The DeanBeat: Nvidia CEO Jensen Huang says AI will auto-populate the 3D imagery of the metaverse

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It takes AI kinds to make a virtual world. Nvidia CEO Jensen Huang said this week during a Q&A at the GTC22 online event that AI will auto-populate the 3D imagery of the metaverse.

He believes that AI will make the first pass at creating the 3D objects that populate the vast virtual worlds of the metaverse — and then human creators will take over and refine them to their liking. And while that is a very big claim about how smart AI will be, Nvidia has research to back it up.

Nvidia Research is announcing this morning a new AI model can help contribute to the massive virtual worlds created by growing numbers of companies and creators could be more easily populated with a diverse array of 3D buildings, vehicles, characters and more.

This kind of mundane imagery represents an enormous amount of tedious work. Nvidia said the real world is full of variety: streets are lined with unique buildings, with different vehicles whizzing by and diverse crowds passing through. Manually modeling a 3D virtual world that reflects this is incredibly time consuming, making it difficult to fill out a detailed digital environment.

This kind of task is what Nvidia wants to make easier with its Omniverse tools and cloud service. It hopes to make developers’ lives easier when it comes to creating metaverse applications. And auto-generating art — as we’ve seen happening with the likes of DALL-E and other AI models this year — is one way to alleviate the burden of building a universe of virtual worlds like in Snow Crash or Ready Player One.

Jensen Huang, CEO of Nvidia, speaking at the GTC22 keynote.

I asked Huang in a press Q&A earlier this week what could make the metaverse come faster. He alluded to the Nvidia Research work, though the company didn’t spill the beans until today.

“First of all, as you know, the metaverse is created by users. And it’s either created by us by hand, or it’s created by us with the help of AI,” Huang said. “And, and in the future, it’s very likely that we’ll describe will some characteristic of a house or characteristic of a city or something like that. And it’s like this city, or it’s like Toronto, or is like New York City, and it creates a new city for us. And maybe we don’t like it. We can give it additional prompts. Or we can just keep hitting “enter” until it automatically generates one that we would like to start from. And then from that, from that world, we will modify it. And so I think the AI for creating virtual worlds is being realized as we speak.”

GET3D details

Trained using only 2D images, Nvidia GET3D generates 3D shapes with high-fidelity textures and complex geometric details. These 3D objects are created in the same format used by popular graphics software applications, allowing users to immediately import their shapes into 3D renderers and game engines for further editing.

The generated objects could be used in 3D representations of buildings, outdoor spaces or entire cities, designed for industries including gaming, robotics, architecture and social media.

GET3D can generate a virtually unlimited number of 3D shapes based on the data it’s trained on. Like an artist who turns a lump of clay into a detailed sculpture, the model transforms numbers into complex 3D shapes.

“At the core of that is precisely the technology I was talking about just a second ago called large language models,” he said. “To be able to learn from all of the creations of humanity, and to be able to imagine a 3D world. And so from words, through a large language model, will come out someday, triangles, geometry, textures, and materials. And then from that, we would modify it. And, and because none of it is pre-baked, and none of it is pre-rendered, all of this simulation of physics and all the simulation of light has to be done in real time. And that’s the reason why the latest technologies that we’re creating with respect to RTX neuro rendering are so important. Because we can’t do it brute force. We need the help of artificial intelligence for us to do that.”

With a training dataset of 2D car images, for example, it creates a collection of sedans, trucks, race cars and vans. When trained on animal images, it comes up with creatures such as foxes, rhinos, horses and bears. Given chairs, the model generates assorted swivel chairs, dining chairs and cozy recliners.

“GET3D brings us a step closer to democratizing AI-powered 3D content creation,” said Sanja Fidler, vice president of AI research at Nvidia and a leader of the Toronto-based AI lab that created the tool. “Its ability to instantly generate textured 3D shapes could be a game-changer for developers, helping them rapidly populate virtual worlds with varied and interesting objects.”

GET3D is one of more than 20 Nvidia-authored papers and workshops accepted to the NeurIPS AI conference, taking place in New Orleans and virtually, Nov. 26-Dec. 4.

Nvidia said that, though quicker than manual methods, prior 3D generative AI models were limited in the level of detail they could produce. Even recent inverse rendering methods can only generate 3D objects based on 2D images taken from various angles, requiring developers to build one 3D shape at a time.

GET3D can instead churn out some 20 shapes a second when running inference on a single Nvidia graphics processing unit (GPU) — working like a generative adversarial network for 2D images, while generating 3D objects. The larger, more diverse the training dataset it’s learned from, the more varied and
detailed the output.

Nvidia researchers trained GET3D on synthetic data consisting of 2D images of 3D shapes captured from different camera angles. It took the team just two days to train the model on around a million images using Nvidia A100 Tensor Core GPUs.

GET3D gets its name from its ability to Generate Explicit Textured 3D meshes — meaning that the shapes it creates are in the form of a triangle mesh, like a papier-mâché model, covered with a textured material. This lets users easily import the objects into game engines, 3D modelers and film renderers — and edit them.

Once creators export GET3D-generated shapes to a graphics application, they can apply realistic lighting effects as the object moves or rotates in a scene. By incorporating another AI tool from NVIDIA Research, StyleGAN-NADA, developers can use text prompts to add a specific style to an image, such as modifying a rendered car to become a burned car or a taxi, or turning a regular house into a haunted one.

The researchers note that a future version of GET3D could use camera pose estimation techniques to allow developers to train the model on real-world data instead of synthetic datasets. It could also be improved to support universal generation — meaning developers could train GET3D on all kinds of 3D shapes at once, rather than needing to train it on one object category at a time.

Prologue is Brendan Greene's next project.
Prologue is Brendan Greene’s next project.

So AI will generate worlds, Huang said. Those worlds will be simulations, not just animations. And to run all of this, Huang foresees the need to create a “new type of datacenter around the world.” It’s called a GDN, not a CDN. It’s a graphics delivery network, battle tested through Nvidia’s GeForce Now cloud gaming service. Nvidia has taken that service and use it create Omniverse Cloud, a suite of tools that can be used to create Omniverse applications, any time and anywhere. The GDN will host cloud games as well as the metaverse tools of Omniverse Cloud.

This type of network could deliver real-time computing that is necessary for the metaverse.

“That is interactivity that is essentially instantaneous,” Huang said.

Are any game developers asking for this? Well, in fact, I know one who is. Brendan Greene, creator of battle royale game PlayerUnknown’s Productions, asked for this kind of technology this year when he announced Prologue and then revealed Project Artemis, an attempt to create a virtual world the size of the Earth. He said it could only be built with a combination of game design, user-generated content, and AI.

Well, holy shit.

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Nvidia and Booz Allen develop Morpheus platform to supercharge security AI 

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One of the biggest challenges facing modern organizations is the fact that security teams aren’t scalable. Even well-resourced security teams struggle to keep up with the pace of enterprise threats when monitoring their environments without the use of security artificial intelligence (AI).

However, today at the 2022 Nvidia GTC conference, Nvidia and enterprise consulting firm Booz Allen announced they are partnering together to release a GPU-accelerated AI cybersecurity processing framework called the Morpheus platform. 

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So far, Booz Allen has used Morpheus to create Cyber Precog, a GPU-accelerated software platform for building AI models at the network’s edge, which offer data ingestion capabilities at 300x the rate of CPUs, and boost AI training by 32x and AI inference by 24x. 


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The new solution will enable public and private sector companies to address some of the cybersecurity challenges around closing the cyberskills gap with AI optimized for using GPUs, enabling much more processing to take place than if it was relying on CPUs. 

Finding threats with digital fingerprinting 

Identifying malicious activity in a network full of devices is extremely difficult to do without the help of automation. 

Research shows that 51% of IT security and SOC decision-makers feel their team is overwhelmed by the volume of alerts, with 55% admitting that they aren’t entirely confident in their ability to prioritize and respond to them. 

Security AI has the potential to lighten the loads of SOC analysts by automatically identifying anomalous — or high-risk — activity, and blocking it. 

For instance, the Morpheus software framework enables developers to inspect network traffic in real time, and identify anomalies based on digital fingerprinting. 

“We call it digital fingerprinting of users and machines, where you basically can get to a very granular model for every user or every machine in the company, and you can basically build the model on how that person should be interacting with the system,” said Justin Boitano, VP, EGX of Nvidia. 

“So if you take a user like myself, and I use Office 365 and Outlook every day, and suddenly me as a user starts trying to log in into build systems or other sources of IP in the company, that should be an event that alerts our security teams,” Boitano said. 

It’s an approach that gives the solution the ability to examine network traffic for sensitive information, detect phishing emails, and alert security teams with AI processing powered by large BERT models that couldn’t run on CPUs alone. 

Entering the security AI cluster category: UEBA, XDR, EDR 

As a solution, Morpheus is competing against a wide range of security AI solutions, from user and entity behavior analytics (UEBA) solutions to extended detection and response (XDR) and endpoint detection and response (EDR) solutions designed to discover potential threats.

One of the organizations competing against Nvidia in the realm of threat detection is CrowdStrike Falcon Enterprise, which combines next-gen antivirus (NGAV), endpoint detection and response, threat hunting, and threat intelligence as part of a single solution to continuously and automatically identify threats in enterprise environments.

CrowdStrike recently announced raising $431 million in revenue during the 2022 fiscal year. 

Another potential competitor is IBM QRadar, an XDR solution that uses AI to identify security risks with automatic root cause analysis and MITRE ATT&CK mapping, while providing analysts with support in the form of automated triaging and contextual intelligence. IBM announced raising $16.7 billion in revenue in 2021. 

With Nvidia recently announcing second quarter revenue of $6.7 billion, and now combining the strength of Nvidia’s GPUs alongside Booz Allen’s expertise, the Morpheus framework stands in a unique position to empower enterprises to conduct greater analytic data processing activities at the edge of the network to help supercharge threat detection. 

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Nvidia advances digital twins for retail, rail and telco

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The concept of digital twins is all about modeling the physical world in the metaverse, enabling humans as well as AI to make better decisions.

Building digital twins requires both hardware and software.  At the Nvidia GTC conference today, the company announced the next generation OVX computing systems to help power metaverse applications, including Nvidia’s Omniverse. The new OVX systems are powered by eight Nvidia L40 GPUs and integrate the ConnectX-7 SmartNIC for high-speed networking and storage.

In a press briefing, Richard Kerris, vice president of Omniverse at Nvidia, said that the new OVX systems have been designed for building complex industrial digital twins.

“The new OVX systems are designed to build virtual worlds using leading 3D software applications from our many software partners to be able to operate immersive digital twin simulations in Nvidia Omniverse enterprise, which is a scalable end-to-end platform enabling enterprises to build and operate metaverse applications,” Kerris said.


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Digital twins come to telco

“Omniverse extends and enhances existing workflows across industries, bringing AI superpowers to multi-trillion dollar industries across telecommunications, transportation, retail energy, media and entertainment and more,” Kerris said.

Among the industries that are embracing the digital twin concept is telecommunications. At GTC, Nvidia announced that Heavy AI is using Nvidia’s Omniverse digital twin technology to help telcos optimize 5G cellular networks. Kerris said that Heavy AI made use of digital twins to help Charter Communications with its network deployment.

“Heavy AI is an AI data analytics company that built an AI accelerated application framework on Omniverse, which enables telcos to develop physically accurate interactive digital twins to plan, build, and operate for and 5g networks at nationwide scale,” Kerris said.

Digital twins in all the aisles at home improvement retailer Lowe’s

Nvidia is also using GTC as a venue to highlight digital twin adoption by Lowe’s, which is one of the world’s largest home improvement retailers with over 2000 stores and over 300,000 retail associates. 

“Lowe’s is now using Omniverse as their platform to design, build and operate digital twins of their stores to optimize operations and enhance the shopping experience,” Kerris said.

Lowe’s store associates can now use augmented reality headsets to see what’s on the shelves and the current status of inventory levels. The digital twin also helps with store planning to make sure it’s as easy as possible for consumers to get what they need.

Riding the digital twin rails with Deutsche Bahn

Another industry use case for digital twins that Nvidia is talking about at GTC is in transportation with Deutsche Bahn.

Kerris said that Deutsche Bahn is the second-largest transport company in the world and the National Railway of Germany. Deutsche Bahn is using Omniverse to build and operate digital twins of over 5700 stations and over 33,000 kilometers of track. Omniverse is also being used for capacity optimization, as they’re using the digital twin to train and validate AI models that can continuously monitor the railways and trains to recognize hazards and situations that could affect network operations. 

“Deutsche Bahn expects to increase capacity and efficiency of the railway and reduce its carbon footprint without building any new tracks,” Kerris said.

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Nvidia and PassiveLogic team up to drive integration for autonomous buildings

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Nvidia has invested $15 million in PassiveLogic, a pioneer in autonomous building control systems. The investment will help drive integration between PassiveLogic’s tools and Nvidia’s Omniverse platform for the industrial metaverse. 

PassiveLogic is developing a growing ecosystem of tools built on top of digital twins to enable generative design, autonomous systems and next-generation artificial intelligence (AI). These help architects, engineers, contractors and building owners improve the efficiency and reduce the cost of building operations.

The platform helps users quickly collaborate around AI controls, test them on digital twins of the building, and then deploy them into operations. The company claims its Hive control platform is ten times faster to install and reduces energy consumption by a third compared to conventional automation solutions. 

PassiveLogic is also driving the quantum digital twin standard for autonomous systems that helps describe system-level interactions between components, equipment, assemblies and environments. 


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Visualization meets automation

According to PassiveLogic’s CEO Troy Harvey, “Nvidia’s Omniverse and PassiveLogic’s Quantum are each focused on different and complementary aspects of describing the world through digital twins.” 

The Omniverse is very focused on geometry and visualization and providing the integration to universal scene description (USD) workflows for industrial digital twins. PassiveLogic’s compiler and compute technology runs the AI for these digital twins at the edge on Nvidia GPU technology. Integration between the platforms will make it easier to embed digital twins into AI control systems to support autonomous building controls that can adapt to changes in user needs, the environment, or the equipment itself. 

“As a partnership, we are really excited about the breadth of applications our combined technology platforms can address,” Harvey said. 

Other components that PassiveLogic’s platform include are: 

  • The Quantum Creator which provides a CAD system for creating digital twins that describe what something is, how it works and why it would do specific actions. 
  • An Autonomy Studio that enables users to build autonomous systems by composing digital twins into systems and environments through a drag-and-drop interface that outputs a system-level digital twin. 
  • The Hive platform consumes these digital twins to provide real-time automation of buildings. 
  • The Passport feature allows individuals to create and share their own personal digital twin reflecting physiological, ergonomic and comfort preferences.

New workflows

These integrations can improve the control systems for any kind of equipment. In the short run, Harvey is most focused on opportunities for autonomous buildings to improve sustainability. He estimates that buildings consume about 41% of the world’s energy, and believes the company’s platform can help reduce that by 30%. 

PassiveLogic can help teams at the beginning of a project to clarify project goals, iterate prototypes with generative design, and then automate control systems. Nvidia’s Omniverse then provides the visualization, animation and 3D exploration of the virtual world, once the project is underway. Omniverse also simplifies USD integration with other tools. 

The new funding from Nvidia brings PassiveLogic’s total funding to more than $80 million. Other investors include building-asset owners, equipment manufacturers and venture investors such as Addition, Brookfield, Keyframe, RET, Era and A/O Proptech. This investment is part of a broader trend around using USD as a core data layer to simplify workflows across various tools. It complements efforts to integrate USD and IFC and Nvidia’s recent partnership with Siemens to grow the industrial metaverse on top of USD.

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How Nvidia is making building a PC much more expensive

Nvidia’s RTX 4090 is on the way. The company revealed as much today during its GeForce Beyond event at GTC 2022. It promises two to four times the performance of the RTX 3090 Ti, which would make it one of the largest jumps we’ve seen in performance between generations.

If you ignore the price, that is. With the RTX 4090 launching at $1,599, the RTX 4080 16GB at $1199, and the RTX 4080 12GB at $899, Nvidia’s new GPUs represent yet another round of price increases for new GPUs, one that makes building a PC more expensive than ever before.

Performance gains negated by higher prices


It used to be the case that newer GPUs had much better bang for the uck than older ones, at least if you’re just comparing MSRP. You could usually expect 30% or more performance per dollar with each new generation, and some generations delivered as much as 50% or even 70% more value than the previous one. For example, the GTX 1080 Ti was about 70% faster than the GTX 980 Ti and it only cost about $50 more; the 1080 Ti’s great value is one of the reasons why it’s such a legendary Nvidia GPU. However, it’s been apparent for a while that Nvidia just isn’t offering the generational value improvements it used to. We saw it in the RTX 20 series, and we also saw it in the RTX 30 series, which was only a partial improvement over its predecessor.

The RTX 40 series is no different. Even if we take Nvidia’s own numbers at face value (the lower end of the range, anyways), the RTX 4080 16GB at about double the performance of the RTX 3080 isn’t all that great when it’s nearly double the price, too. In fact, the 4080 16GB would need to be 2.3 times faster than the 3080 to have even 30% more bang for buck. Sure, the 40 series has better ray tracing performance and more Tensor cores than the 30 series, but we’re still not to the point where that kind of technology is truly standard.

There are also other reasons to care about the price, even if the value is good. First, the cheapest RTX 40 card announced was the RTX 4080 12GB, which has less memory and less cores than the RTX 4080 16GB. It should be called the RTX 4070 really, but maybe Nvidia didn’t want to call it that because of its $899 price tag, which is higher than even that of the RTX 3080. It’s pretty obvious that these GPUs are unaffordable to the vast majority of PC users, even those who once purchased Nvidia flagships like the 1080 Ti.

Three graphics cards on a gray background.

Of course, Nvidia will launch lower-end GPUs with lower prices, and that brings me to my second point. Because we can see how expensive the high-end 40 series models are, it’s hard to see Nvidia not increasing the prices across the whole stack. Usually, we only see maybe $50 to $100 between each GPU throughout the product stack, so it seems very unlikely the RTX 4070 will launch at $499 just like the RTX 3070. Instead, we might see the 4070 launch with an MSRP of $699 or even $799. Will there even be a 40 series card for less than $300?

Nvidia wants to eat its cake and have it too

The RTX 4080 series of graphics cards.

Besides Nvidia wanting to increase its margins and profits, the high price tag for the RTX 40 series is possibly also motivated by the glut of 30 series cards still on the market, particularly higher-end cards from the RTX 3080 to the RTX 3090 Ti. These cards have been falling in price because demand has dipped, and that’s bad for Nvidia’s bottom line. If Nvidia had priced the RTX 40 series lower, then that might have pushed the price of its old flagships down even further.

This is Nvidia’s problem, and in the past when Nvidia or AMD made too many GPUs, they just had to suck it up. For example, after the RTX 20 series launch, Nvidia enjoyed a single quarter of record-breaking revenue and profits, followed by a yearlong slump caused by a glut of GTX 10 series cards still on the market.

Nvidia probably remembers this very well and doesn’t want it to happen again (despite the fact it’s responsible for making too many GPUs in the first place), so the company’s solution is simply that consumers will have to pay more for new RTX 40 series cards or to buy old RTX 30 series cards at prices that are still higher than they ought to be. It’s certainly a bold stance to take given that the world isn’t exactly in prosperous times.

This can really only go one of two ways for Nvidia. In one scenario, Nvidia gets exactly what it wants and everyone accepts the prices for RTX 30 and 40 series GPUs. Nvidia doesn’t have to take on losses or lower-than-desired profits for RTX 30, and RTX 40 sells for great margins. In the other scenario, people balk at the prices and the old and new cards don’t sell nearly as well as they need to, which would be worse than if Nvidia just allowed prices to fall so demand can rise.

AMD Radeon RX 6900 graphics card hovers over an AMD red and black background.

We also can’t forget about AMD, which is set to announce its RX 7000 series GPUs on November 3. AMD has been following Nvidia’s lead on increasing prices in recent years, and the next-generation RX 7000 series is on TSMC’s cutting-edge and expensive 5nm process, which is almost identical to the TSMC 4nm process RTX 40 series uses. AMD has every reason to not price RX 7000 particularly competitively, so Nvidia probably feels pretty safe in its strategy. Yet, AMD does have the power to price RX 7000 competitively if it wants to, and doing so would be disastrous for Nvidia. Setting such high prices is certainly risky in multiple ways.

Disappointing but predictable

The top of the Nvidia RTX 4080 cooler.

Personally, I’m not surprised about the price of Nvidia’s new GPUs. In August, I detailed how CPUs and especially GPUs have been worsening in value improvement every year and how it wasn’t just because of COVID or supply shortages. The RTX 40 series is just the next step in a trend that’s being going on for years.

In all likelihood, the trend will continue for years to come, and it could really hurt desktop gaming. We always see reports that desktops are dying and the show’s over, and those reports always ended up being wrong. But it’s hard to not be concerned when the cheapest GPU in a new generation is significantly more expensive than the priciest flagship from just a few years ago.

Editors’ Choice

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Nvidia GeForce RTX 4080 16GB vs RTX 4080 12GB

Nvidia has surprised us all by announcing two versions of the GeForce RTX 4080 instead of an RTX 4080 and an RTX 4070. Following the RTX 4090, the two 4080s will likely be some of the more popular GPUs in the brand-new RTX 40 “Ada Lovelace” lineup.

While the RTX 4070 didn’t make an appearance yet, the two versions of the RTX 4080 give us plenty to get hyped for. Let’s see how they compare to one another.



While the two GPUs are both called RTX 4080, they differ quite a lot in terms of their specifications. Many leaks suspected that Nvidia would be launching the RTX 4090, RTX 4080, and the RTX 4070 initially. Now, it seems that the RTX 4080 12GB may have inherited some of the specs that were initially leaked as RTX 4070.

The RTX 4080 16GB obviously sports more memory, but interestingly, it’s the RTX 4080 12GB that has slightly higher clock speeds. However, the extra memory and CUDA cores on the RTX 4080 16GB will both have an impact on performance.

Nvidia GeForce RTX 4080 16GB Nvidia GeForce RTX 4080 12GB
CUDA cores 9,728 7,680
Base clock 2,210MHz 2,310MHz
Maximum clock 2,510MHz 2,610MHz
Memory size 16GB GDDR6X 12GB GDDR6X
Memory bus 256-bit 192-bit
TDP 320 watts 285 watts

Expected performance

Comparison of the RTX 4080 16GB and 12GB versions.

Nvidia hasn’t said much about the expected performance of the RTX 4080, so it’s hard to predict how powerful the two GPUs are going to be. We can guess based on their specs, but the real knowledge will come from benchmarks. Fortunately, we’re likely to start seeing them begin to leak out soon, and once the cards are fully out, we should be able to test them ourselves.

The CUDA core volume of the RTX 4080 12GB puts it between the RTX 3070 Ti and the RTX 3080. However, it sports more memory than the RTX 3070 Ti, and also utilizes Nvidia’s latest tech such as DLSS 3 and Shader Execution Reordering (SER). Suffice it to say that we will be seeing an improvement in terms of performance from both cards, but it’s too early to gauge just how they compare to each other.

Nvidia has teased that the RTX 4080 will be two to four times faster than the RTX 3080 Ti, but these numbers may change. It did give us one thing, though — the benchmark linked above that implies the RTX 4080 16GB outperforms the RTX 4080 12GB in each of the three titles, but it’s not a massive difference. However, they both dwarf the RTX 3080 Ti.

Pricing and availability

Nvidia's Ada Lovelace chip.

We don’t have an exact release date for the two RTX 4080 GPUs just yet, but we do know that they will be launching in November this year, so a little later than the flagship RTX 4090.

Once they arrive, the GPUs will be priced at $899 for the RTX 4080 12GB and $1,119 for the RTX 4080 16GB. Custom models from Nvidia’s board partners, such as Gigabyte, Asus, Zotac, MSI, and others, will also be available soon enough, and those might be priced higher depending on their specifications.

It’s a close call

A comparison between the graphics quality without DLSS 3 and with it.

Choosing between the RTX 4080 16GB and the RTX 4080 12GB is going to be a pretty close call once these GPUs are available for sale.

On the one hand, the RTX 4080 12GB is $300 cheaper, and that’s nothing to sneeze at. On the other hand, the 16GB version will, of course, offer better performance, but it’s hard to say whether that difference will be worth $300.

Based on specifications alone, the RTX 4080 16GB will be the better choice, no contest — but if you’re looking for a mix of affordable and powerful, the 12GB option might be the better pick. The RTX 3080 and RTX 3080 Ti will also retain their good value if you’ll be focusing on the price rather than pushing for the latest technologies.

Editors’ Choice

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EVGA is done making GPUs, and it’s because of Nvidia

Among Nvidia’s 3rd-party GPU manufacturers, EVGA is perhaps the most famous. The brand is well known for high-quality RTX and GTX graphics cards with generous consumer policies, as well as power supplies, coolers, and motherboards. The partnership between Nvidia and EVGA, which lasted over two decades, is now over, however, and not only will EVGA stop making Nvidia GPUs, it has no plans on making any GPUs ever again. It’s not a clean breakup either.

EVGA Terminates NVIDIA Partnership, Cites Disrespectful Treatment

In a statement to Gamers Nexus, which broke the news, EVGA stated “this is not a financial decision, it is a principled decision.” EVGA has accused Nvidia of keeping partners out of the loop on future products, cutting GPU prices without warning, and limiting what prices GPUs can be set at. According to an Nvidia staff member who spoke to Gamers Nexus, Nvidia CEO Jensen Huang sometimes wonders “why are these guys [EVGA and other Nvidia partners] making money when they’re not doing much?”

One of the main issues is that Nvidia sells its own Founder’s Edition models for significantly less than models from partners. EVGA reportedly loses hundreds of dollars on each RTX 3080, 3090, and 3090 Ti it sells as it needs to cut prices to remain competitive with Nvidia. That figure only considers manufacturing, however.

Although EVGA says this isn’t a financial decision, finances are certainly at play. Jon Peddie Research noted that the gross margin for Nvidia has continued to increase year after year while the already small margin for GPU partner companies has declined. In its 2022 estimate, Jon Peddie Research believes Nvidia will see about 65% gross margin for its entire business while AIB partners will just see 5%. Declining margins are down to increasing costs for production, R&D, and marketing. According to Jon Peddie Research, making up low margins on volume is no longer appealing.

Gamers Nexus was skeptical of EVGA’s story, however. In its report, host Steve Burke suggest the company was probably ordering too many GPUs during the crypto boom and could have gotten burned by the sudden decline in mining. Burke notes that something similar happened with its RTX 20-series GPUs when the company lost money in the six-digit range.

EVGA CEO Andrew Han might also have personal reasons for ending the partnership. Gamers Nexus says that Han, who is in his 60s and has been CEO since EVGA was founded in 2000, wants to spend more time with his family as he approaches retirement and feels that Nvidia’s allegedly disrespectful attitude is no longer worth the trouble.

EVGA isn’t going out of business, yet

EVGA RTX 3060 sitting on a table.

Although 78% of EVGA’s revenue is derived from its graphics business, the company says it will continue to operate its other ventures. The company’s next largest venture is power supplies, and while it only makes up 20% of EVGA’s revenue, it has four times the gross margin of its graphics business. Losing the vast majority of its revenue is still problematic, however, though EVGA explicitly denied that there would be any layoffs.

Han also denied he would sell EVGA. The company is apparently in a healthy financial position. Furthermore, the CEO didn’t want to deprecate EVGA’s reputation by selling it to another company that might only be interested in profit.

While EVGA could potentially partner up with AMD or Intel to preserve its AIB GPU business, the company has make it clear that it will not be making any GPUs in the future. Gamers Nexus speculates that EVGA’s CEO might have personal reasons for not wanting to pursue a partnership with Nvidia’s competitors, similar to his personal reasons for terminating the partnership with Nvidia.

As for existing EVGA GPUs, the company confirmed it would honor warranties and RMAs as long as supplies last. Its supply of RTX 30-series cards will run out by the end of the year, however, and it’s not certain how easy it will then be for EVGA to uphold its warranties, whether or not the company is willing.

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Nvidia AI chip export ban expected to impact U.S., China AI race

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Last week was filled with reports that U.S. government officials have ordered Nvidia to stop exporting its A100 and H100 GPUs to China, a signal of what Reuters called a “major escalation” of a U.S. campaign to slow China’s technological capabilities. 

Nvidia also said last Thursday that the U.S. government will allow it to continue developing its H100 artificial intelligence (AI) chip in China. 

The move immediately led to speculation about the impact on Chinese firms, including their ability to compete in areas of AI research such as image recognition. Questions also came quickly about the impact on Nvidia’s business in China. 

Nvidia’s upcoming H100 processor has been touted as “the next-generation flagship of Nvidia’s AI processor solution for the data center,” while the A100 is Nvidia’s mainstay enterprise GPU commonly deployed in supercomputers. According to Handel Jones, CEO of International Business Strategies, a chip consultancy, the cost to develop the H100 is in excess of $1 billion, and it took Nvidia, with its very strong R&D organization, over two years to implement the design. 


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Short- and long-term impact on Nvidia

The short-term impact on Nvidia is potential reduction in revenues of $400 million in each of the next two quarters, Jones told VentureBeat by email. Last Wednesday, Nvidia said it had booked $400 million in Chinese sales of the affected chips this quarter, which could be lost if its customers decide not to buy alternative Nvidia products. 

The longer-term impact, however, is the loss of Nvidia’s China market, which “will result in Chinese companies producing their own AI accelerators,” said Jones, whose upcoming book, “When AI Rules the World: China, the U.S., and the Race to Control a Smart Planet,” details how China is progressing in AI. He also claims that by 2030, China will have a leadership position in many of the important areas of AI.

The A100 and H100 products are critical for training AI in data centers, he added, and companies in China that are impacted include Alibaba Group, Tencent, Inspur Group, H3C, Baidu and Lenovo Group.

Overall, IBS projects the semiconductor market to grow between 8% to 10% in 2022 and 2% to 8% in 2023, while the organization expects additional restrictions to be placed on the sales of semiconductor products to China. 

U.S. government concerns

According to Dan Pickard, international trade and national security expert and attorney at Buchanan Ingersoll & Rooney, it is widely believed that China’s state-led industrial policies are intended to advance both their commercial interests and their military development. “It is for these reasons that there is bipartisan support to ensure that U.S. technology is not transferred to China that ends up supporting the People’s Liberation Army,” Pickard told VentureBeat by email. 

The export ban came just one month after President Biden signed the CHIPS and Science Act into law, which includes $52.7 billion to increase domestic semiconductor production. Just two days ago, the U.S. Commerce Department detailed its plans to distribute the money, which includes $28 billion earmarked for underwriting expansions of existing semiconductor manufacturing facilities or the construction of new ones. 

It is “difficult to overstate” the concerns of the U.S. government regarding the semiconductor manufacturing shortages, said Pickard. 

Nvidia export ban impacts Chinese universities

On the Chinese side, a recent Reuters review showed that high-profile universities and state-run research institutes in China have been relying on a U.S. computing chip to power their AI technology, but its export to the country has now been restricted. 

For example, the review showed that Tsinghua University, China’s highest-ranked higher education institution globally, spent over $400,000 last October on two Nvidia AI supercomputers, each powered by four A100 chips.

Still, some Chinese companies have insisted U.S. restrictions would have a limited impact. Yesterday CNBC reported that Chinese electric car maker Nio said U.S. restrictions on Nvidia chip sales to China won’t affect the automaker’s business.

“Based on our estimations, our computing power is sufficient for our autonomous driving technology development in the aspect of AI training for now,” William Li, founder, chairman and CEO of Nio, said, according to the article. “And we have been working very closely with our partner Nvidia.”

More controls likely to come

Pickard pointed out that because of Nvidia’s significant position in the semiconductor industry, “it is front and center in the United States’ contest against China to see who will be the world leader in 21st century technology.” This contest, he said, is “at its hottest in regard to artificial intelligence.” 

Chinese companies that need access to these types of GPUs should definitely be concerned, he added.

“One of the few areas in Washington where there is bipartisan consensus concerns controlling the export of key technologies to China,” he said. “It is reasonable to expect more, not less, controls on exports to China for the foreseeable future.” 

He also pointed out that U.S. companies are also going to need to be hypervigilant in regard to the increasing level of controls on the export of national security-sensitive items to China and Russia.

“The Department of Commerce has been clear that establishing export controls on certain new technologies are essential to the national security of the United States,” he emphasized, adding that the President has wide discretion in regard to prohibiting the export of U.S. goods, technology and services under the existing export controls and economic sanctions laws.

“These laws are powerful foreign policy tools and have been used more aggressively over the past several administrations,” he said. 

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NVIDIA looks set to reveal its next-gen GeForce RTX GPUs on September 20th

NVIDIA’s GPU Technology Conference goes down this month and the company has revealed when CEO Jensen Huang’s keynote will take place. You’ll be able to watch it at 11AM ET on September 20th. The keynote will kick off with a GeForce Beyond special broadcast, which will also stream on and .

The company says the event will include “the latest breakthroughs in gaming, creating and graphics technology.” NVIDIA is expected to reveal its RTX 40-series graphics cards during the broadcast — an image the company shared to promote the event includes the GeForce RTX Logo. NVIDIA previously said it would release its this year. Those will supplant graphics cards with the current Ampere architecture.

It remains to be seen just how well the RTX 40-series cards will perform. In the meantime, the 30-series GPUs after the cryptocurrency market cratered.

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Nvidia has an exciting announcement about the RTX 4000 GPUs

If you’ve been awaiting news about the upcoming Nvidia GeForce RTX 40-Series GPU lineup, it’s officially time to get excited. Nvidia has just announced that a “special broadcast” will take place on September 20.

“PC enthusiasts, don’t miss the GeForce Beyond special broadcast,” said Nvidia in its quick teaser, adding that this is an event you won’t want to miss. Here’s what’s happening and how you can tune in too.


— NVIDIA GeForce (@NVIDIAGeForce) September 7, 2022

While Nvidia doesn’t say it outright, it makes perfect sense for this to be the long-awaited RTX 4000 announcement. The company has hinted as much during a recent earnings call, where Nvidia CEO Jensen Huang said that we can expect to hear more about the next generation of GPUs “next month,” referring to September. This might still change, of course, but all signs point to it being the plan for now.

At 8 a.m. PT on September 20, Huang will present his GTC 2022 keynote. The keynote is said to revolve around Nvidia’s latest breakthroughs in gaming, content creation, and graphics technology. However, before the keynote actually begins, a special GeForce Beyond broadcast will take place. This is, presumably, when Nvidia will break the big news.

What to expect from Nvidia’s special announcement


It seems very likely, if not almost guaranteed, that Nvidia will use this broadcast to finally reveal more about its upcoming “Ada Lovelace” GPU lineup. This doesn’t mean that we’ll get to hear about the entire range, though.

The most likely GPU to lead the way is the flagship Nvidia GeForce RTX 4090, rumored to release ahead of its less powerful counterparts, the RTX 4080 and RTX 4070. Nvidia is almost certainly readying budget-friendly graphics cards too, but most rumors pinpoint the launch of these cheaper cards as early 2023. The graphics cards are expected to deliver a marked leap in performance, with some sources saying that they could even double it compared to the RTX 30-series.

There have also been whispers of a GPU that will allegedly be even more powerful than the RTX 4090. With a monstrous TBP that is said to be as high as 900 watts, this GPU could either be an RTX 4090 Ti or a Titan GPU if Nvidia chooses to bring that back.

It’s difficult to say just how much Nvidia will reveal on September 20, but an announcement of the initial wave of GPUs seems likely. Whether we learn the exact release dates and pricing remains to be seen.

How to watch the announcement

NVIDIA CEO Jensen Huang on stage.

Anyone can watch the keynote live when it takes place on September 20. Nvidia will be streaming it on its GTC 2022 website, and it will also likely end up on YouTube closer to the date.

If you’re not able to watch it, don’t worry — we’ll keep you posted with all the latest news about Nvidia GPUs.

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