NVIDIA’s Gamescom announcements are revealing what’s next for GeForce NOW, with new ways to play, more supported devices and platforms, and even more big PC games headed to the cloud.

New NVIDIA DLSS 4.5 technology controls give members more ways to fine-tune gameplay, while expanded support for new Steam devices, GOG single sign-on, Firefox browser support and more Amazon Fire TV devices continue broadening where and how GeForce RTX-powered PC gaming can be enjoyed.

Gamescom is also bringing a blockbuster lineup of PC games to GeForce NOW. CONTROL Resonant, Gears of War: E-Day, Onimusha: Way of the Sword, The Blood of Dawnwalker and STAR WARS Zero Company are all coming to the cloud at launch. 

For a limited time, members can also get CONTROL Resonant with the purchase of a 12-month GeForce NOW Ultimate membership.

The Gamescom extras don’t stop there. To celebrate, GeForce NOW is giving away more than 40 prizes in our Community Giveaway — including gaming hardware and GeForce NOW Ultimate memberships.

There’s even more fun to come. Check out the list of 13 games joining the GeForce NOW library this week.

Next-Level Cloud Gaming

This fall, GeForce NOW is introducing new NVIDIA DLSS 4.5 controls in the cloud, giving Ultimate members more choice over how supported games look and perform, even without the latest hardware. Powered by NVIDIA RTX graphics, each control offers something different: DLSS Super Resolution sharpens image quality, while Dynamic Frame Generation delivers lower latency and stutter for smoother streaming, and Ray Reconstruction enhances ray-traced visuals. Together, these technologies make it easier to tune supported games for image quality, responsiveness or a preferred balance between the two. 

Stay tuned to GFN Thursdays for details about best practices for enabling DLSS 4.5 overrides and how Dynamic Frame Generation can help Ultimate members enable more graphic settings while still getting smooth streaming.

GeForce NOW is also adding community-requested support for Steam Machine with Steam Controller later this fall, and later for Windows and macOS, delivering more ways to play and making it even easier for longtime Steam players to jump into the cloud using familiar hardware. The additions build on existing Steam Deck support and expand compatibility across the Steam ecosystem.

Launching GOG games just got even easier. With GOG single sign-on now available on GeForce NOW, members can securely link their account once, automatically sync supported games and launch them without having to sign in to GOG again — all with a single click.

With fewer steps between opening the app, skipping any downloads and jumping into gameplay, it’s another way GeForce NOW continues streamlining access to PC gaming libraries, making it easier to play the GOG library of games on TV platforms since gamers are automatically logged into their GOG accounts.

Find popular titles, like Cyberpunk 2077 and The Witcher 3: The Wild Hunt, in the app in the dedicated GOG row. Or search and filter for GOG games, then jump into supported titles from almost anywhere — all streamed from the cloud. Check back on GFN Thursdays for the latest games joining the GeForce NOW library, including newly supported games streaming from GOG.

Plus, GeForce NOW is now officially supported on Firefox, expanding browser access across all major Windows browsers. Members can jump into supported PC games directly from Firefox without downloading a dedicated app. Ultimate members can enjoy GeForce RTX-powered gaming at up to 1440p and 120 frames per second.

Simply download the latest version of Firefox and visit play.geforcenow.com to get started. The cloud handles downloads, installs and updates, keeping local storage free while games stay ready to play in the cloud. 

GeForce NOW is expanding support to additional Amazon Fire TV devices. GeForce NOW first launched on Fire TV devices earlier this year, and will soon expand to the Fire TV Stick 4K Select, a 4K streaming stick that’s ready to game right out of the box. Streaming from the cloud means supported PC games are ready to play using a compatible controller, making it easy to enjoy GeForce NOW on the biggest screen in the house with even more options to play from the comfort of the couch. 

And that’s not all the awesomeness from Amazon. Later this year, Fire TV users will be able to purchase GeForce NOW memberships directly through Amazon, creating a more seamless path from device setup to gameplay.

These new features will arrive alongside some of the biggest games coming to the cloud throughout the year.

Get Ready for Launch

Five newly announced and highly anticipated games are coming to GeForce NOW at launch: CONTROL Resonant, Gears of War: E-Day, Onimusha: Way of the Sword, The Blood of Dawnwalker and STAR WARS Zero Company. 

Better yet, members can lock in one of them early: For a limited time, purchase a 12-month GeForce NOW Ultimate membership and receive CONTROL Resonant at launch at no additional cost, and be first to experience Faden’s next chapter. The offer is available Tuesday, Aug. 25, through Sunday, Sept. 27.

Ultimate members can stream the full lineup with GeForce RTX 5080-class performance in the cloud, including NVIDIA DLSS 4, ray tracing, NVIDIA Reflex and Cinematic Quality Streaming technologies, with up to 5K high dynamic range on supported devices. 

Whether diving into new worlds or returning to iconic franchises, GeForce NOW lets members skip expensive hardware upgrades, massive installs and storage management so they can jump straight into the action the moment each game launches. Here’s a closer look at what’s coming:

Explore a warped Manhattan on the brink of paranatural annihilation in Remedy’s CONTROL Resonant. Harness Dylan Faden’s extraordinary powers to battle the Hiss, the Mold and other reality-bending threats while searching for his sister, Federal Bureau of CONTROL Director Jesse Faden.

Experience the horror and brutality of Emergence Day in the Coalition’s Gears of War: E-Day. Fourteen years before the original Gears of War, join Marcus Fenix and Dominic Santiago in a gripping origin-story campaign as the Locust Horde first erupt from below, igniting a desperate fight for survival. Rally squads and fight on in multiplayer with a reimagination of Gears’ iconic PvE mode, Horde Siege, or go head to head with a refined versus PvP.

Fight across a twisted vision of Edo-period Kyoto in Capcom’s Onimusha: Way of the Sword. Wield the mystical Oni Gauntlet, master intense swordplay and battle relentless demonic Genma beneath mysterious clouds of Malice.

Step into 14th-century Europe in Bandai Namco’s The Blood of Dawnwalker, where war, plague and rising vampire powers have reshaped history. Play as Coen, a young Dawnwalker torn between humanity and cursed strength, and shape his path while fighting to save his family.

Lead an elite squad behind enemy lines in STAR WARS Zero Company, a single-player turn-based game set during the twilight of the Clone Wars. Command former Republic officer Hawks and Zero Company through tactical battles and high-stakes missions where strategy, squad composition and player choices shape the fate of the galaxy.

More Ways to Win

GeForce NOW is marking Gamescom with a community giveaway featuring more than 40 prizes. Gamers will have a chance to score a MacBook Neo, Steam Deck or Chromebook — each paired with a one-year GeForce NOW Ultimate membership — along with Amazon Fire TV Sticks and GeForce NOW Ultimate memberships ranging from one month to one year.

Head over to the GeForce NOW community on Reddit for all the giveaway details and how to enter.

The Hunt Is On

Squad up and fight for survival in Aliens: Fireteam Elite 2, streaming on GeForce NOW this week at launch. Assemble a four-player fireteam of Colonial Marines and battle through swarms of Xenomorphs, deadly new threats and increasingly desperate encounters. With deeper squad mechanics, new and improved classes, smarter enemies and expanded customization, every mission puts teamwork — and nerves — to the test.

Members can also look for the following games joining the cloud this week:

• Aliens: Fireteam Elite 2 (New release on Steam, Aug. 25)
• Resonance: A Plague Tale Legacy (New release on Steam and Xbox, Aug. 27, available on Game Pass)
• STAR WARS Zero Company (New release on EA app and Steam, Aug. 27)
• Baldur’s Gate 3 (GOG)
• Call of Duty: Modern Warfare 4 – Open Beta Weekend (Battle.net, Steam and Xbox available from Aug. 28 10am PDT  to Sep. 1 at 10am PDT. For more information please visit the Call of Duty website).
• Clair Obscur: Expedition 33 (GOG)
• Cold Fear (GOG)
• IRON NEST: Heavy Turret Simulator (Steam)
• Kingdom Come: Deliverance II (GOG)
• Mount & Blade: Bannerlord II (GOG)
• No Man’s Sky (GOG)
• Paradise Killer (Steam)
• The Witcher 3: Wild Hunt (GOG)

Dates listed above reflect when games are released on their respective stores. GeForce NOW availability may vary, as games are onboarded after release and added throughout the week. Keep an eye on GeForce NOW channels and GFN Thursdays for availability updates on announced titles.

To experience GeForce NOW, gamers can start with a GeForce NOW day pass and try premium cloud gaming before committing to a membership. Better yet, the cost of a day pass can be applied toward a first membership purchase, making it even easier to level up when ready.

Check back every GFN Thursday for new games, new features and even more ways to play on GeForce NOW. Which Gamescom announcement has you most excited? Let us know on X or in the comments below. 

The future of PC gaming is on display with GeForce NOW.

DLSS 4.5 controls
Expanded device support
Upcoming AAA games
More ways to play
Community giveaway
Firefox browser support
… and more! #gamescom

Details https://t.co/Rz6RnX0Pt4 pic.twitter.com/sShKRhzrD3

— NVIDIA GeForce NOW (@NVIDIAGFN) August 25, 2026

The next wave of AI is placing new demands on infrastructure. 

As AI agents and trillion-parameter workloads become mainstream, the performance of AI infrastructure depends not only on compute, but on how compute, memory, storage, networking and software are designed together as a unified system.

To help hyperscalers and AI innovators build the next generation of semi-custom AI infrastructure, NVIDIA today expanded NVIDIA NVLink Fusion with NVIDIA NVHBM, a next-generation high-bandwidth memory technology that brings higher memory performance and efficiency to XPUs. It will be validated and offered by leading memory partners, extending this advanced memory capability to NVLink Fusion customers.

Traditional HBM architectures place the memory controller on the XPU die, consuming valuable silicon area that could otherwise be dedicated to compute. NVHBM, built on the same technology that NVIDIA will use for future GPUs, integrates NVIDIA’s custom memory controller into the HBM base die. 

By integrating the memory controller into the 3D HBM stack instead of the XPU, NVHBM delivers up to 30% greater memory bandwidth and 15% lower HBM power consumption, and frees up to 25% more area on XPU compute die compared with standard HBM4E.

NVIDIA is establishing a standard NVHBM implementation, available from multiple memory providers. This reduces the engineering effort required to integrate and qualify memory across multiple suppliers — giving NVLink Fusion customers a faster path for bringing custom AI chips to market. 

Amazon’s Annapurna Labs will be the first to work on NVHBM as part of its broader collaboration with NVIDIA around NVLink Fusion.

AWS and NVIDIA Continue NVLink Fusion Collaboration

Amazon’s Annapurna Labs will work with NVIDIA on NVHBM technology and the NVLink scale-up architecture to enhance performance and efficiency for AI workloads.

This builds on AWS’s previously announced support for NVLink Fusion. Annapurna Labs will support NVLink Fusion with its next-generation Trainium chips starting with Trainium4, which would allow Amazon chips and NVIDIA GPUs to work together with common rack-scale architecture.

“NVHBM represents a new architectural approach to advancing high-bandwidth memory performance and efficiency,” said Nafea Bshara, vice president of Annapurna Labs at Amazon. “We look forward to this technology collaboration to benefit future AWS infrastructure designs.” 

Vertically Integrated and Horizontally Open

NVLink Fusion enables partners to connect custom XPUs and CPUs to NVIDIA’s rack-scale platform.

Partners can access NVIDIA NVLink chiplets, NVLink-C2C, NVLink Switches and NVIDIA MGX systems and racks, as well as a broad ecosystem of CPU partners, ASIC designers, system manufacturers and technology providers. 

Offered with each generation of NVIDIA’s rack-scale system architecture, NVLink Fusion allows hyperscalers and AI-native companies to focus engineering resources on XPU innovation while using a proven technology stack for scale-up and scale-out networking, rack-scale systems and software — creating a faster, lower-risk path to deploying semi-custom AI infrastructure.

Learn more about NVLink and NVLink Fusion.

NVIDIA is bringing the next wave of RTX gaming to the Gamescom conference running this week in Cologne, Germany, with support for new games, anti-cheat technologies and increased visual quality. 

Electronic Arts, Embark and Ubisoft are among the latest game publishers and developers bringing their blockbuster titles to NVIDIA RTX Spark ahead of its launch this fall. They join the publishers that announced RTX Spark support at COMPUTEX in May, including KRAFTON, NetEase, Riot Games and XBOX. 

NVIDIA is working closely with game developers and ecosystem partners to ensure today’s top PC games — including EA’s EA SPORTS F1 25 and Apex Legends, Ubisoft’s Anno 117: Pax Romana and Embark Studios’ ARC Raiders and THE FINALS — run seamlessly on RTX Spark devices.

“We’re excited to bring Anno 117: Pax Romana to a new generation of RTX-powered PCs,” said Stéphane Jankowski, Ubisoft executive producer of Anno 117 : Pax Romana. “Our teams build the game to be seen wide: full districts, long sightlines, thousands of details at once. Seeing RTX Spark hold that level of fidelity from a single chip is genuinely impressive.”

RTX Spark reinvents personal computing, bringing personal AI agents, advanced content creation and high-performance gaming together in a platform engineered from the ground up for the next wave of Windows PCs. For gamers, RTX Spark combines Windows devices with NVIDIA RTX technologies, including DLSS and Reflex. 

Bringing Anti-Cheat to RTX Spark

Supporting modern PC gaming goes beyond running the game itself. Many of today’s biggest online games rely on sophisticated anti-cheat technologies, to enable fair play across the multiple platforms these games run on.  

NVIDIA has been working with EA to bring native EA Javelin Anticheat support to RTX Spark, including for EA’s biggest multiplayer games, such as EA SPORTS F1 25. RTX Spark launches this fall. 

RTX Delivers Next-Generation Gaming

RTX Spark is just one part of NVIDIA’s lineup of gaming announcements at Gamescom, including:

• DLSS 4.5 Ray Reconstruction is available now, featuring NVIDIA’s latest second-generation transformer model to deliver even higher image quality in ray- and path-traced content. It replaces traditional denoisers with an NVIDIA supercomputer-trained AI network to reconstruct higher-quality pixels in the parts of a ray-traced frame where rays were not sampled.
• Path tracing comes to CONTROL Resonant and 007 First Light to deliver new levels of visual fidelity. 
• NVIDIA ACE technologies are coming to Aniimo in early 2027, letting players naturally interact with their creatures.
• Gears of War: E-Day includes RTX Mega Geometry integration, advancing image quality on GeForce RTX GPUs, alongside ray tracing and DLSS 4.5.
• RTX Remix keeps remastering PC classics, with a major Painkiller RTX mod update available now and The Elder Scrolls III: Morrowind RTX coming soon.
• GeForce NOW gives members more ways to play with new DLSS 4.5 controls, expanded support for Steam Machine and other devices and platforms, plus blockbuster PC games including CONTROL Resonant, Gears of War: E-Day and more coming to the cloud at launch.

Find more on NVIDIA’s announcements at Gamescom in this GeForce article. 

To generate intelligence at scale, AI factories run continuously, and their economics are defined by delivered output: tokens per second, tokens per watt, cost per token, utilization and uptime. 

That requires AI infrastructure designed and built as a full factory, not a collection of individual accelerators.

Hyperscalers and AI-native companies building custom XPUs must consider not just XPU design, but the design and development of the entire AI platform, including scale-up and scale-out networking, rack-scale architecture, production factory software and a robust supplier ecosystem. 

At AI factory scale, this path is complex and costly, and represents a fundamental obstacle to getting XPUs to market quickly. 

Breaking the constraint means combining custom XPUs with proven, mature infrastructure — allowing builders to focus innovation where it matters most while harnessing established technology for the rest. 

NVLink Fusion delivers on that need, connecting XPUs to NVIDIA’s world-leading AI infrastructure to increase performance, accelerate time to market and mitigate risk for semi-custom AI factories.

Unlock XPU Performance With Fast Scale-Up

For modern workloads such as running trillion-parameter models, mixture-of-experts architectures and agentic AI, if the scale-up fabric cannot keep up, utilization drops and cost per token rises.

A scale-up networking solution must excel on three dimensions: 

• Delivered performance: End-to-end network performance, in-network compute and  mature software integration.
• Factory resiliency: Uptime, continuous health monitoring and telemetry, and component-level serviceability while the factory keeps running.
• Platform maturity: Reduced operational risk by using a mature technology stack with a demonstrated track record of large-scale deployments and realized return on investment. 

As an example, NVLink Fusion brings XPUs into the NVIDIA NVLink scale-up domain. Sixth-generation NVLink provides leading high-bandwidth, low-latency networking across a 72-XPU domain. The end-to-end latency for XPU-to-XPU transfers is 3x lower than alternative solutions based on off-the-shelf Ethernet, and the packet rate is 10x higher.

For end-to-end performance, NVIDIA GB300 NVL72 systems help deliver significantly higher throughput and better interactivity compared with configurations that don’t use NVL72, and future NVLink roadmap configurations include domains of up to 1,152 accelerators and co-packaged optics.

NVLink Fusion also includes NVIDIA NVLink-C2C for connecting XPUs to NVIDIA Vera CPUs or other ecosystem CPUs, delivering up to 6x the energy efficiency of a PCIe interface — helping remove barriers between control and compute for agentic systems.

A Proven Stack and Ecosystem for Development and Deployment

Teams developing custom XPUs often underestimate the effort and complexity of turning XPU innovation into data center deployment. This includes:

• Integrating high-speed CPU and scale-up interfaces
• Sourcing and validating a scale-up network solution
• Designing compute and switch trays
• Designing and validating a rack architecture, including cooling and power
• Integrating security and storage
• Managing a complex supplier ecosystem

The ideal platform provides all of this, allowing teams to focus on targeted innovation while using proven solutions for the rest.

NVLink Fusion is supported by an ecosystem designed for rapid development, integration and deployment, spanning ASIC design, CPU, and IP and optical interconnect partners.

“NVLink Fusion gives customers the ability to choose the CPU architecture, the performance level, the software capabilities that best meet their needs for the workloads that they care about,” said Tim Wilson, vice president and general manager of data center silicon engineering at Intel.

NVLink Fusion adopters can also use the NVIDIA MGX rack-scale architecture and the same supply chain used for MGX-based systems such as NVIDIA Vera Rubin NVL72. Manufacturing partners manage design and integration, while MGX suppliers provide the building blocks for rack, cooling, power and emerging 800 VDC designs.

“With Vera Rubin [NVL72], we are looking at almost 100% automation of system builds in the manufacturing line,” said Jack Luoh, head of product and solution at QCT and Quanta Computer. “Most of those investments can be leveraged if the XPU leverages NVLink Fusion.”

The NVIDIA AI infrastructure platform is vertically integrated and horizontally open. NVLink Fusion adopters can optionally incorporate NVIDIA Rubin GPUs, Vera CPUs, co-packaged optics switches, ConnectX SuperNICs, BlueField DPUs, Mission Control software and full-rack solutions including NVIDIA Vera Rubin NVL72, Vera CPU Rack, LPX, STX and SPX.

Managing Risk With Infrastructure Standardization

AI factory planning doesn’t wait for silicon. Power procurement, facility design, cooling, rack layout and network architecture begin long before the final accelerator mix is available. A data center locked to one chip can become a schedule risk.

Different workloads may favor different accelerators, including XPUs, GPUs, CPUs and LPUs. GPU systems may work alongside semi-custom systems for training, post-training, reasoning, retrieval and serving.

“The value of the NVLink Fusion program is … [customers] can deploy their rack-level solution with the NVIDIA GPU, and then they can decouple the development of their XPU and put it at a different pace,” said Vince Hu, corporate senior vice president and general manager of the data center and computing business group at MediaTek.

NVLink Fusion addresses these challenges  through a unified architecture. XPU- and GPU-based systems such as Vera Rubin NVL72 can share rack footprints, networking, cooling, power delivery and management systems. Operators can move forward with buildout while deferring the precise silicon mix, then reprovision capacity as workload demand, silicon supply and business priorities change. 

“NVLink Fusion allows the hyperscalers or the custom ASIC designers to integrate their own custom CPU or XPU and bridges the NVIDIA technology with a third-party process to create a unified rack-scale architecture,” said Lie-Szu Juang, chair and chief strategy officer at GUC.

Designed, Validated and Operated as a Factory

Factory buildout is expensive, and mistakes can require costly rework. Infrastructure must be validated before construction begins. NVLink Fusion aligns with the NVIDIA DSX reference architecture for AI factories: codesigning buildings, power, cooling, compute and networking. The NVIDIA Omniverse DSX AI Factory Blueprint provides a digital twin and open reference design for gigawatt-scale AI factories, enabling partners to model facilities and technology together before deployment.

At the rack level, serviceability is part of performance. Reference compute trays feature 100% liquid cooling with no fans, cables or hoses, and allow trays to be removed while the rest of the rack remains operational. NVLink Switch trays are also liquid cooled and support continued operation during service.

“With NVLink Fusion we can use proven NVL72 rack design to have time-to-market, and we can have access to multiple suppliers to help us to deliver more into the hands of our customers,” said CC Lee, senior hardware development manager at Annapurna Labs, an Amazon company.

Software completes the factory. NVIDIA NCCL for distributed workloads, NVIDIA Dynamo and NIXL for disaggregation and NVIDIA Mission Control for cluster management, telemetry and debugging help operators run mixed AI infrastructure as a coordinated system.

With NVLink Fusion, XPUs can now meet a world-class AI platform, enabling hyperscalers and AI-native companies to build unified, semi-custom AI factories that combine the strengths of many builders into infrastructure no one company could build alone.

Learn more about NVLink Fusion.

The next era of AI inference won’t be defined by a single breakthrough chip, network or system. It’ll be defined by how every layer of the AI factory works together. That’s why NVIDIA is extending Vera Rubin NVL72 with fast token generation for agentic systems.

Announced today, the NVIDIA Vera Rubin rack-scale system NVIDIA Groq 3 LPX is in full production. In an Artificial Analysis benchmark running Gemma 4 31B, an open source agentic model, it delivered 3,400 output tokens per second for 100,000-token long-context use cases critical to agentic systems, 4x faster than the nearest alternative platform. 

Industry partners worldwide are adopting Vera Rubin platform solutions. SpaceXAI announced that NVIDIA Vera CPUs will power its next generation of agentic AI. CoreWeave has deployed into production Spectrum-X Multiplane, which connects NVIDIA Vera Rubin racks using multiple parallel switches to provide high-bandwidth, flat and lossless AI networks. Nebius is the first AI cloud to adopt NVIDIA Groq 3 LPX.

As AI shifts from training to reasoning and agentic, inference has become the new frontier. Agentic AI systems are generating more tokens, processing dramatically larger context windows and increasingly collaborating with other AI systems to solve complex problems. 

These workloads demand a new class of infrastructure optimized not just for performance but for throughput, responsiveness and economics at unprecedented scale. 

At the Hot Chips conference this week in Palo Alto, California, NVIDIA is showcasing how extreme codesign is reshaping the AI factory from end to end. By architecting compute, networking and inference acceleration as a unified system, NVIDIA is helping customers build infrastructure purpose-built for the emerging demands of long-context inference and multi-agent systems.

Extreme Codesign Optimizes for Performance

Extreme codesign is the guiding principle behind NVIDIA platforms. Vera Rubin is engineered to accelerate inference as agents reason over increasingly long sequences. 

NVIDIA Spectrum-X Ethernet moves those massive data flows efficiently across AI factories, and NVIDIA Groq 3 LPX is built to generate tokens at ultrafast speeds. Together, they show how NVIDIA is optimizing every stage of the AI pipeline, from context and communication to generation, as part of a single, integrated AI factory architecture.

NVIDIA Groq 3 LPX brings a new low-latency inference architecture designed to work alongside Vera Rubin NLV72, the most versatile AI factory platform, helping enterprises and cloud providers deliver the low latency, extreme throughput and scalable economics required for agentic applications.

Breakthrough performance comes not from optimizing individual components in isolation, but from codesigning every layer of the stack. From networking and context processing to large-scale inference, NVIDIA’s full-stack platform turns AI factories into integrated engines for intelligence, built to turn ever-growing volumes of tokens into revenue.

Tuesday, Aug. 24, 8:00 a.m. PT

NVIDIA Partners Adopt Vera Rubin for Lowest Token Costs

Nebius, a leading AI cloud, is first to adopt NVIDIA Groq 3 LPX, giving developers access to leading token generation speeds for highly responsive agentic AI applications. 

Adding NVIDIA Groq 3 LPX to NVIDIA Vera Rubin NVL72 in  Nebius Token Factory will boost inference performance so developers can build highly interactive agents, coding systems and other real-time AI experiences at scale.

Connecting NVIDIA Vera Rubin racks, CoreWeave is deploying Spectrum-X Multiplane in production, unlocking advances for its AI cloud infrastructure.

Tuesday, Aug. 24, 8:00 a.m. PT

SpaceXAI Adopts NVIDIA Vera CPUs for Agentic AI

SpaceXAI plans to build and scale its future AI architecture around NVIDIA Vera Rubin, from data centers on Earth to orbital satellites. The company plans to deploy NVIDIA Vera CPUs to accelerate the CPU-intensive work behind agentic AI, including orchestration, tool use, code execution, data processing and simulation. 

The SpaceXAI partnership extends NVIDIA’s full-stack AI platform to SpaceXAI, bringing together Vera CPUs, NVIDIA accelerated computing, networking and software to advance AI at unprecedented scale.

Designed for the agentic era, Vera Rubin provides leading per-core performance, exceptional memory bandwidth and predictable performance under load, helping agents complete tasks faster and keeping valuable GPU infrastructure fully utilized.

Tuesday, Aug. 24, 8:00 a.m. PT

NVIDIA Groq 3 LPX: The Interactive AI Inference Accelerator

Codesigned with the Vera Rubin NVL72 platform, NVIDIA Groq 3 LPX is helping AI factories deliver tokens at the lowest latency for agentic workloads.

Agentic AI is creating a new performance challenge: decode latency. As AI agents reason, use tools and interact with other systems, they generate responses one token at a time, causing even tiny delays to multiply across complex chains of work. To keep agents operating at the pace users expect, NVIDIA Groq 3 LPX extends the Vera Rubin NVL72 platform with specialized acceleration for token generation. 

NVIDIA Rubin GPUs handle large-scale context processing while LPX accelerates latency-sensitive decode workloads. The result is faster, more predictable token generation that helps AI factories deliver responsive reasoning, smoother agent interactions and greater infrastructure efficiency. 

Together, Rubin GPUs and LPUs are designed to eliminate the traditional tradeoff between speed and throughput, helping AI providers deliver responsive, large-scale inference for the next generation of agentic AI applications.

Building the Token Factory

As the industry shifts from model training to serving intelligence at scale, infrastructure must evolve into what NVIDIA describes as a “token factory” capable of delivering performance, throughput, intelligence integrity and economic efficiency simultaneously. Agentic AI systems increasingly communicate with other AI systems, access multiple data sources and maintain large amounts of context, creating unprecedented demand for fast inference.

NVIDIA Groq 3 LPX was designed for exactly these workloads. As an extension of the Vera Rubin NVL72, it enables ultrafast responsiveness even across massive context windows while helping service providers maximize throughput and infrastructure utilization. 

Extreme Codesign for Inference

Unlike standalone accelerators, NVIDIA Groq 3 LPX combines the strengths of GPUs and LPUs through extreme codesign. Rubin GPUs and LPUs jointly compute every layer of an AI model, enabling new levels of inference performance for agentic workloads. 

At scale, fleets of LPUs operate as a giant processor optimized for deterministic inference. A rack-scale NVIDIA Groq 3 LPX deployment can include 256 LP30 accelerators connected through direct chip-to-chip links, creating a highly efficient inference engine built for modern AI factories.

Designed for the Agentic AI Era

As reasoning models grow and agentic workflows generate ever more tokens, the infrastructure required to serve them must evolve. NVIDIA Groq 3 LPX extends the Vera Rubin NVL72 platform with a purpose-built inference architecture designed to maximize responsiveness, throughput and efficiency, helping power the next generation of AI factories.

And this is only the beginning, more optimizations, more models, more performance when paired with Vera Rubin NVL72 — new levels of throughput and interactivity are coming. Stay tuned. 

Tuesday, Aug. 24, 8:00 a.m. PT

NVIDIA Spectrum-X Multiplane Enables Massive AI Factory Scale on a Flatter, More Resilient Network​

As AI factories grow massive, the network has become a critical engine of performance. At Hot Chips, NVIDIA is spotlighting Spectrum-X Multiplane — the latest in the hardware-accelerated Spectrum-X Ethernet architecture that lets Ethernet scale to unprecedented size while avoiding the latency, jitter and cost of adding another network tier.

NVIDIA Spectrum-X Ethernet is designed as an end-to-end, AI-optimized Ethernet platform, combining NVIDIA Spectrum-X Ethernet switches, SuperNICs and software to improve the performance and efficiency of Ethernet-based AI infrastructure for AI factories and clouds. The platform is designed to deliver 1.6x better AI networking performance compared with off-the-shelf Ethernet, while providing consistent, predictable performance in multi-tenant environments.

Multiplane Unlocks Scale Without the Tradeoffs of a New Tier

Scaling an AI factory beyond today’s largest clusters traditionally means adding a third network tier, which adds latency, slows things down unpredictably and drives up the cost of cabling, optics and power. Spectrum-X Multiplane takes a simpler approach: It splits each server’s network connection into several independent paths, or “planes,” each running its own lightweight two-tier network. The result is a flat, simple network that scales to 512,000 GPUs, without the added cost and complexity of a third tier.

This all happens automatically. A dedicated hardware engine inside the NVIDIA ConnectX SuperNIC manages traffic across the planes and instantly reroutes around any failure, so applications and software simply see one fast, reliable connection. In an eight-plane topology, if one plane fails, the network still maintains about 90% of its total bandwidth, with hardware recovery that’s 11x faster than software-based multiplane load balancing. This translates to 1.6x higher AI factory output.

Built Through Extreme Codesign

That reliability comes from extreme codesign of Vera Rubin NVL72, spanning switch silicon, SuperNICs and software. Spectrum-X SN6000 series switches, based on the 102.4Tb/s Spectrum-6 Ethernet ASIC and ConnectX-9 SuperNICs, supporting up to 1,600Gb/s per GPU, are purpose-built for Vera Rubin NVL72 AI factories. Spectrum-XGS Ethernet extends that same codesign across data centers, letting multiple facilities function as a single AI super-factory and accelerating multi-site NCCL collectives by 1.9x.

Tuesday, Aug. 24, 8:00 a.m. PT

NVIDIA Introduces Scale-In Infrastructure for Agentic AI Factories, Powered by BlueField-4, DOCA

NVIDIA is introducing NVIDIA Scale-In, the fifth pillar of NVIDIA AI networking and a new class of accelerated network infrastructure for agentic AI factories. Scale-In extends purpose-built acceleration to the infrastructure services that secure, manage and operate the AI factory.

Powered by the NVIDIA BlueField-4 processor and NVIDIA DOCA software platform and connected over NVIDIA Spectrum-X Ethernet, NVIDIA Scale-In transforms the traditional north-south access network into a unified, accelerated infrastructure domain.

Cloud computing brought software-defined networking, composability and elasticity to the data center, enabling users, applications, data and services to scale dynamically. 

Agentic AI represents the next platform shift. AI factories bring together massive accelerated compute with growing numbers of users, applications and autonomous agents, all continuously interacting with data, storage and services. This transforms the demands on the infrastructure that brings AI to life. Networking, storage, cybersecurity and operations must now be accelerated alongside AI compute, combining software-defined flexibility with purpose-built hardware acceleration and full-stack codesign. 

NVIDIA Scale-In delivers multi-tenant networking, high-performance storage access, in-silicon security, elastic provisioning and real-time observability, while keeping infrastructure processing independent of host compute resources. By accelerating and codesigning these services as part of the AI factory, Scale-In helps security, data access and operations scale alongside AI compute. The result is secure, efficient and manageable shared infrastructure for deploying and operating agentic AI at massive scale.

Tuesday, Aug. 24, 8:00 a.m. PT

NVIDIA NVLink Fusion Connects XPUs to NVIDIA’s Leading AI Platform

NVIDIA NVLink Fusion brings custom silicon into NVIDIA’s world-leading AI infrastructure platform, enabling hyperscalers and AI-native companies to build semi-custom AI factories with greater performance, flexibility and speed.

As AI models grow in size and complexity, raw compute alone is not enough. AI factories require high-bandwidth, low-latency scale-up networking, proven rack-scale architectures and a full ecosystem spanning power, cooling, management software and supply chain. NVLink Fusion addresses these challenges by connecting custom XPUs and CPUs to NVIDIA’s scale-up and scale-out technology stack.

The platform includes sixth-generation NVIDIA NVLink and NVLink Switch purpose-built scale-up networking, as well as NVLink-C2C for energy-efficient connectivity between XPUs and CPUs. Through the NVIDIA MGX ecosystem, adopters can also use production-proven rack designs, components, manufacturing partner solutions and open, extensible software for distributed computing, disaggregated workloads and cluster management.

By standardizing GPU- and XPU-based systems on a unified architecture, NVLink Fusion helps decouple data center buildout from silicon readiness. Operators can share rack footprints, networking, cooling, power delivery and management systems, then adjust the mix of GPUs and XPUs as supply and workload requirements evolve.

NVLink Fusion extends the NVIDIA AI platform’s vertically integrated, horizontally open approach to custom silicon. It gives partners the freedom to innovate where they differentiate while drawing on NVIDIA technologies across compute, networking, infrastructure and software — creating a single, flexible AI factory that no one company could build alone.

According to OpenRouter data, agentic AI workloads consume 15x more tokens than a simple chat request. Why? 

Consider what happens when an AI agent researches a company for an investment decision. The agent queries financial databases, searches news and filings, invokes a sub-agent to run peer comparisons and model valuations, then synthesizes everything into a recommendation. Agents and sub-agents keep reasoning until the task is done, driving increased token demand. With every step, the accumulated tokens become the input to the next, making long-context handling central to agentic AI performance.

The same pattern plays out across every agentic use case, from software development to customer service to deep research. 

As agentic AI moves into production across industries, the infrastructure running it needs to meet that token demand efficiently. 

New measured performance data shows NVIDIA Vera Rubin NVL72 systems deliver up to 30x higher throughput per megawatt than NVIDIA GB300 NVL72 on agentic workloads. NVIDIA measured this inference throughput data using the SemiAnalysis AgentX workload, consisting of recorded real-world agentic coding sessions, with actual context growth, tool calls and sub-agent spawning preserved. For power-constrained AI factories, that translates directly into 30x more agentic work for the same energy footprint.

These early results for Vera Rubin NVL72 demonstrate NVIDIA’s accelerated pace of innovation. With continuous software optimizations, performance across both Vera Rubin NVL72 and GB300 NVL72 will continue to improve.

Vera Rubin NVL72: 30x Higher Throughput per Megawatt and 35x Lower Token Cost

Agentic workloads look fundamentally different from chat or document summarization, where input and output sequences typically range from 1K to 8K tokens. In agentic sessions, context accumulates across steps and can reach hundreds of thousands of input tokens, with wide variability in both input and output lengths across requests. Performance measurement must evolve to capture the full agent workflow rather than a single inference request.

The results below reflect performance measured on real-world agentic coding trajectories.

In SemiAnalysis AgentX, the NVIDIA Blackwell platform delivers leading performance across multiple agentic models including Kimi K3, MiniMax M3, GLM5.3, Qwen3.5 and DeepSeek V4 Pro. 

For example, GB300 NVL72 delivers up to 15x better throughput per megawatt than the NVIDIA Hopper architecture on the DeepSeek V4 Pro model, giving customers a high-performance foundation to run agentic workloads. This leap reflects the advantage of a larger scale-up GPU domain and codesigned software in delivering significantly better inference efficiency.

Vera Rubin extends that advantage, lifting the performance across the entire Pareto curve, to deliver as much as 30x higher throughput per megawatt than GB300 NVL72 on the DeepSeek V4 Pro model. These early results, measured using the SemiAnalysis AgentX workload and currently pending SemiAnalysis review, don’t yet reflect Vera CPU performance for tool calling. 

NVIDIA DSX MaxLPS technologies manages power across the GPU, rack and workload levels to provision up to 40% more GPUs within the same megawatt budget, pushing throughput per megawatt further at AI factory scale. 

Throughput per megawatt also directly impacts the cost of every token produced. At up to 35x lower cost per million tokens than GB300 NVL72, Vera Rubin NVL72 can run agents continuously, at scale, across the full breadth of customers’ workloads. 

For power-constrained AI factories, throughput per megawatt determines AI factory revenue and cost per million tokens determines the profit margin on that revenue.

Extreme Codesign for Agentic Scale

Modern inference optimization spans a range of techniques that are especially critical for agentic AI. NVIDIA Vera Rubin NVL72 enables all of these and more through extreme codesign across every layer of the platform to deliver multifold performance gains.

• Disaggregated serving separates context processing (prefill) from response generation (decode) so each scales independently.
• Rate matching synchronizes the speeds at which prefill GPUs and decode GPUs produce tokens to maximize efficiency.
• Large-scale expert parallelism distributes expert sub-networks in mixture-of-experts models across the scale-up GPU domain. 
• Distributed KV-caching extends memory across the scale-up GPU domain, while KV-cache offloading tiers less-active context to host and storage, keeping previously processed context accessible without recomputation.
• KV-aware routing directs incoming requests to the GPUs that already hold the relevant cached context, reducing redundant computation across long sessions. 
• Fused CUDA kernels like MegaMoE combine many computation and inter-GPU communication operations into a single execution pass, keeping GPUs active rather than waiting for data.

NVIDIA Rubin GPUs’ enhanced fifth-generation Tensor Cores and the third-generation Transformer Engine accelerate both prefill and decode stages of inference. NVFP4 quantization compresses model weights to 4-bit precision, reducing memory footprint and increasing throughput without sacrificing output quality. 

The NVL72 scale-up domain, a defining architecture across Vera Rubin and Grace Blackwell, enables the high-bandwidth and low-latency inter-GPU communication essential for techniques such as large-scale expert parallelism and distributed KV-caching. Purpose-built to power this scale-up domain, NVIDIA NVLink interconnect technology and NVLink Switches, now in their sixth generation, deliver 10x higher packet rates and 3x lower latency than off-the-shelf Ethernet alternatives.

Spanning optimized CUDA kernels, inference runtimes like NVIDIA TensorRT LLM and serving frameworks like NVIDIA Dynamo, NVIDIA’s software stack is codesigned with the hardware to enable inference optimizations.

While the results above reflect current Vera Rubin NVL72 performance, the full platform is a seven-chip architecture that also includes the NVIDIA Vera CPU, Groq 3 LPU, NVLink 6 Switch, BlueField-4 DPU, Spectrum-6 SPX and ConnectX-9 SuperNIC, all purpose-built for AI factories deploying agents at scale.

Extreme codesign also extends to NVIDIA’s co-engineering with its partners. Vera Rubin is in full production and is scaling across the ecosystem. 

Learn more about the NVIDIA Vera Rubin platform.

It’s a new way into the cloud. 

GeForce NOW welcomes Firefox support to the cloud, opening up another way to jump into high-performance PC gaming straight from the browser, starting today. Whether on a school laptop or everyday PC, it’s now even easier to play supported PC games without downloading a dedicated app.

Plus, discover 12 new games streaming from the GeForce NOW library this week, led by Gallipoli. 

No Downloads. Just Firefox.

GeForce NOW is now supported on Firefox, expanding access across even more devices. 

Members can outfox big downloads and jump into high-performance PC gaming directly from the browser — no waiting on lengthy game installs or updates. The experience delivers GeForce RTX-powered performance at up to 1440p and 120 frames per second for GeForce NOW Ultimate members. 

Simply download the latest version of Firefox, head to play.geforcenow.com and start playing. The cloud handles the heavy lifting, so games are ready to play without taking up valuable storage space on the device. Firefox joins Chrome, Edge and Opera in supporting GeForce NOW on Windows browsers.

Deploy the Games

Land on the beaches of the Gallipoli Peninsula in BlackMill Games’ Gallipoli, the latest entry in the WW1 Game Series. Fight as soldiers of the British and Ottoman Empires in intense 25v25 objective-based battles across coastal shores, deserts and urban battlefields, with 10 historical classes and over 50 authentic weapons and equipment options.

Teamwork is key in its grounded, one-shot-one-kill combat. Take command as an Officer, provide cover as a Heavy Machine Gunner or Sniper, keep the squad going as a Stretcher Bearer and more while navigating challenging campaigns.

Head to the frontlines from the cloud with no lengthy game downloads or installs taking up local storage space. Play directly in the newly supported Firefox browser, or take the action across PCs, Macs, Chromebooks, handhelds and more with GeForce NOW cloud saves.

In addition, members can look for the following games streaming throughout the week:

• Stars Reach (New release on Steam, Aug. 18)
• The Sinking City 2 (New release on Steam, Aug. 18)
• Gallipoli (New release on Steam, Aug. 20)
• DIVE or DIE – Children of Rain (Steam)
• Escape the Backroom (Xbox, available on Game Pass)
• Hell Is Us (Xbox, available on Game Pass)
• High on Life (Steam)
• High on Life 2 (Steam and Xbox, available on Game Pass)
• Parcel Simulator (Steam)
• Starvester (Steam)
• The Thaumaturge (Xbox, available on Game Pass)
• Tomb Raider IV-VI Remastered (Epic Games Store)

Dates listed above reflect when games are released on their respective stores. GeForce NOW availability may vary, as games are onboarded after release and added throughout the week. Keep an eye on GeForce NOW channels and GFN Thursdays for availability updates on announced titles.

To try GeForce NOW in Firefox, start with a GeForce NOW day pass and experience premium cloud gaming directly from the browser. If members later upgrade to a Performance or Ultimate membership, the cost of the day pass can be applied toward the first membership purchase.

What are you planning to play this weekend? Let us know on X or in the comments below.

AI factories are the defining infrastructure of the AI era — where compute transforms energy and data into intelligence that powers every business, industry and country.

In the AI economy, compute is revenue.

AI factories require a full stack of critical resources: advanced chips, packaging, memory and networking — as well as land, power and shell.

Just as NVIDIA has used its scale, long-term visibility and supply-chain partnerships to secure critical semiconductor resources, we are now applying that same discipline to secure LPS capacity exclusively for NVIDIA AI factories.

Today, we are partnering with SB Energy to secure LPS capacity at the exceptional PORTS-Pike Technology Campus in Portsmouth, Ohio, to host NVIDIA compute. OpenAI will be the tenant.

LPS: The Next Strategic Resource

For the vast majority of NVIDIA customers, securing LPS has long been a part of their infrastructure strategy.

The world’s largest cloud service providers and investment-grade enterprises have balance sheets, infrastructure expertise and long-term contracts to secure LPS independently. They build and operate AI factories using NVIDIA accelerated computing, networking, systems and software.

This model will continue to represent most of NVIDIA’s business.

But frontier AI labs are different.

Frontier AI labs have extraordinary demand for training and inference compute, but many are growing faster than their balance sheets and long-term credit profiles can support. They may have strong customer demand and rapidly growing revenue yet still lack the decades-long infrastructure contracts and investment-grade financing capacity needed to secure the AI factory infrastructure independently.

Their growth is increasingly constrained not by algorithms or customer demand, but by the availability of compute.

For these companies, more compute means more intelligence, more products, more users and more revenue. NVIDIA is helping provide the infrastructure that powers this flywheel.

PORTS-Pike: A Site for Generations of NVIDIA Compute

OpenAI will build and operate a world-class AI factory at PORTS-Pike. The AI factory will use NVIDIA’s full-stack DSX AI factory platform, including GPUs, CPUs, networking and infrastructure software.  

The initial deployment is expected to provide 4.25 gigawatts of AI factory capacity. Each generation of NVIDIA AI factory systems deployed at PORTS-Pike could represent approximately 1.5 million NVIDIA GPUs, or approximately $150 billion to $200 billion in NVIDIA revenue. Over 20 years, the site can support multiple upgrade cycles. 

This is the essential economic point: the LPS commitment secures a long-lived AI factory site, while the NVIDIA compute inside can be upgraded repeatedly. Each new generation can deliver greater production, more intelligence and better economics. 

NVIDIA may also choose to extend the arrangement at PORTS-Pike beyond the initial 4.25 gigawatts to secure the remaining capacity of 3.75 gigawatts. 

OpenAI and NVIDIA Expanding Compute Opportunity

More broadly, OpenAI has committed to substantial deployments of NVIDIA AI infrastructure through 2030. OpenAI’s existing and planned commitments represent approximately 12 gigawatts of NVIDIA compute, with an opportunity to expand to approximately 16 gigawatts if NVIDIA extends the PORTS-Pike arrangement beyond the initial 4.25 gigawatts. 

At these levels, the opportunity represents roughly $600 billion of NVIDIA compute through 2030. 

The Important Questions

What is NVIDIA guaranteeing, and for how long?

NVIDIA is supporting the LPS infrastructure at PORTS-Pike for approximately 4 gigawatts over a 20-year term, securing a site on which NVIDIA compute will be exclusively deployed.  

Our support is limited to defined portions of lease and power payments, along with a specified residual-value commitment — not the full cost of the site or all of the tenant’s obligations.   

The guarantee will become effective in phases as data centers are placed in service between 2028 and 2030. As OpenAI makes lease payments and capacity comes online, NVIDIA’s remaining exposure declines.

Why is NVIDIA guaranteeing PORTS-Pike?

LPS has become a critical constraint on AI factory deployment. NVIDIA is selectively securing exceptional sites where we can host multiple generations of NVIDIA compute and serve durable customer demand. 

The productive life of the site extends through multiple generations of NVIDIA systems, each capable of producing more intelligence and more revenue than the generation before.

Is this circular financing?

No. OpenAI will pay the lease.

NVIDIA uses its scale and long-term visibility to secure PORTS-Pike to host NVIDIA compute. This is the same discipline we apply to supply-chain management: we secure critical inputs when we have visibility into customer demand and when doing so enables long-term productive capacity.

What happens to PORTS-Pike if OpenAI does not use the site in the future?

NVIDIA compute is versatile, fungible and broadly adopted. The capacity can be resold to another qualified tenant across NVIDIA’s global ecosystem of cloud service providers, enterprises, AI labs and startups. 

CUDA makes NVIDIA compute more than hardware. It gives developers and NVIDIA engineers a common platform to continually improve installed systems. 

CUDA makes NVIDIA compute versatile. Versatility makes it fungible. Fungibility drives utilization and durability — making NVIDIA compute a productive asset: rentable and financeable. 

The value of an exceptional site, like PORTS-Pike, is not limited to one customer or one generation of compute. NVIDIA’s standardized platform, broad developer ecosystem and large market of potential users support the ability to redeploy productive capacity over time.

How much LPS will NVIDIA secure?

It will be strategic and disciplined. 

Most NVIDIA customers will continue to secure their own LPS.  The vast majority of LPS hosting NVIDIA compute will continue to be secured directly by CSPs, enterprises, sovereign AI builders and other customers. 

NVIDIA will focus selectively on exceptional sites where visible, durable demand can support multiple generations of NVIDIA compute.

The Infrastructure of Intelligence

PORTS-Pike represents the next step in NVIDIA’s journey. 

We began by building accelerated computing chips. We then expanded to systems, networking, CUDA and full-stack AI factories. Today, we are helping secure the critical infrastructure required to build these factories. 

NVIDIA is the full-stack AI infrastructure platform. 

We are investing in the long-lived foundations of AI factories so our customers can deploy the most productive compute platform in the world, generation after generation. 

By securing the critical resources needed to host NVIDIA compute, we can help the world’s most innovative companies build the AI factories that will power the age of intelligence.

Indonesia is taking charge of its AI future.

This week, the Ministry of Communication and Digital Affairs (Komdigi), Indosat Ooredoo Hutchison (Indosat or IOH), NVIDIA and Universitas Gadjah Mada (UGM) launched the UGM Indosat NVIDIA AI Technology Center (NVAITC) in Yogyakarta — the country’s first university-based AI technology center. Established under Indonesia’s AI Center of Excellence initiative, UGM Indosat NVAITC brings government, industry and academia together to develop AI that addresses Indonesia’s most urgent national priorities.

“The Indonesia AI Center of Excellence reflects our long-term vision to position Indonesia as a nation that not only adopts AI but also develops and contributes AI innovations to the world,” said Meutya Hafid, Indonesia’s Minister of Communication and Digital Affairs. “Through initiatives like this, we are laying the foundations of Indonesia’s AI sovereignty and ensuring AI becomes a driver of economic growth, national competitiveness and solutions to Indonesia’s most pressing challenges.”

“UGM is committed to supporting Indonesia’s AI ambitions through education, research, and innovation that deliver real societal impact,” said Prof. dr. Ova Emilia, Ph.D. “This initiative supports national priorities in higher education, research downstreaming and talent development by accelerating the adoption of AI and preparing future-ready Indonesian talent.”

Compute That Belongs to the Country

Powered by NVIDIA’s full-stack AI platform and GPU Merdeka — Indosat’s sovereign GPU-as-a-service platform — UGM Indosat NVAITC gives UGM’s researchers and students access to enterprise-grade accelerated computing, AI software, open source, pretrained models, development frameworks and technical mentorship. It also connects Indonesian researchers to a worldwide ecosystem of expertise.

“At Indosat, we believe no Indonesian should be left behind in the AI era,” said Vikram Sinha, president director and CEO of Indosat Ooredoo Hutchison. “Through UGM Indosat NVAITC, we are bringing the best of global AI technologies and expertise to Indonesia, while expanding access for the ecosystem of researchers, students, startups, and innovators across the country. By strengthening AI readiness and empowering Indonesian talent, we aim to support the government’s vision for AI and help position Indonesia not only as a user of AI technologies, but as a nation that develops and contributes AI innovations to the world.”

The opportunity is real. Indonesia is the world’s fourth-most populous country, with researchers and developers working on problems of scale and urgency. What they’ve historically lacked is access to the compute, models and infrastructure to move from insight to impact.

“Indonesia is home to an extraordinary community of researchers, developers and innovators with the potential to shape the future of AI,” said Marc Hamilton, vice president of solutions architecture and engineering at NVIDIA. “From healthcare and agriculture to disaster preparedness, the opportunities for AI to drive real change are immense. Through UGM Indosat NVIDIA AI Technology Center, NVIDIA is committed to equipping Indonesian talent with NVIDIA Nemotron open models and expertise to turn that potential into innovation with local and global impact.”

AI for Indonesian Challenges

Three initial projects define what this center is for, focused on healthcare, agriculture and natural disaster response.

Indonesia records over 1 million new tuberculosis (TB) cases every year. TB is curable — but it kills when it goes undetected. Detection in rural and underserved areas has depended on equipment and expertise unavailable at the community level. UGM’s Faculty of Medicine Public Health and Nursing team, led by dr. Dian Kesumapramudya Nurputra, M.Sc, Ph.D, SpA , is developing an AI-powered electronic screening technology — eNose-TB — that screens for TB by analyzing breath samples. The goal: affordable, fast, accessible screening that reaches patients in remote clinics and villages — no specialist or expensive lab required. 

“As researchers, we have always believed that technology developed in Indonesia can solve Indonesian challenges,” said Dian. “Through the center, access to world-class AI infrastructure and expertise will help us accelerate our research and bring us closer to our dream of developing affordable and accessible healthcare technologies that can improve lives across Indonesia.”

Agriculture employs nearly 30% of Indonesia’s workforce. SmartAgri uses multimodal AI — combining satellite imagery, sensor data and local agricultural knowledge — alongside edge computing to deliver precision farming for Indonesian terrain, crops and smallholders. These AI-powered recommendations help farmers know exactly when and how to irrigate, delivering impact that compounds over time.

Indonesia sits on the Pacific Ring of Fire, facing more natural disaster risk than almost anywhere on earth. Tech4Disaster is building a geospatial AI platform using NVIDIA accelerated computing to process satellite and sensor data at speed — giving communities and emergency responders earlier warning, better situational awareness and faster coordination tools when disaster strikes.

Learn more about the UGM Indosat NVIDIA AI Technology Center.

GeForce NOW is giving cloud gaming an extra-credit upgrade just in time for back-to-school season.

The native Linux app for GeForce NOW is officially out of beta. GeForce NOW is also delivering new cloud optimizations that make Frame Generation feel even more responsive while streaming. On top of that, Performance members will see higher frame rates in demanding games thanks to a CPU performance adjustment. 

Plus, learn how GeForce NOW can add high-performance GeForce RTX gaming to the Chromebooks students already rely on for school – and how eligible new owners can take advantage of Chromebook Fast Pass.

It all adds up to more ways to move from assignments to adventures across nearly any device.

The only homework left is to decide what to play. This week, nine new games join the GeForce NOW library.

The Cloud Gets Extra Credit

GeForce NOW keeps getting better.

The native Linux app for GeForce NOW has officially graduated from beta. Built on months of community feedback, the release focuses on performance, stability and polish while continuing to support Ubuntu 24.04 and later. A new Flatpack repository makes getting started and receiving future updates even easier. Visit the download page for more information and instructions.

The cloud handles the rendering, so Linux gamers can tap into the latest GeForce RTX performance while staying on their preferred setup — without local hardware upgrades, compatibility headaches or complicated workarounds.

GeForce NOW has also optimized DLSS Frame Generation in the cloud, reducing latency and making games feel more responsive. The improvements are especially noticeable when streaming at 60 or 120 frames per second in 1440p and 4K, helping every mouse movement and camera pan feel more immediate.

Performance members will also enjoy additional server software optimizations to improve frame rates in select CPU-intensive games, helping demanding titles run more smoothly while delivering an even better gameplay experience. The cloud-side upgrade happens automatically, with nothing for members to install or configure, and at no additional cost.

From Class Mode to Game Mode

Chromebooks already in backpacks can double as a PC gaming powerhouse, with GeForce NOW giving everyday school setups extra credit. 

Chromebooks handling lectures, group projects and browser tabs can transform into high-performance GeForce RTX gaming PCs in just a few clicks.

When class is out, open a supported browser or app and stream more than 2,000 supported PC games from existing libraries, with the cloud handling the heavy lifting. The same flexibility extends to Macs, Surface devices, everyday laptops, Steam Deck and mobile devices.

Plus, eligible new Chromebook and Chromebook+ owners can claim a year of GeForce NOW at no cost with Chromebook Fast Pass, with priority access and no ads. 

Study. Stream PC games. Same device. 

Ready for the Weekend

In addition, members can look for the following new games streaming this week:

• Car Wash Simulator (New release on Steam, Aug. 10)
• Pax Autocratica (New release on Steam, Aug. 10)
• Clawed (New release on Steam, Aug. 13)
• Hell Let Loose: Vietnam (New release on Steam, Aug. 13)
• Sandustry (New release on Steam and Xbox, Aug. 13)
• Cat Mail Co. (Steam)
• Misery (Steam)
• Monster Hunter Wilds Prologue Demo (Steam)
• Pratfall (Steam)

To cap things off, a creator known as “Madelyn” recently shared how GeForce now makes cloud gaming affordable and accessible across nearly all her devices, calling it “a game changer” in her video:

What are you planning to play this weekend? Let us know on X or in the comments below.

NVIDIA founder and CEO Jensen Huang is ranked No. 1 on Glassdoor’s Best CEOs list for 2026.

In the just-released ranking, recognition is earned directly from the people who know their leadership the best — employees. Huang topped the list, with 99% of employees approving of the job he does.

“As AI and shifting expectations redefine the modern workplace, exceptional leadership has never been more vital,” Glassdoor noted. “The top 50 CEOs do more than manage day-to-day operations — they empower their people and drive authentic culture.”

Glassdoor reintroduced the Best CEOs list this year after a hiatus of five years, replacing the Best-Led Companies list, which ranked NVIDIA at No. 1 in 2025, in a shift to sharpen the focus on CEO and executive leadership and its impact on employee experience and company reputation. 

Glassdoor also publishes a list of Best Places to Work in Technology and AI, where NVIDIA ranked No. 1 in January. Some 28 of the 50 Best CEOs 2026 winners also earned a spot on Glassdoor’s 2026 Best Places to Work list, reinforcing that strong CEO leadership and great workplace culture go hand in hand. 

Focus on Leadership

Rounding out the top five spots on this year’s list of 50 Best CEOs are Jason Johnson of Quick Quack Car Wash, Kevin Lobo of Stryker, Alan Schnitzer of Travelers and Chris Czarnecki of Keller Williams.  

This award recognizes CEOs “who are leading with empathy, clarity and purpose” at a time when workers are navigating increased uncertainty in the job market, according to Glassdoor, which analyzes employee ratings and reviews to identify the leaders who have earned the highest levels of employee approval and trust. 

“Trust in leadership is fraying at exactly the moment employees need it most,” said Daniel Zhao, Glassdoor’s chief economist, in a statement. “Our data shows senior leadership ratings hitting their lowest point in nearly a decade. Against that backdrop, the CEOs on this list stand out for doing the opposite: communicating clearly, staying grounded in their company’s mission and earning genuine trust from the people they lead.” 

Tech Companies Lead the List

While the 50 Best-Led Companies list spans multiple industries, technology companies dominate the ranking.

This year, nine tech CEOs are on Glassdoor’s 50 Best CEOs list, making technology the most-represented sector, led by NVIDIA, followed by leaders in real estate, financial services and manufacturing. 

When providing a company review, employees are asked to rate several factors tied to their employment experience, including sentiment regarding their CEO and senior management’s job performance. Employees also share feedback about the company’s culture via Glassdoor’s anonymous reviews.

Here are some recent comments on NVIDIA’s leadership:

• “Jensen is a dynamic and genuine leader. People are friendly and helpful. Work is meaningful.” 
• “CEO is visionary and a true leader.” 
• “Great vision for the company from the CEO.” 
• “CEO is a rock star, company encourages collaboration.” 

And employee comments on NVIDIA’s company culture:

• “The best place I’ve ever worked. The engineering culture, the people-first leadership, the freedom to do things my own way — these are hard to find anywhere else.”
• “It is a rare company that encourages collaboration across teams. First-principle thinking is encouraged from the top.”
• “You get to work on the latest AI technologies — the company that is the backbone of the new tech revolution.”
• “The opportunities to forge a career path aligned to my interests and talents, along with the supportive culture. The benefits afforded to employees are second to none.”

Glassdoor’s Best CEOs list was determined using company reviews from U.S. employees between May 16, 2025, and May 15, 2026. 

Follow @nvidialife on Instagram and learn more about NVIDIA life, culture and careers.

We announced partnerships with Apollo, BlackRock, Blackstone, Brookfield, Goldman Sachs and KKR to establish independent financing platforms designed to mobilize over $500 billion of third-party capital to support the buildout of AI infrastructure over time.

This is a major milestone for NVIDIA and the AI industry. We have moved from an era in which companies bought chips and built data centers project by project to one in which AI factories can be financed as productive infrastructure — with repeatable platforms, long-term institutional capital and a diverse customer base that uses compute to create revenue.

AI has reached an inflection point. It is moving from research into production. AI is creating real value, and the infrastructure behind it is becoming one of the world’s most productive assets. In AI, compute is revenue.

A New Infrastructure Asset

NVIDIA compute is not just a chip. It is a complete AI factory platform including accelerated computing, networking, systems software, AI frameworks and a global developer ecosystem.

NVIDIA DSX AI factories can run the world’s broadest range of AI models, modalities and algorithms — language, vision, speech, biology, physical AI and robotics. One NVIDIA AI factory can serve many customers and many workloads. That makes it flexible and fungible.

It is also built on a globally adopted architecture used across every major cloud, and by systems makers and enterprises around the world. When needs change, the factory can be used by another customer, another cloud or another operator. This broad ecosystem gives NVIDIA compute a deep market of potential users and offtakers, helping protect residual value.

CUDA makes the factory better over time. Every generation of NVIDIA software improves the performance, efficiency and total cost of ownership of already- installed infrastructure. The hardware does not stand still: software innovation allows an AI factory to produce more intelligence at lower cost throughout its life, extending its useful economic value.

NVIDIA A100 is a powerful example. NVIDIA introduced the Ampere-based A100 in 2020, and six years later, it remains in active commercial use for AI training, fine-tuning, inference and high-performance computing. Customers continue to commit capacity for multi-year deployments, extending A100’s economic life toward a decade.

The market is also demonstrating the durability of NVIDIA compute economics. One-year H100 rental pricing rose from about $1.70 per GPU-hour in October 2025 to about $2.35 per GPU-hour in March 2026. Cross-provider on-demand median pricing rose from roughly $2.00 per GPU-hour in October 2025 to $2.70 in June 2026. Blackwell capacity commands a premium, with reported B200 cloud rates spanning approximately $5.30 to $7.05 per GPU-hour.

That is what makes NVIDIA AI factories different. Their value is not fixed at installation: CUDA continuously improves their output; the installed base remains productive well beyond its initial depreciation period; and the same standard architecture serves a deep, growing global market of AI workloads.

These are the characteristics of an investable infrastructure asset: it produces revenue, serves a broad market, improves in performance over time and can be redeployed.

Bringing Capital to AI Factories

The demand for AI infrastructure is extraordinary. But access to capital is uneven. Many great AI companies, enterprises and AI clouds have demand for compute but do not yet have access to financing at the scale or cost required to build quickly.

That is why we are partnering with the world’s leading long-term capital providers.

Apollo, BlackRock, Blackstone, Brookfield, Goldman Sachs and KKR are also among the world’s leading infrastructure investors, with deep expertise in underwriting long-lived, productive assets. Together, we are creating repeatable financing platforms to help the AI ecosystem build the factories it needs.

The platforms are designed to help qualified AI labs, enterprises and AI clouds access AI-factory infrastructure at scale. The more than $500 billion figure represents aggregate third-party capital that these platforms are designed to mobilize over time — the capital is not NVIDIA revenue, a single fund or a commitment to a single customer.

The financial institutions will independently assess each opportunity — the customer, demand, utilization, cash flow and residual value. NVIDIA provides the AI factory platform. The financial institutions provide long-term capital and financing expertise.

The Important Questions

Is this circular financing?

This initiative is designed to address that concern. We are bringing independent, long-term institutional capital into the AI infrastructure market.

The demand is real: it comes from frontier AI labs, AI-native startups, enterprises, cloud providers and countries building AI services. The capital providers independently underwrite each project — including the customer, demand, utilization, cash flow and residual value. NVIDIA provides the platform; the investors make independent financing decisions.

This is the beginning of an open capital market for AI infrastructure.

Why would NVIDIA support financing?

In some cases, NVIDIA may provide a residual-value support mechanism for up to 25% of an opportunity, assessed carefully on a project-by-project basis. That support is limited, residual-value based and designed to complement — not replace — independent underwriting.

This is substantially lower than other compute-financing arrangements. NVIDIA can provide support because NVIDIA compute is unique: it is fungible, universally adopted, software-upgradable and redeployable across a large ecosystem of customers.

Our role is to help unlock a very large pool of independent capital while maintaining disciplined risk exposure.

Can the market absorb this capacity?

The question is not whether we are building data centers. The question is whether we are building productive AI factories.

An AI factory turns energy and data into valuable intelligence. Its customers are broad: frontier AI labs, AI clouds, enterprises and nations. They are building AI because it has become useful — doing valuable work across every industry.

There is discipline in the model. Each financing partner will independently evaluate demand, utilization, cash flow and residual value. Capacity will be built around real customer economics.

Where is the return on investment?

The return is in the usefulness of AI.

Companies are using AI to write software, discover drugs, design products, serve customers, automate operations and build new services. AI factories make this possible. More compute creates better AI; better AI creates more usage; more usage creates more revenue; and more revenue drives more compute.

This is the virtuous cycle of the AI industrial revolution.

The Infrastructure of Intelligence

Every industrial revolution has been built on infrastructure: electricity, transportation, communications and computing, with every buildout enabled by external financing.

AI factories are the infrastructure of the intelligence era.

With these partnerships, NVIDIA and the world’s leading financial institutions are creating a new way to finance the infrastructure that will power this industrial revolution. We will make AI factories more accessible to the companies, industries and nations building the future.

The age of AI is here. Together, we will build the infrastructure to power it.

Every new generation of accelerated computing demands more from the infrastructure underneath it — more compute performance, higher rack density and more efficient, scalable power distribution. The bottleneck isn’t just wattage. It’s how power gets from the grid to the GPU. 

In traditional power delivery, electricity travels from the grid as an alternating current (AC) and gets converted multiple times, each time adding overhead and complexity as racks become denser. At the power levels that next-generation AI compute demands, even small inefficiencies compound quickly. 

800 VDC simplifies that path. By distributing power at higher voltage through a direct current (DC), fewer conversion stages stand between the grid and the accelerator — which means more of the available power reaches the compute. NVIDIA DSX reference designs are built to guide AI factories through the transition from today’s AC infrastructure through hybrid architectures and into fully native 800 VDC facilities. 

NVIDIA, Google and Microsoft have been developing the 800 VDC architecture together through the Open Compute Project (OCP), and published a joint white paper March 2026 and the LVDC Solid-State Transformer Specification v0.3 July 2026. More than 80 equipment manufacturers and infrastructure companies are already building products to this specification. 

Existing Facilities Don’t Have to Wait 

Most of today’s AI factories were designed around AC distribution. The NVIDIA MGX-compatible 800 VDC power rack, arriving in the second half of 2026, creates a hybrid architecture that brings next-generation rack-scale compute performance to facilities that are already built and operational. It’s designed to slot into existing AC infrastructure and deliver 800 VDC to compute racks within the row — no changes to the building’s electrical system required. 

“800 VDC unlocks the compute performance and power density required for AI at scale,” said Vladimir Troy, vice president of data center infrastructure at NVIDIA. “Through OCP, NVIDIA is working with more than 80 ecosystem companies to give AI factories a practical path forward — not just a future vision.” 

For site owners, this matters because the investments already made don’t have to be stranded. Land, power rights, building infrastructure — the hybrid approach preserves all of it while opening the door to higher compute density. 

A Roadmap for Every Stage of Growth 

800 VDC provides AI factories a roadmap to scale, with on-ramps at every stage of growth. 

The power rack is where most operators will start — a near-term, hybrid-compatible path into higher-density compute. For operators building out dedicated AI factory environments, the row power center — a centralized power station for a full rack row — uses an overhead 800 VDC busway to scale power distribution across multiple rack rows, supporting up to 2 megawatts per row, with availability expected in 2027. And for new facilities being designed, the DC power block — a facility-scale unit that converts grid power directly to 800 VDC in a single step — will enable direct medium-voltage conversion at massive scale: the architecture for AI infrastructure being planned for the decade ahead. 

An Open Standard Means a Real Supply Chain 

The 800 VDC architecture specifications define common interfaces so that power hardware from different vendors can work together inside the same 800 VDC facility. With 80+ companies building to the specifications, the supply chain is forming around an open standard. 

These building blocks are captured in NVIDIA DSX reference designs, giving operators a system-level blueprint to connect power architecture, rack-scale computing and facility infrastructure as they scale AI factories.

Wood Mackenzie projects $9 trillion in global AI and data infrastructure investment through 2040. The facilities that can absorb that investment will be the ones that resolved their power architecture before compute demand outran what their infrastructure could deliver. NVIDIA, Google and Microsoft are working with the broader ecosystem to make sure 800 VDC is ready when operators need it — and that existing facilities have a path to get there now. 

Read the 800 VDC OCP blog and the NVIDIA 800 VDC white paper. Learn more about the NVIDIA DSX reference architecture guide. 

The open source ecosystem is making it easier for AI enthusiasts and developers to build, customize and run increasingly capable agents locally. 

Throughout August, NVIDIA is celebrating the partners and open source communities moving local AI forward, along with the models, applications and tools emerging across the ecosystem. That includes NVIDIA’s latest open models, software and developer tools, plus the accelerated computing, libraries and educational resources that help users get started.

It’s shaping up to be a big month for agents. Follow along for the latest developments in this special-edition NVIDIA Local AI blog series, with new updates added over the coming weeks.

Follow NVIDIA RTX Spark on X, Instagram, TikTok and Facebook — and stay informed by subscribing to the NVIDIA Local AI newsletter. Follow NVIDIA Workstation on LinkedIn and X. 

Tuesday, Aug. 25, 6:00 a.m. PT

Perplexity Launches Portable Computer Agent Optimized for NVIDIA DGX Spark

Perplexity’s new local agent app — Portable Computer — lets users run a powerful personal AI agent on NVIDIA DGX Spark, enabling local, private workflows that don’t count towards token limits. 

Portable Computer is ideal for productivity workers, as a polished, simple-to-use agent with connections to many top apps, including Google Drive, Gmail, Slack and GitHub. The option to switch between local and cloud models allows users to use cloud models for complex work, and local models for everyday tasks.

The agent’s local AI capabilities have been optimized for DGX Spark, which provides fast large language model inference and 24×7 always-on operation, perfect for long-running autonomous agents. Support for GeForce RTX and RTX PRO GPUs is coming soon. 

When running locally, Perplexity offers one-click local inference setup and an optimized experience for local NVIDIA hardware — with a specially post-trained Qwen 3.8 27B model. The company is also working on a fine-tuned Nemotron 3.5 Lightning variant for ultra-fast responses, which will be coming soon. Perplexity is also working on support for Windows, as well as DGX Station. 

Check out the Perplexity blog to learn more about how to get started with Portable Computer. Download Perplexity Computer directly from the Perplexity App. 

Tuesday, Aug. 14, 10:00 a.m. PT

NVIDIA RTX GPUs Bring Frontier Quality Qwen3.8-27B Coding Agents to Local AI

NVIDIA’s RTX GPUs give developers a high-performance solution to frontier-quality local coding agents with the Qwen3.8-27B.

As the local companion to Qwen3.8-Max, the 27-billion-parameter open model is sized for a single GPU and built for responsive coding workflows with local files, tools and project context. Developers can keep sensitive code, proprietary data and project context on their own systems while using capable AI assistance throughout the development process.

Day-zero support is available now on NVIDIA RTX GPUs, including NVIDIA RTX PRO GPUs and the NVIDIA GeForce RTX 5090. Optimized with multi-token prediction, Qwen3.8-27B reaches 131 tokens per second on a single GeForce RTX 5090 with MTP, giving developers fast local inference for agentic coding, tool use and longer development tasks.

Developers can get started immediately with llama.cpp, Ollama , Unsloth and LM Studio Bionic, all of which support Qwen3.8-27B on NVIDIA RTX GPUs. Together, NVIDIA RTX performance, the CUDA software ecosystem and familiar open-source tools give developers broad support for new models and a strong foundation for responsive local agent coding workflows.

The model is also supported on DGX Spark and DGX Station — with additional optimizations, including NVFP4 support for all NVIDIA local AI devices, coming soon. Qwen3.8-27B brings frontier-quality local coding agents to NVIDIA’s always-on personal AI systems.

Beyond PCs and personal AI systems, NVIDIA Jetson brings day-zero support for Qwen3.8-27B to the edge. We’ve found the model to be incredibly capable, with strong reasoning and agentic capabilities, while speculative decoding through MTP enables fast, responsive local inference at the edge.

Tuesday, Aug. 11, 10:00 a.m. PT

#ICYMI: New Open Models and More Available Today, Accelerated by NVIDIA GPUs

NVIDIA recently released Cosmos 3 Edge, a 4-billion-parameter open world model for robotics, autonomous vehicles and vision AI. At a quarter the size of Cosmos 3 Nano, it runs on device on NVIDIA DGX Spark and NVIDIA Jetson.

MiniMax-H3 is a 33-billion-parameter open weights model that generates video and natively synchronized stereo audio from text, images, video, audio or a mix of all four. Creators can access it through ComfyUI and run it locally with checkpoints optimized for NVIDIA GPUs.

Poolside AI launched Laguna S 2.1, a 118-billion-parameter open weight agentic coding model that works through hours-long tasks. An NVFP4 checkpoint enables developers to run the model locally on a single NVIDIA DGX Spark with lower compute and memory requirements, without sacrificing accuracy.

DeepSeek recently refreshed DeepSeek-V4-Flash, a 284-billion-parameter MoE model with 13 billion active parameters and a 1 million-token context window. Developers can run it locally on an NVIDIA DGX Station using community-built GGUF versions available today.

Thinking Machines Lab’s Inkling-Small is a frontier-class, open weight multimodal model with native reasoning across text, images and audio, plus adjustable thinking effort. Trained on NVIDIA GB300 NVL72, the 276-billion-parameter model activates just 12 billion parameters per token and runs on a single DGX Station or two DGX Spark systems. An NVFP4 checkpoint optimized for NVIDIA Blackwell is available on Hugging Face.

Unsloth launched Unsloth Desktop on Monday. The product brings local model inference, image and video diffusion, fine-tuning, agent integrations, web research and code execution into one fully open source desktop app. It’s the first desktop app that both trains and runs AI models locally.

Unsloth Desktop brings local AI training and inference into one fully open source desktop app. It’s the first desktop app to train and run AI models locally.

Alibaba recently released Wan-Animate-2, a 14-billion-parameter open weight model that transfers motion and facial expressions from a driving video onto a static character image — human, cartoon, robot or animal. With day-zero support in ComfyUI, it generates up to 26x faster on NVIDIA RTX PRO 6000 Blackwell and 22x faster on an NVIDIA RTX 5090 compared to Apple M3 Ultra.

Tuesday, Aug. 11, 9:00 a.m. PT

LTX Introduces LTX-2.5

LTX-2.5 is the latest state-of-the-art, open-world video generation model from LTX’s leading research team, providing the foundation for use cases across media and entertainment, robotics and real-time generation, and delivering higher-quality video, stronger prompt adherence and more consistent characters, scenes and voices across generations. 

New multishot support enables creators to generate sequences spanning multiple cuts while maintaining continuity, while an upgraded diffusion video decoder improves visual fidelity and reduces artifacts. Creators can also refine existing footage with generative edits, making it easier to fix details and maintain a polished look without starting over.

The new prompt enhancer, featuring Gemma4 E2B and a custom Gemma4 12B text encoder, provides remarkable prompt adherence, giving far greater control over the output.

A new diffusion video decoder complements the variational autoencoder video decoder, improving video quality by giving a second decode path for higher-quality final renders.

LTX-2.5 is optimized for NVIDIA RTX GPUs, DGX Spark and DGX Station systems. On an NVIDIA RTX 6000 PRO GPU, LTX-2.5 delivers up to 20% faster performance and 40% memory savings. Creators can use these NVFP4, FastVideo and ComfyUI enhancements locally through a ready-to-use ComfyUI workflow for text, image and video-to-video generation. 

Learn more about LTX-2.5 to learn how to get started with high-quality, local video generation on NVIDIA hardware.

Tuesday, Aug. 11, 9:00 a.m. PT

NVIDIA Accelerates Meta’s Muse Glimmer for Always-On Local Agentic AI

Today, Meta released Muse Glimmer, a 30-billion-parameter, dense, open weight model with a 120K+ context window. It’s purpose-built for coding and local agentic AI.  

Optimized for NVIDIA GeForce RTX PCs, NVIDIA DGX Spark, DGX Station and NVIDIA Jetson, Muse Glimmer delivers over 200 tokens per second on RTX 5090, enabling always-on agents to process data locally and work through complex, multistep tasks on a single system. 

Its dense architecture and hybrid attention help keep processing and memory demands manageable as agents take on longer tasks, use tools and maintain context across multiple steps.  

Muse Glimmer is a 30-billion-parameter model optimized for always-on local agent workflows. It’s small enough to run on a PC with a single consumer GPU, enabling use cases that range from local agents and function calling to local coding and LLM-as-a-judge evaluation.

AI enthusiasts and developers can build agents using NemoClaw and fine-tune Muse Glimmer locally with NVIDIA NeMo Automodel, using private or specialized data while keeping it on the system. Muse Glimmer is designed for: 

• Custom agents: Adapt the model for specific tasks, tools, data and areas of expertise. 
• Private data processing: Read, summarize and act on local files, documents, emails and messages. 
• Credential handling: Use application programming interface keys, authentication tokens and other sensitive information while keeping inference on the device. 
• Multistep tasks: Complete many sequential tool calls and recover from errors or unexpected results. 
• Long-running workflows: Break larger projects into steps, track progress and resume interrupted sessions with context intact. 

Developers can run Muse Glimmer with popular inference frameworks including vLLM for text generation, image, reasoning and tool use, or llama.cpp for text and image workloads using BF16 and quantized GGUF checkpoints. llama.cpp also supports DFlash speculative decoding to accelerate generation. 

Running on a single NVIDIA RTX 5090, Muse Glimmer enables always-on agents to work across private files, applications and communications while reducing reliance on cloud-based inference. 

Learn more about Meta’s Muse Glimmer and check out the NVIDIA tech blog to learn how to get started with local AI and fine-tuning on NVIDIA hardware. 

Tuesday, Aug. 11, 8:00 a.m. PT

Multiply DGX Spark Performance With NVIDIA Sync Cluster Assistant

New open models such as GLM 5.2 and DeepSeek V4 Flash are bringing cloud-level intelligence to local systems, enabling advanced workloads like coding and research agents. Running these larger models, however, can require multiple GPUs or DGX Spark systems working together.

NVIDIA Sync app updates make it easy to cluster multiple DGX Spark systems together, providing the memory capacity, inference performance and training throughput needed to run larger models, faster. 

Available for Windows and macOS, NVIDIA Sync automatically detects connected systems, provides private and secure remote access through Tailscale and lets developers launch applications across one or more DGX Spark systems without manual networking or copied terminal commands.

The Cluster Assistant in NVIDIA Sync, automates configuring two or more DGX Spark systems as a high-speed cluster. Developers connect the systems through their NVIDIA ConnectX-7 ports, and NVIDIA Sync configures the network, routes workloads across nodes and monitors system health.

To get started with agentic AI on DGX Spark, check out playbooks on NemoClaw, OpenClaw, Hermes Agent and OpenShell. 

See notice regarding software product information.

Tuesday, Aug. 11, 8:00 a.m. PT

Additional DGX Spark Updates

Arriving later in August are two new exciting features for developers.

DGX Spark will be getting Google Chrome as a native ARM64 Linux build — installed in a single click from the DGX Dashboard. That means Google account sync, the full extension ecosystem, cross-device continuity and all the features that come standard on every other platform are now accessible on your DGX Spark. Sign in once and the browser environment follows from laptop to Spark.

The new NVIDIA Sync Resource Monitor will provide real-time and historical views of CPU and GPU usage across an individual DGX Spark or an entire cluster — without requiring additional monitoring software or setup. At-a-glance visualizations help users track workload progress, check available capacity before adding jobs or models, and quickly identify bottlenecks or unexpected behavior. Marquee zooming lets users move from the big picture to specific moments in a workload, helping them troubleshoot faster and get more from their systems.

Tuesday, Aug. 11, 6:00 a.m. PT

NVIDIA Introduces Nemotron 3.5 Lightning for Fast, Specialized Agentic Tasks

Today, NVIDIA expanded its Nemotron 3 model family with Nemotron 3.5 Lightning, a customizable open 30B mixture-of-experts (MoE) model for always-on agents. 

Nemotron 3.5 Lightning delivers up to 4x faster token generation and 30% faster time to completion compared to open models in its class. 

And because Nemotron 3.5 Lightning is open weights, AI enthusiasts and developers can fine-tune it with their own examples to better match specific tasks, interests and workflows. For example, they could train the model to: 

• Write in a preferred style: Follow established tones, formats and terminology when writing emails, reports or other documents.
• Learn a specialty: Better understand the language and common tasks associated with areas such as photography, gaming or 3D design.
• Code a certain way: Follow preferred coding conventions, frameworks and testing approaches when writing, reviewing or refactoring code.

Paired with access to apps, files and other tools, these fine-tuned models can power more personalized local agentic AI experiences — from an assistant that helps manage email and calendars, to a smart-home agent that handles everyday routines, to a coding companion that works alongside developers on a local codebase.

NVIDIA collaborated with vLLM, Ollama, llama.cpp and LM Studio to provide the best local deployment experience for Nemotron 3.5 Lightning models — offering developers choice of NVFP4 and GGUF format of models. Unsloth also provides day-one support with optimized and quantized models for efficient local deployment via Unsloth Studio. 

Nemotron 3.5 Lightning runs locally on NVIDIA RTX PCs, NVIDIA DGX Spark and OEM GB10 systems, and NVIDIA Jetson, and scales up to RTX PRO workstations, NVIDIA DGX Station and GB300 deskside systems, data centers and cloud environments. With NVIDIA Blackwell systems available from Acer, ASUS, Dell Technologies, Exxact, GIGABYTE, HP, Lenovo, MSI and Supermicro, users can choose from a wide range of devices and form factors to fit their needs.

As generative AI adoption grows, enterprises are looking for ways to keep rising token costs in check without sacrificing access to frontier intelligence. NVIDIA NeMo Switchyard, an open source routing library, automatically directs each step of an agent workflow to the best-fit model based on accuracy, speed and cost. It also gives developers the flexibility to work across models and providers for different tasks. 

Internal benchmarks show that NeMo Switchyard, by routing each step across a system of models, helped maintain frontier-level task completion while reducing benchmark completion cost to roughly one-third of Opus 4.8 alone. NeMo Switchyard is available on GitHub.

Visit the Nemotron 3.5 Lightning, NeMo Switchyard and Jetson AI technical blogs to get started. And to build at the edge, start with Jetson AI Lab tutorials and discover real-world Jetson projects. Nemotron 3.5 Lightning is also available through OpenRouter, on build.nvidia.com as an NVIDIA NIM microservice, and through a broad ecosystem of NVIDIA Cloud Partners, post-training platforms, inference platforms and cloud service providers. NeMo Switchyard is available on GitHub.

As AI shifts from chatbots to autonomous agents, open models are serving market demands for full control over where AI runs and how it’s deployed and evolves.

Today, NVIDIA is expanding its Nemotron 3 model family with Nemotron 3.5 Lightning, the highest-efficiency model in its class for long-running agentic AI workloads. This release follows Nemotron 3 Nano and reflects NVIDIA’s commitment to continually improving open models for greater accuracy and speed. 

Built for specialized tasks within larger multi-agent systems, Nemotron 3.5 Lightning, a 30-billion-parameter mixture-of-experts model, helps create smarter and more efficient agentic applications.

Also, NVIDIA is releasing NeMo Switchyard, an open source library for smart routing inside popular agent tools. Enterprises can use it to build a router based on their specific needs. When deployed, NeMo Switchyard can intelligently direct each request to the most capable and suitable model for the job, across developers’ own mix of open, proprietary and NVIDIA models, without requiring developers to rewrite their applications.

Together, Nemotron 3.5 Lightning and NeMo Switchyard deliver greater control over how AI is deployed, where it runs and how efficiently it operates — across PCs, workstations, data centers and the cloud.

Always-On Agents Need a System of Models 

Modern agentic systems — always-on agents — increasingly operate as systems of models, or model ensembles, with different models specialized for different tasks. 

NVIDIA Nemotron open models are designed for this architecture. A frontier reasoning model such as Nemotron 3 Ultra or GPT-5.6 may plan and orchestrate a workflow, while smaller specialized models like Nemotron 3.5 Lightning can perform targeted tasks such as code review, tool use, security alert monitoring and answering billing questions.

Powering High-Volume Specialized Tasks With Nemotron 3.5 Lightning

NVIDIA Nemotron 3.5 Lightning is a fully customizable open model built for high-volume tasks powering always-on agents. It was developed with contributions from the Nemotron Coalition, whose members provided evaluation methodologies, inference software and datasets to help advance the model.

The model delivers up to 4x faster output speed, leading to 30% faster agentic task completion compared with other models in its class. And because it’s open and customizable, Nemotron 3.5 Lightning can be easily post-trained with NVIDIA NeMo on an organization’s own domain data, tools and workflows to improve accuracy for specialized tasks.

AI leaders across industries are customizing Nemotron 3.5 Lightning for their workloads, including CrowdStrike for cybersecurity, Harvey with Trajectory for legal services and CodeRabbit with Baseten for code review, helping improve accuracy for domain-specific agentic tasks. Additionally, Lila Sciences is helping to improve reasoning capabilities for agentic tasks across physical and life sciences, and Fastino Labs customized the model and is seeing leading accuracies for software development, finance and healthcare workloads. 

Nemotron 3.5 Lightning also gives organizations control over privacy and deployment. It can run on local AI systems — including NVIDIA RTX PCs, NVIDIA DGX Spark, NVIDIA DGX Station and NVIDIA Jetson — to help users maximize existing infrastructure investments, or scale across edge AI devices, NVIDIA RTX PRO workstations, data centers and cloud environments for enterprise use cases. And Nemotron 3.5 Lightning can run locally or on premises for high-volume, specialized tasks that require fast responses.

Also, as with every Nemotron launch, NVIDIA publishes as much of the training data and techniques as licensing permits, which allows for traceability, auditing and training of other models. Alongside Lightning, NVIDIA is releasing Nemotron-RL-Agentic-Terminal-Pivot, an agentic reinforcement learning dataset used to post-train it for coding agent capabilities.

More Efficient AI Apps With Model Routing 

Some models are better for coding, some for reasoning, some for lightweight tasks and some are optimized to run locally for greater privacy and efficiency. If customers rely on one default model, they might either overspend or lose quality; if they manage routing manually, it becomes integration work that can slow down a deployment.

NVIDIA NeMo Switchyard is an open source model routing library for AI agents. The technology routes prompts to the most capable and efficient model for each step of an agent workflow automatically, based on specific needs. Agent application developers can tune or modify the router with different routing algorithms to match their priorities, such as quality, latency and cost requirements. In a system of models, enterprises can create powerful AI agents with improved tokenomics. 

Internal benchmarks show that NeMo Switchyard maintains frontier-level accuracy while reducing task completion cost to nearly one-third of Opus 4.8 alone.

NVIDIA is working with partners across the AI ecosystem to bring intelligent model routing into the tools and platforms developers already use. 

• Boomi: Evaluated Switchyard across five routing capabilities, achieving 100% domain-routing accuracy, sending 59% of traffic to a 5x faster fine-tuned model and reducing later-turn latency by 21%.
• Cadence: Improved efficiency by 9.9% by using the ChipStack AI Super Agent for a formal verification use case.
• Classmethod: Is running opencode and Fireworks workloads using NeMo Switchyard internally, with initial testing showing a 27% cost reduction while maintaining quality.
• Cognition: ​Integrated the NVIDIA NeMo Switchyard staged router into Devin Desktop for NVIDIA internal use, achieving near-frontier performance on FrontierCode Main while reducing mean cost by 28% relative to routing all requests to a single underlying frontier model.
• Kong: Delivers routing with NeMo Switchyard natively through Kong AI Gateway.
• LangChain: With NeMo Switchyard, achieved 74% lower cost in 145 multi-turn Deep Agents tasks by routing only 7% of calls to a frontier model, at a 6% accuracy tradeoff.
• LiteLLM: Is adding NeMo Switchyard as a plug-in into its proxy layer so developers can access these benefits without changing their existing stack.
• Nous Research: Integrated NeMo Switchyard into Hermes to provide developers with an easy-to-configure routing system to improve agent efficiency.
• Ramp: Used NeMo Switchyard to match a frontier model’s performance while cutting costs by 58% and runtime by 33% in Ramp SWE-Bench.
• Siemens: Is benchmarking to improve efficiency in its Fuse EDA AI Agent.

Nemotron 3.5 Lightning is available on Hugging Face, ModelScope, OpenRouter and build.nvidia.com as an NVIDIA NIM microservice as well as through a broad ecosystem of NVIDIA Cloud Partners, post-training platforms, inference platforms and cloud service providers. NeMo Switchyard is available on GitHub and coming to partner platforms soon. 

The global buildout of AI infrastructure reached a new milestone today — Firebird, an emerging AI cloud, launched the CIS region’s largest AI factory in Armenia, establishing a new AI computing hub powered by NVIDIA accelerated computing and Dell Technologies high-performance AI infrastructure. 

Nikol Pashinyan, prime minister of the Republic of Armenia; Zhaslan Madiyev, deputy prime minister of the Republic of Kazakhstan; and David Allen, U.S. chargé d`affaires, a.i. in Armenia, attended the AI factory opening ceremony. 

AI factories are the foundational infrastructure for the AI era, providing the computing capacity needed to train, fine-tune and deploy AI models for every domain at scale.

Building the Infrastructure to Create Intelligence at Home

While AI services are available globally, countries also need the capacity to develop and run AI for their own languages, industries and national priorities. Firebird’s AI factory brings that capacity to Armenia, giving developers, startups, enterprises, universities and public institutions the compute to build and scale AI at home.

Firebird plans to deploy more than 70,000 NVIDIA Rubin and Blackwell GPUs and 300 megawatts of AI infrastructure capacity in Armenia by the end of 2027, accelerating the country’s development as a center for AI research, advanced computing and innovation. 

“Our ambition for what we are building in the next 2 years or so is roughly 2 gigawatts of capacity around the world. We’re very focused on merging into frontier markets,” said Alexander Yesayan, co-founder of Firebird.

At this scale, energy efficiency is essential. Built on the NVIDIA DSX platform, this AI factory integrates accelerated computing, networking, power and cooling as one codesigned system. Firebird’s AI factory is designed from the ground up to turn compute into revenue. With DSX, it can run up to 40% more GPUs on the same footprint, producing more tokens per dollar and extracting more value from every megawatt of capacity.

A Magnet for Global AI, a Catalyst for Local Innovation

Firebird’s ambitions extend beyond a single site. With NVIDIA’s support, the company is pursuing an approximately 2-gigawatt AI infrastructure roadmap spanning Armenia, Kazakhstan and additional markets. 

Firebird also announced that NVIDIA intends to invest in the company, following an earlier investment by CoreWeave this year. These investments will help Firebird expand its global infrastructure and operational footprint, and support its efforts to establish the largest and most advanced compute clusters across frontier markets.

Delivered in just over six months, the Armenia AI factory demonstrates Firebird’s ability to turn ambitious infrastructure plans into operational AI capacity with exceptional speed.

Schneider Electric provides the power infrastructure supporting Firebird’s AI factory in Hrazdan, helping Firebird meet its accelerated deployment schedule by rapidly delivering and setting up critical systems, including medium- and low-voltage switchgear, three-phase uninterruptible power supply systems and rack enclosures. This keeps the power buildout moving at the pace of the compute and provides a reliable foundation to bring NVIDIA accelerated computing online at scale.

To support the facility’s thermal needs, Vertiv provided a cooling architecture combining chilled-water technology, advanced controls and Vertiv TrimCooler technology for efficient heat rejection. Vertiv’s iCOM CWM Chilled Water Manager centrally coordinates cooling resources, improving visibility, efficiency and responsiveness as demand shifts with AI workloads.

Early demand is coming from AI-native companies including Perplexity, which is working with Firebird to access high-performance AI infrastructure for its AI agent platform and answer engine. 

As AI becomes essential infrastructure worldwide, Firebird’s expansion can help make the CIS region a magnet for global companies building and running AI — and a catalyst for local developers, researchers and enterprises. 

Powered by NVIDIA’s total AI factory platform — reference architecture, accelerated computing, networking and AI software — and deployed on Dell PowerEdge servers, the new Firebird AI factory will help Armenia’s builders turn energy into intelligence and connect their innovations to the global AI economy.

August is here, bringing 26 new games for GeForce NOW members. 

Command the seas in World of Warships: Legends and discover what’s next in the GeForce NOW library, starting with the eight newly added games this week. 

In addition, GeForce NOW is at the QuakeCon gaming conference this week in Grapevine, Texas, with hands-on experiences awaiting attendees.

Return to QuakeCon 

Visit the NVIDIA booth at QuakeCon to experience GeForce RTX 5080-powered Ultimate cloud gaming in action, with demos showcasing stunning visuals at up to 5K 120 frames per second on an ultrawide display, as well as seamless gameplay on the Lenovo Legion Go S handheld device.

Conference attendees can see how thousands of PC games, including fan-favorite Bethesda titles, can move effortlessly across laptops, Macs, handhelds, mobile devices, TVs and more — letting members pick up where they left off on nearly any supported screen.

Gamers not at the show can try out Ultimate cloud gaming in action with a day pass and jump into Bethesda titles from any device.

All Games on Deck

World of Warships: Legends drops anchor on GeForce NOW this week, bringing free-to-play naval combat. Captains can command destroyers, cruisers and battleships across massive multiplayer battles while exploring the latest update, featuring the Pacific Hammer Campaign, a new line of U.S. destroyers and more.

Chart a course straight into the latest content across devices today without any installs needed, and check out all the games available this week:

• Big Walk (New release on Steam Aug. 4)
• Beacon Pines (Free on Epic Games Store Aug. 6)
• Sovereign Tower (New release on Steam Aug. 6)
• Expeditions: Samurai (New release on Steam Aug. 7)
• The Adventures of Elliot: The Millennium Tales (Xbox, available on the Microsoft Store)
• The Incident at Galley House (Steam)
• Machine Party (Steam)
• World of Warships: Legends (Steam)

And look forward to the games coming throughout the month:

• Pax Autocratica (New release on Steam Aug. 10)
• Car Wash Simulator (New release on Steam Aug. 10)
• Clawed (New release on Steam Aug. 13)
• Hell Let Loose: Vietnam (New release on Steam Aug. 13)
• Sandustry (New release on Steam and Xbox, available on Game Pass, Aug. 13)
• The Sinking City 2 (New release on Steam Aug. 18)
• Mortal Shell II (New release on Steam Aug. 20)
• Gallipoli (New release on Steam, Aug. 20)
• Aliens: Fireteam Elite 2 (New release on Steam Aug. 25)
• Resonance: A Plague Tale Legacy (New release on Steam and Xbox, available on Game Pass, Aug. 27)
• Breathedge 2 (New release on Steam Aug. 30)
• World of Warships: Legends (Steam)
• High on Life (Steam)
• High on Life 2 (Steam and Xbox, available on Game Pass)
• Paradise Killer (Steam)
• Misery (Steam)
• Pratfall (Steam)
• Starvester (Steam)

Extra Joy From July

In addition to the dozen games announced last month, 15 more joined the GeForce NOW library. 

• Breath of Fire IV (Steam)
• Call of Duty: Black Ops 6 (Ubisoft Connect)
• CloverPit (Xbox, available on Game Pass)
• Dinoblade (Steam)
• Dino Crisis (Steam)
• Dino Crisis 2 (Steam)
• Esports Manager 2026 (Steam)
• Funnel Runners (Steam)
• Granblue Fantasy: Relink – Endless Ragnarok Demo (Steam)
• Halo: Campaign Evolved (Steam and Xbox, available on Game Pass)
• Mistfall Hunter (Steam)
• Onimusha: Way of the Sword DEMO (Steam)
• Pathogenic (Steam)
• Sudden Attack Zero Point (Steam)
• The Life and Suffering of Prince Jerian (Steam)

Mistfall Hunter didn’t make it this month. Stay tuned to GFN Thursday for the latest updates.

What are you planning to play this weekend? Let us know on X or in the comments below.

Share a game screenshot or clip that perfectly sums up your day so far.

— NVIDIA GeForce NOW (@NVIDIAGFN) August 4, 2026