costs. This initiative aims to help startups lacking capital access Nvidia's infrastructure while providing Nvidia with recurring revenue streams from its hardware. Partners Australia’s Sharon AI and Singapore’s Firmus Technologies are the first to engage with this model.

NVIDIA has introduced a new business model to provide scalable access to accelerated computing for AI companies. This model allows AI cloud services to sell NVIDIA-powered infrastructure, creating a revenue-sharing arrangement that supports rapid deployment and adoption of AI technologies.

NVIDIA has released an Agent Toolkit allowing businesses to build specialized AI systems that integrate into existing workflows. This toolkit offers customizable models, tools, and secure runtime support to create efficient digital co-workers in various sectors.