NVIDIA today announced the world's first family of open source quantum AI models, NVIDIA Ising, designed to help researchers and enterprises build quantum processors capable of running useful applications. To achieve useful quantum applications at scale, significant breakthroughs are needed in quantum processor calibration and quantum error correction. AI is key for turning today's quantum processors into large-scale, reliable computers. Open models empower developers to build high-performance AI while maintaining total control over their data and infrastructure.

Named after a landmark mathematical model that dramatically simplified the understanding of complex physical systems, the NVIDIA Ising family provides high-performance, scalable AI tools for quantum error correction and calibration — two of the most critical challenges in building hybrid-quantum classical systems. Ising models run the world's best quantum processor calibration and enable researchers to tackle much larger, more complex problems with quantum computers by delivering up to 2.5x faster performance and 3x higher accuracy for the decoding process needed for quantum error correction.

NVIDIA today announced the NVIDIA Nemotron 3 family of open models, data and libraries designed to power transparent, efficient and specialized agentic AI development across industries. The Nemotron 3 models—with Nano, Super and Ultra sizes—introduce a breakthrough hybrid latent mixture-of-experts (MoE) architecture that helps developers build and deploy reliable multi-agent systems at scale.

As organizations shift from single-model chatbots to collaborative multi-agent AI systems, developers face mounting challenges, including communication overhead, context drift and high inference costs. In addition, developers require transparency to trust the models that will automate their complex workflows. Nemotron 3 directly addresses these challenges, delivering the performance and openness customers need to build specialized, agentic AI.

NVIDIA and Google Cloud are expanding access to accelerated computing to transform the full spectrum of enterprise workloads, from visual computing to agentic and physical AI. Google Cloud today announced the general availability of G4 VMs, powered by NVIDIA RTX PRO 6000 Blackwell Server Edition GPUs. Plus, NVIDIA Omniverse and NVIDIA Isaac Sim are now available as virtual machine images (VMIs) on the Google Cloud Marketplace to unlock physical AI-driven applications for key industries like manufacturing, automotive and logistics. This powerful combination creates a versatile, multi-workload platform for enterprises to accelerate their most demanding challenges on Google Cloud.

NVIDIA RTX PRO 6000 Blackwell GPUs excel at high-performance AI inference for multimodal, generative and agentic AI deployments, while also powering complex visual and simulation workloads ranging from computer-aided engineering and content creation to robotics simulation. Customers like WPP are using G4 VMs with NVIDIA Omniverse to instantly generate photorealistic 3D advertising environments at global scale, while Altair is using the platform within Altair One to accelerate demanding simulation and fluid dynamics workloads.

NVIDIA has released the GeForce 581.29 WHQL Game Ready drivers adding optimizations for Borderlands 4 (that will launch this week on September 12) and Dying Light: The Beast. The update also includes Studio Driver support for Microsoft TRELLIS NIM. The drivers fix a critical Marvel Rivals performance issue that affected users on previous 581.xx versions. This should restore expected frame rates and gameplay.

However, some known issues persist. Counter-Strike 2 may display slightly distorted text when running below native resolution, Adobe Premiere Pro users could experience system freezes during hardware-accelerated exports on certain configurations, and Like a Dragon: Infinite Wealth players might notice light flickering on some systems.

DOWNLOAD: NVIDIA GeForce 581.29 WHQL

Agentic AI is redefining the cybersecurity landscape—introducing new opportunities that demand rethinking how to secure AI while offering the keys to addressing those challenges. Unlike standard AI systems, AI agents can take autonomous actions—interacting with tools, environments, other agents and sensitive data. This provides new opportunities for defenders but also introduces new classes of risks. Enterprises must now take a dual approach: defend both with and against agentic AI.

Building Cybersecurity Defense With Agentic AI
Cybersecurity teams are increasingly overwhelmed by talent shortages and growing alert volume. Agentic AI offers new ways to bolster threat detection, response and AI security—and requires a fundamental pivot in the foundations of the cybersecurity ecosystem. Agentic AI systems can perceive, reason and act autonomously to solve complex problems. They can also serve as intelligent collaborators for cyber experts to safeguard digital assets, mitigate risks in enterprise environments and boost efficiency in security operations centers. This frees up cybersecurity teams to focus on high-impact decisions, helping them scale their expertise while potentially reducing workforce burnout. For example, AI agents can cut the time needed to respond to software security vulnerabilities by investigating the risk of a new common vulnerability or exposure in just seconds. They can search external resources, evaluate environments and summarize and prioritize findings so human analysts can take swift, informed action.

AI-powered image generation has progressed at a remarkable pace—from early examples of models creating images of humans with too many fingers to now producing strikingly photorealistic visuals. Even with such leaps, one challenge remains: achieving creative control. Creating scenes using text has gotten easier, no longer requiring complex descriptions—and models have improved alignment to prompts. But describing finer details like composition, camera angles and object placement with text alone is hard, and making adjustments is even more complex.

Advanced workflows using ControlNets—tools that enhance image generation by providing greater control over the output—offer solutions, but their setup complexity limits broader accessibility. To help overcome these challenges and fast-track access to advanced AI capabilities, NVIDIA at the CES trade show earlier this year announced the NVIDIA AI Blueprint for 3D-guided generative AI for RTX PCs. This sample workflow includes everything needed to start generating images with full composition control. Users can download the new Blueprint today.

As enterprises increasingly adopt AI, securing AI factories—where complex, agentic workflows are executed—has never been more critical. NVIDIA is bringing runtime cybersecurity to every AI factory with a new NVIDIA DOCA software framework, part of the NVIDIA cybersecurity AI platform. Running on the NVIDIA BlueField networking platform, NVIDIA DOCA Argus operates on every node to immediately detect and respond to attacks on AI workloads, integrating seamlessly with enterprise security systems to deliver instant threat insights. The DOCA Argus framework provides runtime threat detection by using advanced memory forensics to monitor threats in real time, delivering detection speeds up to 1,000x faster than existing agentless solutions—without impacting system performance.

Unlike conventional tools, Argus runs independently of the host, requiring no agents, integration or reliance on host-based resources. This agentless, zero-overhead design enhances system efficiency and ensures resilient security in any AI compute environment, including containerized and multi-tenant infrastructures. By operating outside the host, Argus remains invisible to attackers—even in the event of a system compromise. Cybersecurity professionals can seamlessly integrate the framework with their SIEM, SOAR and XDR security platforms, enabling continuous monitoring and automated threat mitigation and extending their existing cybersecurity capabilities for AI infrastructure.

Generative AI is unlocking new capabilities for PCs and workstations, including game assistants, enhanced content-creation and productivity tools and more. NVIDIA NIM microservices, available now, and AI Blueprints, in the coming weeks, accelerate AI development and improve its accessibility. Announced at the CES trade show in January, NVIDIA NIM provides prepackaged, state-of-the-art AI models optimized for the NVIDIA RTX platform, including the NVIDIA GeForce RTX 50 Series and, now, the new NVIDIA Blackwell RTX PRO GPUs. The microservices are easy to download and run. They span the top modalities for PC development and are compatible with top ecosystem applications and tools.

The experimental System Assistant feature of Project G-Assist was also released today. Project G-Assist showcases how AI assistants can enhance apps and games. The System Assistant allows users to run real-time diagnostics, get recommendations on performance optimizations, or control system software and peripherals - all via simple voice or text commands. Developers and enthusiasts can extend its capabilities with a simple plug-in architecture and new plug-in builder.