Tag: GPU

AMD RX 7900 GRE gets a free performance boost – is this now the best mid-range GPU you can buy?

Post author By lisa nichols
Post date March 25, 2024
No Comments on AMD RX 7900 GRE gets a free performance boost – is this now the best mid-range GPU you can buy?

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AMD’s RX 7900 GRE became available to buy in the US (and elsewhere) last month, but with an odd catch on the performance front – the good news being that this hiccup related to overclocking the GPU has now been cured.

If you’ve been following the somewhat curious tale of the global launch of the RX 7900 GRE – the ‘Golden Rabbit Edition’ graphics card that was initially exclusive to China – you may recall it was artificially limited to 2.3GHz for the memory clock speed by a bug, as confirmed by AMD. Apparently, this was an issue with an incorrect memory tuning limit.

Well, that glitch has now been remedied with AMD’s new Adrenalin Edition 24.3.1 driver. As Team Red says in the release notes, there’s a fix for the “maximum memory tuning limit [being] incorrectly reported on AMD Radeon RX 7900 GRE graphics products.”

VideoCardz noticed this development and reports that with the new driver, Tech Powerup has found it can ramp up the memory clock by 300MHz, giving a sizeable leap in performance. Running at 2.6GHz rather than 2.3GHz results in a 15% boost in 3DMark (Time Spy).

Note that this is the memory clock, which is distinct from the GPU clock speed, and not to be confused with that. The GPU chip is also limited for overclocking as our previous report highlighted, but AMD hasn’t taken action on that front.

Analysis: Going GREat guns

This is just one synthetic test, so we need to be a bit cautious, but other benchmarking online from Hardware Unboxed shows similarly impressive results (in gaming tests, and a host of them, too).

However, we should point out that other reports online suggest that RX 7900 GRE owners are far from guaranteed to be able to run a VRAM overclock as ambitious as 2.6GHz, or even get past 2.5GHz (for that matter, cresting 2.4GHz is proving challenging for some graphics cards anecdotally).

As ever with any chip, the mileage you’ll get out of your video memory will be different from others – but even so, everyone should be able to realize a worthwhile performance benefit here. If not 10-15%, there should still be some decent headroom now AMD has fixed this bug, and many folks are reporting around a 5% boost or close to that at the very least.

With this extra chunk of frame rates under its belt, the 7900 GRE is now looking an even more tempting proposition. Assuming, of course, that you’re confident enough with PC hardware to engage in overclocking shenanigans – not everyone will want to do so.

The RX 7900 GRE was already a great mid-range performer before this happened, anyway, and at its current price, this seems to be the best GPU in this price bracket now, for those willing to push it with an overclock, certainly. It’s looking better than the rival RTX 4070 Super with this new AMD driver, and the RX 7900 GRE is about 7% cheaper than Team Green’s graphics card going by current pricing on Newegg in the US (for the cheapest models in stock).

Relative pricing may be a different story in your region, but you get the point. Also, with the 7900 GRE being within 10% of the performance of the much pricier 7900 XT now, as Hardware Unboxed points out, it’s a possible alternative to the latter.

We’d be remiss to mention that with the Nvidia RTX 4070 Super comparison, you are losing out on the ray tracing and DLSS 3 front, of course – but for pure rasterization it’s the 7900 GRE all the way as pricing stands, with this extra driver boost. Nvidia and its partners may need to respond here…

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Tags AMD, Boost, Buy, free, GPU, GRE, midrange, Performance

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Startup claims to boost LLM performance using standard memory instead of GPU HBM — but experts remain unconvinced by the numbers despite promising CXL technology

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MemVerge, a provider of software designed to accelerate and optimize data-intensive applications, has partnered with Micron to boost the performance of LLMs using Compute Express Link (CXL) technology.

The company’s Memory Machine software uses CXL to reduce idle time in GPUs caused by memory loading.

The technology was demonstrated at Micron’s booth at Nvidia GTC 2024 and Charles Fan, CEO and Co-founder of MemVerge said, “Scaling LLM performance cost-effectively means keeping the GPUs fed with data. Our demo at GTC demonstrates that pools of tiered memory not only drive performance higher but also maximize the utilization of precious GPU resources.”

Impressive results

The demo utilized a high-throughput FlexGen generation engine and an OPT-66B large language model. This was performed on a Supermicro Petascale Server, equipped with an AMD Genoa CPU, Nvidia A10 GPU, Micron DDR5-4800 DIMMs, CZ120 CXL memory modules, and MemVerge Memory Machine X intelligent tiering software.

The demo contrasted the performance of a job running on an A10 GPU with 24GB of GDDR6 memory, and data fed from 8x 32GB Micron DRAM, against the same job running on the Supermicro server fitted with Micron CZ120 CXL 24GB memory expander and the MemVerge software.

The FlexGen benchmark, using tiered memory, completed tasks in under half the time of traditional NVMe storage methods. Additionally, GPU utilization jumped from 51.8% to 91.8%, reportedly as a result of MemVerge Memory Machine X software’s transparent data tiering across GPU, CPU, and CXL memory.

Raj Narasimhan, senior vice president and general manager of Micron’s Compute and Networking Business Unit, said “Through our collaboration with MemVerge, Micron is able to demonstrate the substantial benefits of CXL memory modules to improve effective GPU throughput for AI applications resulting in faster time to insights for customers. Micron’s innovations across the memory portfolio provide compute with the necessary memory capacity and bandwidth to scale AI use cases from cloud to the edge.”

However, experts remain skeptical about the claims. Blocks and Files pointed out that the Nvidia A10 GPU uses GDDR6 memory, which is not HBM. A MemVerge spokesperson responded to this point, and others that the site raised, stating, “Our solution does have the same effect on the other GPUs with HBM. Between Flexgen’s memory offloading capabilities and Memory Machine X’s memory tiering capabilities, the solution is managing the entire memory hierarchy that includes GPU, CPU and CXL memory modules.”

MemVerge Memory Machine X results

(Image credit: MemVerge)

Is the AI GPU the new mainframe? New open source tech allows users to ‘timeshare’ GPU resources for AI purposes for free — reminiscent of the days where scarce resources fosted computing elitism

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Without an efficient way to squeeze additional computing power from existing infrastructure, organizations are often forced to purchase additional hardware or delay projects. This can lead to longer wait times for results and potentially losing out to competitors. This problem is compounded by the rise of AI workloads which require a high GPU compute load.

ClearML has come up with what it thinks is the perfect solution to this problem – fractional GPU capability for open source users, making it possible to “split” a single GPU so it can run multiple AI tasks simultaneously.

This move recalls the early days of computing when mainframes could be shared among individuals and organizations, giving them the ability to utilize computing power without needing to buy additional hardware.

Fractional capabilities for Nvidia GPUs

ClearML says the new feature allows DevOps professionals and AI Infrastructure leaders to partition their Nvidia GTX, RTX, and data center-grade, MIG-enabled GPUs into smaller units to support multiple AI and HPC workloads, enabling users to switch between small R&D jobs and larger, more demanding training jobs.

The approach supports multi-tenancy, offering secure and confidential computing with hard memory limitation. ClearML says stakeholders can run isolated parallel workloads on a single shared compute resource, increasing efficiency and reducing costs.

“With our new free offering now supporting fractional capabilities for the broadest range of Nvidia GPUs than any other company, ClearML is democratizing access to compute as part of our commitment to help our community build better AI at any scale, faster,” says Moses Guttmann, CEO and Co-founder of ClearML. “We hope that organizations that might have a mixture of infrastructure are able to use ClearML and get more out of the compute and resources they already have.”

The new open source fractional GPU functionality is available for free on ClearML’s GitHub page.

ClearML Fractional GPU Utilization

(Image credit: ClearML)

Meta sheds more light on how it is evolving Llama 3 training — it relies for now on almost 50,000 Nvidia H100 GPU, but how long before Meta switches to its own AI chip?

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Meta has unveiled details about its AI training infrastructure, revealing that it currently relies on almost 50,000 Nvidia H100 GPUs to train its open source Llama 3 LLM.

The company says it will have over 350,000 Nvidia H100 GPUs in service by the end of 2024, and the computing power equivalent to nearly 600,000 H100s when combined with hardware from other sources.

The figures were revealed as Meta shared details on its 24,576-GPU data center scale clusters.

The company explained “These clusters support our current and next generation AI models, including Llama 3, the successor to Llama 2, our publicly released LLM, as well as AI research and development across GenAI and other areas.“

The clusters are built on Grand Teton (named after the National Park in Wyoming), an in-house-designed, open GPU hardware platform. Grand Teton integrates power, control, compute, and fabric interfaces into a single chassis for better overall performance and scalability.

The clusters also feature high-performance network fabrics, enabling them to support larger and more complex models than before. Meta says one cluster uses a remote direct memory access network fabric solution based on the Arista 7800, while the other features an NVIDIA Quantum2 InfiniBand fabric. Both solutions interconnect 400 Gbps endpoints.

“The efficiency of the high-performance network fabrics within these clusters, some of the key storage decisions, combined with the 24,576 NVIDIA Tensor Core H100 GPUs in each, allow both cluster versions to support models larger and more complex than that could be supported in the RSC and pave the way for advancements in GenAI product development and AI research,” Meta said.

Storage is another critical aspect of AI training, and Meta has developed a Linux Filesystem in Userspace backed by a version of its ‘Tectonic’ distributed storage solution optimized for Flash media. This solution reportedly enables thousands of GPUs to save and load checkpoints in a synchronized fashion, in addition to “providing a flexible and high-throughput exabyte scale storage required for data loading”.

While the company’s current AI infrastructure relies heavily on Nvidia GPUs, it’s unclear how long this will continue. As Meta continues to evolve its AI capabilities, it will inevitably focus on developing and producing more of its own hardware. Meta has already announced plans to use its own AI chips, called Artemis, in servers this year, and the company previously revealed it was getting ready to manufacture custom RISC-V silicon.

AMD’s FSR 3.1 promises improved image quality – with less flickering, better motion – and a trick that’ll delight some Nvidia GPU owners

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AMD has announced its next-gen upscaling box of tricks to make frame rates smoother with PC games at GDC 2024.

This is FSR 3.1, and it sounds like a big step forward – and not just for the quality of the upscaling tech, since there’s a surprise in tow for Nvidia (and Intel Arc) GPU owners.

The basics outlined by AMD are that in terms of better image quality, FSR 3.1 will boast ‘improved temporal stability’ which means fewer flickering or shimmering effects and less ‘fizziness’ around objects that are moving. There’ll be a reduction in the level of ghosting seen, too.

FSR 3.1 will provide a cleaner image overall, in short, and we’ll also get better preservation of detail, AMD asserts.

What about the surprise we mentioned? This is “decoupling FSR 3 upscaling from frame generation” so they are separate entities, and as AMD makes clear, this means that frame generation (adding additional artificial frames for a smoother frame rate) could work with other upscaling solutions.

AMD tells us that the first game to use FSR 3.1 will be Insomniac’s Ratchet & Clank: Rift Apart, and it’ll be delivered in an update for the game later this year (sadly we don’t get any idea of a precise timeframe).

FSR 3.1 should be available to game developers from Q2, meaning next month.

Support for Vulkan and the Xbox Game Development Kit is also inbound, and AMD notes a new FidelityFX API will make it “easier for developers to debug” and provide forward compatibility with updated versions of FSR.

Analysis: DLSS turbocharger for older Nvidia GPUs

Better image quality is clearly welcome with FSR 3.1 – even if some feel AMD has been pretty slow to act implementing improvements on this front – but the decoupling of frame generation is equally important.

This means that a PC gamer using Nvidia’s DLSS upscaling could draft in AMD’s frame generation to further rev up frame rates. Okay, so Lovelace GPU owners get DLSS 3 frame generation anyway, but those who don’t have an RTX 4000 graphics card can use DLSS but layer AMD’s frame generation on top, at least in theory. (Remember, while DLSS 3.5 may support older Nvidia GPUs with ray reconstruction, Team Green’s frame generation is still only available to RTX 4000 graphics cards).

Or those with an Intel Arc graphics card making use of XeSS upscaling could do the same thing, of course. AMD has tried to stay as open as possible with FSR and this is another commendable move in that respect.

We’ll need to see all this in action before we can really draw conclusions about how far AMD has advanced with version 3.1 of FSR, and it might be a bit of a wait yet before that happens.

Via VideoCardz

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Tags AMDs, delight, flickering, FSR, GPU, image, Improved, motion, NVIDIA, Owners, promises, Quality, Thatll, trick

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TikTok owner is quietly doing an ‘Apple’ — ByteDance invests in Chinese memory pioneer, months after a similar move in a GPU vendor, as it plans for an Apple Vision Pro VR rival

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TikTok’s parent company ByteDance has reportedly quietly invested in Xinyuan Semiconductors, a Chinese memory chip manufacturer.

A report from Pandaily, a tech media site based in Beijing, the move reportedly positions ByteDance as the third-largest shareholder in the chip maker, holding an indirect stake of 9.5%.

A ByteDance spokesperson confirmed this previously undisclosed investment to Pandaily, stating its aim is to hasten the development of VR headsets. This move aligns with ByteDance’s growing interest in the VR sector, as it plans to take on Meta’s Quest and Apple‘s Vision Pro.

Pushing ahead into VR

Based in Shanghai and established in 2019, Xinyuan Semiconductors specializes in Resistive Random Access Memory (ReRAM) technology and related chip products. The company’s portfolio covers three major application areas: high-performance industrial control and automotive SoC and ASIC chips, Computing in Memory (CIM) IP and chips, and System-on-Memory (SoM) chips.

This investment in Xinyuan Semiconductors isn’t ByteDance’s first venture into the semiconductor industry. In 2021, the tech behemoth also invested in Moore Thread, a Chinese GPU manufacturer.

The company’s strategic investments signal a clear intent to compete in the VR space. TikTok is already available as a native app for Vision Pro.

But while this latest investment could potentially be setting the stage for a showdown with Apple and its Vision Pro headset, ByteDance has another far bigger battle on its hands right now.

The US House of Representatives recently passed a significant bill that could lead to a TikTok ban in America if the Chinese parent company fails to sell its controlling stake of the social media app within the next six months.

AI chip built using ancient Samsung tech is claimed to be as fast as Nvidia A100 GPU — prototype is smaller and much more power efficient but is it just too good to be true?

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Scientists from the Korea Advanced Institute of Science and Technology (KAIST) have unveiled an AI chip that they claim can match the speed of Nvidia‘s A100 GPU but with a smaller size and significantly lower power consumption. The chip was developed using Samsung‘s 28-nanometer manufacturing process, a technology considered relatively old in the fast-moving world of semiconductors.

The team, led by Professor Yoo Hoi-jun at KAIST’s processing-in-memory research center, has developed what it says is the world’s first ‘Complementary-Transformer’ (C-Transformer) AI chip. This neuromorphic computing system mimics the structure and workings of the human brain, using a deep learning model often employed in visual data processing.

“Neuromorphic computing is a technology that even companies like IBM and Intel have not been able to implement, and we are proud to be the first in the world to run the LLM with a low-power neuromorphic accelerator,” Yoo said.

Questions remain

This technology learns context and meaning by tracking relationships within data, such as words in a sentence, which is a key technology for generative AI services like ChatGPT.

During a demonstration at the ICT ministry’s headquarters, team member Kim Sang-yeob showcased the chip’s capabilities. On a laptop equipped with the chip he performed tasks such as Q&A sessions, sentence summation, and translations using OpenAI‘s LLM, GPT-2. The tasks were completed at least three times faster and, in some cases, up to nine times faster than when running GPT-2 on an internet-connected laptop.

Implementing LLMs in generative AI tasks typically requires numerous GPUs and 250 watts of power, but the team claims their semiconductor uses only 1/625 of the power of Nvidia’s GPU for the same tasks. In addition, because it is also 1/41 of the size, measuring just 4.5mm by 4.5mm, it could ultimately be used in devices like mobile phones.

Whether the chip can deliver on its promises in real-world applications remains to be seen, however. As Tom’s Hardware reports, “Though we are told that the KAIST C-Transformer chip can do the same LLM processing tasks as one of Nvidia’s beefy A100 GPUs, none of the press nor conference materials we have provided any direct comparative performance metrics. That’s a significant statistic, conspicuous by its absence, and the cynical would probably surmise that a performance comparison doesn’t do the C-Transformer any favors.“

‘A single chip to outperform a small GPU data center’: Yet another AI chip firm wants to challenge Nvidia’s GPU-centric world — Taalas wants to have super specialized AI chips

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Toronto-based AI chip startup Taalas has emerged from stealth with $50 million in funding and the lofty aim of revolutionizing the GPU-centric world dominated by Nvidia.

Founded by Ljubisa Bajic, Lejla Bajic, and Drago Ignjatovic, all previously from Tenstorrent (the creator of Grayskull), Taalas is developing an automated flow for quickly turning any AI model – Transformers, SSMs, Diffusers, MoEs, etc. – into custom silicon. The company claims that the resulting Hardcore Models are 1000x more efficient than their software counterparts.

The startup also says that one of its chips can hold an entire large AI model without requiring external memory, and the efficiency of hard-wired computation enables a single chip to outperform a small GPU data center.

Casting intelligence directly into silicon

“Artificial intelligence is like electrical power – an essential good that will need to be made available to all. Commoditizing AI requires a 1000x improvement in computational power and efficiency, a goal that is unattainable via the current incremental approaches. The path forward is to realize that we should not be simulating intelligence on general purpose computers, but casting intelligence directly into silicon. Implementing deep learning models in silicon is the straightest path to sustainable AI,” said Ljubisa Bajic, Taalas’ CEO.

“We believe the Taalas ‘direct-to-silicon’ foundry unlocks three fundamental breakthroughs: dramatically resetting the cost structure of AI today, viably enabling the next 10-100x growth in model size, and efficiently running powerful models locally on any consumer device. This is perhaps the most important mission in computing today for the future scalability of AI. And we are proud to support this remarkable n-of-1 team as they do it,” said Matt Humphrey, Partner at Quiet Capital which led the two rounds of funding alongside Pierre Lamond, an advisor at Eclipse Ventures.

Taalas says it will be taping out its first large language model chip in the third quarter of 2024, and aiming to make its chips available to the first customers in Q1 2025.

NVIDIA’s AI personal assistant demo available for RTX GPU PCs

Post author By miranda cosgrove
Post date February 19, 2024
No Comments on NVIDIA’s AI personal assistant demo available for RTX GPU PCs

NVIDIA RTX AI PCs Chat Bot Demo

NVIDIA has recently unveiled a new technology demonstration that is set to enhance the way we interact with artificial intelligence. This new feature, known as “Chat With RTX,” is designed to work seamlessly on Windows RTX PCs, leveraging the power of NVIDIA RTX GPUs to deliver a personalized and efficient chatbot experience. The technology is aimed at providing users with quick, secure, and contextually relevant responses, drawing from their own documents and notes to ensure a private and customized interaction.

At the heart of “Chat With RTX” lies a sophisticated GPT large language model that is capable of tailoring conversations to the user’s specific needs. This is not your average chatbot; it’s an intelligent system that can process a variety of file types, including text documents, PDFs, Word documents, XML files, and even transcriptions from YouTube videos. This versatility allows the chatbot to provide assistance that is highly relevant to the user’s personal content.

NVIDIA RTX AI PCs Chat Bot Demo

One of the key features of NVIDIA’s new tech demo is the use of retrieval-augmented generation (RAG), which significantly enhances the quality of the chatbot’s responses. In addition, the demo incorporates TensorRT-LLM, a tool for optimizing large language models, ensuring that the chatbot operates at peak efficiency. Thanks to RTX acceleration, the chatbot is not only accurate but also incredibly fast, running directly on a user’s Windows RTX PC without the need for cloud processing.

Developers, in particular, may find “Chat With RTX” intriguing as it builds upon the TensorRT-LLM RAG developer reference project available on GitHub. This provides them with a valuable opportunity to explore advanced AI models and potentially integrate similar technologies into their own projects.

For those interested in experiencing “Chat With RTX,” there are certain system requirements that must be met. The user’s PC should be equipped with a GeForce RTX 30 Series GPU or a more advanced model, with a minimum of 8GB of VRAM. Additionally, the PC must be running either Windows 10 or 11 and have the latest NVIDIA drivers installed to ensure compatibility with the demo.

NVIDIA has acknowledged a current installation issue with “Chat With RTX” and has promised to resolve it in a forthcoming update. In the meantime, users are advised to install the application in the default directory to avoid any complications.

Furthermore, NVIDIA is encouraging developers to push the boundaries of generative AI by hosting a contest. Participants are invited to create innovative applications using NVIDIA RTX GPUs, with the chance to win prizes. This contest not only stimulates creativity within the developer community but also showcases the potential of NVIDIA’s technology in driving forward AI applications.

The introduction of “Chat With RTX” is a testament to NVIDIA’s ongoing efforts to advance AI and GPU technology. By focusing on high-performance computing and data privacy, NVIDIA is making it possible to integrate sophisticated AI capabilities into everyday tasks. This technology allows users to benefit from a smart, responsive, and personalized AI assistant, all while keeping their data securely processed on their local machine. As NVIDIA continues to innovate and address any initial teething problems, “Chat With RTX” is poised to become an essential tool for those seeking a more intelligent and responsive computing experience.

Filed Under: Technology News, Top News

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Tags Assistant, demo, GPU, NVIDIAs, PCs, Personal, RTX

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NVIDIA NVIDIA RTX 2000 Ada Generation GPU

Post author By miranda cosgrove
Post date February 13, 2024
No Comments on NVIDIA NVIDIA RTX 2000 Ada Generation GPU

NVIDIA NVIDIA RTX 2000 Ada Generation GPU

NVIDIA has just unveiled a new graphics card that’s set to transform the way professionals work across various industries. The RTX 2000 Ada Generation GPU is not just an upgrade; it’s a leap forward, offering up to 50% more performance than its predecessor, the RTX A2000 12 GB. This new card is designed to fit into compact workstations, yet it packs a punch with 16 GB of memory, making it more than capable of handling complex tasks and managing high-resolution content with ease. The NVIDIA RTX 2000 Ada features the latest technologies in the NVIDIA Ada Lovelace GPU architecture, including:

Third-generation RT Cores: Up to 1.7x faster ray tracing performance for high-fidelity, photorealistic rendering.
Fourth-generation Tensor Cores: Up to 1.8x AI throughput over the previous generation, with structured sparsity and FP8 precision to enable higher inference performance for AI-accelerated tools and applications.
CUDA cores: Up to 1.5x the FP32 throughput of the previous generation for significant performance improvements in graphics and compute workloads.
Power efficiency: Up to a 2x performance boost across professional graphics, rendering, AI and compute workloads, all within the same 70 W of power as the previous generation.
Immersive workflows: Up to 3x performance for virtual-reality workflows over the previous generation.
16 GB of GPU memory: An expanded canvas enables users to tackle larger projects, along with support for error correction code memory to deliver greater computing accuracy and reliability for mission-critical applications.
DLSS 3: Delivers a breakthrough in AI-powered graphics, significantly boosting performance by generating additional high-quality frames.
AV1 encoder: Eighth-generation NVIDIA Encoder, aka NVENC, with AV1 support is 40% more efficient than H.264, enabling new possibilities for broadcasters, streamers and video callers.

Professionals from all walks of life, including architects, engineers, and content creators, stand to benefit from the RTX 2000 Ada’s enhanced capabilities. For those in architecture and urban planning, the GPU accelerates visualization and structural analysis, making it easier to bring projects to life. Product designers and engineers will appreciate the ability to iterate designs more quickly, streamlining the development process. Content creators, on the other hand, will enjoy smooth editing experiences, even when dealing with high-resolution videos and images. The GPU also supports real-time data processing, which is essential for industries that rely on AI-driven intelligence, such as medical devices, manufacturing, and retail.

NVIDIA NVIDIA RTX 2000

In the realm of virtual reality, the RTX 2000 Ada stands out with its robust support for immersive graphics. It utilizes NVIDIA’s DLSS and ray-tracing technologies to create incredibly realistic images, enhancing enterprise workflows in VR and providing users with an experience that’s closer to reality than ever before.

The technological innovations in the RTX 2000 Ada are impressive. It features third-generation RT Cores for quicker ray tracing, fourth-generation Tensor Cores for enhanced AI throughput, and improved CUDA cores for handling both graphics and compute workloads efficiently. Despite these advancements, the GPU remains power-efficient, delivering twice the performance within a 70 W power envelope. It also includes DLSS 3 technology for AI-powered graphics enhancement and an AV1 encoder, which optimizes video streaming and calling.

NVIDIA NVIDIA RTX 2000 features

The feedback from early adopters of the RTX 2000 Ada has been overwhelmingly positive. Companies like Dassault Systèmes, Rob Wolkers Design and Engineering, and WSP have already experienced the GPU’s exceptional performance, versatility, and large memory capacity. These attributes are essential for managing the complex tasks that professionals encounter daily.

NVIDIA ensures that users of the RTX 2000 Ada have the support they need with the latest RTX Enterprise Driver. This driver introduces new features such as Video TrueHDR and Video Super Resolution, enhancing the overall user experience. The GPU is available globally through distribution partners and will be included in systems from major manufacturers like Dell Technologies, HP, and Lenovo starting in April.

Specifications

The release of the NVIDIA RTX 2000 Ada Generation GPU is a significant event for professionals who rely on powerful graphics processing. It’s a versatile tool that caters to a wide range of industries and workflows, reflecting NVIDIA’s commitment to pushing the boundaries of graphics technology. For more information on full specifications jump over to the official NVIDIA product page.

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Tags Ada, Generation, GPU, NVIDIA, RTX