NVIDIA

intel’s-“gaudi-3”-ai-accelerator-chip-may-give-nvidia’s-h100-a-run-for-its-money

Intel’s “Gaudi 3” AI accelerator chip may give Nvidia’s H100 a run for its money

Adventures in Matrix Multiplication —

Intel claims 50% more speed when running AI language models vs. the market leader.

An Intel handout photo of the Gaudi 3 AI accelerator.

Enlarge / An Intel handout photo of the Gaudi 3 AI accelerator.

On Tuesday, Intel revealed a new AI accelerator chip called Gaudi 3 at its Vision 2024 event in Phoenix. With strong claimed performance while running large language models (like those that power ChatGPT), the company has positioned Gaudi 3 as an alternative to Nvidia’s H100, a popular data center GPU that has been subject to shortages, though apparently that is easing somewhat.

Compared to Nvidia’s H100 chip, Intel projects a 50 percent faster training time on Gaudi 3 for both OpenAI’s GPT-3 175B LLM and the 7-billion parameter version of Meta’s Llama 2. In terms of inference (running the trained model to get outputs), Intel claims that its new AI chip delivers 50 percent faster performance than H100 for Llama 2 and Falcon 180B, which are both relatively popular open-weights models.

Intel is targeting the H100 because of its high market share, but the chip isn’t Nvidia’s most powerful AI accelerator chip in the pipeline. Announcements of the H200 and the Blackwell B200 have since surpassed the H100 on paper, but neither of those chips is out yet (the H200 is expected in the second quarter of 2024—basically any day now).

Meanwhile, the aforementioned H100 supply issues have been a major headache for tech companies and AI researchers who have to fight for access to any chips that can train AI models. This has led several tech companies like Microsoft, Meta, and OpenAI (rumor has it) to seek their own AI-accelerator chip designs, although that custom silicon is typically manufactured by either Intel or TSMC. Google has its own line of tensor processing units (TPUs) that it has been using internally since 2015.

Given those issues, Intel’s Gaudi 3 may be a potentially attractive alternative to the H100 if Intel can hit an ideal price (which Intel has not provided, but an H100 reportedly costs around $30,000–$40,000) and maintain adequate production. AMD also manufactures a competitive range of AI chips, such as the AMD Instinct MI300 Series, that sell for around $10,000–$15,000.

Gaudi 3 performance

An Intel handout featuring specifications of the Gaudi 3 AI accelerator.

Enlarge / An Intel handout featuring specifications of the Gaudi 3 AI accelerator.

Intel says the new chip builds upon the architecture of its predecessor, Gaudi 2, by featuring two identical silicon dies connected by a high-bandwidth connection. Each die contains a central cache memory of 48 megabytes, surrounded by four matrix multiplication engines and 32 programmable tensor processor cores, bringing the total cores to 64.

The chipmaking giant claims that Gaudi 3 delivers double the AI compute performance of Gaudi 2 using 8-bit floating-point infrastructure, which has become crucial for training transformer models. The chip also offers a fourfold boost for computations using the BFloat 16-number format. Gaudi 3 also features 128GB of the less expensive HBMe2 memory capacity (which may contribute to price competitiveness) and features 3.7TB of memory bandwidth.

Since data centers are well-known to be power hungry, Intel emphasizes the power efficiency of Gaudi 3, claiming 40 percent greater inference power-efficiency across Llama 7B and 70B parameters, and Falcon 180B parameter models compared to Nvidia’s H100. Eitan Medina, chief operating officer of Intel’s Habana Labs, attributes this advantage to Gaudi’s large-matrix math engines, which he claims require significantly less memory bandwidth compared to other architectures.

Gaudi vs. Blackwell

An Intel handout photo of the Gaudi 3 AI accelerator.

Enlarge / An Intel handout photo of the Gaudi 3 AI accelerator.

Last month, we covered the splashy launch of Nvidia’s Blackwell architecture, including the B200 GPU, which Nvidia claims will be the world’s most powerful AI chip. It seems natural, then, to compare what we know about Nvidia’s highest-performing AI chip to the best of what Intel can currently produce.

For starters, Gaudi 3 is being manufactured using TSMC’s N5 process technology, according to IEEE Spectrum, narrowing the gap between Intel and Nvidia in terms of semiconductor fabrication technology. The upcoming Nvidia Blackwell chip will use a custom N4P process, which reportedly offers modest performance and efficiency improvements over N5.

Gaudi 3’s use of HBM2e memory (as we mentioned above) is notable compared to the more expensive HBM3 or HBM3e used in competing chips, offering a balance of performance and cost-efficiency. This choice seems to emphasize Intel’s strategy to compete not only on performance but also on price.

As far as raw performance comparisons between Gaudi 3 and the B200, that can’t be known until the chips have been released and benchmarked by a third party.

As the race to power the tech industry’s thirst for AI computation heats up, IEEE Spectrum notes that the next generation of Intel’s Gaudi chip, code-named Falcon Shores, remains a point of interest. It also remains to be seen whether Intel will continue to rely on TSMC’s technology or leverage its own foundry business and upcoming nanosheet transistor technology to gain a competitive edge in the AI accelerator market.

Intel’s “Gaudi 3” AI accelerator chip may give Nvidia’s H100 a run for its money Read More »

amd-promises-big-upscaling-improvements-and-a-future-proof-api-in-fsr-3.1

AMD promises big upscaling improvements and a future-proof API in FSR 3.1

upscale upscaling —

API should help more games get future FSR improvements without a game update.

AMD promises big upscaling improvements and a future-proof API in FSR 3.1

AMD

Last summer, AMD debuted the latest version of its FidelityFX Super Resolution (FSR) upscaling technology. While version 2.x focused mostly on making lower-resolution images look better at higher resolutions, version 3.0 focused on AMD’s “Fluid Motion Frames,” which attempt to boost FPS by generating interpolated frames to insert between the ones that your GPU is actually rendering.

Today, the company is announcing FSR 3.1, which among other improvements decouples the upscaling improvements in FSR 3.x from the Fluid Motion Frames feature. FSR 3.1 will be available “later this year” in games whose developers choose to implement it.

Fluid Motion Frames and Nvidia’s equivalent DLSS Frame Generation usually work best when a game is already running at a high frame rate, and even then can be more prone to mistakes and odd visual artifacts than regular FSR or DLSS upscaling. FSR 3.0 was an all-or-nothing proposition, but version 3.1 should let you pick and choose what features you want to enable.

It also means you can use FSR 3.0 frame generation with other upscalers like DLSS, especially useful for 20- and 30-series Nvidia GeForce GPUs that support DLSS upscaling but not DLSS Frame Generation.

“When using FSR 3 Frame Generation with any upscaling quality mode OR with the new ‘Native AA’ mode, it is highly recommended to be always running at a minimum of ~60 FPS before Frame Generation is applied for an optimal high-quality gaming experience and to mitigate any latency introduced by the technology,” wrote AMD’s Alexander Blake-Davies in the post announcing FSR 3.1.

Generally, FSR’s upscaling image quality falls a little short of Nvidia’s DLSS, but FSR 2 closed that gap a bit, and FSR 3.1 goes further. AMD highlights two specific improvements: one for “temporal stability,” which will help reduce the flickering and shimmering effect that FSR sometimes introduces, and one for ghosting reduction, which will reduce unintentional blurring effects for fast-moving objects.

The biggest issue with these new FSR improvements is that they need to be implemented on a game-to-game basis. FSR 3.0 was announced in August 2023, and AMD now trumpets that there are 40 “available and upcoming” games that support the technology, of which just 19 are currently available. There are a lot of big-name AAA titles in the list, but that’s still not many compared to the sum total of all PC games or even the 183 titles that currently support FSR 2.x.

AMD wants to help solve this problem in FSR 3.1 by introducing a stable FSR API for developers, which AMD says “makes it easier for developers to debug and allows forward compatibility with updated versions of FSR.” This may eventually lead to more games getting future FSR improvements for “free,” without the developer’s effort.

AMD didn’t mention any hardware requirements for FSR 3.1, though presumably, the company will still support a reasonably wide range of recent GPUs from AMD, Nvidia, and Intel. FSR 3.0 is formally supported on Radeon RX 5000, 6000, and 7000 cards, Nvidia’s RTX 20-series and newer, and Intel Arc GPUs. It will also bring FSR 3.x features to games that use the Vulkan API, not just DirectX 12, and the Xbox Game Development Kit (GDK) so it can be used in console titles as well as PC games.

AMD promises big upscaling improvements and a future-proof API in FSR 3.1 Read More »

nvidia-announces-“moonshot”-to-create-embodied-human-level-ai-in-robot-form

Nvidia announces “moonshot” to create embodied human-level AI in robot form

Here come the robots —

As companies race to pair AI with general-purpose humanoid robots, Nvidia’s GR00T emerges.

An illustration of a humanoid robot created by Nvidia.

Enlarge / An illustration of a humanoid robot created by Nvidia.

Nvidia

In sci-fi films, the rise of humanlike artificial intelligence often comes hand in hand with a physical platform, such as an android or robot. While the most advanced AI language models so far seem mostly like disembodied voices echoing from an anonymous data center, they might not remain that way for long. Some companies like Google, Figure, Microsoft, Tesla, Boston Dynamics, and others are working toward giving AI models a body. This is called “embodiment,” and AI chipmaker Nvidia wants to accelerate the process.

“Building foundation models for general humanoid robots is one of the most exciting problems to solve in AI today,” said Nvidia CEO Jensen Huang in a statement. Huang spent a portion of Nvidia’s annual GTC conference keynote on Monday going over Nvidia’s robotics efforts. “The next generation of robotics will likely be humanoid robotics,” Huang said. “We now have the necessary technology to imagine generalized human robotics.”

To that end, Nvidia announced Project GR00T, a general-purpose foundation model for humanoid robots. As a type of AI model itself, Nvidia hopes GR00T (which stands for “Generalist Robot 00 Technology” but sounds a lot like a famous Marvel character) will serve as an AI mind for robots, enabling them to learn skills and solve various tasks on the fly. In a tweet, Nvidia researcher Linxi “Jim” Fan called the project “our moonshot to solve embodied AGI in the physical world.”

AGI, or artificial general intelligence, is a poorly defined term that usually refers to hypothetical human-level AI (or beyond) that can learn any task a human could without specialized training. Given a capable enough humanoid body driven by AGI, one could imagine fully autonomous robotic assistants or workers. Of course, some experts think that true AGI is long way off, so it’s possible that Nvidia’s goal is more aspirational than realistic. But that’s also what makes Nvidia’s plan a moonshot.

NVIDIA Robotics: A Journey From AVs to Humanoids.

“The GR00T model will enable a robot to understand multimodal instructions, such as language, video, and demonstration, and perform a variety of useful tasks,” wrote Fan on X. “We are collaborating with many leading humanoid companies around the world, so that GR00T may transfer across embodiments and help the ecosystem thrive.” We reached out to Nvidia researchers, including Fan, for comment but did not hear back by press time.

Nvidia is designing GR00T to understand natural language and emulate human movements, potentially allowing robots to learn coordination, dexterity, and other skills necessary for navigating and interacting with the real world like a person. And as it turns out, Nvidia says that making robots shaped like humans might be the key to creating functional robot assistants.

The humanoid key

Robotics startup figure, an Nvidia partner, recently showed off its humanoid

Enlarge / Robotics startup figure, an Nvidia partner, recently showed off its humanoid “Figure 01” robot.

Figure

So far, we’ve seen plenty of robotics platforms that aren’t human-shaped, including robot vacuum cleaners, autonomous weed pullers, industrial units used in automobile manufacturing, and even research arms that can fold laundry. So why focus on imitating the human form? “In a way, human robotics is likely easier,” said Huang in his GTC keynote. “And the reason for that is because we have a lot more imitation training data that we can provide robots, because we are constructed in a very similar way.”

That means that researchers can feed samples of training data captured from human movement into AI models that control robot movement, teaching them how to better move and balance themselves. Also, humanoid robots are particularly convenient because they can fit anywhere a person can, and we’ve designed a world of physical objects and interfaces (such as tools, furniture, stairs, and appliances) to be used or manipulated by the human form.

Along with GR00T, Nvidia also debuted a new computer platform called Jetson Thor, based on NVIDIA’s Thor system-on-a-chip (SoC), as part of the new Blackwell GPU architecture, which it hopes will power this new generation of humanoid robots. The SoC reportedly includes a transformer engine capable of 800 teraflops of 8-bit floating point AI computation for running models like GR00T.

Nvidia announces “moonshot” to create embodied human-level AI in robot form Read More »

nvidia-unveils-blackwell-b200,-the-“world’s-most-powerful-chip”-designed-for-ai

Nvidia unveils Blackwell B200, the “world’s most powerful chip” designed for AI

There’s no knowing where we’re rowing —

208B transistor chip can reportedly reduce AI cost and energy consumption by up to 25x.

The GB200

Enlarge / The GB200 “superchip” covered with a fanciful blue explosion.

Nvidia / Benj Edwards

On Monday, Nvidia unveiled the Blackwell B200 tensor core chip—the company’s most powerful single-chip GPU, with 208 billion transistors—which Nvidia claims can reduce AI inference operating costs (such as running ChatGPT) and energy consumption by up to 25 times compared to the H100. The company also unveiled the GB200, a “superchip” that combines two B200 chips and a Grace CPU for even more performance.

The news came as part of Nvidia’s annual GTC conference, which is taking place this week at the San Jose Convention Center. Nvidia CEO Jensen Huang delivered the keynote Monday afternoon. “We need bigger GPUs,” Huang said during his keynote. The Blackwell platform will allow the training of trillion-parameter AI models that will make today’s generative AI models look rudimentary in comparison, he said. For reference, OpenAI’s GPT-3, launched in 2020, included 175 billion parameters. Parameter count is a rough indicator of AI model complexity.

Nvidia named the Blackwell architecture after David Harold Blackwell, a mathematician who specialized in game theory and statistics and was the first Black scholar inducted into the National Academy of Sciences. The platform introduces six technologies for accelerated computing, including a second-generation Transformer Engine, fifth-generation NVLink, RAS Engine, secure AI capabilities, and a decompression engine for accelerated database queries.

Press photo of the Grace Blackwell GB200 chip, which combines two B200 GPUs with a Grace CPU into one chip.

Enlarge / Press photo of the Grace Blackwell GB200 chip, which combines two B200 GPUs with a Grace CPU into one chip.

Several major organizations, such as Amazon Web Services, Dell Technologies, Google, Meta, Microsoft, OpenAI, Oracle, Tesla, and xAI, are expected to adopt the Blackwell platform, and Nvidia’s press release is replete with canned quotes from tech CEOs (key Nvidia customers) like Mark Zuckerberg and Sam Altman praising the platform.

GPUs, once only designed for gaming acceleration, are especially well suited for AI tasks because their massively parallel architecture accelerates the immense number of matrix multiplication tasks necessary to run today’s neural networks. With the dawn of new deep learning architectures in the 2010s, Nvidia found itself in an ideal position to capitalize on the AI revolution and began designing specialized GPUs just for the task of accelerating AI models.

Nvidia’s data center focus has made the company wildly rich and valuable, and these new chips continue the trend. Nvidia’s gaming GPU revenue ($2.9 billion in the last quarter) is dwarfed in comparison to data center revenue (at $18.4 billion), and that shows no signs of stopping.

A beast within a beast

Press photo of the Nvidia GB200 NVL72 data center computer system.

Enlarge / Press photo of the Nvidia GB200 NVL72 data center computer system.

The aforementioned Grace Blackwell GB200 chip arrives as a key part of the new NVIDIA GB200 NVL72, a multi-node, liquid-cooled data center computer system designed specifically for AI training and inference tasks. It combines 36 GB200s (that’s 72 B200 GPUs and 36 Grace CPUs total), interconnected by fifth-generation NVLink, which links chips together to multiply performance.

A specification chart for the Nvidia GB200 NVL72 system.

Enlarge / A specification chart for the Nvidia GB200 NVL72 system.

“The GB200 NVL72 provides up to a 30x performance increase compared to the same number of NVIDIA H100 Tensor Core GPUs for LLM inference workloads and reduces cost and energy consumption by up to 25x,” Nvidia said.

That kind of speed-up could potentially save money and time while running today’s AI models, but it will also allow for more complex AI models to be built. Generative AI models—like the kind that power Google Gemini and AI image generators—are famously computationally hungry. Shortages of compute power have widely been cited as holding back progress and research in the AI field, and the search for more compute has led to figures like OpenAI CEO Sam Altman trying to broker deals to create new chip foundries.

While Nvidia’s claims about the Blackwell platform’s capabilities are significant, it’s worth noting that its real-world performance and adoption of the technology remain to be seen as organizations begin to implement and utilize the platform themselves. Competitors like Intel and AMD are also looking to grab a piece of Nvidia’s AI pie.

Nvidia says that Blackwell-based products will be available from various partners starting later this year.

Nvidia unveils Blackwell B200, the “world’s most powerful chip” designed for AI Read More »

nvidia-sued-over-ai-training-data-as-copyright-clashes-continue

Nvidia sued over AI training data as copyright clashes continue

In authors’ bad books —

Copyright suits over AI training data reportedly decreasing AI transparency.

Nvidia sued over AI training data as copyright clashes continue

Book authors are suing Nvidia, alleging that the chipmaker’s AI platform NeMo—used to power customized chatbots—was trained on a controversial dataset that illegally copied and distributed their books without their consent.

In a proposed class action, novelists Abdi Nazemian (Like a Love Story), Brian Keene (Ghost Walk), and Stewart O’Nan (Last Night at the Lobster) argued that Nvidia should pay damages and destroy all copies of the Books3 dataset used to power NeMo large language models (LLMs).

The Books3 dataset, novelists argued, copied “all of Bibliotek,” a shadow library of approximately 196,640 pirated books. Initially shared through the AI community Hugging Face, the Books3 dataset today “is defunct and no longer accessible due to reported copyright infringement,” the Hugging Face website says.

According to the authors, Hugging Face removed the dataset last October, but not before AI companies like Nvidia grabbed it and “made multiple copies.” By training NeMo models on this dataset, the authors alleged that Nvidia “violated their exclusive rights under the Copyright Act.” The authors argued that the US district court in San Francisco must intervene and stop Nvidia because the company “has continued to make copies of the Infringed Works for training other models.”

A Hugging Face spokesperson clarified to Ars that “Hugging Face never removed this dataset, and we did not host the Books3 dataset on the Hub.” Instead, “Hugging Face hosted a script that downloads the data from The Eye, which is the place where ELeuther hosted the data,” until “Eleuther removed the data from The Eye” over copyright concerns, causing the dataset script on Hugging Face to break.

Nvidia did not immediately respond to Ars’ request to comment.

Demanding a jury trial, authors are hoping the court will rule that Nvidia has no possible defense for both allegedly violating copyrights and intending “to cause further infringement” by distributing NeMo models “as a base from which to build further models.”

AI models decreasing transparency amid suits

The class action was filed by the same legal team representing authors suing OpenAI, whose lawsuit recently saw many claims dismissed, but crucially not their claim of direct copyright infringement. Lawyers told Ars last month that authors would be amending their complaints against OpenAI and were “eager to move forward and litigate” their direct copyright infringement claim.

In that lawsuit, the authors alleged copyright infringement both when OpenAI trained LLMs and when chatbots referenced books in outputs. But authors seemed more concerned about alleged damages from chatbot outputs, warning that AI tools had an “uncanny ability to generate text similar to that found in copyrighted textual materials, including thousands of books.”

Uniquely, in the Nvidia suit, authors are focused exclusively on Nvidia’s training data, seemingly concerned that Nvidia could empower businesses to create any number of AI models on the controversial dataset, which could affect thousands of authors whose works could allegedly be broadly infringed just by training these models.

There’s no telling yet how courts will rule on the direct copyright claims in either lawsuit—or in the New York Times’ lawsuit against OpenAI—but so far, OpenAI has failed to convince courts to toss claims aside.

However, OpenAI doesn’t appear very shaken by the lawsuits. In February, OpenAI said that it expected to beat book authors’ direct copyright infringement claim at a “later stage” of the case and, most recently in the New York Times case, tried to convince the court that NYT “hacked” ChatGPT to “set up” the lawsuit.

And Microsoft, a co-defendant in the NYT lawsuit, even more recently introduced a new argument that could help tech companies defeat copyright suits over LLMs. Last month, Microsoft argued that The New York Times was attempting to stop a “groundbreaking new technology” and would fail, just like movie producers attempting to kill off the VCR in the 1980s.

“Despite The Times’s contentions, copyright law is no more an obstacle to the LLM than it was to the VCR (or the player piano, copy machine, personal computer, Internet, or search engine),” Microsoft wrote.

In December, Hugging Face’s machine learning and society lead, Yacine Jernite, noted that developers appeared to be growing less transparent about training data after copyright lawsuits raised red flags about companies using the Books3 dataset, “especially for commercial models.”

Meta, for example, “limited the amount of information [it] disclosed about” its LLM, Llama-2, “to a single paragraph description and one additional page of safety and bias analysis—after [its] use of the Books3 dataset when training the first Llama model was brought up in a copyright lawsuit,” Jernite wrote.

Jernite warned that AI models lacking transparency could hinder “the ability of regulatory safeguards to remain relevant as training methods evolve, of individuals to ensure that their rights are respected, and of open science and development to play their role in enabling democratic governance of new technologies.” To support “more accountability,” Jernite recommended “minimum meaningful public transparency standards to support effective AI regulation,” as well as companies providing options for anyone to opt out of their data being included in training data.

“More data transparency supports better governance and fosters technology development that more reliably respects peoples’ rights,” Jernite wrote.

Nvidia sued over AI training data as copyright clashes continue Read More »

review:-amd-radeon-rx-7900-gre-gpu-doesn’t-quite-earn-its-“7900”-label

Review: AMD Radeon RX 7900 GRE GPU doesn’t quite earn its “7900” label

rabbit season —

New $549 graphics card is the more logical successor to the RX 6800 XT.

ASRock's take on AMD's Radeon RX 7900 GRE.

Enlarge / ASRock’s take on AMD’s Radeon RX 7900 GRE.

Andrew Cunningham

In July 2023, AMD released a new GPU called the “Radeon RX 7900 GRE” in China. GRE stands for “Golden Rabbit Edition,” a reference to the Chinese zodiac, and while the card was available outside of China in a handful of pre-built OEM systems, AMD didn’t make it widely available at retail.

That changes today—AMD is launching the RX 7900 GRE at US retail for a suggested starting price of $549. This throws it right into the middle of the busy upper-mid-range graphics card market, where it will compete with Nvidia’s $549 RTX 4070 and the $599 RTX 4070 Super, as well as AMD’s own $500 Radeon RX 7800 XT.

We’ve run our typical set of GPU tests on the 7900 GRE to see how it stacks up to the cards AMD and Nvidia are already offering. Is it worth buying a new card relatively late in this GPU generation, when rumors point to new next-gen GPUs from Nvidia, AMD, and Intel before the end of the year? Can the “Golden Rabbit Edition” still offer a good value, even though it’s currently the year of the dragon?

Meet the 7900 GRE

RX 7900 XT RX 7900 GRE RX 7800 XT RX 6800 XT RX 6800 RX 7700 XT RX 6700 XT RX 6750 XT
Compute units (Stream processors) 84 (5,376) 80 (5,120) 60 (3,840) 72 (4,608) 60 (3,840) 54 (3,456) 40 (2,560) 40 (2,560)
Boost Clock 2,400 MHz 2,245 MHz 2,430 MHz 2,250 MHz 2,105 MHz 2,544 MHz 2,581 MHz 2,600 MHz
Memory Bus Width 320-bit 256-bit 256-bit 256-bit 256-bit 192-bit 192-bit 192-bit
Memory Clock 2,500 MHz 2,250 MHz 2,438 MHz 2,000 MHz 2,000 MHz 2,250 MHz 2,000 MHz 2,250 MHz
Memory size 20GB GDDR6 16GB GDDR6 16GB GDDR6 16GB GDDR6 16GB GDDR6 12GB GDDR6 12GB GDDR6 12GB GDDR6
Total board power (TBP) 315 W 260 W 263 W 300 W 250 W 245 W 230 W 250 W

The 7900 GRE slots into AMD’s existing lineup above the RX 7800 XT (currently $500-ish) and below the RX 7900 (around $750). Technologically, we’re looking at the same Navi 31 GPU silicon as the 7900 XT and XTX, but with just 80 of the compute units enabled, down from 84 and 96, respectively. The normal benefits of the RDNA3 graphics architecture apply, including hardware-accelerated AV1 video encoding and DisplayPort 2.1 support.

The 7900 GRE also includes four active memory controller die (MCD) chiplets, giving it a narrower 256-bit memory bus and 16GB of memory instead of 20GB—still plenty for modern games, though possibly not quite as future-proof as the 7900 XT. The card uses significantly less power than the 7900 XT and about the same amount as the 7800 XT. That feels a bit weird, intuitively, since slower cards almost always consume less power than faster ones. But it does make some sense; pushing the 7800 XT’s smaller Navi 32 GPU to get higher clock speeds out of it is probably making it run a bit less efficiently than a larger Navi 31 GPU die that isn’t being pushed as hard.

  • Andrew Cunningham

  • Andrew Cunningham

  • Andrew Cunningham

When we reviewed the 7800 XT last year, we noted that its hardware configuration and performance made it seem more like a successor to the (non-XT) Radeon RX 6800, while it just barely managed to match or beat the 6800 XT in our tests. Same deal with the 7900 GRE, which is a more logical successor to the 6800 XT. Bear that in mind when doing generation-over-generation comparisons.

Review: AMD Radeon RX 7900 GRE GPU doesn’t quite earn its “7900” label Read More »

nvidia’s-new-app-doesn’t-require-you-to-log-in-to-update-your-gpu-driver

Nvidia’s new app doesn’t require you to log in to update your GPU driver

Some updates are good, actually —

Removing little-used features also improved responsiveness and shrank the size.

Nvidia app promo image

Nvidia

Nvidia has announced a public beta of a new app for Windows, one that does a few useful things and one big thing.

The new app combines the functions of three apps you’d previously have to hunt through—the Nvidia Control Panel, GeForce Experience, and RTX Experience—into one app. Setting display preferences on games and seeing exactly how each notch between “Performance” and “Quality” will affect its settings is far easier and more visible inside the new app. The old-fashioned control panel is still there if you right-click the Nvidia app’s notification panel icon. Installing the new beta upgrades and essentially removes the Experience and Control Panel apps, but they’re still available online.

But perhaps most importantly, Nvidia’s new app allows you to update the driver for your graphics card, the one you paid for, without having to log in to an Nvidia account. I tested it, it worked, and I don’t know why I was surprised, but I’ve been conditioned that way. Given that driver updates are something people often do with new systems and the prior tendencies of Nvidia’s apps to log you out, this is a boon that will pay small but notable cumulative dividends for some time to come.

Proof that you can, miracle of miracles, download an Nvidia driver update in Nvidia's new app without having to sign in.

Proof that you can, miracle of miracles, download an Nvidia driver update in Nvidia’s new app without having to sign in.

Game performance tools are much easier to use, or at least understand, in the new Nvidia app. It depends on the game, but you get a slider to move between “Performance” and “Quality.” Some games don’t offer more than one or two notches to use, like Monster Train or Against the Storm. Some, like Hitman 3 or Deep Rock Galactic, offer so many notches that you could make a day out of adjusting and testing. Whenever you move the slider, you can see exactly what changed in a kind of diff display.

Changing the settings in <em>Elden Ring</em> with the more granular controls available in Nvidia’s new beta app.” height=”1009″ src=”https://cdn.arstechnica.net/wp-content/uploads/2024/02/Screenshot-2024-02-22-134416.png” width=”1282″></img><figcaption>
<p>Changing the settings in <em>Elden Ring</em> with the more granular controls available in Nvidia’s new beta app.</p>
<p>Nvidia/Kevin Purdy</p>
</figcaption></figure>
<p>If you use Nvidia’s in-game overlay, triggered with Alt+Z, you can test that out, see its new look and feel, set up performance metrics, and change its settings from Nvidia’s beta app. Driver updates now come with more information about what changed, rather than sending you to a website of release notes. On cards with AI-powered offerings, you’ll also get tools for Nvidia Freestyle, RTX Dynamic Vibrance, RTX HDR, and other such nit-picky options.</p>
<p>Not everything available in the prior apps is making it into this new all-in-one app, however. Nvidia notes that GPU overclocking and driver rollback are on the way. And the company says it has decided to “discontinue a few features that were underutilized,” including the ability to broadcast to Twitch and YouTube, share video or stills to Facebook and YouTube, and make Photo 360 and Stereo captures. Noting that “good alternatives exist,” Nvidia says culling these things halves the new app’s install time, improves responsiveness by 50 percent, and takes up 17 percent less disk space.</p>
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		<p class= Nvidia’s new app doesn’t require you to log in to update your GPU driver Read More »

us-funds-$5b-chip-effort-after-lagging-on-semiconductor-innovation

US funds $5B chip effort after lagging on semiconductor innovation

Now hiring? —

US had failed to fund the “science half” of CHIPS and Science Act, critic said.

US President Joe Biden speaks before signing the CHIPS and Science Act of 2022.

Enlarge / US President Joe Biden speaks before signing the CHIPS and Science Act of 2022.

The Biden administration announced investments Friday totaling more than $5 billion in semiconductor research and development intended to re-establish the US as a global leader manufacturing the “next generation of semiconductor technologies.”

Through sizeable investments, the US will “advance US leadership in semiconductor R&D, cut down on the time and cost of commercializing new technologies, bolster US national security, and connect and support workers in securing good semiconductor jobs,” a White House press release said.

Currently, the US produces “less than 10 percent” of the global chips supply and “none of the most advanced chips,” the White House said. But investing in programs like the National Semiconductor Technology Center (NSTC)—considered the “centerpiece” of the CHIPS and Science Act’s four R&D programs—and training a talented workforce could significantly increase US production of semiconductors that the Biden administration described as the “backbone of the modern economy.”

The White House projected that the NSTC’s workforce activities would launch in the summer of 2024. The Center’s prime directive will be developing new semiconductor technologies by “supporting design, prototyping, and piloting and through ensuring innovators have access to critical capabilities.”

Moving forward, the NSTC will operate as a public-private consortium, involving both government and private sector institutions, the White House confirmed. It will be run by a recently established nonprofit called the National Center for the Advancement of Semiconductor Technology (Natcast), which will coordinate with the secretaries of Commerce, Defense, and Energy, as well as the National Science Foundation’s director. Any additional stakeholders can provide input on the NSTC’s goals by joining the NSTC Community of Interest at no cost.

The National Institute of Standards and Technology (NIST) has explained why achieving the NSTC’s mission to develop cutting-edge semiconductor technology in the US will not be easy:

The smallest dimensions of leading-edge semiconductor devices have reached the atomic scale and the complexity of the circuit architecture is increasing exponentially with the use of three-dimensional structures, the incorporation of new materials, and improvements in the thousands of process steps needed to make advanced chips. Into the future, as new applications demand higher-performance semiconductors, their design and production will become even more complex. This complexity makes it increasingly difficult and costly to implement innovations because of the dependencies between design and manufacturing, between manufacturing steps, and between front-end and back-end processes.

The complexity of keeping up with semiconductor tech is why it’s critical for the US to create clear pathways for skilled workers to break into this burgeoning industry. The Biden administration said it plans to invest “at least hundreds of millions of dollars in the NSTC’s workforce efforts,” creating a Workforce Center of Excellence with locations throughout the US and piloting new training programs, including initiatives engaging underserved communities. The Workforce Center will start by surveying best practices in semiconductor education programs, then establish a baseline program to attract workers seeking dependable paths to break into the industry.

Last year, the Semiconductor Industry Association (SIA) released a study showing that the US was not adequately preparing a highly skilled workforce. Between “67,000, or 58 percent, of projected new jobs, may remain unfulfilled at the current trajectory,” SIA estimated.

A skilled workforce is just part of the equation, though. The US also needs facilities where workers can experiment with new technologies without breaking the bank. To that end, the Department of Commerce announced it would be investing “at least $200 million” in a first-of-its-kind CHIPS Manufacturing USA Institute. That institute will “allow innovators to replicate and experiment with physical manufacturing processes at low cost.”

Other Commerce Department investments announced include “up to $300 million” for advanced packaging R&D necessary for discovering new applications for semiconductor technologies and over $100 million in funding for dozens of projects to help inventors “more easily scale innovations into commercial products.”

A Commerce Department spokesperson told Ars that “the location of the NSTC headquarters has not yet been determined” but will “directly support the NSTC research strategy and give engineers, academics, researchers, engineers at startups, small and large companies, and workforce developers the capabilities they need to innovate.” In 2024, NSTC’s efforts to kick off research appear modest, with the center expecting to prioritize engaging community members and stakeholders, launching workforce programs, and identifying early start research programs.

So far, Biden’s efforts to ramp up semiconductor manufacturing in the US have not gone smoothly. Earlier this year, TSMC predicted further delays at chips plants under construction in Arizona and confirmed that the second plant would not be able to manufacture the most advanced chips, as previously expected.

That news followed criticism from private entities last year. In November, Nvidia CEO Jensen Huang predicted that the US was “somewhere between a decade and two decades away” from semiconductor supply chain independence. The US Chamber of Commerce said last August that the reason why the US remained so far behind was because the US had so far failed to prioritize funding in the “science half” of the CHIPS and Science Act.

In 2024, the Biden administration appears to be attempting to finally start funding a promised $11 billion total in research and development efforts. Once NSTC kicks off research, the pressure will be on to chase the Center’s highest ambition of turning the US into a consistent birthplace of life-changing semiconductor technologies once again.

US funds $5B chip effort after lagging on semiconductor innovation Read More »

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Nvidia’s G-Sync Pulsar is anti-blur monitor tech aimed squarely at your eyeball

What will they sync of next? —

Branded monitors can sync pixels to backlighting, refresh rate, and GPU frames.

Motion blur demonstration of G-Sync Pulsar, with

Enlarge / None of this would be necessary if it weren’t for your inferior eyes, which retain the colors of pixels for fractions of a second longer than is optimal for shooting dudes.

Nvidia

Gaming hardware has done a lot in the last decade to push a lot of pixels very quickly across screens. But one piece of hardware has always led to complications: the eyeball. Nvidia is targeting that last part of the visual quality chain with its newest G-Sync offering, Pulsar.

Motion blur, when it’s not caused by slow LCD pixel transitions, is caused by “the persistence of an image on the retina, as our eyes track movement on-screen,” as Nvidia explains it. Prior improvements in display tech, like variable rate refresh, Ultra Low Motion Blur, and Variable Overdrive have helped with the hardware causes of this deficiency. The eyes and their object permanence, however, can only be addressed by strobing a monitor’s backlight.

You can’t just set that light blinking, however. Variable strobing frequencies causes flicker, and timing the strobe to the monitor refresh rate—itself also tied to the graphics card output—was tricky. Nvidia says it has solved that issue with its G-Sync Pulsar tech, employing “a novel algorithm” in “synergizing” its variable refresh smoothing and monitor pulsing. The result is that pixels are transitioned from one color to another at a rate that reduces motion blur and pixel ghosting.

Nvidia also claims that Pulsar can help with the visual discomfort caused by some strobing effects, as the feature “intelligently controls the pulse’s brightness and duration.”

  • The featureless axis labels make my brain hurt, but I believe this chart suggests that G-Sync Pulsar does the work of timing out exactly when to refresh screen pixels at 360 Hz.

    Nvidia

  • The same, but this time at 200 Hz.

    Nvidia

  • And again, this time at 100 Hz. Rapidly changing pixels are weird, huh?

    Nvidia

To accommodate this “radical rethinking of display technology,” a monitor will need Nvidia’s own chips built in. There are none yet, but the Asus ROG Swift PG27 Series G-Sync and its 360 Hz refresh rate is coming “later this year.” No price for that monitor is available yet.

It’s hard to verify how this looks and feels without hands-on time. PC Gamer checked out Pulsar at CES this week and verified that, yes, it’s easier to read the name of the guy you’re going to shoot while you’re strafing left and right at an incredibly high refresh rate. Nvidia also provided a video, captured at 1,000 frames per second, for those curious.

Nvidia’s demonstration of G-Sync Pulsar, using Counter-Strike 2 filmed at 1000 fps, on a 360 Hz monitor, with Pulsar on and off, played back at 1/24 speed.

Pulsar signals Nvidia’s desire to once again create an exclusive G-Sync monitor feature designed to encourage a wraparound Nvidia presence on the modern gaming PC. It’s a move that has sometimes backfired on the firm before. The company relented to market pressures in 2019 and enabled G-Sync in various variable refresh rate monitors powered by VESA’s Display port Adaptive-Sync tech (more commonly known by its use in AMD’s FreeSync monitors). G-Sync monitors were selling for typically hundreds of dollars more than their FreeSync counterparts, and while they technically had some exclusive additional features, the higher price points likely hurt Nvidia’s appeal when a gamer was looking at the full cost of new or upgraded system.

There will not be any such cross-standard compatibility with G-Sync Pulsar, which will only be offered on monitors with a G-Sync Ultimate badge, and then further support Pulsar, specifically. There’s always a chance that another group will develop its own synced-strobe technology that could work across GPUs, but nothing is happening as of yet.

In related frame-rate news, Nvidia also announced this week that its GeForce Now game streaming service will offer G-Sync capabilities to those on Ultimate or Priority memberships and playing on capable screens. Nvidia claims that, paired with its Reflex offering on GeForce Now, the two “make cloud gaming experiences nearly indistinguishable from local ones.” I’ll emphasize here that those are Nvidia’s words, not the author’s.

Nvidia’s G-Sync Pulsar is anti-blur monitor tech aimed squarely at your eyeball Read More »

they’re-not-cheap,-but-nvidia’s-new-super-gpus-are-a-step-in-the-right-direction

They’re not cheap, but Nvidia’s new Super GPUs are a step in the right direction

supersize me —

RTX 4080, 4070 Ti, and 4070 Super arrive with price cuts and/or spec bumps.

Nvidia's latest GPUs, apparently dropping out of hyperspace.

Enlarge / Nvidia’s latest GPUs, apparently dropping out of hyperspace.

Nvidia

  • Nvidia’s latest GPUs, apparently dropping out of hyperspace.

    Nvidia

  • The RTX 4080 Super.

    Nvidia

  • Comparing it to the last couple of xx80 GPUs (but not the original 4080).

    Nvidia

  • The 4070 Ti Super.

    Nvidia

  • Comparing to past xx70 Ti generations.

    Nvidia

  • The 4070 Super.

    Nvidia

  • Compared to past xx70 generations.

    Nvidia

If there’s been one consistent criticism of Nvidia’s RTX 40-series graphics cards, it’s been pricing. All of Nvidia’s product tiers have seen their prices creep up over the last few years, but cards like the 4090 raised prices to new heights, while lower-end models like the 4060 and 4060 Ti kept pricing the same but didn’t improve performance much.

Today, Nvidia is sprucing up its 4070 and 4080 tiers with a mid-generation “Super” refresh that at least partially addresses some of these pricing problems. Like older Super GPUs, the 4070 Super, 4070 Ti Super, and 4080 Super use the same architecture and support all the same features as their non-Super versions, but with bumped specs and tweaked prices that might make them more appealing to people who skipped the originals.

The 4070 Super will launch first, on January 17, for $599. The $799 RTX 4070 Ti Super launches on January 24, and the $999 4080 Super follows on January 31.

RTX 4090 RTX 4080 RTX 4080 Super RTX 4070 Ti RTX 4070 Ti Super RTX 4070 RTX 4070 Super
CUDA Cores 16,384 9,728 10,240 7,680 8,448 5,888 7,168
Boost Clock 2,520 MHz 2,505 MHz 2,550 MHz 2,610 MHz 2,610 MHz 2,475 MHz 2,475 MHz
Memory Bus Width 384-bit 256-bit 256-bit 192-bit 256-bit 192-bit 192-bit
Memory Clock 1,313 MHz 1,400 MHz 1,437 MHz 1,313 MHz 1,313 MHz 1,313 MHz 1,313 MHz
Memory size 24GB GDDR6X 16GB GDDR6X 16GB GDDR6X 12GB GDDR6X 16GB GDDR6X 12GB GDDR6X 12GB GDDR6X
TGP 450 W 320 W 320 W 285 W 285 W 200 W 220 W

Of the three cards, the 4080 Super probably brings the least significant spec bump, with a handful of extra CUDA cores and small clock speed increases but the same amount of memory and the same 256-bit memory interface. Its main innovation is its price, which at $999 is $200 lower than the original 4080’s $1,199 launch price. This doesn’t make it a bargain—we’re still talking about a $1,000 graphics card—but the 4080 Super feels like a more proportionate step down from the 4090 and a good competitor to AMD’s flagship Radeon RX 7900 XTX.

The 4070 Ti Super stays at the same $799 price as the 4070 Ti (which, if you’ll recall, was nearly launched at $899 as the “RTX 4080 12GB“) but addresses two major gripes with the original by stepping up to a 256-bit memory interface and 16GB of RAM. It also picks up some extra CUDA cores, while staying within the same power envelope as the original 4070 Ti. These changes should help it keep up with modern 4K games, where the smaller pool of memory and narrower memory interface of the original 4070 Ti could sometimes be a drag on performance.

Most of the RTX 40-series lineup. The original 4080 and 4070 Ti are going away, while the original 4070 now slots in at $549. It's not shown here, but Nvidia confirmed that the 16GB 4060 Ti is also sticking around at $449.

Enlarge / Most of the RTX 40-series lineup. The original 4080 and 4070 Ti are going away, while the original 4070 now slots in at $549. It’s not shown here, but Nvidia confirmed that the 16GB 4060 Ti is also sticking around at $449.

Nvidia

Finally, we get to the RTX 4070 Super, which also keeps the 4070’s $599 price tag but sees a substantial uptick in processing hardware, from 5,888 CUDA cores to 7,168 (the power envelope also increases, from 200 W to 220 W). The memory system remains unchanged. The original 4070 was already a decent baseline for entry-level 4K gaming and very good 1440p gaming, and the 4070 Super should make 60 FPS 4K attainable in even more games.

Nvidia says that the original 4070 Ti and 4080 will be phased out. The original 4070 will stick around at a new $549 price, $50 less than before, but not particularly appealing compared to the $599 4070 Super. The 4090, 4060, and the 8GB and 16GB versions of the 4060 Ti all remain available for the same prices as before.

  • The Super cards’ high-level average performance compared to some past generations of GPU, without DLSS 3 frame generation numbers muddying the waters. The 4070 should be a bit faster than an RTX 3090 most of the time.

    Nvidia

  • Some RTX 4080 performance comparisons. Note that the games at the top all have DLSS 3 frame generation enabled for the 4080 Super, while the older cards don’t support it.

    Nvidia

  • The 4070 Ti Super vs the 3070 Ti and 2070 Super.

    Nvidia

  • The 4070 Super versus the 3070 and the 2070.

    Nvidia

Nvidia’s performance comparisons focus mostly on older-generation cards rather than the non-Super versions, and per usual for 40-series GPU announcements, they lean heavily on performance numbers that are inflated by DLSS 3 frame generation. In terms of pure rendering performance, Nvidia says the 4070 Super should outperform an RTX 3090—impressive, given that the original 4070 was closer to an RTX 3080. The RTX 4080 Super is said to be roughly twice as fast as an RTX 3080, and Nvidia says the RTX 4070 Ti Super will be roughly 2.5 times faster than a 3070 Ti.

Though all three of these cards provide substantially more value than their non-Super predecessors at the same prices, the fact remains that prices have still gone up compared to past generations. Nvidia last released a Super refresh during the RTX 20-series back in 2019; the RTX 2080 Super went for $699 and the 2070 Super for $499. But the 4080 Super, 4070 Ti Super, and 4070 Super will give you more for your money than you could get before, which is at least a move in the right direction.

They’re not cheap, but Nvidia’s new Super GPUs are a step in the right direction Read More »

2023-was-the-year-that-gpus-stood-still

2023 was the year that GPUs stood still

2023 was the year that GPUs stood still

Andrew Cunningham

In many ways, 2023 was a long-awaited return to normalcy for people who build their own gaming and/or workstation PCs. For the entire year, most mainstream components have been available at or a little under their official retail prices, making it possible to build all kinds of PCs at relatively reasonable prices without worrying about restocks or waiting for discounts. It was a welcome continuation of some GPU trends that started in 2022. Nvidia, AMD, and Intel could release a new GPU, and you could consistently buy that GPU for roughly what it was supposed to cost.

That’s where we get into how frustrating 2023 was for GPU buyers, though. Cards like the GeForce RTX 4090 and Radeon RX 7900 series launched in late 2022 and boosted performance beyond what any last-generation cards could achieve. But 2023’s midrange GPU launches were less ambitious. Not only did they offer the performance of a last-generation GPU, but most of them did it for around the same price as the last-gen GPUs whose performance they matched.

The midrange runs in place

Not every midrange GPU launch will get us a GTX 1060—a card roughly 50 percent faster than its immediate predecessor and beat the previous-generation GTX 980 despite costing just a bit over half as much money. But even if your expectations were low, this year’s midrange GPU launches have been underwhelming.

The worst was probably the GeForce RTX 4060 Ti, which sometimes struggled to beat the card it replaced at around the same price. The 16GB version of the card was particularly maligned since it was $100 more expensive but was only faster than the 8GB version in a handful of games.

The regular RTX 4060 was slightly better news, thanks partly to a $30 price drop from where the RTX 3060 started. The performance gains were small, and a drop from 12GB to 8GB of RAM isn’t the direction we prefer to see things move, but it was still a slightly faster and more efficient card at around the same price. AMD’s Radeon RX 7600, RX 7700 XT, and RX 7800 XT all belong in this same broad category—some improvements, but generally similar performance to previous-generation parts at similar or slightly lower prices. Not an exciting leap for people with aging GPUs who waited out the GPU shortage to get an upgrade.

The best midrange card of the generation—and at $600, we’re definitely stretching the definition of “midrange”—might be the GeForce RTX 4070, which can generally match or slightly beat the RTX 3080 while using much less power and costing $100 less than the RTX 3080’s suggested retail price. That seems like a solid deal once you consider that the RTX 3080 was essentially unavailable at its suggested retail price for most of its life span. But $600 is still a $100 increase from the 2070 and a $220 increase from the 1070, making it tougher to swallow.

In all, 2023 wasn’t the worst time to buy a $300 GPU; that dubious honor belongs to the depths of 2021, when you’d be lucky to snag a GTX 1650 for that price. But “consistently available, basically competent GPUs” are harder to be thankful for the further we get from the GPU shortage.

Marketing gets more misleading

1.7 times faster than the last-gen GPU? Sure, under exactly the right conditions in specific games.

Enlarge / 1.7 times faster than the last-gen GPU? Sure, under exactly the right conditions in specific games.

Nvidia

If you just looked at Nvidia’s early performance claims for each of these GPUs, you might think that the RTX 40-series was an exciting jump forward.

But these numbers were only possible in games that supported these GPUs’ newest software gimmick, DLSS Frame Generation (FG). The original DLSS and DLSS 2 improve performance by upsampling the images generated by your GPU, generating interpolated pixels that make lower-res image into higher-res ones without the blurriness and loss of image quality you’d get from simple upscaling. DLSS FG generates entire frames in between the ones being rendered by your GPU, theoretically providing big frame rate boosts without requiring a powerful GPU.

The technology is impressive when it works, and it’s been successful enough to spawn hardware-agnostic imitators like the AMD-backed FSR 3 and an alternate implementation from Intel that’s still in early stages. But it has notable limitations—mainly, it needs a reasonably high base frame rate to have enough data to generate convincing extra frames, something that these midrange cards may struggle to do. Even when performance is good, it can introduce weird visual artifacts or lose fine detail. The technology isn’t available in all games. And DLSS FG also adds a bit of latency, though this can be offset with latency-reducing technologies like Nvidia Reflex.

As another tool in the performance-enhancing toolbox, DLSS FG is nice to have. But to put it front-and-center in comparisons with previous-generation graphics cards is, at best, painting an overly rosy picture of what upgraders can actually expect.

2023 was the year that GPUs stood still Read More »

magiscan-app-lets-users-create-3d-models-with-their-smartphone

MagiScan App Lets Users Create 3D Models With Their Smartphone

As if our smartphones weren’t already incredible enough, startup company AR-Generation is using them to bridge the gap between the real and virtual worlds. With their new cutting-edge app, you can create 3D models with only your smartphone and use them for any AR or metaverse application.

Introducing MagiScan

Meet MagiScan, an AI-powered 3D scanner app that produces high-quality 3D models for any AR or metaverse application. Developed by AR-Generation, a member of the NVIDIA Inception program, MagiScan is the first and only 3D scanner in NVIDIA Omniverse, a real-time 3D graphics collaboration platform.

The MagiScan app, available on both iOS and Android devices, allows users to capture an image of any object using their smartphone camera and quickly generate its detailed 3D model. AR-Feneration co-founder and CEO, Kiryl Sidarchuk, estimates that this process is up to 100 times less expensive and 10 times faster than manual 3D modeling, making it an accessible and user-friendly option for creators. With MagiScan, creators can easily refine their work and increase accessibility to AR technology.

3D scanning objects with MagiScan

While 3D scanning with smartphones is not new technology, it has significantly improved over the years. In 2015, researchers at Carnegie Mellon University developed a tool for measuring real objects in 3D space using “average” cellphone cameras. They developed their technology to perform accurate measurements so it could be helpful in self-driving cars and virtual shopping for eyeglass frames.

A similar technology was also created in 2021, called the PhotoCatch app, which uses the then-new Apple Object Capture photogrammetry technology.

How MagiScan Works

MagiScan is incredibly easy to use. Simply open the app, scan an object from all angles, and wait for a few seconds for the app to generate a 3D model. Once done, you can export your 3D model in various formats, including STL which allows you to print your model.

In addition to personal use, brands can also use MagiScan for their online platforms. Just enable “Connect MagiScan for Business,” then scan your products and add their 3D models to your website.

Exporting 3D Models Directly to Omniverse

AR-Generation also created an extension allowing MagiScan users to export their 3D models directly to the NVIDIA Omniverse. “We customized our app to allow export of 3D models based on real-world objects directly to Omniverse, enabling users to showcase the models in AR and integrate them into any metaverse or game,” Sidarchuk said.

Magiscan to Omniverse

This extension is made possible by OpenUSD or Universal Scene Description, an open-source software originally developed by Pixar Animation Studios for simulating, describing, composing, and collaborating in the 3D realm. The OpenUSD compatibility is Sidarchuk’s favorite Ominverse feature, and he believes that OpenUSD is the “format of the future.”

The company chose to build an extension for Omniverse because the platform, according to Sidarchuk,  “provides a convenient environment that integrates all the tools for working with 3D and generative AI.”

MagiScan and Augmented Reality’s Impact on E-Commerce

The impact of 3D models and AR is not limited to the gaming and metaverse realms. E-commerce businesses can also benefit from the rapid advancements in this technology.

About 60% of online shoppers consider high-quality images a critical factor in their purchasing decisions. To keep up with the competition, brands must provide more than just photos with a white background. They can also display 3D models of their products on their websites or online marketplaces to provide a more immersive browsing experience.

Through MagiScan, AR-Generation believes that conversion rates can increase up to 94%, while returns can drop up to 58%. Crisp and accurate 3D models allow consumers to visualize a product in real life, helping them make more informed purchasing decisions. This may be the same reason why Carnegie Mellon University researchers developed their 3D scanner to aid people in buying eyeglass frames online.

The Growing Significance of AR in Daily Life

Sidarchuk believes that AR will become an integral part of everyday life. And it’s not hard to see why. AR has grown in popularity over the years and is now widely used in various industries, from gaming to shopping to employee training. With AR, individuals and corporations can experience immersive virtual environments in a safe and secure way.

Thanks to advancements in technology, high-quality 3D experiences are now possible on smartphones. This means that AR and the Omniverse have the potential to impact even the most mundane activities of our daily lives. With this in mind, it’s clear that AR technology is here to stay.

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