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Year: 2024

High Can See Clearly Now: AI-Powered NVIDIA RTX Video HDR Transforms Standard Video Into Stunning High Dynamic Range

RTX Remix open beta adds full ray tracing, DLSS, Reflex and generative AI tools for modders; the new GeForce RTX 4070 Ti SUPER is available now; the January Studio Driver is released; and 3D artist Vishal Ranga creates vibrant scenes using AI this week ‘In the NVIDIA Studio.’
January 24, 2024 by Gerardo Delgado
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Editor’s note: This post is part of our weekly In the NVIDIA Studio series, which celebrates featured artists, offers creative tips and tricks, and demonstrates how NVIDIA Studio technology improves creative workflows. We’re also deep diving on new GeForce RTX 40 Series GPU features, technologies and resources, and how they dramatically accelerate content creation.

RTX Video HDR — first announced at CES — is now available for download through the January Studio Driver. It uses AI to transform standard dynamic range video playing in internet browsers into stunning high dynamic range (HDR) on HDR10 displays.

PC game modders now have a powerful new set of tools to use with the release of the NVIDIA RTX Remix open beta.

It features full ray tracing, NVIDIA DLSS, NVIDIA Reflex, modern physically based rendering assets and generative AI texture tools so modders can remaster games more efficiently than ever.

Pick up the new GeForce RTX 4070 Ti SUPER available from custom board partners in stock-clocked and factory-overclocked configurations to enhance creating, gaming and AI tasks.

Get creative superpowers with the GeForce RTX 4070 Ti SUPER available now.

Part of the 40 SUPER Series announced at CES, it’s equipped with more CUDA cores than the RTX 4070, a frame buffer increased to 16GB, and a 256-bit bus — perfect for video editing and rendering large 3D scenes. It runs up to 1.6x faster than the RTX 3070 Ti and 2.5x faster with DLSS 3 in the most graphics-intensive games.

And this week’s featured In the NVIDIA Studio technical artist Vishal Ranga shares his vivid 3D scene Disowned — powered by NVIDIA RTX and Unreal Engine with DLSS.

RTX Video HDR Delivers Dazzling Detail

Using the power of Tensor Cores on GeForce RTX GPUs, RTX Video HDR allows gamers and creators to maximize their HDR panel’s ability to display vivid, dynamic colors, preserving intricate details that may be inadvertently lost due to video compression.

RTX Video HDR and RTX Video Super Resolution can be used together to produce the clearest streamed video anywhere, anytime. These features work on Chromium-based browsers such as Google Chrome or Microsoft Edge.

To enable RTX Video HDR:

  1. Download and install the January Studio Driver.
  2. Ensure Windows HDR features are enabled by navigating to System > Display > HDR.
  3. Open the NVIDIA Control Panel and navigate to Adjust video image settings > RTX Video Enhancement — then enable HDR.

Standard dynamic range video will then automatically convert to HDR, displaying remarkably improved details and sharpness.

RTX Video HDR is among the RTX-powered apps enhancing everyday PC use, productivity, creating and gaming. NVIDIA Broadcast supercharges mics and cams; NVIDIA Canvas turns simple brushstrokes into realistic landscape images; and NVIDIA Omniverse seamlessly connects 3D apps and creative workflows. Explore exclusive Studio tools, including industry-leading NVIDIA Studio Drivers — free for RTX graphics card owners — which support the latest creative app updates, AI-powered features and more.

RTX Video HDR requires an RTX GPU connected to an HDR10-compatible monitor or TV. For additional information, check out the RTX Video FAQ.

Introducing the Remarkable RTX Remix Open Beta

Built on NVIDIA Omniverse, the RTX Remix open beta is available now.

The NVIDIA RTX open beta is out now.

It allows modders to easily capture game assets, automatically enhance materials with generative AI tools, reimagine assets via Omniverse-connected apps and Universal Scene Description (OpenUSD), and quickly create stunning RTX remasters of classic games with full ray tracing and NVIDIA DLSS technology.

RTX Remix has already delivered stunning remasters, such as Portal with RTX and the modder-made Portal: Prelude RTX. Orbifold Studios is now using the technology to develop Half-Life 2 RTX: An RTX Remix Project, a community remaster of one of the highest-rated games of all time. Check out the gameplay trailer, showcasing Orbifold Studios’ latest updates to Ravenholm:

Learn more about the RTX Remix open beta and sign up to gain access.

Leveling Up With RTX

Vishal Ranga has a decade’s worth of experience in the gaming industry, where he pursues level design.

“I’ve loved playing video games since forever, and that curiosity led me to game design,” he said. “A few years later, I found my sweet spot in technical art.”

Ranga specializes in level design.

His stunning scene Disowned was born out of experimentation with Unreal Engine’s new ray-traced global illumination lighting capabilities.

Remarkably, he skipped the concepting process — the entire project was conceived solely from Ranga’s imagination.

Applying the water shader and mocking up the lighting early helped Ranga set up the mood of the scene. He then updated old assets and searched the Unreal Engine store for new ones — what he couldn’t find, like fishing nets and custom flags, he created from scratch.

Ranga meticulously organizes assets.

“I chose a GeForce RTX GPU to use ray-traced dynamic global illumination with RTX cards for natural, more realistic light bounces.” — Vishal Ranga

Ranga’s GeForce RTX graphics card unlocked RTX-accelerated rendering for high-fidelity, interactive visualization of 3D designs during virtual production.

Next, he tackled shader work, blending in moss and muck into models of wood, nets and flags. He also created a volumetric local fog shader to complement the assets as they pass through the fog, adding greater depth to the scene.

Shaders add extraordinary depth and visual detail.

Ranga then polished everything up. He first used a water shader to add realism to reflections, surface moss and subtle waves, then tinkered with global illumination and reflection effects, along with other post-process settings.

Materials come together to deliver realism and higher visual quality.

Ranga used Unreal Engine’s internal high-resolution screenshot feature and sequencer to capture renders. This was achieved by cranking up screen resolution to 200%, resulting in crisper details.

Throughout, DLSS enhanced Ranga’s creative workflow, allowing for smooth scene movement while maintaining immaculate visual quality.

When finished with adjustments, Ranga exported the final scene in no time thanks to his RTX GPU.

 

Ranga encourages budding artists who are excited by the latest creative advances but wondering where to begin to “practice your skills, prioritize the basics.”

“Take the time to practice and really experience the highs and lows of the creation process,” he said. “And don’t forget to maintain good well-being to maximize your potential.”

3D artist Vishal Ranga.

Check out Ranga’s portfolio on ArtStation.

Follow NVIDIA Studio on Instagram, X and Facebook. Access tutorials on the Studio YouTube channel and get updates directly in your inbox by subscribing to the Studio newsletter. 

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NVIDIA GTC 2026: Live Updates on What’s Next in AI

Rolling coverage from San Jose, including NVIDIA CEO Jensen Huang’s keynote, news highlights, live demos and on‑the‑ground color through March 19.
March 19, 2026 by NVIDIA Writers
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Advancing Open Source AI, NVIDIA Donates Dynamic Resource Allocation Driver for GPUs to Kubernetes Community

In addition, NVIDIA announced at KubeCon Europe a confidential containers solution for GPU-accelerated workloads, updates to the NVIDIA KAI Scheduler and new open source projects to enable large-scale AI workloads.
March 24, 2026 by Justin Boitano
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Artificial intelligence has rapidly emerged as one of the most critical workloads in modern computing.

For the vast majority of enterprises, this workload runs on Kubernetes, an open source platform that automates the deployment, scaling and management of containerized applications.

To help the global developer community manage high-performance AI infrastructure with greater transparency and efficiency, NVIDIA is donating a critical piece of software — the NVIDIA Dynamic Resource Allocation (DRA) Driver for GPUs — to the Cloud Native Computing Foundation (CNCF), a vendor-neutral organization dedicated to fostering and sustaining the cloud-native ecosystem. 

Announced today at KubeCon Europe, CNCF’s flagship conference running this week in Amsterdam, the donation moves the driver from being vendor-governed to offering full community ownership under the Kubernetes project. This open environment encourages a wider circle of experts to contribute ideas, accelerate innovation and help ensure the technology stays aligned with the modern cloud landscape. 

“NVIDIA’s deep collaboration with the Kubernetes and CNCF community to upstream the NVIDIA DRA Driver for GPUs marks a major milestone for open source Kubernetes and AI infrastructure,” said Chris Aniszczyk, chief technology officer of CNCF. “By aligning its hardware innovations with upstream Kubernetes and AI conformance efforts, NVIDIA is making high-performance GPU orchestration seamless and accessible to all.”

In addition, in collaboration with the CNCF’s Confidential Containers community, NVIDIA has introduced GPU support for Kata Containers, lightweight virtual machines that act like containers. This extends hardware acceleration into a stronger isolation, separating workloads for increased security and enabling AI workloads to run with enhanced protection so organizations can easily implement confidential computing to safeguard data.

Simplifying AI Infrastructure

Historically, managing the powerful GPUs that fuel AI within data centers required significant effort. 

This contribution is designed to make high-performance computing more accessible. Key benefits for developers include:

  • Improved Efficiency: The driver allows for smarter sharing of GPU resources, delivering effective use of computing power, with support of NVIDIA Multi-Process Service and NVIDIA Multi-Instance GPU technologies.
  • Massive Scale: It provides native support for connecting systems together, including with NVIDIA Multi-Node NVlink interconnect technology. This is essential for training massive AI models on NVIDIA Grace Blackwell systems and next-generation AI infrastructure.
  • Flexibility: Developers can dynamically reconfigure their hardware to suit their needs, changing how resources are allocated on the fly.
  • Precision: The software supports fine-tuned requests, allowing users to ask for the specific computing power, memory settings or interconnect arrangement needed for their applications.

A Collaborative, Industry-Wide Effort

NVIDIA is collaborating with industry leaders — including Amazon Web Services, Broadcom, Canonical, Google Cloud, Microsoft, Nutanix, Red Hat and SUSE — to drive these features forward for the benefit of the entire cloud-native ecosystem.

“Open source will be at the core of every successful enterprise AI strategy, bringing standardization to the high-performance infrastructure components that fuel production AI workloads,” said Chris Wright, chief technology officer and senior vice president of global engineering at Red Hat. “NVIDIA’s donation of the NVIDIA DRA Driver for GPUs helps to cement the role of open source in AI’s evolution, and we look forward to collaborating with NVIDIA and the broader community within the Kubernetes ecosystem.”

“Open source software and the communities that sustain it are a cornerstone of the infrastructure used for scientific computing and research,” said Ricardo Rocha, lead of platforms infrastructure at CERN. “For organizations like CERN, where efficiently analyzing petabytes of data is essential to discovery, community-driven innovation helps accelerate the pace of science. NVIDIA’s donation of the DRA Driver strengthens the ecosystem researchers rely on to process data across both traditional scientific computing and emerging machine learning workloads.”

Expanding the Open Source Horizon

This donation is just part of NVIDIA’s broader initiatives to support the open source community. For example, NVSentinel — a system for GPU fault remediation — and AI Cluster Runtime, an agentic AI framework, were announced at GTC last week.

In addition, NVIDIA announced at GTC new open source projects including the NVIDIA NemoClaw reference stack and NVIDIA OpenShell runtime for securely running autonomous agents. OpenShell provides fine-grained programmable policy security and privacy controls, and natively integrates with Linux, eBPF and Kubernetes.

NVIDIA also today announced that its high-performance AI workload scheduler, the KAI Scheduler, has been onboarded as a CNCF Sandbox project — a key step toward fostering broader collaboration and ensuring the technology evolves alongside the needs of the wider cloud-native ecosystem. Developers and organizations can use and contribute to the KAI Scheduler today.

NVIDIA remains committed to actively maintaining and contributing to Kubernetes and CNCF projects to help meet the rigorous demands of enterprise AI customers. 

In addition, following the release of NVIDIA Dynamo 1.0, NVIDIA is expanding the Dynamo ecosystem with Grove, an open source Kubernetes application programming interface for orchestrating AI workloads on GPU clusters. Grove, which enables developers to express complex inference systems in a single declarative resource, is being integrated with the llm-d inference stack for wider adoption in the Kubernetes community. 

Developers and organizations can begin using and contributing to the NVIDIA DRA Driver today.

Visit the NVIDIA booth at KubeCon to see live demos of this technology in action.

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NVIDIA, Telecom Leaders Build AI Grids to Optimize Inference on Distributed Networks

AT&T, T‑Mobile, Comcast, Spectrum and others are building AI grids using NVIDIA AI infrastructure, while Personal AI, Linker Vision, Serve Robotics and Decart are deploying real-time AI applications across the grid.
March 17, 2026 by Kanika Atri
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As AI‑native applications scale to more users, agents and devices, the telecommunications network is becoming the next frontier for distributing AI. 

At NVIDIA GTC 2026, leading operators in the U.S. and Asia showed that this shift is underway, announcing AI grids — geographically distributed and interconnected AI infrastructure — using their network footprint to power and monetize new AI services across the distributed edge.  

Different operators are taking different paths. Many are starting by lighting up existing wired edge sites as AI grids they can monetize today. Others harness AI-RAN — a technology that enables the full integration of AI into the radio access network — as a workload and edge inference platform on the same grid.  

Telcos and distributed cloud providers run some of the most expansive infrastructure in the world: about 100,000 distributed network data centers worldwide, spanning regional hubs, mobile switching offices and central offices, with enough spare power to offer more than 100 gigawatts of new AI capacity over time.

AI grids turn this existing real-estate, power and connectivity into a geographically distributed computing platform that runs AI inference closer to users, devices and data, where response and cost per token align best. This is more than an infrastructure upgrade — it’s a structural change in how AI is delivered, putting telecom networks at the center of scaling AI rather than just carrying its traffic. 

Global Operators Turn Distributed Networks Into AI Grids

Across six major operators, AI grids are moving from concept to reality.

AT&T, a leader in connected IoT with over 100 million connections across thousands of device types, is partnering with Cisco and NVIDIA to build an AI grid for IoT. By running AI on a dedicated IoT core and moving AI inference closer to where data is created, AT&T can support mission‑critical, real‑time applications like public‑safety use cases with Linker Vision, enabling faster detection, alerting and response while helping keep sensitive information under customer control at the network edge.

“Scaling AI services that are both highly secure and accessible for enterprises and developers is a core pillar of our IoT connectivity strategy,” said Shawn Hakl, senior vice president of product at AT&T Business. “By combining AT&T’s business‑grade connectivity, localized AI compute and zero‑trust security while working with members of the NVIDIA Inception program and harnessing Cisco’s AI Grid with NVIDIA infrastructure and Cisco Mobility Services Platform, we’re bringing real‑time AI inference closer to where data is generated — accelerating digital transformation and unlocking new business opportunities.”

Comcast is developing one of the nation’s largest low‑latency broadband footprints into an AI grid for real‑time, hyper‑personalized experiences. Working with NVIDIA, Decart, Personal AI and HPE, Comcast has validated that its AI grid keeps conversational agents, interactive media and NVIDIA GeForce NOW cloud gaming responsive and economical even during demand spikes, with significantly higher throughput and lower cost per token.

Spectrum has the network infrastructure to support an AI grid that spans more than 1,000 edge data centers and hundreds of megawatts of capacity less than 10 milliseconds away from 500 million devices. The initial deployment focuses on rendering high-resolution graphics for media production using remote GPUs embedded across Spectrum’s fiber-powered, low-latency network.

Akamai is building a globally distributed AI grid, expanding Akamai Inference Cloud across more than 4,400 edge locations with thousands of NVIDIA RTX PRO 6000 Blackwell Server Edition GPUs. Akamai’s AI grid orchestration platform matches each request to the right tier of compute, improving the token economics of inference while powering low-latency, real-time AI experiences for applications like gaming, media, financial services and retail.

Indosat Ooredoo Hutchison is connecting its sovereign AI factory with distributed edge and AI‑RAN sites across Indonesia to build an AI grid for local innovation. By running Sahabat-AI — a Bahasa Indonesia-based platform — on this grid within Indonesia’s borders, Indosat can bring localized AI services closer to hundred millions of Indonesians across thousands of islands, giving local developers and startups a sovereign platform to build AI applications that are fast, culturally relevant and compliant by design.

T‑Mobile  is working with NVIDIA to explore edge AI applications using NVIDIA RTX PRO 6000 Blackwell Server Edition GPUs, demonstrating how distributed network locations could support emerging AI-RAN and edge inference use cases. Developers including LinkerVision, Levatas, Vaidio, Archetype AI and Serve Robotics are already piloting smart‑city, industrial and retail applications on the grid, connecting cameras, delivery robots and city‑scale agents to real-time intelligence on the network edge. This demonstrates how cell sites and mobile switching offices can support distributed edge AI workloads while continuing to deliver advanced 5G connectivity.

New AI‑Native Services Put Telecom AI Grids to Work

AI grids are becoming foundational to a new class of AI‑native applications — real‑time, hyper‑personalized, concurrent and token-intensive.

Personal AI is using NVIDIA Riva to power human‑grade conversational agents on the AI grid. By running small language models closer to users, it achieves sub-500 millisecond end-to-end latency and over 50% lower cost-per-token, enabling voice experiences that feel natural while remaining economically viable at scale.

Linker Vision is transforming city operations by running real‑time vision AI on the AI grid. By processing thousands of camera feeds across distributed edge sites, it delivers predictable latency for live detection and instant alerting — enabling safer, smarter cities with up to 10x faster traffic accident detection, 15x faster disaster response and sub‑minute alerts for unsafe crowd behavior. 

Decart is redefining hyper‑personalized distributed media by bringing real‑time video generation to AI grids. By running its Lucy models at the network edge, it achieves sub‑12-millisecond network latency, enabling interactive video streams and overlays that adapt instantly to each viewer, delivering smooth, immersive live video experiences even when viewership peaks.

AI Grid Reference Design and Ecosystem

The NVIDIA AI Grid Reference Design defines the building blocks — including NVIDIA accelerated computing, networking and software platforms — for deploying and orchestrating AI across distributed sites.

A growing ecosystem of full‑stack partners including Cisco and infrastructure partners like HPE are bringing AI grid solutions to market on systems built with the NVIDIA RTX PRO 6000 Blackwell Server Edition. Armada, Rafay and Spectro Cloud are among the partners building an AI grid control plane to seamlessly orchestrate workloads across distributed AI infrastructure.

“Physical AI is accelerating the shift from centralized intelligence to distributed decision making at the network edge,” said Masum Mir, senior vice president and general manager provider mobility at Cisco. “Our partnership with NVIDIA brings together the full stack — from NVIDIA GPUs to Cisco’s networking and mobility capabilities — enabling operators to power mission-critical applications, deliver real-time inferencing and participate in the AI value chain.”

Together, this ecosystem is helping telcos and distributed cloud providers redefine their role in the AI value chain — transforming the network edge into a unified intelligence layer that runs, scales and monetizes AI workloads.

Learn more about AI Grid.

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