What GeForce RTX 4090D video cards with 48 GB of memory from China are capable of and why they are needed

While Western enthusiasts continue to chase extra frames in games, Chinese GPU market presents a surprise that will make even seasoned experts think. After all, real monsters have appeared on the horizon, whose equals will be very difficult to find. Masters from the Middle Kingdom managed to increase the memory capacity of top Nvidia video cards up to 48 GB. So now they are not just circumventing American sanctions – they are setting a new bar in the history of graphics computing.

Is it possible to increase the memory capacity of a video card?

The modifications affected two key models of high-performance GPUs from NVIDIA. The first one is GeForce RTX 4090D, a special version of the flagship carddeveloped for the Chinese market to circumvent US export restrictions. Second – GeForce RTX 4080 SUPERa high-end high-end card. Both models have received a significant increase in video memory, which significantly expands their capabilities in tasks that require processing large amounts of data.

RTX 4090D now available in 48GB versionwhich is twice the memory capacity of the standard version. And the RTX 4080 SUPER, in turn, received an upgrade to 32 GB, also doubling its standard memory values. All that remains is to figure out why there is so much?

The appearance of these modified GPUs in China – This is not just a response to American export restrictions or even a demonstration of the technological potential of the Chinese. Because they are not needed for games or editing. The upgrade that Nvidia's top cards have undergone is primarily needed for training AI and neural networks.

The increased memory capacity allows the use of video cards with more complex and voluminous models, accelerating learning processes and AI model inferencewhich were previously limited by the amount of available memory. But this is exactly what Nvidia wanted to avoid by banning the export of branded cards to the Chinese market and releasing a D-modification of its the top-end RTX 4090.

Such modifications are not uncommon in the enthusiast community and service centers. However, the scale at which this occurs in the case of improved Nvidia video cardsspeaks of a serious approach and industrial level of production. For those interested in the technical aspects of modification, it is important to understand that increasing memory capacity is not just about replacing chips. This is a complex task that may include modification of the printed circuit board, redesign of the power system and optimization of the cooling system.

Nvidia RTX 4090D with 48 GB of memory

Therefore RTX 4090D with 48 GB memory is not just an upgrade, but a real technological hybrid:

• Heart Card – Graphic AD102 processor from standard RTX 4090D

• Body – presumably modified PCB from RTX 3090 Tisupporting 24 memory modules instead of the standard 12

• Circulatory system – high speed GDDR6X memory with a throughput of 937 GB/s

This engineering solution overcomes the limitations of the standard RTX 4090D configuration, doubling the memory capacity without significant loss of performance.

The RTX 4080 SUPER with 32GB VRAM is also worth a close look. With double the memory capacity of the standard version (16GB), this card is a powerful tool for tasks that require a lot of VRAM but don't require the maximum performance of the RTX 4090D.

How are video cards and neural networks connected?

Despite the fact that a video card is primarily a tool for solving graphical problems, they work effectively in tasks with a large number of small calculations, unlike the processor. And this is what makes them useful when working with large language models such as GPT.

The RTX 4090D with 48GB of memory offers some serious advantages over the classic version of itself. Not only can it load and process models with a huge number of parameters, but it also does it alone, without requiring the models to be divided between several GPUs. This greatly simplifies the learning process and increases work efficiency.

Using the RTX 4090D, researchers can train a model based on large volumes of medical text and images simultaneously, potentially leading to more accurate and reliable diagnostic systems.

Regarding computer visionthen the RTX 4080 SUPER with 32 GB VRAM becomes the ideal tool for working with ultra-high-resolution images and videos. This is especially valuable in areas such as satellite imaging and remote sensing data analysis.

By using RTX 4080 SUPER It is possible to analyze entire urban agglomerations in great detail, tracking changes in buildings, green spaces or even traffic flows.

Besides, increased memory capacity Both cards allow you to work with more complex and detailed 3D models and textures, which opens up new possibilities in the field of digital content creation.

With their help, architects and urban planners can create incredibly detailed models of entire urban areas, allowing clients and residents to “walk” along unbuilt streets and appreciate every detail of the future project.

In science, the 48GB RTX 4090D opens up new horizons for simulating complex physics processes and objects like simulating plasma behavior in fusion reactors with greater accuracy and detail. This could speed up the development of efficient fusion reactors, bringing us closer to an era of clean and inexhaustible energy.

Video cards with more memory

Why, then, doesn't Nvidia make video cards with this amount of memory on a commercial scale? Well, actually it does. Just within the framework of a different, professional line. After all, for gaming GPUs this much VRAM is simply excessive. A Nvidia RTX A6000 has the same 48 GB. True, it costs almost half a million rubles, while a custom version of the RTX 4090D with an increased amount of memory most likely costs Chinese craftsmen 2 times cheaper. The savings are dramatic.

So it turns out that remake a gaming video cardreplacing its individual components turns out to be more profitable even with a number of serious reservations.

Firstly, the increased memory capacity and, accordingly, increased heat generation require special attention to be paid to cooling the video card. As a rule, such systems either use liquid cooling or simply provide a smaller resource that each specific GPU in the stack will provide.

Secondly, for maximum performance it is critical to use specialized drivers optimized for increased memory. Since the volume is specific, it is possible that Drivers for custom RTX 4080 Super and RTX 4090D enthusiasts themselves write, which also increases the potential cost of the systems where they are used.

Thirdly, in addition to increasing memory capacity, such video cards may require profiling for detailed analysis of VRAM usage and optimization of workloads. This will help you identify bottlenecks in your algorithms and optimize your code to use available memory more efficiently, but it will make the system more difficult to use.

However, there are many techniques that, even with all the reservations, make it possible to extract sufficient efficiency from such systems:

• When working with large language models, special techniques such as model and pipeline parallelism are used to effectively distribute the load across available memory resources.

• Computer vision tasks can use real-time image processing techniques, such as tiling or stream processing, to work even with images larger than the amount of available video memory.

• When creating content for VR and AR, techniques for dynamically loading textures and levels of detail are used (LOD) to take full advantage of the increased memory without sacrificing performance.

• Scientific computing uses libraries optimized for working with large amounts of data on the GPU, such as cuDNN for deep learning or CUDA-accelerated versions of scientific libraries.

Why the RTX 4090D with 48GB is a breakthrough

The appearance of the RTX 4090D with 48 GB and the RTX 4080 SUPER with 32 GB VRAM undoubtedly marked a new stage in development high performance computing. These cards don't just circumvent export restrictions – they open up new opportunities for researchers, developers and specialists in various fields.

From training super-massive language models to creating photorealistic virtual worlds, increased video memory allows you to solve problems that previously seemed unattainable on a single GPU. Now all that’s left to do is learn how to correctly use their potential to 100%. And, perhaps, optimize it for “civilian” tasks such as games or working with graphics.

But if we talk about politics, the most interesting thing is that such video cards have already gone “to the people.” And Nvidia will definitely notice this, and most likely it has already noticed. And now it will be very interesting to see how the corporation behaves. After all, it has two options: simply turn a blind eye to what is happening and not participate in it in any way (because banning it won’t work anyway) or go ahead and reduce the cost of production by starting to produce more affordable video cards with a maximum amount of memory, which will open up vast AI training opportunities for a more reasonable price.